research on healthy lifestyle

• Research article
• Open access
• Published: 29 September 2022

A healthy lifestyle is positively associated with mental health and well-being and core markers in ageing

• Pauline Hautekiet   ORCID: orcid.org/0000-0003-3805-3004 1 , 2 ,
• Nelly D. Saenen 1 , 2 ,
• Dries S. Martens 2 ,
• Margot Debay 2 ,
• Johan Van der Heyden 3 ,
• Tim S. Nawrot 2 , 4 &
• Eva M. De Clercq 1  

BMC Medicine volume  20 , Article number:  328 ( 2022 ) Cite this article

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Studies often evaluate mental health and well-being in association with individual health behaviours although evaluating multiple health behaviours that co-occur in real life may reveal important insights into the overall association. Also, the underlying pathways of how lifestyle might affect our health are still under debate. Here, we studied the mediation of different health behaviours or lifestyle factors on mental health and its effect on core markers of ageing: telomere length (TL) and mitochondrial DNA content (mtDNAc).

In this study, 6054 adults from the 2018 Belgian Health Interview Survey (BHIS) were included. Mental health and well-being outcomes included psychological and severe psychological distress, vitality, life satisfaction, self-perceived health, depressive and generalised anxiety disorder and suicidal ideation. A lifestyle score integrating diet, physical activity, smoking status, alcohol consumption and BMI was created and validated. On a subset of 739 participants, leucocyte TL and mtDNAc were assessed using qPCR. Generalised linear mixed models were used while adjusting for a priori chosen covariates.

The average age (SD) of the study population was 49.9 (17.5) years, and 48.8% were men. A one-point increment in the lifestyle score was associated with lower odds (ranging from 0.56 to 0.74) for all studied mental health outcomes and with a 1.74% (95% CI: 0.11, 3.40%) longer TL and 4.07% (95% CI: 2.01, 6.17%) higher mtDNAc. Psychological distress and suicidal ideation were associated with a lower mtDNAc of − 4.62% (95% CI: − 8.85, − 0.20%) and − 7.83% (95% CI: − 14.77, − 0.34%), respectively. No associations were found between mental health and TL.

Conclusions

In this large-scale study, we showed the positive association between a healthy lifestyle and both biological ageing and different dimensions of mental health and well-being. We also indicated that living a healthy lifestyle contributes to more favourable biological ageing.

Peer Review reports

According to the World Health Organization (WHO), a healthy lifestyle is defined as “a way of living that lowers the risk of being seriously ill or dying early” [ 1 ]. Public health authorities emphasise the importance of a healthy lifestyle, but despite this, many individuals worldwide still live an unhealthy lifestyle [ 2 ]. In Europe, 26% of adults smoke [ 3 ], nearly half (46%) never exercise [ 4 ], 8.4% drink alcohol on a daily basis [ 5 ] and over half (51%) are overweight [ 5 ]. These unhealthy behaviours have been associated with adverse health outcomes like cardiovascular diseases [ 6 , 7 , 8 ], respiratory diseases [ 9 ], musculoskeletal diseases [ 10 ] and, to a lesser extent, mental disorders [ 11 , 12 ].

Even though the association between lifestyle and health outcomes has been extensively investigated, biological mechanisms explaining these observed associations are not yet fully understood. One potential mechanism that can be suggested is biological ageing. Both telomere length (TL) and mitochondrial DNA content (mtDNAc) are known biomarkers of ageing. Telomeres are the end caps of chromosomes and consist of multiple TTAGGG sequence repeats. They protect chromosomes from degradation and shorten with every cell division because of the “end-replication problem” [ 13 ]. Mitochondria are crucial to the cell as they are responsible for apoptosis, the control of cytosolic calcium levels and cell signalling [ 14 ]. Living a healthy lifestyle can be linked with healthy ageing as both TL and mtDNAc have been associated with health behaviours like obesity [ 15 ], diet [ 16 ], smoking [ 17 ] and alcohol abuse [ 18 ]. Furthermore, as biomarkers of ageing, both TL and mtDNAc have been associated with age-related diseases like Parkinson’s disease [ 19 ], coronary heart disease [ 20 ], atherosclerosis [ 21 ] and early mortality [ 22 ]. Also, early mortality and higher risks for the aforementioned age-related diseases are observed in psychiatric illnesses, and it is suggested that advanced biological ageing underlies these observations [ 23 ].

Multiple studies evaluated individual health behaviours, but research on the combination of these health behaviours is limited. As they often co-occur and may cause synergistic effects, assessing them in combination with each other rather than independently might better reflect the real-life situation [ 24 , 25 ]. Therefore, in a general adult population, we combined five commonly studied health behaviours including diet, smoking status, alcohol consumption, BMI and physical activity into one healthy lifestyle score to evaluate its association with mental health and well-being and biological ageing. Furthermore, we evaluated the association between the markers of biological ageing and mental health and well-being. We hypothesise that individuals living a healthy lifestyle have a better mental health status, a longer TL and a higher mtDNAc and that these biomarkers are positively associated with mental health and well-being.

Study population

In 2018, 11611 Belgian residents participated in the 2018 Belgian Health Interview Survey (BHIS). The sampling frame of the BHIS was the Belgian National Register, and participants were selected based on a multistage stratified sampling design including a geographical stratification and a selection of municipalities within provinces, of households within municipalities and of respondents within households [ 26 ]. The study population for this cross-sectional study included 6054 BHIS participants (see flowchart in Additional file 1 : Fig. S1) [ 27 , 28 , 29 , 30 , 31 ]. Minors (< 18 years) and participants not eligible to complete the mental health modules (participants who participated through a proxy respondent, i.e. a person of confidence filled out the survey) were excluded ( n  = 2172 and n  = 846, respectively). Furthermore, of the 8593 eligible participants, those with missing information to create the mental health indicators, the lifestyle score or the covariates used in this study were excluded ( n  = 1642, 788 and 109, respectively).

For the first time in 2018, a subset of 1184 BHIS participants contributed to the 2018 Belgian Health Examination Survey (BELHES). All BHIS participants were invited to participate except for minors (< 18 years), BHIS participants who participated through a proxy respondent and residents of the German Community of Belgium, the latter representing 1% of the Belgian population. Participants were recruited on a voluntary basis until the regional quotas were reached (450, 300 and 350 in respectively Flanders, Brussels Capital Region and Wallonia). These participants underwent a health examination, including anthropological measurements and completed an additional questionnaire. Also, blood and urine samples were collected. Of the 6054 included BHIS participants, 909 participated in the BELHES. Participants for whom we could not calculate both TL and mtDNAc were excluded ( n  = 170). More specifically, participants were excluded because they did not provide a blood sample ( n  = 91) or because they did not provide permission for DNA research ( n  = 32). Twenty samples were excluded from DNA extraction because either total blood volume was too low ( n  = 7), samples were clothed ( n  = 1) or tubes were broken due to freezing conditions ( n  = 12). Twenty-seven samples were excluded because they did not meet the biomarker quality control criteria (high technical variation in qPCR triplicates). This was not met for 3 TL samples, 20 mtDNAc samples and 4 samples for both biomarkers. For this subset, we ended up with a final number of 739 participants. Further in this paper, we refer to “the BHIS subset” for the BHIS participants ( n  = 6054) and the “BELHES subset” for the BELHES participants ( n  = 739).

As part of the BELHES, this project was approved by the Medical Ethics Committee of the University Hospital Ghent (registration number B670201834895). The project was carried out in line with the recommendations of the Belgian Privacy Commission. All participants have signed a consent form that was approved by the Medical Ethics Committee.

Health interview survey

The BHIS is a comprehensive survey which aims to gain insight into the health status of the Belgian population. The questions on the different dimensions of mental health and well-being were based on international standardised and validated questionnaires [ 32 ], and this resulted in eight mental health outcomes that were used in this study. Detailed information on each indicator score and its use is addressed in Additional file 1 : Table. S1. Firstly, the General Health Questionnaire (GHQ-12) provides the prevalence of psychological and severe psychological distress in the population [ 27 ]. On the total GHQ score, cut-off points of + 2 and + 4 were used to identify respectively psychological and severe psychological distress.

Secondly, we used two indicators for the positive dimensions of mental health: vitality and life satisfaction. Four questions of the short form health survey (SF-36) indicate the participant’s vital energy level [ 28 , 33 ]. We used a cut-off point to identify participants with an optimal vitality score, which is a score equal to or above one standard deviation above the mean, as used in previous studies [ 34 , 35 ]. Life satisfaction was measured by the Cantril Scale, which ranges from 0 to 10 [ 29 ]. A cut-off point of + 6 was used to indicate participants with high or medium life satisfaction versus low life satisfaction.

Thirdly, the question “How is your health in general? Is it very good, good, fair, bad or very bad?” was used to assess self-perceived health, also known as self-rated health. Based on WHO recommendations [ 36 ], the answer categories were dichotomised into “good to very good self-perceived health” and “very bad to fair self-perceived health”.

Fourthly, depressive and generalised anxiety disorders were defined using respectively the Patient Health Questionnaire (PHQ-9) and the Generalised Anxiety Disorder Questionnaire (GAD-7). We identified individuals who suffer from major depressive syndrome or any other type of depressive syndrome according to the criteria of the PHQ-9 [ 37 ]. A cut-off point of + 10 on the total sum of the GAD-7 score was used to indicate generalised anxiety disorder [ 31 ]. Additionally, a dichotomous question on suicidal ideation was used: “Have you ever seriously thought of ending your life?”; “If yes, did you have such thoughts in the past 12 months?”. Finally, the BHIS also includes personal, socio-economic and lifestyle information. The standardised Cronbach’s alpha coefficients for the PHQ-9, GHQ-12, GAD-7 and questions on vitality of the SF-36 ranged between 0.80 and 0.90.

Healthy lifestyle score

We developed a healthy lifestyle score based on five different health behaviours: body mass index (BMI), smoking status, physical activity, alcohol consumption and diet (Table 1 ). These health behaviours were defined as much as possible according to the existing guidelines for healthy living issued by the Belgian Superior Health Council [ 38 ] and the World Health Organisation [ 39 , 40 , 41 ]. Firstly, BMI was calculated as a person’s self-reported weight in kilogrammes divided by the square of the person’s self-reported height in metres (kg/m 2 ). BMI was classified into four categories: underweight (BMI < 18.5 kg/m 2 ), normal weight (BMI 18.5–24.9 kg/m 2 ), overweight (BMI 25.0–29.9 kg/m 2 ) and obese (BMI ≥ 30.0 kg/m 2 ). Due to a J-shaped association of BMI with the overall mortality and multiple specific causes of death, obesity and underweight were both classified as least healthy [ 42 ]. BMI was scored as follows: obese and underweight = 0, overweight = 1 and normal weight = 2.

Secondly, smoking status was divided into four categories. Participants were categorised as regular smokers if they smoked a minimum of 4 days per week or if they quit smoking less than 1 month before participation (= 0). Occasional smokers were defined as smoking more than once per month up to 3 days per week (= 1). Participants were classified as former smokers if they quit smoking at least 1 month before the questionnaire or if they smoked less than once a month (= 2). The final category included people who never smoked (= 3).

Thirdly, physical activity was assessed by the question: “What describes best your leisure time activities during the last year?”. Four categories were established and scored as follows: sedentary activities (= 0), light activities less than 4 h/week (= 1), light activities more than 4 h/week or recreational sport less than 4 h/week (= 2) and recreational sport more than 4 h or intense training (= 3). Fourthly, information on the number of alcoholic drinks per week was used to categorise alcohol consumption. The different categories were set from high to low alcohol consumption: 22 drinks or more/week (= 0), 15–21 drinks/week (= 1), 8–14 drinks/week (= 2), 1–7 drinks/week (= 3)and less than 1 drink/week (= 4).

Finally, in line with the research by Benetou et al., a diet score was calculated using the frequency of consuming fruit, vegetables, snacks and sodas [ 43 ]. For fruit as well as vegetable consumption, the frequency was scored as follows: never (= 0), < 1/week (= 1), 1–3/week (= 2), 4–6/week (= 3) and ≥ 1/day (= 4). The frequency of consuming snacks and sodas was scored as follows: never (= 4), < 1/week (= 3), 1–3/week (= 2), 4–6/week (= 1) and ≥ 1/day (= 0). The diet score was then divided into tertiles, in line with the research by Benetou et al. [ 43 ]. A diet score of 0–9 points was classified as the least healthy behaviour (= 0). A diet score ranging from 10 to 12 made up the middle category (= 1), and a score from 13 to 16 was classified as the healthiest behaviour (= 2).

All five previously described health behaviours were combined into one healthy lifestyle score (Table 1 ). The sum of the scores obtained for each health behaviour indicated the absolute lifestyle score. To calculate the relative lifestyle score, each absolute scored health behaviour was given equal weight by recalculating its maximum absolute score to a relative score of 1. The relative lifestyle scores were then summed up to achieve a final continuous lifestyle score, ranging from 0 to 5, with a higher score representing a healthier lifestyle.

Telomere length and mitochondrial DNA content assay

Blood samples were collected during the BELHES and centrifuged for 15 min at 3000 rpm before storage at − 80 °C. After extracting the buffy coat from the blood sample, DNA was isolated using the QIAgen Mini Kit (Qiagen, N.V.V Venlo, The Netherlands). The purity and quantity of the sample were measured with a NanoDrop spectrophotometer (ND-2000; Thermo Fisher Scientific, Wilmington, DE, USA). DNA integrity was assessed by agarose gel electrophoresis. To ensure a uniform DNA input of 6 ng for each qPCR reaction, samples were diluted and checked using the Quant-iT™ PicoGreen® dsDNA Assay Kit (Life Technologies, Europe).

Relative TL and mtDNAc were measured in triplicate using a previously described quantitative real-time PCR (qPCR) assay with minor modifications [ 44 , 45 ]. All reactions were performed on a 7900HT Fast Real-Time PCR System (Applied Biosystems, Foster City, CA, USA) in a 384-well format. Used telomere, mtDNAc and single copy-gene reaction mixtures and PCR cycles are given in Additional file 1 : Text. S1. Reaction efficiency was assessed on each plate by using a 6-point serial dilution of pooled DNA. Efficiencies ranged from 90 to 100% for single-copy gene runs, 100 to 110% for telomere runs and 95 to 105% for mitochondrial DNA runs. Six inter-run calibrators (IRCs) were used to account for inter-run variability. Also, non-template controls were used in each run. Raw data were processed and normalised to the reference gene using the qBase plus software (Biogazelle, Zwijnaarde, Belgium), taking into account the run-to-run differences.

Leucocyte telomere length was expressed as the ratio of telomere copy number to single-copy gene number (T/S) relative to the mean T/S ratio of the entire study population. Leucocyte mtDNAc was expressed as the ratio of mtDNA copy number to single-copy gene number (M/S) relative to the mean M/S ratio of the entire study population. The reliability of our assay was assessed by calculating the interclass correlation coefficient (ICC) of the triplicate measures (T/S and M/S ratios and T, M and S separately) as proposed by the Telomere Research Network, using RStudio version 1.1.463 (RStudio PBC, Boston, MA, USA). The intra-plate ICCs of T/S ratios, TL runs, M/S ratios, mtDNAc runs and single-copy runs were respectively 0.804 ( p  < 0.0001), 0.907 ( p  < 0.0001), 0.815 ( p  < 0.0001), 0.916 ( p  < 0.0001) and 0.781 ( p  < 0.0001). Based on the IRCs, the inter-plate ICC was 0.714 ( p  < 0.0001) for TL and 0.762 ( p  < 0.0001) for mtDNAc.

Statistical analysis

Statistical analyses were performed using the SAS software (version 9.4; SAS Institute Inc., Cary, NC, USA). We performed a log(10) transformation of the TL and mtDNAc data to reduce skewness and to better approximate a normal distribution. Three analyses were done: (1) In the BHIS subset ( n  = 6054), we evaluated the association between the lifestyle score and the mental health and well-being outcomes (separately). These results are presented as the odds ratio (95% CI) of having a mental health condition or disorder for a one-point increment in the lifestyle score. (2) In the BELHES subset ( n  = 739), we evaluated the association between the lifestyle score and both TL and mtDNAc (separately). These results are presented as the percentage difference in TL or mtDNAc (95% CI) for a one-point increment in the lifestyle score. (3) In the BELHES subset ( n  = 739), we evaluated the association between the mental health and well-being outcomes and both TL and mtDNAc (separately). These results are presented as the percentage difference in TL or mtDNAc (95% CI) when having a mental health condition or disorder compared with the healthy group.

For all three analyses, we performed multivariable linear mixed models (GLIMMIX; unstructured covariance matrix) taking into account a priori selected covariates including age (continuous), sex (male, female), region (Flanders, Brussels Capital Region, Wallonia), highest educational level of the household (up to lower secondary, higher secondary, college or university), country of birth (Belgium, EU, non-EU) and household type (single, one parent with child, couple without child, couple with child, others). To capture the non-linear effect of age, we included a quadratic term when the result of the analysis showed that both the linear and quadratic terms had a p -value < 0.1. For the two analyses on TL and mtDNAc, we additionally adjusted for the date of participation in the BELHES. As multiple members of one household participated, we added household numbers in the random statement.

Bivariate analyses evaluating the associations between the characteristics and TL, mtDNAc, the lifestyle score or psychological distress as a parameter of mental health and well-being are evaluated based on the same model. The chi-squared tests (categorical data) and t -tests (continuous data) were used to evaluate the characteristics of included and excluded participants. The lifestyle score was validated by creating a ROC curve and calculating the area under the curve (AUC) of the adjusted association between the lifestyle score and self-perceived health. Adjustments were made for age, sex, region, highest educational level of the household, country of birth and household type.

In a sensitivity analysis, to evaluate the robustness of our findings, we additionally adjusted our main models separately for perceived quality of social support (poor, moderate, strong) and chronic disease (suffering from any chronic disease or condition: yes, no). The third model, evaluating the biomarkers with the mental health outcomes, was also additionally adjusted for the lifestyle score.

Population characteristics

The characteristics of the BHIS and BELHES subset are presented in Table 2 . In the BHIS subset, 48.8% of the participants were men. The average age (SD) was 49.9 (17.5) years, and most participants were born in Belgium (79.5%). The highest educational level in the household was most often college or university degree (53.3%), and the most common household composition was couple with child(ren) (37.7%). The proportion of participants in different regions of Belgium, i.e. Flanders, Brussels Capital Region and Wallonia, was respectively 41.1%, 23.3% and 35.6%. For the BELHES subset, we found similar results except for region and education. We noticed more participants from Flanders and more participants with a high educational level in the household. The mean (SD) relative TL and mtDNAc were respectively 1.04 (0.23) and 1.03 (0.24). TL and mtDNAc were positively correlated (Spearman’s correlation = 0.21, p  < 0.0001).

