- Research article
- Open Access
Device-measured physical activity and incident affective disorders
BMC Medicine volume 20, Article number: 290 (2022)
Studies on physical activity (PA) and mental health are largely limited to self-reported PA. This study aims to use prospective cohort data to investigate the association between device-measured PA and affective disorders.
A total of 37,327 participants from UK Biobank who had not had any prior affective disorder diagnoses were included in this prospective cohort study. Wrist-worn accelerometers were used to measure total, light (LPA), moderate (MPA), and vigorous (VPA) PA. Associations between PA domains and affective disorders were analysed using penalised splines in Cox proportional hazard models. Analyses were adjusted for other intensity-specific PA and sociodemographic and lifestyle factors. Sensitivity analyses were conducted adjusting for body mass index and longstanding illnesses as well as excluding events in the first 2 years of follow-up. Preventable fractions for the population were estimated for MPA and VPA.
Over a median follow-up of 6.8 years, 1262 (3.4%) individuals were diagnosed with affective disorders. Replacing 30 min of sedentary behaviour in a week with MPA (HR 0.95, 95% CI 0.94–0.97) or VPA (HR 0.91, 95% CI 0.85–0.98) was associated with lower risk of affective behaviours, up to 500 and 120 min of MPA and VPA. Assuming causality, 5.14% and 18.88% of affective disorders could have been prevented if MPA ≥150 min/week and VPA ≥75 min/week were achieved, respectively, across the study population.
Device-measured MPA and VPA were associated with lower risk of affective disorders. The potential mental health benefits of MPA continue to accrue above the current World Health Organization recommendation.
Psychiatric disorders are one of the top contributors to the global burden of diseases accounted for 14% of total years living with disability in 2017 . Among psychiatric disorders, affective disorders are the most common type. Current treatment for affective disorders includes medications, such as selective serotonin reuptake inhibitors , psychotherapy , or even exercise on prescription . However, medications often have various side effects  and cognitive behavioural therapy is only moderately effective . In view of these, identifying strategies to prevent affective disorders is important to reduce their public health burden.
One potential modifiable factor for affective disorders is physical activity (PA). Meta-analyses of prospective studies have shown that PA was associated with lower risk of depression  (OR = 0.78) and anxiety  (OR = 0.66). However, the meta-analyses also acknowledged multiple limitations of existing studies: most notably, the reliance on self-reported measurement and the failure to adjust for key confounders, such as ethnicity and chronic conditions [6, 7]. Inconsistency between self-reported and objectively measured PA has been well reported which could substantially distort effect sizes . This issue could be further amplified for affective disorders since the inaccuracy in self-report could be dependent on mental health.
There were experimental evidence supporting the causal effect of PA on affective disorders and general mental health, but most trials compared PA intervention with usual care [9, 10]. One important question that could not be easily answered by trials is the dose-response association between PA and mental health outcomes, as well as whether this depends on the intensity of PA. Focusing on duration, a cross-sectional study of 1.2 million participants reported a U-shaped relationship between PA and mental health, whereby people who self-reported to perform moderate-duration of PA had fewest mental health symptoms . However, as with most previous studies, such findings could be due to bidirectionality or recall bias .
The current study aimed to determine whether, and to what extent, device-measured PA was associated with affective disorders, including depression and anxiety. This study also investigated whether there was any dose-response relationship in regard to PA intensity and explored its public health implications by estimating the preventable fractions for the population (PFPs).
The UK Biobank cohort study enrolled over 500,000 participants aged 37–73 years at baseline from the general population (5.5% response rate) . In brief, between 2006 and 2010, participants attended one of 22 assessment centres across Scotland, England, and Wales [14, 15]. All participants completed a touch-screen questionnaire, had physical measurements taken, and provided blood, urine, and saliva samples at baseline. Among all UK Biobank participants, about half had linked primary care data and were included in this study (Additional file 1: Fig. S1).
Axivity AX3 wrist-worn triaxial accelerometers were used to collect objective PA measurements from 103,686 UK Biobank participants between 2013 and 2015. The dominant wrist of each individual was used over a period of 7 days at 100 Hz and combined into 5-s epochs for analysis . The 7161 participants with insufficient wear time (<72-h wear), missing data, or poor device calibration were excluded, leaving 96,525 participants with valid device-measured PA data. The median wear time was 6.91 days with less than 20% of participants wore < 6 days. More details about data collection and processing can be found elsewhere .
