difference of observation and case study

Research Methods (Case Studies & Observation Studies) 0 Pages | Leaving School | 29/03/2024

• Case Studies & Observation Studies

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Observation Studies

Categories of behaviour: If the researcher is undertaking a natural observation , he may need to divide the behaviour he sees into categories so that a quick record can be made. If the researcher wants to understand how the public respond to a woman collapsing in the street, for example, his categories might include 1.) Ignores and walks on. 2.) Hesitates and walks on. 3.) Checks to see if the woman is ok. 4.) Calls 999.

Inter-observer reliability: In order to test the reliability of an observer’s records, it might be sensible to have two observers who are working to exactly the same category and score sheet, so that they can compare their results at the end of the observation period. If these observations closely match each other then it can be assumed their observations have been accurate. If there is a significant difference it may be necessary to start the observation over again.

Advantages of natural observation Natural observations are high in ecological validity . A string of natural actions can be observed. In a laboratory situation people are often asked to complete unnatural tasks.

Disadvantages of natural observation In the absence ofcontrolled variables it is difficult to establish why someone behaved in a certain way. This type of study is reliant on the accuracy of the observation. There are ethical issues involved in an observation of this kind i.e. the people being observed may not know that this is the case. Should they be told? And if they are told, would their behaviour still be natural? Natural observations can be awkward to plan as well as time consuming.

• Research Methods
• Hypotheses and Experimental Designs
• Standardised Procedures & Instructions
• Ecological Validity & Sampling Methods
• Making Sense of Data & Anomalous Results
• Survey Methods & Ethical Considerations
• Remember it, Test it!

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Methodology

• What Is a Case Study? | Definition, Examples & Methods

What Is a Case Study? | Definition, Examples & Methods

Published on May 8, 2019 by Shona McCombes . Revised on November 20, 2023.

A case study is a detailed study of a specific subject, such as a person, group, place, event, organization, or phenomenon. Case studies are commonly used in social, educational, clinical, and business research.

A case study research design usually involves qualitative methods , but quantitative methods are sometimes also used. Case studies are good for describing , comparing, evaluating and understanding different aspects of a research problem .

Table of contents

When to do a case study, step 1: select a case, step 2: build a theoretical framework, step 3: collect your data, step 4: describe and analyze the case, other interesting articles.

A case study is an appropriate research design when you want to gain concrete, contextual, in-depth knowledge about a specific real-world subject. It allows you to explore the key characteristics, meanings, and implications of the case.

Case studies are often a good choice in a thesis or dissertation . They keep your project focused and manageable when you don’t have the time or resources to do large-scale research.

You might use just one complex case study where you explore a single subject in depth, or conduct multiple case studies to compare and illuminate different aspects of your research problem.

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Once you have developed your problem statement and research questions , you should be ready to choose the specific case that you want to focus on. A good case study should have the potential to:

• Provide new or unexpected insights into the subject
• Challenge or complicate existing assumptions and theories
• Propose practical courses of action to resolve a problem
• Open up new directions for future research

TipIf your research is more practical in nature and aims to simultaneously investigate an issue as you solve it, consider conducting action research instead.

Unlike quantitative or experimental research , a strong case study does not require a random or representative sample. In fact, case studies often deliberately focus on unusual, neglected, or outlying cases which may shed new light on the research problem.

Example of an outlying case studyIn the 1960s the town of Roseto, Pennsylvania was discovered to have extremely low rates of heart disease compared to the US average. It became an important case study for understanding previously neglected causes of heart disease.

However, you can also choose a more common or representative case to exemplify a particular category, experience or phenomenon.

Example of a representative case studyIn the 1920s, two sociologists used Muncie, Indiana as a case study of a typical American city that supposedly exemplified the changing culture of the US at the time.

While case studies focus more on concrete details than general theories, they should usually have some connection with theory in the field. This way the case study is not just an isolated description, but is integrated into existing knowledge about the topic. It might aim to:

• Exemplify a theory by showing how it explains the case under investigation
• Expand on a theory by uncovering new concepts and ideas that need to be incorporated
• Challenge a theory by exploring an outlier case that doesn’t fit with established assumptions

To ensure that your analysis of the case has a solid academic grounding, you should conduct a literature review of sources related to the topic and develop a theoretical framework . This means identifying key concepts and theories to guide your analysis and interpretation.

There are many different research methods you can use to collect data on your subject. Case studies tend to focus on qualitative data using methods such as interviews , observations , and analysis of primary and secondary sources (e.g., newspaper articles, photographs, official records). Sometimes a case study will also collect quantitative data.

Example of a mixed methods case studyFor a case study of a wind farm development in a rural area, you could collect quantitative data on employment rates and business revenue, collect qualitative data on local people’s perceptions and experiences, and analyze local and national media coverage of the development.

The aim is to gain as thorough an understanding as possible of the case and its context.

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In writing up the case study, you need to bring together all the relevant aspects to give as complete a picture as possible of the subject.

How you report your findings depends on the type of research you are doing. Some case studies are structured like a standard scientific paper or thesis , with separate sections or chapters for the methods , results and discussion .

Others are written in a more narrative style, aiming to explore the case from various angles and analyze its meanings and implications (for example, by using textual analysis or discourse analysis ).

In all cases, though, make sure to give contextual details about the case, connect it back to the literature and theory, and discuss how it fits into wider patterns or debates.

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Normal distribution
• Degrees of freedom
• Null hypothesis
• Discourse analysis
• Control groups
• Mixed methods research
• Non-probability sampling
• Quantitative research
• Ecological validity

Research bias

• Rosenthal effect
• Implicit bias
• Cognitive bias
• Selection bias
• Negativity bias
• Status quo bias

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Non-Experimental Research

32 Observational Research

Learning objectives.

• List the various types of observational research methods and distinguish between each.
• Describe the strengths and weakness of each observational research method. 

What Is Observational Research?

The term observational research is used to refer to several different types of non-experimental studies in which behavior is systematically observed and recorded. The goal of observational research is to describe a variable or set of variables. More generally, the goal is to obtain a snapshot of specific characteristics of an individual, group, or setting. As described previously, observational research is non-experimental because nothing is manipulated or controlled, and as such we cannot arrive at causal conclusions using this approach. The data that are collected in observational research studies are often qualitative in nature but they may also be quantitative or both (mixed-methods). There are several different types of observational methods that will be described below.

Naturalistic Observation

Naturalistic observation  is an observational method that involves observing people’s behavior in the environment in which it typically occurs. Thus naturalistic observation is a type of field research (as opposed to a type of laboratory research). Jane Goodall’s famous research on chimpanzees is a classic example of naturalistic observation. Dr.  Goodall spent three decades observing chimpanzees in their natural environment in East Africa. She examined such things as chimpanzee’s social structure, mating patterns, gender roles, family structure, and care of offspring by observing them in the wild. However, naturalistic observation  could more simply involve observing shoppers in a grocery store, children on a school playground, or psychiatric inpatients in their wards. Researchers engaged in naturalistic observation usually make their observations as unobtrusively as possible so that participants are not aware that they are being studied. Such an approach is called disguised naturalistic observation .  Ethically, this method is considered to be acceptable if the participants remain anonymous and the behavior occurs in a public setting where people would not normally have an expectation of privacy. Grocery shoppers putting items into their shopping carts, for example, are engaged in public behavior that is easily observable by store employees and other shoppers. For this reason, most researchers would consider it ethically acceptable to observe them for a study. On the other hand, one of the arguments against the ethicality of the naturalistic observation of “bathroom behavior” discussed earlier in the book is that people have a reasonable expectation of privacy even in a public restroom and that this expectation was violated. 

In cases where it is not ethical or practical to conduct disguised naturalistic observation, researchers can conduct  undisguised naturalistic observation where the participants are made aware of the researcher presence and monitoring of their behavior. However, one concern with undisguised naturalistic observation is  reactivity. Reactivity refers to when a measure changes participants’ behavior. In the case of undisguised naturalistic observation, the concern with reactivity is that when people know they are being observed and studied, they may act differently than they normally would. This type of reactivity is known as the Hawthorne effect . For instance, you may act much differently in a bar if you know that someone is observing you and recording your behaviors and this would invalidate the study. So disguised observation is less reactive and therefore can have higher validity because people are not aware that their behaviors are being observed and recorded. However, we now know that people often become used to being observed and with time they begin to behave naturally in the researcher’s presence. In other words, over time people habituate to being observed. Think about reality shows like Big Brother or Survivor where people are constantly being observed and recorded. While they may be on their best behavior at first, in a fairly short amount of time they are flirting, having sex, wearing next to nothing, screaming at each other, and occasionally behaving in ways that are embarrassing.

Participant Observation

Another approach to data collection in observational research is participant observation. In  participant observation , researchers become active participants in the group or situation they are studying. Participant observation is very similar to naturalistic observation in that it involves observing people’s behavior in the environment in which it typically occurs. As with naturalistic observation, the data that are collected can include interviews (usually unstructured), notes based on their observations and interactions, documents, photographs, and other artifacts. The only difference between naturalistic observation and participant observation is that researchers engaged in participant observation become active members of the group or situations they are studying. The basic rationale for participant observation is that there may be important information that is only accessible to, or can be interpreted only by, someone who is an active participant in the group or situation. Like naturalistic observation, participant observation can be either disguised or undisguised. In disguised participant observation , the researchers pretend to be members of the social group they are observing and conceal their true identity as researchers.

In a famous example of disguised participant observation, Leon Festinger and his colleagues infiltrated a doomsday cult known as the Seekers, whose members believed that the apocalypse would occur on December 21, 1954. Interested in studying how members of the group would cope psychologically when the prophecy inevitably failed, they carefully recorded the events and reactions of the cult members in the days before and after the supposed end of the world. Unsurprisingly, the cult members did not give up their belief but instead convinced themselves that it was their faith and efforts that saved the world from destruction. Festinger and his colleagues later published a book about this experience, which they used to illustrate the theory of cognitive dissonance (Festinger, Riecken, & Schachter, 1956) [1] .

In contrast with undisguised participant observation ,  the researchers become a part of the group they are studying and they disclose their true identity as researchers to the group under investigation. Once again there are important ethical issues to consider with disguised participant observation.  First no informed consent can be obtained and second deception is being used. The researcher is deceiving the participants by intentionally withholding information about their motivations for being a part of the social group they are studying. But sometimes disguised participation is the only way to access a protective group (like a cult). Further, disguised participant observation is less prone to reactivity than undisguised participant observation. 

Rosenhan’s study (1973) [2]   of the experience of people in a psychiatric ward would be considered disguised participant observation because Rosenhan and his pseudopatients were admitted into psychiatric hospitals on the pretense of being patients so that they could observe the way that psychiatric patients are treated by staff. The staff and other patients were unaware of their true identities as researchers.

Another example of participant observation comes from a study by sociologist Amy Wilkins on a university-based religious organization that emphasized how happy its members were (Wilkins, 2008) [3] . Wilkins spent 12 months attending and participating in the group’s meetings and social events, and she interviewed several group members. In her study, Wilkins identified several ways in which the group “enforced” happiness—for example, by continually talking about happiness, discouraging the expression of negative emotions, and using happiness as a way to distinguish themselves from other groups.

One of the primary benefits of participant observation is that the researchers are in a much better position to understand the viewpoint and experiences of the people they are studying when they are a part of the social group. The primary limitation with this approach is that the mere presence of the observer could affect the behavior of the people being observed. While this is also a concern with naturalistic observation, additional concerns arise when researchers become active members of the social group they are studying because that they may change the social dynamics and/or influence the behavior of the people they are studying. Similarly, if the researcher acts as a participant observer there can be concerns with biases resulting from developing relationships with the participants. Concretely, the researcher may become less objective resulting in more experimenter bias.

Structured Observation

Another observational method is structured observation . Here the investigator makes careful observations of one or more specific behaviors in a particular setting that is more structured than the settings used in naturalistic or participant observation. Often the setting in which the observations are made is not the natural setting. Instead, the researcher may observe people in the laboratory environment. Alternatively, the researcher may observe people in a natural setting (like a classroom setting) that they have structured some way, for instance by introducing some specific task participants are to engage in or by introducing a specific social situation or manipulation.

Structured observation is very similar to naturalistic observation and participant observation in that in all three cases researchers are observing naturally occurring behavior; however, the emphasis in structured observation is on gathering quantitative rather than qualitative data. Researchers using this approach are interested in a limited set of behaviors. This allows them to quantify the behaviors they are observing. In other words, structured observation is less global than naturalistic or participant observation because the researcher engaged in structured observations is interested in a small number of specific behaviors. Therefore, rather than recording everything that happens, the researcher only focuses on very specific behaviors of interest.

Researchers Robert Levine and Ara Norenzayan used structured observation to study differences in the “pace of life” across countries (Levine & Norenzayan, 1999) [4] . One of their measures involved observing pedestrians in a large city to see how long it took them to walk 60 feet. They found that people in some countries walked reliably faster than people in other countries. For example, people in Canada and Sweden covered 60 feet in just under 13 seconds on average, while people in Brazil and Romania took close to 17 seconds. When structured observation  takes place in the complex and even chaotic “real world,” the questions of when, where, and under what conditions the observations will be made, and who exactly will be observed are important to consider. Levine and Norenzayan described their sampling process as follows:

“Male and female walking speed over a distance of 60 feet was measured in at least two locations in main downtown areas in each city. Measurements were taken during main business hours on clear summer days. All locations were flat, unobstructed, had broad sidewalks, and were sufficiently uncrowded to allow pedestrians to move at potentially maximum speeds. To control for the effects of socializing, only pedestrians walking alone were used. Children, individuals with obvious physical handicaps, and window-shoppers were not timed. Thirty-five men and 35 women were timed in most cities.” (p. 186).

Precise specification of the sampling process in this way makes data collection manageable for the observers, and it also provides some control over important extraneous variables. For example, by making their observations on clear summer days in all countries, Levine and Norenzayan controlled for effects of the weather on people’s walking speeds.  In Levine and Norenzayan’s study, measurement was relatively straightforward. They simply measured out a 60-foot distance along a city sidewalk and then used a stopwatch to time participants as they walked over that distance.

As another example, researchers Robert Kraut and Robert Johnston wanted to study bowlers’ reactions to their shots, both when they were facing the pins and then when they turned toward their companions (Kraut & Johnston, 1979) [5] . But what “reactions” should they observe? Based on previous research and their own pilot testing, Kraut and Johnston created a list of reactions that included “closed smile,” “open smile,” “laugh,” “neutral face,” “look down,” “look away,” and “face cover” (covering one’s face with one’s hands). The observers committed this list to memory and then practiced by coding the reactions of bowlers who had been videotaped. During the actual study, the observers spoke into an audio recorder, describing the reactions they observed. Among the most interesting results of this study was that bowlers rarely smiled while they still faced the pins. They were much more likely to smile after they turned toward their companions, suggesting that smiling is not purely an expression of happiness but also a form of social communication.

In yet another example (this one in a laboratory environment), Dov Cohen and his colleagues had observers rate the emotional reactions of participants who had just been deliberately bumped and insulted by a confederate after they dropped off a completed questionnaire at the end of a hallway. The confederate was posing as someone who worked in the same building and who was frustrated by having to close a file drawer twice in order to permit the participants to walk past them (first to drop off the questionnaire at the end of the hallway and once again on their way back to the room where they believed the study they signed up for was taking place). The two observers were positioned at different ends of the hallway so that they could read the participants’ body language and hear anything they might say. Interestingly, the researchers hypothesized that participants from the southern United States, which is one of several places in the world that has a “culture of honor,” would react with more aggression than participants from the northern United States, a prediction that was in fact supported by the observational data (Cohen, Nisbett, Bowdle, & Schwarz, 1996) [6] .

When the observations require a judgment on the part of the observers—as in the studies by Kraut and Johnston and Cohen and his colleagues—a process referred to as   coding is typically required . Coding generally requires clearly defining a set of target behaviors. The observers then categorize participants individually in terms of which behavior they have engaged in and the number of times they engaged in each behavior. The observers might even record the duration of each behavior. The target behaviors must be defined in such a way that guides different observers to code them in the same way. This difficulty with coding illustrates the issue of interrater reliability, as mentioned in Chapter 4. Researchers are expected to demonstrate the interrater reliability of their coding procedure by having multiple raters code the same behaviors independently and then showing that the different observers are in close agreement. Kraut and Johnston, for example, video recorded a subset of their participants’ reactions and had two observers independently code them. The two observers showed that they agreed on the reactions that were exhibited 97% of the time, indicating good interrater reliability.

One of the primary benefits of structured observation is that it is far more efficient than naturalistic and participant observation. Since the researchers are focused on specific behaviors this reduces time and expense. Also, often times the environment is structured to encourage the behaviors of interest which again means that researchers do not have to invest as much time in waiting for the behaviors of interest to naturally occur. Finally, researchers using this approach can clearly exert greater control over the environment. However, when researchers exert more control over the environment it may make the environment less natural which decreases external validity. It is less clear for instance whether structured observations made in a laboratory environment will generalize to a real world environment. Furthermore, since researchers engaged in structured observation are often not disguised there may be more concerns with reactivity.

Case Studies

A  case study   is an in-depth examination of an individual. Sometimes case studies are also completed on social units (e.g., a cult) and events (e.g., a natural disaster). Most commonly in psychology, however, case studies provide a detailed description and analysis of an individual. Often the individual has a rare or unusual condition or disorder or has damage to a specific region of the brain.