We compared (1) the characteristics of the 6054 eligible BHIS participants that were included in the BHIS subset with the 2539 eligible participants that were excluded from the BHIS subset (Additional file 1 : Table S2) and (2) the 739 participants from the BHIS subset that were included in the BELHES subset with the 5315 participants that were excluded from the BELHES subset (Additional file 1 : Table S3). Except for sex and nationality in the latter, all other covariates showed differences between the included and excluded groups. On the other hand, population data from 2018 indicates that the average age (SD) of the adult Belgian population was 49.5 (18.9) with a distribution over Flanders, Brussels Capital Region and Wallonia of respectively 58.2%, 10.2% and 31.6% and that 48.7% were men. The distribution of our sample according to age and sex thus largely corresponds to the age and sex distribution of the adult Belgian population figures. The large difference in the regional distribution is due to the oversampling of the Brussels Capital Region in the BHIS.

Bivariate associations evaluating the characteristics with TL, mtDNAc, the lifestyle score or psychological distress as a parameter of mental health are presented in Additional file 1 : Table S4. Briefly, men had a − 6.41% (95% CI: − 9.10 to − 3.65%, p  < 0.0001) shorter TL, a − 8.03% (95% CI: − 11.00 to − 4.96%, p  < 0.0001) lower mtDNAc, lower odds of psychological distress (OR = 0.59, 95% CI: 0.53 to 0.66, p  < 0.0001) and a lifestyle score of − 0.28 (95% CI: − 0.32 to − 0.24, p  < 0.0001) points less compared with women. Furthermore, a 1-year increment in age was associated with a − 0.64% (− 0.73 to − 0.55%, p  < 0.0001) shorter TL and a − 0.19% (95% CI: − 0.31 to − 0.08%, p  = 0.00074) lower mtDNAc.

Mental health prevalence and lifestyle characteristics

Within the BHIS subset, 32.3% and 18.0% of the participants had respectively psychological and severe psychological distress. 86.7% had suboptimal vitality, 12.0% indicated low life satisfaction and 22.0% had very bad to fair self-perceived health. The prevalence of depressive and generalised anxiety disorders was respectively 9.0% and 10.8%, respectively. 4.4% of the participants indicated to have had suicidal thoughts in the past 12 months. Similar results were found for the BELHES subset (Table 3 ).

Within the BHIS subset, the average lifestyle score (SD) was 3.1 (0.9) (Table 4 ). A histogram of the lifestyle score is shown in Additional file 1 : Fig. S2. 16.6% were regular smokers, and 4.9% reported 22 alcoholic drinks per week or more. 29.7% reported that their main leisure time included mainly sedentary activities, and 18.6% were underweight or obese. 29.2% were classified as having an unhealthy diet score. The participants of the BELHES subset were slightly more active, but no other dissimilarities were found (Table 4 ). The ROC curve shows an area under the curve (AUC) of 0.74, indicating a 74% predictive accuracy for the lifestyle score as a self-perceived health predictor (Additional file 1 : Fig. S3).

Healthy lifestyle and mental health and well-being

Living a healthier lifestyle, indicated by having a higher lifestyle score, was associated with lower odds of all mental health and well-being outcomes (Table 5 ). After adjustment, a one-point increment in the lifestyle score was associated with lower odds of psychological (OR = 0.74, 95% CI: 0.69, 0.79) and severe psychological distress (OR = 0.69, 95% CI: 0.64, 0.75). Similarly, for the same increment, the odds of suboptimal vitality, low life satisfaction and very bad to fair self-perceived health were respectively 0.62 (95% CI: 0.56, 0.68), 0.62 (95% CI: 0.56, 0.68) and 0.56 (95% CI: 0.52, 0.61). Finally, the odds of having depressive disorder, generalised anxiety disorder or suicidal ideation were respectively 0.57 (95% CI: 0.51, 0.63), 0.63 (95% CI: 0.57, 0.69) and 0.63 (95% CI: 0.55, 0.72) for a one-point increment in the lifestyle score.

The biomarkers of ageing

After adjustment, living a healthy lifestyle was positively associated with both TL and mtDNAc (Table 6 ). A one-point increment in the lifestyle score was associated with a 1.74 (95% CI: 0.11, 3.40%, p  = 0.037) higher TL and a 4.07 (95% CI: 2.01, 6.17%, p  = 0.00012) higher mtDNAc.

People suffering from severe psychological distress had a − 4.62% (95% CI: − 8.85, − 0.20%, p  = 0.041) lower mtDNAc compared with those who did not suffer from severe psychological distress. Similarly, people with suicidal ideation had a − 7.83% (95% CI: − 14.77, − 0.34%, p  = 0.041) lower mtDNAc compared with those without suicidal ideation. No associations were found for the other mental health and well-being outcomes, and no associations were found between mental health and TL (Table 6 ).

Sensitivity analysis

Additional adjustment of the main analyses for perceived quality of social support, chronic disease or lifestyle score (in the association between the mental health outcomes and the biomarkers of ageing) did not strongly change the effect of our observations (Additional file 1 : Tables S5-S7). However, we noticed that most of the associations between severe psychological distress or suicidal ideation and mtDNAc showed marginally significant results.

In this study, we evaluated the associations between eight mental health and well-being outcomes, a healthy lifestyle score and 2 biomarkers of biological ageing: telomere length and mitochondrial DNA content. Having a healthy lifestyle was positively associated with all mental health and well-being indicators and the markers of biological ageing. Furthermore, having had suicidal ideation or suffering from severe psychological distress was associated with a lower mtDNAc. However, no association was found between mental health and TL.

In the first part of this research, we evaluated the association between lifestyle and mental health and well-being and showed that living a healthy lifestyle was positively associated with better mental health and well-being outcomes. Similar trends were found in previous studies for each of the health behaviours separately [ 11 , 12 , 46 , 47 , 48 ]. Although evaluating these health behaviours separately provides valuable information, assessing them in combination with each other rather than independently might better reflect the real-life situation as they often co-occur and may exert a synergistic effect on each other [ 24 , 25 , 49 ]. For example, 68% of the adults in England engaged in two or more unhealthy behaviours [ 25 ]. Especially, smoking status and alcohol consumption co-occurred, but half of the studies in the review by Noble et al. indicated clustering of all included health behaviours [ 24 ].

To date, the number of studies evaluating the combination of multiple health behaviours and mental health and well-being in adults is limited, and most of them use a different methodology to assess this association [ 50 , 51 , 52 , 53 , 54 , 55 , 56 ]. Firstly, differences are found between the included health behaviours. Most studies included the four “SNAP” risk factors, i.e. smoking, poor nutrition, excess alcohol consumption and physical inactivity. Other health behaviours that were sometimes included were BMI/obesity, sleep duration/quality and psychological distress [ 50 , 53 , 54 , 56 ]. Secondly, differences are found in the scoring of the health behaviours and the use of the lifestyle score. Whereas in this study the health behaviours were scored categorically, studies often dichotomised the health behaviours and/or the final lifestyle score [ 50 , 52 , 53 , 56 ]. Also, two studies performed clustering [ 54 , 55 ]. Health behaviours can cluster together at both ends of the risk spectrum, but less is known about the middle categories. This is avoided by using the cluster method where participants are clustered based on similar behaviours. On the other hand, a lifestyle score can be of better use and more easily be interpreted when aiming to compare healthy versus unhealthy lifestyles, as was the case for this study.

Despite these different methods, all previously mentioned studies show similar results. Together with our findings, which also support these results, this provides clear evidence that an unhealthy lifestyle is associated with poor mental health and well-being outcomes. Important to notice is that, like our research, most studies in this field have a cross-sectional design and are therefore not able to assume causality. Therefore, mental health might be the cause or the consequence of an unhealthy lifestyle. Further prospective and longitudinal studies are warranted to confirm the direction of the association.

Healthy lifestyle and biomarkers of ageing

How lifestyle affects our health is not yet fully understood. One possible pathway is through oxidative stress and biological ageing. An unhealthy lifestyle has been associated with an increase in oxidative stress [ 57 , 58 , 59 ], and in turn, higher concentrations of oxidative stress are known to negatively affect TL and mtDNAc [ 60 ]. In this study, we showed that living a healthy lifestyle was associated with a longer TL and a higher mtDNAc. Our results showed a stronger association of lifestyle with mtDNAc compared with TL. TL is strongly determined by TL at birth [ 61 ]. On the other hand, mtDNAc might be more variable in shorter time periods. Although mtDNAc and TL were strongly correlated, this could explain why lifestyle is more strongly associated with mtDNAc. However, we can only speculate about this, and further research is necessary to confirm our results.

Similar as for the association with mental health, in previous studies, the biomarkers have been associated with health behaviours separately rather than combined [ 62 , 63 , 64 , 65 ]. To our knowledge, we are the first to evaluate the associations between a healthy lifestyle score and mtDNAc. Our results are in line with our expectations. As TL and mtDNAc are known to be correlated [ 60 ], we would expect similar trends for both biomarkers. In the case of TL, few studies included a combined lifestyle score in association with this biomarker. Consistent with our results, in a study population of 1661 men, the sum score of a healthier lifestyle was correlated with a longer TL [ 66 ]. Similar results were found by Sun et al. where a combination of healthy lifestyles in a female study population was associated with a longer TL compared with the least healthy group [ 67 ]. Also, improvement in lifestyle has been associated with TL maintenance in the elderly at risk for dementia [ 68 ], and a lifestyle intervention programme was positively associated with leucocyte telomere length in children and adolescents [ 69 ]. These results suggest that on a biological level, a healthy lifestyle is associated with healthy ageing. Within this context, a study on adults aged 60 and older showed that maintaining a normal weight, not smoking and performing regular physical activity were associated with slower development of disability and a reduction in mortality [ 70 ]. Similarly, midlife lifestyle factors like non-smoking, higher levels of physical activity, non-obesity and good social support have been associated with successful ageing, 22 years later [ 71 ].

Mental health and well-being and biomarkers of ageing

Finally, we evaluated the association between the biomarkers of ageing and the mental health and well-being outcomes. The hypothesis that biological ageing is associated with mental health has been supported by observations showing that chronically stressed or psychiatrically ill persons have a higher risk for age-related diseases like dementia, diabetes and hypertension [ 23 , 72 , 73 ]. Important to notice is that, like our research, the majority of studies on this topic have a cross-sectional design and therefore are unable to identify causality. Therefore, it is currently unknown whether psychological diseases accelerate biological ageing or whether biological ageing precedes the onset of these diseases [ 74 ].

Our results showed a lower mtDNAc for individuals with suicidal ideation or severe psychological distress but not for any of the other mental health outcomes. Evidence on the association between mtDNAc and mental health is inconsistent. Women above 60 years old with depression had a significantly lower mtDNAc compared with the control group [ 75 ]. Furthermore, individuals with a low mtDNAc had poorer outcomes in terms of self-rated health [ 76 ]. In contrast, Otsuka et al. showed a higher peripheral blood mtDNAc in suicide completers [ 77 ], and studies on major depressive syndrome [ 78 ] and self-rated health [ 79 ] showed the same trend. Finally, Vyas et al. showed no significant association between mtDNAc and depression status in mid-life and older adults [ 80 ]. These differences might be due to the differences in study population and methods. For example, the two studies indicating lower mtDNAc in association with poor mental health both had an elderly study population, and one study population consisted of psychiatrically ill patients. Next to that, differences were found in the type of samples, mtDNAc assays and questionnaires or diagnostics. The inconsistency of these studies and our results calls for further research on this association and for standardisation of methods within studies to enable clear comparisons.

As for TL, we did not find an association with any of the mental health and well-being outcomes. Previous studies in adults showed a lower TL in association with current but not remitted anxiety disorder [ 81 ], depressive [ 82 ] and major depressive disorder [ 73 , 83 ], childhood trauma [ 84 ] suicide [ 77 , 85 ], depressive symptoms in younger adults [ 86 ] and acculturative stress and postpartum depression in Latinx women [ 87 ]. Also, in a meta-analysis, psychiatric disorders overall were associated with a shorter leucocyte TL [ 88 ]. However, other studies failed to demonstrate an association between TL and mental health outcomes like major depressive disorder [ 89 ], late-life depression [ 90 ] and anxiety disorder [ 91 ]. Again, this could be due to a different method to assess the mental health outcomes, a different study design, uncontrolled confounding factors and the type of telomere assay. For example, a meta-analysis showed stronger associations with depression when using southern blot or FISH assay compared with qPCR to measure telomere length [ 92 ].

Strengths and limitations

An important strength of this study is the use of a validated lifestyle score that can easily be reproduced and used for other research on lifestyle. Secondly, we were able to use a large sample size for our analyses in the BHIS subset. Thirdly, by assessing multiple dimensions of mental health and well-being, we were able to give a comprehensive overview of the mental health status. To our knowledge, we are the first to evaluate the associations between a healthy lifestyle score and mtDNAc.

Our results should however be interpreted with consideration for some limitations. As mentioned before, the study has a cross-sectional design, and therefore, we cannot assume causality. Secondly, for the lifestyle score, we used self-reported data, which might not always represent the actual situation. For example, BMI values tend to be underestimated due to the overestimation of height and underestimation of weight [ 93 ], and also, smoking behaviour is often underestimated [ 94 ]. Also, equal weights were used for each of the health behaviours as no objective information was available on which weight should be given to a specific health behaviour. Thirdly, there is a distinct time lag between the completion of the BHIS questionnaire and the collection of the BELHES samples. The mean (SD) number of days is 52 (35). This is less than the period for suicidal ideation, assessed over the 12 previous months, but there might be a more limited overlap with the period for assessment of the other mental health variables, such as vitality and psychological distress, assessed over the last few weeks, and depressive and generalised anxiety disorders, assessed over the last 2 weeks. Fourthly, due to a non-response bias, the lowest socio-economic classes are less represented in our study population. This will not affect our dose–response associations but might affect the generalisability of our findings to the overall population. Finally, we do not have data on blood cell counts, which has been associated with mtDNAc [ 95 ].

In this large-scale study, we showed that living a healthy lifestyle was positively associated with mental health and well-being and, on a biological level, with a higher TL and mtDNAc, indicating healthy ageing. Furthermore, individuals with suicidal ideation or suffering from severe psychological distress had a lower mtDNAc. Our findings suggest that implementing strategies to incorporate healthy lifestyle changes in the public’s daily life could be beneficial for public health, and might offset the negative impact of environmental stressors. However, further studies are necessary to confirm our results and especially prospective and longitudinal studies are essential to determine causality of the associations.

Availability of data and materials

The dataset used for this study is available through a request to the Health Committee of the Data Protection Authority.

Abbreviations

Area under the curve

Body mass index

Confidence intervals

Generalised Anxiety Disorder Questionnaire

General Health Questionnaire

Inter-run calibrator

• Mitochondrial DNA content

Patient Health Questionnaire

Relative operating characteristic curve

Short Form Health Survey

• Telomere length

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Acknowledgements

We are grateful to all BHIS and BELHES participants for contributing to this study.

The HuBiHIS project is financed by Sciensano (PJ) N°: 1179–101. Dries Martens is a postdoctoral fellow of the Research Foundation—Flanders (FWO 12X9620N).

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Sciensano, Risk and Health Impact Assessment, Juliette Wytsmanstraat 14, 1050, Brussels, Belgium

Pauline Hautekiet, Nelly D. Saenen & Eva M. De Clercq

Centre for Environmental Sciences, Hasselt University, 3500, Hasselt, Belgium

Pauline Hautekiet, Nelly D. Saenen, Dries S. Martens, Margot Debay & Tim S. Nawrot

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TL, mtDNAc and single copy-gene reaction mixture and PCR cycling conditions. Table S1. The mental health indicators with their scores and uses. Table S2. Comparison of the characteristics of the 6,054 eligible BHIS participants that were included in the BHIS subset compared to the 1,838 eligible participants that were excluded from the BHIS subset. Table S3. Comparison of the characteristics of the 739 participants from the BHIS subset that were included in the BELHES subset compared to the 5,315 participants that were excluded from the BELHES subset. Table S4. Bivariate associations between the characteristics and telomere length (TL), mitochondrial DNA content (mtDNAc), the lifestyle score or psychological distress. Table S5. Results of the sensitivity analysis of the association between lifestyle and mental health. Table S6. Results of the sensitivity analysis of the association between lifestyle and the biomarkers of ageing. Table S7. Results of the sensitivity analysis of the association between mental health and the biomarkers of ageing. Fig. S1. Exclusion criteria. The BHIS subset consisted of 6,055 BHIS participants and the BELHES subset consisted of 739 BELHES participants. Fig. S2. Histogram of the lifestyle score. Fig. S3. Validation of the lifestyle score. ROC curve for the lifestyle score as a predictor for good to very good self-perceived health. The model was adjusted for age, sex, region, highest educational level in the household, household composition and country of birth.

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Hautekiet, P., Saenen, N.D., Martens, D.S. et al. A healthy lifestyle is positively associated with mental health and well-being and core markers in ageing. BMC Med 20 , 328 (2022). https://doi.org/10.1186/s12916-022-02524-9

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Contributions and Challenges in Health Lifestyles Research

Stefanie mollborn, elizabeth m lawrence, jarron m saint onge.

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Direct all correspondence to: Stefanie Mollborn, UCB 483, University of Colorado Boulder, Boulder, CO 80309-0483; [email protected] ; telephone: 303-735-3796.

The concept of health lifestyles is moving scholarship beyond individual health behaviors to integrated bundles of behaviors undergirded by group-based identities and norms. Health lifestyles research merges structure with agency, individual- with group-level processes, and multifaceted behaviors with norms and identities, shedding light on why health behaviors persist or change and on the reproduction of health disparities and other social inequalities. Recent contributions have applied new methods and life course perspectives, articulating health lifestyles’ dynamic relationships to social contexts and demonstrating their implications for health and development. Culturally focused work has shown how health lifestyles function as signals for status and identity and perpetuate inequalities. We synthesize literature to articulate recent advances and challenges and demonstrate how health lifestyles research can strengthen health policies and inform scholarship on inequalities. Future work emphasizing health lifestyles’ collective nature and attending to upstream social structures will further elucidate complex social processes.

INTRODUCTION

Health behaviors have important consequences for health and well-being; display strong and consistent disparities across age, race/ethnicity, gender, and socioeconomic status; and are influenced by a complex interplay of social factors. The past two decades have seen intensive health promotion, yet many indicators of health behaviors have worsened over time, suggesting that new approaches are needed ( Becker, Sewell, Bian et al. 2020 ). Individual health behaviors have been studied extensively within many disciplines, but only recently have researchers begun to harness theoretical and empirical insights that result from considering multiple health behaviors simultaneously ( Prochaska, Spring and Nigg 2008 ). Yet frequently, these efforts lack theoretical rigor to conceptualize and integrate the meanings and implications of health behaviors beyond individual risk factors and health outcomes.