Minutes per week (min/week) of light (LPA), moderate (MPA), and vigorous (VPA) PA were determined as the time spent between >30 milligravities (mg) and 125 mg, >125 mg and 400 mg, and >400 mg, respectively [17, 18], extrapolated from fraction of time spent over the total wear time. This assumes the time spent in various PA was similar in measured and unmeasured period. Total PA was expressed as total metabolic equivalent of task (MET)-minutes/week accounting for both intensity and duration and was calculated as time spent in LPA × 2 + MPA × 4 + VPA × 8, adapting the International Physical Activity Questionnaire scoring recommendation .
Incident affective disorders were extracted from both hospital and primary care records to ensure comprehensive ascertainment. Dates and causes of hospital admissions were identified via record linkage to Health Episode Statistics (England and Wales) and the Scottish Morbidity Records (Scotland). Details of the linkage procedure can be found at http://content.digital.nhs.uk/services. The start of follow-up was the date where all device-measured PA measurements were completed. Participants with affective disorders prior to that date, based on retrospective record linkage, were excluded from the analyses (Additional file 1: Fig. S1). Retrospective linked data dated back to 2004 were used as the Quality and Outcomes Framework in primary care practices that were implemented that year. Hospital admission data were available until September 2021 in England, July 2021 in Scotland, and February 2018 in Wales. Using the International Classification of Diseases, 10th revision (ICD-10), affective disorders were defined as F30-F39 and F40-F44, depression as F32-33, and anxiety as F40-44.
Age, when PA data were collected, was determined from dates of birth and PA assessment. Ethnicity was self-reported and categorised into White, South Asian, Black, Chinese, mixed ethnic background, and others. Area-based deprivation was derived from postcode of residence, using the Townsend score . Educational attainment was based on self-report of the highest level of qualification. Sleep duration was self-reported in hours as there were no reliable objectively measured data. Smoking status was self-reported and categorised as never, former, or current smoker. Alcohol consumption was calculated based on self-reported frequency and volume of drinking. Dietary intake of fruit and vegetables, red meat, processed meat, and oily fish was self-reported. Height and body weight were measured by trained nurses during the baseline assessment. Body mass index (BMI) was calculated as (weight in kg)/(height in m)2. Longstanding illnesses were self-reported as any longstanding illness, disability, or infirmary.
Descriptive characteristics by quartile of total PA are presented as means with standard deviation (SD) for continuous variables and frequencies with percentages for categorical variables.
Nonlinear associations between PA and incident affective disorders were investigated using penalised cubic splines fitted in Cox proportional hazard models. The penalised spline is chosen as it is not sensitive to knot numbers and placements . In these analyses, PA variables were truncated to be between 2000 and 12,000 MET-min/week for total PA, 300 and 3600 min/week for MPA, 0 and 1800 min/week for MPA, and 0 and 240 min/week for VPA to ensure the reliability at the tail end of the splines. These truncation thresholds were roughly the 1st and 99th percentile for each of the variables. The reference points were the minimum of the truncated PA variables. An analysis assuming linearity was performed in the range where the associations appeared linear to extract easily interpreted estimates. This was replicated to use MET-minutes/week instead of time spent. Analyses were adjusted for these confounders: age, sex, ethnicity, deprivation, education, sleep duration, smoking, alcohol intake, and dietary intake based on the causal hypothesis shown in Additional file 1: Fig. S1. For analysis of LPA, MPA, and VPA, the three PA variables were mutually adjusted. Because LPA, MPA, VPA, and sleep duration were all included in the model, the hazard ratios of PA could be interpreted as replacing the time spent on sedentary behaviour by that PA .
To illustrate the joint association of MPA and VPA with affective disorders, a risk matrix was constructed using the categorised MPA and VPA variables: 0–<150, 150–<300, 300–<600, and ≥600 min for MPA and 0–<15, 15–<45, 45–<75, and ≥75 min for VPA. These cut-offs were chosen based on the current WHO PA recommendations  and the shapes of associations, as well as the distributions. The least active was used as the reference groups.
Furthermore, three sensitivity analyses were performed. Firstly, PA were not mutually adjusted as LPA and MPA (r = 0.42) and MPA and VPA (r = 0.49) were moderately correlated which may lead to unstable HRs. Secondly, BMI and longstanding illnesses reported at baseline were additionally adjusted as these could limit the ability of participants to perform PA, but could also be mediators (Additional file 1: Fig. S1). Thirdly, a 2-year landmark analysis was conducted excluding participants who developed affective disorders in the first 2 years of follow-up (n = 795), in order to minimise the risk of reverse causation due to sub/pre-clinical mental health issues at baseline.