Like many observational research methods, case studies tend to be more qualitative in nature. Case study methods involve an in-depth, and often a longitudinal examination of an individual. Depending on the focus of the case study, individuals may or may not be observed in their natural setting. If the natural setting is not what is of interest, then the individual may be brought into a therapist’s office or a researcher’s lab for study. Also, the bulk of the case study report will focus on in-depth descriptions of the person rather than on statistical analyses. With that said some quantitative data may also be included in the write-up of a case study. For instance, an individual’s depression score may be compared to normative scores or their score before and after treatment may be compared. As with other qualitative methods, a variety of different methods and tools can be used to collect information on the case. For instance, interviews, naturalistic observation, structured observation, psychological testing (e.g., IQ test), and/or physiological measurements (e.g., brain scans) may be used to collect information on the individual.

HM is one of the most notorious case studies in psychology. HM suffered from intractable and very severe epilepsy. A surgeon localized HM’s epilepsy to his medial temporal lobe and in 1953 he removed large sections of his hippocampus in an attempt to stop the seizures. The treatment was a success, in that it resolved his epilepsy and his IQ and personality were unaffected. However, the doctors soon realized that HM exhibited a strange form of amnesia, called anterograde amnesia. HM was able to carry out a conversation and he could remember short strings of letters, digits, and words. Basically, his short term memory was preserved. However, HM could not commit new events to memory. He lost the ability to transfer information from his short-term memory to his long term memory, something memory researchers call consolidation. So while he could carry on a conversation with someone, he would completely forget the conversation after it ended. This was an extremely important case study for memory researchers because it suggested that there’s a dissociation between short-term memory and long-term memory, it suggested that these were two different abilities sub-served by different areas of the brain. It also suggested that the temporal lobes are particularly important for consolidating new information (i.e., for transferring information from short-term memory to long-term memory).

The history of psychology is filled with influential cases studies, such as Sigmund Freud’s description of “Anna O.” (see Note 6.1 “The Case of “Anna O.””) and John Watson and Rosalie Rayner’s description of Little Albert (Watson & Rayner, 1920) [7] , who allegedly learned to fear a white rat—along with other furry objects—when the researchers repeatedly made a loud noise every time the rat approached him.

The Case of “Anna O.”

Sigmund Freud used the case of a young woman he called “Anna O.” to illustrate many principles of his theory of psychoanalysis (Freud, 1961) [8] . (Her real name was Bertha Pappenheim, and she was an early feminist who went on to make important contributions to the field of social work.) Anna had come to Freud’s colleague Josef Breuer around 1880 with a variety of odd physical and psychological symptoms. One of them was that for several weeks she was unable to drink any fluids. According to Freud,

She would take up the glass of water that she longed for, but as soon as it touched her lips she would push it away like someone suffering from hydrophobia.…She lived only on fruit, such as melons, etc., so as to lessen her tormenting thirst. (p. 9)

But according to Freud, a breakthrough came one day while Anna was under hypnosis.

[S]he grumbled about her English “lady-companion,” whom she did not care for, and went on to describe, with every sign of disgust, how she had once gone into this lady’s room and how her little dog—horrid creature!—had drunk out of a glass there. The patient had said nothing, as she had wanted to be polite. After giving further energetic expression to the anger she had held back, she asked for something to drink, drank a large quantity of water without any difficulty, and awoke from her hypnosis with the glass at her lips; and thereupon the disturbance vanished, never to return. (p.9)

Freud’s interpretation was that Anna had repressed the memory of this incident along with the emotion that it triggered and that this was what had caused her inability to drink. Furthermore, he believed that her recollection of the incident, along with her expression of the emotion she had repressed, caused the symptom to go away.

As an illustration of Freud’s theory, the case study of Anna O. is quite effective. As evidence for the theory, however, it is essentially worthless. The description provides no way of knowing whether Anna had really repressed the memory of the dog drinking from the glass, whether this repression had caused her inability to drink, or whether recalling this “trauma” relieved the symptom. It is also unclear from this case study how typical or atypical Anna’s experience was.

Case studies are useful because they provide a level of detailed analysis not found in many other research methods and greater insights may be gained from this more detailed analysis. As a result of the case study, the researcher may gain a sharpened understanding of what might become important to look at more extensively in future more controlled research. Case studies are also often the only way to study rare conditions because it may be impossible to find a large enough sample of individuals with the condition to use quantitative methods. Although at first glance a case study of a rare individual might seem to tell us little about ourselves, they often do provide insights into normal behavior. The case of HM provided important insights into the role of the hippocampus in memory consolidation.

However, it is important to note that while case studies can provide insights into certain areas and variables to study, and can be useful in helping develop theories, they should never be used as evidence for theories. In other words, case studies can be used as inspiration to formulate theories and hypotheses, but those hypotheses and theories then need to be formally tested using more rigorous quantitative methods. The reason case studies shouldn’t be used to provide support for theories is that they suffer from problems with both internal and external validity. Case studies lack the proper controls that true experiments contain. As such, they suffer from problems with internal validity, so they cannot be used to determine causation. For instance, during HM’s surgery, the surgeon may have accidentally lesioned another area of HM’s brain (a possibility suggested by the dissection of HM’s brain following his death) and that lesion may have contributed to his inability to consolidate new information. The fact is, with case studies we cannot rule out these sorts of alternative explanations. So, as with all observational methods, case studies do not permit determination of causation. In addition, because case studies are often of a single individual, and typically an abnormal individual, researchers cannot generalize their conclusions to other individuals. Recall that with most research designs there is a trade-off between internal and external validity. With case studies, however, there are problems with both internal validity and external validity. So there are limits both to the ability to determine causation and to generalize the results. A final limitation of case studies is that ample opportunity exists for the theoretical biases of the researcher to color or bias the case description. Indeed, there have been accusations that the woman who studied HM destroyed a lot of her data that were not published and she has been called into question for destroying contradictory data that didn’t support her theory about how memories are consolidated. There is a fascinating New York Times article that describes some of the controversies that ensued after HM’s death and analysis of his brain that can be found at: https://www.nytimes.com/2016/08/07/magazine/the-brain-that-couldnt-remember.html?_r=0

Archival Research

Another approach that is often considered observational research involves analyzing archival data that have already been collected for some other purpose. An example is a study by Brett Pelham and his colleagues on “implicit egotism”—the tendency for people to prefer people, places, and things that are similar to themselves (Pelham, Carvallo, & Jones, 2005) [9] . In one study, they examined Social Security records to show that women with the names Virginia, Georgia, Louise, and Florence were especially likely to have moved to the states of Virginia, Georgia, Louisiana, and Florida, respectively.

As with naturalistic observation, measurement can be more or less straightforward when working with archival data. For example, counting the number of people named Virginia who live in various states based on Social Security records is relatively straightforward. But consider a study by Christopher Peterson and his colleagues on the relationship between optimism and health using data that had been collected many years before for a study on adult development (Peterson, Seligman, & Vaillant, 1988) [10] . In the 1940s, healthy male college students had completed an open-ended questionnaire about difficult wartime experiences. In the late 1980s, Peterson and his colleagues reviewed the men’s questionnaire responses to obtain a measure of explanatory style—their habitual ways of explaining bad events that happen to them. More pessimistic people tend to blame themselves and expect long-term negative consequences that affect many aspects of their lives, while more optimistic people tend to blame outside forces and expect limited negative consequences. To obtain a measure of explanatory style for each participant, the researchers used a procedure in which all negative events mentioned in the questionnaire responses, and any causal explanations for them were identified and written on index cards. These were given to a separate group of raters who rated each explanation in terms of three separate dimensions of optimism-pessimism. These ratings were then averaged to produce an explanatory style score for each participant. The researchers then assessed the statistical relationship between the men’s explanatory style as undergraduate students and archival measures of their health at approximately 60 years of age. The primary result was that the more optimistic the men were as undergraduate students, the healthier they were as older men. Pearson’s  r  was +.25.

This method is an example of  content analysis —a family of systematic approaches to measurement using complex archival data. Just as structured observation requires specifying the behaviors of interest and then noting them as they occur, content analysis requires specifying keywords, phrases, or ideas and then finding all occurrences of them in the data. These occurrences can then be counted, timed (e.g., the amount of time devoted to entertainment topics on the nightly news show), or analyzed in a variety of other ways.

Media Attributions

• What happens when you remove the hippocampus? – Sam Kean by TED-Ed licensed under a standard YouTube License
• Pappenheim 1882  by unknown is in the  Public Domain .
• Festinger, L., Riecken, H., & Schachter, S. (1956). When prophecy fails: A social and psychological study of a modern group that predicted the destruction of the world. University of Minnesota Press. ↵
• Rosenhan, D. L. (1973). On being sane in insane places. Science, 179 , 250–258. ↵
• Wilkins, A. (2008). “Happier than Non-Christians”: Collective emotions and symbolic boundaries among evangelical Christians. Social Psychology Quarterly, 71 , 281–301. ↵
• Levine, R. V., & Norenzayan, A. (1999). The pace of life in 31 countries. Journal of Cross-Cultural Psychology, 30 , 178–205. ↵
• Kraut, R. E., & Johnston, R. E. (1979). Social and emotional messages of smiling: An ethological approach. Journal of Personality and Social Psychology, 37 , 1539–1553. ↵
• Cohen, D., Nisbett, R. E., Bowdle, B. F., & Schwarz, N. (1996). Insult, aggression, and the southern culture of honor: An "experimental ethnography." Journal of Personality and Social Psychology, 70 (5), 945-960. ↵
• Watson, J. B., & Rayner, R. (1920). Conditioned emotional reactions. Journal of Experimental Psychology, 3 , 1–14. ↵
• Freud, S. (1961).  Five lectures on psycho-analysis . New York, NY: Norton. ↵
• Pelham, B. W., Carvallo, M., & Jones, J. T. (2005). Implicit egotism. Current Directions in Psychological Science, 14 , 106–110. ↵
• Peterson, C., Seligman, M. E. P., & Vaillant, G. E. (1988). Pessimistic explanatory style is a risk factor for physical illness: A thirty-five year longitudinal study. Journal of Personality and Social Psychology, 55 , 23–27. ↵

Research that is non-experimental because it focuses on recording systemic observations of behavior in a natural or laboratory setting without manipulating anything.

An observational method that involves observing people’s behavior in the environment in which it typically occurs.

When researchers engage in naturalistic observation by making their observations as unobtrusively as possible so that participants are not aware that they are being studied.

Where the participants are made aware of the researcher presence and monitoring of their behavior.

Refers to when a measure changes participants’ behavior.

In the case of undisguised naturalistic observation, it is a type of reactivity when people know they are being observed and studied, they may act differently than they normally would.

Researchers become active participants in the group or situation they are studying.

Researchers pretend to be members of the social group they are observing and conceal their true identity as researchers.

Researchers become a part of the group they are studying and they disclose their true identity as researchers to the group under investigation.

When a researcher makes careful observations of one or more specific behaviors in a particular setting that is more structured than the settings used in naturalistic or participant observation.

A part of structured observation whereby the observers use a clearly defined set of guidelines to "code" behaviors—assigning specific behaviors they are observing to a category—and count the number of times or the duration that the behavior occurs.

An in-depth examination of an individual.

A family of systematic approaches to measurement using qualitative methods to analyze complex archival data.

Research Methods in Psychology Copyright © 2019 by Rajiv S. Jhangiani, I-Chant A. Chiang, Carrie Cuttler, & Dana C. Leighton is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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Case-control and Cohort studies: A brief overview

Posted on 6th December 2017 by Saul Crandon

Introduction

Case-control and cohort studies are observational studies that lie near the middle of the hierarchy of evidence . These types of studies, along with randomised controlled trials, constitute analytical studies, whereas case reports and case series define descriptive studies (1). Although these studies are not ranked as highly as randomised controlled trials, they can provide strong evidence if designed appropriately.

Case-control studies

Case-control studies are retrospective. They clearly define two groups at the start: one with the outcome/disease and one without the outcome/disease. They look back to assess whether there is a statistically significant difference in the rates of exposure to a defined risk factor between the groups. See Figure 1 for a pictorial representation of a case-control study design. This can suggest associations between the risk factor and development of the disease in question, although no definitive causality can be drawn. The main outcome measure in case-control studies is odds ratio (OR) .

Figure 1. Case-control study design.

Cases should be selected based on objective inclusion and exclusion criteria from a reliable source such as a disease registry. An inherent issue with selecting cases is that a certain proportion of those with the disease would not have a formal diagnosis, may not present for medical care, may be misdiagnosed or may have died before getting a diagnosis. Regardless of how the cases are selected, they should be representative of the broader disease population that you are investigating to ensure generalisability.

Case-control studies should include two groups that are identical EXCEPT for their outcome / disease status.

As such, controls should also be selected carefully. It is possible to match controls to the cases selected on the basis of various factors (e.g. age, sex) to ensure these do not confound the study results. It may even increase statistical power and study precision by choosing up to three or four controls per case (2).

Case-controls can provide fast results and they are cheaper to perform than most other studies. The fact that the analysis is retrospective, allows rare diseases or diseases with long latency periods to be investigated. Furthermore, you can assess multiple exposures to get a better understanding of possible risk factors for the defined outcome / disease.

Nevertheless, as case-controls are retrospective, they are more prone to bias. One of the main examples is recall bias. Often case-control studies require the participants to self-report their exposure to a certain factor. Recall bias is the systematic difference in how the two groups may recall past events e.g. in a study investigating stillbirth, a mother who experienced this may recall the possible contributing factors a lot more vividly than a mother who had a healthy birth.

A summary of the pros and cons of case-control studies are provided in Table 1.

Table 1. Advantages and disadvantages of case-control studies.

Cohort studies

Cohort studies can be retrospective or prospective. Retrospective cohort studies are NOT the same as case-control studies.

In retrospective cohort studies, the exposure and outcomes have already happened. They are usually conducted on data that already exists (from prospective studies) and the exposures are defined before looking at the existing outcome data to see whether exposure to a risk factor is associated with a statistically significant difference in the outcome development rate.

Prospective cohort studies are more common. People are recruited into cohort studies regardless of their exposure or outcome status. This is one of their important strengths. People are often recruited because of their geographical area or occupation, for example, and researchers can then measure and analyse a range of exposures and outcomes.

The study then follows these participants for a defined period to assess the proportion that develop the outcome/disease of interest. See Figure 2 for a pictorial representation of a cohort study design. Therefore, cohort studies are good for assessing prognosis, risk factors and harm. The outcome measure in cohort studies is usually a risk ratio / relative risk (RR).

Figure 2. Cohort study design.

Cohort studies should include two groups that are identical EXCEPT for their exposure status.

As a result, both exposed and unexposed groups should be recruited from the same source population. Another important consideration is attrition. If a significant number of participants are not followed up (lost, death, dropped out) then this may impact the validity of the study. Not only does it decrease the study’s power, but there may be attrition bias – a significant difference between the groups of those that did not complete the study.

Cohort studies can assess a range of outcomes allowing an exposure to be rigorously assessed for its impact in developing disease. Additionally, they are good for rare exposures, e.g. contact with a chemical radiation blast.

Whilst cohort studies are useful, they can be expensive and time-consuming, especially if a long follow-up period is chosen or the disease itself is rare or has a long latency.

A summary of the pros and cons of cohort studies are provided in Table 2.

The Strengthening of Reporting of Observational Studies in Epidemiology Statement (STROBE)

STROBE provides a checklist of important steps for conducting these types of studies, as well as acting as best-practice reporting guidelines (3). Both case-control and cohort studies are observational, with varying advantages and disadvantages. However, the most important factor to the quality of evidence these studies provide, is their methodological quality.

• Song, J. and Chung, K. Observational Studies: Cohort and Case-Control Studies .  Plastic and Reconstructive Surgery.  2010 Dec;126(6):2234-2242.
• Ury HK. Efficiency of case-control studies with multiple controls per case: Continuous or dichotomous data .  Biometrics . 1975 Sep;31(3):643–649.
• von Elm E, Altman DG, Egger M, Pocock SJ, Gøtzsche PC, Vandenbroucke JP; STROBE Initiative.  The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies.   Lancet 2007 Oct;370(9596):1453-14577. PMID: 18064739.

Saul Crandon

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No Comments on Case-control and Cohort studies: A brief overview

Very well presented, excellent clarifications. Has put me right back into class, literally!

Very clear and informative! Thank you.

very informative article.

Thank you for the easy to understand blog in cohort studies. I want to follow a group of people with and without a disease to see what health outcomes occurs to them in future such as hospitalisations, diagnoses, procedures etc, as I have many health outcomes to consider, my questions is how to make sure these outcomes has not occurred before the “exposure disease”. As, in cohort studies we are looking at incidence (new) cases, so if an outcome have occurred before the exposure, I can leave them out of the analysis. But because I am not looking at a single outcome which can be checked easily and if happened before exposure can be left out. I have EHR data, so all the exposure and outcome have occurred. my aim is to check the rates of different health outcomes between the exposed)dementia) and unexposed(non-dementia) individuals.

Very helpful information

Thanks for making this subject student friendly and easier to understand. A great help.

Thanks a lot. It really helped me to understand the topic. I am taking epidemiology class this winter, and your paper really saved me.

Happy new year.

