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Long Term Research: What it is with examples

Long-term research is well recognized as a vital tool, and we can find examples for various systems and species. For this reason, today, we will explain everything related to this exciting research methodology in depth.

What is long-term research?

Long-term research is a study design that considers observing the same variables/entities over time. It is also called a longitudinal survey . We can find examples of its application in environmental studies, agricultural studies, evolution, medicine, human health , Emerging technologies, and Land use.

It is usually mainly used in studies where we can analyze the object of study over time from different perspectives and periods.

The length of a study of this type usually varies depending on the needs and objectives, but all those projects that take about 5 years to complete can be considered long-term research.

Pros & cons of long term research

Long-term research has particular applications, so it has some advantages and disadvantages to take into account; below we list some of them.

Long term research Pros:

• Long-term research is essential to record changes over a prolonged period.
• It can help you identify patterns that may occur over a long period.
• It allows higher levels of validity.
• Data collected/determined is unique.
• It enables you to identify a path or a trend towards development.

Long term research Cons:

• It’s gradual, and this involves time-consuming processes.
• There’s always a factor of uncertainty/unpredictability.
• The cost is usually high and direct compared to the other forms of research.
• The process is dependent on the research skills of the researchers.
• Reliance on individual interpretations can introduce alterations.

At the end of the day, the advantages are more notorious than the disadvantages when it comes to long-term research.

Long-Term Research Example

Below we share an actual case where long-term research was the methodology used to provide answers to clear objectives.

• Acid rain is caused due to sulfur and nitric acid pollution in the atmosphere, resulting in fish kills and forest decline across the Northeast.
• The evidence came from a series of rain collectors at the Hubbard Brook Experimental Forest.
• Researchers not only noticed that the pH of the rain and snow was low, but when they compared it to previous years of data they realized it was abnormally low. After the problem was recognized, practitioners and politicians came together to reduce pollution, which in turn reduced acid rain to safer levels.
• Without Hubbard Brook’s long-term dataset, identification of acid rain as the cause of forest decline might have taken much longer or never happened at all.

As you may have noticed, long-term research usually has many uses in the field of medicine and ecology, although it can also be used for ethnographic and social research .

Other Notable Examples of Long-Term Investigations

• Climate change studies: There are several studies around the world that use long-term research as their main tool, in addition to including other observational research methods to study changes on the planet as a result of this phenomenon.
• Pitch drop experiment : It is an experiment that has been running since 1927 and its purpose is to calculate the density of the material poured into the decanter. The guardians of this experiment keep a record of each droplet which can take several months to happen, thanks to this they can calculate and better understand the density of liquids and their behavior.

Importance of using a data repository in a long-term investigation

One of the biggest challenges faced by researchers who lead this type of project is in the planning and administration of data, this is where technology has played an important role in recent years.

The research repositories allow researchers to easily add, manage and consult the data collected over time, this type of system allows proper management of all this information thanks to its focus on the classification and administration of all the data poured into them.

This greatly facilitates the task of researchers over the years, not only allowing them to maintain a backup of their information but also to be able to consult the information they need in seconds using intelligent filters that allow them to make elaborate and precise conclusions over time. throughout the duration of the investigation.

At QuestionPro we offer researchers of all kinds not only data collection tools like our survey software but also have insights repository for long-term research of all kinds.

If you are interested in having a demo or reading more about it, we invite you to visit the Insight Hub to learn more about this amazing product.

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Harvard study, almost 80 years old, has proved that embracing community helps us live longer, and be happier

Part of the Tackling Issues of Aging series

A series on how Harvard researchers are tackling the problematic issues of aging.

W hen scientists began tracking the health of 268 Harvard sophomores in 1938 during the Great Depression, they hoped the longitudinal study would reveal clues to leading healthy and happy lives.

They got more than they wanted.

After following the surviving Crimson men for nearly 80 years as part of the Harvard Study of Adult Development , one of the world’s longest studies of adult life, researchers have collected a cornucopia of data on their physical and mental health.

Of the original Harvard cohort recruited as part of the Grant Study, only 19 are still alive, all in their mid-90s. Among the original recruits were eventual President John F. Kennedy and longtime Washington Post editor Ben Bradlee. (Women weren’t in the original study because the College was still all male.)

In addition, scientists eventually expanded their research to include the men’s offspring, who now number 1,300 and are in their 50s and 60s, to find out how early-life experiences affect health and aging over time. Some participants went on to become successful businessmen, doctors, lawyers, and others ended up as schizophrenics or alcoholics, but not on inevitable tracks.

“Loneliness kills. It’s as powerful as smoking or alcoholism.” Robert Waldinger, psychiatrist, Massachusetts General Hospital

During the intervening decades, the control groups have expanded. In the 1970s, 456 Boston inner-city residents were enlisted as part of the Glueck Study, and 40 of them are still alive. More than a decade ago, researchers began including wives in the Grant and Glueck studies.

Over the years, researchers have studied the participants’ health trajectories and their broader lives, including their triumphs and failures in careers and marriage, and the finding have produced startling lessons, and not only for the researchers.

“The surprising finding is that our relationships and how happy we are in our relationships has a powerful influence on our health,” said Robert Waldinger , director of the study, a psychiatrist at Massachusetts General Hospital and a professor of psychiatry at Harvard Medical School . “Taking care of your body is important, but tending to your relationships is a form of self-care too. That, I think, is the revelation.”

Close relationships, more than money or fame, are what keep people happy throughout their lives, the study revealed. Those ties protect people from life’s discontents, help to delay mental and physical decline, and are better predictors of long and happy lives than social class, IQ, or even genes. That finding proved true across the board among both the Harvard men and the inner-city participants.

“The people who were the most satisfied in their relationships at age 50 were the healthiest at age 80,” said Robert Waldinger with his wife Jennifer Stone.

Rose Lincoln/Harvard Staff Photographer

The long-term research has received funding from private foundations, but has been financed largely by grants from the National Institutes of Health, first through the National Institute of Mental Health, and more recently through the National Institute on Aging.

Researchers who have pored through data, including vast medical records and hundreds of in-person interviews and questionnaires, found a strong correlation between men’s flourishing lives and their relationships with family, friends, and community. Several studies found that people’s level of satisfaction with their relationships at age 50 was a better predictor of physical health than their cholesterol levels were.

“When we gathered together everything we knew about them about at age 50, it wasn’t their middle-age cholesterol levels that predicted how they were going to grow old,” said Waldinger in a popular TED Talk . “It was how satisfied they were in their relationships. The people who were the most satisfied in their relationships at age 50 were the healthiest at age 80.”

He recorded his TED talk, titled “What Makes a Good Life? Lessons from the Longest Study on Happiness,” in 2015, and it has been viewed 13,000,000 times.

The researchers also found that marital satisfaction has a protective effect on people’s mental health. Part of a study found that people who had happy marriages in their 80s reported that their moods didn’t suffer even on the days when they had more physical pain. Those who had unhappy marriages felt both more emotional and physical pain.

Those who kept warm relationships got to live longer and happier, said Waldinger, and the loners often died earlier. “Loneliness kills,” he said. “It’s as powerful as smoking or alcoholism.”

According to the study, those who lived longer and enjoyed sound health avoided smoking and alcohol in excess. Researchers also found that those with strong social support experienced less mental deterioration as they aged.

In part of a recent study , researchers found that women who felt securely attached to their partners were less depressed and more happy in their relationships two-and-a-half years later, and also had better memory functions than those with frequent marital conflicts.

“When the study began, nobody cared about empathy or attachment. But the key to healthy aging is relationships, relationships, relationships.” George Vaillant, psychiatrist

“Good relationships don’t just protect our bodies; they protect our brains,” said Waldinger in his TED talk. “And those good relationships, they don’t have to be smooth all the time. Some of our octogenarian couples could bicker with each other day in and day out, but as long as they felt that they could really count on the other when the going got tough, those arguments didn’t take a toll on their memories.”

Since aging starts at birth, people should start taking care of themselves at every stage of life, the researchers say.

“Aging is a continuous process,” Waldinger said. “You can see how people can start to differ in their health trajectory in their 30s, so that by taking good care of yourself early in life you can set yourself on a better course for aging. The best advice I can give is ‘Take care of your body as though you were going to need it for 100 years,’ because you might.”

The study, like its remaining original subjects, has had a long life, spanning four directors, whose tenures reflected their medical interests and views of the time.

Under the first director, Clark Heath, who stayed from 1938 until 1954, the study mirrored the era’s dominant view of genetics and biological determinism. Early researchers believed that physical constitution, intellectual ability, and personality traits determined adult development. They made detailed anthropometric measurements of skulls, brow bridges, and moles, wrote in-depth notes on the functioning of major organs, examined brain activity through electroencephalograms, and even analyzed the men’s handwriting.

Now, researchers draw men’s blood for DNA testing and put them into MRI scanners to examine organs and tissues in their bodies, procedures that would have sounded like science fiction back in 1938. In that sense, the study itself represents a history of the changes that life brings.

6 factors predicting healthy aging According to George Vaillant’s book “Aging Well,” from observations of Harvard men in long-term aging study

Physically active.

Absence of alcohol abuse and smoking

Having mature mechanisms to cope with life’s ups and downs

Healthy weight

Stable marriage.

Psychiatrist George Vaillant, who joined the team as a researcher in 1966, led the study from 1972 until 2004. Trained as a psychoanalyst, Vaillant emphasized the role of relationships, and came to recognize the crucial role they played in people living long and pleasant lives.

In a book called “Aging Well,” Vaillant wrote that six factors predicted healthy aging for the Harvard men: physical activity, absence of alcohol abuse and smoking, having mature mechanisms to cope with life’s ups and downs, and enjoying both a healthy weight and a stable marriage. For the inner-city men, education was an additional factor. “The more education the inner city men obtained,” wrote Vaillant, “the more likely they were to stop smoking, eat sensibly, and use alcohol in moderation.”

Vaillant’s research highlighted the role of these protective factors in healthy aging. The more factors the subjects had in place, the better the odds they had for longer, happier lives.

“When the study began, nobody cared about empathy or attachment,” said Vaillant. “But the key to healthy aging is relationships, relationships, relationships.”

“We want to find out how it is that a difficult childhood reaches across decades to break down the body in middle age and later.” Robert Waldinger

The study showed that the role of genetics and long-lived ancestors proved less important to longevity than the level of satisfaction with relationships in midlife, now recognized as a good predictor of healthy aging. The research also debunked the idea that people’s personalities “set like plaster” by age 30 and cannot be changed.

“Those who were clearly train wrecks when they were in their 20s or 25s turned out to be wonderful octogenarians,” he said. “On the other hand, alcoholism and major depression could take people who started life as stars and leave them at the end of their lives as train wrecks.”

The study’s fourth director, Waldinger has expanded research to the wives and children of the original men. That is the second-generation study, and Waldinger hopes to expand it into the third and fourth generations. “It will probably never be replicated,” he said of the lengthy research, adding that there is yet more to learn.

“We’re trying to see how people manage stress, whether their bodies are in a sort of chronic ‘fight or flight’ mode,” Waldinger said. “We want to find out how it is that a difficult childhood reaches across decades to break down the body in middle age and later.”

Lara Tang ’18, a human and evolutionary biology concentrator who recently joined the team as a research assistant, relishes the opportunity to help find some of those answers. She joined the effort after coming across Waldinger’s TED talk in one of her classes.

“That motivated me to do more research on adult development,” said Tang. “I want to see how childhood experiences affect developments of physical health, mental health, and happiness later in life.”

Asked what lessons he has learned from the study, Waldinger, who is a Zen priest, said he practices meditation daily and invests time and energy in his relationships, more than before.

“It’s easy to get isolated, to get caught up in work and not remembering, ‘Oh, I haven’t seen these friends in a long time,’ ” Waldinger said. “So I try to pay more attention to my relationships than I used to.”

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Taking the Long View: U.S. Scientists Affirm Value of Long Term Research

Note: Yale School of the Environment (YSE) was formerly known as the Yale School of Forestry & Environmental Studies (F&ES). News articles and events posted prior to July 1, 2020 refer to the School's name at that time.

Long-term research has been a primary tool for being able to understand how global changes are happening on the ground, particularly as a result of climate change.

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Taking the long view: US scientists affirm value of long term research

Ecologists, evolutionary scientists reveals surprise findings.

For many years, long-term research has played a key role in revealing the planet's complex ecological and evolutionary dynamics. But some scientists argue that there's a need to revise strategies for long-term research to fill gaps in research, better examine underrepresented fields, and address limits in design and data collection.

What's more, many contend that the benefits and failings of long-term research are typically argued only by a limited number of scientists who have published reports in the field.

A Yale-led survey of 1,179 ecological and evolutionary scientists, published in the journal Ecological Monographs , provides a detailed glimpse into how the U.S. ecological community views the direction of long-term research, the important role it plays in the advancement of knowledge, and specific research areas scientists believe should be treated as priorities. (The researchers defined "long-term research" as projects lasting at least five years.)

According to the survey, which was done in collaboration with polling experts from the Yale Program on Climate Change Communication, nearly 80 percent of respondents believe that long-term experiments have contributed a "great deal" to improved ecological understanding.

In fact, multi-site, long-term research -- in comparison with, for instance, short-term, single-site, modeling, or lab experiments -- was by far the most highly ranked approach for developing new theory. Observational research methods (monitoring) and experimental approaches were considered equally important.

Respondents also called for a more supportive research environment and funding structure, including stronger institutional acknowledgement of the contributions of long-term research and greater support during the establishment and maintenance of research programs.

When asked which topics or questions should be targeted in future long-term research, respondents most commonly identified the impacts of global change -- including climate change, invasion by non-native species, and anthropogenic disturbance.

"Long-term research has been a primary tool for being able to understand how global changes are happening on the ground, particularly as a result of climate change," said Sara Kuebbing, a postdoctoral associate at the Yale School of Forestry & Environmental Studies (F&ES) and lead author of the study. "Almost everyone agrees that it is critically important and needs to be continued."

View the article, "Long-term research in ecology and evolution (LTREE): A survey of challenges and opportunities."

That most ecologists and evolutionary scientists believe long-term research is important might not be surprising, said Mark Bradford, a professor of soils and ecosystem ecology at F&ES and co-author of the paper. But he didn't expect that its importance would be rated above all other research approaches.

"There are many different kinds of research going on -- including lots of short-term experiments and single-site experiments," he said. "But what came out of this survey is that long-term, multi-site, observational and experimental research was the approach that is generating the most knowledge."

That sentiment stands in notable contrast to some recent research funding decisions. For instance, the U.S. Department of Energy in the past decade pulled the funding on large-scale -- and expensive -- open-air projects that were aimed to produce a better understanding of how increased carbon dioxide concentrations were affecting tree growth in forests. It was estimated that DOE was spending $3 million annually at an individual site.

"One argument at the time was that we'd learned as much as we could so it was time to redirect the funding, to put it toward other problems," said Bradford. "I think that is a fair justification and a number of scientists were arguing that all these long-term experiments have caveats, so the longer you do them the more they become devalued over time."

"But overwhelmingly the responses that came back in the survey did not support this view. The response for the most part was that experiments don't decrease in value over time, and that caveats don't undermine that long-term value."

Critically, these long-term research projects sometimes yield answers to questions that scientists didn't even know to ask, Kuebbing said. Monthly readings of atmospheric levels of carbon dioxide at the Mauna Loa Observatory in Hawaii, begun in 1958, would eventually provide critical clues about the changing global climate. At the Hubbard Brook Ecosystem Study in New Hampshire during the 1970s, Yale scientists monitoring the long-term effects of deforestation on ecosystems accidentally discovered the impacts of acid rain.

"In this context they had data to show the accumulation of what was happening -- and not because they had set out to discover acid rain," said Kuebbing." "This is one of the benefits that many respondents to the survey talked about; long-term research can yield surprise findings that you wouldn't have been able to discover until it was too late."

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Story Source:

Materials provided by Yale School of Forestry & Environmental Studies . Original written by Kevin Dennehy. Note: Content may be edited for style and length.

Journal Reference :

• Sara E. Kuebbing et al. Long-term research in ecology and evolution: a survey of challenges and opportunities . Ecological Monographs , 2018 DOI: 10.1002/ecm.1289

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Surface Transportation Environmental Research: A Long-Term Strategy -- Special Report 268 (2002)

Chapter: 8 establishment of a long-term research strategy, 8 establishment of a long-term research strategy.

In 1996, the General Accounting Office recommended the development of a long-term research strategy for surface transportation (GAO 1996). In 1998, in the Transportation Equity Act for the 21st Century, Congress called for the establishment of a surface transportation environmental cooperative research program, along with the development of a national research agenda on transportation, energy, and the environment. Congress recognized that a research program with a targeted national agenda is critically needed to help transportation decision makers effectively provide for mobility while simultaneously protecting and enhancing the environment.

In the preceding chapters, the Advisory Board has outlined a proposed agenda for the cooperative research program called for by Congress. The work to be carried out under the program encompasses further developing the research agenda and updating it from year to year; sponsoring and coordinating the research itself; and fostering increased coordination, cooperation, and communication among research entities as part of a long-term national research strategy on transportation and the environment.

Research can be used to identify new policies and approaches for resolving, mitigating, and managing adverse environmental impacts resulting from transportation, as well as for enhancing the environment and contributing to public health and community integrity. For example, research could be conducted to refine the current sketchy understanding of the impact of the expansion of transportation facilities on travel behavior, enabling policymakers to predict associated land use changes more accurately. As another example, additional research could lead to better definition of the relationship between

roadway width and wildlife habitat fragmentation, thereby assisting transportation planners in protecting and reestablishing wildlife migratory routes.

Current research on transportation and the environment is making valuable contributions, but its ultimate impact is limited because of low levels of funding, inadequate coordination across research entities, and the short-term focus that characterizes most of the work done to date. The Advisory Board has concluded that there is a pressing need for a new strategy for transportation environmental research. This strategy is necessary to ( a ) fill gaps in the existing research programs of the multiple agencies with interests in transportation, energy, and the environment; ( b ) extend the scope of research to system-level and long-term issues as well as short-term needs; ( c ) ensure that all related research initiatives are coordinated; ( d ) provide for broad dissemination of research findings; and ( e ) present research findings in forms and formats that are easily accessible to transportation professionals and policymakers. Absent such a strategy, a fragmented, primarily short-term approach to research will persist without a strong relationship to transportation investment policies and operating practices (GAO 1996).

A long-term, coordinated research strategy with sufficient funding is the only way of adequately preparing transportation policymakers to confront the substantial challenges of an increasing population and expanding economy. In this chapter components of a national research agenda for a proposed surface transportation environmental cooperative research program are outlined; the characteristics of successful cooperative research programs are detailed; and mechanisms for funding and implementing the proposed program, as well as fostering increased cooperation and coordination between the surface transportation community and other research entities, are examined.

NATIONAL RESEARCH AGENDA FOR A SURFACE TRANSPORTATION ENVIRONMENTAL COOPERATIVE RESEARCH PROGRAM

The first step in formulating a long-term research strategy for transportation and the environment is to develop a national research agenda that responds to current and anticipated problems and policy choices. That first step has been taken in the previous chapters of this report, in which problems and policy choices have been outlined and six key areas in which targeted research is needed have been identified:

Human health,

Ecology and natural systems,

Environmental and social justice,

Emerging technologies,

Land use, and

Planning and performance measures.

As noted earlier in the report, these six areas were chosen because they represent the points of intersection between transportation and the human and natural environments. While research is being conducted in each of these areas, important gaps exist. Without new research in each of the six areas, decision makers will lack important information needed to make sound decisions on transportation and the environment.

A new surface transportation environmental cooperative research program is needed to ensure that the national research agenda on these critical issues is implemented. This would be accomplished under the program proposed herein in two ways: in some cases, the program would serve as the sponsoring organization for research, while in others the program would serve as the coordinating body for research carried out by others.

Typically, cooperative research programs are formed for two primary reasons. The first is to leverage financial and other resources, as is the case with the National Cooperative Highway Research Program (NCHRP). For example, NCHRP was formed by state departments of transportation (DOTs) for the express purpose of pooling funds to conduct research of common value. State DOTs realized that many of the problems that were occurring in one state were also occurring in many others. Pooling funds for research to solve these problems instead of duplicating efforts enables the state DOTs not only to carry out more research than would be possible on an individual state basis, but also to conduct this research in a more cost-effective manner.

The second primary reason for forming cooperative research programs is to resolve political stalemates and scientific debates. In other words, when tough political choices are made more difficult by scientific uncertainty or conflict, forming a cooperative research program can be the key to producing credible research results that are accepted by multiple parties. For example, the Health Effects Institute (HEI) was formed in 1980 to produce research necessary for resolving disputes between the automotive industry and the Environmental Protection Agency (EPA) regarding the health effects attributable to vehicle emissions. By thus bringing EPA and the automotive manufacturers

together, it was possible to reach agreement on the questions that needed to be researched and the methodologies to be employed. Interpretations of the results may still vary, but underlying conflicts over basic data have been resolved. Essentially, the primary benefit of such cooperative research programs is the ability to conduct research in a manner that is both transparent to and accepted by all relevant parties.

Today, transportation policy choices have become more polarized than necessary because interested parties hold different positions on the likely answers to important but unresolved scientific questions. For example, what combination of transit and automobile-oriented investments will best provide for population growth while minimizing environmental harm? Do noise barriers produce more benefit or harm to wildlife near highways? Will denser land use configurations lead to decreased automobile dependency? Does transportation influence land use, or is the converse true? Can travel behavior be significantly altered if the right incentives are provided? What are the right incentives? The list of questions currently unanswered ranges from the complex to the relatively simple. Unfortunately, in the areas in which research has been conducted, definitive results have remained elusive. The primary reasons for this are that the research generated is often narrowly scoped, frequently because funding is limited; there is not always agreement on the scientific interpretations, the conclusions, or even the fundamental methodology used to conduct the research; and new forums for discussion of the findings and their interpretations and implications have emerged. Further, research findings are often narrowly disseminated, so that they are known within academe but not the broader community of professionals, or are known in the state where the work was conducted but not elsewhere. If a surface transportation environmental cooperative research program were established, all parties representing contending perspectives could be brought together to define the research questions, review the research methodology, and jointly interpret the findings, thereby increasing the likelihood of acceptance of the results.

CHARACTERISTICS OF A SUCCESSFUL COOPERATIVE RESEARCH PROGRAM

Cooperative research programs must have certain characteristics to succeed. These characteristics, listed below, are designed to instill a basic level of trust

and confidence in the integrity of the research process, which is just as important as the research that is produced (Deen and Harder 1999):

A clearly articulated mission from which a strategic focus and research priorities can be ascertained;

An institutional structure that provides for complete scientific independence from outside influences;

A credible and openly competitive process for soliciting and evaluating research proposals based on merit review;

A rigorous standard for evaluating research products;

A mechanism for widely disseminating research findings and for involving a wide array of stakeholders; and

An ability to obtain competent staff and to secure a stable funding source.

The Advisory Board reviewed the organizational structures of four leading cooperative research programs: HEI, 1 EPA’s Science to Achieve Results Program (STAR), 2 NCHRP, and the Transit Cooperative Research Program (TCRP). 3 Summaries of these programs are included in Appendix B . On the basis of this review, the following specific program elements appear to be most important for success:

Core partners —entities that should contribute to the overall governance of the program, the primary customers and recipients of the research products. 4 Three categories of core partners should be considered when forming a surface transportation environmental cooperative research program: public entities, the private sector, and nongovernmental/nonprofit organizations.