Preventable fractions for the population (PFPs)  were estimated to estimate the proportions of all incident affective disorders that could have been prevented if the individuals in a specific PA categories were as active as the most active group, assuming that the association was causal. It should be noted that this study could not ascertain causality and this analysis only illustrates the relative importance of intensity-specific PA for the study population. All analyses were conducted using the ‘survival’ packages in R 4.2.0. The proportional hazard assumption was checked using Schoenfeld residuals. A P-value below 0.05 was considered statistically significant.
UK Biobank was approved by the North-West Multi-Centre Research Ethics Committee (Ref: 11/NW/0382). Written informed consent was obtained from all participants.
Of the 502,459 UK Biobank participants, 96,519 had valid device-measured PA data (Additional file 1: Fig. S2). Of these, 53,480 were excluded because no primary care data were available. There were no significant differences in demographics and device-measured PA (e.g. average acceleration 27.89 vs. 27.90 mg) between participants with and without linked primary care data. In the overall analysis of affective disorders, an additional 5712 were excluded due to prior affective disorder diagnoses, resulting in an effective sample size of 37,327. The median (interquartile range [IQR]) follow-up period was 6.8 (6.3–7.4) years. The overall incidence rates for overall affective disorders, depression, and anxiety were 50.8, 25.5, and 30.8 per 10,000 person-years, respectively.
Participant characteristics by device-measured total PA are shown in Table 1. Participants who undertook more PA were generally younger, less deprived, more likely to be female and White, less likely to smoke, and consumed more fruit and vegetables and less red and processed meat. They also had lower BMI and were less likely to have longstanding illnesses. Participant characteristics by LPA, MPA, and VPA are shown in Additional file 1: Tables S1-S3, respectively.
Total PA, and MPA VPA were associated with lower risk of incident affective disorders when adjusted for sociodemographic and lifestyle (Fig. 1) factors. Compared with the least active participants, performing PA was associated with reduced risk of affective disorders up to 6000 MET-minutes/week, after which the association plateaued. Participants who replaced sedentary behaviours with 150–300 min/week of MPA (HR 0.53 95% CI 0.48–0.58) or 75–150 min/week of VPA (HR 0.71, 95% CI 0.56–0.91) were at lower risk of affective disorders. The association of MPA with affective disorders was broadly linear until 500 min/week and then plateaued. That of VPA was J-shaped with the lowest risk at 120 min of VPA. The associations of MPA with depression and anxiety were broadly similar to total PA, but that of VPA with depression was more U-shaped and that with anxiety was more linear. There were no significant associations for LPA.
When intensity-specific PA were not mutually adjusted, the association of MPA remained similar but those of VPA were less J-shaped and were significant for all three outcomes (Additional file 1: Fig. S3). However, LPA was still not significantly associated with all of the outcomes. When BMI and longstanding illnesses were also added to the models (Additional file 1: Fig. S4), the associations were attenuated but still significant for MPA. Similar results were found in the 2-year landmark analyses (Additional file 1: Fig. S5).
Since the associations were broadly linear up to 500 and 120 min/week of MPA and VPA as shown in the splines in Fig. 1, analysis assuming linearity was conducted for these ranges (Table 2). Replacing 30 min/week of sedentary behaviour with MPA and VPA was associated with 5% (HR 0.95, 95% CI 0.94–0.97) and 9% (HR 0.91, 95% CI 0.85–0.98) lower risk of affective disorders, respectively. The results were similar when PA was adjusted for intensity. When intensity PA were not mutually adjusted, the association of MPA was similar but that of VPA was noticeably stronger.
The risk matrix of the joint associations between MPA and VPA and affective disorders is shown in Fig. 2. LPA was not included since it was generally not associated with affective disorders even when not adjusted for other intensity-specific PA. Assuming causality, a sedentary individual with no MPA or VPA would have 41% risk reduction in affective disorder if they replace 150–299 min of sedentary time in a week with MPA. If that individual further replaces ≥75 min of sedentary time in a week with VPA, their risk reduction would be 56%.