Wow its amazing n simple way of briefing ,which i was enjoyed to learn this.its very easy n quick to pick ideas .. Thanks n stay connected

Saul you absolute melt! Really good work man

am a student of public health. This information is simple and well presented to the point. Thank you so much.

very helpful information provided here

really thanks for wonderful information because i doing my bachelor degree research by survival model

Quite informative thank you so much for the info please continue posting. An mph student with Africa university Zimbabwe.

Thank you this was so helpful amazing

Apreciated the information provided above.

So clear and perfect. The language is simple and superb.I am recommending this to all budding epidemiology students. Thanks a lot.

Great to hear, thank you AJ!

I have recently completed an investigational study where evidence of phlebitis was determined in a control cohort by data mining from electronic medical records. We then introduced an intervention in an attempt to reduce incidence of phlebitis in a second cohort. Again, results were determined by data mining. This was an expedited study, so there subjects were enrolled in a specific cohort based on date(s) of the drug infused. How do I define this study? Thanks so much.

thanks for the information and knowledge about observational studies. am a masters student in public health/epidemilogy of the faculty of medicines and pharmaceutical sciences , University of Dschang. this information is very explicit and straight to the point

Very much helpful

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Introduction to Observational and Experimental Studies

These two studies are the cornerstones of scientific inquiry, each offering a distinct approach to unraveling the mysteries of the natural world.

Observational studies allow us to observe, document, and gather data without direct intervention. They provide a means to explore real-world scenarios and trends, making them valuable when manipulating variables is not feasible or ethical. From surveys to meticulous observations, these studies shed light on existing conditions and relationships.

Experimental studies , in contrast, put researchers in the driver's seat. They involve the deliberate manipulation of variables to understand their impact on specific outcomes. By controlling the conditions, experimental studies establish causal relationships, answering questions of causality with precision. This approach is pivotal for hypothesis testing and informed decision-making.

At Santos Research Center, Corp., we recognize the importance of both observational and experimental studies. We employ these methodologies in our diverse research projects to ensure the highest quality of scientific investigation and to answer a wide range of research questions.

Observational Studies: A Closer Look

In our exploration of research methodologies, let's zoom in on observational research studies—an essential facet of scientific inquiry that we at Santos Research Center, Corp., expertly employ in our diverse research projects.

What is an Observational Study?

Observational research studies involve the passive observation of subjects without any intervention or manipulation by researchers. These studies are designed to scrutinize the relationships between variables and test subjects, uncover patterns, and draw conclusions grounded in real-world data.

Researchers refrain from interfering with the natural course of events in controlled experiment. Instead, they meticulously gather data by keenly observing and documenting information about the test subjects and their surroundings. This approach permits the examination of variables that cannot be ethically or feasibly manipulated, making it particularly valuable in certain research scenarios.

Types of Observational Studies

Now, let's delve into the various forms that observational studies can take, each with its distinct characteristics and applications.

Cohort Studies:  A cohort study is a type of observational study that entails tracking one group of individuals over an extended period. Its primary goal is to identify potential causes or risk factors for specific outcomes or treatment group. Cohort studies provide valuable insights into the development of conditions or diseases and the factors that influence them.

Case-Control Studies:  Case-control studies, on the other hand, involve the comparison of individuals with a particular condition or outcome to those without it (the control group). These studies aim to discern potential causal factors or associations that may have contributed to the development of the condition under investigation.

Cross-Sectional Studies:  Cross-sectional studies take a snapshot of a diverse group of individuals at a single point in time. By collecting data from this snapshot, researchers gain insights into the prevalence of a specific condition or the relationships between variables at that precise moment. Cross-sectional studies are often used to assess the health status of the different groups within a population or explore the interplay between various factors.

Advantages and Limitations of Observational Studies

Observational studies, as we've explored, are a vital pillar of scientific research, offering unique insights into real-world phenomena. In this section, we will dissect the advantages and limitations that characterize these studies, shedding light on the intricacies that researchers grapple with when employing this methodology.

Advantages: One of the paramount advantages of observational studies lies in their utilization of real-world data. Unlike controlled experiments that operate in artificial settings, observational studies embrace the complexities of the natural world. This approach enables researchers to capture genuine behaviors, patterns, and occurrences as they unfold. As a result, the data collected reflects the intricacies of real-life scenarios, making it highly relevant and applicable to diverse settings and populations.

Moreover, in a randomized controlled trial, researchers looked to randomly assign participants to a group. Observational studies excel in their capacity to examine long-term trends. By observing one group of subjects over extended periods, research scientists gain the ability to track developments, trends, and shifts in behavior or outcomes. This longitudinal perspective is invaluable when studying phenomena that evolve gradually, such as chronic diseases, societal changes, or environmental shifts. It allows for the detection of subtle nuances that may be missed in shorter-term investigations.

Limitations: However, like any research methodology, observational studies are not without their limitations. One significant challenge of statistical study lies in the potential for biases. Since researchers do not intervene in the subjects' experiences, various biases can creep into the data collection process. These biases may arise from participant self-reporting, observer bias, or selection bias in random sample, among others. Careful design and rigorous data analysis are crucial for mitigating these biases.

Another limitation is the presence of confounding variables. In observational studies, it can be challenging to isolate the effect of a specific variable from the myriad of other factors at play. These confounding variables can obscure the true relationship between the variables of interest, making it difficult to establish causation definitively. Research scientists must employ statistical techniques to control for or adjust these confounding variables.

Additionally, observational studies face constraints in their ability to establish causation. While they can identify associations and correlations between variables, they cannot prove causality or causal relationship. Establishing causation typically requires controlled experiments where researchers can manipulate independent variables systematically. In observational studies, researchers can only infer potential causation based on the observed associations.

Experimental Studies: Delving Deeper

In the intricate landscape of scientific research, we now turn our gaze toward experimental studies—a dynamic and powerful method that Santos Research Center, Corp. skillfully employs in our pursuit of knowledge.

What is an Experimental Study?

While some studies observe and gather data passively, experimental studies take a more proactive approach. Here, researchers actively introduce an intervention or treatment to an experiment group study its effects on one or more variables. This methodology empowers researchers to manipulate independent variables deliberately and examine their direct impact on dependent variables.

Experimental research are distinguished by their exceptional ability to establish cause-and-effect relationships. This invaluable characteristic allows researchers to unlock the mysteries of how one variable influences another, offering profound insights into the scientific questions at hand. Within the controlled environment of an experimental study, researchers can systematically test hypotheses, shedding light on complex phenomena.

Key Features of Experimental Studies

Central to statistical analysis, the rigor and reliability of experimental studies are several key features that ensure the validity of their findings.

Randomized Controlled Trials:  Randomization is a critical element in experimental studies, as it ensures that subjects are assigned to groups in a random assignment. This randomly assigned allocation minimizes the risk of unintentional biases and confounding variables, strengthening the credibility of the study's outcomes.

Control Groups:  Control groups play a pivotal role in experimental studies by serving as a baseline for comparison. They enable researchers to assess the true impact of the intervention being studied. By comparing the outcomes of the intervention group to those of survey members of the control group, researchers can discern whether the intervention caused the observed changes.

Blinding:  Both single-blind and double-blind techniques are employed in experimental studies to prevent biases from influencing the study or controlled trial's outcomes. Single-blind studies keep either the subjects or the researchers unaware of certain aspects of the study, while double-blind studies extend this blindness to both parties, enhancing the objectivity of the study.

These key features work in concert to uphold the integrity and trustworthiness of the results generated through experimental studies.

Advantages and Limitations of Experimental Studies

As with any research methodology, this one comes with its unique set of advantages and limitations.

Advantages:  These studies offer the distinct advantage of establishing causal relationships between two or more variables together. The controlled environment allows researchers to exert authority over variables, ensuring that changes in the dependent variable can be attributed to the independent variable. This meticulous control results in high-quality, reliable data that can significantly contribute to scientific knowledge.

Limitations:  However, experimental ones are not without their challenges. They may raise ethical concerns, particularly when the interventions involve potential risks to subjects. Additionally, their controlled nature can limit their real-world applicability, as the conditions in experiments may not accurately mirror those in the natural world. Moreover, executing an experimental study in randomized controlled, often demands substantial resources, with other variables including time, funding, and personnel.

Observational vs Experimental: A Side-by-Side Comparison

Having previously examined observational and experimental studies individually, we now embark on a side-by-side comparison to illuminate the key distinctions and commonalities between these foundational research approaches.

Key Differences and Notable Similarities

Methodologies

• Observational Studies : Characterized by passive observation, where researchers collect data without direct intervention, allowing the natural course of events to unfold.
• Experimental Studies : Involve active intervention, where researchers deliberately manipulate variables to discern their impact on specific outcomes, ensuring control over the experimental conditions.
• Observational Studies : Designed to identify patterns, correlations, and associations within existing data, shedding light on relationships within real-world settings.
• Experimental Studies : Geared toward establishing causality by determining the cause-and-effect relationships between variables, often in controlled laboratory environments.
• Observational Studies : Yield real-world data, reflecting the complexities and nuances of natural phenomena.
• Experimental Studies : Generate controlled data, allowing for precise analysis and the establishment of clear causal connections.

Observational studies excel at exploring associations and uncovering patterns within the intricacies of real-world settings, while experimental studies shine as the gold standard for discerning cause-and-effect relationships through meticulous control and manipulation in controlled environments. Understanding these differences and similarities empowers researchers to choose the most appropriate method for their specific research objectives.

When to Use Which: Practical Applications

The decision to employ either observational or experimental studies hinges on the research objectives at hand and the available resources. Observational studies prove invaluable when variable manipulation is impractical or ethically challenging, making them ideal for delving into long-term trends and uncovering intricate associations between certain variables (response variable or explanatory variable). On the other hand, experimental studies emerge as indispensable tools when the aim is to definitively establish causation and methodically control variables.

At Santos Research Center, Corp., our approach to both scientific study and methodology is characterized by meticulous consideration of the specific research goals. We recognize that the quality of outcomes hinges on selecting the most appropriate method of research study. Our unwavering commitment to employing both observational and experimental research studies further underscores our dedication to advancing scientific knowledge across diverse domains.

Conclusion: The Synergy of Experimental and Observational Studies in Research

In conclusion, both observational and experimental studies are integral to scientific research, offering complementary approaches with unique strengths and limitations. At Santos Research Center, Corp., we leverage these methodologies to contribute meaningfully to the scientific community.

Explore our projects and initiatives at Santos Research Center, Corp. by visiting our website or contacting us at (813) 249-9100, where our unwavering commitment to rigorous research practices and advancing scientific knowledge awaits.

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Experiment vs Observational Study: Similarities & Differences

An experiment involves the deliberate manipulation of variables to observe their effect, while an observational study involves collecting data without interfering with the subjects or variables under study.

This article will explore both, but let’s start with some quick explanations:

• Experimental Study : An experiment is a research design wherein an investigator manipulates one or more variables to establish a cause-effect relationship (Tan, 2022). For example, a pharmaceutical company may conduct an experiment to find out if a new medicine for diabetes is effective by administering it to a selected group (experimental group), while not administering it to another group (control group).
• Observational Study : An observational study is a type of research wherein the researcher observes characteristics and measures variables of interest in a subset of a population, but does not manipulate or intervene (Atkinson et al., 2021). An example may be a sociologist who conducts a cross-sectional survey of the population to determine health disparities across different income groups. 

Experiment vs Observational Study

1. experiment.

An experiment is a research method characterized by a high degree of experimental control exerted by the researcher. In the context of academia, it allows for the testing of causal hypotheses (Privitera, 2022).

When conducting an experiment, the researcher first formulates a hypothesis , which is a predictive statement about the potential relationship between at least two variables.

For instance, a psychologist may want to test the hypothesis that participation in physical exercise ( independent variable ) improves the cognitive abilities (dependent variable) of the elderly.

In an experiment, the researcher manipulates the independent variable(s) and then observes the effects on the dependent variable(s). This method of research involves two or more comparison groups—an experimental group that is subjected to the variable being tested and a control group that is not (Sampselle, 2012).

For instance, in the physical exercise study noted above, the psychologist would administer a physical exercise regime to an experimental group of elderly people, while a control group would continue with their usual lifestyle activities .

One of the unique features of an experiment is random assignment . Participants are randomly allocated to either the experimental or control groups to ensure that every participant has an equal chance of being in either group. This reduces the risk of confounding variables and increases the likelihood that the results are attributable to the independent variable rather than another factor (Eich, 2014).

For instance, in the physical exercise example, the psychologist would randomly assign participants to the experimental or control group to reduce the potential impact of external variables such as diet or sleep patterns.

1. Impacts of Films on Happiness: A psychologist might create an experimental study where she shows participants either a happy, sad, or neutral film (independent variable) then measures their mood afterward (dependent variable). Participants would be randomly assigned to one of the three film conditions.

2. Impacts of Exercise on Weight Loss: In a fitness study, a trainer could investigate the impact of a high-intensity interval training (HIIT) program on weight loss. Half of the participants in the study are randomly selected to follow the HIIT program (experimental group), while the others follow a standard exercise routine (control group).

3. Impacts of Veganism on Cholesterol Levels: A nutritional experimenter could study the effects of a particular diet, such as veganism, on cholesterol levels. The chosen population gets assigned either to adopt a vegan diet (experimental group) or stick to their usual diet (control group) for a specific period, after which cholesterol levels are measured.

Read More: Examples of Random Assignment

Strengths and Weaknesses

Read More: Experimental Research Examples

2. Observational Study

Observational research is a non-experimental research method in which the researcher merely observes the subjects and notes behaviors or responses that occur (Ary et al., 2018).

This approach is unintrusive in that there is no manipulation or control exerted by the researcher. For instance, a researcher could study the relationships between traffic congestion and road rage by just observing and recording behaviors at a set of busy traffic lights, without applying any control or altering any variables.

In observational studies, the researcher distinguishes variables and measures their values as they naturally occur. The goal is to capture naturally occurring behaviors , conditions, or events (Ary et al., 2018).

For example, a sociologist might sit in a cafe to observe and record interactions between staff and customers in order to examine social and occupational roles .

There is a significant advantage of observational research in that it provides a high level of ecological validity – the extent to which the data collected reflects real-world situations – as the behaviors and responses are observed in a natural setting without experimenter interference (Holleman et al., 2020)

However, the inability to control various factors that might influence the observations may expose these studies to potential confounding bias , a consideration researchers must take into account (Schober & Vetter, 2020).

1. Behavior of Animals in the Wild: Zoologists often use observational studies to understand the behaviors and interactions of animals in their natural habitats. For instance, a researcher could document the social structure and mating behaviors of a wolf pack over a period of time.

2. Impact of Office Layout on Productivity: A researcher in organizational psychology might observe how different office layouts affect staff productivity and collaboration. This involves the observation and recording of staff interactions and work output without altering the office setting.

3. Foot Traffic and Retail Sales: A market researcher might conduct an observational study on how foot traffic (the number of people passing by a store) impacts retail sales. This could involve observing and documenting the number of walk-ins, time spent in-store, and purchase behaviors.

Read More: Observational Research Examples

Experimental and Observational Study Similarities and Differences

Experimental and observational research both have their place – one is right for one situation, another for the next.

Experimental research is best employed when the aim of the study is to establish cause-and-effect relationships between variables – that is, when there is a need to determine the impact of specific changes on the outcome (Walker & Myrick, 2016).

One of the standout features of experimental research is the control it gives to the researcher, who dictates how variables should be changed and assigns participants to different conditions (Privitera, 2022). This makes it an excellent choice for medical or pharmaceutical studies, behavioral interventions, and any research where hypotheses concerning influence and change need to be tested.

For example, a company might use experimental research to understand the effects of staff training on job satisfaction and productivity.

Observational research , on the other hand, serves best when it’s vital to capture the phenomena in their natural state, without intervention, or when ethical or practical considerations prevent the researcher from manipulating the variables of interest (Creswell & Poth, 2018).

It is the method of choice when the interest of the research lies in describing what is, rather than altering a situation to see what could be (Atkinson et al., 2021).

This approach might be utilized in studies that aim to describe patterns of social interaction, daily routines, user experiences, and so on. A real-world example of observational research could be a study examining the interactions and learning behaviors of students in a classroom setting.

I’ve demonstrated their similarities and differences a little more in the table below:

Experimental and observational research each have their place, depending upon the study. Importantly, when selecting your approach, you need to reflect upon your research goals and objectives, and select from the vast range of research methodologies , which you can read up on in my next article, the 15 types of research designs .

Ary, D., Jacobs, L. C., Irvine, C. K. S., & Walker, D. (2018). Introduction to research in education . London: Cengage Learning.

Atkinson, P., Delamont, S., Cernat, A., Sakshaug, J. W., & Williams, R. A. (2021). SAGE research methods foundations . New York: SAGE Publications Ltd.

Creswell, J.W., and Poth, C.N. (2018). Qualitative Inquiry and Research Design: Choosing among Five Approaches . New York: Sage Publications.

Eich, E. (2014). Business Research Methods: A Radically Open Approach . Frontiers Media SA.

Holleman, G. A., Hooge, I. T., Kemner, C., & Hessels, R. S. (2020). The ‘real-world approach’and its problems: A critique of the term ecological validity. Frontiers in Psychology , 11 , 721. doi: https://doi.org/10.3389/fpsyg.2020.00721  

Privitera, G. J. (2022). Research methods for the behavioral sciences . Sage Publications.