Institutional arrangement —establishment of the research program in a setting that provides for independence and credibility. A partnership between industry and the public sector enhances a cooperative research program’s ability to create a cohesive, integrated research environment and should offer the added attraction of securing private investments while

establishing a firewall of independence around the program. Representatives from nongovernmental organizations can form the third leg of the partnership, thereby differentiating the model from that of HEI. 5

Strategic focus —program direction, as established by the core partners and stakeholders of the program and guided by the national research agenda. To ensure that all core partners and relevant stakeholders are in agreement on the priorities of the cooperative research program, a mechanism is needed for formulating, articulating, and periodically updating the program’s strategic focus areas. HEI and STAR, for example, develop multiyear strategic plans.

Solicitation and evaluation of research proposals —the ability to exercise vital quality control mechanisms through open competition and merit review. Establishing clear criteria aids in the selection of the most appropriate research projects, thereby ensuring a selection process that is transparent, fair, and subject to open competition. HEI, STAR, NCHRP, and TCRP all use clear selection criteria in addition to insisting that the selected research projects undergo external merit review. Although the merit review processes differ among the four programs, each incorporates the fundamental elements of both external and internal merit review, as well as open competition.

Evaluation of research —peer review and other approaches designed to ensure the validity and credibility of both the research and the operating functions of the cooperative research program. In addition to evaluating specific research projects upon completion, periodic programmatic evaluations can serve to increase confidence in the operations of the program (NRC 1999) and may be important when a significant number of end users/customers/stakeholders are to be satisfied.

Dissemination of research —mechanisms for effectively disseminating research findings. Too often, the research process ends with the publication of findings. Unless research programs make a concerted effort to inform other researchers and research institutions, practicing professionals, decision makers, and the general populace about those findings, a system of fragmented, uncoordinated research initiatives will persist.

Funding —stable funding that enables support of long-term basic and applied research (see below).

Competency and availability of staff —the ability to attract and retain highly skilled and independent staff. Next to stable and sufficient funding, the support of competent staff is the most crucial element in achieving a successful program. If the program is to avail itself of the best and most experienced representatives of the multiple disciplines needed for the research efforts, long-term stable arrangements and flexible contracting processes must be offered with established and emerging institutions that can demonstrate the ability to assemble appropriate teams and complete the tasks assigned. The staffs and institutions involved must also be shielded from political interests and undue influence from key stakeholders and core partners; that is, they must be granted full independence so they can apply the highest standards of scientific rigor to their work (NRC 1999).

Stakeholder involvement/communication —mechanisms for providing for meaningful stakeholder involvement in the selection, evaluation, and coordination of the research. 6 To maintain stakeholder confidence, the process for determining research priorities, selecting and structuring studies, and reviewing the final research products must be clearly understood and readily transparent. All four of the research programs reviewed share common features for actively soliciting stakeholder involvement. Stakeholders are invited to participate in the formulation of the program’s strategic plan and strategic focus areas, to submit research proposals, to participate in merit reviews of the research proposals, to participate in workshops at which ongoing research is presented, and to participate in peer reviews of the research products. They are also apprised of the research findings and recommendations.

In summary, the structure and characteristics of a cooperative research program designed to improve the quality and credibility of research in the surface transportation arena should have the following elements:

Core partners

Public partners

Private partners

Board of directors

Formed by core partners

Solicits stakeholder input

Develops strategic focus

Manages priority setting

Proposal solicitation

Request for proposals

Open competition

Merit review—project selection criteria

Stakeholder involvement

Evaluation of research proposals

Expert peer review

Publication of final report with reviewers’ comments

Dissemination of research results

Set-aside funds for distribution of reports

Internet, professional databases, libraries, and so forth

One-page summaries of reports for the nonscientific community

Presentations of research at workshops

Periodic program evaluation (measures of success)

Strategic plan

Open solicitation of research proposals

Merit review of research proposals

Research presentations at workshops

Peer review of final research

Dissemination of research reports

Adequate and stable funding must be available for the proposed program on a multiyear basis to support administrative, contracting, and sponsorship activities, including the ability to enter into partnerships with other public and private entities; to support long-term research, both basic and applied; and to sponsor workshops and demonstrations of implementation.

Congress first established a continuing commitment to transportation research beyond the budget of the then Bureau of Public Roads of the Department of Agriculture through the Hayden-Cartwright Act of 1934, enabling the

states to use federal transportation funds for surveys, planning, and engineering investigations in support of future highway improvements (FHWA 1976). The scope of this commitment was subsequently expanded to encompass broader planning and research activities. In these early years, the development of fundamental science and engineering knowledge to support transportation improvements and operations was essential.

The Federal-Aid Highway Act of 1962 (Public Law 87-866) first enabled the states to pool funds collaboratively to further research interests. NCHRP was established to conduct research in acute problem areas that affected highway planning, design, construction, operation, and maintenance nationwide. In 1991, the Intermodal Surface Transportation Efficiency Act increased funding for planning and research to 2 percent of the funding for certain federal-aid highway program categories and required that at least 25 percent of those funds (now known as State Planning and Research Funds) be spent for research. These funds, along with limited direct apportionments to DOT, remain the principal source of transportation research support today.

With passage of the National Environmental Policy Act of 1969, transportation agencies were given broader responsibilities and required to coordinate with environmental organizations. However, no funding commensurate with such responsibilities was ever identified. Nor was any mechanism created to further collaborative approaches rather than regulatory research initiatives. While transportation-derived funding remains one of the largest sources of investment in many environmental research areas, most of the activity associated with this funding is tied directly to individual transportation projects instead of to the development of knowledge that would assist in enhancing the overall condition of the environment. Also, little research into the effects of past projects and current operations is undertaken, the focus being primarily on new endeavors.

Because transportation–environment research has been underfunded during the past 30 years, a significant investment is now needed to address both the backlog of issues requiring attention and the issues that continue to arise. Current programs are not designed to address the full range of pressing research needs in surface transportation and the environment. HEI focuses on only one such issue—the health effects of air pollution. NCHRP and TCRP focus on their respective surface transportation modes, and while the scope of both programs includes topics in planning, economics, and environmental protection, program resources are devoted each year primarily to modal concerns. NCHRP research dollars are used mainly for the design and

construction of highway structures and pavements, and on issues related to safety and operations; TCRP focuses on transit concerns. In contrast, the transportation–environment research agenda proposed in this report would address all surface transportation modes (highways, transit, and rail) and would cover the full range of environmental impacts, with research spanning the six priority areas identified above. Both research addressing short-term needs and longer-term efforts aimed at uncovering fundamental relationships and devising basic new approaches to transportation and the environment would be funded.

While a precise budgetary estimate has not been developed, it is the judgment of the Advisory Board that full implementation of the agenda proposed in this report would necessitate a multiyear investment of well over a $100 million. In reviewing the NCHRP, TCRP, and HEI programs, the Advisory Board ascertained that the minimum operating budget of each was $8 million. The multiyear investment envisioned for the proposed transportation–environment research agenda would be an important complement to these other expenditures. Indeed, by developing new knowledge and helping to put that knowledge into practice, a focused, coordinated investment in transportation–environment research would enhance the efficacy of these existing programs. 7

A large-scale research program requires a careful startup phase and review of success before a sustainable and effective annual level of research activity can be determined. Thus it is prudent to anticipate that full implementation of the proposed program should and will be phased in over several years. However, there is little reason for growth of the program to be constrained by a lack of funding. At current federal funding levels, as little as 0.05 percent of annual authorizations of certain federal-aid program categories produces approximately $150 million. While a precise annual budgetary estimate for the eventual surface transportation cooperative research program cannot be established until after the startup phase, it is the Advisory Board’s opinion that the budget will not exceed such a small fraction of the overall federal transportation program. A national commitment of this scale would not pose a significant financial challenge to the federal transportation program, and given the importance of the environmental issues at stake in the proposed research program, could be expected to produce benefits many times as large.

IMPLEMENTATION

The Advisory Board envisions that representatives from appropriate federal government agencies would establish a relationship with an independent entity capable of implementing and managing a national surface transportation environmental cooperative research program having the characteristics of a successful program outlined above. The implementing organization would be directed to report the program’s activities each year to the appropriate federal agency representatives and to Congress.

INCREASED COOPERATION, COORDINATION, AND COMMUNICATION

The proposed surface transportation environmental cooperative research program could serve as a coordinating body for many entities involved in such research. Such entities might include federal agencies conducting environmental and transportation research, universities, the private sector, non-governmental organizations, state transportation and environmental agencies, international bodies, and other research institutions. Specifically, the program could be tasked with ( a ) annually surveying the research being conducted in the area both domestically and internationally; ( b ) providing specific recommendations for enhancing coordination, eliminating duplication, and identifying research gaps and priorities, with particular attention to long-term research needs; ( c ) enabling and supporting ongoing information sharing and collaborative debate; ( d ) sponsoring demonstrations and operational tests of promising research findings in cooperation with other parties; ( e ) soliciting stakeholder input to the management and evaluation of the program; and ( f ) fostering further education and capacity building in both research and professional practice.

Abbreviations

FHWA Federal Highway Administration

GAO General Accounting Office

NRC National Research Council

TRB Transportation Research Board

Deen, T. B., and B. T. Harder. 1999. NCHRP Synthesis of Highway Practice 280: Seven Keys to Building a Robust Research Program. TRB, National Research Council, Washington, D.C.

FHWA. 1976. America’s Highways 1776–1976. U.S. Department of Transportation, Washington, D.C.

GAO. 1996. Surface Transportation: Research Funding, Federal Role, and Emerging Issues. Report GAO/RCED-96-233.

NRC. 1999. Evaluating Federal Research Programs: Research and the Government Performance and Results Act. National Academy Press, Washington, D.C.

TRB. 2001. Special Report 260: Strategic Highway Research: Saving Lives, Reducing Congestion, Improving Quality of Life. National Research Council, Washington, D.C.

TRB Special Report 268 - Surface Transportation Environmental Research: A Long-Term Strategy defines a broad and ambitious research program to address and inform major public policy debates about the effects of surface transportation facilities and operations on the human and natural environments. The committee that conducted the study identified major gaps in knowledge that could be filled through a cooperative program of research involving federal agencies, states, and environmental organizations. The committee recommended creation of a new cooperative research program to carry out its recommended research agenda. Special Report 268 Summary

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Article Contents

Conceptual framing, climate change at lter sites, environmental forcing and human activities, ecosystem responses to climate change, future work for lter, conclusions, references cited.

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Long-Term Ecological Research on Ecosystem Responses to Climate Change

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Julia A Jones, Charles T Driscoll, Long-Term Ecological Research on Ecosystem Responses to Climate Change, BioScience , Volume 72, Issue 9, September 2022, Pages 814–826, https://doi.org/10.1093/biosci/biac021

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In this article marking the 40th anniversary of the US National Science Foundation's Long Term Ecological Research (LTER) Network, we describe how a long-term ecological research perspective facilitates insights into an ecosystem's response to climate change. At all 28 LTER sites, from the Arctic to Antarctica, air temperature and moisture variability have increased since 1930, with increased disturbance frequency and severity and unprecedented disturbance types. LTER research documents the responses to these changes, including altered primary production, enhanced cycling of organic and inorganic matter, and changes in populations and communities. Although some responses are shared among diverse ecosystems, most are unique, involving region-specific drivers of change, interactions among multiple climate change drivers, and interactions with other human activities. Ecosystem responses to climate change are just beginning to emerge, and as climate change accelerates, long-term ecological research is crucial to understand, mitigate, and adapt to ecosystem responses to climate change.

Accelerating climate change, including extreme  climate events, has intensified the need to understand the response across ecosystems. This special issue marks the 40th anniversary of the US National Science Foundation's Long Term Ecological Research (LTER) Network. Although the LTER research agenda and sites were not explicitly designed to address climate change, the LTER program offers sustained ecological research among diverse ecosystems undergoing climate change. In this article, we demonstrate how a long-term ecosystem perspective emerging from four decades of LTER research provides key insights about ecosystem responses to climate change.

Long-term ecological research applies ecological principles over scales of time and space great enough to evaluate long-term change (Callahan 1984 , Waide and Kingsland 2021 ). Since its inception in 1980, LTER has addressed major environmental issues based on cross-site comparative research, producing broadly applicable ecological principles (Franklin et al. 1990 ). The decade-to-century life span of LTER permits identification of the complex nature of temporal change, as well as spatial patterns of change (Hobbie et al. 2003 ). Long-term ecological research also addresses environmental stewardship, including policy, management, and conservation (Driscoll et al. 2012 ). The sustained nature of LTER research communities enhances the impacts of science on environmental policy relative to short-term studies (Hughes et al. 2017 ). The commitment of the LTER community to serve broad public interests, and the LTER culture of openness, dispersed leadership, and partnership with policymakers and resource managers has been key to advancing basic science that supports the societal need to address major environmental challenges (Swanson et al. 2021 ). LTER research addresses environmental change in a human-dominated world (Robertson et al. 2012 ).

Four decades of LTER research have solidified three elements of a long-term ecosystem perspective: the invisible present, the invisible place, and spatiotemporal disturbance dynamics. The invisible present is the time scale “within which our responsibilities for planet Earth are most evident, [encompassing] our lifetimes and the lifetimes of our children and our grandchildren” (Magnuson 1990 ). Sustained long-term research places events or changes in their broader context and reveals lagged and cascading effects through time (Magnuson 1990 ). LTER research has demonstrated how legacies of human activities and natural events continue to influence ecosystems for decades and even centuries (Foster et al. 2003 ). With 40 years of record, augmented by pre-LTER data (Jones and Nelson 2021 ), the time frame of LTER research is now long enough to begin to distinguish responses to long-term climate change from short-term or cyclical variation (e.g., Bahlai et al. 2021 , Magnuson 2021 ).

The invisible place, by analogy, is the spatial scale within which events and ecosystem processes operate. It addresses how events and processes are influenced by their location along flow paths of matter and energy through landscapes and seascapes (Swanson and Sparks 1990 ). Examination of the ecological effects of global climate change requires multiscale research that uses knowledge of coarser scales of resolution to provide context for interpretation of fine-scale system behavior and knowledge of finer-scale processes to explain the mechanisms of patterns observed at coarser scales (Swanson and Sparks 1990 ). With 28 sites ranging from the Arctic and Antarctic to the tropics, the spatial extent of LTER research can connect site-level ecosystem responses to regionally varying climate change processes.

Long-term ecological research also contributes to understanding the temporal and spatial contexts of disturbance. The LTER program encompasses many ecosystem types subject to multiple disturbances, documents slow or infrequent events, and provides a long-term baseline against which to detect change and measure ecosystem responses (Turner et al. 2003 ). LTER research examines how environmental drivers of disturbance act on ecosystem properties via specific mechanisms (Peters et al. 2011 ). LTER studies can identify disturbance mechanisms (specific stressors such as heat, impact force, abrasion, and burial that damage or kill organisms) associated with various disturbance types (phenomena such as fire, flood, wind, and wave action; Dale et al. 2005 ). With 28 sites and more than 900 site years of study, LTER research may detect how climate change is altering disturbance regimes and ecosystem responses (Gaiser et al. 2020 ).

Entering its fifth decade, LTER research now plays a vital role in deepening our understanding of ecosystem responses to climate change. This special issue evaluates 40 years of LTER research on climate change effects on ecosystems and suggests how LTER findings and approaches may guide continued research and policy in the coming decades. The 28 LTER sites are being subjected to varied types of environmental forcing from climate change, as well as other human activities that collectively affect a suite of ecosystem processes and ecosystem services, and ultimately, climate change itself (figure  1 ). LTER sites sample diverse ecosystems (figure  2 ) that are being subjected to varying types and rates of climate change ( Hayhoe et al. 2018 , IPCC 2021 ). The core research areas of LTER—disturbance, primary production, cycling of organic and inorganic matter, and dynamics of populations and communities—permit comparison of ecosystem responses among sites. Therefore, our specific objectives in the present article are to describe and apply a conceptual framework for ecosystem responses to climate change that arises from a long-term ecosystem perspective; to describe how ecosystem responses to climate change vary among ecosystem types, their climatic and geographic settings, and ecosystem processes, based on findings and perspectives from LTER research; and to propose LTER synthesis and outreach efforts to address ecosystem responses to climate change.

Conceptual diagram depicting climate change effects on ecosystem processes at LTER sites addressed in this article and special issue. Global climate change alters local temperature and moisture (*), producing varied types of environmental forcing. Ecosystem processes respond to environmental forcing and to non-climate-change human actions, and these responses affect ecosystem services. Both responses and ecosystem services feed back to climate change.

Locations of sites in the US Long-Term Ecological Research network, as of 2020, coded by group used in this special issue: marine pelagic, coastal, dryland, forest and freshwater.

The articles in this special issue consider a set of linked processes, including climatic forcing, environmental forcing, ecosystem response, feedback loops to the climate system, and ecosystem services (figure  1 ). Increased concentrations of greenhouse gases are altering global temperature and atmospheric circulation, producing local changes in temperature and moisture. These processes result in environmental forcings that affect ecosystems, such as temperature and moisture stress; altered growing seasons and shorter winters; increased floods, drought, wildfire, and hurricanes; rising sea level, saltwater intrusion; altered winds, waves, and currents; increased freshwater inputs to oceans; and enhanced ocean stratification and acidification. Environmental forcings alter disturbance, primary production, cycling of organic and inorganic matter, and population and community dynamics, and these changes can feed back to the climate system. Ecosystem processes are simultaneously responding to non-climate-related human actions, such as air pollution, land management, fishing, and introduced species. Collectively, these changes affect ecosystem services that shape human livelihoods, well-being, and survival and alter human behaviors in ways that feed back to affect climate change (Collins et al. 2011 ).

In the articles in this special issue, the 28 LTER sites active in 2019 were placed into one of four groups: forest and freshwater ( n  = 9), dryland ( n  = 8), coastal ( n  = 6), and marine pelagic ( n  = 5) ecosystems. Each group encompasses varied geographic and climate settings (table  1 , figures  2 and  3 , supplemental figure S7). Forests are ecologically and physically connected to freshwater ecosystems through surface and groundwater flows, and the forest and freshwater LTER group includes boreal, temperate, and tropical forests and associated streams and lakes, all spanning tropical to sub-Arctic latitudes. Drylands encompass nonforested terrestrial ecosystems with higher ratios of temperature to precipitation indicating greater moisture stress than forest and freshwater LTER sites (figure  3 ). The dryland LTER group includes hot deserts, cold deserts, tundra, tallgrass prairie, and row crops, all spanning subtropical to polar latitudes. Coastal LTER sites are ecosystems at land margins, where terrestrial processes and freshwater interact with the ocean and oceanographic processes. Ecosystems in the coastal LTER group include barrier islands, seagrass meadows, mangrove forests, salt marshes, coral reefs, and kelp forests, all spanning tropical to temperate latitudes. Marine pelagic LTER sites are ocean ecosystems along continental margins, where processes within the water column are influenced by proximity to the coasts and by deep water beyond continental shelves. The marine pelagic LTER group includes ecosystems with a wide range of terrestrial influences spanning tropical to polar latitudes. Each group represents diverse ecosystem types located across the Western Hemisphere (figure  2 ) encompassing wide ranges in mean annual precipitation and mean annual temperature (figure  3 ). Collectively these 28 LTER sites provide unique opportunities for broadscale synthesis. But the LTER network is much more than a collection of sites: It is an open, interactive, diverse community of researchers committed to mechanistic research, synthesis of findings, and collaboration with policymakers (Driscoll et al. 2012 , Swanson et al. 2021 ).

Mean annual air temperature and mean annual precipitation by ecosystem type at 28 LTER sites, based on data from the Global Historical Climatology Network site nearest to each LTER site (see the supplemental material for details of data sets and analyses). See table  1 for data and site names.

Names, abbreviations, locations, and biomes of 28 LTER sites in four groups of ecosystems, and mean annual temperature and mean annual precipitation, 1950–2020, based on data from nearest site in the Global Historical Climatology Data network (see the supplemental material).

Abbreviations: MAP, mean annual precipitation; MAT, mean annual temperature.

a Beaufort Lagoon is a shallow sea ice-dominated coastal marine LTER.

Climate change affects environmental forcing and ecosystem responses through changes in average and extreme temperature and moisture on multiple time scales. We quantified change in air and sea surface temperature and moisture at LTER sites by ecosystem group and geographic setting, for four time periods (1980–2020 versus the twentieth century, 1930–2020, 1950–2020, and 1980–2020) using global gridded data sets (see the supplemental material).

Most LTER sites warmed 0.3 to 0.4 degrees Celsius per decade from 1980 to 2020, and the rates of warming were lowest at tropical LTER sites and highest at polar LTER sites (figure  4 , supplemental table S4). Of the 22 LTER sites with Standardized Precipitation–Evaporation Index data (see the supplemental material), 14 became functionally wetter (LTER sites in the Arctic, the eastern United States, and Puerto Rico), whereas 8 became functionally drier (LTER sites in the southwest United States) from 1980 to 2020 (figure  5 ). The rate of warming has accelerated at almost all LTER sites over the LTER period (1980–2020) relative to the twentieth century. Most LTER sites warmed at rates two to three times faster in 1980 to 2020 than in 1930 to 2020, and the warming rates increased by three to ten times at six LTER sites (figure  6 , table S4).

Rates of change in temperature from 1980 to 2020 based on Goddard Institute for Space Studies air and sea surface temperature anomalies for LTER sites, by latitude (see the supplemental material for details of data sets and analyses).

Controlling for temperature change, over the LTER period (1980 to 2020), some LTER sites are becoming functionally wetter, whereas others are becoming functionally drier, based on the Standardized Precipitation–Evaporation Index (SPEI; see the supplemental material for details of data and analyses). SPEI data are not calculated for open ocean sites or Antarctic sites (CCE, NES, NGA, MCM, MCR, or PAL). Excluding the Alaska sites, which are all warming faster than the continental US or Puerto Rico, warming rates are higher at sites that have become drier and lower at sites that have become wetter.

The rate of temperature change during the LTER period (1980 to 2020) has accelerated at almost all LTER sites compared to the rate from 1930 to 2020. Warming has accelerated by more than two times and up to ten times. Some sites with relatively low warming rates from 1930 to 2020 have experienced large acceleration in 1980 to 2020.

Changes in extremes and warming rates provide different perspectives of climate change effects on LTER ecosystems. Absolute change, measured in degrees Celsius, indicates change in heat energy to the ecosystem, whereas relative warming, measured in percentiles, can indicate changes in extremes, such as the hottest and coldest conditions. Some LTER sites, especially at subtropical and tropical latitudes, are not warming very fast in absolute terms, but the number of extreme hot months from 1980 to 2020 was three to four times higher than it was in the twentieth century (figure  7 , supplemental table S5, supplemental figure S3). Other LTER sites, especially at subpolar latitudes, are warming rapidly in absolute terms but have lesser increases in extreme hot months (figure  7 , table S5).

Extremes of temperature became more frequent at LTER sites during the LTER period (1980 to 2020) both in absolute terms (increase in months per year with air temperature more than 2 degrees Celsius warmer than in the twentieth century, x -axis) and relative terms (increase in months per year with air temperature above the 90th percentile, twentieth century, y -axis). Some LTER sites with little increase in absolute extremes had large increases in relative extremes (e.g., FCE, LUQ, MCR), and vice versa (e.g., ARC, BLE, BNZ, CDR, KBS, NGA, NTL, PAL).