Table 3 shows the proportions of preventable affective disorders. Assuming the associations to be causal, almost one-fourth of incident affective disorders in the study population were attributed to <150 min/week of MPA (5.14%) and <75 min/week of VPA (18.88%). The proportion attributable to MPA was small because only 4% of the study participants performed <150 min of MPA. Conversely, over 90% of participants performed <75 min of VPA, resulting in a larger number of cases attributed to VPA. However, a total of 20.98% of the cases could have been prevented if all participants could perform ≥600 min of MPA a week.
This study provided robust evidence of an association between device-measured PA and affective disorders, including depression and anxiety. A higher level of PA was associated with lower risk of affective disorders up to 500 min of MPA and 120 min of VPA per week. At an equivalent amount of time, VPA was associated with lower risk of affective disorders than MPA. However, consistent with the literature , the potential benefits of MPA continue to accrue after achieving the current PA recommendations (Fig. 2). A larger proportion of population-level mental health burden was attributed to low VPA because a substantial amount of people did not perform sufficient VPA (Table 3).
This study has several important strengths. First and foremost, it utilised device-measured PA, which is less prone to measurement errors and free of self-reporting bias compared to PA questionnaires. This study also ascertained affective disorders from multiple electronic health records, thus improving data completeness and protecting against reporting bias. To minimise reverse causation due to sub/pre-clinical mental health problems, we conducted a landmark analysis, excluding participants who were diagnosed with affective disorders within the first 2 years of follow-up. BMI and longstanding illnesses could be mediators or confounders and were additionally adjusted in a sensitivity analysis. All of the results were consistent, increasing confidence in the findings. However, this study still has several caveats. Firstly, UK Biobank is not representative of the UK population, with evidence of a healthy volunteer selection bias. Estimates of effect size were found to be consistent with population-representative cohorts , but PA levels are higher among UK Biobank participants. Participants with worse mental health might not be recruited or willing to participate from the accelerometer measurements. Therefore, the calculated PFPs are likely to be an underestimate of the contribution of PA to affective disorders in the general population. PFPs assume the estimates to be causal which cannot be confirmed in this study. Even though using clinical diagnosis in linked primary care and hospital data should be relatively robust, it is not possible to differentiate the severity of the mental health outcomes. In spite of adjustment for a wide range of potential confounders, unobserved and residual confounding cannot be ruled out. Reverse causation due to sub-/pre-clinical mental health symptoms is still possible despite the 2-year landmark analysis. Additionally, the wrist-worn accelerometers could overestimate PA levels and may not differentiate light PA from sedentary time as well as in waist-worn accelerometers. Last but not least, even though PA were categorised by intensity, variations within categories could not be captured, and the number and duration of bouts have not been explored.
Even though causality cannot be confirmed in this observational study, the relationship between PA and affective disorders has met most of the Branford Hill criteria for causation . This study has shown the associations to be strong in magnitude (HR < 0.5) with evidence of a dose-response relationship. The associations between PA and lower risk of affective disorders were consistent with previous meta-analyses [6, 7]. There is experimental evidence, including multiple trials, supporting the protective effects of PA on mental well-being across all age ranges [9, 10]. A bidirectional Mendelian randomisation (MR) study also supported a causal relationship between device-measured PA and lower risk of depression, but not the other way round . Interestingly, there was also no association between self-reported PA and depression in the MR study, highlighting the importance of objectively measured PA on reducing recall bias and regression dilution bias. The causal relationship between PA and affective disorders is biologically plausible given the observed changes in several neurotransmitters (including endorphin) after PA . There are several potential mechanisms between PA and affective disorders including neurobiological and psychosocial pathways [30, 31]. The differential associations of VPA with depression and anxiety indicate that there might be different mechanistic pathways, as shown in studies on the signalling of serotonin, norepinephrine, and dopamine . It should be noted that even though similar relationships have been reported in previous meta-analyses and randomised controlled trials [9, 10], the current study adds meaningful evidence through its comparison between device-measured PA of different intensities, examination of dose-response relationships, and consideration of both individual- and population-level implications. However, due to the nature of accelerometer data collection, this study was not able to distinguish between occupational and leisure time PA and its associations with the mental health outcomes of interest.