Sampselle, C. M. (2012). The Science and Art of Nursing Research . South University Online Press.

Schober, P., & Vetter, T. R. (2020). Confounding in observational research. Anesthesia & Analgesia , 130 (3), 635.

Tan, W. C. K. (2022). Research methods: A practical guide for students and researchers . World Scientific.

Walker, D., and Myrick, F. (2016). Grounded Theory: An Exploration of Process and Procedure . New York: Qualitative Health Research.

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Dr. Chris Drew is the founder of the Helpful Professor. He holds a PhD in education and has published over 20 articles in scholarly journals. He is the former editor of the Journal of Learning Development in Higher Education. [Image Descriptor: Photo of Chris]

• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd/ 5 Top Tips for Succeeding at University
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Creating a Corporate Social Responsibility Program with Real Impact

• Emilio Marti,
• David Risi,
• Eva Schlindwein,
• Andromachi Athanasopoulou

Lessons from multinational companies that adapted their CSR practices based on local feedback and knowledge.

Exploring the critical role of experimentation in Corporate Social Responsibility (CSR), research on four multinational companies reveals a stark difference in CSR effectiveness. Successful companies integrate an experimental approach, constantly adapting their CSR practices based on local feedback and knowledge. This strategy fosters genuine community engagement and responsive initiatives, as seen in a mining company’s impactful HIV/AIDS program. Conversely, companies that rely on standardized, inflexible CSR methods often fail to achieve their goals, demonstrated by a failed partnership due to local corruption in another mining company. The study recommends encouraging broad employee participation in CSR and fostering a culture that values CSR’s long-term business benefits. It also suggests that sustainable investors and ESG rating agencies should focus on assessing companies’ experimental approaches to CSR, going beyond current practices to examine the involvement of diverse employees in both developing and adapting CSR initiatives. Overall, embracing a dynamic, data-driven approach to CSR is essential for meaningful social and environmental impact.

By now, almost all large companies are engaged in corporate social responsibility (CSR): they have CSR policies, employ CSR staff, engage in activities that aim to have a positive impact on the environment and society, and write CSR reports. However, the evolution of CSR has brought forth new challenges. A stark contrast to two decades ago, when the primary concern was the sheer neglect of CSR, the current issue lies in the ineffective execution of these practices. Why do some companies implement CSR in ways that create a positive impact on the environment and society, while others fail to do so? Our research reveals that experimentation is critical for impactful CSR, which has implications for both companies that implement CSR and companies that externally monitor these CSR activities, such as sustainable investors and ESG rating agencies.

• EM Emilio Marti is an associate professor at the Rotterdam School of Management, Erasmus University. His research focuses on corporate sustainability with a specific focus on sustainable investing.
• DR David Risi is a professor at the Bern University of Applied Sciences and a habilitated lecturer at the University of St. Gallen. His research focuses on how companies organize CSR and sustainability.
• ES Eva Schlindwein is a professor at the Bern University of Applied Sciences and a postdoctoral fellow at the University of Oxford. Her research focuses on how organizations navigate tensions between business and society.
• AA Andromachi Athanasopoulou is an associate professor at Queen Mary University of London and an associate fellow at the University of Oxford. Her research focuses on how individuals manage their leadership careers and make ethically charged decisions.

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Post-bariatric pregnancy is associated with vitamin K1 deficiency, a case control study

• Brit Torunn Bechensteen 1 , 2 ,
• Cindhya Sithiravel 3 ,
• Ellen Marie Strøm-Roum 4 ,
• Heidi Kathrine Ruud 2 ,
• Gunnhild Kravdal 3 ,
• Jacob A. Winther 1 &
• Tone G. Valderhaug 1  

BMC Pregnancy and Childbirth volume  24 , Article number:  229 ( 2024 ) Cite this article

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Maternal obesity is associated with adverse outcome for pregnancy and childbirths. While bariatric surgery may improve fertility and reduce the risk of certain pregnancy-related complications such as hypertension and gestational diabetes mellitus, there is a lack of evidence on the optimal nutritional monitoring and supplementation strategies in pregnancy following bariatric surgery. We aimed to assess the impact of bariatric surgery on micronutrients in post-bariatric pregnancy and possible differences between gastric bypass surgery and sleeve gastrectomy.

In this prospective case control study, we recruited 204 pregnant women (bariatric surgery n  = 59 [gastric bypass surgery n  = 26, sleeve gastrectomy n  = 31, missing n  = 2] and controls n  = 145) from Akershus university hospital in Norway. Women with previous bariatric surgery were consecutively invited to study participation at referral to the clinic for morbid obesity and the controls were recruited from the routine ultrasound screening in gestational week 17–20. A clinical questionnaire was completed and blood samples were drawn at mean gestational week 20.4 (SD 4.5).

The women with bariatric surgery had a higher pre-pregnant BMI than controls (30.8 [SD 6.0] vs. 25.2 [5.4] kg/m2, p  < 0.001). There were no differences between groups regarding maternal weight gain (bariatric surgery 13.3 kg (9.6) vs. control 14.8 kg (6.5), p  = 0.228) or development of gestational diabetes ( n  = 3 [5%] vs. n  = 7 [5%], p  = 1.000). Mean levels of vitamin K1 was lower after bariatric surgery compared with controls (0.29 [0.35] vs. 0.61 [0.65] ng/mL, p  < 0.001). Multiadjusted regression analyses revealed an inverse relationship between bariatric surgery and vitamin K1 (B -0.26 ng/mL [95% CI -0.51, -0.04], p  = 0.047) with a fivefold increased risk of vitamin K1 deficiency in post-bariatric pregnancies compared with controls (OR 5.69 [1.05, 30.77] p  = 0.044). Compared with sleeve gastrectomy, having a previous gastric bypass surgery was associated with higher risk of vitamin K1 deficiency (OR 17.1 [1.31, 223.3], p  = 0.030).

Post-bariatric pregnancy is negatively associated with vitamin K1 with a higher risk of vitamin K1 deficiency in pregnancies after gastric bypass surgery compared with after sleeve gastrectomy. Vitamin K1 deficiency in post-bariatric pregnancy have potential risk of hypocoaguble state in mother and child and should be explored in future studies.

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Obesity is common in women of reproductive age, increasing the risk of several complications for mother and child [ 1 , 2 ]. Maternal metabolism in obesity may reduce the likelihood of successful pregnancy [ 3 ]. Moreover, given that weight loss before pregnancy mitigates the adverse outcomes of pregnancy related outcomes from obesity, bariatric surgery in women of reproductive age in increasing [ 4 , 5 ]. However, although bariatric surgery may reduce the risks of certain obesity related complications in pregnancy, pregnancy after bariatric surgery may carry adverse events such as malnutrition, vitamin deficiencies and inadequate weight gain as well as changes in endocrine and metabolic homeostasis [ 6 , 7 , 8 , 9 , 10 ]. Pregnancy following bariatric surgery has been associated with increased risk of preterm birth, nutritional deficiency and small for gestational age [ 7 , 8 , 11 , 12 , 13 , 14 ]. The causality of these effects are not known, but personalized nutritional counseling during post-bariatric pregnancy has been shown to improve nutrient intake of mothers and may contribute to higher weight of offspring [ 15 ].

There is a growing body of evidence suggesting that maternal nutrition and lifestyle affect fetal growth and development [ 16 , 17 ]. Micronutrients are vitamins and minerals that enable the body to produce enzymes, hormones and other substances essential for normal growth and development [ 18 ]. Micronutrient deficiencies contribute to poor growth, intellectual impairments and increased risk of morbidity and mortality [ 19 ]. Widespread global micronutrient deficiencies exist, with pregnant women and young children at highest risk [ 19 ]. Micronutrient interventions such as supplementation of folate to prevent neural tube defects zinc to reduce risk of preterm birth, and iron to reduce the risk of low birthweight are established [ 20 , 21 , 22 ]. The micronutritional deficiencies seen after bariatric surgery might be explained by poor dietary pattern in combination with gastrointestinal modification and reduced intestinal transit time [ 23 , 24 , 25 , 26 , 27 ]. Deficiencies of fatty soluble vitamins seem to be particularly prevalent in post-bariatric pregnancies, with potential risks of impaired vision, neuronal disorders, impairment of the immune system and hypocoagulability for mother and child [ 24 , 28 , 29 , 30 ].

While sleeve gastrectomy is the most common surgical procedure for the treatment of obesity worldwide, there is conflicting evidence on the optimal surgical procedure before subsequent pregnancy [ 10 , 31 ]. A large registry study showed no difference between gastric bypass and sleeve gastrectomy for preterm birth or small for gestational age [ 12 ]. Studies indicates increased risk of prematurity in pregnancy occurring less than 2 years after bariatric surgery [ 12 , 32 ]. However, other studies have not confirmed increased risks in pregnancies related to time-interval between bariatric surgery and conception [ 13 , 33 ]. As such, there is an evident knowledge gap on the impact of bariatric surgery on micronutrient status in pregnancies as well as outcomes for mother and child in order to provide optimal obstetric care in this group.

The aim of this study was to assess the impact of bariatric surgery on concentrations of micronutrients in post-bariatric pregnancies compared with non-surgical controls. Specifically, we hypothesized that fatty soluble micronutrients, including vitamin K1, was impaired after bariatric surgery. We also wanted to assess differences in maternal micronutrients concentrations following sleeve gastrectomy versus gastric bariatric surgery.

Materials and methods

Design and study population.

This observational case control study compared micronutritional status in pregnancy after bariatric surgery with non-surgical controls. Study participants were recruited from Akershus university hospital, between October 18th 2018 and December 9th 2022. Pregnant women with previous bariatric surgery were consecutively invited to study participation at referral to the clinic for morbid obesity and the controls were recruited from the routine ultrasound screening in gestational week 17–20. A total of 59 women with a previous bariatric surgery was included in the study and information on surgical procedure was available for 57 women (gastric bypass surgery n  = 26 and sleeve gastrectomy n  = 31). All women with post-bariatric pregnancies were closely monitored individually by a clinical doctor and a registered clinical dietitian focusing on micronutrient status and gestational weight gain. The controls received standard hospital care and dietary advice with additional advice if the blood samples revealed deficiencies.

A total of 204 women were included in this study with 92% of Caucasian ethnicity ( n  = 185). We compared micronutrient status in pregnancy in women with previous bariatric surgery ( n  = 59) to controls ( n  = 145). Women with known intestinal conditions (i.e. known inflammatory bowel disease, uncontrolled coeliac disease) were not included in the study. The study was approved by the Regional Committee for Medical and Health Research Ethics (reference 25829). All study participants provided written informed consent before study commencement, and the study was performed in accordance with the Declaration of Helsinki [ 34 ].

Definitions

The reference intervals for micronutrients in non-pregnant women and the chosen cut-offs defining micronutrient deficiencies in pregnancy are presented in Table  1 . We defined micronutrient deficiency according to known physiological changes in blood during pregnancy combined with established reference intervals in a non-pregnant population [ 30 , 35 , 36 , 37 ]. Time interval between bariatric surgery and conception was categorized into < 18 and ≥ 18 months.

Data collection

Clinical and laboratory data were retrieved at mean gestational week 20.4 (SD 4.5) (Bariatric surgery 23.9 [6.5] vs. controls 19.0 [2.0] weeks, p  < 0.001). Follow-up blood sample was available in a subgroup of 32 women with post-bariatric pregnancies at mean 30.4 (SD 5.6) gestational week. All patients completed a questionnaire on comorbidities, medications and dietary supplements. Additional information including maximum weight, time of bariatric surgery, type of bariatric surgery was retrieved during the first visit.

Blood samples and analysis

The blood samples were obtained by venipuncture and collected in Vacuette® tubes. EDTA tubes were used for analysis of hemoglobin, hemoglobin A1c and thiamine (vitamin B1). Lithium heparin gel tubes were used for analysis of zinc and selenium, and serum gel tubes for the remaining analyses. All the blood samples were non-fasting. After blood collection, all tubes were handled according to established procedures. The standard clinical chemistry parameters were analysed at the laboratory at Akershus University Hospital. Hemoglobin was analysed on Sysmex instruments (Sysmex Corporation, Kobe, Japan) and hemoglobin A1c on Tosoh instruments (Tosoh Corporation, Tokyo, Japan). Magnesium and homocysteine were analysed on Vitros 5.1 FS (Ortho Clinical Diagnostics, Raritan, NJ) until May 2021, thereafter on cobas c503 (Roche Diagnostics, Mannheim, Germany). Folate, cobalamin, ferritin and vitamin D were analysed on cobas e801 (Roche Diagnostics). Zinc and selenium were analysed using inductive coupled plasma – mass spectrometry (ICP-MS) and methylmalonic acid (MMA) with a liquid chromatography – mass spectrometry method (LC-MS/MS). Thiamine, pyridoxal 5-phosphate (vitamin B6), vitamin A and vitamin E were analysed at Oslo University Hospital, Aker and vitamin K1 was analysed at Fürst Medical Laboratorium, Oslo, all with chromatographic methods.

Statistical analysis

We estimated that the prevalence of micronutrient deficiency would be 30% in post-bariatric pregnancies and 5% in controls. To confirm a similar difference with a statistical power of more than 80% and a significance level (α) of 0.05, a total of 200 patients had to be included in the study with a 4:1 ratio of cases vs. controls (40 post-bariatric pregnancies and 160 controls). Proportions are reported as numbers with percent, continuous variables as mean ± standard deviation (SD) as appropriate. Differences between treatment groups were analysed using Pearson’s chi-square test or Fishers exact test for categorical data and Student’s t-test for continuous data. Paired sample t-test was used to assess paired observations of micronutrients in baseline and follow-up blood samples. Skewed distributed data were log-transformed to achieve normal distribution. Correlations between possible confounders and vitamin K1 variables were assessed by Spearman’s correlation (rho). Two-sided P values < 0.05 were considered statistically significant. The Bonferroni Holm correction was applied to mitigate the risk of type 1 statistical error. We used linear regression analyses to explore possible associations between bariatric surgery and vitamin K1 and logistic regression analyses to explore possible associations between bariatric surgery and vitamin K1 deficiency. Possible confounders were identified using a stepwise selection approach in which variables with p-values below 0.10 were included along with clinically significant confounders. Coefficients and odds ratio (OR) from regression analysis are presented with 95% confidence interval (CI). The analyses were performed using IBM SPSS Statistics (version 729.0.0).

We included 204 women in the study (bariatric surgery n  = 59 and controls n  = 145). Data on the specific type of surgical procedure were available for 57 women who had undergone bariatric surgery prior to conception (gastric bypass surgery n  = 26 and sleeve gastrectomy = 31). The women in the surgical group lost on average 39.0 (16.9) kg from the time of surgery to the start of pregnancy and the time interval from bariatric surgery to pregnancy was mean 63.7 (39.2) months. Patients’ characteristics by surgical status are presented in Table  2 .

The women with bariatric surgery had a higher pre-pregnant body mass index (BMI) compared with controls (30.8 [SD 6.0] vs. 25.2 [5.4] kg/m 2 , p  < 0.001). There was no difference between groups regarding age (32.1 [5.7] vs. 31.2 [4.2] years, p  = 0.215), maternal weight gain (13.3 [9.6] vs. 14.8 [6.5] kg, p  = 0.228), HbA1c (30.2 [7.1] vs. 31.1[3.6] mmol/mol, p  = 0.234) or development of gestational diabetes (5% vs. 5%, p  = 1.000). Fewer women with bariatric surgery had completed higher education and more women with bariatric surgery currently smoked compared with controls (24 [43%] vs. 103 [72%], p  < 0.001 and 5 [9%] vs. 0, p  = 0.001, respectively. Children of post-bariatric pregnancies had lower gestational age and lower birthweight, however neither reached statistical significance (38.5[3.1] vs. 39.3[2.1] weeks, p  = 0.054 and 3363 [624] vs. 3520 [521] g, p  = 0.081, respectively).

Dietary supplements and micronutrient status by surgical status are presented in Table  3 . Concentrations of ferritin, magnesium, pyridoxal 5-phosphate, vitamin A, E and K1 and selenium were significantly lower post-bariatric pregnancies compared with controls. Using micronutrients as categorical variables (deficiency yes/no) conferred a higher prevalence of micronutrient deficiencies such as iron, magnesium, pyridoxal 5-phosphate, vitamin K1 and selenium in pregnancies after bariatric surgery compared with controls and a higher prevalence of vitamin K1 deficiency after gastric bariatric surgery vs. sleeve gastrectomy (Fig.  1 ). The distribution of vitamin K1 concentrations in women with post-bariatric pregnancies and controls is presented in Fig.  2 . Paired sample t-test showed increased concentrations vitamin K1 in a subgroup of women with post-bariatric pregnancies (0.29 [0.29] ng/mL to 0.64 [0.92] ng/mL, p  = 0.070).

