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BMJ Mental Health logoLink to BMJ Mental Health
. 2024 Apr 8;27(1):e300915. doi: 10.1136/bmjment-2023-300915

Healthy lifestyle and the risk of depression recurrence requiring hospitalisation and mortality among adults with pre-existing depression: a prospective cohort study

Zhi Cao 1,2, Jiahao Min 1, Yu-Tao Xiang 3, Xiaohe Wang 1, Chenjie Xu 1,
PMCID: PMC11015220  PMID: 38589227

Abstract

Background

Although lifestyle-based treatment approaches are recommended as important aspects of depression care, the quantitative influence of aggregated healthy lifestyles on depression recurrence and mortality remains unknown.

Objective

To investigate the association between healthy lifestyle and the risks of first-time hospitalisation for recurrent depression and mortality.

Methods

26 164 adults with depression (mean (SD) age, 56.0 (7.9) years) were included from UK Biobank between 2006 and 2010 and followed up until 2022. Depression was defined as a physician’s diagnosis in hospital admissions or the use of prescribed antidepressant medication. A weighted healthy lifestyle score (HLS) was calculated based on smoking, alcohol consumption, diet, sleep pattern, physical activity, social health, employment status and greenspace interaction.

Findings

Over a 13.3-year follow-up, 9740 cases of first-time hospitalisation due to depression recurrence and 1527 deaths were documented. Compared with the lowest HLS tertile, the highest tertile was associated with a 27% lower risk (HR=0.73, 95% CI 0.69 to 0.77) of first-time hospitalisation for depression recurrence and a 22% (HR=0.78, 95% CI 0.68 to 0.91) lower risk of mortality among adults with depression. Lower risks of first-time hospitalisation for depression recurrence were observed among those who smoked less, drank more alcohol, followed healthier diets and sleep patterns, spent more time employed in current job or had greater exposure to greenspace.

Conclusion and implications

Greater adherence to healthy lifestyle was associated with a lower risk of hospitalisation and mortality among adults with pre-existing depression. Incorporating behaviour modification as an essential part of clinical practice for depressed patients could complement medication-based therapies.

Keywords: Depression, PSYCHIATRY, Depression & mood disorders


WHAT IS ALREADY KNOWN ON THIS TOPIC

  • Current guidelines recommended lifestyle-based treatment approaches as crucial components of depression care. However, the quantitative impact of aggregated healthy lifestyles remains unknown.

WHAT THIS STUDY ADDS

  • Adherence to a healthy lifestyle was associated with lower risk of hospitalisation and mortality in adults with pre-existing depression.

  • Participants who smoked less, drank more alcohol, followed healthier diets and sleep patterns, spent more time employed or had greater exposure to greenspace could experience greater benefits.

HOW THIS STUDY MIGHT AFFECT RESEARCH, PRACTICE OR POLICY

  • Behaviour modification could be implemented as an essential part of clinical practice for depressed patients, complementing medication-based therapies.

Background

The Global Burden of Disease Study in 2019 has revealed that mental disorders have consistently ranked among the top 10 leading causes of burden worldwide since 1990, with depressive disorders accounting for 37.3% of mental disorder disability-adjusted life years in 2019, affecting approximately 4.7% of the global population.1 Recurrent depression in adults with pre-existing depression presents a significant challenge throughout their lifespan, with estimates ranging as high as 75–90%.2 Recurrent depression has a profound impact on individuals’ quality of life, relationships and daily functioning. Therefore, developing prognostic interventions for adults with pre-existing depression is necessary to prevent further episodes.

Psychotherapies and pharmacotherapies are often the first-line treatments for depression. However, the existing study has demonstrated a ceiling effect on the effectiveness of psychotherapies and pharmacotherapies.3 The use of antidepressant medication often leads to iatrogenic comorbidity due to accompanying side effects such as gastrointestinal symptoms, genitourinary symptoms, sexual dysfunction and central nervous system disturbances.4 Meanwhile, the treatment coverage for depression remains low across various income levels and regions worldwide, primarily due to financial and resourcing-related barriers.5 Consequently, directing the focus towards modifiable risk factors in adults with pre-existing depression has become a priority for both public health and clinical practice.

There have been numerous studies investigating the efficacy of lifestyle-based approaches for managing depression, which typically involve applying environmental, behavioural and motivational principles to self-care and self-management of lifestyle-related health issues. Several reviews have demonstrated that exercise can serve as an accessible and cost-effective alternative or adjunctive intervention for reducing depressive symptoms.6 7 Additionally, other studies have suggested that maintaining a balanced diet, obtaining sufficient sleep and using stress management techniques such as mindfulness mediation may also be advantageous for reducing the likelihood of depression recurrence.8 9 However, the relatively small sample size and short follow-up precluded extensive investigations of how depression prognosis would be modified by lifestyle factors. Moreover, inconsistencies in defining and measuring healthy lifestyle factors across studies hinder the ability to draw definitive conclusions. Meanwhile, the focus of the most studies has predominantly been on individual lifestyle factors, neglecting to examine their aggregated effects. This may overlook the potential correlation and interaction between various lifestyle factors. It is therefore necessary to consider aggregated effect and elucidate overall patterns of healthy lifestyle.