There have been limited studies comparing PA of different intensities, especially LPA. The study found no significant association between LPA and the affective disorders regardless of adjustment of MPA and VPA. This may indicate that intensity does play an important role in the benefits of PA in mental health. This is indeed true as shown in the linear analysis that for an equivalent amount of time, VPA, compared with MPA, had a stronger association with affective disorder. A cross-sectional study reported slightly stronger effect size for self-reported VPA (OR = 0.58–0.78) than MPA (OR = 0.68–0.92) . However, the PA variables were dichotomised based on a previous recommendation from the American Heart Association (AHA) (MPA ≥ 150; VPA ≥ 60 min/week) . The World Health Organization and the AHA have subsequently updated their recommendation to at least 150–300 or 75–150 min/week of MPA and VPA, respectively . Our study, using the updated recommendation, found that the effect size for achieving the recommendations was slightly weaker for VPA (HR = 0.68) than for MPA (HR = 0.55), even though the confidence intervals overlapped substantially. It should also be noted that since the majority of the participants who performed 75–150 min/week of VPA already were performing a high level of MPA, the potential benefits of VPA estimated could be plateaued.
Only 5.2% of all the affective disorders, including depression and anxiety, could be attributable to not achieving the recommendation of MPA, but 16.5% could be attributable to that of VPA. This is largely due to the substantially different distributions of MPA and VPA. A large majority (95.9%) of study participants fulfilled the WHO recommendation of ≥150 min of MPA (including walking at a moderate speed ) but only 9.6% of the participants had ≥75 min of VPA, which could be because the WHO recommendation was largely derived from studies using self-reported PA considering only bouted (e.g. >10 min) PA, and/or that the UK Biobank participants had healthier lifestyle. Regardless, to our knowledge, this is a novel finding as previous studies often considered combined moderate- and vigorous-intensity physical activities rather than MPA, and VPA separately. It is also worth to note that more affective disorders could have been prevented if more participants could perform more MPA than the current recommendations. Future studies should investigate how the current recommendations based on self-reported data should be translated to device-measured PA, given that wearable devices reporting activity time are increasingly common.
The current study, along with previous evidence on causality [9, 28], suggested that promoting MPA and VPA in the population could substantially reduce the burden of affective disorders. Because the associations did not plateau until a very high level of MPA, people who are physically active could still gain benefit from increasing their MPA, echoing the current WHO recommendation. For people who already performed a reasonable amount of MPA (>300 min/week) or for people who would like to achieve more benefits for the same amount of time, they should consider undertaking or increasing VPA. At the population level, strategies to promote VPA should be explored because most people performed minimal VPA. Nonetheless, it should be noted that PA with very high intensity could be associated with discomfort and might not be easy to adopt or maintain [37, 38]. There were also reports on VPA-associated adverse events in trials among older adults [39, 40]. There were not sufficient numbers of participants with high level of VPA but low level of MPA to explore such activity pattern is associated with mental health benefits. Similarly, it is not certain whether performing VPA over the current recommendations of 75–150 min/week would confer to mental health benefits.
In conclusion, device-measured PA was associated with lower risk of affective disorders. Promoting physical activity in the population, even beyond the current recommendations, is associated with substantially lower burden of affective disorders.
Data can be requested from UK Biobank (http://ukbiobank.ac.uk/).
Body mass index
International Classification of Diseases, 10th revision
Light-intensity physical activity
Metabolic equivalent of task
- mg :
Moderate-intensity physical activity
Preventable fraction for the population
Vigorous-intensity physical activity
James SL, Abate D, Abate KH, Abay SM, Abbafati C, Abbasi N, et al. Global, regional, and national incidence, prevalence, and years lived with disability for 354 diseases and injuries for 195 countries and territories, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet. 2018;392(10159):1789–858.
Arroll B, Macgillivray S, Ogston S, Reid I, Sullivan F, Williams B, et al. Efficacy and tolerability of tricyclic antidepressants and SSRIs compared with placebo for treatment of depression in primary care: a meta-analysis. Ann Fam Med. 2005;3(5):449–56.
Carpenter JK, Andrews LA, Witcraft SM, Powers MB, Smits JA, Hofmann SG. Cognitive behavioral therapy for anxiety and related disorders: a meta-analysis of randomized placebo-controlled trials. Depress Anxiety. 2018;35(6):502–14.
NICE. Physical activity: exercise referral schemes. Public health guideline [PH54]. London: National Institute for Health and Care Excellence; 2014. [Available from: https://www.nice.org.uk/guidance/ph54]
Kato M, Serretti A. Review and meta-analysis of antidepressant pharmacogenetic findings in major depressive disorder. Mol Psychiatry. 2010;15(5):473–500.