The increase in extreme hot months and the corresponding loss of extreme cold months has accelerated during the LTER period (1980–2020). By the 2000s,  the frequencies of extreme hot and cold months were outside the range of variation from the 1930s to the 1970s at almost all LTER sites: The frequency of extreme hot months more than tripled at five of six coastal LTER sites, four of five marine pelagic LTER sites, three of eight dryland LTER sites, and two of nine forest and freshwater LTER sites, with a corresponding loss of extreme cold months (figure  8 ). Wetness and dryness extremes also have become more frequent since 2000 but at fewer sites. Extreme wet months were more frequent in the 2000s or 2010s than in the 1930s through the 1970s at three of nine forest and freshwater LTER sites (Hubbard Brook, Luquillo, North Temperate Lakes) and two of eight dryland LTER sites (Cedar Creek, Kellogg Biological Station), whereas extreme dry months were more frequent at four dryland LTER sites (Arctic, Central Arizona–Phoenix, Jornada, Sevilleta; figure  9 ). Both wet and dry extremes have increased at some LTER sites (e.g., Luquillo).

Decadal frequency of numbers of extreme hot months (a–d) and extreme cold months (e–h) in relative terms for LTER sites from the 1930s to 2010s,  based on Goddard Institute for Space Studies air and sea surface temperature anomaly data (see the supplemental material for details of data sets and analyses). (a, e) forest and freshwater sites, (b, f) dryland sites, (c, g) coastal sites, (d, h) marine pelagic sites. Vertical dashed line indicates beginning of LTER period. Horizontal dashed line is the expected frequency of months per decade in the highest 90% and lowest 10% of the distribution for the twentieth century (i.e., 12 months per decade).

Decadal frequency of numbers of extreme wet months (a–d) and extreme dry months (e–h) in relative terms for LTER sites from the 1930s to 2010s,  based on Standardized Precipitation–Evaporation Index (SPEI) data (see the supplemental material for details of data sets and analyses). (a, e) forest and freshwater sites, (b, f) dryland sites, (c, g) coastal sites, (d, h) marine pelagic sites. Vertical dashed line indicates beginning of LTER period. Horizontal dashed line is the expected frequency of months per decade in the highest 90% and lowest 10% of the distribution for the twentieth century (i.e., 12 months per decade). SPEI data do not exist for CCE, NES, NGA, MCM, MCR, or PAL.

Trends in temperature and wetness or dryness varied by season and location, with rapid warming at LTER sites in the austral fall and winter in Antarctica; in fall and winter in Alaska; in spring, summer, and fall in the continental United States; and year-round in the tropics (supplemental table S6, supplemental figure S5) and with drier winter and spring at LTER sites in Alaska, the Rocky Mountains, and the southwestern United States and wetter autumn at LTER sites in the north central and northeastern United States (supplemental table S7, supplemental figure S6).

LTER sites are being exposed to many types of local environmental forcing driven by climate change, as is described in the articles in this special issue (figure  1 ). Heat waves have imposed temperature stress on all ecosystem types. Drought has exacerbated moisture stress and wildfire in some forest and freshwater and dryland ecosystems. The loss of ice, snow, and permafrost is altering runoff patterns; increasing flooding in forest, freshwater, and dryland ecosystems; and augmenting freshwater delivery to some marine pelagic ecosystems. Altered winds, waves, and increased hurricanes compounded by sea level rise and saltwater intrusion are affecting coastal ecosystems, whereas currents and upwelling patterns are changing in coastal and marine pelagic ecosystems. Ocean ecosystems are experiencing stronger stratification and acidification.

Environmental forcing from climate change (figure  1 ) differs among the four ecosystem types and by location. In forest and freshwater systems, climate change melts snow and ice, thaws permafrost, and alters precipitation (Campbell et al. 2022 , this issue). Extreme heat and increased evaporative demand impose temperature and moisture stress. Forest and freshwater sites in the eastern United States and the tropics have experienced increases in storm intensity, including more frequent and intense hurricanes, floods, and ice storms, whereas those in the western United States and Alaska have experienced increased dry conditions and wildfire (figure  1 ).

In dryland ecosystems, climate change increases temperature and moisture stress, flooding, and wind erosion (Hudson et al. 2022 , this issue). Heat waves and reduced humidity accentuate moisture stress. Increased precipitation and flooding have affected dryland systems in the central and north central United States, whereas increased wet–dry extremes have intensified flooding, drought, erosion, and dust transport in southwestern deserts. At the Arctic tundra LTER, the air has become warmer and drier, increasing climatic drought and even wildfire, and permafrost thaw has simultaneously increased streamflow (figure  1 ).

In coastal systems, climate change contributes to temperature stress, altered wind and waves, and saltwater intrusion (Reed et al. 2022 , this issue). Reduced coastal upwelling, ocean acidification, and other biogeochemical changes in soil, sediment, and water are affecting coastal ecosystems. Less frequent freezing events, increased marine heat waves, and reduced vertical ocean mixing impose temperature stress. Rising sea levels have increased inundation and saltwater intrusion. Coastal ecosystems have experienced increased extreme waves and wind, higher storm surges, and coastal erosion, as well as increased flooding and drought (figure  1 ).

In marine pelagic systems, climate change contributes to changes in water circulation, warming, and effects on sea ice (Ducklow et al. 2022 , this issue). Circulation and mixing effects include reduced upwelling, increased stratification due to changing temperature and salinity and decreased vertical mixing. Marine heat waves have imposed temperature stress at all latitudes. In Arctic and Antarctic latitudes, melt and retreat of glaciers increase freshwater inputs. Higher sea surface and air temperature and evaporation increase precipitation (rain or snow). Sea ice changes include later advance and earlier retreat of seasonal ice and reduced ice duration (figure  1 ).

Non-climate-related human activities interact with environmental forcing from climate change to influence ecosystem responses (figure  1 , supplemental table S3). Marine pelagic LTER sites have been affected by fishing, whaling and the cessation of whaling, and, more recently, by microplastics. In coastal LTER sites, fishing and marine mammal extirpation have altered community composition, whereas urbanization and coastal development have altered water, sediment, and nutrient dynamics. Dryland LTER sites have been affected by clearing, agriculture, and field abandonment; the extirpation of native grazers and the introduction of domestic grazers; diversion of water; elevated atmospheric deposition and ozone; and urbanization and exurban development. Forest and freshwater LTER sites have been altered by forest clearing, agriculture, grazing, and field abandonment; logging and road construction; fire suppression; elevated nitrogen and sulfur deposition and ozone; introduced species and plant pathogens; and urbanization and exurban development.

The ecosystem responses to climate change at LTER sites are extremely varied and, in many cases, just emerging. As is demonstrated in the articles in this special issue, a long-term ecosystem perspective offers important insights into how ecosystems are responding to changing climate. A few simple principles emerge, but for the most part, ecosystem responses are highly individualistic. Responses are most evident at ecosystems exposed to the most rapid rates of change or experiencing the most severe climate-related disturbances. Moreover, ecosystem responses can be most clearly linked to climate change where warming has led to a phase transition of water from ice to liquid water or to water vapor. Beyond these, the responses to climate change are quite variable among ecosystems.

Several additional principles from a long-term ecosystem perspective help explain the variability of ecosystem responses to climate change. First, the direct effects of climate change (i.e., changing temperature and moisture) vary by latitude, by region, and by season. Second, an ecosystem's response depends on environmental forcing, which differs among biomes and locations. Responses vary across the LTER core research areas of disturbance, primary production, organic and inorganic matter cycling, and population dynamics. Non-climate-related human activities and their legacies interact with various types of environmental forcing to produce effects. Finally, climate change results in a cascade of interactions and legacies over time (the invisible present, disturbance cascades), and in space (the invisible place), which play out over decades to centuries. We briefly discuss these principles below. Examples are taken from the companion articles in this special issue and from articles accessible in publication lists linked at lternet.edu.

Responses are greatest where warming is most rapid

In general, across the 28 LTER sites, ecosystem responses to climate change are most prominent where the greatest increases in absolute temperature have occurred (i.e., high-latitude sites). For example, increased wildfire has altered ecosystem processes and communities in the boreal forest (Bonanza Creek) and tundra (Arctic) LTER sites, and changes in freshwater inflows, sediment, and light have affected a shallow sea ice-dominated coastal marine LTER (Beaufort Lagoon Ecosystem). Ecosystem responses also are evident at subtropical or tropical sites, which have experienced the greatest relative increases in temperature. Increased hurricanes and heat waves have altered ecosystem processes in tropical rainforest (Luquillo) and in a tropical coral reef (Moorea).

Responses are notable where ice is melting

Many ecosystem responses to changing climate are associated with a threshold change in the phase of water. Liquid water alters energy exchange, transports nutrients and sediment, and enables growth and metabolism. In marine pelagic ecosystems, freshwater outflow from the melting Greenland icecap is increasing stratification of the Atlantic Ocean near New England (Northeast Shelf), permafrost thaw is promoting a shift to shrubs in the Alaskan tundra (Arctic), permafrost thaw and increased river discharge have altered processes in a shallow sea ice-dominated coastal marine ecosystem (Beaufort Lagoon), and a shift from snow to rain may be drowning penguin chicks in Antarctica (Palmer). In coastal ecosystems, reductions in freezing events have enabled mangroves to expand their northern range limit (Florida Coastal Everglades) and permitted native woody species to expand seaward (Virginia Coastal Reserve). In forest and freshwater ecosystems, a shift from snow to rain has altered plant function and streamflow (Baltimore, Hubbard Brook, Harvard Forest) and increased stream water phosphorus inputs and algal blooms (North Temperate Lakes), whereas permafrost thaw has expanded shrub cover in alpine tundra (Niwot Ridge). In drylands, permafrost thaw has increased transport of organic and inorganic material to lakes in the tundra (Arctic), the loss of snowpack has increased greenhouse gas emissions (Kellogg), and a glacial melt outburst flood altered stream and lake ecosystems in Antarctica (McMurdo Dry Valleys). A shift in the phase of water from liquid to vapor (i.e., increased evaporation) also is linked to increased dust transport in the US Southwest (Central Arizona–Phoenix), reduced primary production in deserts (Jornada), and increased wildfire in Alaska (Bonanza) and the US Pacific Northwest (Andrews).

Responses vary by core research area

Ecosystem responses to climate change are evident in all core research areas of LTER designated at the inception of the program: disturbance, primary production, cycling of organic and inorganic matter, and populations and communities, but the responses differ among the core research areas (figure  1 ). Most LTER sites report increases in the frequency and severity of disturbance events associated with climate change, and many sites have been exposed to unprecedented types of disturbance. Altered disturbances include increased frequency and severity of terrestrial and marine heat waves, tropical cyclones, flooding, ice storms, and larger and more severe wildfires.

Changes in primary production also are being observed at LTER sites. Increased intensity of weather events involving heat, drought, wind, waves, floods, or wildfire are associated with losses but also some gains in live biomass in forest, dryland, coastal, and marine pelagic ecosystems. Net primary production has increased in some ecosystems because of a loss of sea ice, snow, and glaciers, and it has declined in other ecosystems because of a variety of mechanisms including combustion, physical abrasion, heat stress, moisture stress, defoliation, wave action, and the loss of light from burial or suspended sediment.

Movement and storage of organic matter also have been altered by climate change at many LTER sites. Changes include remineralization of buried carbon in seagrass meadows as a result of marine heat waves (Georgia Coastal), peat collapse in salt marshes due to saltwater intrusion (Florida Coastal Everglades), losses due to wildfire (Arctic, Andrews), increased decomposition of soil organic matter (Harvard Forest), increased dissolved organic matter delivery to streams and lakes in part due to increased temperature and runoff (Hubbard Brook), and altered carbon fixation and export to the deep ocean due to sea surface warming (California Current, Northern Gulf of Alaska).

Changes in temperature, precipitation intensity, and phase of water have altered transport of inorganic material at some LTER sites. Some changes increase transport. Thawing permafrost increased the available pool of nitrogen in the boreal forest (Bonanza), hurricanes have produced pulses of nitrate and potassium in a tropical rain forest (Luquillo), and extreme precipitation events have augmented nutrient inputs to lakes (North Temperate Lakes). In contrast, other changes decrease transport of inorganic material. Increased soil carbon has reduced nitrogen availability in a temperate deciduous forest (Hubbard Brook). Increased freshwater inputs to marine pelagic systems may reduce salinity because of dilution (Northeast Shelf) and sea surface warming can suppress upwelling and delivery of inorganic material (California Current).

Populations and communities at LTER sites are responding to climate change, but many of these effects also are attributable to non-climate-change related human activities (see below). Examples of direct effects of climate change on populations are few but include declines in cold water dependent fish species and increased cyanobacteria that drive harmful algal blooms (North Temperate Lakes), reductions in seabird (Adélie penguin) populations possibly in response to changes in snow (Palmer Antarctic), and changes in fish species abundance in kelp forests (Santa Barbara Coastal).

Responses vary depending on climate change drivers

The diverse ecosystem types represented by LTER sites are being subjected to different combinations of increased temperature and changes in moisture (figure 5), as well as different seasonal timings of these changes. Marine pelagic sites and coastal LTER sites respond to change in air and water temperatures and may respond to changes in moisture only indirectly through changes in terrestrial freshwater inputs. Some terrestrial LTER sites are becoming warmer and drier, whereas others are becoming warmer and wetter, and at one LTER (Arctic Tundra) the air is warming and drying, but permafrost thaw is producing wetter soils. Trends in temperature and moisture also differ by season among LTER sites, likely contributing to differential effects on seasonally driven ecological processes.

Responses vary with environmental forcing

Environmental forcings are the proximal drivers of ecosystem responses to climate change, but they differ among biomes, even among nearby LTER sites subjected to the same changes in temperature and moisture. For example, in Antarctica, increased snow is a primary environmental forcing at the marine pelagic LTER (Palmer), whereas glacial melt-driven floods are a primary forcing at the dryland LTER (McMurdo Dry Valleys). In Alaska, primary environmental forcings include a marine heat wave at the marine pelagic LTER (Northern Gulf of Alaska), increased wildfire at the forested LTER (Bonanza Creek) and the tundra LTER (Arctic), and increased wave action from the loss of sea ice at the marine LTER (Beaufort Lagoon Ecosystem). In the northeast and north central United States, primary environmental forcings include increased intensity of precipitation in forest and freshwater LTER sites (Baltimore, Harvard Forest, Hubbard Brook, North Temperate Lakes) and some dryland LTER sites (Cedar Creek, Kellogg Biological Station), changes in ocean currents and freshwater mixing in the marine pelagic LTER (Northeast Shelf), and rising sea level and salt water intrusion at the coastal LTER (Plum Island).

Responses depend on other human activities

LTER research has demonstrated that ecosystem and ecological processes continue to respond to past human activities and other nonclimate disturbances for decades or centuries (Foster et al. 2003 , Turner et al. 2003 ). The legacies of these past and ongoing human activities and other natural disturbances interact with climate change, producing diverging ecosystem responses even among LTER sites in similar biomes with similar environmental forcing. For example, among marine pelagic LTER sites, ecosystem responses to marine heat waves vary based on past and ongoing fisheries management, including krill harvests (Palmer Antarctic) and twentieth century fisheries collapse (Northeast Shelf, California Current Ecosystem, Northern Gulf of Alaska). Ecosystem responses to climate change at dryland LTER sites are mediated by past removal of native grazers and introduction of domestic cattle (Konza Prairie, Jornada). Responses in coastal LTER sites depend on adjacent local development (Santa Barbara Coastal), changes in river flow input due to upstream development (Georgia Coastal, Plum Island), and diversions of freshwater inputs (Florida Coastal Everglades). Ecosystem responses to climate change in forest and freshwater LTER sites are affected by diverse histories of logging (Andrews, Hubbard Brook), fire suppression (Coweeta), forest clearing and agriculture (Baltimore, Luquillo), air pollution (Hubbard Brook, Niwot Ridge), and introduced pathogens (Coweeta, Harvard Forest). Human responses, such as salvage logging conducted in response to wildfire (Andrews) or forest mortality from insects (Harvard Forest), compound ecosystem responses to climate change. The 40-year history of LTER demonstrates the value of a long-term perspective that integrates multiple drivers of ecosystem response.

Insights from a long-term ecosystem perspective

A long-term ecosystem perspective provides insights into how climate change affects all aspects of populations, communities, and ecosystem processes and into how these effects cascade through time and space. The invisible present permits the comparison of responses to successive hurricane events as the record becomes longer at a tropical forest (Luquillo). It reveals lagged responses of wildfire and dust storms to changes in primary production in US Southwest desert LTER sites (Jornada, Sevilleta, Central Arizona–Phoenix). But despite the many changes observed, so far, climate change is implicated in very few major shifts in ecosystem type or state at LTER sites. A long-term perspective can reveal ecosystem responses such as resilience to multiple disturbances (in coral reefs, Moorea; in tropical forest, Luquillo), slow succession (in boreal forest, Bonanza), or changing community composition with continued ecosystem function (in prairies, Konza and Cedar Creek; in deserts, Jornada, Sevilleta; and in polar oceans, Palmer). A long-term ecosystem perspective also reveals the role of spatial location (the invisible place), such as how the location of lakes in the landscape affects their response to climate change (North Temperate Lakes) or how the position of a forest in the landscape affects its exposure to wildfire (Andrews). In addition, a long-term ecosystem perspective considers spatial and temporal cascades of disturbances, such as increased upwind storm intensity affecting defoliation of mangroves (Florida Coastal Everglades) or glacial melt outburst flood delivery of sediment suppressing lake primary production (McMurdo Dry Valleys). Cascading effects of climate change through direct and indirect effects and non-climate-change related human actions are being observed at numerous LTER sites across various biomes (Bahlai et al. 2021 ).

This examination of ecosystem responses to climate change from 40 years of LTER research indicates that ecosystem responses to climate change are just emerging, differ among ecosystem types and locations, and are likely to accelerate in coming decades. These findings raise two key issues: a need for LTER to promote sustained and cross-site deliberation about how different ecosystems are responding to climate change and an imperative for LTER to promote environmental stewardship by engaging governmental agencies, nongovernmental organizations, land trusts, environmental managers, and other actors in dialogue about science, policy, and management.

Sustained long-term cross-LTER research

Crucial insights into major ecological principles about ecosystem responses to climate change require not only sustaining long-term research at individual sites but also creating and maintaining forums for cross-LTER synthesis, which are lacking. Such forums could address key questions that emerge from this overview, including the following: How do disturbance effects on ecosystems change as disturbances become more frequent and increase in intensity? In which ecosystems is increasing climate variability leading to state changes in ecosystem structure or function, and why? What forms of non-climate-related human activities mitigate or magnify ecosystem responses to climate change? Why do ecosystem responses differ among ecosystems subjected to the same or similar climate or environmental forcing? Although there are many forums to discuss climate change, there is no sustained effort to identify ecological principles governing ecosystem responses to climate change. A significant additional investment to create and sustain such fora, building on LTER, would provide a unique contribution to basic and applied science and provide an effective mechanism for the research community to interact with resource managers.

Ecosystem services and environmental stewardship

Climate change is altering ecosystem services at LTER sites, including sustaining, regulating, provisioning, and cultural services (figure  1 ). For example, increased drought, heat, and the associated disruption of soil biocrust formation may increase wind erosion and dust storms, damaging human health in US Southwest desert LTER sites. Increased precipitation is overwhelming flood protection capacities of some forest and freshwater LTER sites, whereas increased heat and more frequent disturbance, including wildfire, is reducing carbon storage in forest and tundra LTER sites. In coastal ecosystems, increased wave height and a loss of coastal vegetation and sand barriers reduce storm protection capacity and coastal resilience to storm surges and sea level rise. In marine pelagic systems, marine heat waves and changes in upwelling may suppress or alter primary production, damaging fisheries. LTER studies also indicate that ecosystem responses alter ecosystem services, affecting human outcomes differently among regions and ecosystem types. Indeed, it appears that climate change effects are local and should be managed accordingly. The LTER community should actively collaborate with policymakers, land managers, and resource managers to promote locally relevant environmental stewardship in the face of climate change.

The 40th anniversary of the establishment of the US Long-Term Ecological Research Network provides an opportunity to ask what we have learned about ecosystem responses to climate change from long-term ecological research. Long-term ecological research provides an integrated perspective that permits linking changes in greenhouse gases to environmental forcing, ecosystem responses and their effects on ecosystem services and climate feedback loops. The syntheses of LTER research in this special issue indicate that virtually all ecosystem and ecological processes are being affected by changing climate but often differentially by region or ecosystem type. On the other hand, so far, climate change is implicated in very few major shifts in ecosystem type or state at LTER sites. Many other past and ongoing human activities continue to produce ecological change at LTER sites and most places on Earth, and those activities and their legacies interact with climate change over multiple decades to centuries.

The present article and the others in this special issue assess place-based LTER research in the context of anthropogenic climate change. Ecosystems are responding to changes in temperature, moisture, and environmental forcing that vary among regions and biomes. Ecosystem responses to climate change are just emerging, are extremely varied, and are likely to intensify as climate change accelerates. The observed ecosystem responses at US LTER sites and ongoing work at other sites and networks globally attest to the value of and need for continued long-term ecological research in order to understand, mitigate, and adapt to ecosystem responses to global climate change.

Author Biographical

Julia Jones ( [email protected] ) is a distinguished professor of geography in the College of Earth, Ocean, and Atmospheric Sciences at Oregon State University, in Corvallis, Oregon, in the United States. Charles Driscoll ( [email protected] ) is a distinguished and university professor of civil and environmental engineering at Syracuse University, in Syracuse, New York, in the United States.

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The Methodological Approach of the Long-Term Study

• Open Access
• First Online: 17 January 2019

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• Ingrid Paus-Hasebrink 6 ,
• Jasmin Kulterer 7 &
• Philip Sinner 8  

Part of the book series: Transforming Communications – Studies in Cross-Media Research ((TCSCMR))

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This chapter outlines our study’s specific methodological approach as derived from its theoretical foundation. We deal with the questions of collecting, processing and analysing data from a qualitative longitudinal study over 12 years from 2005 to 2017 but also with questions of recruitment and panel maintenance, as well as ethical issues. This chapter, therefore, focuses on the ways in which the methodological design was revised and judiciously complemented, in order to grasp the complexity of the topic. Following the logic of triangulation, we developed and implemented a rich design, where all the components draw on, complement, and monitor each other in the processes of data collection and analysis. This means that we have to discuss the importance of transparency and comprehensibility in terms of intersubjectivity, paying special attention to the qualitative and longitudinal character of the study.

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4.1 Introduction

Lasting from 2005 to 2017, this study’s methodological approach to the role of media in the socialisation of socially disadvantaged children and adolescents in Austria illustrates how the complex praxeological approach to researching children’s and adolescent’s media usage within the process of socialisation can be operationalised. The approach we selected for dealing with this challenge involved a qualitative longitudinal panel and was conducted successfully in six waves of data collection over a period of twelve years. The starting point was the question: How to operationalise the theoretical approach presented in the preceding chapter, in order to provide a fruitful basis for analysing the role of media within socialisation on a methodological level? As growing up in a mediatised world is a complex process, it is abundantly clear that looking merely superficially at how children deal with media will not suffice. Instead, it is essential to investigate how they subjectively make sense of media as a source for coping with factors of which they are not yet aware. The question: “How do children, and also their parents, make sense of media?” is closely connected with the structures of their everyday lives on the micro-level of the child or adolescent, on the meso-level of the family, but also of peers and friends, as well as on the macro-level of the country/society (see Fig.  4.1 ). Their media-related practices may be understood as subjectively given answers to the challenges of their everyday lives. Against this background, we consider media usage a practice within a socially constructed everyday life and thus a form of observable practical ability.