The aim of this study was to examine the associations between aggregated and individual healthy lifestyle factors with the risk of first-time hospitalisation for the recurrence of depression and mortality among adults with pre-existing depression in the UK Biobank (UKB). Furthermore, we explored the potential effect modification by antidepressant medication use and other potential risk factors (online supplemental file 1).

Supplementary data

bmjment-2023-300915supp001.pdf (2.6MB, pdf)

Methods

Study population and participants

This prospective cohort study used data from the UKB, a large-scale population-based study in the UK that has comprehensively collected health and lifestyle information on over 500 000 individuals aged between 37 and 73 years. Participants attended one of the 22 assessment centres located throughout England, Scotland and Wales to complete touchscreen questionnaires and physical examinations, and provide biological samples for collection between 2006 and 2010.10

Participants with pre-existing depression were identified through the integration of multiple data sources, including self-reported depression, prescribed antidepressant medication usage (codes of antidepressants used in the UKB can be seen in online supplemental table 1) and codes F32–F33 in the electronic health records (England and Wales: Health Episode Statistics; Scotland: Scottish Morbidity Records) based on the 10th Revision of the International Classification of Diseases (ICD-10). The prevalence and characteristics of probable depression within UKB have been previously described.11 Briefly, 47 043 participants with pre-existing depression were included; we then excluded the individuals with missing lifestyle factors and covariates, remaining 26 164 participants in the final analysis (online supplemental figure 1). All participants provided informed consent for linkage to national electronic health-related datasets. This study follows the Strengthening the Reporting of Observational Studies in Epidemiology reporting guideline for cohort studies.

Supplementary data

bmjment-2023-300915supp002.pdf (152.6KB, pdf)

Lifestyle assessment and scoring

Healthy lifestyle factors adopted in this study conformed with the clinical guidelines developed by the World Federation of Societies for Biological Psychiatry, and included eight domains of behaviours for depression healthcare8: (1) pack years of smoking, (2) alcohol consumption, (3) diet score, (4) sleep score, (5) physical activity, (6) social health score, (7) time spent on work and (8) greenspace interaction. These lifestyle factors were collected through a touchscreen questionnaire at baseline (2006–2010), subsequent to the diagnosis of depression in participants.

The aggregated healthy lifestyle score (HLS) was calculated through the following formula, whereby a higher score indicates a healthier lifestyle: (β1×pack years of smoking+β2×alcohol consumption+β3×diet score+β4×sleep score+β5×physical activity+β6×social health score+β7×time spent on work+β8×greenspace interaction)×(8/sum of the β coefficients). Cox regression models were used to derive β coefficient for each lifestyle factor, with the exact values of β1–8 detailed in online supplemental table 2. In subsequent multivariable analyses, the HLS was divided into tertiles, with the highest tertile representing the healthiest lifestyle, and the lowest tertile representing the least healthy lifestyle. The individual lifestyle factors were used as continuous variables in the analyses. Detailed methods for assessing individual lifestyles and calculating are provided in the online supplemental eMethods.

Clinical outcome ascertainment

The primary analytical outcomes of interest were the hazards of first-time hospitalisation due to recurrence of depression and all-cause mortality. Hospital-treated depression recurrence was assessed via linkage data to the UK National Health Service (NHS) Hospital Episode Statistics (HES) database for hospital admissions according to the ICD-10 codes (F32–F33), and mortality was determined through the NHS Central Registry, from March 1995 until May 2021. Follow-up of this study was censored at the date of incident recurrence recorded in HES, death or the end of follow-up (19 December 2022), whichever occurred first.

Covariates

Covariates were selected based on a prior-defined directed acyclic graph (online supplemental figure 2). Our study finally included age, gender (female and male), ethnicity (white and non-white), education attainment (college or university degree, professional qualifications and other), Townsend Deprivation Index (TDI), body mass index (BMI, kg/m2, weight in kilograms divided by height in meters squared), C reactive protein (CRP, mg/L), overall health rating (healthy/unhealthy), self-reported long-standing illness, disability or infirmity (yes and no), self-reported other serious medical conditions or disabilities diagnosed by doctors (yes/no) and use of antidepressant medication (yes/no). TDI is a composite index of deprivation that takes into account unemployment, lack of car and cottage ownership and household overcrowding. Higher values indicate lower socioeconomic status.12 Covariates with a missing rate of less than 1% (eg, TDI and ethnicity) were excluded, covariates with missing rate greater than 1% (eg, education attainment) were either coded as an additional category for categorical variables or replaced by mean values for continuous variables (missingness of the covariates can be seen in online supplemental table 3). All covariates were evaluated during the baseline phase of the UKB study, spanning 2006–2010, subsequent to the diagnosis of depression in participants.

