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Mental health and sleep correlates of self-reported outdoor daylight exposure in over 13,000 adults with depression

Published online by Cambridge University Press:  17 February 2025

Jacob J. Crouse Open the ORCID record for Jacob J. Crouse [Opens in a new window] ,
Shin Ho Park Open the ORCID record for Shin Ho Park [Opens in a new window] ,
Brittany L. Mitchell Open the ORCID record for Brittany L. Mitchell [Opens in a new window] ,
Enda M. Byrne Open the ORCID record for Enda M. Byrne [Opens in a new window] ,
Sarah E. Medland Open the ORCID record for Sarah E. Medland [Opens in a new window] ,
Tian Lin Open the ORCID record for Tian Lin [Opens in a new window] ,
Jan Scott Open the ORCID record for Jan Scott [Opens in a new window] ,
Zsofi de Haan Open the ORCID record for Zsofi de Haan [Opens in a new window] ,
Emiliana Tonini Open the ORCID record for Emiliana Tonini [Opens in a new window]  and
Frank Iorfino Open the ORCID record for Frank Iorfino [Opens in a new window]
...Show all authors
Jacob J. Crouse*
Affiliation:
Youth Mental Health and Technology Team, Brain and Mind Centre, The University of Sydney, NSW, Australia
Shin Ho Park
Affiliation:
Youth Mental Health and Technology Team, Brain and Mind Centre, The University of Sydney, NSW, Australia
Brittany L. Mitchell
Affiliation:
Mental Health and Neuroscience Program, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
Enda M. Byrne
Affiliation:
Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia Child Health Research Centre, The University of Queensland, Brisbane, QLD, Australia
Sarah E. Medland
Affiliation:
Mental Health and Neuroscience Program, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
Tian Lin
Affiliation:
Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia
Jan Scott
Affiliation:
Youth Mental Health and Technology Team, Brain and Mind Centre, The University of Sydney, NSW, Australia Academic Psychiatry, Institute of Neuroscience, Newcastle University, Newcastle upon Tyne, UK
Zsofi de Haan
Affiliation:
Youth Mental Health and Technology Team, Brain and Mind Centre, The University of Sydney, NSW, Australia
Emiliana Tonini
Affiliation:
Youth Mental Health and Technology Team, Brain and Mind Centre, The University of Sydney, NSW, Australia
Frank Iorfino
Affiliation:
Youth Mental Health and Technology Team, Brain and Mind Centre, The University of Sydney, NSW, Australia
Naomi R. Wray
Affiliation:
Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia Queensland Brain Institute, The University of Queensland, Brisbane, QLD, Australia Department of Psychiatry, The University of Oxford, Oxford, UK Oxford Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
Nicholas G. Martin
Affiliation:
Mental Health and Neuroscience Program, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
Ian B. Hickie
Affiliation:
Youth Mental Health and Technology Team, Brain and Mind Centre, The University of Sydney, NSW, Australia
*
Corresponding author: Jacob Crouse; Email: jacob.crouse@sydney.edu.au

Abstract

Background

Increasing daylight exposure might be a simple way to improve mental health. However, little is known about daylight-symptom associations in depressive disorders.

Methods

In a subset of the Australian Genetics of Depression Study (N = 13,480; 75% female), we explored associations between self-reported number of hours spent in daylight on a typical workday and free day and seven symptom dimensions: depressive (overall, somatic, psychological); hypo-manic-like; psychotic-like; insomnia; and daytime sleepiness. Polygenic scores for major depressive disorder (MDD); bipolar disorder (BD); and schizophrenia (SCZ) were calculated. Models were adjusted for age, sex, shift work status, employment status, season, and educational attainment. Exploratory analyses examined age-stratified associations (18–24 years; 25–34 years; 35–64 years; 65 and older). Bonferroni-corrected associations (p < 0.004) are discussed.