Relevant contextual factors for children’s socialisation

To sum up, the approach implies the following four methodological requirements: first and foremost, since the main analytical concepts refer to entangled and interlinked processes of interaction between children and their parents within their everyday lives, the methodological approach has to be located within a qualitative research paradigm (Clarke, 2005 , 2011 ; Flick, 2014 ; Wilson, 1970 ). Researching within this paradigm allows us to retrace and reconstruct how individuals subjectively make sense of their social contexts and act in them. Furthermore, it allows us to detect the underlying structures of individuals’ utterances and actions. Secondly, in order to be able to reconstruct and to review the processes of doing family , data have to be collected from children as well as from parents. Thirdly, in order to include the broader social contexts that have an impact on a family, particular attention has to be paid to its socio-structural conditions. And fourthly, in order to grasp the dynamic character of the socialisation processes, a longitudinal design is needed.

4.2 Recruitment of the Families

We conducted a panel study of twenty (reduced to 18 after the second wave due to drop-outs) socially disadvantaged families with children (boys and girls), who were, respectively, almost five or six years old in 2005 (see Paus-Hasebrink & Bichler, 2008 , pp. 132–141; Paus-Hasebrink & Kulterer, 2014 ; Paus-Hasebrink, Sinner, Kulterer, & Oberlinner, 2017 , pp. 48–49). They were not yet in school and still attending kindergarten. So, from the preparatory theoretical work, we deduced the following selection criteria as being central to researching social inequality and then recruited the 20 families accordingly.

Apparent markers of social disadvantage following Hradil ( 1987 , 1999 ): for example, unemployment, low income (defined as relatively poor, less than 50% of the national median income, and as at risk of poverty, less than 60% of the national median income), lower formal education, residential neighbourhood, bad housing conditions—singly or in combination.

Family structure: families were so chosen that the sample would contain different family configurations, like single-parent families, large families (more than five children) and nuclear families.

Migrant background: households with at least one foreign member (understood as a non EU- or EFTA-citizen) belong to the groups at risk of poverty or being marginalised (see Statistik Austria, 2017 , pp. 21, 25).

Area of living: urban and rural areas/areas with a poor infrastructure (for example, mountain areas, areas with bad public transport connections, no accessible railway stations) (see Table  4.1 ).

It was not our aim to depict statistical distribution throughout the population but to select typical and “ information - rich cases ” (Patton, 2002 , p. 230, emphasis in original) in our field of research, a procedure that is called “purposeful sampling” (Rapley, 2014 , p. 56): “Information-rich cases are those from which one can learn a great deal about issues of central importance to the purpose of inquiry, thus the term purposeful sampling” (Patton, 2002 , p. 230, emphasis in original). Based on a literature review (Paus-Hasebrink & Bichler, 2008 , pp. 135–136) conducted to acquire “knowledge of the phenomenon” (Rapley, 2014 , p. 50), we selected markers that define the characteristic living conditions of socially disadvantaged families, in order to make a “thoughtful and rigorous” (Rapley, 2014 , p. 49) choice.

Lack of money is the most important marker of social disadvantage. On the one hand, family income depends on the specific living conditions of a family’s wage earners, on the other hand, the availability of material resources defines the living conditions of a family as a whole. In order to include families in our sample, we had to be able to easily survey suitable markers, but with the provision that they would be capable of representing a wide range of socially disadvantaged families. Accordingly, for our sampling we decided to consider those factors that increase the risk of poverty (see Chapter 2 ): low income, unemployment, lower formal education of the parents, single-parent families, large families and families with migrant background. In addition, we decided to select children between five and six years old, who were not yet attending school, in order to capture the important transition period from kindergarten to primary school in our second wave of surveying our panel. We decided in advance on a total of 20 cases and based our decision on the qualitative orientation of the study, the complex methodological approach and the available resources (temporal, personal and financial). Both the distribution of families across rural and urban areas and the distribution between the sexes were to be equal. For practical reasons, we recruited families in the Federal States of Salzburg and Upper Austria.

The process of recruitment was supported by a number of social organisations and social facilities like the Organisation of Single Mothers and Single Fathers , Caritas , the Youth Welfare Office and numerous kindergartens, as well as by public authorities like the Department of Families and the relevant ministers from the two states. We informed potential participants about the project, invited them to contact the principal investigator and offered an expense allowance (50€ per data collection) to the families, an amount which remained unchanged until 2016. The children, and later the adolescents, received small non-monetary gifts (for example, sweets, pens or handicraft kits). However, socially disadvantaged people do have a record of rejecting government institutions and participation in research projects, so that the process of recruitment was quite hard and time-consuming (see also Patton, 2002 , pp. 310–311). In order to achieve our goals, we were forced to take further action: we started to cooperate with additional social organisations and social facilities, used personal contacts and political support, and we placed advertisements in local and regional newspapers and journals.

Table  4.1 provides an overview of the sample’s main characteristics at the beginning of the study (see Chapter 5 for a description of the families). Due to the fact that low income in the beginning was a major selection criterion to become a part of the panel this criterion is not listed in the table—at that time all families had a low income.

The focus of the research was on one child at the right age from each family. However, the siblings figured in the interviews, too. Since two families did not participate throughout, by the end of the project the sample consisted of ten boys and eight girls. Over the study period, the families’ changing circumstances altered other characteristics as well, so the sample looked different at the end of the project, a point extensively discussed below (see Chapter 8 ). The aim of choosing children between the ages of five and six was to be able to investigate the relevant phases of a child’s development from kindergarten, through mid-childhood to youth over six waves of data collection (2005, 2007, 2010, 2012, 2014, and 2016). To complete it, we conducted a telephone survey in winter 2016/2017, in order to get the latest information about the development of the adolescents and their families.

4.3 The Challenges of Managing a Long-Term Study

The whole project consists of three separate phases (waves 1 and 2, waves 3 and 4, waves 5 and 6 and call-back interviews). All three project phases had to be applied for singly. Not only did they have to pass the blind peer-review process, but they also had to fulfil the requirements and restrictions of the OeNB Anniversary Fund ( 2015 ) (Fund of the Austrian National Bank). In addition, the proposals needed the approval of Department of Communications of the University of Salzburg, the approval of the Research Service of the University of Salzburg ( 2018 ) and the approval of the Rectorate of the University of Salzburg. Due to the limited time frames, we had to plan the three project waves separately, not as a single twelve-year project. However, we did manage to acquire three consecutive grants, but there was always the risk of terminating the study after its first or second phase. Financing over a period of years is one of the most challenging tasks in conducting long-term research, due to reduced overall funding for research, but also given the existing structures (in Austria) for research funding. As a consequence, we had to work hard on maintaining our panel. On the one hand, we had to keep contact-data up to date, as our clients moved house and changed telephone numbers, email addresses and other contact details quite often. Furthermore, they often neglected official letters, sometimes not even opening them. Consequently, we had to visit some of the families over the time, just in order to stay in touch and to re-recruit them for our next wave of data collection. On the other hand, we had to deal with the uncertainties over the project funding. Every time we had to invite the families to participate again in future, even though we could not guarantee if, and when, the next wave of data collection would take place. This required great commitment from both sides, families and researchers, but in the end, we were able to achieve a very low drop-out quota.

4.4 Data Collection

In order to reconstruct, describe and explain how children make sense of media against the background of their everyday lives, we have applied a broad repertoire of qualitative methods (Morse & Maddox, 2014 , pp. 524–525; see also Paus-Hasebrink et al., 2017 , pp. 51–58; see Figs.  4.2 and 4.3 ). However, we have gone one step further, using a rich design which is characterised “as one that is not restricted to one theory and method, or one set of categories or instruments, but which embraces diverse and multiple perspectives brought together with coherence and harmony. It is more than a multi-method design per se” (Paus-Hasebrink, Prochazka, & Sinner, 2013 , p. 23): we conducted guided in-depth interviews (semi-structured) with the children. Commencing with the fifth wave (beginning of adolescence, 2014), we added three more methods of data collection:

Methods of data collection in the different waves of research at a glance

Methods of data collection

(1) Thinking aloud about a social networking tool (for example Facebook or WhatsApp) selected by each child, (2) In addition, we asked them to produce their own network maps of media and relationship structures, and (3) to take a set of photos (favourite spot, place of work, preferred spot for media usage). We also conducted guided in-depth interviews (semi-structured) with the parents or one single parent. In addition, we asked them to independently complete a standardised questionnaire on their living conditions. In order to get an overall impression of the respective families, we used observation protocols to provide a report on the visit by the interviewers.

These components of our rich design draw on, complement, and monitor each other during data collection and analysis, a process which simultaneously makes the research transparent and intersubjectively traceable: “The goal of such rich design approaches may be to eliminate weaknesses and blind spots perceived in one method by using complementary approaches that have specific strengths in such areas. A sensitive combination of methods can therefore shed light on aspects that cannot be covered adequately by only one method. Such rich designs feature a high density of data and a high level of reflection on the research process itself” (Paus-Hasebrink et al., 2013 , p. 23). This is in accordance with Denzin’s ( 1989 , p. 307) concept of triangulation: “By combining multiple observers, theories, methods, and data sources, [researchers] can hope to overcome the intrinsic bias that comes from single-methods, single-observer, and single-theory studies” (see also Denzin & Lincoln, 2011 , p. 2). The capacity for “comparing and cross-checking the consistency of information” (Patton, 2002 , p. 559) allows researchers to generate greater added value.

The strategies of research were guided by the analytical concepts presented in Chapter 3 : options for action , outlines for action and competences for action . In order to operationalise these concepts, we used several reactive and non-reactive methods. The standardised questionnaire for the parents was used in order to examine the options for actions . Observational methods served as an additional tool to investigate how the child and the parents conduct their everyday life and how they deal with the conditions of their specific social situation in doing family (for example, conflicts, proximity and so on). In order to define the outlines for action , we combined these questionnaires and the named observations with guided in-depth interviews conducted separately with the child and the parents—mostly the mother, in some cases both parents—on different aspects of their everyday lives and their usage of media. In order to gather evidence regarding competences for action , we used indications from interviews and observations of a wide range of aspects, for instance, a child’s cognitive and motivational resources, their attitudes towards kindergarten, as well as school and education in general, parents’ strategies for bringing up their children, together with their ability to implement them, and for mediating their media usage, and both children’s and parents’ media-related skills. All these aspects were investigated from both the children’s and the parents’ perspective. This made it possible to discover discrepancies or commonalities in how children and parents mutually perceive their competences. Differences between the answers of parents and children suggested conclusions regarding family climate and doing family (relationships between parents and children and within the family as a whole). Furthermore, this approach allowed conclusions concerning the applied mediation strategies and their acceptance, as well as their relevance for the process of socialisation. In addition, it became possible to track the process of growing up and the process of distancing from parents as the children became older and subsequently adolescent (at the beginning of puberty and youth) (see Paus-Hasebrink et al., 2017 , p. 51). All the data collected from these different methods were used to create and to update a global characteristic (Charlton & Neumann, 1986 ; Schütze, 1977 ; see also Paus-Hasebrink & Bichler, 2008 , pp. 142–145; Paus-Hasebrink & Kulterer, 2014 , pp. 57–59; Paus-Hasebrink et al., 2017 , p. 51) for each family investigated.

4.4.1 Standardised Questionnaire

At each visit, the parents were asked to complete a standardised questionnaire on their living conditions. These questionnaires covered central aspects about living together, the constellation within individual families, housing and socio-economic circumstances. It consisted of the following main categories:

family constellation and family characteristics: marital status, number of family members living together, number of children, overall and in residence, ages of children and parents, nationality, religious affiliation;

professional career and activity of the parents: schooleducation and apprenticeship, (un)employment and self-employment, training courses;

economic situation of the family: householdincome (single earner or not), social benefits, further sources of income (for example, rental income, interest, inheritances), assets and/or estates, amount of pocket money (children), later on also, income of the adolescents;

housing situation: residential area and neighbourhood, size and state of flat or house, ownership or renting a flat or house, duration of residence, plans for relocation.

4.4.2 Guided In-Depth Interview with the Parents

Since its inception, we tried to keep changes or modifications to the guideline at a minimum, in order to sustain a maximum of comparability throughout the investigation period. The guidelines for both parents and children were closely coordinated. However, we questioned the parents more intensively about their children than the other way round. Our interviews included questions about how, in the first instance, the parents—but then, to a lesser extent, their children and other family members—dealt with and experienced possible challenges, such as unemployment or poorly-paid jobs, shortage of money, bad housing conditions, living in a single parent family and/or in the context of a large family, and so on. We also asked them how they interacted with their family and if they felt integrated in it, in their neighbourhood, peer-groups, friendships, kindergarten or class, and social life in general, and what kinds of media offerings they used, to what extent, for what purposes, and with whom. Additionally, we asked children and parents in each wave of data collection for their ideas, preferences, goals, plans and motives for action and what they were planning for their future. However, in the interviews with parents some topics were examined more intensively than they were with the children. There were, for example, questions about their mediation strategies, questions not only about the child of particular interest but also about the siblings, and questions concerning developments in different aspects of individual families’ lives. In order to provide an overview, the guidelines for parents followed four main themes:

how the family felt situated socially—the everyday life of the family—the participation of parents and children within society, that is, the social conditions of growing up, the changes that have happened in living conditions, the climate and relationships within the family, daily routines and aspects of everyday life, the preferred leisure activities of parents, and of children, their shared interests, social activities and involvement in associations, cultural life and active citizenship;

their attitudes towards the media—the media usage of parents, child and siblings—the behaviour within the family as regards the media, meaning the importance of different media for the parents, the individual patterns of media usage of the family members, the relevance of joint media practices, the sources of information, the expertise within the family when dealing with the media;

media repertoires: the media ownership of the family and of single members of the family, the financing of access to the media, the media usage of parents and children, the individual media repertoires of the family members, the importance of media for the children in general/in their everyday lives, the roles and functions of the media for the children, but also for the parents, the modes of media usage (for example, where, when, how long, with whom, on which device), the existence of mediation strategies, the importance of media education for parents, the regulation of media usage by the parents;

the communication and transferring of values—the extent, importance and credibility of the family—the extent, importance and credibility of other contexts of socialisation (for example, media, kindergarten, school, friends, peers) and the changes over time, the sources of knowledge transfer, the sources of value transfer, role models and idols, the importance of the family along with other players, the changes due to the development of the child.

4.4.3 Guided In-Depth Interview with the Children and Adolescents

In order to sustain maximum comparability, changes were reduced to a minimum, with the main themes remaining the same over time. Although the main aim of the interviews with the children was to investigate their media usage within a mediatised world, we also asked them about the circumstances of their lives (for example, family life, kindergarten and school, friends and peers, financial worries, housing, their own room, wishes and dreams and so on). Special attention was paid to the growing importance of online media and internet-based (communication) services. We did not enquire about the usage of single devices, but rather more about individual media repertoires, based on functions and interests (see Hasebrink, 2014 ; Hasebrink & Domeyer, 2012 ; Hasebrink & Popp, 2006 ). Thus, some aspects of our guidelines were restructured over time. This meant specifically adding new questions, removing others, or rewording some, in order to cater for new technological developments. In the interests of a pleasant and more relaxed atmosphere, boys were always interviewed by male members of the team and girls by female members. Whenever possible, we tried to send the same interview teams to the same families over several years and waves of investigation. On the one hand, the interviewer gained special knowledge about the respective family and its members, on the other hand, this approach created a particular relationship of trust. For the same reasons, we tried to send more experienced interviewers to more challenging families (see Chapter 8 ).

The situation of the child’s life in society—the everyday life of the family—the participation of the child within society—leisure activities—the social conditions of growing up and living conditions, the climate and relationships within families, the daily routines and aspects of everyday life, the preferred leisure activities of the children, their social activities, involvement in associations, cultural life and active citizenship;

Media ownership—their media repertoires—their media usage and media behaviour—the importance of media—the roles and functions of media for the child—the structure of media repertoires, the accessibility of media for the children, the personal ownership of media, the media used by the children and the role of media in the children’s everyday lives;

The behaviour of parents and siblings as regards the media—their attitudes towards media—the media usage within the family—media education and the regulation of media usage—the role of different media for individual members of the family, the behaviour of individual family members as regards the media, the media practices shared within the family, the mediation strategies, media education and the media expertise within the family;

The ways of adopting values—the role of the family concerning communication and transfer of knowledge and values—the role of other contexts of socialisation (for example, media, kindergarten, school, friends, peers)—the contexts in which children adopt values and codes of practice—the role models and idols for children.

In contrast to the guidelines for parents, the guidelines for children contained so-called role-playing questions, in order to better understand the importance of media and media personalities for the children. Different layers of media processing—cognitive, emotional and social—formed our analytic distinctions. However, in real life they are closely linked and intertwined. Therefore, it is quite difficult for the adolescents to articulate themselves concerning these fields (Cakici & Bayir, 2012 , pp. 1076–1077; Cohen, Manion, & Morrison, 2011 , pp. 513–522; Jörg, 1994 ; Paus-Haase, 1998 , p. 104; Stahlke, 2005 , pp. 496–507; Tilemann, 2017 , p. 393). To answer role-playing questions may be helpful in expressing complex contexts of experience and subjective understanding (Paus-Haase, 1998 , p. 165; Sader, 1995 , p. 194). We used three different types of role-playing questions. In an “island-question”, the adolescents were asked to explain which persons and which objects they would take with themselves to a desert island and why. In the “100 Euro-question” (later “500 Euro-question”) they were asked to tell us what they would do with 100 (or 500) Euro. In the “wishing question”, they were asked at the end of the interview what they would do if they had one wish. We found the answers to these questions were particularly important sources of information.

4.4.4 Observation Protocol

Finally, we will now outline our observation protocol (Mason, 2002 , pp. 96–98). This completed our survey of the families from the interviewers’ point of view by basically fulfilling two purposes: on the one hand, it systematically recorded the respective families’ living and housing conditions and their media hardware. On the other hand, it served as a guideline for the participatory observation, in order to grasp how all family members were doing family . We kept the criteria of the protocol the same over time (see also Paus-Hasebrink & Bichler, 2008 , pp. 131–132; Paus-Hasebrink & Kulterer, 2014 , p. 381):

the state of the house or flat and rooms: cleanliness, neatness, furniture, differences between the children’s rooms and the rest of the rest of the houses or flats;

the media hardware: devices, number of them, their positioning, media-related items, media hardware in the children’s bedrooms;

the pets: species, number, location, behaviour, cleanliness;

the family members: garments, smoking, alcohol, behaviour and manner of children, parents and siblings;

any further particular features.

As criteria like cleanliness, neatness or the conditions of furniture and clothing may be viewed subjectively, it was essential to reach a common understanding. Therefore, we applied a two-step procedure: firstly, both interviewers had to fill out the protocol together, immediately after finishing the visit in the family. They were instructed to compare their personal impressions with their experiences in other families they visited. Secondly, in order to consolidate a common understanding of a given sample, the research team discussed all recent observation protocols and compared them to the data from the previous waves.

4.4.5 Complementary Methods for Adolescents: Thinking Aloud, Network Maps, Photos

With the fifth wave of data collection in 2014, the children we were observing entered on a new stage of life, adolescence. So, we decided to add three more qualitative methods of data collection to our already existing range. On the one hand, we wanted to do justice to our adolescents by acknowledging the changes in their everyday lives, their new interests, behavioural patterns and options. On the other hand, we had to adjust to the technological and medial changes in a mediatised world between our waves of interviews. These newly added methods, then, allowed a better understanding of the media behaviour of the adolescents and their social inclusion in family and non-family relationships. This approach was successful in revealing hidden information, not only when it was difficult for the adolescents to express their thoughts and ideas, but also when they were not fully aware of certain facts and structures (see Paus-Hasebrink, 2005 , p. 224; 2017 , pp. 277–278; Paus-Hasebrink et al., 2017 , pp. 55–56).

4.4.5.1 Thinking Aloud About Favourite Social Media Tools

With the first supplementary method, we could stay abreast of changes caused by the increasing importance of social networking tools. Based on the method of thinking aloud (Bilandzic, 2005 , pp. 362–364; 2017 , pp. 407–408), we asked the adolescent to talk about their favourite social networking tools (for example, Facebook, WhatsApp, YouTube). They were askedto showus their profilesand settings (for example, privacy settings, friends list, photos, groups, single chats, followed pages and channels and so on) and to talk about how they make use of the applications and features (see Rose, 2016 , pp. 301–302). Our aim was to better understand what is really important to the adolescents (see Marotzki, Holze, & Verständig, 2014 , p. 457), so the guideline for this method contained only a few questions and topics (Toerien, 2014 , pp. 330–331), but did include questions about the structures of the respective social networking tools, for example core or advanced features but also restrictions (see also Trappel, 2007 , pp. 156–158). However, we likewise used the method of thinking aloud in order to reveal how competent and knowledgeable the adolescents were about the social networking tools, although they had not been willing or able to talk about them during the formal interview.

4.4.5.2 Network Maps Drawn by Our Subjects

In order to cater for the increased relevance of social relationships within the everyday lives of the adolescents (Cotterell, 2007 , p. 91), we decided to integrate network maps into our repertoire of methods (Coffey, 2014 , p. 367; Samuelsson, Thernlund, & Ringström, 1996 , pp. 327–330) as these can visualise connections and communication paths and routines (Hepp & Düvel, 2010 , p. 271). Hence, they are suitable for researching not only media repertoires but also information repertoires and relationship structures. We decided to apply a two-step approach by firstly asking the adolescents to write down the names of all relevant persons on a sheet of paper with the word “me” already printed in its centre to symbolise the position of the respective adolescents, around which they then grouped other relevant persons. The number of persons, their position in relation to the adolescents and the links between them were up to the adolescents (Crosnoe, 2000 , p. 379). When they had finished, we asked them to elaborate on the spatial distances, on the choice of people (maybe on people who had been left out or forgotten and so on) and on the connections between themselves and the people nominated. In a second step, we asked the adolescents to add the media services they used and their media devices to the map. If possible, they might indicate connections between certain media (devices and services) and (groups of) persons (for example, PlayStation with friend XY, WhatsAppgroup for best friends, TV-series with brother and sister). All this resulted in network maps about everyday communication, the media used (devices and services) and connections among the elements cited. This method shed light on the importance of (groups of) persons and media thus selected, in order to visualise their status (see Hepp & Düvel, 2010 , pp. 271–273; Hepp, Berg, & Roitsch, 2011 , pp. 10–12). However, it was also very informative to note the persons and media not present, or no more than distant from the respective adolescents.

4.4.5.3 Photos by Our Subjects

Inspired by the concept of “bedroom culture” (Bovill & Livingstone, 2001 , p. 2; Lincoln, 2013 , p. 315), we asked the adolescents to take a set of photos in their own room, in order to better understand their personalities and their media behaviour. In practical terms, they were asked to take three pictures (see Harper, 2005 , pp. 231, 233; Marshall & Rossman, 2016 , pp. 184–187; Rose, 2016 , pp. 310–312): a photo of their favourite spot, a photo of their place of work and a photo of their preferred spot for media usage. However, they could also indicate where one photo could cover two or even three spots (Mason, 2002 , pp. 112–113). Such photos do provide deep insights into the private spheres of the adolescents (Rose, 2016 , pp. 360–362). They reveal rooms where they spend a lot of time, and ones which are today strongly affected by media (see Bovill & Livingstone, 2001 , p. 3). In their own realm within the house or flat they can realise their own ideas and express themselves (see Lincoln, 2013 , pp. 318–320). Examples of their self-expression are individual schemes for painting walls and the positioning of media devices, but also the use of posters or merchandising products and selected furniture and how these are set out in the room.