Statistical analysis

In descriptive analyses, continuous variables were expressed as means and SDs, categorical variables were represented by frequency and percentage. Cox proportional hazards models were performed to evaluate the associations of HLS with first-time hospitalisation due to recurrence of depression and mortality, with the calculation of HRs and 95% CIs. Linear trends were examined by entering the median value of each tertile of HLS as a continuous variable into the models. Three multivariable-adjusted models were constructed to account for potential confounding. Model 1 was adjusted for age (timescale) and sex; model 2 was adjusted as in model 1 and for TDI, ethnicity, education attainment and BMI; model 3 was adjusted as in model 2 and for health rating, long-standing illness, disability or infirmity, other serious medical conditions diagnosed by doctor, CRP and use of antidepressants. The proportional hazard assumption was assessed for all Cox models using Schoenfeld residuals, and no evidence of violation was detected. Dose-response associations between HLS, individual lifestyle factors and the risk of first-time hospitalisation for the recurrence of depression and mortality were evaluated using restricted cubic splines (RCS) fitted in the Cox models.

Stratified analyses were conducted according to use of antidepressant medication (yes/no), age (<60 and ≥60 years), sex (female and male), ethnicity (white and non-white), education attainment (college or university degree, professional qualifications and others), TDI (<median and ≥median), BMI (<25 and ≥25 kg/m2), CRP (≤10 and >10 mg/L), self-reported long-standing illness, disability or infirmity (yes/no), self-reported other serious medical conditions or disabilities diagnosed by doctors (yes/no). Interactions were tested by a likelihood ratio test comparing models with and without product terms between HLS and stratified factors.

Sensitivity analyses were also conducted to assess the robustness of our results. First, we excluded participants with poor self-rated health status (categorised as healthy and unhealthy according to questionnaire), considering that they were less likely to adopt healthy lifestyles. Second, we excluded participants with limited follow-up (≤2 years) to avoid the potential risk of reverse causation. Third, complete case analyses were used to assess the associations between HLS with first-time hospitalisation due to recurrence of depression and mortality.

All UKB analyses were conducted using Stata V.16.0 (StataCorp, College Station, Texas) and R software (V.4.1.3). The statistical significance was set as p<0.05 (two-sided test).

Results

Population characteristics

Table 1 shows the baseline characteristics of the study participants according to tertiles of HLS. Among 26 164 participants with a mean age of 56.0 years (SD, 7.9 years), 17 259 (65.96%) were female. The majority self-identified as white accounting for 24 088 participants (92.07%), and 7584 participants had completed at least some college education (28.99%). Compared with those within the lowest tertile of HLS, participants who were categorised into the highest tertile were generally younger, had lower TDI values, higher levels of education attainment, lower BMI and CRP levels, self-reported healthier status, less likely to have a prevalence of long-standing illness, disability or infirmity, serious medical conditions or use of antidepressant medications.

Table 1.

Baseline characteristics of adults with pre-existing depression according to aggregated healthy lifestyle scores (HLS)

Baseline characteristics Total HLS
Lowest tertile Medium tertile Highest tertile
Participants (n) 26 164 8722 8721 8721
Age (years), mean (SD) 55.99 (0.05) 56.48 (7.92) 56.33 (8.06) 55.16 (7.55)
Female 17 259 (65.96) 5108 (58.56) 6016 (68.98) 6135 (70.35)
White ethnicity 24 088 (92.07) 7808 (89.52) 8045 (92.25) 8235 (94.43)
TDI, mean (SD) −0.84 (0.02) 0.46 (3.54) −1.03 (3.13) −1.94 (2.68)
Education attainment
 College or university degree 7584 (28.99) 1824 (20.91) 2562 (29.38) 3198 (36.67)
 Professional qualifications 3019 (11.54) 1077 (12.35) 998 (11.44) 944 (10.82)
 Other 10 525 (40.23) 3372 (38.66) 3 533 (40.51) 3620 (41.51)
BMI (kg/m²), mean (SD) 28.38 (0.03) 29.43 (5.98) 28.33 (5.36) 27.39 (5.00)
CRP (mg/L), mean (SD) 3.31 (0.03) 4.15 (5.66) 3.17 (4.96) 2.62 (4.22)
Overall health rating
 Healthy 13 289 (50.79) 2715 (31.13) 4726 (54.19) 5848 (67.06)
 Unhealthy 12 875 (49.21) 6007 (68.87) 3995 (45.81) 2873 (32.94)
Long-standing illness, disability or infirmity
 No 10 552 (40.33) 2171 (24.89) 3679 (42.19) 4702 (53.92)
 Yes 14 950 (57.14) 6313 (72.38) 4828 (55.36) 3809 (43.68)
Serious medical conditions or disabilities
 No 15 962 (61.01) 4405 (50.50) 5462 (62.63) 6095 (69.89)
 Yes 9401 (35.93) 3936 (45.13) 3004 (34.45) 2461 (28.22)
Use of antidepressant medication
 No 5969 (22.81) 1719 (19.71) 2014 (23.09) 2236 (25.64)
 Yes 20 195 (77.19) 7003 (80.29) 6707 (76.91) 6485 (74.36)

Values are numbers (percentages) unless stated otherwise.

BMI, body mass index; CRP, C reactive protein; HLS, healthy lifestyle score; TDI, Townsend Deprivation Index.