Results

Adults with depression reported spending a median of one hour in daylight on workdays and three hours on free days. More daylight exposure on workdays and free days was associated with lower depressive (overall, psychological, somatic) and insomnia symptoms (p’s<0.001), but higher hypo-manic-like symptoms (p’s<0.002). Genetic loading for MDD and SCZ were associated with less daylight exposure in unadjusted correlational analyses (effect sizes were not meaningful). Exploratory analyses revealed age-related heterogeneity. Among 18–24-year-olds, no symptom dimensions were associated with daylight. By contrast, for the older age groups, there was a pattern of more daylight exposure and lower insomnia symptoms (p < 0.003) (except for 25–34-year-olds on free days, p = 0.019); and lower depressive symptoms with more daylight on free days, and to some extent workdays (depending on the age-group).

Conclusions

Exploration of the causal status of daylight in depression is warranted.

Keywords

daylightsunlightinsomniacircadianchronobiologymood disorders

Information

Type
Research Article
Information
European Psychiatry , Volume 68 , Issue 1 , 2025 , e41
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited.
Copyright
© The Author(s), 2025. Published by Cambridge University Press on behalf of European Psychiatric Association

Introduction

Mental disorders are associated at multiple levels with circadian disruption [Reference Walker1–Reference Crouse, Park, Byrne, Mitchell, Chan, Scott, Medland, Martin, Wray and Hickie4], and it has been suggested that robust circadian rhythmicity is essential for supporting optimal mental health [Reference Fishbein, Knutson and Zee5–Reference Hickie7]. In humans, a central “circadian” (~24-hour) clock in the brain regulates the timing of most physiological processes and behaviours including mood, energy, activity, and cognition [Reference Mure8–Reference Schmidt10]. Daily exposure to sunlight—a free, readily available resource—is the primary input that regulates circadian rhythms and their alignment to the day-night cycle [Reference Boivin11–Reference Zeitzer17]. Circadian rhythms evolved over millions of years under a relatively stable pattern of bright light during the day and limited light at night [Reference Stevens and Zhu18]. Industrialisation led to the widespread use of artificial indoor lighting, which is too dim during the day for optimal entrainment [Reference Powell19]. The suboptimal intensity and patterns of typical artificial light exposure during the day, and the fact that industrialised societies spend ~90% of their time indoors and only ~2.5 hours in daylight per day [Reference Burns20,Reference Klepeis21], is a major diversion in the light patterns under which our circadian system evolved. Ultimately, this challenges the stability of circadian rhythms, which may impact mental health conditions including depressive disorders [Reference Hickie and Crouse22].

Greater exposure to daylight appears to have a positive effect on mental health in the general population [Reference Wang23–Reference Brown and Jacobs25]. Two studies performed using UK Biobank (N = 85,000–400,000) reported that higher self-rated and objective daylight exposure were associated with lower levels of low mood, anhedonia, antidepressant usage, and major depressive disorder (controlling for nighttime light exposure, photoperiod, sleep quality, sociodemographic factors, physical activity, and cardio-metabolic health) [Reference Burns20,Reference Burns26]. Some community-based studies report opposing findings. One study exploring the effects of sunlight exposure in a community sample reported a lack of association between sunlight and depressive symptoms [Reference Kerr27], and a study of >13,000 university graduates reported that males living in locations with longer daylight hours had a higher (not lower) risk of depression [Reference Henríquez-Sánchez28]. Several small clinical studies suggest a beneficial pattern of association. In subjects with seasonal affective disorder (SAD), those allocated to a 1-hour morning natural light therapy (walking outdoors) reported a larger reduction in depressive symptoms compared to those allocated to daily artificial light therapy [Reference Wirz-Justice29]. And a meteorological study of people with SAD observed that shorter daylight duration and lower daylight exposure were associated with worse depressive symptoms a week later [Reference Sarran30].

Two gaps are worth noting. First, most studies exploring the daylight-depression relationship focus narrowly on sum scores from depressive symptom scales, which usually combine psychological and somatic/physical symptoms. These may represent separate dimensions [Reference Couvy-Duchesne31]. To the best of our knowledge, only one study has examined specific depressive symptom types, finding that participants exposed to more sunlight were less likely to report suicidal thoughts, and more likely to report changes in sleep, appetite, and feelings of worthlessness/guilt [Reference Komulainen32]. Second, studies have neglected other symptom dimensions experienced by people with depression, including psychotic-like phenomena and hypo/mania, which might be influenced by light, as in bipolar disorder [Reference Esaki33–Reference Roguski, Ritter and Smith35]. Examining a link between daylight and hypo/manic symptoms is important, as at lower levels of severity, hypo/mania may actually reflect positive states of increased mood and energy, rather than psychopathology.