4.4.6 Final Call-Back Interview

The sixth and last planned wave of surveys had already taken place in January and February 2016. Since it was our aim to bring the data up to date before completing the study, we shortened our guidelines and conducted an additional call-back interview in January 2017. To integrate the results with preceding waves, we talked to the adolescents and also to at least one of the guardians, in most cases the mothers. We kept to our protocol of girls being interviewed by a female team member and boys by a male team member. The focus in these call-back interviews was on the current living situations, life-changing experiences and our subjects’ perception of the changes. However, against the backdrop of the sixth wave, questions about future prospects and developments, working life and unemployment were particularly important. At this time, some of the adolescents had to face a period of uncertainty. They had to decide about their plans for the future, whether to continue schooling or start an apprenticeship or even working life itself. Finding a (appropriate) training place was not easy for all of them, so they had to deal with unemployment and with training activities at job centres. The following topics were part of our guidelines:

current living situation;

schooleducation or apprenticeship;

future prospects and unemployment;

family life and satisfaction;

friends, peers and (new) relationships;

favourite media;

role of the media in everyday life.

In the data processing, we processed the call-back interviews just like the guided interviews from the previous six waves of data collection. This included, in particular, transcription, anonymisation of data and subsequent analysis. However, we did not intend the call-back interviews as a substitute for a seventh wave of data collection, due to temporal, financial and personal restrictions. They were only used to record the latest developments in the topics mentioned above to wrap up before finishing the study. Compared to the sixth wave of data collection, we had to accept two adolescents dropping out: one boy was not reachable in any way, one girl missed all appointments for a telephone call.

4.5 Data Processing and Data Analysis

The wide range of data collectionmethods required a common strategy to analyse the huge amount of heterogeneous data from different sources. The guided interviews and their analysis were at the heart of the process, but all other sources were taken into account at every stage of the analysis as well (Banks, 2014 , pp. 394, 402; Bilandzic, 2005 , pp. 362–364; 2017 , pp. 407–408; Coffey, 2014 , pp. 367, 377–373; Crosnoe, 2000 , pp. 379, 387; Marotzki, Holze, & Verständig, 2014 , p. 457; Marvasti, 2014 , pp. 359–361; Samuelsson et al., 1996 , pp. 327–330). We divided the analysis of the collected data into a number of steps (see Fig.  4.4 ), which are closely linked to each other, so that the team is able to go back and forth between original data and, for example, the coded material, in order to clarify issues of interpretation (see Paus-Hasebrink, Sinner, Prochazka, & Kulterer, 2018 for a more detailed description of the process of analysing qualitative data in a long-term study).

Overview of data processing and data analysis

The transcription of interviews, including consistent and thorough anonymisation of names and places throughout all successive waves of interviews with panel members : The literary transcription of the audio files of the interviews (including the call-back interviews) was the starting point in each wave. Due to the sensitive character of the data and the possibility of unmasking our interview partners within small-scale communities, we rigorously practiced a strict and consistent anonymisation, including the separate storage of the original audio files, the anonymised transcripts and the anonymisation protocol with proper names and aliases (see Marshall & Rossman, 2016 , pp. 212–213; Mertens, 2014 , p. 510). However, we made use of anonymisation not only for proper names (family, friends, peers, teachers and so on) but also for pets’ names, locations (stores, schools, facilities) and locations of leisure activities. Strict rules were essential for us to coordinate our subjective impressions from all stages of the project. However, over time we had to pass material from one staff member to another. To maintain transparent processes, we, therefore, conducted employee training and meetings of the coders at regular intervals. In order to support the process of transcription, we used the advanced transcription software f4transkript (Windows) and f5transkript (MacOS) (for example, automated switching of speakers, time-markers, slowed playback speed (Dresing, Pehl, & Schmieder, 2015 ; Marshall & Rossman, 2016 , pp. 209–210).

The development of a comprehensive coding scheme for the analyses of all the forms of data, and computer - assisted coding of data using qualitative data analysis software (MAXQDA) : We applied thematic coding (Flick, 2013 ; Kuckartz, 2010 , p. 10) combining deductive and inductive steps. Based on theoretical considerations of grounded theory research (Strauss & Corbin, 1996 , 1998 ), we made use of opencoding, axialcoding and selectivecoding. Categories were defined partly deductively, based on the above-mentioned considerations, and inductively, based on the actual material. The coding schemes (one for parents, one for children) were continuously enhanced throughout the successive waves of interviews with panel members, in order to grasp new phenomena (for example, new media services). However, we tried to keep it consistent over time, and coders were not authorised to make changes on their own. Instead, we used memos and meetings of the coders to discuss any changes within our schemes and any later additions.

The data analysis followed an approach developed by Paus - Haase and Keuneke (Paus-Haase, Hasebrink, Mattusch, Keuneke, & Krotz, 1999 , pp. 143, 147) As a first step, we conducted a “ focused analysis ” of all data across all families : This was conducted in alignment with the main categories of the coding schemes: the role of family, of friends and peers, school, media usage, leisure activities, financial aspects and personal ambitions. Comparability across the waves of data collection was ensured by thematically structured matrices for each family, which included data from all waves and all methods of data collection. These matrices are organised by year (2005, 2007, 2010, 2012, 2014, and 2016) and category. Due to the long-term character of the study, the knowledge we generated was not always linear and chronological. In some cases, we were able to clarify situations in later years, or we were able to find out more about past events. In such cases, we updated not only the matrix of the family but also family profiles and cases. In order to support the processing, we also made use of the summary-function and the summary-grid-function included in MAXQDA. This meant we were able to portray developments over the years systematically.

For the next step, we conducted a “contextual in - depth analysis ” (Paus-Haase et al., 1999 , pp. 143, 191) using the three analytical concepts, options for action , outlines for action , and competences for action : Based on a qualitative analysis of the entire data set, we created a characterisation for each of our children within its respective family. By accounting for the contexts, we were able to understand how the child developed its cognitive and motivational resourcesand gained competences for action , which, against the backdrop of the options for action , are reflected in the realisation of the child’s and the parents’ outlines for actions (see Paus-Hasebrink et al., 2017 , pp. 64–66; 2018 , pp. 218–221; Paus-Hasebrink & Kulterer, 2014 , p. 70).

Case studies : On completion of the second wave, we went on to select families of particular significance. We examined these case studies even more intensively as regards options for action , outlines for action , and competences for action . In order to assure that this qualitative analysis was comprehensible to all our researchers, we applied two safety mechanisms: on the one hand, we used strict guidelines for composing these particular cases and describing them. On the other hand, two team members always dealt with these case examples together, in order to check each other’s approach, but also in order to support each other’s understanding. Later on, we compiled fact sheets for all the other families too and developed them into full family profiles. Consequently, there is no difference in our treatment of the individual families in this study as we applied identical methodology to all of them (see Chapter 5 ).

The family characterisations, as determined in the two previous steps, served as the basis for identifying family types (Kluge, 2000 ): Using our three analytical concepts, we defined three criteria, which, in turn, indicated meaningful differences between the families: socio-economic circumstances as indicator of options and for action ; socio-emotional climate as indicator of outlines for action ; copingresources as indicator of competences for action . Each family was characterised by these criteria indicating a specific pattern for each, and based on them, we were able to define similar cases as types (see Chapter 8 and Paus-Hasebrink et al., 2018 , pp. 221–222).

4.6 Ethical Challenges

As we have shown above, conducting a qualitative long-term study over twelve years means intensive and close contact to the families studied, because our data collection was conducted at their homes and not in a laboratory or at a neutral location (see Marshall & Rossman, 2016 , pp. 146–147). Conducting (qualitative) research with children and subsequently with adolescents does, then, require particular care (see also Marshall & Rossman, 2016 , p. 162). Qualitative researchers “situate themselves in consciously value-laden territory in which human relationships and critical self-reflection loom prominently. This positioning leads to the emergence of ethical dilemmas throughout the conduct of research that go beyond legal requirements or many professional standards for ethically responsible research” (Mertens, 2014 , p. 510). How do researchers interact with their subjects without being either patronising or didactic? How do they retain their role and not become a form of consultant, who might influence the subjects of research? How do they deal with severe problems and conflicts within the families, for example, psychiatric disorders, physical violence, experiences of abuse or suspicion about (sexual) abuse? At what point are previously defined limits exceeded, so that researchers cannot remain in their role and are obligated to interfere or to provide support (see also Mertens, 2014 , pp. 516–518; Patton, 2002 , pp. 326–327, 405–415)? Furthermore, we had to deal with the small-scale structures of Austrian communities, where individuals might be recognised easily. Given the sensitive topic of our study, we had to find ways to guarantee absolute anonymity to our participants while going deeper and deeper into their personal spheres (see data processing; see Marshall & Rossman, 2016 , p. 126).

All the difficulties outlined in the paragraph above occurred during the twelve years of research. Each time, we had to deal with a particular case based on individual circumstances. We discussed each situation before carefully deciding whether or not to interfere. Any intervention then resulted from further discussion of the correct procedure. In some cases, we involved a psychologist at the university, not only to request support in deciding our own action, but also to possibly offer professional help to affected families (see also Lobe, Livingstone, Ólafsson, & Simões, 2008 , pp. 29–31, 47–49, 52–53, 57–59; Ólafsson, Livingstone, & Haddon, 2013 , pp. 39–42, 75–76; Paus-Hasebrink, 2008 , p. 57).

4.7 Conclusion

In this chapter, we have explicated the methodological design behind operationalising our theoretical approach to researching the role of media within children’s and adolescents’ socialisation in socially disadvantaged families. We conducted a longitudinal study to understand the complex interplay of subjective and structural factors which shape the lives of children as they grow up. We applied a complex design, convinced that “triangulation (…) increases credibility and quality by countering the concern (or accusation) that a study’s findings are simply an artefact of a single method, a single source or a single investigator’s blinders” (Patton, 2002 , p. 563). However, we stress that “it is important to avoid the use of triangulation simply as an end in itself. Rich design or triangulation is only valid if it is applicable to the research question” (Paus-Hasebrink et al., 2013 , p. 23). Over the years of our study, this challenging approach proved suitable for researching complex processes. Our experience shows, however, how important it is, when conducting a qualitative long-term study, to follow strict rules and to work intersubjectively traceable, ensuring that all aspects remain comprehensible to the researchers involved. At the same time, we found it essential to react to the new challenges presented not only by medial and technological changes but also by social changes and by the development of the children themselves.

Conducting a complex research project is always a sophisticated process. Methodical procedures and ethical guidelines need to be followed and fulfilled. Moreover, the research team has to deal with restriction in relation to financial, temporal and personnel resources. But every kind of project has specific peculiarities. To conclude, we want to highlight two aspects that turned out to be of particular relevance conducting our longitudinal study over twelve years. At the same time, to meet these aspects was extremely challenging: The first point concerns the maintenance of the panel. Not only we had to convince our families to remain part of our project over and over again, but also, we had to engage in keeping the contacts alive. Therefore, we had to learn to gather as much contact details as possible (for example, different e-mail addresses and telephone numbers of mothers and fathers, not only a single one), because the contact details were changing quite quickly. Furthermore, when the children were older, it turned out to be purposeful to ask them for contact details and the permission to contact them, too. The second point concerns the handling but also the storage of the data. As said before, the strict compliance of rules and intersubjective working methods are indispensable in order to generate valid data in a qualitative longitudinal study. In this context, a major challenge was to handle our sensitive data with care, this included not only the processing of data, but also the safe storage of data and the administration of access rights. Our solution included a mirrored database with backups on different hard drives in spatially separate rooms and strict access rules to the entire data. As mentioned above, this included as well the separate storage of anonymised data and the anonymisation protocol. Furthermore, we had to deal with rapidly increasing volumes of data. This development was caused by additional methods of data collection on the one hand, but also by more extensive answers of our subjects (due to more complex media repertoires and living situations) on the other hand. Having these experiences in mind, it seems to be essential to plan data handling, data storage and panel maintenance very carefully from the beginning, when it comes to developing a longitudinal research project comparable to our study.

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Paus-Hasebrink, I., Kulterer, J., Sinner, P. (2019). The Methodological Approach of the Long-Term Study. In: Social Inequality, Childhood and the Media. Transforming Communications – Studies in Cross-Media Research. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-030-02653-0_4

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The Need for Long-term Research

Long-term research is vital, but rare. Long Now Board Member Kevin Kelly on what we can do to make it common and incentivized.

By Kevin Kelly

Jul 1, 02022

I am imagining a type of institution that would specialize in long-range research. These institutions (plural) would manage research that had a 25+ year horizon. This might be “pure” basic research with no apparent value within 25 years, as well as long-term longitudinal studies that take 25 years or more to complete. Some programs would be large-scale projects with ambitious decades-long goals. Others would be small individual limited projects with a single PI. Research with a 25-year horizon to either completion or payoff would create a generational research agenda.

At first approximation, this is the type of research that universities are supposed to be doing now. However it is rarely done by universities. Rather the typical research project might last 4 years at the longest because that is the max duration of a PhD and post-doc. An average PhD candidate wants their own unique project; they don’t want to continue someone else’s project. So the academic world is a rapid succession of short-lived projects. There are many advantages to this frothy, bubbling, rapid pace, but what we do not get are the results gained from ambitious multi-generation work. There are some hard things that require long-durations to reveal, and many other discoveries that require work beyond the scope of single investigators.

Long-range research is the type of work that is sometimes funded by DARPA in the US, NASA, and some international agencies and research programs. Sending telescopes and probes into deep space, or building high-speed particle accelerators are long-lived research agendas. These are also huge-budget projects, with attendant high-profile politics. We need more long-term research projects that require smaller budgets and resources to endure. That would help make them common rather than rare.

Some of the research labs of big tech companies (AT&T, Microsoft, IBM, Google) are occasionally engaged in this extended time horizon. This long horizon is harder to justify to their stockholders, so the percentage of long-range research they do is a small fraction of all their research, and it is rarely on the 25-year horizon, but in the mix this longer-term commercial research is helpful and important.

We can’t really expect corporate enterprises to develop 25-year programs. Governments should continue to fund as much long-horizon research as they can. I see two frontiers where long-range research might blossom. One is in international cooperation. Pooling resources from dozens if not hundreds of nations reduces political risk and makes it possible to fund things that may never pay off or pay off in another generation. More likely, non-profits can operate here. In many ways, they are the best-shaped organization to do multi-generational work. They don’t have to show results next quarter, they are free of many political mandates, they can make failures without punishment, and they can aim for the future.

We might create incentives for non-profits to focus on long-term projects. Perhaps in addition to the usual tax advantages we give to non-profit donations, maybe there are additional advantages if they aim to “payout” the next generation. Maybe a donor gets twice the tax write-off if the chosen non-profit funds long-term research, or long-term projects with a plus 25-year horizon. For example, maybe the non-profit is doing solar-panel material research that might take a generation to arrive, or perhaps they want to develop a system of planetary ocean monitors that would take a generation to install and track. We could invent tax and legal incentives to promote this kind of time horizon.

Today, long-term research is rare. It is hard for corporations and national governments to do. However we could encourage non-profits, universities, and international agencies to do more by developing additional incentives. And by promoting their successes when they do so.

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Large study provides scientists with deeper insight into long COVID symptoms

NIH-funded research effort identifies most common symptoms, potential subgroups, and initial symptom-based scoring system – with aim of improving future diagnostics and treatment.

Initial findings from a study of nearly 10,000 Americans, many of whom had COVID-19, have uncovered new details about long COVID, the post-infection set of conditions that can affect nearly every tissue and organ in the body. Clinical symptoms can vary and include fatigue, brain fog, and dizziness, and last for months or years after a person has COVID-19. The research team, funded by the National Institutes of Health, also found that long COVID was more common and severe in study participants infected before the 2021 Omicron variant.

The study, published in JAMA , is coordinated through the NIH’s Researching COVID to Enhance Recovery (RECOVER) initiative, a nationwide effort dedicated to understanding why some people develop long-term symptoms following COVID-19, and most importantly, how to detect, treat, and prevent long COVID. The researchers hope this study is the next step toward potential treatments for long COVID, which affects the health and wellbeing of millions of Americans.

“Americans living with long COVID want to understand what is happening with their bodies,” said ADM Rachel L. Levine, M.D., Assistant Secretary for Health. “RECOVER, as part of a broader government response, in collaboration with academia, industry, public health institutions, advocacy organizations and patients, is making great strides toward improving our understanding of long COVID and its associated conditions.”

Researchers examined data from 9,764 adults, including 8,646 who had COVID-19 and 1,118 who did not have COVID-19. They assessed more than 30 symptoms across multiple body areas and organs and applied statistical analyses that identified 12 symptoms that most set apart those with and without long COVID: post-exertional malaise, fatigue, brain fog, dizziness, gastrointestinal symptoms, heart palpitations, issues with sexual desire or capacity, loss of smell or taste, thirst, chronic cough, chest pain, and abnormal movements.

They then established a scoring system based on patient-reported symptoms. By assigning points to each of the 12 symptoms, the team gave each patient a score based on symptom combinations. With these scores in hand, researchers identified a meaningful threshold for identifying participants with long COVID. They also found that certain symptoms occurred together and defined four subgroups or “clusters” with a range of impacts on health.

Based on a subset of 2,231 patients in this analysis who had a first COVID-19 infection on or after Dec. 1, 2021, when the Omicron variant was circulating, about 10% experienced long-term symptoms or long COVID after six months. The results are based on a survey of a highly diverse set of patients and are not final. Survey results will next be compared for accuracy against an array of lab tests and imaging.

To date, more than 100 million Americans have been infected with SARS-CoV-2, the virus that causes COVID-19. As of April, the federal government’s Household Pulse survey estimates that about 10% of adults infected with the virus continue to experience and suffer from the many symptoms termed together as long COVID. Patients and researchers have identified more than 200 symptoms associated with long COVID.

“This study is an important step toward defining long COVID beyond any one individual symptom,” said study author Leora Horwitz, M.D., director of the Center for Healthcare Innovation and Delivery Science, and co-principal investigator for the RECOVER Clinical Science Core, at NYU Langone Health. “This approach — which may evolve over time — will serve as a foundation for scientific discovery and treatment design.” The researchers explain studying the underlying biological mechanisms of long COVID is central to advancing informed interventions and identifying effective treatment strategies.

In addition to establishing the scoring system, the researchers found that participants who were unvaccinated or who had COVID-19 before the Omicron strain emerged in 2021 were more likely to have long COVID and more severe cases of long COVID. Further, reinfections were also linked to higher long COVID frequency and severity, compared to people who only had COVID-19 once.

“While the score developed in this study is an important research tool and early step toward diagnosing and monitoring patients with long COVID, we recognize its limitations,” said David C. Goff, M.D., Ph.D., director of the Division of Cardiovascular Sciences at the National Heart, Lung, and Blood Institute, part of NIH. Goff serves as an epidemiology lead for NIH RECOVER. “All patients suffering from long COVID deserve the attention and respect of the medical field, as well as care and treatment driven by their experiences. As treatments are developed, it will be important to consider the complete symptom profile.”

The ongoing RECOVER research serves as the foundation for planned clinical trials, whose interventions are rooted in many of the symptoms outlined in this study. RECOVER clinical trials are expected to begin enrolling patient participants in 2023.

This research was funded by NIH agreements OT2HL161841 , OT2HL161847 , and OT2HL156812 . Additional support came from grant R01 HL162373 . For more information on RECOVER, visit https://recovercovid.org .                                                                 

About RECOVER : The National Institutes of Health Researching COVID to Enhance Recovery (NIH RECOVER) Initiative is a $1.15 billion effort, including support through the American Rescue Plan Act of 2021, that seeks to identify how people recuperate from COVID-19, and who are at risk for developing post-acute sequelae of SARS-CoV-2 (PASC). Researchers are also working with patients, clinicians, and communities across the United States to identify strategies to prevent and treat the long-term effects of COVID – including long COVID. For more information, please visit recovercovid.org .

HHS Long COVID Coordination : This work is a part of the National Research Action Plan (opens pdf), a broader government-wide effort in response to the Presidential Memorandum directing the Secretary for the Department of Health and Human Services to mount a full and effective response to long COVID. Led by Assistant Secretary for Health Admiral Rachel Levine, the Plan and its companion Services and Supports for Longer-term Impacts of COVID-19 (opens pdf) report lay the groundwork to advance progress in the prevention, diagnosis, treatment, and provision of services for individuals experiencing long COVID.

About the National Institutes of Health (NIH): NIH, the nation's medical research agency, includes 27 Institutes and Centers and is a component of the U.S. Department of Health and Human Services. NIH is the primary federal agency conducting and supporting basic, clinical, and translational medical research, and is investigating the causes, treatments, and cures for both common and rare diseases. For more information about NIH and its programs, visit www.nih.gov .

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Thaweethai T, Jolley SE, Karlson EW, et al. Development of a Definition of Postacute Sequelae of SARS-CoV-2 Infection.  JAMA.  Published online May 25, 2023. doi: 10.1001/jama.2023.8823

Change Note

On May 31, 2023, a statistic in the seventh paragraph on the percentage of adults continuing to experience symptoms of COVID-19 was updated from 6% to 10%.

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New Research Sheds Light on Long-Term Pulmonary Outcomes for Bronchopulmonary Dysplasia Patients

Published on Apr 22, 2024

Division of Pulmonary and Sleep Medicine

Researchers revealed new insights into the long-term effects of bronchopulmonary dysplasia (BPD), a chronic lung disease that primarily affects premature infants, on patient health. The new paper, co-authored by Dr. Sharon A. McGrath-Morrow, MBA, MD , Associate Chief of the Division of Pulmonary and Sleep Medicine at Children’s Hospital of Philadelphia (CHOP) and published in the Journal of Perinatology , underscores the importance of early intervention and comprehensive care for individuals with BPD, with potential respiratory consequences lasting throughout their lives.

Dr. McGrath-Morrow and her colleagues thoroughly evaluated data from patients with BPD from infancy through adulthood. The paper provides critical insights into the long-term effects of BPD and emphasize the importance of early intervention and comprehensive care.

Key findings highlight that individuals who have a history of BPD:

• Significantly benefit from early intervention and treatment in mitigating long-term respiratory outcomes. This includes minimizing barotrauma due to mechanical ventilation, optimizing nutrition to promote somatic growth, vaccine prophylaxis and judicious use of respiratory medications such as corticosteroids to manage symptoms and complications of BPD.
• Face persistent respiratory issues into adulthood. These challenges encompass reduced lung function, limited exercise capacity, heightened susceptibility to respiratory infections, and an increased risk of developing chronic obstructive pulmonary disease (COPD).
• Experience a diminished quality of life related to their respiratory health, leading to higher rates of hospitalization and increased reliance on healthcare services.

To address these complex needs, researchers recommend a multidisciplinary care model involving pulmonologists, pediatricians, and other specialists is crucial. They also stressed the need for ongoing research to help healthcare professionals improve the respiratory health and overall well-being of individuals affected by this chronic lung disease.