Aggregated and individual healthy lifestyle and first-time hospitalisation due to recurrence of depression

Over a mean follow-up period of 13.3 years, 9740 cases of first-time hospitalisation due to recurrence of depression were documented. The risk of depression recurrence decreased with increasing HLS (HR=0.91, 95% CI 0.90 to 0.92). Compared with the lowest tertile of HLS, the adjusted HRs for depression recurrence were 0.86 (95% CI 0.82 to 0.91) and 0.73 (95% CI 0.69 to 0.77) across the medium and highest tertiles of HLS, respectively (P for trend <0.001, table 2). Regardless of the level of HLS, there was a reverse linear association between HLS and depression recurrence (P for non-linearity=0.329, figure 1).

Table 2.

Multivariable associations of aggregated healthy lifestyle scores (HLS) with first-time hospitalisation for the recurrence of depression and mortality among adults with pre-existing depression

Cases Incidence per 1000 person-years Model 1
HR (95% CI)
Model 2
HR (95% CI)
Model 3
HR (95% CI)
Depression recurrence
HLS
 Lowest tertile 3972 34.7 (33.6–35.8) 1 (Reference) 1 (Reference) 1 (Reference)
 Medium tertile 3171 27.2 (26.3–28.2) 0.76 (0.73 to 0.80) 0.81 (0.77 to 0.85) 0.86 (0.82 to 0.91)
 Highest tertile 2597 22.1 (21.2–22.9) 0.59 (0.56 to 0.62) 0.65 (0.61 to 0.68) 0.73 (0.69 to 0.77)
 P for trend <0.001 <0.001 <0.001
 Per 1 score of increment 9740 27.9 (27.4–28.5) 0.86 (0.85 to 0.87) 0.88 (0.87 to 0.89) 0.91 (0.90 to 0.92)
Mortality
HLS
 Lowest tertile 740 6.6 (6.2–7.1) 1 (Reference) 1 (Reference) 1 (Reference)
 Medium tertile 439 3.8 (3.5–4.2) 0.60 (0.53 to 0.68) 0.66 (0.58 to 0.74) 0.76 (0.67 to 0.86)
 Highest tertile 348 3.0 (2.7–3.3) 0.53 (0.47 to 0.61) 0.63 (0.55 to 0.72) 0.78 (0.68 to 0.91)
 P for trend <0.001 <0.001 <0.001
 Per 1 score increment 1527 4.4 (4.2–4.7) 0.86 (0.83 to 0.89) 0.86 (0.83 to 0.89) 0.92 (0.87 to 0.95)

Model 1: adjusted for age (timescale) and sex. Model 2: further adjusted for ethnicity, education attainment, Townsend Deprivation Index and body mass index. Model 3: fully adjusted for self-assessed overall health rating, self-reported long-standing illness, self-reported serious medication conditions or disabilities, C reactive protein levels and use of antidepressant medication.

CI, confidence interval; HLS, healthy lifestyle score; HR, hazard ratio.

Figure 1.

Figure 1

Dose-response associations between HLS and individual lifestyle factors with the risk of first-time hospitalization for the recurrence of depression among adults with pre-existing depresssion. A: Healthy lifestyle score. B: Pack years of smoking. C: Alcohol consumption. D: Diet score. E: Sleep score. F: Physical activity. G: Social health score. H: Time spent on work. I: Greenspace interaction. Models were adjusted for age, sex, ethnicity, education attainment, Townsend Deprivation Index, body mass index, self-assessed overall health rating, self-reported long-standing illness, self-reported serious medication conditions or disabilities, C reactive protein levels and use of antidepressant medication.

The associations of individual lifestyle factors with depression recurrence were also evaluated separately (figure 2). After adjusting for potential confounders, the HR for depression recurrence in participants with the highest smoking intensity compared with the lowest category was 1.14 (95% CI 1.09 to 1.19). Notably, the highest level of physical activity adherence was associated with a 5% higher risk of depression recurrence in this study. In contrast, we observed a decreased risk of depression recurrence among those who consumed higher amounts of alcohol (HR=0.84, 95% CI 0.79 to 0.88), adhered to healthier diet (HR=0.88, 95% CI 0.82 to 0.94) and sleep pattern (HR=0.86, 95% CI 0.82 to 0.91), spent more time employed in current job (HR=0.81, 95% CI 0.76 to 0.85) or engaged more frequently with greenspace (HR=0.87, 95% CI 0.83 to 0.92) when compared with those who exhibited the lowest levels of these individual lifestyle factors. Results from the multivariable-adjusted RCS regression showed that the risk of depression recurrence decreased linearly with increasing diet and sleep score (P for non-linearity=0.179 and 0.508, respectively), whereas the associations between pack years of smoking, alcohol consumption, time spent on work, greenspace interaction and depression recurrence were more non-linear (all P for non-linearity <0.05). In addition, increased physical activity was linearly associated with increased risk of depression recurrence (P for non-linearity=0.877) (figure 1).

Figure 2.

Figure 2

Multivariable-adjusted associations of individual lifestyle factor with first-time hospitalization for the recurrence of depression and mortality among adults with pre-existing depression. Models were adjusted for age, sex, ethnicity, education attainment, Townsend Deprivation Index, body mass index, self-assessed overall health rating, self-reported long-standing illness, self-reported serious medication conditions or disabilities, C reactive protein levels and use of antidepressant medication.