Using a volunteer cohort of adults with depression (Australian Genetics of Depression Study), our objective was to explore the associations between self-reported number of hours of outdoor natural light exposure (on workdays and free days) and seven symptom dimensions. Given this cohort is genetically informative, we also explored whether genetic vulnerability to mood and psychotic disorders is associated with lower levels of daylight exposure. We hypothesised that more hours of self-reported daylight exposure would be associated with lower levels of depressive symptoms (overall, psychological, somatic dimensions), insomnia symptoms, daytime sleepiness, and psychotic-like experiences, but higher levels of increased activity and mood (i.e., sub-threshold hypo-mania-like phenomena). Examination of differences in light-symptom associations on workdays vs free days was exploratory. Our analyses and hypotheses were not pre-registered.

Methods

Participants and study design

The Australian Genetics of Depression Study (AGDS) is a volunteer cohort of adults diagnosed with, and/or treated for, a depressive disorder. Recruitment procedures and sample characteristics are described elsewhere [Reference Byrne36]. Participants joined AGDS after responding to a media campaign or a letter from the Australian Government’s Department of Human Services, mailed to 110,000 Australian residents who had received four or more antidepressant prescriptions for any of the 10 most common antidepressants in Australia, over the prior 4.5 years.

Participants completed an online survey about physical and mental health, treatment for depression, and factors related to mental health [Reference Byrne36]. Participants could donate a saliva sample for genetic analyses; >75% returned a sample via mail-out kit [Reference Gomez37].

Sources of bias in AGDS include: (a) high educational attainment; (b) recruited mostly via media appeal (>85%) compared to prescription history invitation; (c) higher female-to-male ratio than population prevalence of unipolar depression (3:1 vs 2:1); and (d) highly recurrent course of depression (modal number of episodes = 13+) [Reference Byrne36].

The study received ethical approval from the QIMR Berghofer Medical Research Institute Human Research Ethics Committee in Brisbane, Australia. Written informed consent was obtained.

Assessments

Demographics

Age, biological sex, marital status, and educational attainment were collected using standard questions.

Daylight exposure

Participants were asked: “On average, how much time do you spend outdoors in natural light per day?”, separately for workdays and free days (e.g., weekend). Responses were in hours and minutes. We employed data cleaning rules to remove implausible values: (a) values like ‘360 hours’ (meant to be ‘360 minutes’); and (b) values greater than the maximum hours of daylight on the longest day of the year (15 hours). 17 and 28 values were deleted for workdays and free days, respectively (<0.001% of data).

Depressive symptoms

The 12-item Somatic and Psychological Health Report (SPHERE-12) [Reference Hickie38] assessed somatic and psychological symptoms of depression. Participants responded to items with the prompt, “Over the past few weeks have you been troubled by…” Responses were coded: “Never or some of the time” (=1); “A good part of the time” (=2); and “Most of the time” (=3). We used a total score and sub-scale scores for the somatic (SOMA-6) and psychological dimensions (PSYCH-6).

Hypo/manic-like symptoms

A 5-item screener adapted from the Altman Self-Rating Mania Scale (ASRMS) [Reference Altman39] assessed lifetime, brief periods of hypo-manic-like symptoms. Participants responded to items with the prompt, “Have you ever experienced a definite period where for more than 2 or 3 days…” Responses were coded: ‘Don’t know’ (=0), ‘No’ (=0), and ‘Yes’ (=1). We used a total score (range=0-5).

Psychotic-like symptoms

A 6-item screener partially adapted from the Community Assessment of Psychic Experiences (CAPE) [Reference Konings40] assessed lifetime psychotic-like symptoms. Participants responded to items with the prompt, “Have you ever…” Responses were coded: “No” (=0) and “Yes” (=1). We used a total score (range = 0–6).

Insomnia symptoms

The 7-item Insomnia Severity Index (ISI) [Reference Morin41] assessed current (last 2 weeks) severity of insomnia symptoms. We used a total score (range = 0–28).