"Our review underlines the enduring challenges individuals with BPD face," said McGrath-Morrow. "The findings also highlight the need for ongoing, detailed studies to clearly map the full trajectory of BPD’s impact over a lifetime and identify opportune moments for intervention.”

Contact: Jennifer Lee, The Children’s Hospital of Philadelphia, 267-426-6084 or [email protected]

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The Determinants of Living with Long-Term Conditions: An International Cross-Sectional Study

Silvia corchon.

1 Nursing Department, University of Valencia, 46010 Valencia, Spain; [email protected]

Carmen Rodríguez-Blázquez

2 National Centre of Epidemiology and CIBERNED, Carlos III Institute of Health, 28029 Madrid, Spain; se.iiicsi@bdorc

Alfonso Meneses

3 Faculty of Nursing, Physiotherapy and Podiatry, University Complutense of Madrid, 28040 Madrid, Spain; se.mcu.fne@sesenema

Marta Aranda-Gallardo

4 Department of Internal Medicine, Costa del Sol Hospital, Marbella, 29603 Malaga, Spain; se.sch@adnaram

Lorena López

5 Madrid Healthcare System, 28823 Madrid, Spain; moc.liamg@5081arabrabanerol

Maria Eugenia Ursúa

6 Navarra Healthcare System, 31008 Navarra, Spain; [email protected]

Maria Victoria Navarta-Sanchez

7 Faculty of Medicine, Autonomous University of Madrid, 28029 Madrid, Spain; [email protected]

Mari Carmen Portillo

8 NIHR ARC Wessex, Faculty of Health Sciences, University of Southampton, Southampton SO17 1BJ, UK; [email protected]

Leire Ambrosio

Associated data.

The data presented in this study are available on request from the corresponding author. The data are not publicly available due to confidentiality of participants.

It is essential that healthcare and social professionals understand the daily lives of people with chronic diseases, and the variables that influence them. The aim of this study was to identify the determinants influencing the process of living with long-term conditions. To investigate this, an observational, international, cross-sectional study was carried out. A consecutive sample of 1788 Spanish-speaking population living with chronic obstructive pulmonary disease, chronic heart failure and type 2 diabetes mellitus were included. Descriptive statistics and multiple linear regression models were performed. The linear regression models identified that social support (β = 0.39, p < 0.001) and the satisfaction with life (β = 0.37, p < 0.001) were the main determinants in the process of living with a long-term condition (49% of the variance). Age (β = −0.08, p = 0.01) and disease duration (β = 0.07, p = 0.01) were determinants only in the chronic heart failure subgroup, and country was significant in the chronic obstructive pulmonary disease subgroup (β = −0.15, p = 0.002). Satisfaction with life and social support were key determinants influencing the process of living with long-term conditions. As such, those aspects should be included in the design of interventions focused on the achievement of a positive living in people with long-term conditions.

1. Introduction

Changes in life expectancy, demographics, lifestyles, healthcare, and social factors over the last century have led to a significant increase in chronic diseases or long-term conditions (LTCs) worldwide [ 1 ]. LTCs constitute one of the greatest challenges for healthcare and social systems, and are currently the leading cause of disability, morbidity, and costs [ 1 ]. Moreover, the projections of global mortality and the burden of diseases in the coming years estimate that LTCs will account for approximately three-quarters of all deaths globally in 2030, with huge socioeconomic impacts due to the exorbitant costs of often lengthy and expensive treatments [ 1 , 2 ]. Therefore, there is a growing need for the development of well-coordinated and cost-effective long-term care policies to address the consequences of this situation [ 3 ].

Among LTCs, chronic heart failure (HF), chronic obstructive pulmonary disease (COPD) and type 2 diabetes mellitus (T2DM) are the three most prevalent chronic conditions, constituting the principal causes of death worldwide [ 1 , 4 ]. HF is leading the LTC, with a prevalence of 10% in individuals older than 70 years of age [ 1 , 4 , 5 ]. COPD has become the third leading cause of death worldwide, with a prevalence of 251 million cases according to the Global Burden of Disease Study [ 4 , 6 , 7 ]. T2DM ranks seventh among the principal causes of death and its incidence has been estimated to increase to 693 million by 2045 [ 8 ].

In addition to this, it is important to consider that these conditions progressively worsen over time [ 2 , 6 ]. This leads people to experience an intensification of symptoms and limitations, which, in turn, affects their daily living, quality of life, and satisfaction with life [ 9 , 10 ]. When living with LTCs such as COPD, HF, and T2DM, people must adapt their daily routines and implement multiple adjustment behaviours [ 11 , 12 , 13 ]. The desired outcomes for these people are related to maintaining or improving their functional status, social life, and quality of life [ 9 , 11 , 12 ]. To achieve these goals, it could be helpful to understand how a person lives with an LTC and the determinants that could impact on this process. According to the World Health Organization [ 14 ], social determinants of health are defined as “the conditions in which people are born, grow, work, live and the set of forces and systems shaping the conditions of daily life”. In this sense, determinants encompass a wide array of variables that include social factors and similar broadly defined factors [ 15 ]. Living with LTCs is understood as a complex process that also includes internal processes [ 16 ]. It is not a static and linear process as people could shift through its different attributes moving from negative to positive living and vice versa [ 16 ].

It is essential that healthcare and social professionals understand the daily lives of people with LTCs and the variables that influence them. This understanding will generate the required knowledge to provide comprehensive, individualized, and person-centred care for those living with LTCs [ 17 , 18 ]. Until now, recent research has studied the variables related to a better quality of life or satisfaction with life in people living with LTCs [ 19 , 20 , 21 , 22 ]. These variables include age, gender, marital status, educational level [ 20 , 21 ], disease duration, symptom management, multimorbidity [ 19 , 22 ], social support [ 23 , 24 ], and satisfaction with life [ 25 ]. Based on this, to our knowledge, quality of life and satisfaction with life are two main consequences of the complex process of living with LTCs [ 16 ]. In this sense, the determinants of quality of life or satisfaction with life should not be generalized to those influencing the process of living with LTCs. Consequently, identification of the determinants of the process of living with LTCs from a comprehensive view and from the person’s perspective represents an important gap in the literature.

The aim of this study was to identify the determinants influencing the process of living with LTCs, such as COPD, HF, and T2DM.

2. Materials and Methods

2.1. design.

An observational, international, and cross-sectional study [ 26 ] was carried out.

2.2. Setting and Participants

This was a multicentre study that included public and private hospitals, primary and secondary specialized units, and patient associations or community groups of Spain and Colombia. The sample for this study was composed of outpatients who met the following inclusion and exclusion criteria ( Table 1 ):

Inclusion and exclusion criteria.

COPD: chronic obstructive pulmonary disease; HF: heart failure; T2DM: type 2 diabetes mellitus.

Consecutive case sampling was performed [ 27 , 28 ].

To obtain the convenience sample of people living with COPD, HF, or T2DM from both countries, a minimum sample size of 260 people per pathology and country was established [ 29 ]. This sample size was calculated for a factory analysis process as part of the LW-CI scale validation [ 30 , 31 ]. In this sense, a total of 780 people per country were established, with a consecutive total sample size of 1560 people living with COPD, HF, and T2DM.

2.3. Study Variables and Instruments

The dependent variable of this study was living with LTCs. The independent variables of the study included sociodemographic variables, namely country, gender, age, marital status, educational level, disease duration, employment situation, social support, satisfaction with life, and severity of the illness perceived by the person. The Spanish validated version of the following instruments was used to measure those variables.

The Living with Chronic Illness Scale (LW-CI scale) [ 32 ] is a self-reported measuring scale to evaluate the complex process of living with an LTC through 26 items grouped into the domains of acceptance (4 items), coping (7 items), self-management (4 items), integration (5 items), and adjustment (6 items) [ 32 ]. All items are answered using a 5-point Likert scale from never or nothing (0) to always or a lot (4), except for the domain acceptance, which is reversely scored from never or nothing (4) to always or a lot (0). In this way, the LW-CI scale has total score value from 0 points, indicating negative living with the LTC, to 104 points, reflecting positive living with the LTC [ 32 ].

The Duke-UNC Functional Social Support questionnaire (DUFSS) [ 33 , 34 ] is an 11-item scale that was used to evaluate perceived social support of the person when living with the LTC including areas, such as confidant, affective, and instrumental support [ 33 , 34 ]. Each item is scored from 1 (much less than I would like) to 5 (as much as I would like). The total score ranged from 11 (the lowest level of support) to 55 (the highest level of perceived social support) [ 33 , 34 ].

A modified version of the Satisfaction with Life Scale (SLS-6) [ 35 ] was used to evaluate satisfaction with life during the process of living with an LTC. The SLS-6 is a 6-item scale related to physical area, psychological wellbeing, social relations, leisure, and financial situation. Each item is scored on a Likert scale from 0 (totally unsatisfied with life) to 10 (totally satisfied with life) [ 35 ].

The Patients-based Global Impression of Severity Scale (PGIS) [ 36 , 37 ] was used to evaluate the self-perception of the disease severity. This is rated on a 6-point Likert scale, using a range of responses from 0 (not ill at all) to 5 (extremely ill) [ 36 , 37 ].

2.4. Ethical Issues

The study was approved by the Ethics Committees of the participant hospitals and the participating universities in Spain (reference number 2017.099) and Colombia (reference number 013). The study followed the Declaration of Helsinki and the standard operating procedures that guaranteed compliance with good clinical practice. After receiving pertinent oral and written information and before inclusion in the study, all participants signed informed consent forms.

2.5. Data Collection

As explained previously in publications regarding LW-CI scale validation in different LTCs [ 30 , 31 ] for data collection, the principal researcher developed a detailed protocol indicating the steps of the study with the aim of reducing potential errors and heterogeneity in the process. This protocol was then sent to the responsible researcher of the centre that was undertaking the data collection. Moreover, a meeting was organised to provide researchers an opportunity to ask questions and clarify doubts. Thus, before starting data collection, the principal investigator ensured that all researchers involved in this process understood the established steps [ 30 , 31 ].

All the researchers and centres of the study followed the detailed protocol for data collection: (1) healthcare professionals (nurses and physicians) approached all the potential participants giving initial oral information about the study, (2) interested people received an invitation letter and the participant information sheet with detailed written information, and (3) participants completed the questionnaires during the routine clinical visits. Although all the scales were self-reported, the researcher was available to resolve any doubts that could arise during their completion [ 30 , 31 ].

2.6. Statistical Analysis

Descriptive statistics were applied to characterize the participants’ sociodemographic and disease-related data. Multiple linear regression models were performed using the LW-CI scale total score and the total score of the scale for each LTC (LW-CI-HF scale [ 31 ], LW-CI-COPD scale, and LW-CI-T2DM scale) as the dependent variable. Independent variables included sociodemographic aspects, social support (DUFSS), satisfaction with life (SLS-6), and persons’ perception of the severity of the illness (PGIS).

Assumptions for the linear regression model (normality, homoscedasticity, independence of errors, and absence of multicollinearity) were assessed; therefore, some variables, such as age at disease onset, were excluded due to collinearity.

The enter method was used for the regression models to simultaneously assess the effect of each explanatory variable in each model, considering that multicollinearity was previously discarded. p -values of 0.05 or less were considered statistically significant. All calculations were performed using IBM SPSS 25.0 statistical software.

A total sample of 1788 people living with LTCs from Spain and Colombia were included in this study. As shown in Table 2 , 52.1% of participants from the total sample were men. The mean age of the sample was 68.9 years (standard deviation, SD 12.3), and most participants were married (55.4%), retired (35.3%), and had a primary or basic educational level (62.1%). The mean disease duration was 8.7 (SD 7.9) years.

Sociodemographic and disease characteristics of the total sample.

LTC: long-term condition.

The sociodemographic characteristics of the sample per LTC and historical data of each condition are presented in Supplementary Material S1 .

Regarding linear regression models, using the LW-CI total scale score for the whole sample, the main determinants were the DUFSS (standardized beta, β = 0.39, p < 0.001) and SLS-6 (β = 0.37, p < 0.001) scales (see Table 3 ). This model accounted for 49% of the variance.

Multiple linear regression models.

LW-CI: Living with Chronic Illness scale; LW-CI-HF: Living with Chronic Illness—heart failure scale; LW-CI-T2DM: Living with Chronic Illness—type 2 diabetes mellitus; LW-CI-COPD: Living with Chronic Illness—chronic obstructive pulmonary disease; DUFSS: Duke-UNC Functional Social Support Questionnaire; SLS-6: Satisfaction with Life Scale. * Independent variables with at least one significant result ( p < 0.05). Other variables included in the models were: gender, educational level, employment situation, and Patient-based Global Impression of Severity Scale (PGIS).

DUFSS (β = 0.33, p < 0.001) and SLS-6 (β = 0.45, p < 0.001) together with age (β = −0.08, p = 0.01), and disease duration (β = 0.07, p = 0.01) were also the main determinants for living with HF, accounting for 56% of the variance (see Table 3 ).

The LW-CI-T2DM results also showed that DUFSS (β = 0.39, p < 0.001) and SLS-6 (β = 0.31, p < 0.001) were the main determinants in a model accounting for 41% of the variance ( Table 3 ).

As shown in Table 4 , for LW-CI-COPD, the country was a significant variable (β = −0.15, p = 0.002). Thus, separate models were performed for Spain and Colombia.

Multiple linear regression models of LW-CI-COPD by country.

LW-CI-COPD: Living with Chronic Illness—chronic obstructive pulmonary disease; DUFSS: Duke-UNC Functional Social Support scale; SLS-6: Satisfaction with life scale. * Other variables included in the models: age, gender, educational level, employment situation, disease duration, and Patient-based Global Impression of Severity Scale (PGIS).

For Spain, only marital status was significant (β = 0.38, p < 0.01), accounting for 1% of the variance. For Colombia, DUFSS (β = 0.51, p < 0.001), SLS-6 (β = 0.26, p < 0.001) and marital status (β = 0.10, p < 0.01) were significantly associated with LW-CI-COPD, accounting for 52% of the variance ( Table 4 ).

4. Discussion

To our knowledge, this is the first study that has focused on the determinants in the process of living with LTCs from a comprehensive perspective.

Data were captured on a wide sample of people with LTCs from two different Spanish-speaking countries: Spain and Colombia. This allowed an understanding of the process of living with LTCs in people with different backgrounds and cultural contexts. Specifically, it explored the determinants associated with living with COPD, HF, and T2DM, pathologies with high prevalence worldwide and great impact on people’s life due to their symptoms and potential exacerbations [ 4 ]. Therefore, the sample size and its heterogeneity regarding the most prevalent conditions currently as well as the sociodemographic characteristics support its generalization to people living with these LTCs.

Regarding the variables that influenced the process of living with LTCs, the results indicate that the perceived social support and satisfaction with life were key aspects for people in this study. To our knowledge, social support includes health professionals’ support as well as family, partners, friends, community groups, and voluntary or charity organisations [ 11 , 12 , 13 , 38 ]. This support contributes to relieving people of stress, improving their acceptance, coping, adjustment to the disease and reinforcing self-care and psychological well-being [ 11 , 12 , 13 , 38 ]. Our results are congruent with previous studies investigating people living with different LTCs [ 38 , 39 , 40 , 41 ], stressing that social support was related to better reported general and emotional health in people as well as well-being and quality of life. For example, our study mirrors previous studies that investigated people living with Parkinson’s disease [ 42 ], wherein social support was strongly correlated with the process of living with the illness. This means that social support is a significant and independent influence on the process of living with different LTCs, such as Parkinson’s disease, COPD, HF, and T2DM. This supports the fact that there are important parallels between different LTCs that could result in common care pathways and interventions. Consequently, it is important to develop interventions to foster person’s living with LTCs, promoting better quality of life, psychosocial wellbeing, and health-related outcomes [ 16 ]. Other studies that investigated a COPD population have identified associations between the social support received through a comprehensive intervention and the perceived symptoms and person’s quality of life [ 39 , 43 , 44 , 45 ]. Regarding people living with HF, some authors [ 24 ] highlighted that psychological health and social relationships were strongly related to the daily living of people, whereas physical health presented a slight association with living with HF. However, these results should be taken with caution as it was a qualitative study undertaken in a non-generalizable HF population. Other studies [ 11 , 12 , 46 ] conducted in a population with T2DM also stressed the crucial role of social support, especially from the family, in people’s experience with the illness. Similar results were drawn in studies conducted with people living with neurological conditions, such as chronic stroke [ 47 ] and Parkinson’s disease [Ambrosio 2019] where the positive effect of social support programs had on the persons’ mental health and well-being is highlighted. Regarding satisfaction with life, previous studies also found that a more satisfactory life was related to better daily living with LTCs, such as HF [ 24 , 48 ], COPD [ 25 ], and T2DM [ 49 ]. Therefore, it could be highlighted that social support and satisfaction with life seem to be key factors in the process of living with LTCs. These results are paramount for the development of mental health programs and person-centred pathways to promote positive living with LTCs and maximise quality of life, wellbeing, and health-related outcomes such as satisfaction with life.

In line with previous publications that have found strong associations of emotional and social support and participants’ self-reported health, wellbeing, and quality of life [ 40 , 41 ], findings emerged in this study that revealed some differences in the determinants of positive living depending on the pathology and people’s characteristics. For example, according to the results of this study, age and the duration of the illness are determinants in people living with HF. This is coherent with existing literature, showing that people living with HF have an important reduction of their quality of life due to the disease progression, particularly in patients with HF in New York Heart Association Classification classes II (mild symptoms and slight limitation during ordinary activity) and III (marked limitation in activity due to symptoms, comfortable only at rest) [ 50 ]. This variation has not been identified in people with other LTCs such as T2DM and COPD, so patients with HF may need a more extensive follow-up as the disease progresses [ 50 ].

Treatment and its characteristics did not seem to be determinants in the process of living with the illness in any of the three pathologies (COPD, T2DM, and HF) examined. In other words, it appears that independent of the received treatment, the person could experience negative or positive living with the LTC. Similar results have been identified in previous studies of people living with other LTCs, such as Parkinson’s disease or chronic stroke [ 47 ]. Therefore, it could be concluded that in these LTCs (COPD, T2DM, HF, Parkinson’s disease, or chronic stroke), living with the pathology is a process intrinsically related to the individual characteristics rather than to the illness and the treatment itself. In fact, of the independent variables introduced in this study (age, gender, educational level, employment situation, disease duration, and person’s perception of LTC severity), only social support and satisfaction with life seemed to be determinants in the process of living with LTCs. However, regarding the treatment, it is important to highlight the particular risk of medical errors. These errors, which in many cases occur in domestic settings [ 51 ], may have a dramatic impact on people with LTCs, and may potentially be responsible for countless adverse effects and even the death of these persons [ 52 ]. Therefore, these issues and the potential impact on the well-being and quality of life of people with different LTCs should be further explored in future research studies.

Regarding possible differences between countries, this was only noted in people living with COPD. For the Colombian population, social support, satisfaction with life, and marital status were determinant factors in the process of living living with the condition. However, in the Spanish population, only marital status was a determinant. This unexpected result could be explained by differences in the characteristics of people, or in the care provided to this specific population in Latin America and European countries. To our understanding, most knowledge of COPD has been based on research carried out in Europe or North America and there is a gap of information in people from Latin America about the prevalence, person’s characteristics, and changes in lung function over time [ 53 , 54 ]. Nevertheless, the model for the Spanish population accounted for only 1% of the variance. Therefore, it seems that in Spanish people living with COPD, there are other variables that could be determinant and have not been taken into account in this study. However, due to the lack of previous studies focused on these identified potential differences, it is not easy to provide an explanation for this finding, and more research is needed to explore these potential differences in detail.

This study has some limitations that should be taken into account. Although the variance explained by the models is relatively high, there could be other determinant variables in the process of living with LTCs that have not been included in this study (such as comorbidity or multimorbidity) [ 21 ]. This study included people from two different countries and with three specific LTCs. As such, the determinants in the process of living with LTCs could vary in people with other pathologies or living in different contexts. Therefore, caution is needed to extrapolate these results to other prototypical LTCs and other Spanish-speaking countries with different cultures. Moreover, this was a cross-sectional study, so it is difficult to establish causal relationships. Therefore, further analytic studies that include people from different countries and with other LTCs are highly recommended.

This study also presented several strengths that should be highlighted: the large sample size; a heterogeneous representation of people living with different prototypical and highly prevalent LTCs (T2DM, COPD and HF); and participants from different settings, such as health care centres and community centres, as well as two different Spanish-speaking countries. Therefore, this study could contribute to knowledge across countries to identify synergies between professionals of different disciplines and sectors to address the process of living with LTCs from a person-centred perspective.

5. Conclusions

In conclusion, this study has allowed us to identify the variables associated with the process of living with LTCs. Here, we highlighted the necessity of a comprehensive approach involving health and social care that focuses on the person and not on the disease. Satisfaction with life and social support have been identified as determinants for people living with LTCs. Therefore, social support assessment should be addressed in the health care and social system. This work has led to a new understanding of essential elements that self-management programs and health and social care interventions need to target for a more positive living with LTCs. In this sense, this research has highlighted the necessity of increasing the focus on capturing the determinants that are particularly important for people. Understanding these determinants could enhance their health outcomes, quality of life, and process of living with LTCs. This study provides valuable information for the development of effective long-term care policies for the management of LTCs, one of the principal challenges faced by modern society.

Acknowledgments

The authors sincerely thank each person living with LTCs such as COPD, HF, and T2DM who participated in this study. The authors would also like to thank the primary and secondary healthcare centres involved in the data collection in Spain and Colombia. Moreover, the authors would like to thank the following members of the research team for their work during the data collection procedure: Eva Timonet (Costa del Sol Hospital), Neus Caparros (University of La Rioja), Manuel Ignacio Ruiz de Ocenda (La Rioja Healthcare System), Jorge Caro-Bautista (Andalusian Public Health System), David Perez-Manchon (Camilo Jose Cela University), Nerea Elizondo (Navarra Healthcare System), Gloria Carvajal (University de La Sabana) and Alejandra Fuentes-Ramirez (University de La Sabana). Finally, the authors also thank the Spanish Ministry of Science, Innovation and Universities (FEDER/Ministerio de Ciencia, Innovación y Universidades—Agencia Estatal de Investigación/Proyecto (CSO2017-82691-R)) and the University of La Sabana (EMF-28-2019) for the financial support received.

Supplementary Materials

The following are available online at https://www.mdpi.com/article/10.3390/ijerph181910381/s1 , Table S1: Sociodemographic characteristics of the sample per long-term condition and historical data of the condition.

Author Contributions

Conceptualization, L.A. and C.R.-B.; methodology, L.A. and C.R.-B.; software, C.R.-B.; validation, L.A. and C.R.-B.; formal analysis, L.A. and C.R.-B.; resources, L.A., C.R.-B., S.C., A.M., M.A.-G., L.L., M.E.U., M.V.N.-S., M.C.P.; data curation, L.A., S.C., A.M., M.A.-G., L.L., M.E.U., M.V.N.-S.; writing—original draft preparation, L.A., S.C. and C.R.-B.; writing—review and editing, L.A., S.C., C.R.-B.; visualization, L.A. and C.R.-B.; supervision, L.A.; project administration, L.A.; funding acquisition, L.A. All authors have read and agreed to the published version of the manuscript.

This research was funded by Ministry of Science, Innovation and University of the Spanish Government (FEDER/Ministerio de Ciencia, Innovación y Universidades—Agencia Estatal de Investigación/Proyecto), grant number CSO2017-82691-R.