Micronutritional deficiency in pregnancy. A : pregnancy following bariatric surgery vs. non-surgical controls. B : pregnancy after gastric bypass surgery vs. sleeve gastrectomy. * denotes statistically significance after corrections for multiple comparisons

Distribution of vitamin K1 concentrations in women with post-bariatric pregnancies and controls

The women with gastric bariatric surgery underwent surgery at a younger age and with a longer time-interval between surgery and conception compared with the women with sleeve gastrectomy (23.5 vs. 27.5 years, p  = 0.002 and 85 [40] vs. 45 [28] months, p = < 0.001, respectively). One woman (4%) after gastric bariatric surgery and five women (16%) after sleeve gastrectomy, p  = 0.205 became pregnant < 18 months after surgery. Both surgical groups had lost comparable weight since surgery (gastric bypass surgery 41.4 [17.1] vs. sleeve gastrectomy 37.0 [16.8] kg, p  = 0.342) and they had comparable pre-pregnant BMI (gastric bypass surgery 31.9 [5.5] vs. sleeve gastrectomy 29.9 [6.4] kg/m 2 , p  = 0.222). The proportion of women with vitamin K1 deficiency was higher after gastric bariatric surgery compared with sleeve gastrectomy (gastric bypass surgery 9 [38%] vs. 1 [3%], p  = 0.003 and Fig.  1 ).

Univariate linear regression analysis showed that bariatric surgery was inversely associated with vitamin K1 levels (B -0.33 [95% CI -0.51, -0.15, p  < 0.001]. The result remained statistically significant after multivariable adjustments (-0.26 ng/mL [-0.51, -0.04], p  = 0.047) (Table  4 A). In addition, compared with sleeve gastrectomy, gastric bariatric surgery was inversely associated with vitamin K1 in univariate linear regression analysis (0.20 [0.019, 0.387], p  = 0.031), but not after multivariate adjustment (Table  4 B). Using vitamin K1 as a categorical variable (deficiency yes/no), bariatric surgery was associated with a fivefold increased risk of vitamin K1 deficiency compared with controls and that gastric bariatric surgery was associated with higher adjusted risk of vitamin K1 deficiency compared with sleeve gastrectomy (Table  5 ).

In this study, we compare micronutrient concentrations in post-bariatric pregnancy with matched non-surgical controls. The study shows that the concentrations of vitamin K1, magnesium, and selenium were significantly impaired in post-bariatric pregnancies vs. controls. Moreover, our results show that bariatric surgery was consistently associated with vitamin K1 levels, both as a continuous outcome variable and as a categorical variable (vitamin K1 deficiency) in post-bariatric pregnancy compared with controls. Moreover, the associations might be driven by gastric bariatric surgery rather than sleeve gastrectomy. However, the number of pregnant women with vitamin K1 concentration below the lower reference limit was overall small and the confidence intervals were large. Thus, these results should be interpreted with caution.

Maternal nutrition and micronutrients in pregnancy after bariatric surgery

In pregnancy, there is an increased need for nutrients to support fetal and placental growth and development [ 20 ]. A detailed dietary information was not available in this study and we cannot exclude that the women with bariatric surgery had a different nutritional composition compared with controls. In a subgroup of women with post-bariatric pregnancies, an increment in vitamin K was seen. However, the changes did not reach statistical significance. Follow-up blood samples for the controls were not available. A healthy diet after bariatric surgery may differ from the general population in the composition of lean protein, fruits and vegetables and starchy carbohydrates. Nonetheless, the combination of diet, intestinal modifications and increased metabolism in pregnancy might explain the deficiencies in fatty soluble vitamins seen in this study [ 23 , 24 , 25 , 26 , 27 ]. Improved nutrient intake of mothers was seen after personalized nutritional counseling during post-bariatric pregnancy and might contribute to higher birth weight of offspring [ 15 ]. Given the complexity and heterogeneity of nutritional status in post-bariatric pregnancies, focusing on sub-groups including pre-gestational nutritional deficiencies, and type of surgery performed is of vital importance. A recent consensus report recommended specialized care in pregnancies after bariatric surgery [ 38 ]. There is however a paucity of data to support clinical practice [ 38 , 39 ]. As such, there is an imperative need to identify pregnancy and trimester specific reference intervals and clinical decision limits in order to help clinical advice on dietary supplement.

Lifelong dietary supplement is recommended after bariatric surgery, however adherence to adequate dietary supplements seems to decrease over time [ 26 , 40 , 41 ]. Our study also confers inadequate use of dietary supplements in pregnancy after bariatric surgery with 30–70% of the women not taking recommended post-bariatric surgery dietary supplements (Table  3 ). Thus, a need for increased awareness to ensure adequate microntutrional care before, during and after pregnancy is imperative.

The role of vitamin K1 in pregnancy after bariatric surgery

In line with our results, a systematic review on vitamin K1 concentrations in patients with a history of bariatric surgery reported high risk of vitamin K1 deficiency after bariatric surgery and opted for better monitoring [ 23 ]. Our results also cohere with another study of 49 pregnant women with previous bariatric surgery, showing that vitamin K1 concentrations were lower in women with a history of bariatric surgery compared with 27 controls [ 30 ]. The increased fat storage in pregnancy may lead to less bioavailability for activation of fatty soluble vitamins [ 42 ]. Furthermore, the highly fat-soluble vitamin K1 depend upon conjugated bile salts for adequate absorption. Consequently, reduced stomach acid production, reduced absorption surface and shorter interaction time between conjugated bile salts and vitamin K1 might explain the lower serum concentrations of vitamin K1 after bariatric surgery [ 43 ]. Screening for vitamin K1 deficiency is usually recommended after malabsorptive surgical procedures including biliopancreatic diversion with or without duodenal switch [ 43 ]. However, restrictive procedures may also cause vitamin deficiencies due to digestive symptoms such as vomiting and food intolerance. Interestingly, lower levels of vitamin K1 were found in the first trimester compared to a control group of women without bariatric surgery [ 30 ]. Vomiting and food intolerance may also be the main symptoms of hyperemesis gravidarum, which calls for increased vigilance of vitamin K1 insufficiency in post-bariatric pregnancies in women with symptoms of hyperemesis in pregnancy.

The impact of vitamin K1 deficiency in post-bariatric pregnancies is not clear. Low circulating levels of vitamin K1 might lead to a hypocoaguble state in mother and child [ 30 ]. Some cases of neonatal intracranial bleeding have been reported, possible due to vitamin K1 deficiency [ 44 ]. Another study reported that obesity had stronger impact on hypercoagulability than pregnancy itself [ 45 ]. Nonetheless, insufficient data exist in order to recommend interventions of vitamin K1 deficiency in post-bariatric pregnancy [ 38 ]. While optimal monitoring of vitamin K1 during pregnancy following bariatric surgery remains unclear, a major concern is raised about the consistent finding of vitamin K1 deficiency in post-bariatric pregnancy.

Bariatric surgery before pregnancy: timing and selection of procedure – dose it matter?

Few studies have assessed the impact of different surgical procedures before pregnancy. One study of 119 pregnant women found no effect of maternal weight gain on maternal and perinatal outcome after sleeve gastrectomy [ 46 ]. However, the study did not include pregnancies after gastric bariatric surgery for comparison. Another retrospective observational study showed no differences between gastric bariatric surgery and sleeve gastrectomy regarding re-interventions or obstetric outcomes [ 4 ]. Conflicting evidence exists on the possible adverse effects of sleeve gastrectomy such as dyspepsia and weight regain as compared with gastric bariatric surgery [ 47 , 48 , 49 ]. Our study adds important knowledge about the different surgical procedures, suggesting that gastric bariatric surgery holds greater risk of vitamin K1 deficiency compared with sleeve gastrectomy. The optimal surgical procedure for obesity treatment in women of reproductive age is however not clear and a person-centered approach should be advocated in future guidelines.

The timing of pregnancy after bariatric surgery is moreover under debate. Current recommendations suggest waiting at least 12 months after bariatric surgery before planning a pregnancy [ 12 , 38 , 50 ]. In our study, women with previous sleeve gastrectomy had a shorter time interval between surgery and conception than the women with gastric bariatric surgery. This might reflect that the women who underwent gastric bariatric surgery underwent surgery in an era where gastric bariatric surgery was the most common surgical procedure for weight loss [ 31 ]. Interestingly, after adjustments for the time interval since bariatric surgery, gastric bariatric surgery was not associated with vitamin K1 in the linear regression model (Table  4 B). Thus, as adherence to dietary supplements is reduced with time after bariatric surgery, we cannot rule out that patient’ adherence to dietary supplement might have influenced the differences between surgical procedure seen in the present study [ 26 , 40 , 41 ]. On the other hand, the time interval between sleeve gastrectomy and conception did not impact maternal and neonatal outcomes in a study of 15 women conceived > 18 months after surgery. The authors concluded that pregnancy after sleeve gastrectomy was overall safe and well-tolerated [ 33 ]. Furthermore, a study of 30 women who became pregnant within a mean time of 17 months after gastric bariatric surgery did not appear to confer any serious risks in pregnancy with 90% of the children were born at term with normal birthweight [ 13 ]. In our study, only six patients (11%) became pregnant earlier than 18 months after surgery and the study was not designed to assess pregnancy or birth related complications.

Future implications?

The results of this study underscore the need for increased awareness of nutritional and microntutrional status to ensure adequate obstetric care both before and during post-bariatric pregnancies. Also, this study present important information on adherence to dietary supplement that should be considered in the planning of post-bariatric pregnancies. Moreover, the results of our study rises important questions on the impact of micronutrients deficiencies on future child development.

Strengths and limitations

The strengths of this study include the prospective design with matched controls. Moreover, definitions for the chosen cut-offs for micronutrient deficiency were chosen according to pregnancy specific reference intervals if established. However, we cannot rule out that the concentrations of the micronutrients change in pregnancy. Thus, the validity of the chosen cut-offs for defining micronutrient deficiency should be assessed in future studies. This study was a small single center study and did not have the statistical power to assess pregnancy related or birth related complications. The majority of the women in this study was Caucasian and the results may not be valid in populations of other ethnicities. The observational design does not provide any causality between variables. Also, we cannot rule out if the difference in gestational week for blood sampling or non-fasting blood samples might have influenced the micronutrient analyses. Finally, use of dietary supplements was self-reported and we cannot be sure that all the study participants adhered with the recommendation.

This study shows that concentrations of the micronutrients vitamin K1, magnesium, and selenium were significantly impaired in post-bariatric pregnancies compared with controls. We found a negative association between bariatric surgery and vitamin K1 and a higher risk of vitamin K1 deficiency after gastric bariatric surgery compared with sleeve gastrectomy. Vitamin K1 deficiency in post-bariatric pregnancy have potential risk of hypocoaguble state in mother and child and should be assessed in future studies.

Data availability

The data used in the present study is not open access or publicly available. The datasets are available from the corresponding author on reasonable request.

Abbreviations

Body mass index

Confidence interval

Standard deviation

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Acknowledgements

We acknowledge the work of the staff at the Section for Morbid Obesity at Akershus University Hospital HF for the persistent effort of data collection.

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Brit Torunn Bechensteen, Jacob A. Winther & Tone G. Valderhaug

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TGV and EMRS designed the study. BTB, EMRS and TGV collected the data for the study. TGV analysed the data. BTB and TGV drafted the manuscript. CS ad GK were responsible for the laboratory analyses. TGV and JAW were responsible for the statistical analyses. All authors contributed to the interpretation of data, reviewed and edited the manuscript and gave their final approval of the final version to be published.

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Bechensteen, B.T., Sithiravel, C., Strøm-Roum, E.M. et al. Post-bariatric pregnancy is associated with vitamin K1 deficiency, a case control study. BMC Pregnancy Childbirth 24 , 229 (2024). https://doi.org/10.1186/s12884-024-06407-0

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Published on 4.4.2024 in Vol 26 (2024)

Impacts of an Acute Care Telenursing Program on Discharge, Patient Experience, and Nursing Experience: Retrospective Cohort Comparison Study

Authors of this article:

Original Paper

• Courtenay R Bruce, MA, JD   ; 
• Steve Klahn, RN, MBA   ; 
• Lindsay Randle, MBA   ; 
• Xin Li, BS   ; 
• Kelkar Sayali, BS   ; 
• Barbara Johnson, BSN, MBA, DNP   ; 
• Melissa Gomez, MBA   ; 
• Meagan Howard, MHA   ; 
• Roberta Schwartz, PhD   ; 
• Farzan Sasangohar, PhD  

Houston Methodist, Houston, TX, United States

Corresponding Author:

Courtenay R Bruce, MA, JD

Houston Methodist

8100 Greenbriar Drive

Houston, TX, 77030

United States

Phone: 1 281 620 9040

Email: [email protected]

Background: Despite widespread growth of televisits and telemedicine, it is unclear how telenursing could be applied to augment nurse labor and support nursing.

Objective: This study evaluated a large-scale acute care telenurse (ACTN) program to support web-based admission and discharge processes for hospitalized patients.

Methods: A retrospective, observational cohort comparison was performed in a large academic hospital system (approximately 2100 beds) in Houston, Texas, comparing patients in our pilot units for the ACTN program (telenursing cohort) between June 15, 2022, and December 31, 2022, with patients who did not participate (nontelenursing cohort) in the same units and timeframe. We used a case mix index analysis to confirm comparable patient cases between groups. The outcomes investigated were patient experience, measured using the Hospital Consumer Assessment of Health Care Providers and Systems (HCAHCPS) survey; nursing experience, measured by a web-based questionnaire with quantitative multiple-choice and qualitative open-ended questions; time of discharge during the day (from electronic health record data); and duration of discharge education processes.

Results: Case mix index analysis found no significant case differences between cohorts ( P =.75). For the first 4 units that rolled out in phase 1, all units experienced improvement in at least 4 and up to 7 HCAHCPS domains. Scores for “communication with doctors” and “would recommend hospital” were improved significantly ( P =.03 and P =.04, respectively) in 1 unit in phase 1. The impact of telenursing in phases 2 and 3 was mixed. However, “communication with doctors” was significantly improved in 2 units ( P =.049 and P =.002), and the overall rating of the hospital and the ”would recommend hospital” scores were significantly improved in 1 unit ( P =.02 and P =04, respectively). Of 289 nurses who were invited to participate in the survey, 106 completed the nursing experience survey (response rate 106/289, 36.7%). Of the 106 nurses, 101 (95.3%) indicated that the ACTN program was very helpful or somewhat helpful to them as bedside nurses. The only noticeable difference between the telenursing and nontelenursing cohorts for the time of day discharge was a shift in the volume of patients discharged before 2 PM compared to those discharged after 2 PM at a hospital-wide level. The ACTN admissions averaged 12 minutes and 6 seconds (SD 7 min and 29 s), and the discharges averaged 14 minutes and 51 seconds (SD 8 min and 10 s). The average duration for ACTN calls was 13 minutes and 17 seconds (SD 7 min and 52 s). Traditional cohort standard practice (nontelenursing cohort) of a bedside nurse engaging in discharge and admission processes was 45 minutes, consistent with our preimplementation time study.

Conclusions: This study shows that ACTN programs are feasible and associated with improved outcomes for patient and nursing experience and reducing time allocated to admission and discharge education.

Introduction

Telemedicine, particularly video televisits, has greatly expanded in the wake of the COVID-19 pandemic [ 1 , 2 ]. Televisits have shown promise as a robust, practical, efficacious, and scalable alternative to in-person office visits that could ameliorate labor supply shortages [ 3 , 4 ]. The published evidence suggests a generally positive attitude toward televisit appointments for chronic care, focused on addressing financial and transportation barriers and improving patients’ access to care [ 5 - 7 ]. Despite the promise shown by televisits, limited attention has been paid to applying this method in the acute care setting and, in particular, on how this promising technology can be leveraged to support nurses.

Estimates suggest that approximately 200,000 open nursing positions will become available each year between 2021 and 2031 [ 8 ]. Telenursing can augment nursing labor supply, decrease nursing workload, maintain patient and nurse safety, and positively impact nursing and patient experiences [ 9 ]. However, the impact of telenursing on outcomes in acute care settings remains a research gap.

To address this gap, this study aimed to evaluate the outcomes associated with a large-scale acute care telenurse (ACTN) program to support web-based admission and discharge processes for hospitalized patients compared to patients who did not undergo the ACTN program intervention. Admission and discharge are 2 substantive and time-consuming acute care nursing tasks that involve tedious documentation in the electronic health record (EHR) and extensive interaction with patients and families to gather history and provide patient education [ 10 , 11 ]. We aimed to develop an ACTN program to augment nursing care by conducting admission and discharge processes through telenursing in a large health system. Subsequently, we discuss the impacts on 4 end points: patient experience, nursing experience, time of discharge during the day, and length of time for discharge education processes. We hypothesized that the ACTN program would be associated with higher patient experience scores and improved nursing experience compared to standard admission and discharge practices.

This study was conducted in a large academic hospital system (approximately 2100 beds) in Houston, Texas. The preimplementation methods are reported more extensively in the studies by Hehman et al [ 12 ] and Schwartz et al [ 13 ]. Program implementation was first informed by nursing time and workload surveys and pilot implementation in 4 comparatively understaffed units. The chief innovation officer, along with nursing leaders and ACTN program administrators, met with the bedside nursing staff of these 4 understaffed units to solicit their input on where and how ACTN would add value to their workflow. Bedside nursing staff provided critical input on admission processes that could be delegated to individuals working remotely with no perceived negative impact on patient experience. We conducted participatory workflow design sessions with bedside nursing staff on the ACTN program to cocreate workflow integration points where the remote team could assist [ 13 ].