Schuch FB, Vancampfort D, Firth J, Rosenbaum S, Ward PB, Silva ES, et al. Physical activity and incident depression: a meta-analysis of prospective cohort studies. Am J Psychiatry. 2018;175(7):631–48.
McDowell CP, Dishman RK, Gordon BR, Herring MP. Physical activity and anxiety: a systematic review and meta-analysis of prospective cohort studies. Am J Prev Med. 2019;57(4):545–56.
Celis-Morales CA, Perez-Bravo F, Ibañez L, Salas C, Bailey ME, Gill JM. Objective vs. self-reported physical activity and sedentary time: effects of measurement method on relationships with risk biomarkers. PLoS One. 2012;7(5):e36345.
Rebar AL, Stanton R, Geard D, Short C, Duncan MJ, Vandelanotte C. A meta-meta-analysis of the effect of physical activity on depression and anxiety in non-clinical adult populations. Health Psychol Rev. 2015;9(3):366–78.
Ho FKW, Louie LHT, Wong WH-S, Chan KL, Tiwari A, Chow CB, et al. A sports-based youth development program, teen mental health, and physical fitness: An RCT. Pediatrics. 2017;140(4):e20171543.
Chekroud SR, Gueorguieva R, Zheutlin AB, Paulus M, Krumholz HM, Krystal JH, et al. Association between physical exercise and mental health in 1· 2 million individuals in the USA between 2011 and 2015: a cross-sectional study. Lancet Psychiatry. 2018;5(9):739–46.
Stubbs B, Vancampfort D, Smith L, Rosenbaum S, Schuch F, Firth J. Physical activity and mental health. Lancet Psychiatry. 2018;5(11):873.
Collins R. What makes UK Biobank special? Lancet. 2012;379(9822):1173–4.
Palmer LJ. UK Biobank: bank on it. Lancet. 2007;369(9578):1980–2.
Sudlow C, Gallacher J, Allen N, Beral V, Burton P, Danesh J, et al. UK Biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS Med. 2015;12(3):e1001779.
Doherty A, Jackson D, Hammerla N, Plötz T, Olivier P, Granat MH, et al. Large scale population assessment of physical activity using wrist worn accelerometers: the UK Biobank study. PLoS One. 2017;12(2):e0169649.
Strain T, Wijndaele K, Dempsey PC, Sharp SJ, Pearce M, Jeon J, et al. Wearable-device-measured physical activity and future health risk. Nat Med. 2020;26(9):1385–91.
White T, Westgate K, Hollidge S, Venables M, Olivier P, Wareham N, et al. Estimating energy expenditure from wrist and thigh accelerometry in free-living adults: a doubly labelled water study. Int J Obs. 2019;43(11):2333–42.
IPAQ. Guidelines for data processing and analysis of the International Physical Activity Questionnaire (IPAQ): IPAQ; [Available from: https://sites.google.com/site/theipaq/scoring-protocol].
Townsend PPM, Beattie A. Health and deprivation. Inequality and the North. London (GB): Croom Helm; 1988.
Govindarajulu US, Malloy EJ, Ganguli B, Spiegelman D, Eisen EA. The comparison of alternative smoothing methods for fitting non-linear exposure-response relationships with Cox models in a simulation study. Int J Biostat. 2009;5(1):Article 2.
Mekary RA, Willett WC, Hu FB, Ding EL. Isotemporal substitution paradigm for physical activity epidemiology and weight change. Am J Epidemiol. 2009;170(4):519–27.
Pate RR, Pratt M, Blair SN, Haskell WL, Macera CA, Bouchard C, et al. Physical activity and public health: a recommendation from the Centers for Disease Control and Prevention and the American College of Sports Medicine. JAMA. 1995;273(5):402–7.
Shield KD, Parkin DM, Whiteman DC, Rehm J, Viallon V, Micallef CM, et al. Population attributable and preventable fractions: cancer risk factor surveillance, and cancer policy projection. Curr Epidemiol Rep. 2016;3(3):201–11.
Warburton DE, Charlesworth S, Ivey A, Nettlefold L, Bredin SS. A systematic review of the evidence for Canada’s Physical Activity Guidelines for Adults. Int J Behav Nutr Phys Act. 2010;7(1):1–220.