Aggregated and individual healthy lifestyle and mortality

We documented 1527 deaths over a mean follow-up period of 13.3 years. In the fully adjusted model, higher HLS were significantly associated with a lower risk of mortality; multivariable HR of the highest tertile compared with lowest tertile for mortality was 0.92 (95% CI 0.87 to 0.95) (table 2). The RCS model indicates a linear association between HLS and mortality (P for non-linearity=0.442) (figure 3).

Figure 3.

Figure 3

Dose-response association between HLS and individual lifestyle factors with mortality among adults with pre-existing depression. A: healthy lifestyle score. B: pack years of smoking. C: alcohol consumption. D: diet score. E: sleep score. F: physical activity. G: social health score. H: time spent on work. I: green space interaction. Models were adjusted for age, sex, ethnicity, education attainment, Townsend Deprivation Index, body mass index, self-assessed overall health rating, self-reported long-standing illness, self-reported serious medication conditions or disabilities, C reactive protein levels and use of antidepressant medication.

The associations between individual lifestyle factors and mortality were shown in figure 2. Similarly, participants with the highest smoking intensity experienced a 55% increase in mortality compared with those with the lowest smoking intensity (HR=1.55, 95% CI 1.38 to 1.74). Conversely, a decreased risk of mortality was observed among those who adhered to a healthy diet (HR=0.78, 95% CI 0.66 to 0.93), engaged in higher levels of physical activity (HR=0.81, 95% CI 0.72 to 0.93), achieved higher social health scores (HR=0.79, 95% CI 0.63 to 0.98) and spent more time in their current job (HR=0.74, 95% CI 0.64 to 0.87). Results from the multivariable-adjusted RCS regression indicated a linear decrease in mortality risk with increasing diet score (P for non-linearity=0.262). Conversely, the relationship between physical activity and time spent on work exhibited greater non-linearity (all P for non-linearity <0.05). Furthermore, an increase in pack years of smoking was associated with a non-linear increase in mortality risk (figure 3).

Effect modification and sensitivity analyses

When considering the heterogeneity of whether adults with pre-existing depression take medication, we found a significant interaction between the use of antidepressant medication and HLS (P for interaction <0.001, as shown in table 3). The inverse association with first-time hospitalisation due to recurrence of depression was more pronounced among participants who reported using antidepressant medication than those who did not take medication. Conversely, the association with mortality was more pronounced among participants who reported no use of antidepressant medication than those who took.

Table 3.

Associations of aggregated healthy lifestyle scores (HLS) with first-time hospitalisation for the recurrence of depression and mortality among patients with pre-existing depression stratified by antidepressant medication use

User of antidepressant medication Non-user of antidepressant medication Pinteraction
HR (95% CI) HR (95% CI)
Depression recurrence
HLS <0.001
 Lowest tertile 1 (Reference) 1 (Reference)
 Medium tertile 0.84 (0.79 to 0.89) 0.96 (0.86 to 1.08)
 Highest tertile 0.71 (0.67 to 0.76) 0.79 (0.70 to 0.90)
 P for trend <0.001 <0.001
Mortality
HLS <0.001
 Lowest tertile 1 (Reference) 1 (Reference)
 Medium tertile 0.78 (0.68 to 0.90) 0.65 (0.47 to 0.90)
 Highest tertile 0.81 (0.69 to 0.95) 0.67 (0.47 to 0.94)
 P for trend 0.002 0.018

All analyses were fully adjusted for age (timescale) and sex; ethnicity, education attainment, Townsend Deprivation Index and body mass index; and self-assessed overall health rating, self-reported long-standing illness, self-reported serious medication conditions or disabilities and C reactive protein levels.

CI, confidence interval; HLS, healthy lifestyle score; HR, hazard ratio.

When stratified by other potential risk factors, no statistically significant interactions were observed between HLS and all stratified factors except for BMI (online supplemental tables 4 and 5). The association between HLS and first-time hospitalisation due to recurrence of depression was stronger among participants with lower BMI than higher BMI (P for interaction=0.001). Similar results were noted regarding the association between HLS and mortality (P for interaction=0.004).

The findings remained largely consistent with the main analyses when further excluding participants with poor self-rated health status (online supplemental table 6), those with limited follow-up of ≤2 years (online supplemental table 7) or conducting complete analyses by dropping all missing values of covariates (online supplemental table 8).

Discussion

In this large prospective cohort study of 26 164 adults with pre-existing depression from UKB, we found that adhering to a healthy lifestyle, particularly for less smoking, healthy diet or more time employed in current job, was associated with a lower risk of first-time hospitalisation for the recurrence of depression and mortality (online supplemental file 1). Notably, the associations remained regardless of the use of antidepressant medication. These findings highlight the benefits of adopting a healthy lifestyle in reducing the risks for depression recurrence and mortality among adults with depression.