Pilot Implementation and Procedures

Before implementation, the ACTN administrators trained bedside nurses in pilot units by demonstrating the use of technology during shift huddles. Then, the trainers presented slides on contact information and available support and provided a role demarcation process map, showing what the remote telenurse staff would be doing compared to what the bedside nurses needed to do to launch and conduct discharge education. Furthermore, the trainers invited the nursing staff to observe several discharges to learn how to conduct them. A software with Health Insurance Portability and Accountability Act compliance was uploaded to iPads (Apple Inc) and stored on each unit. Handheld iPads were available, and roaming iPads were made available for patients who could not hold an iPad.

The pilot implementation was staggered in a phased rollout, consisting of 3 sequenced phases, as shown in Figure 1 . Upon admission, the acute care bedside nurse contextualized the ACTN program with patients and families by handing the patient an iPad with a preloaded remote program app (Caregility) and then pressing a soft key to allow the ACTN to enter the patient’s room via the iPad screen. The ACTN introduced themselves, completed the nursing admission profile in the EHR, placed a request for a consultation, and notified the bedside nurse that the admission was completed using secure SMS text messaging [ 13 ]. A similar process was followed for discharge workflow processes, where the ACTN completed patient education on discharge instructions, confirmed the patient’s pharmacy details, confirmed discharge transportation, and arranged for departure.

Bedside nurses used their discretion regarding which patients would be appropriate for the ACTN program. They based this determination principally on whether documentation was needed and whether the patient could benefit from the undivided attention the ACTN program could afford. Furthermore, they excluded patients from the ACTN program if the patients expressed discomfort using an iPad. After the initial rollout, patients’ input was sought on their experience with the ACTN program to identify where and how improvements could be made, and this feedback was incorporated into iterative revisions in subsequent rollouts.

Pilot Outcomes Monitoring

A retrospective, observational cohort comparison was performed, in which all patients in our pilot units for the ACTN program (telenursing cohort) between June 15, 2022, and December 31, 2022, were compared with all patients who did not participate (nontelenursing cohort) in the same units in the same timeframe.

Our primary outcomes were patient experience and nursing experience. Patient experience scope was any process observable by patients [ 14 ]. We compared patient experiences in the telenursing and nontelenursing cohorts by evaluating patients’ responses to the widely used Hospital Consumer Assessment of Health Care Providers and Systems (HCAHPS) survey [ 15 ], which represented 8 aspects (called dimensions) of patient satisfaction. Each dimension was measured using a continuous variable (0 to 100 points).

For the telenursing cohort, we analyzed bedside nurses’ collective responses using a Forms (Microsoft Corp) survey conducted in April 2023. The survey consisted of 5 questions, asking them to indicate whether the ACTN program was helpful using a Likert scale with 5 items (very helpful to very unhelpful). Nurses were asked to provide open-ended comments to explain the reasons for their evaluation. At the end of the survey, we included 2 open-ended fields for nurses to describe opportunities for improvement in future rollouts and provide any additional comments.

Furthermore, we explored the time at which discharge occurred using the EHR admission, discharge, and transfer date and time. We compared the hour of the day the patient was discharged in the telenursing cohort with the hour of the day the patient was discharged in the nontelenursing cohort, hypothesizing a priori that patients might be discharged earlier in the day in the telenursing cohort. Finally, we analyzed the duration of discharge education for both cohorts, measured in minutes.

Data Analysis

The patient demographic data were available for all patients. To confirm that the telenursing cohort had similar patient demographics as the nontelenursing cohort (and therefore to confirm that nurse biases in patient selection for the ACTN program were unlikely), we conducted a case mix index (CMI) evaluation. We first isolated the population of both cohorts into adults (aged ≥18 y). We compared only those patients who were discharged home and excluded those who were on extracorporeal membrane oxygenation or those who underwent a tracheostomy. The remaining population was evaluated to determine whether there was a difference in patient acuity and severity. After confirming that patient acuity and severity were of no significant difference, we included the inpatient and observation populations to evaluate the intervention results.

For the patient experience data, independent sample t tests (2-tailed) were used to compare the telenursing and nontelenursing cohorts across different HCAHPS dimensions and units. Analysis was conducted using R software (R Foundation for Statistical Computing). For the nursing experience survey data, we used Excel (Microsoft Corp) to analyze the responses to multiple-choice, discrete questions and thematic analysis to evaluate the open-text fields. Thematic analysis allows for eliciting key themes that emerge based on recurring statements [ 16 ]. The analysis followed an inductive approach. This approach uses open-ended questions, allowing themes to emerge with a few previously articulated assumptions on responses. Given the limited content, CRB served as the primary coder. Coding labels were used for data contextualizing, allowing for new themes to emerge throughout the coding process, using a codebook [ 16 , 17 ]. We stored emergent patterns and themes in an electronic format.

Ethical Considerations

The hospital’s review board determined that the ACTN pilot would not be considered regulated human subjects research. All data reported in this study were aggregated and deidentified.

The demographics of the telenursing and nontelenursing cohorts were relatively similar. Both cohorts had an average age of 60 years with an SD of 16.91; had a similar distribution in race and ethnicity (approximately 92/2319, 3.96% Asian; 525/2319, 22.64% Black; 425/2319, 18.33% Hispanic; 70/2319, 3.02% Native American, declined to identify, or other categories; and 1202/2319, 51.83% White); and had a similar distribution in female participants versus male participants (1249/2319, 53.86% vs 1070/2319, 46.14%). To further understand the population, the CMI analysis for acuity and severity showed that the CMI was slightly higher in the telenursing cohort than in the nontelenursing cohort, but the difference was not statistically significant ( P =.75).

Patient Experience

Among the first 4 units that rolled out in phase 1, all units experienced improvement in at least 4 and up to 7 HCAHPS domains (Table S1 in Multimedia Appendix 1 ). On average, 6 out of 8 HCAHPS domains were improved for patients in the telenursing cohort. All 4 units experienced improvements in the “overall rating” domain, and 3 of the 4 units experienced improvements in “likelihood to recommend” domain for patients in the telenursing cohort compared to those in the nontelenursing cohort within the same units. The improvement scores ranged from 1.4% for the neurosurgery unit (36 beds) to 11.6% for the medical unit (37 beds). Furthermore, all 4 units in the first phase of roll out experienced improved scores in the “responsiveness” domain by >4 points (ranging from 5% to 10.1%). A total of 2 out of the 4 units also experienced improvements in the “communication with nurses” (ranging from 1.7% to 3%) and “communication about medicines” (ranging from 3.3% to 11.7%) domains. The 2 units that did not experience improvement in the communication domains were the combined medical and surgery neurology and neurosurgical units (36 beds). Only the neurosurgical unit showed statistically significant improvements in 2 dimensions: “communication with doctors” ( P =.03) and “would recommend hospital” ( P =.04).

For the 7 units that rolled out during phase 2, only 1 orthopedic surgery unit (28 beds) experienced improvements in every domain (ranging from 0.9% to 12.5%). Medical observation unit 1 also improved in 5 areas. However, only improvements in “communication with doctors” ( P =.002), “overall rating of hospital” ( P =.02), and “would recommend hospital” ( P =.04) were statistically significant . The remaining units experienced improvements in some domains for the telenursing cohort compared to the nontelenursing cohort, with no improvement in other domains. However, the scores for “communication with nurses” and “communication with doctors” domains were improved for most of the units that rolled out in phase 2 (Table S2 in Multimedia Appendix 1 ).

For the 2 units that rolled out in phase 3, both of which were surgical cardiac units with 36 beds, 1 unit experienced improvement in every domain except “responsiveness” (ranging from 1% to 12%). The other unit only experienced improvement in the “communication with doctors” (4.9%) and “care transitions” domains (1.1%). However, none of these improvements were statistically significant (Table S3 in Multimedia Appendix 1 ).

Nursing Experience

Of the 289 nurses who were invited to participate in the survey, 106 completed the survey (36.7% response rate). Of the 106 nurses, 101 (95.3%) indicated that the ACTN program was “very helpful” or “somewhat helpful” to them as bedside nurses.

Quantitative Findings

The main reasons nurses gave for the program’s helpfulness included that it saved them time (94/106, 88.7%), allowed them to focus on more urgent clinical needs (90/106, 84.9%), allowed them to focus on activities they felt were more in line with their skill level (55/106, 51.9%), and allowed patients to have undivided attention for their discharge education (52/106, 49.1%). Among the 5 nurses who indicated that the ACTN program was somewhat unhelpful or very unhelpful, 3 (60%) indicated that workflows were not clear or needed further refinement or clarification. Furthermore, the nurse respondents shared several barriers and provided opportunities for improvement, with 91 (85.8%) out of 106 nurses offering suggestions.

Qualitative Findings

For the free-text explanation fields, all but 3 nurses (103/106, 97.2%) provided additional comments on the ACTN program helpfulness. Three themes emerged from the qualitative analysis of the free-text comments: (1) most of the nurses’ comments reflected that telenurses help bedside nurses save time, (2) respondents indicated that extra hands provided emotional and physical support in providing patient care, and (3) respondents perceived an improvement in patient safety by having a telenurse who could “catch missed” issues.

Time Saving

One of the perceived benefits of the telenursing program was saving time. One nurse said the following:

... Just putting in home medications alone takes up so much time. This new telenurse service helps [save time]

Several nurses highlighted that admission and discharge processes are so complex and time-consuming that shifting this work to the ACTN program freed nurses to perform other activities, as reflected by this nurse:

The tele RN is able to spend as much time possible sufficiently educating an admission or discharge while allowing me time to respond to the needs of my other patients saving me time on one patient especially charting.

Emotional and Physical Support

For the second theme, several responses focused less on time management and perceived efficiencies and instead centered more on the emotional appeal and support in having an extra hand, as one nurse mentioned:

Being in such a fast-paced unit, it can be a bit stressful with so many discharges and admissions. Having a helpful hand is beneficial.

Improved Patient Safety

Finally, the third theme was perceived improvement in patient safety by having a telenurse who could “catch missed” issues (eg, an incorrectly identified pharmacy details), simultaneously allowing the primary bedside nurse to focus more intensely on other needs, essentially creating a 2-fold safety promotion. Some nurses noted that they could begin carrying out orders while the telenurses began completing the admission, facilitating quicker treatment and resolution of care needs, thereby improving the safety and quality of care. One nurse mentioned the following:

Allows [telenurses] to take on thorough and accurate admissions, while also preventing any rushing the patient might experience from the primary RN.

When asked for areas of improvement, the most recurring theme was having 24 hours of support during the weekend and during the week. The second theme for improvement was the reduced time to connect to a telenurse. The third theme was the availability of iPads. Nurses mentioned that iPads could sometimes be unavailable in patients’ rooms or they may not be fully charged.

Time of Discharge

The time of day distribution is presented in Figure 2 . The only noticeable difference between the telenursing and nontelenursing cohorts was a shift in the volume of patients discharged before 2 PM compared with those discharged after 2 PM at a hospital-wide level ( Table 1 ). At an individual unit level, these results were not consistent and could be further explored by patient population and their needs to discharge. The variation was further illustrated when reviewing the length of stay of patients in the telenursing and nontelenursing cohorts. Only 5 out of the 12 units showed a decrease in the average inpatient length of stay.

Discharge Length

The ACTN admissions averaged 12 minutes and 6 seconds (SD 7 min and 29 s), and the discharges averaged 14 minutes and 51 seconds (SD 8 min and 10 s). The average duration for ACTN calls was 13 minutes and 17 seconds (SD 7 min and 52 s). Traditional cohort standard practice of a bedside nurse engaging in discharge and admission processes was 45 minutes, consistent with our preimplementation nursing time study.

Principal Findings

Our results suggest that the ACTN program was associated with positive nursing experiences because it saved time. Furthermore, the ACTN program was associated with higher HCAHPS scores in several domains but only in the first series of units that piloted the intervention. In phase 1, the improvement in “communication with doctors” and “would recommend hospital” scores in 1 unit was statistically significant. In phase 2, the improvement in “communication with doctors” score was significant in 2 units and that in “overall rating of hospital” and “would recommend hospital” scores were significant in 1 unit. The time of day discharge was nearly the same in both the telenursing and nontelenursing cohorts. The duration for discharge processes was less than half in the ACTN cohort compared to the nonintervention cohort.

At the time of writing this paper, the United States was experiencing a critical nursing shortage that will likely reach an epidemic level in the next few decades [ 8 ]. Despite the promise shown by telenursing, to our knowledge, only 1 existing paper documents the impact of ACTN programs on HCAHPS-measured patient satisfaction using a small cohort of patients in a single, time-limited pre- and posttelenursing analysis [ 18 ]. A study by Schuelke et al [ 18 ] revealed a 6.2% increase in “communication with meds” and 12.7% increase in “communication with nursing” domain scores; other HCAHPS domains were not evaluated. This research builds upon the promising work of Schuelke et al [ 18 ], evaluating the impact of an ACTN program on several units with a much larger cohort of patients using a staggered rollout and comparing all HCAHPS domains between telenursing and nontelenursing cohorts within the same time frame and in the same units.

By conducting granular HCAHPS analyses, we identified what we believed to be a time sequence variability in that units that rolled out in phase 1 performed considerably stronger in HCAHPS impacts than units that rolled out in later phases. An explanation for this sequence effect might be that some later adopters had less potential for high effect size, given that the first 4 units of the rollout were specifically chosen for their staffing problems compared to later units. ACTN support might have augmented the staffing support to such a degree that allowed the impacts of the program to be more salient. An alternative explanation is that the early adopters and promoters tend to have greater diffusion uptake, greater saturation and adoptability, and greater impacts compared to late adopters or those resistant to adoption [ 19 , 20 ]. Our anecdotal evidence suggests that early adopters might have wanted the telenursing program to succeed; therefore, they applied consistent implementation practices to ensure success. Adopters in later stages were more aware of barriers and potential downsides and might have been more ambivalent about telenursing and, therefore, less likely to modify their behaviors to promote the telenursing program’s success.

Another interesting finding was that the ACTN program seemed to be effective for both medical and surgical units of all specialties. Phase 1 was a mix of medical and surgical units; however, all units experienced increases in scores. Phases 2 and 3 experienced mixed results, without a clear lead for one specialty over the other. This may suggest that ACTN programs are broadly applicable across acute settings and that success depends most crucially on the need and desire of unit leaders.

Our time of day discharge findings showed only a few quantitative positive efficiencies. However, our discharge duration analysis and nursing experience survey results showed that ACTN has major time-saving benefits for nurses, suggesting a discrepancy between perceived and actual time savings versus time-of-day discharge savings. One explanation for this discrepancy may be that many factors beyond nursing impact the time of the day a patient is discharged; therefore, while the bedside nurses’ time is saved, the remaining discharge processes beyond nurses remain unaffected. Specifically, there are 3 segments of time during discharge processes: (1) the time for the discharge order and medication reconciliation [ 21 ] to the time the after-visit summary (AVS) is populated and printed [ 22 ]; (2) the time the AVS is completed and printed to the time the discharge instructions are provided; and (3) the time from providing the discharge instructions to the actual discharge ( Figure 3 ). Notably, telenurses’ involvement is currently limited to only the second segment of time. Specifically, telenurses’ involvement is not initiated until the AVS is printed by the nurse, which means that telenurses cannot positively impact any discharge activity that occurs between the time the discharge order is written and the time the AVS is printed. However, there are inefficiencies and bottlenecks in discharge processes that occur well before the AVS is printed [ 23 , 24 ]. For instance, the discharging physician may write a conditional discharge order early in the morning, listing conditions that cannot be fulfilled within a few hours or it may take bedside nursing longer than anticipated time to print the AVS.

To create a wider cascade effect for positively impacting the discharge processes for all segments of time, we are currently trying to obtain greater transparency through EHR reporting in what occurs for segments 1 and 3. For instance, at present, we know that at least 2 hospitals in our 8-hospital system have high incidence rates of conditional discharge orders that should be reduced. One hospital anecdotally reports that the discharging physician identifies incorrect pharmacies, which requires a nurse to send the scripts back to the discharging pharmacist to reconcile before discharge education can occur [ 25 ]; however, the prevalence and location of these issues remain speculative. Segment 3 is a black box of time [ 26 ]—the time it takes for hospital transport or an ambulance to arrive and move the patient to their destination and the time it takes for the family to pick up the patient. All these factors impact the discharge processes and need to be fully elucidated, explored, and streamlined. Furthermore, we hope to facilitate processes that enable telenurses to print the AVS, to remove the dependency on bedside nurses to begin the discharge education process.

Limitations

This study has several noteworthy limitations. First, the study was conducted in 1 health system and the results may not be generalizable to other settings with different patient populations, processes, and implementation strategies [ 27 ]. Second, in this study, we did not control for other factors that could impact patient and provider satisfaction as well as discharge times; telenursing can only improve upon one component in a complex set of factors limiting discharge efficiency and satisfaction outcomes. Finally, participating nurses were aware of the ongoing study, and this knowledge might have affected their behavior [ 28 ].

Future Directions

After the completion of this pilot study, the ACTN admission and discharge program has been rolled out to pilot medical units and all surgical and observation units. Our rationale for expansion rested on the premise that nursing experience is important to maintain and strengthen, particularly at a time when turnover is high in the health care industry in general. It is important to reduce staff inefficiencies in workload as a means of preserving or strengthening organizational morale and cost saving. Because our nursing experience findings for the ACTN program heavily supported the program, this served as the primary motivation for expansion. The nursing experience findings, coupled with the findings related to time-savings in discharge education and modest improvement, though not negative, in the HCAHPS findings for the ACTN program compared to the nontelenursing cohort, further supported expansion.