Batty GD, Gale CR, Kivimäki M, Deary IJ, Bell S. Comparison of risk factor associations in UK Biobank against representative, general population based studies with conventional response rates: prospective cohort study and individual participant meta-analysis. BMJ. 2020;368: m131.
Fedak KM, Bernal A, Capshaw ZA, Gross S. Applying the Bradford Hill criteria in the 21st century: how data integration has changed causal inference in molecular epidemiology. Emerg Themes Epidemiol. 2015;12(1):1–9.
Choi KW, Chen C-Y, Stein MB, Klimentidis YC, Wang M-J, Koenen KC, et al. Assessment of bidirectional relationships between physical activity and depression among adults: a 2-sample mendelian randomization study. JAMA Psychiat. 2019;76(4):399–408.
Dishman RK, O'Connor PJ. Lessons in exercise neurobiology: the case of endorphins. Ment Health Phys Act. 2009;2(1):4–9.
Peluso MAM, Andrade LHSG. Physical activity and mental health: the association between exercise and mood. Clinics. 2005;60(1):61–70.
Ho FKW, Louie LHT, Chow CB, Wong WHS, Ip P. Physical activity improves mental health through resilience in Hong Kong Chinese adolescents. BMC Pediatr. 2015;15(1):1–9.
Liu Y, Zhao J, Guo W. Emotional roles of mono-aminergic neurotransmitters in major depressive disorder and anxiety disorders. Front Psychol. 2018;9:2201.
Asztalos M, De Bourdeaudhuij I, Cardon G. The relationship between physical activity and mental health varies across activity intensity levels and dimensions of mental health among women and men. Public Health Nutr. 2010;13(8):1207–14.
Haskell WL, Lee I-M, Pate RR, Powell KE, Blair SN, Franklin BA, et al. Physical activity and public health: updated recommendation for adults from the American College of Sports Medicine and the American Heart Association. Circulation. 2007;116(9):1081.
WHO. WHO guidelines on physical activity and sedentary behaviour. Geneva (CH): World Health Organization; 2020.
Aguiar EJ, Gould ZR, Ducharme SW, Moore CC, McCullough AK, Tudor-Locke C. Cadence-based classification of minimally moderate intensity during Overground walking in 21-to 40-year-old adults. J Phys Act Health. 2019;16(12):1092–7.
Ekkekakis P, Parfitt G, Petruzzello SJ. The pleasure and displeasure people feel when they exercise at different intensities. Sports Med. 2011;41(8):641–71.
Biddle SJ, Batterham AM. High-intensity interval exercise training for public health: a big HIT or shall we HIT it on the head? Int J Behav Nutr Phys Act. 2015;12(1):1–8.
Keating CJ, Montilla JÁP, Román PÁL, Del Castillo RM. Comparison of high-intensity interval training to moderate-intensity continuous training in older adults: a systematic review. J Aging Phys Act. 2020;28(5):798–807.
Raymond MJ, Bramley-Tzerefos RE, Jeffs KJ, Winter A, Holland AE. Systematic review of high-intensity progressive resistance strength training of the lower limb compared with other intensities of strength training in older adults. Arch Phys Med Rehabil. 2013;94(8):1458–72.
We are grateful to UK Biobank participants. This research has been conducted using the UK Biobank Resource under application number 7155. We thank Miss Rachel SY Chung for her help in preparing this manuscript.
Ethics approval and consent to participate
UK Biobank was approved by the North-West Multi-Centre Research Ethics Committee (Ref: 11/NW/0382). Written informed consent was obtained from all participants.
Consent for publication
The authors declare that they have no competing interests.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Additional file 1: Table S1
. Participant characteristics by LPA quartiles. Table S2. Participant characteristics by MPA quartiles. Table S3. Participant characteristics by VPA quartiles. Figure S1. Hypothesised causal diagram. Figure S2. Participant flowchart. Figure S3. The associations between device-measured PA and affective disorders without adjusting for other intensity-specific PA. Figure S4. The associations between device-measured PA and affective disorders adjusting for BMI and longstanding illnesses. Figure S5. Two-year landmark analysis for the associations between device-measured PA and affective disorders.
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
About this article
Cite this article
Ho, F.K., Petermann-Rocha, F., Parra-Soto, S. et al. Device-measured physical activity and incident affective disorders. BMC Med 20, 290 (2022). https://doi.org/10.1186/s12916-022-02484-0
- Physical activity
- Mental health