Despite a growing body of evidence showing associations between combined and individual lifestyle factors and the risk of new-onset depression, to our knowledge, this is the first study to assess whether aggregated modifiable risk factors are associated with prognosis outcomes in adults with pre-existing depression. Our findings suggest that higher scores on measures of healthier lifestyle were associated with reduced first-time hospitalisation due to recurrence of depression and improved survival rates among adults diagnosed with depression. Moreover, we observed that the inverse association between HLS and first-time hospitalisation due to recurrence of depression was more pronounced among participants who reported using antidepressant medication than those who did not take medication. This could be attributed to the effectiveness of antidepressants in relieving depressive symptoms and the implementation of lifestyle-based interventions.13 However, the mortality risk was higher among participants who did not report using antidepressant medication compared with those who did. Several observational studies have substantiated the potential detrimental effects of these medications on various outcomes such as falls, fracture, epilepsy, hyponatraemia, attempted suicide, self-harm, stroke and transient ischaemic attack.14 15 Consequently, in this study, the association between HLS and mortality risk was found to be stronger among the non-medicated population.

Moreover, the aggregated HLS in this study encompass eight domains, providing a more comprehensive insight into the impacts of lifestyle and environmental factors on individuals with pre-existing depression. The role of lifestyle-based approaches in depression managing may involve multiple biological and psychological mechanisms. Randomised controlled trials have demonstrated that diet interventions can augment the concentration of antioxidant substances in plasma and mitigate oxidative stress. Furthermore, it can significantly elevate serum levels of serotonin, a crucial neurotransmitter strongly associated with alleviating symptoms of depression.16 Extensive reviews have explained on the potential impact of smoking, sleep, physical activity and psychosocial stress on inflammation and oxidative and nitrosative stress that underlie depression progression.17 18 Interventions targeting lifestyle and environmental domains can also extend into the management of substance use disorders through an increase in positive affect, acute reduction in cravings and urges and improvement in comorbid physical disease. These mechanisms are pertinent as substance use can be a means of ‘self-medicating’ psychological distress, past and ongoing trauma and mental illness.19

When examining the role of individual lifestyles, participants who smoked less, adhered to a healthier diet and sleep pattern, spent more time employed in their current jobs and had greater exposure to greenspace were associated with significantly lower risk of depression recurrence. Similarly, adopting healthy lifestyles such as less smoking, healthy diet, physically active and more time employed in current job was associated with a lower risk of mortality among adults with depression. Interestingly, moderate alcohol consumption was significantly associated with the lowest risk of depression recurrence (HR=0.77, 95% CI 0.73 to 0.81). This finding is not entirely unexpected given the conflicting evidence and competing hypotheses surrounding the relationship between alcohol consumption and depression, which may not follow a linear pattern.20 21 Meanwhile, we have found that adherence to the highest level of physical activity was marginally linked to an increased risk of depression recurrence (HR=1.05, 95% CI 1.00 to 1.11). The findings of this study may diverge from those of other studies that have reported more substantial enhancements in physical activity for depressive symptoms.22 Some potential biological mechanisms could explain these findings. High-intensity physical activity might prompt an increased inflammatory response, such as higher levels of interleukin-6 and tumour necrosis factor-alpha, which are closely associated with depression. In addition, high volume of physical activity may cause an increase in the release of stress hormones such as cortisol, which could lead to reduced sleep quality, increased anxiety and the onset of depressive symptoms.23–25 Furthermore, the efficacy of physical activity as a treatment for depression may be contingent on factors such as sample size, research design and methodology.

Evidence from previous reviews has indicated that prolonged exposure to long working hours may lead to depression.26 27 This could be attributed to the limited decision-making autonomy, job-related stress and harassment experienced by both male and female employees, leading to a gradual increase in depressive symptoms over time. However, our findings suggest that longer job engagement in the current position can serve as a protective factor against depression recurrence and mortality. It may serve as compelling evidence for the implementation of a crucial preventive strategy in managing depression within clinical settings. We have also discovered that adults diagnosed with depression could benefit from the long-term exposure to residential greenspace, which is consistent with the results of the previous published cross-sectional study.16 The findings of this study serve as a crucial reference for promoting mental health and addressing depression, particularly in the face of rapid urbanisation and dwindling greenspace resources. Therefore, strengthening the protection and development of urban greenspaces is highly significant for enhancing public mental health. We did not observe a significant association between social health and the remission of the depression, despite the social health in our study was defined according to frequency of friend and family visits, engagement in leisure and social activities and ability to confide.

We also found BMI-dependent effects of HLS on the risks of first-time hospitalisation for the recurrence of depression requiring hospitalisation and mortality, with the HRs being relatively lower at milder BMI (<30 kg/m2) and the effect sizes decreasing with increasing BMI. The relationship between depression and obesity is confirmed to be bidirectional, the presence of one increases the risk for developing the other. The association between depression and obesity can be elucidated by the shared biological pathways, which encompass genetics, alterations in systems involved in homeostatic adjustments (such as the hypothalamic-pituitary-adrenal axis, immunoinflammatory activation, neuroendocrine regulators of energy metabolism including leptin and insulin, and microbiome), as well as brain circuitries integrating homeostatic and mood regulatory responses.28 29 Further investigations are warranted to validate these associations.