The initial scope for expansion included a complete system-wide implementation for all admissions and discharges. Furthermore, we are planning to expand the ACTN program beyond admissions and discharges. Responsive to qualitative feedback reported earlier, the next phase of the ACTN program will add safeguards on high-risk medications by having telenurses conduct double-checks, skin assessments, hourly rounding assistance, and auditing of safety functions and educational activities. These activities were chosen because they are time-intensive for nursing staff on the patient floors. Additional support in these areas would be a staff morale booster in addition to improved efficiencies for bedside nursing. Conducting hourly rounding using the ACTN program will require more time and resources; however, conducting high-quality, uninterrupted hourly rounds is known to be effective at improving patient safety and patient experience outcomes [ 29 ]. Therefore, we suspect that the ACTN program will have some positive impacts if rounds are consistently conducted, even if conducted virtually.

In addition, the ACTNs have been motivating other specialties to adopt or consider a similar program as the ACTN program to support stretched staffing. These specialties include respiratory care, in which virtual support can quickly identify patients in need of intensive on-site support; pharmacy, in which direct communication with staff on medications and patient training can happen through virtual means; infection control, in which room environments can be reviewed through virtual audits, moving quickly from floor to floor; and guest relations and spiritual care, in which patients can be visited virtually upon patient request. Furthermore, physicians who wish to either virtually enter inpatient rooms during their clinic days or from home can quickly drop in to see patients using the virtual program. For these groups to further develop advanced inpatient telemedicine programs, additional technology will be required, including cameras that can zoom into various portions of the room and advanced sound capabilities. Future work could expand programs similar to ACTN to specialties such as respiratory therapy, pharmacy, infection prevention, and spiritual care.

Conclusions

This study provides preliminary evidence suggesting that telenursing may effectively address nursing shortages in acute care settings and positively impact patient and provider satisfaction as well as admission and discharge times. More work is needed to validate the findings in other settings, use other satisfaction metrics, and investigate the impact of telenursing on the quality of care and cost.

Acknowledgments

The authors would like to thank Jacob M Kolman, MA, ISMPP CMPP, senior scientific writer, Houston Methodist Academic Institute, for the critical review and for providing formatting feedback on this manuscript. The authors would also like to thank Amir Hossein Javid for his help with statistical analysis.

Data Availability

Data sharing is not applicable as no data sets were generated during this study.

Authors' Contributions

All authors were involved in the conceptualization, review and approval, and writing of the manuscript. LR, BJ, MG, RS, SK, and MH were extensively involved in the implementation of the project. BJ, MH, SK, and MG conducted the training. SK and XL conducted the analyses. CRB wrote and edited the manuscript, inserted and refined the citations, and provided critical feedback during implementation and analyses. CRB and FS were involved in all stages of writing and publication. All authors meaningfully contributed to the drafting, writing, brainstorming, executing, finalizing, and approving of the manuscript.

Conflicts of Interest

None declared.

Additional outcome information for Hospital Consumer Assessment of Health Care Providers and Systems, time of day discharges, and discharge education processes.

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Abbreviations

Edited by T de Azevedo Cardoso, G Eysenbach; submitted 06.11.23; peer-reviewed by C Jensen; comments to author 08.12.23; revised version received 16.01.24; accepted 17.02.24; published 04.04.24.

©Courtenay R Bruce, Steve Klahn, Lindsay Randle, Xin Li, Kelkar Sayali, Barbara Johnson, Melissa Gomez, Meagan Howard, Roberta Schwartz, Farzan Sasangohar. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 04.04.2024.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.

Pregnancy and COVID-19: What are the risks?

You may wonder how coronavirus disease 2019 (COVID-19) could affect your risk of illness, birth plan or time bonding with your baby. You also might have questions about the safety of the COVID-19 vaccines. Here's what you need to know.

COVID-19 risks during pregnancy

Pregnant people seem to catch the virus that causes COVID-19 at about the same rate as people who aren't pregnant. Also, pregnant people usually get better without needing care in the hospital. But pregnancy is a factor that raises the risk of severe COVID-19. That risk stays higher for at least a month after giving birth.

And the risk continues to go up if a pregnant person has other health issues linked to severe COVID-19. Examples of these health issues are obesity, diabetes, high blood pressure or lung disease.

Being very sick with COVID-19 means that a person's lungs don't work as well as they should. Severe or critical COVID-19 is treated in the hospital with oxygen and other medical help to treat damage throughout the body. Severe COVID-19 can lead to death.

Pregnant people with severe COVID-19 also may be more likely to develop other health problems as a result of COVID-19. They include heart damage, blood clots and kidney damage. Moderate to severe symptoms from COVID-19 have also been linked to higher rates of preterm birth, high blood pressure or preeclampsia.

These risks may shift as the virus that causes COVID-19 changes. Risks also may change as disease prevention and treatment evolve. But risks are lowered significantly when a pregnant person gets the COVID-19 vaccine.

Preventing COVID-19 during pregnancy and breastfeeding

The Centers for Disease Control and Prevention recommends getting a 2023-2024 COVID-19 vaccine if:

• You are planning or trying to get pregnant.
• You are pregnant now.
• You are breastfeeding.

Staying up to date on your COVID-19 vaccine helps prevent severe COVID-19 illness. It also may help a newborn avoid getting COVID-19 if you are vaccinated during pregnancy.

People at higher risk of serious illness can talk to a healthcare professional about additional COVID-19 vaccines or other precautions. It also can help to ask about what to do if you get sick so you can quickly start treatment.

While you’re pregnant, it’s important for you and those in your household to:

• Test for COVID-19. If you have COVID-19 symptoms, test for the infection. If you are exposed, test five days after you came in contact with the virus. In the United States, the Food and Drug Administration, also known as the FDA, approves or authorizes the tests. On the FDA website, you can find a list of the tests that are validated and their expiration dates. You also can check with your healthcare professional before buying a test if you have any concerns.
• Keep some distance. Avoid close contact with anyone who is sick or has symptoms, if possible.
• Wash your hands. Wash your hands well and often with soap and water for at least 20 seconds. Or use an alcohol-based hand sanitizer with at least 60% alcohol.
• Cover your coughs and sneezes. Cough or sneeze into a tissue or your elbow. Then wash your hands.
• Clean and disinfect high-touch surfaces. For example, clean doorknobs, light switches, electronics and counters regularly.

Try to spread out in crowded public areas, especially in places with poor airflow. This is important if you have a higher risk of serious illness.

The CDC recommends that people wear a mask in indoor public spaces if you're in an area with a high number of people with COVID-19 in the hospital. They suggest wearing the most protective mask possible that you'll wear regularly, that fits well and is comfortable.

COVID-19 and prenatal care

Unlike earlier in the pandemic, in-person prenatal visits typically are not disrupted by COVID-19.

If you test positive for COVID-19, your healthcare professional will want to discuss your options with you. That might mean a virtual or in-person appointment to figure out how to best keep track of your health. It may help to know that in most cases, the COVID-19 infection doesn't spread to the unborn baby.

If you test positive for COVID-19 and have symptoms, your healthcare team will monitor you closely. A healthcare professional may ask about your symptoms, review your other medical conditions and determine your risk of serious illness. You may be offered medicine to block the infection from getting worse. Treatment with these medicines may be a pill that you swallow, or a liquid given through a needle into a vein.

You also may be asked to use a device to monitor your oxygen level, called a pulse oximeter.

After the infection, your healthcare professional may plan on extra imaging tests to make sure the unborn baby is growing as expected.

COVID-19 and giving birth

If you test positive for COVID-19 close to when you give birth, you may not need to change your birth plan.

But it's also possible that your healthcare professional will suggest a change in timing or delivery options for your safety. People who also are managing high blood pressure linked to pregnancy or preeclampsia are more likely to be monitored in the hospital if they get COVID-19.

After the baby is born, research suggests it's safe for your baby to stay with you even if you have COVID-19. If you are too ill to care for your baby, your healthcare professional may suggest the baby stay in another hospital area.

To limit your baby's exposure to the virus, wear a well-fitting face mask and have clean hands when caring for your newborn. Stay a reasonable distance from your baby when not feeding, if possible.

Breastfeeding and COVID-19

If you have COVID-19 but feel well enough, there is no need to stop breastfeeding or stay separate from your baby. To avoid spreading the infection, wash your hands before breastfeeding. Also, wear a well-fitting face mask whenever you are in close contact with your baby.

If you're pumping breast milk, wash your hands before touching any pump or bottle parts and follow instructions for pump cleaning. If you need care in the hospital, you may be able to keep pumping.

COVID-19 concerns after giving birth

Staying healthy can be a big concern for new parents. Worry about COVID-19 illness for yourself or your newborn may be an added burden. But it is typical for newborns to get their first illness during their first year of life. In fact, your baby may have mild illness regularly during this first year as the baby comes in contact with the world.

If you find that worry over COVID-19 or other illness is affecting your or your baby's health, talk to your healthcare professional.

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• COVID-19: People with certain medical conditions. Centers for Disease Control and Prevention. https://www.cdc.gov/coronavirus/2019-ncov/need-extra-precautions/people-with-medical-conditions.html. Accessed March 18, 2024.
• Goldman L, et al., eds. COVID-19: Epidemiology, clinical manifestations, diagnosis, community prevention, and prognosis. In: Goldman-Cecil Medicine. 27th ed. Elsevier; 2024. https://www.clinicalkey.com. Accessed March 20, 2024.
• Smith, ER. Clinical risk factors of adverse outcomes among women with COVID-19 in the pregnancy and postpartum period: A sequential, prospective meta-analysis. American Journal of Obstetrics and Gynecology. 2023; doi:10.1016/j.ajog.2022.08.038.
• COVID-19 vaccines while pregnant or breastfeeding. Centers for Disease Control and Prevention. https://www.cdc.gov/coronavirus/2019-ncov/vaccines/recommendations/pregnancy.html. Accessed March 18, 2024.
• Barros, FC. Maternal vaccination against COVID-19 and neonatal outcomes during omicron: INTERCOVID-2022 study. American Journal of Obstetrics and Gynecology. 2024; doi: 10.1016/j.ajog.2024.02.008.
• Interim clinical considerations for use of COVID-19 vaccines in the United States. Centers for Disease Control and Prevention. https://www.cdc.gov/vaccines/covid-19/clinical-considerations/interim-considerations-us.html. Accessed March 20, 2024.
• How to protect yourself and others. Centers for Disease Control and Prevention. https://www.cdc.gov/coronavirus/2019-ncov/prevent-getting-sick/prevention.html. Accessed March 20, 2024.
• COVID-19 overview and infection prevention and control priorities in non-U.S. healthcare settings. Centers for Disease Control and Prevention. https://www.cdc.gov/coronavirus/2019-ncov/hcp/non-us-settings/overview/index.html. Accessed March 20, 2024.
• Use and care of masks. Centers for Disease Control and Prevention. https://www.cdc.gov/coronavirus/2019-ncov/prevent-getting-sick/about-face-coverings.html. Accessed March 20, 2024.
• Hughes BL, et al. COVID-19: Antepartum care of pregnant patients with symptomatic infection. https://www.uptodate.com/contents/search. Accessed March 18, 2024.
• AskMayoExpert. COVID-19: Pregnancy. Mayo Clinic; 2024.
• CDER scientific review documents supporting emergency use authorizations for drug and biological therapeutic products, COVID-19. U.S. Food and Drug Administration. https://www.fda.gov/drugs/coronavirus-covid-19-drugs/cder-scientific-review-documents-supporting-emergency-use-authorizations-drug-and-biological. Accessed March 18, 2024.
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• COVID-19 testing: What you need to know. Centers for Disease Control and Prevention. https://www.cdc.gov/coronavirus/2019-ncov/symptoms-testing/testing.html. Accessed March 18, 2024.
• At-home OTC COVID-19 diagnostic tests. U.S. Food and Drug Administration. https://www.fda.gov/medical-devices/coronavirus-covid-19-and-medical-devices/home-otc-covid-19-diagnostic-tests. Accessed March 20, 2024.
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Direct observation methods: A practical guide for health researchers

Gemmae m. fix.

a VA Center for Healthcare Organization and Implementation Research, Bedford and Boston, MA, USA

b General Internal Medicine, Boston University School of Medicine, Boston, MA, USA

c Department of Psychiatry, Harvard Medical School, Boston, MA, USA

Mollie A. Ruben

d Department of Psychology, University of Maine, Orono, ME, USA

Megan B. McCullough

e Department of Public Health, University of Massachusetts Lowell, Lowell, MA, USA

To provide health research teams with a practical, methodologically rigorous guide on how to conduct direct observation.

Synthesis of authors’ observation-based teaching and research experiences in social sciences and health services research.

This article serves as a guide for making key decisions in studies involving direct observation. Study development begins with determining if observation methods are warranted or feasible. Deciding what and how to observe entails reviewing literature and defining what abstract, theoretically informed concepts look like in practice. Data collection tools help systematically record phenomena of interest. Interdisciplinary teams--that include relevant community members-- increase relevance, rigor and reliability, distribute work, and facilitate scheduling. Piloting systematizes data collection across the team and proactively addresses issues.

Observation can elucidate phenomena germane to healthcare research questions by adding unique insights. Careful selection and sampling are critical to rigor. Phenomena like taboo behaviors or rare events are difficult to capture. A thoughtful protocol can preempt Institutional Review Board concerns.

This novel guide provides a practical adaptation of traditional approaches to observation to meet contemporary healthcare research teams’ needs.

Graphical abstract

• • Health research study designs benefit from observations of behaviors and contexts
• • Direct observation methods have a long history in the social sciences
• • Social science approaches should be adapted for health researchers’ unique needs
• • Health research observations should be feasible, well-defined and piloted
• • Multidisciplinary teams, data collection tools and detailed protocols enhance rigor

1. Introduction

Health research studies increasingly include direct observation methods [ [1] , [2] , [3] , [4] , [5] ]. Observation provides unique information about human behavior related to healthcare processes, events, norms and social context. Behavior is difficult to study; it is often unconscious or susceptible to self-report biases. Interviews or surveys are limited to what participants share. Observation is particularly useful for understanding patients’, providers’ or other key communities’ experiences because it provides an “emic,” insider perspective and lends itself to topics like patient-centered care research [ 1 , 5 , 6 ]. This insider perspective allows researchers to understand end users’ experiences of a problem. For example, patients may be viewed as “non-compliant,” while observations can reveal daily lived experiences that impede adherence to recommended care [ [7] , [8] , [9] , [10] ]. Observation can examine the organization and structure of healthcare delivery in ways that are different from, and complementary to, methods like surveys, interviews, or database reviews. However, there is limited guidance for health researchers on how to use observation.

Observation has a long history in the social sciences, with participant observation as a defining feature of ethnography [ [11] , [12] , [13] ]. Observation in healthcare research differs from the social sciences. Traditional social science research may be conducted by a single individual, while healthcare research is often conducted by multidisciplinary teams. In social science studies, extended time in the field is expected [ 11 ]. In contrast, healthcare research timelines are often compressed and conducted contemporaneous with other work. Compared to social science research questions, healthcare studies are typically targeted with narrowly defined parameters.

These disciplinary differences may pose challenges for healthcare researchers interested in using observation. Given observation’s history in the social sciences there is a need to tailor observation to the healthcare context, with attention to the dynamics and needs of the research team. This paper provides contemporary healthcare research teams a practical, methodologically rigorous guide on when and how to conduct observation.

This article synthesizes the authors’ experiences conducting observation in social science and health services research studies, key literature and experiences teaching observation. The authors have diverse training in anthropology (GF, MM), systems engineering (BK) and psychology (MR). To develop this guide, we reflected on our own experiences, identified literature in our respective fields, found common considerations across these, and had consensus-reaching discussions. We compiled this information into a format initially delivered through courses, workshops, and conferences. In keeping with this pedagogical approach, the format below follows the linear process of study development.

Following the trajectory of a typical health research project, from study development through data collection, analysis and dissemination ( Fig. 1 ), we describe how to design and conduct observation in healthcare related settings. We conclude with data analysis, dissemination of findings, and other key guidance. Importantly, while illustrated as a linear process, many steps inform each other. For example, analysis and dissemination, can inform data collection.

Direct observation across a health research study.

3.1. Study development

3.1.1. study design and research questions.

In developing research using observation, the first step is determining if observation is appropriate. Observation is ideal for studies about naturally occurring behaviors, actions, or events. These include explorations of patient or provider behaviors, interactions, teamwork, clinical processes, or spatial arrangements. The phenomena must be feasible to collect. Sensitive or taboo topics like substance use or sexual practices are better suited to other approaches, like one-on-one interviews or anonymous surveys. Additionally, the phenomena must occur frequently enough to be captured. Trying to observe rare events requires considerable time while yielding little data. Early in the study design process, the scope and resources should be considered. The project budget and the timeline need to account for staffing, designing data collection tools, and pilot testing.

Research questions establish the study goals and inform the methods to accomplish them. In a study examining patients’ experiences of recovery from open heart surgery, the ethnographic study design included medical record data, in-depth interviews, surveys, and observations of patients in their homes, collected over three months following surgery [ 7 ]. By observing patients in their homes GF saw how the household shaped post-surgical diet and exercise. Table 1 provides additional examples of healthcare studies using observation, often as part of a larger, mixed-method design [ 14 , 15 ].