A particularly promising concept is health lifestyles . Health lifestyles are constellations of health behaviors underpinned by group-level identities and norms, which are consequential for health and well-being. Such behaviors include engaging in or abstaining from specific activities, and encompass different domains, such as food consumption or physical activities. Substantial research has documented important associations among a person’s health behaviors, finding that an individual’s health lifestyle does not simply reflect the accumulation of their agentic choices, but rather comprises a meaningful combination of behaviors that reflects social positions, class status, and group identities ( Burdette, Needham, Taylor et al. 2017 ; Cockerham 2005 ; Cockerham, Wolfe and Bauldry 2020 ; Lawrence, Mollborn and Hummer 2017 ; Saint Onge and Krueger 2017 ). That is, although we usually observe health lifestyles in individuals, they are also a group-level phenomenon ( Cockerham 2005 ; Krueger, Bhaloo and Rosenau 2009 ).

We highlight core contributions of health lifestyles research since 2000 and identify current theoretical and methodological challenges. We propose future conceptual, methodological, theoretical, and policy directions to build on prior contributions, address these challenges, and produce new understandings. Twenty-first century health lifestyles research is more empirical than previous work, driving further theoretical advancements. Readily applicable to quantitative and qualitative methods, a health lifestyles approach provides a practical application of theory to advance empirical research and policy.

Health lifestyles offer a perspective for understanding social behavior that engages macro, meso, and micro levels of sociological analysis . To examine health lifestyles, we must recognize structural constraints and opportunities, as well as individual agency and preferences. Cockerham (2005) emphasized the importance of focusing on structural influences on health lifestyles, and recent literature has risen to this challenge. Health lifestyles are emergent indicators of population health, responsive to macrolevel policies and norms and local contexts. Yet adopting a health lifestyle also marks one’s identity at the micro level, an expression of preference and style reflective of social class and other group memberships ( Bourdieu 1986a ; Cockerham, Rütten and Abel 1997 ; Korp 2008 ; Williams 1995 ). Health lifestyles both influence and are influenced by social network ties at the meso level. By articulating how social structures both shape and are shaped by individuals’ configurations of health behaviors, health lifestyles connect racism, sexism, and other interlocking systems of oppression to disparities in health and mortality.

Health lifestyles thus offer a lens into the production and maintenance of social inequalities.

Health behaviors are a key mechanism through which socioeconomic status (SES) influence health and longevity. However, healthfulness can also justify policies that exacerbate social inequalities. Lifestyle behaviors both serve as a scapegoat for blaming disadvantaged individuals and allow the advantaged to praise themselves for earning and deserving their good health, representing “embodied neoliberalism” ( Luna 2019 ). Despite these attributions, health lifestyles research has demonstrated that the lifestyles of those most advantaged are not uniformly healthful, nor are those of those least advantaged consistently harmful ( Saint Onge and Krueger 2017 ).

CONTRIBUTIONS

Table 1 summarizes key contributions from recent health lifestyles literature . Largely following the structure of this section, the table begins with a definition, highlights the influences on health lifestyles, identifies sources of change in health lifestyles and their consequences, and considers how they perpetuate inequalities.

Contributions of Recent Literature to Understanding Health Lifestyles

Conceptualizations of Health Lifestyles

Today’s “lifestylization” ( Knudsen and Triantafillou 2020 ) of health, which focuses on individual responsibility for health behaviors, is so widespread that it is easy to forget that modifying individual behaviors has not always been at the center of health promotion. Indeed, health behaviors are a recent social construct that has assumed importance and become a target of heath policy and individuals’ health-promoting efforts in the past few decades ( Armstrong 2009 ). Specific health behaviors—such as children’s sleep guidelines or diet recommendations—are socially constructed, changing over time and place. For example, seat belt use is only relevant in a society with cars and roads where belts can mitigate injuries from crashes. Yet public health often decontextualizes health behaviors, approaching them in isolation as targets for intervention.

The idea of health lifestyles that transcend specific health behaviors is even more recent but builds on a long history of social theory about lifestyles (e.g., Bourdieu 1986a ; Giddens 1984 ; Simmel 1950 ; Sobel 1981 ; Weber [1921] 1978 ). In particular, Weber articulated lifestyles as visible everyday manifestations of social inequalities ( Cockerham 2021 ). Cockerham and colleagues (1997 :321) drew on this literature to develop theory about health lifestyles, defined as “collective patterns of health-related behavior based on choices from options available to people according to their life chances.” Health lifestyles are theorized to be a blend of social structure and individual agency, including conscious behaviors and gradually acquired habitus, “taken-for-granted, routinised knowledge and practices which, for the most part, we carry out unthinkingly and unreflectively” ( Williams 1995 :598).

Health lifestyles comprise both an individual’s health behaviors and group-level identities, norms, and understandings of health ( Cockerham et al. 1997 ; Cockerham 2005 ). Health lifestyles are collective, “a group attribute resulting from the interaction between social conditions and behavior” ( Frohlich and Potvin 1999 :S11). Alcohol use exemplifies these tensions between group and individual levels and between behaviors and social psychological phenomena such as identities and norms. People abstain from alcohol use for individual-level reasons such as past problem drinking, taste preferences, or health conditions and group-level reasons such as family norms or religious adherence. The relationship between abstention and mortality is different depending on the reason why a person abstains—although the behavior is the same ( Rogers, Krueger, Miech et al. 2013 ). Group contexts and individual factors similarly influence young adults’ binge drinking, ranging from partying to dinners with friends to stress-related drinking while alone ( Wamboldt, Khan, Mellins et al. 2019 ). In these examples, both binge drinking and abstention co-occur with other health behaviors in patterned ways according to different identities, norms, and group contexts. To understand the consequences of alcohol use and effective policy solutions, then, individual and group factors must be synthesized along with underlying behaviors and social psychological phenomena. Using a health lifestyles framework to understand health behaviors achieves this goal.

Measurement of Health Lifestyles

Health lifestyles are difficult to measure because of two ways in which they are conceptualized as having an inherently dual nature. First, as discussed, they are both a group-level phenomenon and an individual-level characteristic. Second, they include both a set of health behaviors and the norms, identities, and understandings that underlie those behaviors. These dualities, along with the diversity and complexity of health behaviors, have posed fundamental measurement challenges, resulting in theoretical developments outpacing empirical investigations of health lifestyles. They have also led scholars to articulate a need for both qualitative and quantitative data (e.g., Blaxter 1990 ; Cockerham et al. 1997 ; Frohlich and Potvin 1999 ).

Several quantitative measurement approaches have provided empirical information about health lifestyle behaviors but not yet captured their dualities. Methods including exploratory factor analysis and cluster analysis have been used to group observations by shared behavioral characteristics. Some empirical studies have used an explicit or implicit health lifestyles lens but analyzed specific health behaviors separately (e.g., Ferraro, Schafer and Wilkinson 2016 ; Krueger et al. 2009 ; Øvrum, Gustavsen and Rickertsen 2014 ).

Other research has combined multiple health behaviors into a single measure of healthfulness, using varied terminology such as a “healthy lifestyle score” (e.g., Costa-Tutusaus and Guerra-Balic 2016 ; Saneei, Esmaillzadeh, Keshteli et al. 2016 ). Capturing the overall level of healthfulness of a person’s health lifestyle behaviors can distill information in a more straightforward and replicable way than can a more inductive approach, such as a latent class analysis (LCA) approach described below. Supporting this approach, some articulations of health lifestyles theory expect lifestyles to vary in their objective healthfulness, ranging from generally healthy to generally unhealthy (e.g., Cockerham et al. 1997 ; Cockerham 2005 ). Yet, this approach has drawbacks. Importantly, people’s behaviors do not always coalesce into concordantly “healthy” or “unhealthy” patterns as implied by a single scale of healthfulness ( de la Haye, D’Amico, Miles et al. 2014 ). Discordance in lifestyle behaviors may represent important information that is lost when condensing data into a single healthfulness score. And the designation of behaviors as “healthy” or “unhealthy” is a social construction that can change across time and place (e.g., amounts of fat, sugar, and protein in a “healthy” diet).

Recognizing this, much recent health lifestyles research has expected that the diverse and often inconsistent motivations, identities, and norms underlying health lifestyles often lead not to concordant health lifestyles in which an individual’s behaviors are either uniformly healthy or unhealthy, but to discordant lifestyles comprised of healthier and unhealthier behaviors within the same person ( Cockerham 2021 ; Daw, Margolis and Wright 2017 ; Saint Onge and Krueger 2017 ). Studies that empirically model a set of discrete health lifestyles that may be concordant or discordant typically use variants of latent class analysis ( Collins and Lanza 2010 ). LCA uses a structural equation modeling approach to identify latent, or unobserved, subgroups using observed, categorical variables—in this case, multiple health behaviors. LCA and similar methods such as latent profile analysis for continuous variables are particularly well suited to identifying health lifestyles because they take a person-centered, rather than variable-centered, approach, allowing relationships among variables within people to emerge inductively from data. LCA ignores researchers’ expectations about groupings or concordance. LCA has drawbacks: It is sensitive to the behaviors included in analyses, often leading to different predominant health lifestyles across studies. LCA is also limited in addressing underlying group-level phenomena such as norms or identities.

Health Lifestyles as Structural Manifestations of Inequalities

Our discussion has so far focused on quantitative, survey-based, behavior-focused studies that comprise the bulk of health lifestyles scholarship, especially in North America. This research has made empirical strides in combining individuals’ health behaviors in innovative ways, but it has ignored one side of each of the two dualities of health lifestyles, measuring individual behaviors but not the group level, and operationalizing behaviors but not identities, norms, or understandings of health. Quantitative and behavior-focused health lifestyles research has yielded intriguing findings about what health lifestyles are, how they serve as structural manifestations of inequalities, and their links to health, development, and status attainment across global settings. We focus on four key contributions here: discordance, social contexts, stability and change, and consequences.

1. Health lifestyles are often discordant combinations of healthier and unhealthier behaviors within individuals,

as described above ( Saint Onge and Krueger 2017 ). Using LCA, which allows a population’s predominant patterns of health behaviors to emerge inductively, researchers have now identified substantial discordance within many health lifestyles, as well as some typical types of discordance that emerge across multiple studies and datasets. This discordance is theoretically interesting because it provides strong indirect evidence for the importance of other factors besides maximizing health—such as group-based norms, identities, and understandings of health—for understanding health lifestyles. An academic, for example, might display low consumption of junk food, high vegetable consumption, abstinence from tobacco use, high caffeine and alcohol intake, limited sleep, and frequent sedentary time. This combination of behaviors is discordant in its healthfulness but may be consistent with the norms of that individual’s social group and may be difficult to alter without understanding nonbehavioral aspects of the health lifestyle.

In other examples, higher-SES Costa Rican adults more frequently have high-calorie diets but also exercise more ( Rosero-Bixby and Dow 2009 ). Similarly, in Denmark, higher SES simultaneously spurs greater interest in both cooking, leading to higher body mass index (BMI), and exercise, leading to lower BMI ( Christensen and Carpiano 2014 ). Among US adolescents in 1995, 15% had a health lifestyle comprised of unusually good nutrition and exercise levels, typical amounts of screentime and sleep, high rates of cigarette smoking, and near-universal binge drinking ( Burdette et al. 2017 ). To better understand causes of such health lifestyle concordance and discordance within individuals, health lifestyles research has made substantial strides in translating insights from the life course theoretical perspective ( Elder 1994 ) into informative findings ( Cockerham 2021 ). The other three contributions described here have arisen from such work.

2. Health lifestyles are structured by social and geographic contexts.

Multidimensional and dynamic conceptualizations of social contexts, a key facet of the life course perspective ( Elder 1994 ), are important for understanding health lifestyles. Parents, siblings, peers, social networks, and schools have all been shown to matter for health behaviors and lifestyles ( Daw, Margolis and Verdery 2015 ; Mollborn and Lawrence 2018 ). For example, one study of US adolescents found that both people’s selection into friendship groups and the influence of their friends shape their health lifestyles ( adams et al. 2020 ). Teens select into and are influenced by social networks across multiple behaviors, strengthening the conclusion that lifestyles, and not just specific behaviors, spread within networks ( Gremmen, Berger, Ryan et al. 2019 ).

But less empirical evidence has established pathways through which social contexts shape health lifestyles. One important exception is research on families, which influence children’s health lifestyles through parents’ sociodemographic group memberships, resources, health behaviors, and parenting practices ( Mollborn, Lawrence, and Krueger 2020 ). Family influences encompass material and cultural components and are linked to macro- and meso-level contexts. This work further hints at the importance of child agency and reciprocal socialization between parents and children for understanding health lifestyle development in early life, but data limitations have prevented the establishment of directional relationships. Beyond families, theory would suggest that changes to social or geographic contexts can shift a person’s health lifestyle through changing structural constraints, group memberships, exposure to norms, and identities. Blaxter (1990) demonstrated that socioeconomic context and living environment impact a person’s ability to practice effective health lifestyles, with higher-status individuals more likely to have resources to achieve healthy lifestyles.

Geographic contexts have received less attention as potentially powerful influences on health lifestyles. Health lifestyles theory expects group norms and understandings of “healthy” behavior to vary from one place to another. Prevalent health behaviors and lifestyles are locally specific ( Lee, Seo, Middlestadt et al. 2015 ), perhaps responding to local differences in social contexts. Regional differences in prevalent health behaviors and lifestyles appear to be one reason why the relationship between education and health varies from place to place ( Kemp and Montez 2020 ); more research examining geographic variation in health lifestyles is needed.

3. Health lifestyles display both continuity and change across a person’s life.

Developmental processes also shape lifestyles over time. Life course theory posits that earlier experiences are associated with later ones, while the meanings and expectations across different roles and contexts, such as attending school or being a parent, shift over life stages ( Settersten 2004 ). Infants engage in health behaviors via a “received health lifestyle” ( Mollborn et al. 2014 ), but many behaviors are beyond their autonomous control. Health-related parenting ( Augustine, Prickett and Kimbro 2017 ) attempts to regulate behaviors such as feeding and sleep duration. Parents often seek to socialize children into becoming “health-promoting actors” ( Christensen 2004 ) who exercise agency in ways that parents deem healthy or appropriate. Growing children gain agency, developing habitus as a form of implicit or routinized agency ( Kohli 2019 ). The habitus makes many health behaviors largely automatic and potentially more difficult to adopt or change later in life ( Bourdieu 1986a ; Cockerham 2018 ), even though habitus can also be flexible ( Cockerham 2018 ); for example, upwardly mobile people often try to change their class-related habitus and distance themselves from the health behaviors of their groups of origin ( Curl, Lareau and Wu 2018 ). Shifts in social roles also appear influential for health lifestyles ( Mize 2017 ). As parents’ control over young people’s health behaviors lessens, they transition to an “achieved health lifestyle” in adulthood ( Mollborn et al. 2014 ), informed by earlier lifestyles and often more stable than at younger ages.

Both SES and gender matter for health lifestyles ( Olson, Hummer and Harris 2017 ; Södergren, Wang, Salmon et al. 2014 ), but they do not predict health lifestyles consistently across life ( Øvrum et al. 2014 ). Extant health lifestyles theory has articulated ideas about childhood gender socialization leading to typically gendered health behaviors ( Cockerham 2018 )—with women generally displaying more favorable health behaviors except for physical activity. But gendered patterns are not consistently reflected in US adolescents and early young adults, emerging first in late young adulthood ( Mollborn, Lawrence, and Hummer 2020 ). Furthermore, differences in health behaviors within genders far outstrip the magnitude of differences between genders ( Mollborn, Lawrence, and Hummer 2020 ). Evidence on the gender-health lifestyles relationship across the transition to adulthood supports the importance of a dynamic, life course, and multilevel perspective that articulates macro-, meso-, and microlevel influences. Although other status categories such as race, ethnicity, or sexual identity have not received sufficient attention (see Cockerham et al. 2017 for an exception), similar processes may apply, and unique findings have yet to be documented.

Research on the SES-health lifestyles relationship has similarly documented a mixture of stability and fluidity across age. Although higher parental SES tends to predict healthier lifestyles in adolescence, and higher levels of socioeconomic attainment in late young adulthood are related to healthier lifestyles, the SES-health lifestyle relationship in early young adulthood (ages 18-24) is absent or even reverses ( Lawrence, Mollborn, Goode et al. 2020 ). As others have found for health behaviors such as smoking and alcohol use (e.g., Chen and Jacobson 2012 ), at this age, socioeconomically or racially privileged young people often have higher levels of risky health behaviors as they pursue a socially sanctioned “age of independence” ( Rosenfeld 2007 ) from adults’ normative controls and adult responsibilities. Yet in later life there is evidence of socially classed health lifestyle “lock-in,” with relatively little change within individuals ( Rees Jones et al. 2011 ). These studies support a conceptualization of SES and its relationship to health lifestyles that incorporates both stability and change across life.

4. Health lifestyles matter for health and development across different life phases.

The life course perspective stresses that earlier life circumstances influence later ones ( Elder 1994 ). Much health lifestyles research has focused on adults, but recently there has been expansion to children and adolescents (e.g., Burdette et al. 2017 ; Mollborn and Lawrence 2018 ). (Research has used observational data, establishing associations and time ordering but not causality.) In early childhood, health lifestyles have been linked to parent-rated health status, cognitive development, and socioemotional development ( Mollborn et al. 2014 ). The finding that children who are otherwise sociodemographically similar, but who have health lifestyles with differing levels of health risk, experience distinct developmental outcomes ( Mollborn et al. 2014 ) provides additional support for the conclusion that health lifestyles matter for development and health. Adolescents’ health lifestyles are associated with health outcomes in adolescence and young adulthood ( Burdette et al. 2017 ). Similarly, adolescent and young adult health lifestyles predict young adult health outcomes ( Lawrence et al. 2017 ), and adults’ health lifestyles are associated with health and mortality ( Saint Onge and Krueger 2017 ; Zhang 2020 ). There are many complexities to consider, as relationships can vary according to the different behaviors enacted in a lifestyle, as well as having different health effects from the same behaviors. For instance, routinized physical activity required in a manual labor job may provide exercise but also increase health risks through repetitive motion injuries and reduced exercise variation.

New research has begun documenting complex interrelationships between health lifestyles and other aspects of health. Depending on a person’s situation, life phase, and social contexts, worsening health can shape their health lifestyle in favorable or unfavorable ways. Qualitative research from class-privileged communities found that having a family member with a disability or serious health condition can improve health lifestyles of all family members because they no longer take health for granted ( Rigles 2019 ). Further, experiencing new health problems in later adulthood can compromise health lifestyles ( Cockerham et al. 2020 ), limiting healthy behaviors such as exercise, which in turn could compromise health. In sum, health lifestyles are structural manifestations of social inequalities that matter for people’s future health and development in complex ways.

Health Lifestyles as Cultural Drivers of Inequalities

So far, we have emphasized one side of each duality by focusing on behaviors and individuals. Another active body of theory, developed primarily by European and North American scholars and supported by some qualitative research, lays out a culturally focused perspective that extends beyond behaviors to explicitly consider identities and norms (e.g., Korp 2010 ; Teuscher, Bukman, van Baak et al. 2015 ). Not all of this research engages explicitly with the term “health lifestyles.” In this perspective, health lifestyles are a visible manifestation of social inequalities used by individuals, groups, and institutions as a cultural tool to perpetuate further inequalities . As Ortner (1998) noted, lifestyles reflect class-based habitus and are more socially acceptable to discuss and assign blame for than social class. And in the US context, race and class are intimately intertwined in people’s lifestyles ( Ortner 1998 ), making exclusion on the basis of both class and race feasible through leveraging lifestyles. We highlight four core contributions of this perspective: health lifestyles as cultural capital, symbolic boundaries, links to morality, and implications for social disparities.