Limitations

The main strengths of this study lie in the development of an aggregated HLS and the utilisation of a large-scale population with pre-existing depression, which have enabled us to investigate the relationship between healthy lifestyles and both first-time hospitalisation due to recurrence of depression and mortality. However, there are several limitations still needed to be considered. First, the information on healthy lifestyle factors was primarily obtained from the self-reported questionnaires, which may be subject to recall bias. Additionally, we cannot rule out the potential influence of lifestyle changes over time, as exposure data were only collected at baseline and no time-varying variables were assessed during follow-up. Second, the primary outcome is first-time hospitalisation due to recurrence of depression and mortality. Therefore, we are unable to account for any comorbidities that may have arisen after depression diagnosis or treatment, such as anxiety disorders, sleep disturbances and physical illnesses. Third, it is difficult to make causality conclusions in an observational cohort study. Despite controlling for numerous relevant confounding factors, unmeasured variables could introduce bias into our findings. Nevertheless, through a series of sensitivity analyses, we have substantiated the robustness of our findings.

Conclusion

To sum up, adherence to a healthy lifestyle was associated with lower risk of first-time hospitalisation for the recurrence of depression and mortality among adults with pre-existing depression. Our findings emphasise the importance of incorporating lifestyle recommendations as an essential component of clinical practice for managing depression, complementing medication-based therapies that may be used alone or in combination.

Acknowledgments

This study was conducted using the UKB (application 79095). We express our sincere thanks to the participants of the UKB and the members of the survey, development and management teams of this project.

Footnotes

Contributors: ZC: conceptualisation, methodology, software, formal analysis, investigation, data curation, writing-original draft. JM: methodology, visualisation, writing-review and editing. YX: conceptualisation, methodology, writing-review and editing. XW: writing-review and editing. CX: guarantor, conceptualisation, methodology, investigation, data acquisiton, data curation, writing-review and editing, project administration, funding acquisition.

Funding: This work was supported by the National Natural Science Foundation of China (grant number 72204071), the Zhejiang Provincial Natural Science Foundation of China (grant number LY23G030005), and the Scientific Research Foundation for Scholars of HZNU (grant number 4265C50221204119).

Competing interests: None declared.

Provenance and peer review: Not commissioned; externally peer reviewed.

Supplemental material: This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.

Data availability statement

Data are available upon reasonable request. The data that support the findings of this study are available from UKB project site, subject to registration and application process. Further details can be found at https://www.ukbiobank.ac.uk.

Ethics statements

Patient consent for publication

Consent obtained directly from patient(s).

Ethics approval

The studies involving human participants were reviewed and approved by NHS National Research Ethics Service (NW/0382). Participants gave informed consent to participate in the study before taking part.