Example studies that use observation.

3.1.2. Data collection procedures

The phenomena to observe should be clearly defined. Research team discussions create a unified understanding of the phenomena, clarify what to observe and record, and ensure data collection consistency. This explication specifies what to look for during observation. For example, a team might operationalize the concept of patient-centered care into specific actions, like how the provider greets the patient. Further, additional nuances within broader domains (e.g., patient-centered care) could be identified while observations are ongoing. The team may identify unanticipated ways that providers enact patient-centered care (e.g., raising non-clinical, but relevant psychosocial topics- like vacations or hobbies- prior to gathering biomedical information). It is also important to look for negative instances, or behaviors that did not happen that should have, or surprising, unexpected findings. A surprise finding during observation was the impetus for further analysis examining how HIV providers think about their patients. While observing HIV care, a provider made an unexpected, judgmental comment about patients who seek pre-exposure prophylaxis (PrEP) to prevent HIV. This statement was documented in the fieldnotes (see 3.1.3 for a further description of fieldnotes) and later discussed with the team, leading to review of other study data and an eventual paper (see Fix et al 2018) [ 1 ]. Leaving room, both literally on the template and conceptually, can provide space for new, unexpected insights.

The sampling strategy outlines the frequency and duration of what is observed and recorded. It requires determining the unit of observation and the observation period. Units of observation are sometimes called “slices” of data. Ambady and Rosenthal [ 20 ] coined the term thin slices, using brief exposures of behavior (6s, 15s, and 30s) to predict teacher effectiveness. While thin slices are predominantly used in psychology, healthcare researchers can apply this concept by recording data for set blocks of time in a larger process, such as recording emergency department activity for the first 15 minutes of each hour.

The unit of observation can be a person (e.g., patient, provider), their behavior (e.g., smiling, eye rolling), an event (e.g., shift change) or interaction (e.g., clinical encounter). Using interactions as the unit of observation requires consideration for repeat observations of some individuals. For example, a fixed number of providers may be repeatedly observed with different patients.

Observation frequency will depend on the frequency of the phenomena. Enough data is needed for variation while also achieving “saturation,” a concept from qualitative methods, which means the point in data collection when no new information is obtained [ 21 ]. For quantitative studies, when examining the relationship between a direct observation measure (e.g., patient smiling) and an outcome (e.g., patient satisfaction), effect sizes from past research should dictate the number of interactions needed to achieve power to detect an effect. The duration of observation (the data slice) can be constrained using parameters as broad as a clinic workday, to distinct events like a clinical encounter.

Observation data can be collected on a continuous, rolling basis, or at predefined intervals. Continuous sampling is analogous to a motion picture—the recorded data mirrors the flow of information captured in a video [ 22 ]. Continuous observation is ideal for understanding what happens throughout an event. It is labor intensive and time-consuming and may result in a small number of observations, although each observation can yield considerable data. For example, a team may want to know about the patient-centeredness of patient-provider interactions. Continuous sampling of a clinical encounter could start when the patient arrives through when they leave, with detailed data collected about both the verbal and nonverbal communication. This could be considered an N of one observation but would yield substantial data. This information could be collected over a continuous day of encounters across several providers and patients, resulting in a considerable amount of data for a small group of people.

In contrast, instantaneous sampling can be conceptualized as snapshots, and is analogous to the thin slice methodology. Psychology research sometimes uses random intervals, while in healthcare research it may be preferable to use predetermined criteria or intervals [ 23 ]. Instantaneous sampling is economical and data collection can happen flexibly across a variety of individuals or times of day or weeks. Disadvantages include losing some of the context that is gained through continuous sampling.

3.1.3. Data collection tools

Data collection tools enable systematic observations, codifying what to observe and record. These tools vary from open-ended to highly structured, depending on the research question(s) and what is known a priori. We describe below three general tool categories—descriptive fieldnotes, semi-structured templates, and structured templates.

3.1.3.1. Descriptive fieldnotes

Descriptive fieldnotes, common in anthropology, are open-ended notes recorded with minimal a priori fields. Descriptive fieldnotes are ideal for research questions where less is known. An almost blank page is used to record the phenomena of interest. Key information such as date, time, location, people present and who recorded the information are useful for later analysis. These notes are jotted sequentially in real-time to maximize data collection, and are filled out and edited later for clarity and details. The flexible and open format facilitates the capture of unanticipated events or interactions.

Descriptive fieldnotes describe in detail what is observed (e.g., who is present, paraphrased statements), while leaving out interpretation. Analytic notes, that interpret what is being observed, can accompany the descriptive notes (e.g., the doctor is frowning and seems skeptical of what the patient is saying), but these analytic notes should be clearly marked as interpretation. One author (GF) demarcates interpretive portions of her fieldnotes using [closed brackets] to identify this portion of the fieldnote as distinct from the descriptive data. Interpretive notes should explain why the observer thinks this might be the case, using supporting data from the observation. Building on the example above, an accompanying interpretive note might say, “[the doctor raised their eyebrows, and does not seem to believe what the patient is saying, similar to what was observed in another encounter- see site 5 fieldnote). This information can be valuable during analysis to contextualize what was recorded and used in a later report or paper. Observation experience builds comfort and expertise with the open-ended, unstructured format.

3.1.3.2. Semi-structured templates

A semi-structured template comprises both open-ended and structured fields ( Fig. 2 ). It includes the same key information described above (i.e., date, time, etc.), then provides prompts for a priori concepts underlying the research questions, often derived from a theoretical model. These literature-based, theoretical concepts should be clearly defined and operationalized. For example, drawing from Street et al’s [ 24 ] framework for patient-centered communication, we can use their six functions (fostering the patient-clinician relationship, exchanging information, responding to emotions, managing uncertainty, making decisions, and enabling self-management) to develop categories for semi-structured coding a template. Like descriptive fieldnotes, the template also provides open-ended space for capturing contextual details about the a priori data recorded in the structured section.

Semi-Structured Observation Template.

3.1.3.3. Structured templates

A structured template in the form of a checklist or recording sheet captures specific, pre-determined phenomena. Structured templates are most useful when the phenomena are known. These templates are commonly used in psychology and engineering. Structured observations are more deductive and based on theoretical models or literature-based concepts. The template prompts the observer to record whether a phenomenon occurred, its frequency, and sometimes its duration or quality. See Keen [ 5 ] or Roter [ 25 ] for example structured templates for recording patient-centered care or patient-provider communication.

All templates should include key elements like the date, time and observer. Descriptive fieldnotes and semi-structured templates should be briefly filled out during the observation, and then written more thoroughly immediately afterwards. Setting aside time during data collection, such as a few hours at the end of each day, facilitates completion of this step. Recording information immediately, rather than weeks or months later, enhances data quality by minimizing recall bias. If written too much later, the recorder might fill in holes in their memory with inaccurate information. Further, small details, written while memories are fresh, may seem unremarkable but later provide critical insights.

For the semi-structured and structured templates, which contain prepopulated fields, there should be an accompanying “codebook” of definitions describing the parameters for each field. For example, building on the previous example using Street et al’s constructs, the code “responding to emotions” could identify instances where patients appear to be sad or worried and the provider responds to these emotions (also termed empathic opportunities and empathic responses) by eliciting, exploring, and validating the patients’ emotions [ 25 , 26 ]. This process operationally defines each concept and facilitates more reliable data capture. If space allows, the codebook can be included in the template and referenced during data collection. Codebooks should be updated through team discussion and as observations are piloted. Definitions from the codebook can be used in later reports and manuscripts.

3.2. Piloting

Given the real-world context within which observation data is collected, pilot-testing helps ensure that ideas work in practice. Piloting provides an opportunity to ensure the research plan works and reduce wasted resources. For example, piloting could reveal issues with the sampling plan (e.g., the phenomena do not happen frequently enough), staffing capacity (e.g., there are too many people to follow) or the codebook (e.g., few of the items specified in the data collection template are observed). Further, piloting gives the team a chance to systematize data collection and address issues before they interfere with the overall study integrity. This process guides what refinements need to be made to the data collection procedures. Piloting should be done at least once in a setting comparable to the intended setting.

3.3. Collecting data, analysis and dissemination

Healthcare studies are commonly conducted by interdisciplinary teams. The observation team should include at minimum two people, including someone with prior observation experience. Having more than one person collecting data increases capacity, distributes the workload and facilitates scheduling flexibility. Multiple observers complement each other’s perspectives and can provide diverse analytic insights. The observers should be engaged early in the research process. Having regular debriefing meetings during data collection ensures data quality and reliability in data collection. Adding key members of relevant communities to the team, such as patients or providers, can further enhance the relevance and help the research team think about the implications of the work.

Observational data collection often takes place in fast-paced clinical settings. For paper-based data collection, consolidating the materials on a clipboard and/or using colored papers or tabs, facilitates access. An electronic tablet to enter information directly bypasses the need for later, manual data entry.

Data analysis should be considered early in the research process. The analytic plan will be informed by both the principles of the epistemological tradition from which the overall study design is drawn and the research questions. Studies using observation are premised on a range of epistemological traditions. Analytical approaches, standards, and terminology differ between anthropologically informed qualitative observations recorded using descriptive fieldnotes versus structured, quantitative checklists premised on psychological or systems engineering principles. A full description of analysis is thus beyond the scope of this guide. Analytic strategies can be found in discipline-specific texts, such as Musante and DeWalt [ 27 ], anthropology; Suen and Ary [ 28 ], psychology; or Lopetegui et al [ 29 ], systems engineering. Regardless of discplinary tradition, analytic decisions should be made based on the study design, research question(s), and objective(s).

Dissemination is a key, final step of the research process. Observation data lends itself to a rich description of the phenomena of interest. In health research, this data is often part of a larger mixed methods study. The observation protocol should be described in a manuscript’s methods section; the results should report on what was observed. Similar to reporting of interview data, the observed data should include key descriptors germane to the research question, like actors, site number, or setting. See Fix et al [ 1 ] and McCullough et al [ 4 ] for examples on how to include semi-structured, qualitative observation data in a manuscript and Waisel et al [ 17 ] and Kuhn et al [ 19 ] for examples of reporting structured, quantitative data in a manuscript.

3.4. Institutional review boards

Healthcare Institutional Review Boards may be unfamiliar with observation. Being explicit about data collection can proactively address concerns. The protocol should detail which individuals will be observed, if and how they will be consented and what will and will not be recorded. Using a reference like the Health Insurance Portability and Accountability Act (HIPAA) identifiers (e.g., name, street address) can guide what identifiable information is collected. The protocol should also describe how the team will protect data, especially while in the field (e.g., “immediately after data collection, written informed consents will be taken to an office and locked in a filing cabinet”).

There are unique risks in studies using observation because data is collected in “the field.” Precautions attentive to these settings protect both participants and research team members. A detailed protocol should describe steps to address potential issues, including rare or distressing events, or what to do if a team member witnesses a clinical emergency or a participant discloses trauma. Additionally, team members may need to debrief after distressing experiences.

4. Discussion & conclusion

4.1. discussion.

The ability to improve healthcare is limited if real-world data are not taken into account. Observation methods can elucidate phenomena germane to healthcare’s most vexing problems. Considerable literature documents the discrepancy between what people report and their behavior [ [30] , [31] , [32] ]. Direct observation can provide important insights into human behavior. In their ethnographic evaluation of an HIV intervention, Evans and Lambert [ 31 ] found, “observation of actual intervention practices can reveal insights that may be hard for [participants] to articulate or difficult to pinpoint, and can highlight important points of divergence and convergence from intervention theory or planning documents.” Further, they saw ethnographic methods as a tool to understand “hidden” information in what they call “private contexts of practice.” While in Rich et al.’s work [ 32 ], asthmatic children were asked about exposure to smoking. Despite not reporting smoking in the home, videos recorded by the children—part of the study design—documented smokers outside their home. The use of observation can help explain research questions as diverse as patients’ health behaviors [ 7 , 10 , 32 ], healthcare delivery [ 3 , 4 ] or the outcomes of a clinical trial [ 9 , 33 ].

A common critique in healthcare research is that observing behavior will change behavior, a concept known as the Hawthorne Effect. Goodwin’s study [ 34 ], using direct observation of physician-patient interactions, explicitly examined this phenomena and found a limited effect. We authors have observed numerous instances of unexpected behavior of healthcare employees such as making disparaging comments about patients, eye rolling, or eating in sterile areas. Thus, those of us who conduct observation often say that if behavior change were as easy as observing people, we could simply place observers in problematic healthcare settings.

The descriptions above on how to use observation are applicable to fields like health services research and implementation and improvement sciences which have similarly adapted other social science approaches.[ [35] , [36] , [37] , [38] , [39] , [40] ] Notably, unlike the social sciences, many health researchers work in teams and thus this guide is written for team-based work. Yet, health researchers sometimes also conduct observations without support from a larger team. While this may be done because of resource constraints, it may raise concerns about the validity of the observations. First, social sciences have a long history of solo researchers collecting and analyzing data, yielding robust, rigorous findings [ 13 , [41] , [42] , [43] ]. Using strategies, such as those outlined above (i.e., writing detailed, descriptive fieldnotes immediately; keeping interpretations separate from the data; looking for negative cases) can enhance rigor. Further, constructs like validity are rooted in quantitative, positivist epistemologies and need to be adapted for naturalistic study designs, like those that include direct observation [ 44 ].

4.2. Innovation

Social science-informed research designs, such as those that include observation, are needed to tackle the dynamic, complex, “wicked problem” that impede high quality healthcare [ 45 ]. Thoughtful, rigorous use of observation tailored to the unique context of healthcare can provide important insights into healthcare delivery problems and ultimately improve healthcare.

Additionally, observation provides several ways to involve key communities, like patients or providers, as participants. Observing patient participants can provide information about healthcare processes or structures, and inform research about patient experiences of care or the extent of patient-centeredness. With the movement towards engaging end users in research, these individuals can contribute more meaningfully [ 46 , 47 ]. As team members, they can define the problem, inform what to observe, how to observe, help interpret data and disseminate findings.

4.3. Conclusion

Observation’s long history in the social sciences provides a robust body of work with strategies that can be inform healthcare research. Yet, traditional social science approaches, such as extended, independent fieldwork may be untenable in healthcare settings. Thus, adapting social science approaches can better meet healthcare researchers’ needs.

This paper provides an innovative, yet practical adaptation of social science approaches to observation that can be feasibly used by health researchers. Team meetings, developing data collection tools and protocols, and piloting, each enhance study quality. During development, teams should determine if observation is an appropriate method. If so, the team should then discuss what and how to collect the data, as described above. Piloting improves data collection procedures. While many aspects of observation can be tailored to health research, analysis is informed by epistemological traditions. Having clear steps for health researchers to follow can increase the rigor or credibility of observation.

Rigorous utilization of observation can enrich healthcare research by adding unique insights into complex problems. This guide provides a practical adaptation of traditional approaches to observation to meet healthcare researchers’ needs and transform healthcare delivery.

This work was supported by the US Department of Veterans Affairs, Veterans Health Administration, Office of Research and Development, Health Services Research and Development. Dr. Fix is a VA HSR&D Career Development awardee at the Bedford VA (CDA 14-156). Drs. Fix, Kim and McCullough are employed at the Center for Healthcare Organization and Implementation Research, where Dr. Ruben was a postdoctoral fellow. The authors received no financial support for the research, authorship, and/or publication of this article.

Declaration of Competing Interest

All authors declared no conflict of interests.

Acknowledgements

This work has been previously presented as workshops at the 2015 Veteran Affairs Health Services Research & Development / Quality Enhancement Research Initiative National Meeting (Philadelphia, PA) and the 2016 Academy Health Annual Research Meeting (Boston, MA). We would like to acknowledge Dr. Shihwe Wang for participating in the 2015 workshop; Dr. Adam Rose for encouragement and helpful comments; and the VA Anthropology Group for advancing the utilization of direct observation in the US Department of Veteran Affairs. The views expressed in this article are those of the authors and do not necessarily reflect the position or policy of the Department of Veterans Affairs or the United States government.

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Title: efficient and sharp off-policy evaluation in robust markov decision processes.

Abstract: We study evaluating a policy under best- and worst-case perturbations to a Markov decision process (MDP), given transition observations from the original MDP, whether under the same or different policy. This is an important problem when there is the possibility of a shift between historical and future environments, due to e.g. unmeasured confounding, distributional shift, or an adversarial environment. We propose a perturbation model that can modify transition kernel densities up to a given multiplicative factor or its reciprocal, which extends the classic marginal sensitivity model (MSM) for single time step decision making to infinite-horizon RL. We characterize the sharp bounds on policy value under this model, that is, the tightest possible bounds given by the transition observations from the original MDP, and we study the estimation of these bounds from such transition observations. We develop an estimator with several appealing guarantees: it is semiparametrically efficient, and remains so even when certain necessary nuisance functions such as worst-case Q-functions are estimated at slow nonparametric rates. It is also asymptotically normal, enabling easy statistical inference using Wald confidence intervals. In addition, when certain nuisances are estimated inconsistently we still estimate a valid, albeit possibly not sharp bounds on the policy value. We validate these properties in numeric simulations. The combination of accounting for environment shifts from train to test (robustness), being insensitive to nuisance-function estimation (orthogonality), and accounting for having only finite samples to learn from (inference) together leads to credible and reliable policy evaluation.

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