1. Health lifestyles are an increasingly important form of cultural capital ( Bourdieu 1986b )

used by advantaged families and communities to their benefit. For example, in two middle-class US communities, parents sought to instill particular health behaviors, norms, and understandings of health in their children that would lead them to engage in classed and raced “performances of health” (e.g., possessing a thin body, engaging in specific types of physical activity, and refusing to eat inexpensive “fast food”) that presumably yield future socioeconomic and health benefits ( Mollborn, Rigles and Pace 2020 ). Many parents deliberately chose communities and schools that reinforced the same health lifestyle messages for their children, further leveraging class and race advantages into children’s cultural capital. Cultural tastes shape network ties in ways that stratify people by privilege, thus converting cultural capital into social capital that yields important resources ( Lewis and Kaufman 2018 ).

2. Health lifestyles function as meaningful political and symbolic boundaries ( Lamont and Molnár 2002 ).

The cultural and social capital they yield makes health lifestyles an effective form of distinction to set advantaged people apart from lower-status groups ( Abel 2008 ; Pampel 2005 ). Individual tastes in health behaviors have rich historical precedents and symbolic meanings. Examples include class- and race-based variation in alcohol (e.g., beer versus wine) and tobacco use (e.g., menthol cigarettes versus vaping products). Thus, lifestyles are an increasingly important form of distinction and intensifying form of social control ( Abrutyn and Carter 2015 ; Carlisle, Hanlon and Hannah 2008 ). This distinction must constantly be maintained and updated to be effective because those with less status often seek to mimic higher-status lifestyles ( Ridgeway 2014 ). As new health information becomes available, it is often “appropriated” by those with more resources who change their behaviors, improving the health of those who need it the least ( Carlisle et al. 2008 ; Link and Phelan 1995 ). And even when actual behaviors look similar across statuses, people’s portrayals and justifications of behaviors can create distinctions that feel meaningful ( Katainen 2010 ).

Thus, as Korp (2008 :25) noted, “The notion of ‘healthy lifestyle’ is fundamentally political. It exercises symbolic power in the sense that it legitimises certain ways of thinking, feeling and acting at the expense of others.” People “do health” in political ways. The symbolic boundaries that health lifestyles create are particularly effective because health behaviors are often rooted in habitus that is developed over long periods of time early in life, making privileged habitus hard for the less privileged to replicate ( Bourdieu 1986a ). Particular behaviors, norms, and understandings of health, rooted in social group memberships, can become routinized, so people do not need to make conscious decisions to enact a particular health lifestyle. For example, putting on a bicycle helmet may be done out of habit. People also make conscious choices about their health in ways that differentiate groups ( Talukdar and Linders 2013 ). Because conscious effort can alter behaviors, understanding health lifestyles as solely resulting from habitus does not attend sufficiently to dominance hierarchies or symbolic power ( Korp 2010 ).

3. Health lifestyles’ links to morality make them especially effective at perpetuating inequalities.

Morality, which is socially constructed and intimately tied to symbolic power and social inequalities, is important for understanding how health lifestyles perpetuate inequalities. Health lifestyles are increasingly linked to self-control, discipline, and hard work in ways that imbue them with morality ( Fielding-Singh 2019 ; Luna 2019 ). Cairns and Johnston (2015 :171) summarized the cultural logic through which scrutiny of someone’s body and physical attractiveness is seen to yield useful information about their health and morality: “The healthy feminine subject chooses wisely in the interest of health and will ‘look good’ as a natural outcome.” This logic leads to social judgments based on physical appearance that can be consequential for future prospects. Someone who does not embody the correct “performance of health” may be considered morally suspect and lose access to socioeconomic opportunities or appropriate health care. This strong link between health lifestyle and morality is particularly evident among parents across the spectrum of privilege, who face high social standards for ensuring the health of their children’s bodies ( Elliott and Bowen 2018 ).

4. Research from the cultural perspective shows how health lifestyles comprise a powerful contemporary mechanism for increasing health disparities and socioeconomic inequalities.

Those with more resources can more easily meet guidelines or expectations, particularly ones that change frequently. For example, pressure on mothers to feed children in increasingly intensive ways can increase inequalities because highly resourced families can devote more time and money ( Elliott and Bowen 2018 ). Although much of this research has focused on individuals or families, research on communities’ collective health lifestyles may be increasingly important. Socioeconomic segregation of neighborhoods is strong, perhaps in part a result of higher-SES US adults self-segregating to form “overrider enclaves” that are intended to protect against a “default American lifestyle” comprised of multiple unhealthy behaviors ( Mirowsky and Ross 2015 ). Because communities are important for understanding how health lifestyle-related habitus and cultural capital are instilled ( Mollborn et al. 2020 ), this segregation may cause increasing social disparities in health lifestyles that could lead to strengthening inequalities in people’s health and socioeconomic attainment.

CRITICAL DIRECTIONS FOR FUTURE RESEARCH

The health lifestyles framework contributes to understandings of health, health disparities, and social inequalities by integrating individual- and group-level influences and synthesizing constellations of health behaviors with underlying social psychological phenomena including norms and identities. While health lifestyles research is advancing rapidly, many challenges and opportunities remain. Concentrated efforts are needed to conceptually map and integrate extant research into a cohesive narrative that facilitates an interdisciplinary field of research and improves understanding of health lifestyles.

Conceptualization Challenges

Conceptualizing and operationalizing health lifestyles poses a challenge to researchers who must contend with both theoretical and practical considerations, including data availability. Most studies focus only on measuring behaviors. Yet as Cockerham, Rütten, and Abel (1997 :338) have noted, “A health lifestyle is not simply a collection of behaviors nor is it merely a variable.” Broader operationalizations beyond behaviors are needed. For example, large-scale surveys could measure definitions of health, multiple group-level identities, and group-based norms (through perceived embarrassment at engaging in a behavior or through perceptions of others’ approval of a behavior). In surveys with group-based sampling such as schools or social networks, these measures plus health behaviors could be aggregated to identify prevalent health lifestyles among specific friendship or identity groups.

Even within behaviorally focused studies, configurations and patterns of behaviors included in quantitative lifestyles research reflect current national-level health objectives that are most often linked to negative health outcomes among adults (e.g., cigarette smoking). When new behaviors emerge (e.g., vaping), data are not immediately available to capture them. Research continues to be limited by questions included in prominent data collection efforts, which lag behind changes in populations’ actual behaviors.

Further, studies often use the same measures over time, life course stages, and contexts, promoting consistency but suppressing innovation. More nuanced and dynamic measurement of health-related behaviors could lead to new insights. For instance, new techniques in nicotine consumption, variation in legal cannabis use, engagement in telemedicine, or social distancing and mask wearing during pandemics were not foreseeable health behaviors until recently. Nontraditional health behaviors such as gun ownership are becoming increasingly integrated into public health research ( Wertz, Azrael, Hemenway et al. 2018 ). Measuring different health behaviors across historical periods and life stages more accurately captures their consequences and how and why these behaviors are enacted. Yet using different health behaviors to operationalize health lifestyles makes it unlikely that the same health lifestyle groups can be identified across analyses. Greater consensus about which domains to measure could improve consistency in findings across studies.

Conceptualizing agency is another research challenge ( Abel and Frohlich 2012 ). The agency-structure divide is messy, and agency can be conceptualized in different ways depending on age and neurotypicality ( Landes and Settersten 2019 ). Behaviors differ in their health effects, visibility, importance to the individual or social group, and level of agency relative to constraints.

Measuring health lifestyles locally requires additional consideration. Health lifestyles are group- and local-level phenomena with meanings unique to one’s school, neighborhood, city, state, country, etc. While there is some consistency in patterns within and across countries, lifestyle meanings remain localized ( Cockerham, Hinote, Abbott et al. 2004 ; Saint Onge and Krueger 2011 ). Multilevel approaches could consider health lifestyles’ meanings in various settings. The ability to achieve health is structured by local norms and opportunities, further complicated by selection into areas and local meanings of health behaviors. Religious groups tend to be geographically concentrated and frequently regulate health lifestyle behaviors such as diets, substance use, and health care seeking ( Hill, Ellison, Burdette et al. 2007 ). Politically conservative areas may provide limited opportunities for some behaviors (e.g., cannabis use, multiple sexual partners) due to local laws and punitive social norms. Rural areas may have inadequate infrastructure for sustained exercise, preventive health services, or food options, with implications for collective lifestyles. Deviating from local, geographic norms may affect identity and social standing in one’s community.

Methodological Challenges

Methodological advancements can benefit health lifestyles research, which in turn offers opportunities to apply new methods. While latent class approaches are a predominant quantitative method for identifying lifestyles, research lacks consensus on how to address current limitations or analyze complex substantive questions. Regardless of methodological choices, assessing conceptual frameworks will require further consideration of behavioral relationships to control variables, mediating mechanisms, or distal outcomes, as well implementation of moderating effects or causal inference over multiple levels of analyses. Current approaches are unsatisfactory for multiple reasons, but there are emergent statistical solutions to some problems (see Bakk and Kuha 2018 ; Masyn 2017 ), and best practices may soon be feasible in existing software.

Longitudinal

studies add further methodological complexity. Longitudinal methods seeking to identify groupings over time, such as latent transition analysis or latent class growth models, allow researchers to model their stability and change. Because these approaches require the same variables over time, they are suitable for health lifestyles studies conducted across ages when meanings of health behaviors are stable (e.g., Burgard, Lin, Segal et al. 2020 ). More frequently, modeling health lifestyle development within individuals can be challenging using current approaches, both because the health behaviors people typically engage in change with age and because ways in which behaviors combine within individuals likely change over time. For such studies, there is no definitive longitudinal method, and consensus around best practices is needed. Continued empirical advances in grouped trajectory models or age-period-cohort approaches have potential for modeling stability and change in lifestyles.

The inductive nature of commonly used quantitative methods nearly guarantees inconsistencies in the lifestyles being identified in different studies, depending on how variables are included and measured. This is frustrating but also an area for advancement. New theoretical conceptualizations grounded in findings are more important than the compositions and prevalence of specific classes, per se. Strict adherence to model selection based on mechanical criteria can overshadow an intention to summarize data in a parsimonious and theoretically informed fashion. The operationalization of health lifestyles should be aimed at representing approximations or snapshots of a much more complex reality. Caution must be taken not to portray bundles of behaviors as reified social facts. Indeed, the utility of these analyses arises from the research questions, approach, and interpretation more than from model precision.

Qualitative methods

show promise for bridging the dualities of health lifestyles that have not been captured in quantitative research, by analyzing health lifestyles as both individual- and group-level phenomena and as both collections of behaviors and underlying identities, norms, and understandings of health. Multiple qualitative methods are necessary to paint this complex picture but are resource intensive. For example, studying how health lifestyles relate to cultural capital, Mollborn, Rigles, and Pace (2020) sampled families within two communities and observed families, interviewed parents and community members who worked with children, and facilitated focus group interactions with multiple parents in the same community.

The methods used to study health lifestyles are interdisciplinary, but sociology can offer particular insight into how to decolonize these tools ( Connell 2018 ). Health lifestyles offer researchers the opportunity to examine inequality from perspectives other than a deficit model focused on problems or disadvantages. Health lifestyles research puts advantages and disadvantages on equal footing, considering structure, agency, resiliency, and constraints across multiple dimensions of (dis)advantage. The cultural perspective articulates how allegedly “healthy” lifestyles perpetuate inequalities, which is at odds with the goal of reducing health disparities through promoting healthy lifestyles. Health lifestyles research could challenge hegemony through decentering dominant groups, shifting research questions from population prevalence and composition of health lifestyles that often center majority groups to considering contextual factors that give rise to different or similar lifestyles for diverse populations. Health lifestyles research would benefit broadly from consideration and examination of health lifestyles across different ethnic and national origins within various international settings beyond the dominant Western health perspective. We advocate for reflecting on researchers’ positionality in conceptualization and measurement. For example, although great strides have been made in analyzing how age, gender, and social class shape health lifestyles, race and ethnicity are rarely deeply studied in a health lifestyles framework, reflecting the Western-, White-dominated demographics of health lifestyles researchers.

Health lifestyles should heed methodological pitfalls common in multiple disciplines. Arbitrary significance values should be used thoughtfully and in conjunction with other information. Fortunately, many health lifestyles studies provide answers by synthesizing a wide variety of data points rather than relying on a single significance test. Such complex studies can be difficult to publish, but we expect improved understandings of the interpretation of statistical results will ease this process. We caution against replication studies of health lifestyles because current approaches are highly sensitive to a number of specifications. Further, researchers should take care to recognize that data on health lifestyles can be used as marketing tools or to justify further marginalizing disadvantaged groups ( Lamont and Molnár 2001 ).

Theoretical Challenges

Health lifestyles research has had a consistent theory-building tradition that will continue in future work. One promising direction is further integration of ideas laid out here. Quantitative and qualitative approaches—both focused on the reciprocal relationship between health lifestyles and social inequalities—are spawning distinct literatures that rarely converse. Yet it is not inherently contradictory to acknowledge that health lifestyles are both quantitatively measurable configurations of behaviors and a cultural means to perpetuate inequalities. Integrating these perspectives’ insights can push health lifestyles theory forward but requires communication across methodological and disciplinary boundaries.

The social psychological underpinnings of health lifestyles—including group-based norms, identities, social control, and understandings of health—must be more clearly articulated. Theoretical advances within social psychology and cultural sociology have laid fruitful groundwork for understanding why health lifestyles are often discordant, why they change, and how they influence people. Cultural sociologists have studied how sets of behaviors are cognitively abstracted as schemas ( Strang and Soule 1998 ) that diffuse through networks and result in automatic cultural differentiation ( Goldberg and Stein 2018 ). Lifestyles may follow similar processes. Sociological research has articulated ways in which culture becomes a source of group distinction and social stratification (e.g., Eliasoph and Lichterman 2003 ). And the proliferation of research on agency ( Hitlin and Elder 2007 ), dual-process cultural models ( Vaisey 2009 ), moral identities ( Stets and Carter 2012 ), norms ( Horne 2001 ), political ideology ( Metzl 2019 ), and stigma ( Link and Phelan 2014 ) provide fertile ground for developing health lifestyles theory.

Integrating research and theories on power and systems of oppression offer insights into health lifestyles. For example, state and local mandates for social distancing and face coverings have faced political resistance during the COVID-19 pandemic, bolstered by the proliferation of misinformation campaigns, suggesting a need to understand the roles of power and politics. Critical race theory ( Graham et al. 2011 ) can also elucidate how health lifestyles operate as cultural tools. Better measures of systemic racism or discriminatory contexts can improve understanding of health lifestyle mechanisms. Further, examining how historical and contemporary institutions and organizations shape constraints, opportunities, motives, and means that guide health lifestyle routines could reveal structural mechanisms perpetuating and creating inequalities in health lifestyles and subsequent outcomes.

Health lifestyles research should further integrate intersectional , transitional approaches. For instance, health lifestyles should be further integrated into theories of gender identity. Sexuality, immigrant status, disability, and other identities are important to accurately portray health lifestyles and can also inform social processes resulting in marginalization or health disparities.

Policy Challenges

Health promotion has focused heavily on changing individuals’ health behaviors ( Knudsen and Triantafillou 2020 ). Although biomedical, treatment-focused health care is still prioritized, this move partially upstream is more cost-effective than waiting to treat health problems that result from unhealthy behaviors ( Benmarhnia, Dionne, Tchouaket et al. 2017 ). Adopting a health lifestyles perspective moves farther upstream by emphasizing social/contextual factors that underlie groups of behaviors, rather than blaming or rewarding individuals for specific behaviors. This upstream focus cannot be achieved unless health lifestyles researchers begin to study both individuals’ behaviors and group-based processes such as norms and identities, as well as the structural environment that envelops health-related decisions and actions. Fulfilling the promise of health lifestyles theory in future empirical research can simultaneously achieve the goal of moving policy implications upstream.

Findings from health lifestyles research have important implications for health policies, public health, and medical practice. For example, health behaviors combine in ways that make it essential to intervene differently for different lifestyle groups ( Wamboldt et al. 2019 ). Efforts to curb binge drinking may be more successful if they account for the fact that all binge drinkers do not have the same health lifestyle ( Burdette et al. 2017 ; Lawrence et al. 2017 ). Another implication of health lifestyles research is that the timing of intervention is important. Because health lifestyles are partially path dependent throughout life, earlier intervention is generally better, although there may be important critical periods. Bauldry and colleagues (2012 :1311) have argued that “efforts to promote healthy behaviors in young adulthood, after the completion of secondary school, may be especially strategic in the promotion of health in later adulthood.” Yet health lifestyles have potential for change across life.

Many health behavior interventions have not worked ( Baum and Fisher 2014 ). Perhaps in response to these limited successes when targeting single health behaviors, some interventions have taken a modified health lifestyle approach targeting multiple behaviors, experiencing varying degrees of success upon evaluation (e.g., Ling, Robbins, Wen et al. 2017 ). Evidence suggests such interventions can lead to identity changes that have unintended consequences for other health behaviors. For example, safety interventions on oil platforms that reframed masculinity as taking care of the collective good changed men’s identities and interactions in a multitude of ways ( Ely and Meyerson 2010 ). But other lifestyle-focused interventions that initially seemed promising were ultimately not effective (e.g., Lloyd, Creanor, Logan et al. 2018 ). Both basic research on health lifestyles and initial findings from interventions suggest that a contextualized approach that considers sets of behaviors, group-based factors, and structural drivers is promising. More multidimensional approaches to targeting health behavior changes are needed, especially “salutogenic” approaches that encourage healthy behaviors more than discouraging unhealthy ones ( Becker et al. 2020 ).

But a health lifestyles approach to policy cannot universally solve the problem of social inequalities. Insights from the cultural health lifestyle perspective are particularly valuable for understanding the perils of using a health lifestyles framework to inform intervention programs . Caution must be taken to avoid “lifestyle drift” from broad social determinants toward emphasis on and investment in individual-level lifestyle behaviors ( Williams and Fullagar 2019 ). Promoting certain behaviors and lifestyles as inherently “healthy” instills them with moral values and sets them up as a goal to which people may aspire but ultimately be limited in achieving because of unequal resources and life chances. Labeling behaviors as “healthy” or “unhealthy” can further heighten intergroup distinctions by race and class. Inequalities can be perpetuated by seemingly well-intended promotion of health behaviors that only the privileged can realistically enact. Effective health lifestyle interventions must ensure that any promotion of a specific lifestyle is paired with full access to the necessary resources to enact and sustain that lifestyle.