Daytime sleepiness

The 8-item Epworth Sleepiness Scale (ESS) [Reference Johns42] assessed excessive daytime sleepiness. Participants responded to a prompt about their likelihood of dozing in eight situations. Responses were coded: ‘Would never doze’ (=0); “Slight chance of dozing” (=1); ‘Moderate chance of dozing’ (=2); and ‘High chance of dozing’ (=3). We used a total score (range = 0–24).

Employment and shift work

Participants were asked “How many days do you work on average?” and we coded an employment status variable as: “full-time” (4–7 days); “part-time” (1–3 days); and “not working” (0 days). Participants were asked “Which of the following best describes your current work arrangements?” and we coded a “Shift work status” variable as: “shift-worker” (1 = shift work with rotating shifts of irregular shifts; 0 = regular work schedule).

Polygenic risk scores (PGS)

Genotyping was conducted on the Illumina Global Screening Array V2. Samples were merged with the 1000 Genomes [Reference Abecasis43] project samples and genetic principal components were calculated using a set of single nucleotide polymorphisms (SNPs) not in linkage disequilibrium. Pre-imputation quality control was done using PLINK 1.9 [Reference Purcell44,Reference Chang45], including removing SNPs with a minor allele frequency <0.005, SNP call rate <97.5%, and identification of participants with genetic similarity to a European reference group (>4 SD from Ancestry Principal Components [PCs] PC1/PC2 centroid) [46] and Hardy–Weinberg equilibrium (p < 1 × 10−6), before imputation using the Haplotype Reference Consortium 1.1 reference panel [47]. Over 95% of AGDS is of European ancestry and PGS were created only for those of European ancestry. Summary statistics from recent genome-wide association studies (GWAS) were used to create PGS for major depressive disorder (MDD) [Reference Howard48], bipolar disorder (BD) [Reference Stahl49], and schizophrenia (SCZ) [Reference Pardiñas50]. SBayesRc [Reference Zheng51], a Bayesian PGS method, was used to generate allele weights for each PGS. The posterior SNP effects for each disorder were used to generate PGS for each participant using the PLINK – score function [Reference Purcell44]. As some AGDS participants have been included in depression GWAS [Reference Howard48], the depression-PGS was calculated using summary statistics excluding AGDS participants.

Statistical analysis

Analyses were conducted using R (version 4.2.2) [Reference Team52]. The sample size was not pre-determined for this analysis. We included all participants with data about light exposure and any mental health and sleep data. Before analysis, we normalised the variables of the reported hours of daylight exposure on free days and workdays, given their positive skew. Correlations among hours of daylight exposure on workdays and free days and MDD, BD, and SCZ PGS were examined using Spearman correlations (rho). Fourteen regression models were used to examine the associations among the seven symptom total or sub-scale scores (SPHERE-12; PSYCH-6; SOMA-6; ASRMS; CAPE; ISI; ESS) and the self-reported hours of daylight exposure on workdays and free days. We ran separate regression models for workdays and free days given their moderate correlation (r = 0.47). Models were adjusted for relevant confounders (age, sex, employment status, shift worker status, and season). We reserve the use of ‘significant’ for associations with a Bonferroni-corrected p < 0.004 (0.05/14).

Results

The analytic sample included up to 13,480 participants (75% female, mean age = 43.1 years; Table 1). The sample is highly educated (28% have a postgraduate degree, 36% have a degree, and 87% have a post-high school qualification). As reported previously [Reference Byrne36], these rates are higher than other Australian general population control samples such as QSkin (56% with a post-high school qualification).

Table 1.

Socio-demographic characteristics of the sample (N = 13,480)

Most met the criteria for a DSM-5 lifetime major depressive episode (Table 2). Depressive symptoms were in the low-to-moderate range (SPHERE-12; mean = 8.85); 28.2% had suggestive clinical insomnia of moderate severity (ISI = 15–21), 6.2% had suggestive severe clinical insomnia (ISS = 22–28); and 3.4% had excessive sleepiness (ESS = 16–24). The sample reported a median of 3 hours of outdoor light exposure on free days (IQR = 3; range = 0–14) and a median of 1 hour on workdays (IQR = 1.5; range = 0–14) (Figure 1).

Table 2.