Institutional Review Board Statement

The study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Ethics Committee of University of Navarra (reference number: 2017.099).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

Conflicts of interest.

The authors declare no conflict of interest.

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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• Published: 13 April 2024

Insights into early recovery from Long COVID—results from the German DigiHero Cohort

• Sophie Diexer 1 ,
• Bianca Klee 1 ,
• Cornelia Gottschick 1 ,
• Anja Broda 1 ,
• Oliver Purschke 1 ,
• Mascha Binder 2 , 3 ,
• Michael Gekle 4 ,
• Matthias Girndt 5 ,
• Jessica I. Hoell 6 ,
• Irene Moor 7 ,
• Daniel Sedding 8 ,
• Jonas Rosendahl 9 &
• Rafael Mikolajczyk 1  

Scientific Reports volume  14 , Article number:  8569 ( 2024 ) Cite this article

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• Epidemiology
• Infectious diseases

65 million people worldwide are estimated to suffer from long-term symptoms after their SARS-CoV-2 infection (Long COVID). However, there is still little information about the early recovery among those who initially developed Long COVID, i.e. had symptoms 4–12 weeks after infection but no symptoms after 12 weeks. We aimed to identify associated factors with this early recovery. We used data from SARS-CoV-2-infected individuals from the DigiHero study. Participants provided information about their SARS-CoV-2 infections and symptoms at the time of infection, 4–12 weeks, and more than 12 weeks post-infection. We performed multivariable logistic regression to identify factors associated with early recovery from Long COVID and principal component analysis (PCA) to identify groups among symptoms. 5098 participants reported symptoms at 4–12 weeks after their SARS-CoV-2 infection, of which 2441 (48%) reported no symptoms after 12 weeks. Men, younger participants, individuals with mild course of acute infection, individuals infected with the Omicron variant, and individuals who did not seek medical care in the 4–12 week period after infection had a higher chance of early recovery. In the PCA, we identified four distinct symptom groups. Our results indicate differential risk of continuing symptoms among individuals who developed Long COVID. The identified risk factors are similar to those for the development of Long COVID, so people with these characteristics are at higher risk not only for developing Long COVID, but also for longer persistence of symptoms. Those who sought medical help were also more likely to have persistent symptoms.

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Introduction

Based on conservative estimates, 65 million people worldwide suffer from long-term symptoms after their SARS-CoV-2 infection 1 . These persistent symptoms are commonly referred to as Long COVID, but there are several different terms and definitions. The World Health Organization (WHO) refers to it as “post COVID-19 condition” and defines it as symptoms persisting in individuals with a history of probable or confirmed SARS-CoV-2 infection that cannot be explained by an alternative diagnosis. For the definition to be fulfilled, these symptoms should be present three months after infection and last for at least two months 2 . The UK National Institute for Health and Care Excellence (NICE) guideline suggests a distinction between symptoms that are present between 4 and 12 weeks after infection (ongoing symptomatic COVID-19) and symptoms that persist beyond 12 weeks (post-acute COVID-19 syndrome). The term “Long COVID” is meant to include both 3 .

Long COVID comprises a wide range of symptoms. The most common symptoms include fatigue, headache, shortness of breath, muscle weakness and joint pain 4 , 5 , 6 . Furthermore, individuals suffering from Long COVID symptoms report worse health-related quality of life 7 . These symptoms can vary in severity and duration. Some studies have reported that symptoms persist for 24 months after infection and investigated factors associated with the recovery of symptoms 8 , 9 , 10 . One study showed that younger, male participants without pre-existing depression, anxiety, or cardiovascular disease were more likely to experience improvement of long-term dyspnea 11 . However, there is limited knowledge about the recovery in individuals who initially develop Long COVID symptoms and recover at an early stage.

In this study, we aimed to identify factors associated with the early recovery from Long COVID (i.e. no symptoms 12 weeks after SARS-CoV-2 infection among those who had symptoms 4–12 weeks after infection). Furthermore, we wanted to identify symptom groups present at 4–12 weeks after infection and how those are associated with early recovery.

Study design

The sample used in this study is part of the population-based prospective cohort study for digital health research in Germany (DigiHero, DRKS Registration-ID: DRKS00025600). The questionnaire and design of the study was described elsewhere 12 . In brief, DigiHero started in the city of Halle (Saxony-Anhalt, Germany) in January 2021 and was later extended to other federal states in Germany. Participants' addresses were taken from population registers and invitations were sent by post. After an online registration, participants received a baseline questionnaire with questions regarding socio-demographic characteristics. The current analysis is based on 48,826 participants, of which 17,008 reported at least one infection, recruited until June 15, 2022.

Questionnaire and measures

In the baseline questionnaire, participants were asked several sociodemographic questions, including their month of birth, sex, country of birth, and education. Education was categorized into three categories (low, medium, high) based on the International Standard Classification of Education (ISCED-97) 13 . If either the participant or one of their parents was not born in Germany, we considered this as having a migration background.

Furthermore, we repeatedly asked participants if they ever had a SARS-CoV-2 infection and those who answered “yes” were subsequently invited to a dedicated questionnaire. In the questionnaire on SARS-CoV-2 infections, we asked the participants about their infection and vaccination dates. In addition, we asked whether they had symptoms and visited a doctor at the time of infection, 4–12 weeks after infection, and 12 or more weeks after infection (“Yes” and “No”). If participants reported that they had any symptoms at the specific time windows, they were asked to rate the severity of 24 different symptoms on a 6-point Likert scale from “not at all” to “very severe” and an additional option “I don’t know” (the last option was treated as a missing value in the analyzes). We categorized this into “presence of symptom” if any of the options apart from “not at all” was selected. Furthermore, participants were asked to rate their course of the acute infection (“no symptoms”, “mild”, “moderate, “severe”, and “very severe”). The last two categories were combined (“severe/very severe”). The SARS-CoV-2 variants were classified based on the reported infection date and periods of dominance of specific variants from official surveillance in Germany 14 . We classified participants as having Long COVID if they reported having symptoms 4–12 weeks after infection. Early recovery was classified if they did not report symptoms anymore for the period 12 or more weeks after infection.

For this analysis, we considered only the first infection per participant. In addition, we only included participants for whom the difference between the date of infection and the completion of the survey was more than 12 weeks, so that they could report symptoms for this period. This definition includes 11,333 participants.

Statistical analysis

Descriptive analysis is presented using frequencies and percentages. Backward stepwise logistic regression based on the Akaike Information Criterion was used to identify possible factors associated with the early symptom recovery. The ten variables selected for inclusion in the regression analysis included the available sociodemographic factors and factors associated with the infection (sex, age, education, migration background, federal state, living in a city, self-assessed course of acute infection, virus variant combined with information on the number of previous vaccinations, whether the participant visited a doctor 4–12 weeks after infection, and an interaction term between age and sex). The variables found in the final model were used as adjustments in additional models to determine which individual symptoms present at 4–12 weeks after infection are associated with the early recovery from Long COVID.

Principal components analysis (PCA) was conducted on all symptoms for the time window 4–12 weeks after infection using the symptom scale as metric variable to identify symptom groups. To assist interpretation of the results promax rotation was used, this oblique rotation allows the factors to be intercorrelated 15 . We selected four components for the main analysis, using the scree plot (Fig. S1). To determine if a specific symptom should be included in a symptom group, a score of at least 0.40 on the primary loadings of items after rotation was used as a cutoff. The component scores were used as independent variables in a logistic regression to determine the association between the symptom groups and symptom recovery. The model was adjusted for the variables previously found to be associated with Long COVID recovery in the stepwise logistic regression.

Additionally we performed a sensitivity analysis, with a more conservative definition of Long COVID. A participant had to report at least one symptom as “moderate” to be defined as a Long COVID case and subsequently, persistence was defined only if a having long term symptoms at the time window 4–12 after infection, as well as the time window 12 weeks or more.

We report 95% confidence intervals (CI) for all analyses. All analyses were performed in R (Version 4.2.0) 16 .

Ethical approval

The Ethics Committee of the Martin Luther University Halle-Wittenberg (2020-076) approved the study.

Informed consent

The study was conducted following the Helsinki Declaration and informed consent was obtained from all individual participants included in the study.

Characteristics of participants

In total, 5098 (45%) of 11,333 infected individuals reported symptoms for the time window 4–12 weeks after infection, of whom 2441 (48%) reported no symptoms for the time window after 12 weeks. The majority of the analyzed sample were female, with high education, and had no migration background (Table 1 ). The mean age was 46 (standard deviation = 14). Around 45% of the participants were infected during the Omicron SARS-CoV-2 period. Almost 50% of the participants classified their course of acute infection as “moderate”. Of the 5098 individuals, only 181 (4%) were hospitalized during acute infection.

Factors associated with Long COVID recovery

Of the ten variables tested in the stepwise regression, the variables included in the final model were sex, age, self-assessed course of acute infection, the variant and vaccination status, and if participants visited a doctor in the time window 4–12 weeks after their infection. Specifically, women were less likely to recover than men were (Odds Ratio (OR) 0.80, 95% CI 0.69; 0.93). Furthermore, participants between 50 and 69 years old were more likely to still report symptoms after 12 weeks compared to the reference category (18–29 years old, OR 0.73 and 0.75, 95% CI 0.58; 0.91 and 0.58; 0.98). Participants infected during the Omicron period, independent of vaccination status, were most likely to recover early compared to all other considered variants. In addition, participants were more likely to recover early (OR 2.32, 95% CI 2.01; 2.67) if they did not seek medical care 4–12 weeks after infection (Table 2 ).

In the sensitivity analysis, using a more conservative definition for Long COVID, we identified the same variables using the stepwise regression. While the overall number of participants fulfilling the more restrictive definition of Long COVID was lower, the relative estimates were similar to the estimates for the initial definition, reported in Table 2 (Table S1).

Single symptoms associated with early recovery from Long COVID

We investigated the association of the presence of symptoms at 4–12 weeks after infection with the early recovery until 12 weeks. Hereby, cough was the only symptom identified that had a positive association with early recovery of symptoms (OR 1.18, 95% CI 1.03; 1.35). There was no association with early recovery for having a sore throat, fever, or congested nose. All other symptoms were associated negatively with early recovery (Fig.  1 ).

Association of symptoms present at 4–12 weeks after infection with early recovery from Long COVID, adjusted for sex, age, self-assessed course of acute infection, variant + vaccination status, and if a participant visited a doctor 4–12 weeks after infection.

Symptom groups associated with early recovery from Long COVID

We identified four distinct groups of symptoms in PCA, and four single symptoms that were not grouped (ear pain, premenstrual syndrome—PMS, swollen lymph nodes and eye conjunctivitis). The first group included diverse symptoms, described as typical symptoms associated with Long COVID like cognitive impairment and fatigue. The second group contained symptoms that could be described as symptoms of an acute infection (congested nose, sore throat, cough, and fever). The third group, termed gastrointestinal symptoms, included the symptoms abdominal pain, diarrhea, and nausea. Lastly, the fourth group was characterized by cardio-respiratory symptoms (chest pain, shortness of breath, and arrhythmia). The total variance explained by the four-factor model was 45% (Table S2).

In the logistic regression using the PCA scores, we found that symptom group 1 and 4 were negatively associated with an early recovery, while symptom group 2 was positively associated with early recovery, and symptom group 3 had no association (Table 3 ).

In the sensitivity analysis, with a more restrictive definition of Long COVID, the four identified groups were very similar. The symptoms headache, vertigo, and smell and taste disorder were not grouped anymore, however the estimates from the logistic regression using the PCA resulted in similar associations as the model presented in Table 3 (data not shown).

Recovery from specific symptoms

The three most commonly reported symptoms at 4–12 weeks after infection were fatigue, shortness of breath and cognitive impairment. This did not change at the time window after 12 weeks. The greatest reductions were seen in fatigue, shortness of breath and cough (Fig.  2 ).

Proportion of individuals with symptoms 4 to 12 weeks and more than 12 weeks after infection.

Using a large sample of individuals suffering from symptoms in the time window 4–12 weeks after SARS-CoV-2 infection, we studied factors associated with the early recovery from Long COVID. These factors included male sex, younger age, a milder self-assessed course of acute infection, being infected during SARS-CoV-2 Omicron dominance, and not seeking medical 4–12 weeks after infection. Additionally, having a cough at 4–12 weeks was positively associated with early recovery. Fatigue, shortness of breath, and cognitive impairment were the symptoms reported most frequently at both time windows. Furthermore, we identified four symptom groups that can be described as diverse symptoms including typical Long COVID symptoms, symptoms of an acute infection, gastrointestinal symptoms, and cardiorespiratory symptoms. The first and fourth group were both negatively associated with early recovery from Long COVID while the second group was positively associated with early recovery. This could be an indicator that there were two groups of individuals suffering from Long COVID in the initial phase. One group with symptoms, such as fatigue, that appear quickly after infection and persist later, and another group that is still dealing with lingering symptoms of an acute infection, but who will eventually recover at an early stage.

Multiple studies tried to identify Long COVID symptom clusters and patterns 17 , 18 , 19 , 20 , 21 . One study that looked at clusters in relation to the SARS-CoV-2 variants identified three groups of symptoms that clustered consistently across variants. These three groups included a cardiorespiratory cluster, a central neurological cluster, and a multi-organ systemic inflammatory cluster. However, overall the number of clusters differed per variant 18 . Comparable to our results one study found five clusters including gastrointestinal, airway, and cardiopulmonary clusters 19 . Another study described three clusters, where cluster one was characterized by symptoms related to pain and the other by cardiorespiratory symptoms. The third one was generally associated with less symptoms 20 . Furthermore, one study identified four distinct clusters, categorized as diverse systemic, neurocognitive, cardiorespiratory, and musculoskeletal 17 . Lastly, other research suggested three clusters where cluster 1 could be described as diverse systemic, cluster 2 included cardiorespiratory symptoms like shortness of breath, and the last one is dominated by neurological symptoms 21 . All of these studies have found a group of symptoms that include cardiorespiratory symptoms, which is similar to the symptom group 4 we identified. However, these studies used different analytic approaches to identify Long COVID symptom groups, which makes it difficult to compare the findings. Nevertheless, our findings are in line with previous studies and additionally could help in the early identification of individuals whose symptoms persist longer.

Multiple studies have identified cough as a common Long COVID symptom 4 , 5 , 6 , 21 , while we found that cough was associated with an early recovery of symptoms. However, we do not see a contradiction between these studies and our findings. Almost 20% of participants with symptoms after 12 weeks still report cough as a symptom, and while cough was associated with early symptom recovery in our study, this doesn't imply universal recovery. In our analysis, cough was grouped with symptoms such as sore throat, whereas a separate group encompassed more severe respiratory symptoms like shortness of breath, which was linked to prolonged symptom persistence. This leads us to the hypothesis that distinct groups of individuals exist, with cough potentially manifesting as either a chronic symptom or a lingering remnant of acute infection.

Most previous studies focused on identifying risk factor in regards to developing Long COVID, in contrast, there is limited information on early recovery from Long COVID. One study found that male sex is associated with recovery 22 , while another study found an association of recovery and COVID-19 severity 23 . This is in line with our findings. Several risk factors for Long COVID have been identified including female sex, younger age, smoking, a high Body-Mass-Index, and comorbidities 21 , 24 , and it is likely that risk factors for Long COVID also influence the symptom recovery. However, a recent study in Germany found that men were less likely to recover from cognitive deficits 25 . This is contrary to our finding that men are more likely to recover. Future studies should investigate if individual symptom recovery differs by sex. Furthermore, several studies investigated the influence of different SARS-CoV-2 variants on Long COVID risk and showed a strong risk reduction in individuals infected with Omicron SARS-CoV-2 12 , 17 , 26 , 27 , 28 . These findings are consistent with our results which show that having been infected during the Omicron dominance is associated with an early recovery from Long COVID. Nevertheless, more research is needed to understand which factors influence the (early) recovery of Long COVID.

We found that individuals suffering from symptoms who visited a doctor 4–12 weeks after their SARS-CoV-2 infection were less likely to recover early. A possible explanation could be that the symptoms of individuals who do not seek medical care are less severe and these individuals will then eventually recover fully. Another explanation could be that patients are already concerned about their symptoms at an early time point and therefore want to consult a general practitioner. A study identified that the “wait-and-see approach” was a common non-pharmacological intervention of German general practitioners 29 . This approach is also recommended by the German S1 guideline “Long/ Post-COVID”, in case of clinical stability of symptoms after a basic diagnosis 30 . Furthermore, a study observed the importance for patients of being believed and listened to, and at the same time that it was difficult to find a general practitioner who believed their symptoms were real 31 . Furthermore, patients participating in a German study reported that their general practitioner did not take their Long COVID symptoms seriously 32 . This could lead to an overall disappointment and mistrust. Notably, a general lack of knowledge about Long COVID was identified among healthcare professionals 33 . We believe that clinicians' understanding of Long COVID needs to be improved and that special attention should be given to individuals who seek help early. Furthermore, more research regarding Long COVID diagnosis and treatment is needed to help clinicians. Particular emphasis should be placed on the importance of early intervention for individuals experiencing persistent symptoms following SARS-CoV-2 infection. Prompt identification and management of Long COVID can mitigate the impact on patients' quality of life and long-term health outcomes.

The strength of our study is the large sample systematically recruited from the population. In contrast to studies following patients after hospital stay due to COVID-19, our sample includes mainly participants who did not require hospital treatment. Nevertheless, there are also limitations of this study. All of the information is based on retrospective self-reports, which may introduce recall bias. This could lead to an overestimation of the proportion of people suffering from Long COVID. However, we were able to show that the results were similar for a more restrictive definition of Long COVID. Additionally, we did not use an official classification for the course of acute infection, which could bias the results. Self-reporting could also lead to misclassification of infections, vaccinations and variants. In addition, we do not have information on why participants visited a doctor and what help, if any, was received. This would provide valuable insights into the care individuals receive at an early stage and their satisfaction with that care. In addition, the results might be limited to countries, like Germany, where healthcare is widely available to everyone. As the study is set in Germany, we therefore did not consider that there might be limiting factors in receiving appropriate healthcare that could negatively affect the recovery of symptoms. Furthermore, other known risk factors of Long COVID, like smoking status and comorbidities could not be taken into account, as this information was not available for DigiHero participants yet. This could lead to biased results and especially other comorbidities could also have an impact on the symptom groups. We also could not include an adequate control group with individuals not infected with SARS-CoV-2 to identify if the symptoms are unique to infected individuals. While our study offers valuable insights into Long COVID, it's essential to interpret the findings within the context of these limitations and consider avenues for future research to address these gaps comprehensively.

In summary, we identified factors and symptoms associated with the early recovery from Long COVID. There are indications that there are distinct groups of people suffering from Long COVID, those who still report lingering symptoms of an acute infection but who will recover early and the others whose symptoms will persist longer. Having sought medical help for COVID symptoms was an indicator for a higher risk of persistence.

Data availability

The anonymized data reported in this study can be obtained from the corresponding author upon request. The dataset includes individual data and an additional data dictionary will be provided. The beginning of data availability starts with the date of publication and the authors will support any requests in the three following years. Data requests should include a proposal for the planned analyses. Decisions will be made according to data use by the access committee of the DigiHero study, and data transfer will require a signed data access agreement.

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Acknowledgements

We thank all DigiHero participants for their efforts and contributions and Mareike Kunze for excellent technical assistance.

Open Access funding enabled and organized by Projekt DEAL. There was no specific funding for the conducted survey. The DigiHero study is funded by internal resources of the Medical Faculty of the Martin Luther University Halle-Wittenberg and part of the recruitment was co-funded by the Ministry of Economy, Science and Digitalization of the Federal State of Saxony-Anhalt (Germany).

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Conceptualization: S.D., B.K., C.G., A.B., O.P., M.B., M.G. (Michael Gekle), M.G. (Matthias Girndt), J.I.H., I.M., D.S., J.R., R.M. Methodology: S.D. Formal analysis and investigation: S.D. Writing—original draft preparation: S.D. Writing—review and editing: S.D., B.K., C.G., A.B., O.P., M.B., M.G. (Michael Gekle), M.G. (Matthias Girndt), J.I.H., I.M., D.S., J.R., R.M. Supervision: R.M. All authors have read and agreed to the published version of the manuscript.

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Would you be happy as a long-term single? The answer may depend on your attachment style

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Are all single people insecure? When we think about people who have been single for a long time, we may assume it’s because single people have insecurities that make it difficult for them to find a partner or maintain a relationship.

But is this true? Or can long-term single people also be secure and thriving?

Our latest research published in the Journal of Personality suggests they can. However, perhaps unsurprisingly, not everybody tends to thrive in singlehood. Our study shows a crucial factor may be a person’s attachment style.

Singlehood is on the rise

Singlehood is on the rise around the world. In Canada, single status among young adults aged 25 to 29 has increased from 32% in 1981 to 61% in 2021 . The number of people living solo has increased from 1.7 million people in 1981 to 4.4 million in 2021.

People are single for many reasons: some choose to remain single, some are focusing on personal goals and aspirations , some report dating has become harder , and some become single again due to a relationship breakdown.

People may also remain single due to their attachment style. Attachment theory is a popular and well-researched model of how we form relationships with other people. An Amazon search for attachment theory returns thousands of titles. The hashtag #attachmenttheory has been viewed over 140 million times on TikTok alone.

What does attachment theory say about relationships?

Attachment theory suggests our relationships with others are shaped by our degree of “anxiety” and “avoidance”.

Attachment anxiety is a type of insecurity that leads people to feel anxious about relationships and worry about abandonment. Attachment avoidance leads people to feel uncomfortable with intimacy and closeness.

People who are lower in attachment anxiety and avoidance are considered “securely attached”, and are comfortable depending on others, and giving and receiving intimacy.

Read more: Is attachment theory actually important for romantic relationships?

Single people are often stereotyped as being too clingy or non-committal . Research comparing single and coupled people also suggests single people have higher levels of attachment insecurities compared to people in relationships.

At the same time, evidence suggests many single people are choosing to remain single and living happy lives .

Single people represent a diverse group of secure and insecure people

In our latest research, our team of social and clinical psychologists examined single people’s attachment styles and how they related to their happiness and wellbeing.

We carried out two studies, one of 482 younger single people and the other of 400 older long-term singles. We found overall 78% were categorised as insecure, with the other 22% being secure.

Looking at our results more closely, we found four distinct subgroups of singles:

secure singles are relatively comfortable with intimacy and closeness in relationships (22%)

anxious singles question whether they are loved by others and worry about being rejected (37%)

avoidant singles are uncomfortable getting close to others and prioritise their independence (23% of younger singles and 11% of older long-term singles)

fearful singles have heightened anxiety about abandonment, but are simultaneously uncomfortable with intimacy and closeness (16% of younger singles and 28% of older long-term singles).

Insecure singles find singlehood challenging, but secure singles are thriving

Our findings also revealed these distinct subgroups of singles have distinct experiences and outcomes.

Secure singles are happy being single, have a greater number of non-romantic relationships, and better relationships with family and friends. They meet their sexual needs outside romantic relationships and feel happier with their life overall. Interestingly, this group maintains moderate interest in being in a romantic relationship in the future.

Anxious singles tend to be the most worried about being single, have lower self-esteem, feel less supported by close others and have some of the lowest levels of life satisfaction across all sub-groups.

Avoidant singles show the least interest in being in a romantic relationship and in many ways appear satisfied with singlehood. However, they also have fewer friends and close relationships, and are generally less satisfied with these relationships than secure singles. Avoidant singles also report less meaning in life and tend to be less happy compared to secure singles.