But even if that difficult goal is achieved and social disparities in particular health lifestyles are reduced, new lifestyle-based forms of distinction will probably arise because health practices and forms of intergroup distinction are always evolving to maintain inequalities ( Carlisle et al. 2008 ; Link and Phelan 1995 ; Pampel 2005 ). Thus, interventions that focus on promoting or discouraging specific health lifestyles, rather than addressing their upstream causes, may well result in an endless game of “whack-a-mole” in which new lifestyle inequalities arise when old ones are successfully addressed—or may even inadvertently exacerbate the intergroup distinctions they are seeking to reduce. For this reason, attending to upstream causes of health lifestyles and reducing underlying group-level disparities may be the most effective strategies for health policy.

Health lifestyles are highly consequential for health and longevity. They are also quotidian, present in every person’s daily routine, whether consciously or not. Health lifestyles research is inclusive, relevant for all populations: Everyone has a health lifestyle. Health lifestyles are a promising construct for understanding social disparities in health by combining insights from disparate literatures within and beyond sociology. They bridge structure and agency; individual and population health; micro-, meso-, and macrolevel explanations; behaviors and social psychological group processes; and conceptions of health as an objective status to be attained and a cultural tool deployed to maintain inequalities.

Sociology is leading the way in understanding health lifestyles, but they are relevant for numerous disciplines. Although theoretical development has outpaced substantive research, the latter is expanding rapidly, improving knowledge about how health lifestyles are transmitted across generations, developed within individuals, shaped by social contexts, and consequential for health and status attainment. Studies integrating life course approaches with health lifestyles show that understanding lifestyles within and across historical periods, birth cohorts, and developmental stages or biological age sheds light on how and why they develop, change, or remain stable ( Burgard et al. 2020 ; Cockerham et al. 2020 ).

Health lifestyles highlight the inherently social nature of health behavior, applying a sociological imagination to reveal how health behaviors are not isolated individual decisions, but embedded in our deeply complicated social lives. Future research must push health lifestyles beyond a behavior-only operationalization that risks perpetuating the individualization of health. Viewing health behavior as individualized elides the group-level nature of health lifestyles and the powerful structural forces that constrain people’s behaviors. Extant research has made important strides in identifying groups and upstream influences, documenting fluidity and discordance in health lifestyles that underscore the importance of moving beyond specific health behaviors to take a more integrated approach. Future research articulating group-level lifestyle processes that underlie bundles of health behaviors can continue to shift scholarly attention to groups and to upstream policy influences on health. Integrating a cultural perspective on health lifestyles as a tool that maintains inequality is another important future goal. Achieving these aims will provide an evidence base that can further inform policies, potentially making them more effective at addressing social inequalities.

Acknowledgments

This research is based on work supported by a grant from the National Science Foundation (SES 1729463). We are grateful to the NIH/NICHD funded CU Population Center (P2CHD066613) and the Lund University Centre for Economic Demography (Sweden) for general support. We thank Kim Truong-Vu, Laurie James-Hawkins, and Richard Rogers for their contributions to this work.

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Healthy Living Guide 2020/2021

A digest on healthy eating and healthy living.

As we transition from 2020 into 2021, the COVID-19 pandemic continues to affect nearly every aspect of our lives. For many, this health crisis has created a range of unique and individual impacts—including food access issues, income disruptions, and emotional distress.

Although we do not have concrete evidence regarding specific dietary factors that can reduce risk of COVID-19, we do know that maintaining a healthy lifestyle is critical to keeping our immune system strong. Beyond immunity, research has shown that individuals following five key habits—eating a healthy diet, exercising regularly, keeping a healthy body weight, not drinking too much alcohol, and not smoking— live more than a decade longer than those who don’t. Plus, maintaining these practices may not only help us live longer, but also better. Adults following these five key habits at middle-age were found to live more years free of chronic diseases including type 2 diabetes, cardiovascular disease, and cancer.

While sticking to healthy habits is often easier said than done, we created this guide with the goal of providing some tips and strategies that may help. During these particularly uncertain times, we invite you to do what you can to maintain a healthy lifestyle, and hopefully (if you’re able to try out a new recipe or exercise, or pick up a fulfilling hobby) find some enjoyment along the way.

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A healthy lifestyle that involves moderate alcohol consumption, a healthy diet, regular physical activity, healthy sleep and frequent social connection, while avoiding smoking and too much sedentary behaviour, reduces the risk of depression, new research has found.

Although our DNA – the genetic hand we’ve been dealt – can increase our risk of depression, we’ve shown that a healthy lifestyle is potentially more important. Barbara Sahakian

In research published today in Nature Mental Health , an international team of researchers, including from the University of Cambridge and Fudan University, looked at a combination of factors including lifestyle factors, genetics, brain structure and our immune and metabolic systems to identify the underlying mechanisms that might explain this link.

According to the World Health Organization, around one in 20 adults experiences depression, and the condition poses a significant burden on public health worldwide. The factors that influence the onset of depression are complicated and include a mixture of biological and lifestyle factors.

To better understand the relationship between these factors and depression, the researchers turned to UK Biobank, a biomedical database and research resource containing anonymised genetic, lifestyle and health information about its participants.

By examining data from almost 290,000 people – of whom 13,000 had depression – followed over a nine-year period, the team was able to identify seven healthy lifestyle factors linked with a lower risk of depression. These were:

• moderate alcohol consumption
• healthy diet
• regular physical activity
• healthy sleep
• never smoking
• low-to-moderate sedentary behaviour
• frequent social connection

Of all of these factors, having a good night’s sleep – between seven and nine hours a night – made the biggest difference, reducing the risk of depression, including single depressive episodes and treatment-resistant depression, by 22%.

Frequent social connection, which in general reduced the risk of depression by 18%, was the most protective against recurrent depressive disorder.

Moderate alcohol consumption decreased the risk of depression by 11%, healthy diet by 6%, regular physical activity by 14%, never smoking by 20%, and low-to-moderate sedentary behaviour by 13%.

Based on the number of healthy lifestyle factors an individual adhered to, they were assigned to one of three groups: unfavourable, intermediate, and favourable lifestyle. Individuals in the intermediate group were around 41% less likely to develop depression compared to those in the unfavourable lifestyle, while those in the favourable lifestyle group were 57% less likely.

The team then examined the DNA of the participants, assigning each a genetic risk score. This score was based on the number of genetic variants an individual carried that have a known link to risk of depression. Those with the lowest genetic risk score were 25% less likely to develop depression when compared to those with the highest score – a much smaller impact than lifestyle.

In people at high, medium, and low genetic risk for depression, the team further found that a healthy lifestyle can cut the risk of depression. This research underlines the importance of living a healthy lifestyle for preventing depression, regardless of a person's genetic risk.

Professor Barbara Sahakian, from the Department of Psychiatry at the University of Cambridge, said: “Although our DNA – the genetic hand we’ve been dealt – can increase our risk of depression, we’ve shown that a healthy lifestyle is potentially more important.

“Some of these lifestyle factors are things we have a degree control over, so trying to find ways to improve them – making sure we have a good night’s sleep and getting out to see friends, for example – could make a real difference to people’s lives.”

To understand why a healthy lifestyle might reduce the risk of depression, the team studied a number of other factors.

First off, they examined MRI brain scans from just under 33,000 participants and found a number of regions of the brain where a larger volume – more neurons and connections – was linked to a healthy lifestyle. These included the pallidum, thalamus, amygdala and hippocampus.

Next, the team looked for markers in the blood that indicated problems with the immune system or metabolism (how we process food and produce energy). Among those markers found to be linked to lifestyle were the C-reactive protein, a molecule produced in the body in response to stress, and triglycerides, one of the primary forms of fat that the body uses to store energy for later.

These links are supported by a number of previous studies. For example, exposure to stress in life can affect how well we are able to regulate blood sugar, which may lead to a deterioration of immune function and accelerate age-related damage to cells and molecules in the body. Poor physical activity and lack of sleep can damage the body’s ability to respond to stress. Loneliness and lack of social support have been found to increase the risk of infection and increase markers of immune deficiency.

The team found that the pathway from lifestyle to immune and metabolic functions was the most significant. In other words, a poorer lifestyle impacts on our immune system and metabolism, which in turn increases our risk of depression.

Dr Christelle Langley, also from the Department of Psychiatry at the University of Cambridge, said: “We’re used to thinking of a healthy lifestyle as being important to our physical health, but it’s just as important for our mental health. It’s good for our brain health and cognition, but also indirectly by promoting a healthier immune system and better metabolism.”

Professor Jianfeng Feng, from Fudan University and Warwick University, added: “We know that depression can start as early as in adolescence or young adulthood, so educating young people on the importance of a healthy lifestyle and its impact on mental health should begin in schools.”

This study was supported by grants from organisations including the National Natural Science Foundation of China and the Ministry of Science, China*.

Reference Zhao, Y & Yang, L et al. The brain structure, immunometabolic and genetic mechanisms underlying the association between lifestyle and depression. Nature Mental Health; 11 Sept 2023; DOI: 10.1038/s44220-023-00120-1

*A full list of funders can be found in the paper.

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• Published: 06 December 2017

Healthy food choices are happy food choices: Evidence from a real life sample using smartphone based assessments

• Deborah R. Wahl 1   na1 ,
• Karoline Villinger 1   na1 ,
• Laura M. König   ORCID: orcid.org/0000-0003-3655-8842 1 ,
• Katrin Ziesemer 1 ,
• Harald T. Schupp 1 &
• Britta Renner 1  

Scientific Reports volume  7 , Article number:  17069 ( 2017 ) Cite this article

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Research suggests that “healthy” food choices such as eating fruits and vegetables have not only physical but also mental health benefits and might be a long-term investment in future well-being. This view contrasts with the belief that high-caloric foods taste better, make us happy, and alleviate a negative mood. To provide a more comprehensive assessment of food choice and well-being, we investigated in-the-moment eating happiness by assessing complete, real life dietary behaviour across eight days using smartphone-based ecological momentary assessment. Three main findings emerged: First, of 14 different main food categories, vegetables consumption contributed the largest share to eating happiness measured across eight days. Second, sweets on average provided comparable induced eating happiness to “healthy” food choices such as fruits or vegetables. Third, dinner elicited comparable eating happiness to snacking. These findings are discussed within the “food as health” and “food as well-being” perspectives on eating behaviour.

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Introduction.

When it comes to eating, researchers, the media, and policy makers mainly focus on negative aspects of eating behaviour, like restricting certain foods, counting calories, and dieting. Likewise, health intervention efforts, including primary prevention campaigns, typically encourage consumers to trade off the expected enjoyment of hedonic and comfort foods against health benefits 1 . However, research has shown that diets and restrained eating are often counterproductive and may even enhance the risk of long-term weight gain and eating disorders 2 , 3 . A promising new perspective entails a shift from food as pure nourishment towards a more positive and well-being centred perspective of human eating behaviour 1 , 4 , 5 . In this context, Block et al . 4 have advocated a paradigm shift from “food as health” to “food as well-being” (p. 848).

Supporting this perspective of “food as well-being”, recent research suggests that “healthy” food choices, such as eating more fruits and vegetables, have not only physical but also mental health benefits 6 , 7 and might be a long-term investment in future well-being 8 . For example, in a nationally representative panel survey of over 12,000 adults from Australia, Mujcic and Oswald 8 showed that fruit and vegetable consumption predicted increases in happiness, life satisfaction, and well-being over two years. Similarly, using lagged analyses, White and colleagues 9 showed that fruit and vegetable consumption predicted improvements in positive affect on the subsequent day but not vice versa. Also, cross-sectional evidence reported by Blanchflower et al . 10 shows that eating fruits and vegetables is positively associated with well-being after adjusting for demographic variables including age, sex, or race 11 . Of note, previous research includes a wide range of time lags between actual eating occasion and well-being assessment, ranging from 24 hours 9 , 12 to 14 days 6 , to 24 months 8 . Thus, the findings support the notion that fruit and vegetable consumption has beneficial effects on different indicators of well-being, such as happiness or general life satisfaction, across a broad range of time spans.

The contention that healthy food choices such as a higher fruit and vegetable consumption is associated with greater happiness and well-being clearly contrasts with the common belief that in particular high-fat, high-sugar, or high-caloric foods taste better and make us happy while we are eating them. When it comes to eating, people usually have a spontaneous “unhealthy = tasty” association 13 and assume that chocolate is a better mood booster than an apple. According to this in-the-moment well-being perspective, consumers have to trade off the expected enjoyment of eating against the health costs of eating unhealthy foods 1 , 4 .

A wealth of research shows that the experience of negative emotions and stress leads to increased consumption in a substantial number of individuals (“emotional eating”) of unhealthy food (“comfort food”) 14 , 15 , 16 , 17 . However, this research stream focuses on emotional eating to “smooth” unpleasant experiences in response to stress or negative mood states, and the mood-boosting effect of eating is typically not assessed 18 . One of the few studies testing the effectiveness of comfort food in improving mood showed that the consumption of “unhealthy” comfort food had a mood boosting effect after a negative mood induction but not to a greater extent than non-comfort or neutral food 19 . Hence, even though people may believe that snacking on “unhealthy” foods like ice cream or chocolate provides greater pleasure and psychological benefits, the consumption of “unhealthy” foods might not actually be more psychologically beneficial than other foods.

However, both streams of research have either focused on a single food category (fruit and vegetable consumption), a single type of meal (snacking), or a single eating occasion (after negative/neutral mood induction). Accordingly, it is unknown whether the boosting effect of eating is specific to certain types of food choices and categories or whether eating has a more general boosting effect that is observable after the consumption of both “healthy” and “unhealthy” foods and across eating occasions. Accordingly, in the present study, we investigated the psychological benefits of eating that varied by food categories and meal types by assessing complete dietary behaviour across eight days in real life.

Furthermore, previous research on the impact of eating on well-being tended to rely on retrospective assessments such as food frequency questionnaires 8 , 10 and written food diaries 9 . Such retrospective self-report methods rely on the challenging task of accurately estimating average intake or remembering individual eating episodes and may lead to under-reporting food intake, particularly unhealthy food choices such as snacks 7 , 20 . To avoid memory and bias problems in the present study we used ecological momentary assessment (EMA) 21 to obtain ecologically valid and comprehensive real life data on eating behaviour and happiness as experienced in-the-moment.

In the present study, we examined the eating happiness and satisfaction experienced in-the-moment, in real time and in real life, using a smartphone based EMA approach. Specifically, healthy participants were asked to record each eating occasion, including main meals and snacks, for eight consecutive days and rate how tasty their meal/snack was, how much they enjoyed it, and how pleased they were with their meal/snack immediately after each eating episode. This intense recording of every eating episode allows assessing eating behaviour on the level of different meal types and food categories to compare experienced eating happiness across meals and categories. Following the two different research streams, we expected on a food category level that not only “unhealthy” foods like sweets would be associated with high experienced eating happiness but also “healthy” food choices such as fruits and vegetables. On a meal type level, we hypothesised that the happiness of meals differs as a function of meal type. According to previous contention, snacking in particular should be accompanied by greater happiness.

Eating episodes

Overall, during the study period, a total of 1,044 completed eating episodes were reported (see also Table  1 ). On average, participants rated their eating happiness with M  = 77.59 which suggests that overall eating occasions were generally positive. However, experienced eating happiness also varied considerably between eating occasions as indicated by a range from 7.00 to 100.00 and a standard deviation of SD  = 16.41.

Food categories and experienced eating happiness

All eating episodes were categorised according to their food category based on the German Nutrient Database (German: Bundeslebensmittelschlüssel), which covers the average nutritional values of approximately 10,000 foods available on the German market and is a validated standard instrument for the assessment of nutritional surveys in Germany. As shown in Table  1 , eating happiness differed significantly across all 14 food categories, F (13, 2131) = 1.78, p  = 0.04. On average, experienced eating happiness varied from 71.82 ( SD  = 18.65) for fish to 83.62 ( SD  = 11.61) for meat substitutes. Post hoc analysis, however, did not yield significant differences in experienced eating happiness between food categories, p  ≥ 0.22. Hence, on average, “unhealthy” food choices such as sweets ( M  = 78.93, SD  = 15.27) did not differ in experienced happiness from “healthy” food choices such as fruits ( M  = 78.29, SD  = 16.13) or vegetables ( M  = 77.57, SD  = 17.17). In addition, an intraclass correlation (ICC) of ρ = 0.22 for happiness indicated that less than a quarter of the observed variation in experienced eating happiness was due to differences between food categories, while 78% of the variation was due to differences within food categories.

However, as Figure  1 (left side) depicts, consumption frequency differed greatly across food categories. Frequently consumed food categories encompassed vegetables which were consumed at 38% of all eating occasions ( n  = 400), followed by dairy products with 35% ( n  = 366), and sweets with 34% ( n  = 356). Conversely, rarely consumed food categories included meat substitutes, which were consumed in 2.2% of all eating occasions ( n  = 23), salty extras (1.5%, n  = 16), and pastries (1.3%, n  = 14).

Left side: Average experienced eating happiness (colour intensity: darker colours indicate greater happiness) and consumption frequency (size of the cycle) for the 14 food categories. Right side: Absolute share of the 14 food categories in total experienced eating happiness.

Amount of experienced eating happiness by food category

To account for the frequency of consumption, we calculated and scaled the absolute experienced eating happiness according to the total sum score. As shown in Figure  1 (right side), vegetables contributed the biggest share to the total happiness followed by sweets, dairy products, and bread. Clustering food categories shows that fruits and vegetables accounted for nearly one quarter of total eating happiness score and thus, contributed to a large part of eating related happiness. Grain products such as bread, pasta, and cereals, which are main sources of carbohydrates including starch and fibre, were the second main source for eating happiness. However, “unhealthy” snacks including sweets, salty extras, and pastries represented the third biggest source of eating related happiness.

Experienced eating happiness by meal type

To further elucidate the contribution of snacks to eating happiness, analysis on the meal type level was conducted. Experienced in-the-moment eating happiness significantly varied by meal type consumed, F (4, 1039) = 11.75, p  < 0.001. Frequencies of meal type consumption ranged from snacks being the most frequently logged meal type ( n  = 332; see also Table  1 ) to afternoon tea being the least logged meal type ( n  = 27). Figure  2 illustrates the wide dispersion within as well as between different meal types. Afternoon tea ( M  = 82.41, SD  = 15.26), dinner ( M  = 81.47, SD  = 14.73), and snacks ( M  = 79.45, SD  = 14.94) showed eating happiness values above the grand mean, whereas breakfast ( M  = 74.28, SD  = 16.35) and lunch ( M  = 73.09, SD  = 18.99) were below the eating happiness mean. Comparisons between meal types showed that eating happiness for snacks was significantly higher than for lunch t (533) = −4.44, p  = 0.001, d  = −0.38 and breakfast, t (567) = −3.78, p  = 0.001, d  = −0.33. However, this was also true for dinner, which induced greater eating happiness than lunch t (446) = −5.48, p  < 0.001, d  = −0.50 and breakfast, t (480) = −4.90, p  < 0.001, d  = −0.46. Finally, eating happiness for afternoon tea was greater than for lunch t (228) = −2.83, p  = 0.047, d  = −0.50. All other comparisons did not reach significance, t  ≤ 2.49, p  ≥ 0.093.