Mental health and sleep characteristics of the sample (N = 13,480)

Note: DSM-5=Diagnostic and Statistical Manual of Mental Disorders (version 5); SPHERE-12=Somatic and Psychological Health Report; CAPE=Community Assessment of Psychic Experiences; ASRMS=Altman Self-Rating Mania Scale; ISI=Insomnia Severity Index; ESS=Epworth Sleepiness Scale.

Figure 1.

Self-reported hours of natural daylight exposure on free days and workdays in adults with depression (N = 13,480). Note: black vertical line represents the median.

We explored whether three PGS (which we would expect to be associated with the symptom measures) were associated with daylight exposure. Neither the MDD, BD, or SCZ PGS were associated with daylight exposure on workdays (correlation coefficients <0.05); however, there were very small but significant associations between fewer hours of daylight exposure on free days and higher PGS for MDD (rho = −0.03; p = 0.019) and SCZ (rho = −0.03; p = 0.006). Given the size of these correlations, we elected to exclude the PGS as covariates in the regression models.

Light exposure and mental and sleep health

In Figure 2, we summarise the results of the regression models exploring associations among symptoms and self-reported daylight exposure on workdays and free days.

Figure 2.

Self-reported number of hours of daylight exposure on free days and workdays and dimensions of mental and sleep health. Note: Coefficients to the left of the broken line indicate that more outdoor daylight exposure is associated with a lower level of symptoms. Adjusted models include covariates of age, sex, shift worker status, employment status, educational attainment, and season. SPHERE-12 = Somatic and Psychological Health Report; CAPE = Community Assessment of Psychic Experiences; ASRMS = Altman Self-Rating Mania Scale; ISI = Insomnia Severity Index; ESS = Epworth Sleepiness Scale.

On workdays (adjusting for covariates), more self-reported hours of daylight exposure was associated with lower overall depressive symptoms (SPHERE-12; β = −0.08; p < 1.94 × 10−11); lower somatic symptoms (SOMA-6; β = −0.06; p < 1.00 × 10−7); lower psychological symptoms (PSYCH-6; β = −0.08; p < 9.65 × 10−11); lower insomnia symptoms (ISI; β = −0.09; p < 8.15 × 10−12); and higher hypo-mania-like symptoms (ASRMS; β = 0.04; p = 0.001). Associations with psychotic-like symptoms (CAPE; β = 0.01; p = 0.250) and excessive daytime sleepiness (ESS; β = 0.02; p = 0.050) were not significant. Age was associated with all seven outcomes (p’s < 0.008), with increasing age associated with higher daytime sleepiness (ESS), but lower symptoms on the remaining six scales. Model outputs are in the Supplementary Materials.

On free days (adjusting for covariates), more self-reported hours of daylight exposure was associated with lower overall depressive symptoms (SPHERE-12; β = −0.11; p < 1.19 × 10−19); lower somatic symptoms (SOMA-6; β = −0.09; p < 5.61 × 10−13); lower psychological symptoms (PSYCH-6; β = −0.11; p < 2.50 × 10−18); lower insomnia symptoms (ISI; β = −0.10; p < 7.24 × 10−17); and higher hypo-mania-like symptoms (ASRMS; β = 0.05; p < 1.01 × 10−5). Associations with psychotic-like symptoms (CAPE; β = −0.02; p = 0.214) and daytime sleepiness were not significant (ESS; β = −0.01; p = 0.322). Age was associated with all seven outcomes (p’s < 0.002), and in the same direction as the workday models. Model outputs are in the Supplementary Materials.

Exploratory analyses

Given the associations between symptoms and age (Spearman’s r = −0.11 to −0.26) and the small correlations between age and self-reported light exposure on workdays and free days (rho = 0.09–0.12), we conducted exploratory analyses of light-symptom associations by age-group. We used categories commonly used in national and epidemiologic studies (e.g., Australian Bureau of Statistics, Australian Institute of Health and Welfare) [53,54]: 18–24 years; 25–34 years; 35–64 years; and 65+. We re-examined associations between PGS for depression, schizophrenia, and bipolar disorder within each age group; correlation coefficients were small: 18–24 years (rho = −0.03 to 0.02); 25–34 years (rho = −0.05 to −0.01); 35–64 years (rho = −0.05 to −0.01); and 65+ (rho = 0–0.03). We elected to exclude them as covariates in the age-stratified models. Table 3 summarises the light exposure, sleep, and mental health ratings across the age groups.