References

  • 1. GBD 2019 Mental Disorders Collaborators . Global, regional, and national burden of 12 mental disorders in 204 countries and territories, 1990-2019: a systematic analysis for the global burden of disease study 2019. Lancet Psychiatry 2022;9:137–50. 10.1016/S2215-0366(21)00395-3 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 2. Ferrari AJ, Somerville AJ, Baxter AJ, et al. Global variation in the prevalence and incidence of major depressive disorder: a systematic review of the epidemiological literature. Psychol Med 2013;43:471–81. 10.1017/S0033291712001511 [DOI] [PubMed] [Google Scholar]
  • 3. Leichsenring F, Steinert C, Rabung S, et al. The efficacy of psychotherapies and pharmacotherapies for mental disorders in adults: an umbrella review and meta-analytic evaluation of recent meta-analyses. World Psychiatry 2022;21:133–45. 10.1002/wps.20941 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 4. Carvalho AF, Sharma MS, Brunoni AR, et al. The safety, tolerability and risks associated with the use of newer generation antidepressant drugs: a critical review of the literature. Psychother Psychosom 2016;85:270–88. 10.1159/000447034 [DOI] [PubMed] [Google Scholar]
  • 5. Moitra M, Santomauro D, Collins PY, et al. The global gap in treatment coverage for major depressive disorder in 84 countries from 2000-2019: a systematic review and bayesian meta-regression analysis. PLoS Med 2022;19:e1003901. 10.1371/journal.pmed.1003901 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 6. Kandola A, Ashdown-Franks G, Hendrikse J, et al. Physical activity and depression: towards understanding the antidepressant mechanisms of physical activity. Neurosci Biobehav Rev 2019;107:525–39. 10.1016/j.neubiorev.2019.09.040 [DOI] [PubMed] [Google Scholar]
  • 7. Ross RE, VanDerwerker CJ, Saladin ME, et al. The role of exercise in the treatment of depression: biological underpinnings and clinical outcomes. Mol Psychiatry 2023;28:298–328. 10.1038/s41380-022-01819-w [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8. Marx W, Manger SH, Blencowe M, et al. Clinical guidelines for the use of lifestyle-based mental health care in major depressive disorder: world federation of societies for biological psychiatry (WFSBP) and Australasian society of lifestyle medicine (ASLM) taskforce. World J Biol Psychiatry 2023;24:333–86. 10.1080/15622975.2022.2112074 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 9. Cho HJ, Lavretsky H, Olmstead R, et al. Sleep disturbance and depression recurrence in community-dwelling older adults: a prospective study. Am J Psychiatry 2008;165:1543–50. 10.1176/appi.ajp.2008.07121882 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10. Fry A, Littlejohns TJ, Sudlow C, et al. Comparison of sociodemographic and health-related characteristics of UK biobank participants with those of the general population. Am J Epidemiol 2017;186:1026–34. 10.1093/aje/kwx246 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11. Smith DJ, Nicholl BI, Cullen B, et al. Prevalence and characteristics of probable major depression and bipolar disorder within UK biobank: cross-sectional study of 172,751 participants. PLoS One 2013;8:e75362. 10.1371/journal.pone.0075362 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12. BA TP: health and deprivation: inequality and the North. London, 1988. [Google Scholar]
  • 13. Cipriani A, Furukawa TA, Salanti G, et al. Comparative efficacy and acceptability of 21 antidepressant drugs for the acute treatment of adults with major depressive disorder: a systematic review and network meta-analysis. Lancet 2018;391:1357–66. 10.1016/S0140-6736(17)32802-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14. Coupland C, Hill T, Morriss R, et al. Antidepressant use and risk of adverse outcomes in people aged 20-64 years: cohort study using a primary care database. BMC Med 2018;16:36. 10.1186/s12916-018-1022-x [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15. Coupland C, Dhiman P, Morriss R, et al. Antidepressant use and risk of adverse outcomes in older people: population based cohort study. BMJ 2011;343:d4551. 10.1136/bmj.d4551 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16. Gascon M, Sánchez-Benavides G, Dadvand P, et al. Long-term exposure to residential green and blue spaces and anxiety and depression in adults: a cross-sectional study. Environ Res 2018;162:231–9. 10.1016/j.envres.2018.01.012 [DOI] [PubMed] [Google Scholar]
  • 17. Berk M, Williams LJ, Jacka FN, et al. So depression is an inflammatory disease, but where does the inflammation come from? BMC Med 2013;11:200. 10.1186/1741-7015-11-200 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18. Lopresti AL, Hood SD, Drummond PD. A review of lifestyle factors that contribute to important pathways associated with major depression: diet, sleep and exercise. J Affect Disord 2013;148:12–27. 10.1016/j.jad.2013.01.014 [DOI] [PubMed] [Google Scholar]
  • 19. Smith LL, Yan F, Charles M, et al. Exploring the link between substance use and mental health status: what can we learn from the self-medication theory? J Health Care Poor Underserved 2017;28:113–31. 10.1353/hpu.2017.0056 [DOI] [PubMed] [Google Scholar]
  • 20. Grønkjær M, Wimmelmann CL, Mortensen EL, et al. Prospective associations between alcohol consumption and psychological well-being in Midlife. BMC Public Health 2022;22:204. 10.1186/s12889-021-12463-4 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21. Li J, Wang H, Li M, et al. Effect of alcohol use disorders and alcohol intake on the risk of subsequent depressive symptoms: a systematic review and meta-analysis of cohort studies. Addiction 2020;115:1224–43. 10.1111/add.14935 [DOI] [PubMed] [Google Scholar]
  • 22. Pearce M, Garcia L, Abbas A, et al. Association between physical activity and risk of depression: a systematic review and meta-analysis. JAMA Psychiatry 2022;79:550–9. 10.1001/jamapsychiatry.2022.0609 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23. Kiecolt-Glaser JK, Derry HM, Fagundes CP. Inflammation: depression fans the flames and feasts on the heat. Am J Psychiatry 2015;172:1075–91. 10.1176/appi.ajp.2015.15020152 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24. Paolucci EM, Loukov D, Bowdish DME, et al. Exercise reduces depression and inflammation but intensity matters. Biol Psychol 2018;133:79–84. 10.1016/j.biopsycho.2018.01.015 [DOI] [PubMed] [Google Scholar]
  • 25. Hill EE, Zack E, Battaglini C, et al. Exercise and circulating cortisol levels: the intensity threshold effect. J Endocrinol Invest 2008;31:587–91. 10.1007/BF03345606 [DOI] [PubMed] [Google Scholar]
  • 26. Theorell T, Hammarström A, Aronsson G, et al. A systematic review including meta-analysis of work environment and depressive symptoms. BMC Public Health 2015;15:738. 10.1186/s12889-015-1954-4 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27. Rugulies R, Sørensen K, Di Tecco C, et al. The effect of exposure to long working hours on depression: a systematic review and meta-analysis from the WHO/ILO joint estimates of the work-related burden of disease and injury. Environ Int 2021;155:106629. 10.1016/j.envint.2021.106629 [DOI] [PubMed] [Google Scholar]
  • 28. Rao WW, Zong QQ, Zhang JW, et al. Obesity increases the risk of depression in children and adolescents: results from a systematic review and meta-analysis. J Affect Disord 2020;267:78–85. 10.1016/j.jad.2020.01.154 [DOI] [PubMed] [Google Scholar]
  • 29. Milaneschi Y, Simmons WK, van Rossum EFC, et al. Depression and obesity: evidence of shared biological mechanisms. Mol Psychiatry 2019;24:18–33. 10.1038/s41380-018-0017-5 [DOI] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary data

bmjment-2023-300915supp001.pdf (2.6MB, pdf)

Supplementary data

bmjment-2023-300915supp002.pdf (152.6KB, pdf)

Data Availability Statement

Data are available upon reasonable request. The data that support the findings of this study are available from UKB project site, subject to registration and application process. Further details can be found at https://www.ukbiobank.ac.uk.


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