Experienced eating happiness per meal type. Small dots represent single eating events, big circles indicate average eating happiness, and the horizontal line indicates the grand mean. Boxes indicate the middle 50% (interquartile range) and median (darker/lighter shade). The whiskers above and below represent 1.5 of the interquartile range.

Control Analyses

In order to test for a potential confounding effect between experienced eating happiness, food categories, and meal type, additional control analyses within meal types were conducted. Comparing experienced eating happiness for dinner and lunch suggested that dinner did not trigger a happiness spill-over effect specific to vegetables since the foods consumed at dinner were generally associated with greater happiness than those consumed at other eating occasions (Supplementary Table  S1 ). Moreover, the relative frequency of vegetables consumed at dinner (73%, n  = 180 out of 245) and at lunch were comparable (69%, n  = 140 out of 203), indicating that the observed happiness-vegetables link does not seem to be mainly a meal type confounding effect.

Since the present study focuses on “food effects” (Level 1) rather than “person effects” (Level 2), we analysed the data at the food item level. However, participants who were generally overall happier with their eating could have inflated the observed happiness scores for certain food categories. In order to account for person-level effects, happiness scores were person-mean centred and thereby adjusted for mean level differences in happiness. The person-mean centred happiness scores ( M cwc ) represent the difference between the individual’s average happiness score (across all single in-the-moment happiness scores per food category) and the single happiness scores of the individual within the respective food category. The centred scores indicate whether the single in-the-moment happiness score was above (indicated by positive values) or below (indicated by negative values) the individual person-mean. As Table  1 depicts, the control analyses with centred values yielded highly similar results. Vegetables were again associated on average with more happiness than other food categories (although people might differ in their general eating happiness). An additional conducted ANOVA with person-centred happiness values as dependent variables and food categories as independent variables provided also a highly similar pattern of results. Replicating the previously reported analysis, eating happiness differed significantly across all 14 food categories, F (13, 2129) = 1.94, p  = 0.023, and post hoc analysis did not yield significant differences in experienced eating happiness between food categories, p  ≥ 0.14. Moreover, fruits and vegetables were associated with high happiness values, and “unhealthy” food choices such as sweets did not differ in experienced happiness from “healthy” food choices such as fruits or vegetables. The only difference between the previous and control analysis was that vegetables ( M cwc  = 1.16, SD  = 15.14) gained slightly in importance for eating-related happiness, whereas fruits ( M cwc  = −0.65, SD  = 13.21), salty extras ( M cwc  = −0.07, SD  = 8.01), and pastries ( M cwc  = −2.39, SD  = 18.26) became slightly less important.

This study is the first, to our knowledge, that investigated in-the-moment experienced eating happiness in real time and real life using EMA based self-report and imagery covering the complete diversity of food intake. The present results add to and extend previous findings by suggesting that fruit and vegetable consumption has immediate beneficial psychological effects. Overall, of 14 different main food categories, vegetables consumption contributed the largest share to eating happiness measured across eight days. Thus, in addition to the investment in future well-being indicated by previous research 8 , “healthy” food choices seem to be an investment in the in-the moment well-being.

Importantly, although many cultures convey the belief that eating certain foods has a greater hedonic and mood boosting effect, the present results suggest that this might not reflect actual in-the-moment experiences accurately. Even though people often have a spontaneous “unhealthy = tasty” intuition 13 , thus indicating that a stronger happiness boosting effect of “unhealthy” food is to be expected, the induced eating happiness of sweets did not differ on average from “healthy” food choices such as fruits or vegetables. This was also true for other stereotypically “unhealthy” foods such as pastries and salty extras, which did not show the expected greater boosting effect on happiness. Moreover, analyses on the meal type level support this notion, since snacks, despite their overall positive effect, were not the most psychologically beneficial meal type, i.e., dinner had a comparable “happiness” signature to snacking. Taken together, “healthy choices” seem to be also “happy choices” and at least comparable to or even higher in their hedonic value as compared to stereotypical “unhealthy” food choices.

In general, eating happiness was high, which concurs with previous research from field studies with generally healthy participants. De Castro, Bellisle, and Dalix 22 examined weekly food diaries from 54 French subjects and found that most of the meals were rated as appealing. Also, the observed differences in average eating happiness for the 14 different food categories, albeit statistically significant, were comparable small. One could argue that this simply indicates that participants avoided selecting bad food 22 . Alternatively, this might suggest that the type of food or food categories are less decisive for experienced eating happiness than often assumed. This relates to recent findings in the field of comfort and emotional eating. Many people believe that specific types of food have greater comforting value. Also in research, the foods eaten as response to negative emotional strain, are typically characterised as being high-caloric because such foods are assumed to provide immediate psycho-physical benefits 18 . However, comparing different food types did not provide evidence for the notion that they differed in their provided comfort; rather, eating in general led to significant improvements in mood 19 . This is mirrored in the present findings. Comparing the eating happiness of “healthy” food choices such as fruits and vegetables to that of “unhealthy” food choices such as sweets shows remarkably similar patterns as, on average, they were associated with high eating happiness and their range of experiences ranged from very negative to very positive.

This raises the question of why the idea that we can eat indulgent food to compensate for life’s mishaps is so prevailing. In an innovative experimental study, Adriaanse, Prinsen, de Witt Huberts, de Ridder, and Evers 23 led participants believe that they overate. Those who characterised themselves as emotional eaters falsely attributed their over-consumption to negative emotions, demonstrating a “confabulation”-effect. This indicates that people might have restricted self-knowledge and that recalled eating episodes suffer from systematic recall biases 24 . Moreover, Boelsma, Brink, Stafleu, and Hendriks 25 examined postprandial subjective wellness and objective parameters (e.g., ghrelin, insulin, glucose) after standardised breakfast intakes and did not find direct correlations. This suggests that the impact of different food categories on wellness might not be directly related to biological effects but rather due to conditioning as food is often paired with other positive experienced situations (e.g., social interactions) or to placebo effects 18 . Moreover, experimental and field studies indicate that not only negative, but also positive, emotions trigger eating 15 , 26 . One may speculate that selective attention might contribute to the “myth” of comfort food 19 in that people attend to the consumption effect of “comfort” food in negative situation but neglect the effect in positive ones.

The present data also show that eating behaviour in the real world is a complex behaviour with many different aspects. People make more than 200 food decisions a day 27 which poses a great challenge for the measurement of eating behaviour. Studies often assess specific food categories such as fruit and vegetable consumption using Food Frequency Questionnaires, which has clear advantages in terms of cost-effectiveness. However, focusing on selective aspects of eating and food choices might provide only a selective part of the picture 15 , 17 , 22 . It is important to note that focusing solely on the “unhealthy” food choices such as sweets would have led to the conclusion that they have a high “indulgent” value. To be able to draw conclusions about which foods make people happy, the relation of different food categories needs to be considered. The more comprehensive view, considering the whole dietary behaviour across eating occasions, reveals that “healthy” food choices actually contributed the biggest share to the total experienced eating happiness. Thus, for a more comprehensive understanding of how eating behaviours are regulated, more complete and sensitive measures of the behaviour are necessary. Developments in mobile technologies hold great promise for feasible dietary assessment based on image-assisted methods 28 .

As fruits and vegetables evoked high in-the-moment happiness experiences, one could speculate that these cumulate and have spill-over effects on subsequent general well-being, including life satisfaction across time. Combing in-the-moment measures with longitudinal perspectives might be a promising avenue for future studies for understanding the pathways from eating certain food types to subjective well-being. In the literature different pathways are discussed, including physiological and biochemical aspects of specific food elements or nutrients 7 .

The present EMA based data also revealed that eating happiness varied greatly within the 14 food categories and meal types. As within food category variance represented more than two third of the total observed variance, happiness varied according to nutritional characteristics and meal type; however, a myriad of factors present in the natural environment can affect each and every meal. Thus, widening the “nourishment” perspective by including how much, when, where, how long, and with whom people eat might tell us more about experienced eating happiness. Again, mobile, in-the-moment assessment opens the possibility of assessing the behavioural signature of eating in real life. Moreover, individual factors such as eating motives, habitual eating styles, convenience, and social norms are likely to contribute to eating happiness variance 5 , 29 .

A key strength of this study is that it was the first to examine experienced eating happiness in non-clinical participants using EMA technology and imagery to assess food intake. Despite this strength, there are some limitations to this study that affect the interpretation of the results. In the present study, eating happiness was examined on a food based level. This neglects differences on the individual level and might be examined in future multilevel studies. Furthermore, as a main aim of this study was to assess real life eating behaviour, the “natural” observation level is the meal, the psychological/ecological unit of eating 30 , rather than food categories or nutrients. Therefore, we cannot exclude that specific food categories may have had a comparably higher impact on the experienced happiness of the whole meal. Sample size and therefore Type I and Type II error rates are of concern. Although the total number of observations was higher than in previous studies (see for example, Boushey et al . 28 for a review), the number of participants was small but comparable to previous studies in this field 20 , 31 , 32 , 33 . Small sample sizes can increase error rates because the number of persons is more decisive than the number of nested observations 34 . Specially, nested data can seriously increase Type I error rates, which is rather unlikely to be the case in the present study. Concerning Type II error rates, Aarts et al . 35 illustrated for lower ICCs that adding extra observations per participant also increases power, particularly in the lower observation range. Considering the ICC and the number of observations per participant, one could argue that the power in the present study is likely to be sufficient to render the observed null-differences meaningful. Finally, the predominately white and well-educated sample does limit the degree to which the results can be generalised to the wider community; these results warrant replication with a more representative sample.

Despite these limitations, we think that our study has implications for both theory and practice. The cumulative evidence of psychological benefits from healthy food choices might offer new perspectives for health promotion and public-policy programs 8 . Making people aware of the “healthy = happy” association supported by empirical evidence provides a distinct and novel perspective to the prevailing “unhealthy = tasty” folk intuition and could foster eating choices that increase both in-the-moment happiness and future well-being. Furthermore, the present research lends support to the advocated paradigm shift from “food as health” to “food as well-being” which entails a supporting and encouraging rather constraining and limiting view on eating behaviour.

The study conformed with the Declaration of Helsinki. All study protocols were approved by University of Konstanz’s Institutional Review Board and were conducted in accordance with guidelines and regulations. Upon arrival, all participants signed a written informed consent.

Participants

Thirty-eight participants (28 females: average age = 24.47, SD  = 5.88, range = 18–48 years) from the University of Konstanz assessed their eating behaviour in close to real time and in their natural environment using an event-based ambulatory assessment method (EMA). No participant dropped out or had to be excluded. Thirty-three participants were students, with 52.6% studying psychology. As compensation, participants could choose between taking part in a lottery (4 × 25€) or receiving course credits (2 hours).

Participants were recruited through leaflets distributed at the university and postings on Facebook groups. Prior to participation, all participants gave written informed consent. Participants were invited to the laboratory for individual introductory sessions. During this first session, participants installed the application movisensXS (version 0.8.4203) on their own smartphones and downloaded the study survey (movisensXS Library v4065). In addition, they completed a short baseline questionnaire, including demographic variables like age, gender, education, and eating principles. Participants were instructed to log every eating occasion immediately before eating by using the smartphone to indicate the type of meal, take pictures of the food, and describe its main components using a free input field. Fluid intake was not assessed. Participants were asked to record their food intake on eight consecutive days. After finishing the study, participants were invited back to the laboratory for individual final interviews.

Immediately before eating participants were asked to indicate the type of meal with the following five options: breakfast, lunch, afternoon tea, dinner, snack. In Germany, “afternoon tea” is called “Kaffee & Kuchen” which directly translates as “coffee & cake”. It is similar to the idea of a traditional “afternoon tea” meal in UK. Specifically, in Germany, people have “Kaffee & Kuchen” in the afternoon (between 4–5 pm) and typically coffee (or tea) is served with some cake or cookies. Dinner in Germany is a main meal with mainly savoury food.

After each meal, participants were asked to rate their meal on three dimensions. They rated (1) how much they enjoyed the meal, (2) how pleased they were with their meal, and (3) how tasty their meal was. Ratings were given on a scale of one to 100. For reliability analysis, Cronbach’s Alpha was calculated to assess the internal consistency of the three items. Overall Cronbach’s alpha was calculated with α = 0.87. In addition, the average of the 38 Cronbach’s alpha scores calculated at the person level also yielded a satisfactory value with α = 0.83 ( SD  = 0.24). Thirty-two of 38 participants showed a Cronbach’s alpha value above 0.70 (range = 0.42–0.97). An overall score of experienced happiness of eating was computed using the average of the three questions concerning the meals’ enjoyment, pleasure, and tastiness.

Analytical procedure

The food pictures and descriptions of their main components provided by the participants were subsequently coded by independent and trained raters. Following a standardised manual, additional components displayed in the picture were added to the description by the raters. All consumed foods were categorised into 14 different food categories (see Table  1 ) derived from the food classification system designed by the German Nutrition Society (DGE) and based on the existing food categories of the German Nutrient Database (Max Rubner Institut). Liquid intake and preparation method were not assessed. Therefore, fats and additional recipe ingredients were not included in further analyses, because they do not represent main elements of food intake. Further, salty extras were added to the categorisation.

No participant dropped out or had to be excluded due to high missing rates. Missing values were below 5% for all variables. The compliance rate at the meal level cannot be directly assessed since the numbers of meals and snacks can vary between as well as within persons (between days). As a rough compliance estimate, the numbers of meals that are expected from a “normative” perspective during the eight observation days can be used as a comparison standard (8 x breakfast, 8 × lunch, 8 × dinner = 24 meals). On average, the participants reported M  = 6.3 breakfasts ( SD  = 2.3), M  = 5.3 lunches ( SD  = 1.8), and M  = 6.5 dinners ( SD  = 2.0). In comparison to the “normative” expected 24 meals, these numbers indicate a good compliance (approx. 75%) with a tendency to miss six meals during the study period (approx. 25%). However, the “normative” expected 24 meals for the study period might be too high since participants might also have skipped meals (e.g. breakfast). Also, the present compliance rates are comparable to other studies. For example, Elliston et al . 36 recorded 3.3 meal/snack reports per day in an Australian adult sample and Casperson et al . 37 recorded 2.2 meal reports per day in a sample of adolescents. In the present study, on average, M  = 3.4 ( SD  = 1.35) meals or snacks were reported per day. These data indicate overall a satisfactory compliance rate and did not indicate selective reporting of certain food items.

To graphically visualise data, Tableau (version 10.1) was used and for further statistical analyses, IBM SPSS Statistics (version 24 for Windows).

Data availability

The dataset generated and analysed during the current study is available from the corresponding authors on reasonable request.

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Acknowledgements

This research was supported by the Federal Ministry of Education and Research within the project SmartAct (Grant 01EL1420A, granted to B.R. & H.S.). The funding source had no involvement in the study’s design; the collection, analysis, and interpretation of data; the writing of the report; or the decision to submit this article for publication. We thank Gudrun Sproesser, Helge Giese, and Angela Whale for their valuable support.

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Deborah R. Wahl and Karoline Villinger contributed equally to this work.

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Department of Psychology, University of Konstanz, Konstanz, Germany

Deborah R. Wahl, Karoline Villinger, Laura M. König, Katrin Ziesemer, Harald T. Schupp & Britta Renner

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B.R. & H.S. developed the study concept. All authors participated in the generation of the study design. D.W., K.V., L.K. & K.Z. conducted the study, including participant recruitment and data collection, under the supervision of B.R. & H.S.; D.W. & K.V. conducted data analyses. D.W. & K.V. prepared the first manuscript draft, and B.R. & H.S. provided critical revisions. All authors approved the final version of the manuscript for submission.

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Wahl, D.R., Villinger, K., König, L.M. et al. Healthy food choices are happy food choices: Evidence from a real life sample using smartphone based assessments. Sci Rep 7 , 17069 (2017). https://doi.org/10.1038/s41598-017-17262-9

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7 Positive Lifestyle Factors That Promote Good Health

How to Live Long and Well

You can't change your genes, or even much of the environment around you, but there are lifestyle choices you can make to boost your health. Being informed and intentional about diet, activity, sleep, and smoking can reduce your health risks and potentially add years to your life.

This article looks at seven lifestyle factors that are backed by the best evidence when it comes to your health over the long run. It shows you why they matter and how to begin making positive changes.

Getting the Right Amount of Sleep

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Getting the right amount of sleep, and doing so regularly, is first on the list. It's often missed because people focus on diet and exercise, but the link between sleep and life expectancy is supported by research.

What surprises some people is that the relationship is a U-shaped curve. This means that too little and too much sleep can affect your health. In one study, sleeping for a long duration (defined as more than 10 hours a night) was associated with psychiatric diseases and higher body mass index BMI.

Another study found that sleeping nine or more hours a night had an increased incidence of stroke of 23% compared to those sleeping seven to eight hours a night. Those who slept over nine hours and napped for 90 minutes or more had an 85% increased stroke risk.

A 2021 study of 1.1 million people in Europe and the United States found that 25% of people slept less than what is recommended for their ages. More than half of all teens don't get enough sleep. Adults do better but have more insomnia and poor sleep quality.

A good night's sleep is important to recharge both the body and mind. It helps the body repair cells and get rid of wastes. It also is important in making memories, and sleep deprivation leads to forgetfulness.

Even if you intend to sleep well, health issues can disrupt your plan. Sleep apnea , for example, can greatly increase health risks.

Sleep apnea affects millions of people, but it's believed that many cases are being missed. Part of the reason is that symptoms like snoring, or waking up gasping for air, don't happen in every case. Sleep apnea can present with a number of surprising signs and symptoms , such as teeth grinding and depression.

If you have any concerns, talk to your healthcare provider about a sleep study . There are treatments, like CPAP , that lower risk and improve quality of life. Changes in your sleep patterns can signal other health issues too, so see your healthcare provider for a checkup if anything changes.

Eating Well-Balanced Meals

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A healthy diet gives you energy and lowers your risk for heart disease, diabetes, cancer, and other diseases. Some of these conditions have proven links to food and nutrition, as is the case with red meat and colorectal cancer.

Taking steps toward a lifelong change in diet will help more than jumping on the latest fad diet does. You may have heard author Michael Pollan's signature phrase: "Eat food. Not too much. Mostly plants." Of those plants, choose a rainbow of colors to make sure you get all the nutrients you need.

One place to begin is with the well-regarded Mediterranean diet. It's rich in many of the healthiest foods and naturally limits less healthy choices. The more you follow the Mediterranean diet, the lower your risk of a host of diseases.

A 2018 review looked at over 12 million people and the risk of over a dozen chronic diseases. The researchers found that people who chose a Mediterranean diet lowered their risk of heart disease, stroke, cancer, and other diseases.

The Mediterranean diet includes a lot of fruits and vegetables, whole grains, "good" oils, and plenty of herbs and spices. It doesn't recommend highly processed foods, refined grains, or added sugar.