Table 3.

Differences in light exposure and mental health and sleep ratings across age groups

Age-stratified analyses revealed age-related heterogeneity (Figure 3). For the youngest group (18–24 years; N = 1,636), there were no significant associations between any symptom measure and light exposure on workdays or free days. For the three older age groups, there was a generally consistent association of lower insomnia symptoms (ISI) and more hours of daylight exposure on workdays and free days; but not for the 25–35-year-olds on free days. Similarly, there was a generally consistent association of lower overall depressive symptoms (SPHERE-12) and more hours of daylight exposure on free days for the two groups aged 35+; and to a lesser extent on workdays, which was significant for only the two groups aged 25–64. Model outputs are in the Supplementary Materials.

Figure 3.

Exploratory analyses of self-reported hours of daylight exposure on free days and workdays and sleep and mental health across age-groups. Note: Models are adjusted for age, sex, shift worker status, employment status, educational attainment, and season. We illustrate unadjusted associations in Supplementary Figure 2. SPHERE-12 = Somatic and Psychological Health Report; CAPE = Community Assessment of Psychic Experiences; ASRMS = Altman Self-Rating Mania Scale; ISI = Insomnia Severity Index; ESS=Epworth Sleepiness Scale.

Sensitivity analysis

Finally, we explored whether the associations between daylight exposure and sleep and mental health were robust to adjustment for self-rated chronotype and insomnia (two variables associated with daylight exposure and mental health [Reference Freeman55–Reference Wong59]). After adding the reduced Morningness-Eveningness Questionnaire (rMEQ) total score and the ISI to separate regression models, we found that the patterns of association were similar, with many of the previously significant associations remaining nominally significant (p < 0.05), albeit attenuated in magnitude. The general pattern of more daylight on workdays and free days with lower depressive symptoms (SPHERE-12, SOMA-6, PSYCH-6) and higher hypo-mania-like symptoms was robust to these adjustments in analyses of the whole sample (Supplementary Figures 3–4). In the age-stratified analyses, the pattern of more daylight on workdays and free days with lower depressive symptoms (SPHERE-12, SOMA-6, PSYCH-6) was robust to adjustments in the 35–64-year-old group, while adjusting for ISI had a more significant attenuating influence in the 25–34-year-olds and 65+ year olds (Supplementary Figures 5–6).

Discussion

In an Australian volunteer cohort of people with depression, adults aged 25+ who report spending more time in natural light on a typical day generally reported lower levels of recent depressive and insomnia symptoms. Alongside other studies [Reference Zeitzer17,Reference Wang23], our findings support a link between daylight and depressive phenomena. Sleepiness and psychosis-like symptoms were largely unrelated to daylight exposure. The results for hypo-mania-like symptoms and somatic and psychological depressive symptoms varied more strongly according to age group. Before discussing these findings, several limitations are worth mentioning.

First, exposure to natural light was self-reported using a single-item question that is less comprehensive than other scales (e.g., Harvard Light Exposure Assessment [Reference Bajaj60]). Relatedly, differences in built environments (e.g., window access) may cause differences in indoor daylight exposure between people reporting the same hours of outdoor exposure. Furthermore, the timing of daylight and nocturnal light exposure were not ascertained, which matters given the associations between both day-time [Reference Burns26] and night-time light exposure and depression [Reference Burns26,Reference Paksarian61]. Third, although assessments of mental health, sleep, and daylight exposure were completed concurrently, our data lacked temporal information. Inferences about directionality and causality are not possible, especially given that these states (and light exposure) are dynamic [Reference Hickie, Merikangas, Carpenter, Iorfino, Scott, Scott and Crouse62]. Fourth, the strength of the PGSs is reliant on the strength of the GWAS, and the PGS may be underpowered to detect the effect on light exposure. Fifth, this cohort is biased toward high educational attainment, which is associated with mental and sleep health and time spent indoors [Reference Schweizer63]. Associations between daylight exposure and mental health and sleep may be affected by collider bias induced by the selection of high educational attainment [Reference Munafò64]. The magnitude of this is unknown, but we note that coefficients were similar when including/excluding educational attainment as a covariate (Supplementary Figure 1). Sixth, there are risks associated with overexposure to ultraviolet light. Recommendations about increasing daylight exposure for the benefit of sleep and mental health should also communicate how to safeguard potential harms. Finally, possible confounders such as physical activity and day length at the time of assessment were unavailable, and while data on lifetime medications was recorded, data on current medications which may increase (e.g., citalopram) [Reference McGlashan65] or decrease light sensitivity (e.g., lithium) [Reference Roguski, Ritter and Smith35] were unavailable. We now discuss interpretations of these findings and areas for innovation.