Fearful singles reported more difficulties navigating close relationships than secure singles. For instance, they were less able to regulate their emotions, and were less satisfied with the quality of their close relationships relative to secure singles. They also reported some of the lowest levels of life satisfaction across all sub-groups.

It’s not all doom and gloom

These findings should be considered alongside several relevant points. First, although most singles in our samples were insecure (78%), a sizeable number were secure and thriving (22%).

Further, simply being in a romantic relationship is not a panacea. Being in an unhappy relationship is linked to poorer life outcomes than being single.

It is also important to remember that attachment orientations are not necessarily fixed. They are open to change in response to life events.

Read more: Single doesn’t mean being lonely or alone

Similarly, sensitive and responsive behaviours from close others and feeling loved and cared about by close others can soothe underlying attachment concerns and foster attachment security over time.

Our studies are some of the first to examine the diversity in attachment styles among single adults. Our findings highlight that many single people are secure and thriving, but also that more work can be done to help insecure single people feel more secure in order to foster happiness.

• Relationships
• Attachment theory
• Personality
• Personality types
• Relationship psychology
• New research
• Personality psychology
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Also in this issue:

• Investing in the Great Outdoors
• The End of the Student Loan Repayment Moratorium
• Ulrike Malmendier | Interview
• Tipping: From Scourge of Democracy to American Ritual
• When Economists Navigate by the Stars
• Back to Table of Contents

Ulrike Malmendier

On law versus economics, the long-term effects of inflation, and the remembrance of crises past

Image: Courtesy Noah Berger

Over the course of her career, much of the research of University of California, Berkeley economist Ulrike Malmendier has been in the areas of behavioral economics and behavioral corporate finance — looking at the effects of various psychological biases, such as overconfidence, on the decisions of consumers, investors, and executives.

Malmendier's more recent work has taken a turn that has made her the Marcel Proust of economics — focusing, like the French novelist, on the subjective nature of human experience and its enduring influence. In this research, she has been analyzing "experience effects": how individuals living through financial crises and other significant economic events respond to these experiences in their future financial behavior. In her view, a major difference between homo economicus (the hypothetical person of classical economics who is perfectly rational and perfectly informed) and actual people is that, as she puts it, "The homo economicus is more of a robot who processes data rather than a living organism whose mind and body absorb these experiences."

In addition to faculty appointments at Berkeley's economics department and Haas School of Business, she is faculty director of Berkeley's new O'Donnell Center for Behavioral Economics, which she co-founded with her husband and Berkeley economics colleague Stefano DellaVigna.

A native of Germany, where she studied ancient Roman law before moving to economics, Malmendier has seen her research published in, among other journals, the American Economic Review , the Quarterly Journal of Economics , and the Journal of Finance . She has received numerous awards, including, in 2013, the American Finance Association's prestigious Fischer Black Prize, awarded biennially to a leading finance scholar under the age of 40 for significant contributions to the field. She is also a fellow of the Econometric Society and the American Academy of Arts and Sciences. The German federal government appointed her in 2022 to the five-member German Council of Economic Experts, sometimes called the Five Sages.

David A. Price interviewed Malmendier by phone in January.

EF: How did you become interested in economics?

Malmendier: There were a couple of motivations that played a role. One is that my father had experienced the after-effects of World War II in Germany, so he had a strong notion that you better go for a job where you could earn a safe living. I did pretty well in high school, yet my dad insisted that it would be better to first go to a bank and do one of these German-type apprenticeships. It was practical. I know how to evaluate you for a loan, open your account, and so on. And you study a little bit; I did a two-year degree in business economics. So I'm a publicly certified banker. It was very much a result of this scarring from the past, the idea that we never know what's going to happen.

When I actually started studying, I went to the University of Bonn. I was interested in both economics and law. I was initially more leaning toward law, specifically ancient Roman law; in fact, I ended up doing a whole Ph.D. in law. But since my bank experience, I had economics always in the back of my mind. In the Juridicum building in Bonn, where the law students are taught, the economics students are also taught. So I managed to also get into the economics program. Formally, it was actually not possible to enroll in both degree programs, but when somebody dropped out, I applied for their slot and got it.

What I experienced in the program was theory, mechanism design, the beauty of math, which kind of led me back into economics. The very mathematical, not very real-world-oriented way in which we were taught economics in Bonn just intellectually attracted me. I had some excellent teachers there. That's really the way I found my path into economics.

EF: That sounds like a big switch from law.

Malmendier: In the civil law systems like you have in Germany, and which go back to Roman law, it's not math, but it's pretty close. You really have to learn the whole big model and how to filter through the case at hand and come to the answer. It's quite stimulating intellectually in a way that seems very related to math. At 8 p.m. on Thursdays, we would meet in the Roman Law Institute, sit between the old books and then open up the Corpus Iuris Civilis , the big work of Roman law, and take a piece of the Latin text, translate it, and discuss the logic and how it flows. That was an exercise with an almost mathematical feel to it.

EF: Turning to your research, one of the things you've found is that people's likelihood of buying a home rather than renting is influenced by their experiences with inflation. Please explain.

Malmendier: I'll step back for the bigger picture here. In general, I have been very interested in the question of how our personal lifetime experiences tend to change us, tend to change the outlook we have of the world, the way we form beliefs. They might also influence our preferences, although my work is a bit more on the beliefs side.

I mentioned how my early life path was influenced by my dad experiencing World War II and how everything can get destroyed — the house gets destroyed, you lose all your possessions and savings, and maybe your country's currency isn't worth anything anymore. One way of looking at the effects of this is simply in terms of information: After such an experience, you have new data about what can happen. That's the traditional economic view. But I'd argue that there's an element beyond the intellectual. When it's your own life, you tend to put a lot of weight on what has happened to you. You're pushed toward overweighing outcomes that have happened to you.

I first worked on that in the context of the stock market, with a paper Stefan Nagel and I wrote on Depression babies in the U.S. We showed that people who experience big crashes of the stock market tend to shy away for years and decades from investing anything in the stock markets. We then turned to another experience, inflation.

Here, the example of Germany was our motivation. Within the EU, the Germans are somewhat notorious for being preoccupied with inflation being a terrible thing and distrusting the European Central Bank to handle it well. That's our reputation. But where does it come from? Many people think that it might have something to do with Germany going through the hyperinflation in the Weimar times and that experience affecting the German populace strongly — so strongly that the adverse reaction was even transmitted to the next generations.

With that big motivation in mind, we thought experience effects might also apply to inflation. Suppose I've lived through a period of high inflation, such as the Great Inflation in the U.S. of the late 1970s, early '80s. Even if I am an economist and work on monetary policy and inflation, I'm still going to be affected by that personal experience. If I'm asked to forecast inflation on the margin, I may overweigh what I saw happening; I may overweigh the probability that prices can spiral out of control.

If that's the case, it's going to influence my financial decision-making. I would want to protect myself against inflation. So how can I protect myself? I put my money into protected assets. In addition to gold and the stock market and so on, one way is to invest in real estate. And so one prediction is that people who are worried about their money being worth much less in the future might want, on the margin, to buy a home rather than rent.

Also, if I can finance this home purchase with a fixed-rate mortgage, so I'm borrowing at a fixed rate — but I think inflation will go up — I believe that it's going to be a good deal. I don't really like variable-rate mortgages at all in this case because I'm worried about the risk of nominal rates adjusting upward. So that's the link between inflation experience and making financial decisions that protect yourself against inflation.

EF: Many people are familiar with the idea that Depression-era youth were affected by that experience throughout their lives. How do you think the experiences of the past several years will tend to affect young Americans of today?

Malmendier: For starters, look at inflation, which started creeping up since 2021, and then in 2022 you were getting close to the double digits. There was such a sharp contrast between the long period of the Great Moderation and all of a sudden that price shock kicking in. For older people, who have seen high inflation before in the '80s or even the '70s, I'm predicting they're just taking that into the average of the long period of low inflation since the early 1980s and of their experience of high inflation in the 1970s and early 1980s. Given their long history of experiences, the new spike does not get too much weight. It just goes up a bit.

But for young people in the United States who basically had seen no inflation at all outside of textbooks, it's a different story. All of their life before they had experienced very low inflation, and then all of a sudden there's the spike. Initially, then, they might be a little slow to react. But if the spike in inflation lasts long enough — it isn't just a two-month blip — they realize, whoa, the world I live in is different than the world I thought I was living in, where high inflation happens only in textbooks.

So the weight they put on that experience increases and can in fact end up being much higher than for older generations because the new experience makes up a much larger part of their lives after it has happened for two years or so. Applied to the current situation, we are now moving slowly and steadily toward the 2 percent inflation target, and we might avoid the complete scarring effects.

One area where I do expect big experience effects from recent years is living through the COVID-19 crisis and many of us being relegated to working from home. I do expect there to be a lasting change in how we view the value of social interaction, the value of working from home versus working at your workplace.

The leadership here at the Haas School of Business, where I am right now, is encountering exactly this issue. They wonder why the same people who were happily coming in five days a week before COVID absolutely refuse to do so now. It's clearly an experience that has changed people. In the classical economic model, you would just talk about the information obtained from that experience and maybe the setup cost of learning Zoom. But that can't explain everything. We knew the length of our commutes before COVID.

And yet, personally experiencing what remote work and cutting out your commute means for your personal life makes an enormous difference. You have to experience it first, not because of lack of information, not because you cannot add and subtract hours spent in the car versus not, but because it just enters your decision-making differently if you have physically experienced it.

EF: If I'm, let's say, on the Federal Open Market Committee, am I also subject to these forces of experience?

Malmendier: Yes, you are. And that is maybe the most surprising aspect to many economists. Allow me to step back again: When behavioral economics and behavioral finance started playing more of a role in our profession, the applications initially focused on individual investors or individual consumers — the man or woman on the street, so to speak. We would have not thought that these biased beliefs play a role for the highly informed, highly trained, highly intelligent, successful leader of a company, a Federal Reserve Bank president, a Federal Reserve Board governor.

Even before I was working on the research on experience effects, I was wondering about that. Because biases reflect something our brain is wired to do, it doesn't need to be negatively correlated with intelligence. So my earliest work in behavioral finance in fact was about overconfident CEOs. And I vividly remember when presenting this paper on the job market two decades ago how certain audiences would tell me, look, I know several CEOs, they're very smart, how can you argue they are biased? But it turns out biases do apply, even to the most successful CEOs.

Going back to experience effects, our work here is based on basic neuroscience underpinnings: Namely, that as we are walking through life and making experiences, neurons fire and so cause connections between neurons, synapses, to form. When experiences are repeated and last longer, then these connections become stronger. So, if I've gone through a period of high inflation and seeing a price increase triggers fear and worry, well, that's also happening to highly informed and well-trained and knowledgeable policymakers, even at the very highest level. That's why their past personal experiences can help us to predict who is leaning more on the hawkish or the dovish side. We have actually found strong evidence of it.

And I've asked the same question about bankers. I've looked at the reports of banks' financial situations — provided thanks to the Fed — on how close they might have been to a bank run, how close they have been to financial distress, and whether that affects their lending behavior in later years. For instance, if a bank experienced the Russian debt default crisis in 1998, their situation during this crisis has a lasting influence on their future choice of exposure in these kind of debt markets.

EF: It seems like you're quite interested in the psychological level of explanation for economic behavior. What drew you to studying these kinds of issues?

Malmendier: Partly it goes back to those times at the University of Bonn, where I was initially sitting in my law lectures, and then I was venturing over to the very mathematical theoretical economics lectures. As beautiful as the modeling and analysis of equilibria was, I was struck by the sharp contrast between the human behavior we analyzed in my law classes and how human behavior was modeled in my economics lectures. In law, humans make mistakes and emotions play a role. For example, for how the penal code considers somebody's attempts to kill somebody, it matters whether that person was being driven by the moment or cold-bloodedly planned the murder. It makes a difference in how law assesses and penalizes this behavior. In economics, there was no consideration of motives or emotions.

And then, when I started studying at Harvard for my second Ph.D., the economics Ph.D., I was lucky that there was rising interest in behavioral economics. It was still a time when it was not broadly accepted, when advisers told me that I might not want to go on the job market with behavioral economics research, but it was slowly changing. For me, behavioral economics really clicked. It injected the psychological realism we need to make good predictions and have good suggestions for policy.

Now I'm trying to go beyond that. We see in classical economics the homo economicus who is perfectly optimizing — taking all the information and coming to the perfect decision. Behavioral economics came around and said, well, that's unrealistic. Let's inject some psychological realism. Let's introduce overconfidence, self-control problems, etc. And that was all good.

But here is the thing that was still missing: If you think about the homo economicus as a computer with a program that perfectly solves the problem at hand, behavioral economics was still kind of dealing with humans like computers. They now had flawed software or maybe occasionally short circuited. But however you program them initially — with overconfidence and so on — they are running that program for the rest of their lives.

This newer agenda on experience effects emphasizes much more that, no, humans are not just software, flawed or not flawed. They are living, breathing organisms. As they walk through life, they adapt and change their outlook on the world. That means that we as economists have a lot to learn, not just from social psychology, which was great for behavioral finance, but also from other fields — from neuroscience, from psychiatry, from endocrinology, etc. People who have lived through a monetary or financial crisis come out of that scarring experience with their brains rewired, and they will make different decisions.

They will keep overweighing this outcome happening again. But I think there's much more to learn. For example, the neuropsychiatrists tell us if you do live through a crisis but you feel like "you can do something about your situation" — what they call controllability — then you tend to do better. You don't tend to be so affected, so traumatized by it.

So I'm personally of the opinion that there's robust evidence in medicine, biology, neuropsychiatry, cognitive science, which we haven't incorporated as much as we should. I'm a bit on a mission to get economists more broadly, not just behavioral economists, to open up to that — of course, acknowledging that behavioral economics, the first round, got us a big step forward.

EF: Are there strategies that people can use to overcome the effects of their negative experiences and make better decisions?

Malmendier: Yes, absolutely.

For contrast, let me start, though, from the strategy that a lot of policymakers and economists believe in but that works much less well than we used to think. That strategy is teaching people. That's the strategy I naturally like as a professor. I used to think that if only I teach people about the equity premium puzzle and about diversification, then they will understand they need to put their money in a broadly diversified low-fee fund rather than having it in some savings account, or worse, checking account, etc., and they would all be better off.

Hence the emphasis on financial literacy. But so far, the process has been muted. Now, I still think financial literacy training is useful; it's important. But it tends to be less effective than we professors often hope compared to the effect of personal experiences with the stock market or other financial instruments.

Theoretical knowledge is just less powerful than we used to think. People might not act on information, and it is not because of asymmetric information, frictions, and access to information. All of that exists and is relevant. But even if you have full access to the relevant information, if you've understood it, if you've processed it, you might still not act on it unless you've seen it work in practice.

That brings me to the more direct answer to your question. If you feel that due to past info exposure, you are acting in a somewhat biased way, and you want to remedy it, the best recommendation is to slowly expose yourself to doing the alternative action or environment and personally experience the resulting outcome and in that way rewiring your brain.

From neuroscience, we don't just learn that life experiences rewire our brain and infer that, after a high-inflation period, we might be scared and get triggered when we see price increases. We also learn that throughout our lives, our brain has a high plasticity — maybe less than when we're young, but throughout our lives, we are pruning synapses that we don't need anymore, we are strengthening others, so we can affect how we think about the world. If we manage to expose ourselves to the right setting, that helps us not only to intellectually understand, but almost physically understand, why a certain type of decision is the right one. We change our wiring.

If somebody is really scared about the stock market, doesn't want to go there, the literature on experience-based learning would suggest something like a cognitive behavioral therapy approach. Namely, let's just take $50 or $100 and put it in a broadly diversified low-fee fund. In the worst case, that's not too much loss. After a year, we look back and see what happened to it and realize, huh, that wasn't so scary. That worked out pretty well even at a bad time. That way, we are rewiring our brain and maybe coming around to the conclusion that, to accumulate wealth, we should be doing more of that.

EF: In recent research, you've found that the experience of leading a company during the Great Recession tended to make CEOs age faster. What's going on there?

Malmendier: It's very connected to this high-level view I have of the evolution of what economics is about and should be about. The mind and the body are altered in many ways as we are walking through life. In the work on experience effects, I've mostly looked at how our beliefs are altered and how financial decisions or inflation expectations are then affected. But I mean it quite literally when I say we need to look at mind and body. Leading your company through that stressful period of the Great Recession probably makes you a different person beyond just having more information.

Working with people from our computer science department, I was exposed to machine learning and convolutional neural networks and learned about this subfield that looks at face recognition and visual machine learning. I thought we could apply it to detect signs of stress and aging. That led us to collect pictures of CEOs before and after crises and to show that we actually age in a crisis. In a severe enough crisis — if I take the usual corporate finance definition, the median firm in your industry undergoing a 30 percent or higher stock price decline — it makes you look an additional one year older.

And this visual effect really does seem to translate into effects on your health. While I couldn't get measurements of cortisol levels or heart rates or the like, I was able to get data on longevity. And what we saw is that if you look one year older, you are actually aging faster in the sense that you unfortunately die one year earlier. So it translated pretty much 1-to-1 into longevity.

What I'm hoping is that with this paper, we can further strengthen the point that we need to think about humans with all their biology. We have a lot to learn that's relevant for predicting career paths, education, all the usual outcome variables we economists are interested in.

EF: What are you working on now?

Malmendier: The physical realm of what crises do to you is something that is staying with me. I have been interested in digging deeper. What is the most stressful aspect of it all? What are the actual stressors? In a related project on CEOs, we ask what kinds of specific situations or decisions trigger these adverse effects in your body and on your health. For CEOs, it turns out to be layoff decisions. It's really hard on a leader to have to let a large fraction of their employees go, particularly if they've been with the company for a long time.

Also, going back to the inflation topic: The recent bout of inflation, not just here in the U.S., but also in Europe, has gotten me interested in how the lower-income parts of the population are affected by inflation. When studying inflation and inflation expectations, economists tend to look at the professional forecasters and market participants who have an impact on markets outcomes. The low-income populations are less studied. But they are, of course, the people for whom the marginal price increase in groceries has the highest marginal utility impact.

I'm trying to estimate to what extent inflation affects their consumption behavior. As goods become more expensive, what can they still afford? And what do they want to afford? That is, is the effect of inflation on their spending coming fully, or almost fully, through the channel of constraints, or do beliefs play a role? Also, is there a nonstandard element in their belief formation? There's a lot of research on hand-to-mouth consumers, about adjustment frictions of consumption that could play a role. But present-biased preferences could also play a role; limited attention could play a role.

We got access to a fairly new dataset on low-income consumers and are exploiting the recent bout of inflation as a source of variation. We ran a survey on that sample to tease out what factors play a role. So far, we are finding that, first of all, it's not just all constraints; beliefs do matter. And they are correlated with difficulties in managing debt. People who have difficulties managing their debt are reacting to inflation in an unexpected way, moving further toward overconsuming relative to what the data say they should be doing. This suggests there might be some nonstandard factor at play that got them into difficulties in managing debt to begin with.

That's what the preliminary results suggest. I hope to learn more about this population and the impact of inflation on them.

• Present Positions Cora Jane Flood Professor of Finance, Haas School of Business, University of California, Berkeley; Professor of Economics, University of California, Berkeley; Faculty Director, O'Donnell Center for Behavioral Economics, University of California, Berkeley
• Selected Additional Affiliations Research Affiliate, Centre for Economic Policy Research; Faculty Research Fellow, Institute for the Study of Labor (IZA); Research Associate, National Bureau of Economic Research
• Education Ph.D. (2002), Harvard University; Ph.D. (2000), University of Bonn; B.A. (1996), University of Bonn; B.A. (1995), University of Bonn

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Tesla Pre EPS Turmoil: 3 Long-Term Factors to Consider

Beyond Bitcoin, finding a more controversial, “battleground” investment than Tesla ( ( TSLA Quick Quote TSLA - Free Report ) ) is difficult.Depending on whom you ask and their investment timeframe, the stock is either a massive winner or a considerable underperformer. It’s possible both can be true – TSLA shares mindboggling 11,000% since going public back in 2010, dramatically outperforming the big automakers such as Toyota (TM), General Motors ( ( GM Quick Quote GM - Free Report ) ), and Ford ( ( F Quick Quote F - Free Report ) ), as well as EV competition like LI Auto ( ( LI Quick Quote LI - Free Report ) ) and Rivian ( ( RIVN Quick Quote RIVN - Free Report ) ). That said, the stock has dramatically underperformed as of late, losing nearly half its value year-to-date. Is Tesla a buy here, or is it a classic value trap?

Below are 3 reasons Tesla may be close to a long-term bottom, including:

The Worst May be (close to) Priced In

Last weekend, Tesla slashed $2,000 off the price of its popular Model Y, Model S, and Model X vehicles in the United States. Meanwhile, the company has continuously dropped prices in China as competition heats up from formidable competitors like LI Auto and Nio ( ( NIO Quick Quote NIO - Free Report ) ). The price cuts, due to rising interest rates and the aforementioned Chinese competition, have put a heavy weight on costs for the company. Gross profit margins have plunged since their highs in 2022 and are back to near-pandemic levels.

While the current price situation looks bleak for Tesla, it’s important for investors to understand that markets discount the future. From a forward-looking perspective, two factors potentially benefit TSLA. First, though the path to interest rates have been pushed back due to stubborn inflation, “hawkish” fears may be overblown. For example, BlackRock ( ( BLK Quick Quote BLK - Free Report ) ), the largest asset manager in the world, still sees potential for two Fed Rate Cuts in 2024. Second, though Chinese EV competition is heating up, a more robust economy could lift all ships, including Tesla. The IShares China Large Cap ETF ( ( FXI Quick Quote FXI - Free Report ) ) is up 7% over the past three months, outperforming the S&P 500 Index.

Rock Bottom Valuation

Tesla’s EPS growth slowed to just 8% year-over-year last quarter as high interest rates, slowing demand, and rising costs adversely impacted the company. However, an essential piece of the investment puzzle for Tesla is to understand that the company is in a transitional period. Cybertruck production is ramping up while the company focuses on its Robotaxi potential. If you believe, like me, that Tesla will ultimately be successful in these areas and growth will reaccelerate, then TSLA is as big a bargain as it’s ever been. TSLA’s price-to-book ratio is hovering near all-time lows. The last time the p/b ratio was this low, the stock went from a split-adjusted $11.80 to $414.50.

Sentiment is Very Negative

Investor sentiment towards TSLA is extremely negative, and it shows up in the stock chart. Tesla’s Relative Strength Index (RSI), a measure of momentum, has only been more oversold twice in its history – in 2019 and late 2022. Each instance led to massive gains over the next few months.

Bottom Line

Tesla’s stock is stuck in a debilitating downtrend, and there is significant risk into earnings tonight. However, for long-term investors with a risk appetite, TSLA appears to be attractive for a long-term investment once the short-term volatility and smoke settle.

See More Zacks Research for These Tickers

Normally $25 each - click below to receive one report free:.

Ford Motor Company (F) - free report >>

BlackRock, Inc. (BLK) - free report >>

General Motors Company (GM) - free report >>

Tesla, Inc. (TSLA) - free report >>

iShares China Large-Cap ETF (FXI) - free report >>

NIO Inc. (NIO) - free report >>

Li Auto Inc. Sponsored ADR (LI) - free report >>

Rivian Automotive, Inc. (RIVN) - free report >>

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