Making Time for Physical Activity

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Thirty minutes a day of physical activity protects heart health. It also lowers the amount of bone loss as you age, and with it the risk of osteoporosis . It's so important that a 2021 study of colon cancer survivors found that living in a "green" community that is friendly for exercise reduced the risk of death.

A 2017 review in Lancet found that people participating in moderate physical activity every day had a lower risk of heart disease and overall mortality, no matter what their income level.

Best of all, physical activity is a low-cost way to boost your health and even save you money. Sometimes your health may limit your exercise options, but you can keep moving by washing your windows, mowing your lawn, sweeping a sidewalk, and other basic tasks.

Once you are past age 65, you may benefit by adding balance and flexibility exercises, but keep moving too. Whether you dance, garden, swim, or go biking, choose moderate-intensity exercise that you know you'll enjoy.

Keeping a Healthy Body Weight

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Obesity is associated with a shorter lifespan and a higher risk of many diseases. The good news is that just being somewhat overweight does not reduce your longevity. In fact, for those over age 65, it's better to be on the high side of normal than the low side.

A 2018 study looked at body mass index (BMI) and mortality over a period of 24 years. A BMI considered between 19 and 24 is considered "normal" or healthy. For those who were in the range classified as obesity, a BMI of 30 to 35 meant a 27% increase in mortality. A BMI of 35 to 40 was linked to a 93% increase.

Among those with a BMI in the overweight range (BMI 25 to 30), mortality was only higher among those who smoked. People with a BMI on the high side of normal (BMI 24, for example) had the lowest death risks.

BMI is a dated, flawed measure. It does not take into account factors such as body composition , ethnicity, sex, race, and age. Even though it is a biased measure , BMI is still widely used in the medical community because it’s an inexpensive and quick way to analyze a person’s potential health status and outcomes.

There isn't any real magic when it comes to keeping a healthy weight. Eating a nutritious diet and exercising daily   are the true secrets for most people. If you're struggling, talk with your healthcare provider. But keep in mind that fad diets don't work, and your greatest hope for success lies in making long-term changes.

Avoiding Smoking or Chewing Tobacco

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Smoking accounts for some 480,000 deaths per year in the United States alone. Added to this are another 16 million people who are alive but coping with a smoking-related illness. If you want the chance to live well for however long you live, don't smoke or chew tobacco.

The list of diseases and cancers linked to smoking is long. If you're finding it hard to quit, and you think illness comes only later in life, it may help to think of more short-term goals. Perhaps it's too expensive, or indoor smoking bans limit your social outings.

Or maybe the midlife concerns will help you! Smoking speeds up wrinkling of the skin. There's also a link between smoking and erectile dysfunction in men. Quitting, or avoiding tobacco in the first place, will save lives but protect its quality too.

Limiting or Avoiding Alcohol

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Despite the hype over red wine and longevity , alcohol should be used only in moderation, and for many people, not at all. Red wine has been found to offer some protective health effects, but there are other ways to get these benefits.

Red wine is rich in flavonoids, particularly the nutrient resveratrol . Resveratrol, however, is also found in red grapes themselves, in red grape juice, and even peanuts.

Moderate alcohol consumption (one drink per day for women, two for men) may lower heart disease risk. Yet a link between alcohol and breast cancer suggests that even this amount should be used with caution.

Women who have three drinks per week have a 15% higher risk of breast cancer and the risk goes up another 10% for every additional drink they have each day.

It is important to note that alcohol is classified as a Group 1 carcinogen by the International Agency for Research on Cancer. Group 1 is the highest-risk group, which also includes asbestos, radiation, and tobacco. Alcohol causes at least seven types of cancer. The more alcohol you drink, the higher your cancer risk.

Higher levels of alcohol can lead to health and other problems, including a greater risk for:

• High blood pressure
• Heart disease
• Some cancers

Moderate intake of alcohol may be part of a healthy lifestyle in special moments, as long as you have no personal or family problems with alcohol abuse. As long as everyone understands the risks, there are times you may drink a toast to your good health!

Managing Mental Health

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Mental health includes emotional, psychological, and social well-being. It affects how we think, feel, act, and relate to others.

Managing mental health includes factors such as managing stress and maintaining social connections. Research shows that people who report being happier live as much as four to 10 years longer than less happy people.

One way to increase happiness is to manage stress. Although we can't eliminate stress entirely, there are some ways to limit it:

• Take time to unwind , such as doing deep breathing exercises, yoga, meditation, taking a bath, or reading a book. Schedule regular times for these and other healthy activities.
• Take breaks from watching, reading, or listening to news and social media.
• Practice gratitude by reminding yourself daily of things you are grateful for. Be specific. Write them down at night, or replay them in your mind.
• Focus on the positive by identifying and challenging your negative and unhelpful thoughts.
• Find a hobby. Research shows activities like gardening, singing, playing a musical instrument, and other hobbies are linked to living longer, healthier lives Hobbies may reduce stress and provide mental stimulation.

Research also shows that staying socially connected positively impacts health and longevity. Getting together regularly with friends or family members can provide emotional support and pleasure. Other ways to foster connection may include:

• Connecting with community or faith-based groups
• Volunteering with others
• Joining a local group, such as a hiking club, knitting group, or other interest group

For a long, healthy life, the seven key lifestyle behaviors include getting enough sleep, eating a healthy diet, being physically active, maintaining a healthy body weight, not smoking, limiting alcohol, and managing mental health.

These factors may seem like a part of the common-sense advice, but there's a reason for that. They're all backed by data, and new medical research continues to point in the same healthy direction.

Frequently Asked Questions

To help strengthen your bones, try the following tips:

• Eat foods that are good sources of calcium and vitamin D.
• Get 30 minutes of exercise a day, especially weight-bearing and strength-building activities like walking, dancing, climbing stairs, and lifting weights.
• Avoid smoking.
• Prevent falls. Exercise may help you improve your balance. Also, remember to check for tripping hazards in your home.

Making healthy lifestyle choices can reduce your risk of high blood pressure, heart attack, and stroke. In a study of 55,000 people, those who made healthy lifestyle choices such as avoiding smoking, eating healthy, and exercising lowered their heart disease risk by about 50%.

The World Cancer Research Fund says at least 18% of cancers in the United States are related to preventable risk factors, including obesity, lack of exercise, poor nutrition, and drinking alcohol.

American College of Cardiology. Getting good sleep could add years to your life .

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Zhou L, Yu K, Yang L, et al.  Sleep duration, midday napping, and sleep quality and incident stroke: the Dongfeng-Tongji cohort .  Neurology . 2019;94(4):e345-e356. doi:10.1212/WNL.0000000000008739

Kocevska D, Lysen TS, Dotinga A, et al. Sleep characteristics across the lifespan in 1.1 million people from the Netherlands, United Kingdom and United States: a systematic review and meta-analysis .  Nat Hum Behav . 2021;5(1):113-122. doi: 10.1038/s41562-020-00965-x

National Institute of Neurological Disorders and Stroke. Sleep apnea .

Gurjao C, Zhong R, Haruki K, et al. Discovery and features of an alkylating signature in colorectal cancer .  Cancer Discov . candisc;2159-8290.CD-20-1656v2. doi: 10.1158/2159-8290.cd-20-1656

Dinu M, Pagliai G, Casini A, Sofi F. Mediterranean diet and multiple health outcomes: an umbrella review of meta-analyses of observational studies and randomized trials. Eur J Clin Nutr . 2018;72(1):30-43. doi:10.1038/ejcn.2017.58

Wiese D, Stroup AM, Maiti A, et al. Measuring neighborhood landscapes: associations between a neighborhood’s landscape characteristics and colon cancer survival .  IJERPH . 2021;18(9):4728. doi: 10.3390/ijerph18094728

Lear, S., Hu, W., Rangarajan, S. et al. The effect of physical activity on mortality and cardiovascular disease in 130 000 people from 17 high-income, middle-income, and low-income countries: the PURE study . Lancet. 2017. 390(10113):2643-2654. doi:10.1016/S0140-6736(17)31634-3

Xu H, Cupples LA, Stokes A, Liu CT. Association of obesity with mortality over 24 years of weight history: findings from the Framingham Heart Study. JAMA Netw Open . 2018;1(7):e184587. doi:10.1001/jamanetworkopen.2018.4587

Centers for Disease Control and Prevention. Tobacco-related mortality .

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Breastcancer.org. Drinking alcohol.

World Health Organization. No level of alcohol consumption is safe for our health .

Centers for Disease Control and Prevention. Alcohol and cancer .

Centers for Disease Control and Prevention. Alcohol use and your health .

National Institute of Mental Health. Caring for your mental health .

Diener E, Chan MY. Happy people live longer: Subjective well-being contributes to health and longevity . Social Science Research Network; 2013.

Evans GF, Soliman EZ. Happier countries, longer lives: an ecological study on the relationship between subjective sense of well-being and life expectancy . Glob Health Promot. 2019 Jun;26(2):36-40. doi: 10.1177/1757975917714035

Soga M, Gaston KJ, Yamaura Y. Gardening is beneficial for health: A meta-analysis . Prev Med Rep. 2016 Nov 14;5:92-99. doi: 10.1016/j.pmedr.2016.11.007

McCrary JM, Altenmüller E, Kretschmer C, et al. Association of music interventions with health-related quality of life: a systematic review and meta-analysis . JAMA Netw Open. 2022;5(3):e223236. doi: 10.1001/jamanetworkopen.2022.3236

Tomioka K, Kurumatani N, Hosoi H. Relationship of having hobbies and a purpose in life with mortality, activities of daily living, and instrumental activities of daily living among community-dwelling elderly adults . J Epidemiol . 2016 Jul 5;26(7):361-70. doi: 10.2188/jea.JE20150153

Holt-Lunstad J. Why social relationships are important for physical health: A systems approach to understanding and modifying risk and protection . Annu Rev Psychol . 2018 Jan 4;69:437-458. doi: 10.1146/annurev-psych-122216-011902

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Chaput J-P, Dutil C, Sampasa-Kanyinga H. Sleeping hours: what is the ideal number and how does age impact this?   Nat Sci Sleep . 2018;10:421-430.

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By Kirsti A. Dyer MD, MS, FT Kirsti A. Dyer, MD, MS, FT, is a board-certified expert in grief and bereavement, and an associate adjunct professor in hospice and palliative studies.

• Open access
• Published: 29 April 2021

Association between healthy lifestyle practices and life purpose among a highly health-literate cohort: a cross-sectional study

• Nobutaka Hirooka 1 ,
• Takeru Kusano 1 ,
• Shunsuke Kinoshita 1 ,
• Ryutaro Aoyagi 1 &
• Nakamoto Hidetomo 1  

BMC Public Health volume  21 , Article number:  820 ( 2021 ) Cite this article

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The national health promotion program in the twenty-first century Japan (HJ21) correlates life purpose with disease prevention, facilitating the adoption of healthy lifestyles. However, the influence of clustered healthy lifestyle practices on life purpose, within the context of this national health campaign remains uninvestigated. This study assessed the association between such practices and life purpose, in line with the HJ21.

We performed a nationwide cross-sectional survey on certified specialists in health management. Participants’ demographic information, lifestyle, and purpose in life were measured using a validated tool. The cohort was median-split into two groups based on their clustered health-related lifestyle score. The values for health-related lifestyle and purpose were compared between the two groups and the correlation between health-related lifestyle and purpose in life was measured.

Data from 4820 participants were analyzed. The higher-scoring health-related lifestyle group showed a significantly higher life purpose than the lower group (35.3 vs 31.4; t  = 23.6, p  < 0.001). There was a significant association between the scores of clustered healthy lifestyle practices and life purpose ( r  = 0.401, p  < 0.001). The higher-scoring health-related lifestyle group achieved a higher life purpose than the lower-scoring group. This association between healthy lifestyle practices and life purpose denotes a positive and linear relationship.

Conclusions

Our results suggest that individuals who have a better health-related lifestyle gain a higher sense of life purpose. In other words, a healthy lifestyle predicts a purpose in life. Our findings posit that examining the causal relationship between healthy lifestyle and purpose in life may be a more efficient approach toward health promotion.

Peer Review reports

Several studies have investigated the implications of life purpose, and literature has shown that a strong sense of purpose in life was positively associated with positive health outcomes [ 1 , 2 , 3 , 4 , 5 , 6 ]. Thus, having a sense of purpose in life is a vital component of human life. Due to a rapidly aging society in Japan, a national health promotion program in the twenty-first century—Health Japan twenty-first century (HJ21)—considers purpose in life as one of the major target goals of health promotion [ 7 ].

Purpose in life is defined as “a self-organizing life aim that stimulates goals” [ 1 ] and is known to promote healthy behaviors and give life meaning [ 8 , 9 ]. Ikigai is a Japanese word for what is considered an important factor for achieving better health and a fulfilling life [ 10 ]. Ikigai is defined as something to live for, exemplifying the joy and the goal of living [ 11 ]. Although Ikigai may not be fully comparable to purpose in life, it does contain the respective concept and plays a cardinal role in yielding positive health-related outcomes [ 12 ].

Notably, health outcomes associated with life purpose or Ikigai include physical [ 1 , 12 , 13 ] and mental health [ 3 , 13 ], quality of life [ 4 ], disease mortality [ 1 , 12 ], and longevity [ 12 ]. Possessing a strong sense of purpose in life has been associated with a lower risk of mortality and cardiovascular diseases [ 1 ] (relative risk: 0.83 and 0.83, respectively). The study concluded that purpose in life tends to yield health benefits. One of the mechanisms considered in the literature was the benefits associated with a healthy lifestyle. People who have adopted a higher purpose in life tend to follow healthier lifestyle practices, which may decrease the incidence of non-communicable chronic diseases, such as cardiovascular diseases or cancer.

Healthcare personnel are responsible for the health of their patients. Studies have shown that healthcare personnel are more likely to encourage healthy lifestyle behaviors among their patients if they engage in such behaviors themselves. Our study population comprises certified specialists in health management who routinely provide advice on health to individuals in their community. Investigating the relationship between lifestyle and purpose in life among healthcare personnel, our target population, is therefore of great scientific interest.

There is a hierarchy of causality among chronic diseases. Non-communicable diseases, such as diabetes, stroke, cancer, and coronary artery disease, have risk factors. In the case of risk factors, such as hypertension, smoking, dyslipidemia, hyperglycemia, studies typically signified proximal causes [ 14 , 15 ]. A healthy lifestyle is a central causality for these risk factors and thus basic lifestyle should be considered a fundamental and proximal risk factor for the aforementioned non-communicable diseases. Studies also highlight that healthy lifestyle practices prevent many similar chronic diseases [ 16 , 17 ], and that intervening to promote healthier lifestyle decreases mortality due to non-communicable diseases [ 18 , 19 ]. Hence, the notion that health benefits are brought through a healthy lifestyle may be supported if the lifestyle strongly correlates with purpose in life.

In this context, however, research exploring the association between purpose in life and healthy lifestyle practices remain scarce. Moreover, existing literature usually considers a single health behavior in relation to purpose in life. To determine the relationship between purpose in life and clustered health-related lifestyle—the fundamental and proximal cause of many health outcomes—the potential benefits of purpose in life towards disease prevention and health must be deciphered.

This study aimed to investigate the association between health-related lifestyles, in line with the HJ21, and purpose in life, measured with a validated tool to better understand the relational mechanisms.

Study design

The design was a cross-sectional study on a cohort of nationwide certified specialists in health management. We surveyed health-related lifestyles similar to those in the questionnaire used for the HJ21. Our questionnaire is based on the one of the oldest national health surveys around the world, the National Health and Nutrition Survey conducted by Japanese Government [ 20 ]. This survey is the oldest of all national health examination surveys currently conducted worldwide and serves as a comprehensive database for risk factors related to non-communicable diseases in Japan. The survey includes questions on demographic data and health-related habits, such as physical activity and exercise, nutrition and diet, smoking, stress, and alcohol intake. Purpose in life was measured with a validated tool in Japanese using the purposeful life scale (Ikigai-9) [ 21 ]. The ethics committee of the Saitama Medical University approved the study (ID: 896, 2018).

Participants

Study participants were certified specialists in health management who actively pursued professional growth provided by the Japanese Association of Preventive Medicine for Adult Disease [ 22 ]. This certification is sponsored by the Ministry of Education, Culture, Sports, Science and Technology, Japan. We excluded specialists who did not actively engage in continuing education or health promotion activities. These specialists are expected to engage the community and the society they live in to promote health and wellbeing. Specialists in health management are certified in multiple processes of study. Candidates study various aspects within the course, including health promotion, lifestyle-related diseases, mental health, nutrition, environment and health, physical activity and exercise, emergency medicine, life support, and health care system. To register, candidates must pass the final written examination. The Japanese Association of Preventive Medicine for Adult Disease encourages specialists to participate in numerous activities by facilitating health promotion workshops, speeches, and activities after registration. Among these individuals who met our inclusion criteria ( N  = 9149), 4820 agreed to participate in the survey.

Variables and measurements

Variables measured in this study were demographic characteristics; health-related habits, including physical activity and exercise, nutrition and diet, smoking, stress, and alcohol intake; and purpose in life. There were eleven health-related lifestyle questions, of which five were two-scaled (“Intention to maintain ideal weight,” “Exercise,” “Alcohol intake,” “Manage lifestyle to prevent disease,” and “Smoking”). For these items, a score of “1” was assigned for an unhealthy lifestyle and a score of “4” was assigned for a healthy lifestyle. The rest of the six health-related habits (“Reading nutritional information labels,” “Maintaining a balanced diet in daily life,” “Intention for exercise,” “Stress,” “Rest,” and “Sleep”) were to be answered on a four-point scale, from “4” (most favorable) to “1” (least favorable). Finally, we added the values of each answer to the questions on the health-related lifestyle of the participants as their clustered health-related lifestyle scores. To measure purpose in life, we used the Ikigai-9 scale, a validated tool to quantify purpose in life. The Ikigai-9 is a psychometric tool that measures across the dimensions of (1) optimistic and positive emotions toward life, (2) active and positive attitudes towards one’s life, and (3) acknowledgement of the meaning of one’s existence [ 23 ]. The Ikigai-9 scale consists of nine questions on various aspects of life purpose and each question must be answered on a five-point scale, from “1” (Strongly disagree) to “5” (Strongly agree). These variables and measurements were previously described elsewhere [ 24 ]. Considering the variables, age, weight, height, BMI, volume of alcohol intake, and purpose in life scores were numeric. Sex, healthy lifestyle, smoking, alcohol intake, and stress comprised either binary or ordinal data.

Descriptive statistics (i.e., mean, standard deviation, range) were used to describe participants’ characteristics. The cohort was divided into two groups (i.e., a higher and lower group, with a cut-off using the median score) based on the clustered health-related lifestyle scores. The correlations between age and lifestyle score and between age and purpose in life score were analyzed. The difference in the Ikigai-9 score between the two clustered health-related lifestyle score groups was investigated. Further, the effect size of the difference in Ikigai-9 score between the two groups was calculated with using Cohen’s d . The association between the clustered health-related lifestyle score and the Ikigai-9 score was also analyzed as a bivariate correlation and a correlation coefficient was calculated to see whether the health-related lifestyles accounted for life purpose. A multiple regression analysis was performed to determine the association between the clustered health-related lifestyle score and the purpose in life score, after controlling for age. All statistical tests were two-tailed and the software IBM SPSS Statistics (Version 26.0. Armonk, NY) was used for the analysis.

The demographic and health-related lifestyle characteristics of the study participants are shown in Table  1 . In total, 4820 certified specialists in health management were included in the analysis. There were 3190 women (66.2%) and 1630 men (33.8%). The mean ( SD ) age of all study participants was 55.4 (±12.2) years. The majority of the participants (85.0%) were non-obese and “intended to keep ideal weight” and “maintain a healthy lifestyle (82.6% and 89.2%, respectively) to prevent lifestyle-related disease,” such as obesity, metabolic syndrome, and cardiovascular disease. We also found that more than 80% of the study participants “read nutritional information labels” and more than 90% “maintained a balanced diet in daily life.” Regarding exercise and physical activity, more than 80% of the study participants “intended to exercise” and approximately 64% of them achieved the recommended levels. These findings reflected a low rate of obesity among the participants, which was 15.0% in the study. While most of the participants reported resting and sleeping adequately, the rate of taking on stress was high (74.4%). The descriptive analysis of the Ikigai-9 scores confirmed that it was normally distributed, based on the histogram and P-P plot.

Table  2 shows the demographics and healthy lifestyle practices for both the higher and lower clustered health-related lifestyle score groups. We found consistent favorable results in all measured health-related habits in the higher clustered health-related lifestyle score group. There was a significant difference in the scores of purpose in life between the higher group and the lower clustered health-related lifestyle score group ( t  = 23.6, p  < .0001). In the higher group, the average score of purpose in life was 35.3 (95% CI; [35.1–35.5]), while for the lower group, the average score for purpose in life was 31.4 (95% CI; [31.2–31.7]). The differences in the Ikigai-9 purpose in life scores of the two groups and its effect sizes (Cohen’s d) were 3.8 (95% CI; [3.5–4.2]) and 0.68, respectively. Moreover, there was a significant association between the clustered health-related lifestyle score and purpose in life score, r  = .401, p  < .001. The significance remained after controlling for age. Correlation between age and both lifestyle and purpose in life were significant (Pearson r  = 0.29 and 0.15, respectively; both p  < .05).

We found that the higher-scoring clustered health-related lifestyle group showed a statistically significant higher purpose in life than the lower-scoring clustered health-related lifestyle group. The study also highlighted a significant positive association between the clustered health-related lifestyle score and the Ikigai-9 score. To the best of our knowledge, this study was the first to show that a strong sense of purpose in life correlates with clustered health-related lifestyles in the context of a national health campaign. Several studies indicate a positive relationship between purpose in life and health-related lifestyles [ 1 , 25 , 26 , 27 ]. Furthermore, many publications reveal a correlation between a single healthy habit and purpose in life. Therefore, our findings—that affirm a positive relationship between purpose in life and clustered health-related lifestyle—are consistent with previously reported results and help broaden the evidence of this association.

Exploring the mechanistic link of purpose in life with a healthy lifestyle may help us understand this relationship. While studies highlight the positive relationship between purpose in life and health-related lifestyle, a few studies’ results are inconsistent with our findings. For example, an existing prospective study did not observe a positive association between purpose in life and healthy sleep patterns [ 28 ]. In other studies, the purpose of life was not associated with smoking [ 29 , 30 ]. Notably, the mechanistic link between health-related lifestyle and purpose in life has not been well examined. Hooker et al. proposed a hypothesized model linking purpose in life with health [ 31 ]. They summarized the relationship between life purpose and health outcomes by utilizing the concept of self-regulation. In the model, they proposed that life purpose influenced health through three self-regulatory processes and skills: stress-buffering, adaptive coping, and health behaviors. Health-related lifestyle, one of the self-regulatory processes, is the result of individuals setting goals, monitoring their progress, and using feedback to modify their lifestyle [ 31 ]. Thus, a purpose provides the foundation and motivation for engaging in a healthy lifestyle. Kim et al. also suggested that sense of purpose in life enhances the likelihood for engagement in restorative health-related lifestyle practices (e.g., physical activity, healthy sleep quality, use of preventive health care services) from cardiovascular disease to the indirect effect of behavior [ 32 ].

There is an alternative explanation for the mechanistic link between purpose in life and health-related lifestyle. A reverse causality model suggested that engaging in healthy lifestyle practices could predict a greater purpose in life [ 31 , 33 ]. Our results denoted that the group with a higher score in purpose in life performed healthier lifestyle practices and behaviors (Table 2 ), which can be supported by either of the hypothesized models. Age statistically significantly influenced both lifestyle and purpose in life in this study, while gender did not. However, age did not change overall relation between lifestyle and purpose in life. This infers that age may act as a moderator on the association. Further research is needed to clarify the mechanism and the directionality of the association, including any modifying factors. The mechanism to explain the causal relationship between life purpose and healthy lifestyle practices helped prepare for healthy aging by preventing diseases, increasing health longevity, and imbuing a health-oriented drive, which are the major goals of the HJ21.

Additionally, the difference in life purpose scores between the two groups (35.3 vs 31.4), shown in Table 2 , should be further explored, whilst we found a statistically significant difference and a correlation between healthy lifestyle practices and purpose in life. Rather than being a single concept, purpose in life has several elements and a more comprehensive construct. The majority of measurement tools concerned with meaning in life assess two distinct concepts: coherence and purpose [ 34 ]. Coherence is a sense of comprehensibility, or one’s life “making sense,” which is descriptive and value-neutral. Purpose means a sense of core goals, aims, and direction in one’s life, which is more evaluative and value-laden in concept. Ikigai is the Japanese concept meaning a sense of life worth living. The Ikigai-9 scale used in this study has three constructs for measuring the purpose in life; (1) optimistic and positive emotions toward life, (2) active and positive attitudes towards one’s life, and (3) acknowledgement of the meaning of one’s existence. The scale seems to measure more similarly to the purpose; however, the total score does not distinguish between the association of specific constructs and healthy lifestyle practices. Thus, further methodological sophistication regarding the evaluation of a specific concept encompassed within life purpose needs to be reached. This aspect broadens our understanding of purpose in life and its relation to health. This particular cohort of certified specialists shared many features of high health literacy through the process of professional development and certification, combined with life-long learning and activities related to their role as health management specialists. Further, health-related lifestyle practices mean that the certified specialists were far healthier than the national average. These characteristics represent an individual’s health literacy. Health literacy is considered to be an individuals’ capacity to obtain and understand basic health information and services and to make appropriate health-related decisions based on this information [ 35 ]. Therefore, health literacy is directly associated with disease mortality [ 36 ], overall health status [ 37 ], disease prevention [ 38 , 39 ], and health behaviors. These can be attributed to purpose in life [ 2 ].

Thus, health literacy and health-related lifestyle appear to have a similar relationship with disease prevention and better health outcomes. The mediating effect of health literacy on the relationship between healthy lifestyle and life purpose should be investigated. Such inquiries in a prospective cohort study can better explain the mechanism of the causal link between purpose in life, health-related lifestyle, and health literacy.

Limitations

There are several limitations to this study. First, all the measurements were self-reported, which can be a source of bias. Second, while the survey questionnaires are widely used in national health promotion, they have not been fully validated. Third, the real-life meaning of purpose in life has not been determined yet. The Ikigai-9 score, one of the tools used to measure the life purpose score, was validated in a small and limited population; however, the instrument may not capture it holistically. This limitation was implicated by the previously reported systematic review. Furthermore, Zheng et al. found variability in the strength of correlation among the questionnaire for quality of life, part of which included questions regarding a purposeful life [ 40 ]. Lastly, the correlational analysis did not include an adjustment for confounding factors other than age. Hence, little is known about factors influencing the relationship between a healthy lifestyle and purpose in life. We need to establish other potential influencing factors and determine which variables have mediating, moderating, and confounding effects on purpose in life to understand the causal relationship between healthy lifestyle practices and life purpose [ 41 ]. This exploration proposes a promising model for future intervention programs.

Despite these limitations, this study has several strengths. First, the study sample size, N  = 4820, was large and distributed throughout Japan. This aspect of the study increases generalizability. According to the previous review, numerous studies on purpose in life focused on older adults [ 42 ], whereas only a few were concerned with younger or middle-aged adults. In the present study, the majority of the participants were younger and middle-aged adults. Second, previous studies used relatively simple questions or did not employ validated tools to measure purpose in life. However, we used a validated tool, Ikigai-9, in this study. This aspect allows the study results to increase the reliability and validity of the measurement of purpose in life and also hold applicability in other studies. Lastly, study participants were certified specialists in health management who have shown high health literacy. This inclusion criterion provides guidance on improving healthy lifestyle practices through health literacy as an approach to health promotion.

In conclusion, a healthy lifestyle was found to be positively associated with purpose in life among a cohort of highly health-literate professionals. Healthcare personnel who receive specific training for health management may play important roles in promoting a population’s health and wellbeing. However, the mechanism to explain the relationship between purpose in life and health-related lifestyle remains unknown. Therefore, causal relations between improving healthier lifestyles and increasing purpose in life should be tested.

Availability of data and materials

The datasets used in the current study are available from the corresponding author upon reasonable request.

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Nobutaka Hirooka, Takeru Kusano, Shunsuke Kinoshita, Ryutaro Aoyagi & Nakamoto Hidetomo

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All authors contributed to the study conception and design. Material preparation and data analysis were performed by Nobutaka Hirooka, Takeru Kusano, and Shunsuke Kinoshita. Nobutaka Hirooka, Shunsuke Kinoshita, and Ryutaro Aoyagi collected the data. Nobutaka Hirooka, Takeru Kusano, and Hidetomo Nakamoto interpreted the analysis. The first draft of the manuscript was written by Nobutaka Hirooka and all authors commented on drafted versions of the manuscript. All authors read and approved the final version of the manuscript.

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Hirooka, N., Kusano, T., Kinoshita, S. et al. Association between healthy lifestyle practices and life purpose among a highly health-literate cohort: a cross-sectional study. BMC Public Health 21 , 820 (2021). https://doi.org/10.1186/s12889-021-10905-7

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Scientists Calculated How Much Longer You Can Live With a Healthy Lifestyle

S tudy after study reminds us that as challenging as it can be, sticking with healthy habits— eating right , exercising regularly, not smoking , maintaining a healthy weight , and controlling how much alcohol you drink—can help us to live longer . But tacking on extra years isn’t so appealing if some or most of them are riddled with heart disease, diabetes or cancer.

In a 2018 study , an international group of researchers led by scientists at Harvard T.H. Chan School of Public Health found that adopting five healthy habits could extend life expectancy by 14 years for women and by 12 years for men:

• eating a diet high in plants and low in fats
• exercising at a moderate to vigorous level for several hours a week
• maintaining a healthy body weight
• not smoking
• consuming no more than one alcoholic drink a day for women and two for men

To follow up on that data, the researchers wanted to know how many of those added years were healthy ones, free of three common chronic diseases: heart disease, type 2 diabetes and cancer. And in a study published Jan. 8 in BMJ , they report that a healthy lifestyle can indeed contribute to more—and more disease-free—years of life. The results suggest that women can extend their disease-free life expectancy after age 50 by about 10 years, and men can add about eight years more, than people who don’t have these habits.

“It’s important to look at disease-free life expectancy because that has important implications in terms of improving quality of life and reducing overall health care costs,” says Dr. Frank Hu, chair of the department of nutrition at Harvard T.H. Chan School of Public Health and senior author of the paper. “Extending lifespan is not sufficient, we want to extend health span, so the longer life expectancy is healthy and free of major chronic diseases and disabilities associated with those diseases.”

To figure out those patterns, the researchers analyzed data collected from more than 111,000 U.S. women and men who were between the ages of 30 and 75 when they enrolled in the Nurses Health Study or the Health Professionals Follow-Up Study beginning in 1980 and 1986, respectively. The participants answered questionnaires about their lifestyle habits and their health every two years through to 2014. Based on their answers, each participant was given a “lifestyle” score from 0-5, with higher scores representing better adherence to healthy guidelines. The researchers then attempted to correlate these scores to how long the participants lived without heart disease, cancer or diabetes.

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Women who reported satisfying four or five of the healthy habits lived on average 34 more years without those diseases after age 50 compared to 24 years for women who said they did not follow any of the healthy habits. Men who reported fulfilling four or five of the lifestyle habits lived on average 31 more years free of disease after age 50 while those who adopted none of them lived on average 23 more years after age 50.

Hu says that none of the five factors stood out as more important than the others; the benefits in saving people from disease and in extending life were similar across all five. Further, the evidence suggests that the contributions of each factor are additive—the number of years of disease-free life gained increased with each additional healthy habit people followed. “People shouldn’t be discouraged from adopting them if they find one or two factors difficult to follow,” says Hu.

And because all of the participants in the study were over age 30, the findings also suggest that “it’s never too late to change,” Hu says. “It’s always better to adopt healthy lifestyle habits as early as possible, but even adopting them relatively late in life is still going to have substantial health benefits later on.”

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Physical Activity Is Good for the Mind and the Body

Health and Well-Being Matter is the monthly blog of the Director of the Office of Disease Prevention and Health Promotion.

Everyone has their own way to “recharge” their sense of well-being — something that makes them feel good physically, emotionally, and spiritually even if they aren’t consciously aware of it. Personally, I know that few things can improve my day as quickly as a walk around the block or even just getting up from my desk and doing some push-ups. A hike through the woods is ideal when I can make it happen. But that’s me. It’s not simply that I enjoy these activities but also that they literally make me feel better and clear my mind.

Mental health and physical health are closely connected. No kidding — what’s good for the body is often good for the mind. Knowing what you can do physically that has this effect for you will change your day and your life.

Physical activity has many well-established mental health benefits. These are published in the Physical Activity Guidelines for Americans and include improved brain health and cognitive function (the ability to think, if you will), a reduced risk of anxiety and depression, and improved sleep and overall quality of life. Although not a cure-all, increasing physical activity directly contributes to improved mental health and better overall health and well-being.

Learning how to routinely manage stress and getting screened for depression are simply good prevention practices. Awareness is especially critical at this time of year when disruptions to healthy habits and choices can be more likely and more jarring. Shorter days and colder temperatures have a way of interrupting routines — as do the holidays, with both their joys and their stresses. When the plentiful sunshine and clear skies of temperate months give way to unpredictable weather, less daylight, and festive gatherings, it may happen unconsciously or seem natural to be distracted from being as physically active. However, that tendency is precisely why it’s so important that we are ever more mindful of our physical and emotional health — and how we can maintain both — during this time of year.

Roughly half of all people in the United States will be diagnosed with a mental health disorder at some point in their lifetime, with anxiety and anxiety disorders being the most common. Major depression, another of the most common mental health disorders, is also a leading cause of disability for middle-aged adults. Compounding all of this, mental health disorders like depression and anxiety can affect people’s ability to take part in health-promoting behaviors, including physical activity. In addition, physical health problems can contribute to mental health problems and make it harder for people to get treatment for mental health disorders.

The COVID-19 pandemic has brought the need to take care of our physical and emotional health to light even more so these past 2 years. Recently, the U.S. Surgeon General highlighted how the pandemic has exacerbated the mental health crisis in youth .

The good news is that even small amounts of physical activity can immediately reduce symptoms of anxiety in adults and older adults. Depression has also shown to be responsive to physical activity. Research suggests that increased physical activity, of any kind, can improve depression symptoms experienced by people across the lifespan. Engaging in regular physical activity has also been shown to reduce the risk of developing depression in children and adults.

Though the seasons and our life circumstances may change, our basic needs do not. Just as we shift from shorts to coats or fresh summer fruits and vegetables to heartier fall food choices, so too must we shift our seasonal approach to how we stay physically active. Some of that is simply adapting to conditions: bundling up for a walk, wearing the appropriate shoes, or playing in the snow with the kids instead of playing soccer in the grass.

Sometimes there’s a bit more creativity involved. Often this means finding ways to simplify activity or make it more accessible. For example, it may not be possible to get to the gym or even take a walk due to weather or any number of reasons. In those instances, other options include adding new types of movement — such as impromptu dance parties at home — or doing a few household chores (yes, it all counts as physical activity).

During the COVID-19 pandemic, I built a makeshift gym in my garage as an alternative to driving back and forth to the gym several miles from home. That has not only saved me time and money but also afforded me the opportunity to get 15 to 45 minutes of muscle-strengthening physical activity in at odd times of the day.

For more ideas on how to get active — on any day — or for help finding the motivation to get started, check out this Move Your Way® video .

The point to remember is that no matter the approach, the Physical Activity Guidelines recommend that adults get at least 150 minutes of moderate-intensity aerobic activity (anything that gets your heart beating faster) each week and at least 2 days per week of muscle-strengthening activity (anything that makes your muscles work harder than usual). Youth need 60 minutes or more of physical activity each day. Preschool-aged children ages 3 to 5 years need to be active throughout the day — with adult caregivers encouraging active play — to enhance growth and development. Striving toward these goals and then continuing to get physical activity, in some shape or form, contributes to better health outcomes both immediately and over the long term.

For youth, sports offer additional avenues to more physical activity and improved mental health. Youth who participate in sports may enjoy psychosocial health benefits beyond the benefits they gain from other forms of leisure-time physical activity. Psychological health benefits include higher levels of perceived competence, confidence, and self-esteem — not to mention the benefits of team building, leadership, and resilience, which are important skills to apply on the field and throughout life. Research has also shown that youth sports participants have a reduced risk of suicide and suicidal thoughts and tendencies. Additionally, team sports participation during adolescence may lead to better mental health outcomes in adulthood (e.g., less anxiety and depression) for people exposed to adverse childhood experiences. In addition to the physical and mental health benefits, sports can be just plain fun.

Physical activity’s implications for significant positive effects on mental health and social well-being are enormous, impacting every facet of life. In fact, because of this national imperative, the presidential executive order that re-established the President’s Council on Sports, Fitness & Nutrition explicitly seeks to “expand national awareness of the importance of mental health as it pertains to physical fitness and nutrition.” While physical activity is not a substitute for mental health treatment when needed and it’s not the answer to certain mental health challenges, it does play a significant role in our emotional and cognitive well-being.

No matter how we choose to be active during the holiday season — or any season — every effort to move counts toward achieving recommended physical activity goals and will have positive impacts on both the mind and the body. Along with preventing diabetes, high blood pressure, obesity, and the additional risks associated with these comorbidities, physical activity’s positive effect on mental health is yet another important reason to be active and Move Your Way .

As for me… I think it’s time for a walk. Happy and healthy holidays, everyone!

Yours in health, Paul

Paul Reed, MD Rear Admiral, U.S. Public Health Service Deputy Assistant Secretary for Health Director, Office of Disease Prevention and Health Promotion

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