As hypothesised, the associations between time in daylight and mental/sleep health largely indicated a relationship between better mental/sleep health with more time in daylight. This was clearest for insomnia and depressive symptoms (Figures 2 and 3). Associations were generally stronger on free days, wherein people have more control over how they spend their time. Our first interpretation of these associations is a causal effect of daylight on symptoms. There may be a beneficial effect of light on the neural regulation of mood circuits, as observed in animals whereby light information is signalled via photosensitive retinal ganglion cells to neurons in the perihabenular hub of the thalamus that are purported to control mood-related behaviours [Reference Fernandez66–Reference Weil70]. There may also be a beneficial effect of daylight on the entrainment and stabilisation of circadian rhythms, which regulate mood, energy, and sleep [Reference Wright71,Reference Blume, Garbazza and Spitschan72]. An alternative interpretation is noncausal effect, whereby opportunities for behaviours benefiting mood/sleep (e.g., socialising, exercise) [Reference Lowe73] may occur more in daylight. Conversely, these associations might be explained by an effect of symptoms on daylight: people with depression may be more likely to stay indoors when depressed or sleeping poorly [Reference Fried and Nesse74]. Finally, low daylight exposure and symptoms might be caused by common genetic or unmeasured factors (e.g., sedentary behaviour). These questions might be clarified by applying causal inference methods to longitudinal designs that measure dynamic mental states and objective recordings of sleep, activity, and light [Reference Cain75].

One noteworthy (exploratory) finding was the age-related differences in light exposure and light-symptom associations. On free days, the 18–24-year and 25–34-year age groups reported similar hours of light exposure, while the 35-64-year and 65+ groups reported more hours of daylight (Table 3). By contrast, on workdays, the 65+ age group reported more daylight exposure than the 18–24-year, 25–34-year, and 35–64-year groups. One study reported that although all age groups spend more than half of their waking hours in dim or moderate room light intensity, older adults spend more of their waking day in brighter light [Reference Scheuermaier, Laffan and Duffy76]. In the three older groups, there were stronger associations between depressive and insomnia symptoms and daylight exposure on workdays and free days, with some slight differences depending on the specific association (Figure 3). By contrast, daylight and symptoms were unrelated in the 18–24-year-olds. One interpretation is that, if a causal effect of daylight exposure on symptoms is real, it may be less relevant to youth, for whom psychopathology may be more strongly influenced by other factors (e.g., stressors around the transition to adulthood; academic pressure; bullying; neurobiological maturation; sensitivity to social influence) [Reference McGorry77–Reference Herrman79]. By contrast, the mental health and sleep of individuals in the older age-groups may be more strongly dependent on light exposure or activities correlated with time spent outdoors (e.g., socialising, exercise).

Another noteworthy finding was that more daylight exposure was associated with higher lifetime hypo-mania-like symptoms. A simple interpretation is that some symptoms (e.g., increased activity) may cause people to spend more time outdoors. An alternative interpretation is that the items used are based on a screening tool that does not ask about severity/impairment, and as such, these items may be indexing positive mental states related to being outside (e.g., being active, feeling elated, increased self-confidence) rather than psychopathology.

In conclusion, we observed that in adults with depression (and particularly those aged 25 and older), more reported time spent in daylight is associated with better mental and sleep health, and especially depressive and insomnia symptoms. Exploration of the causal status of daylight on mental and sleep states is warranted [Reference Hickie and Crouse80]. Better estimation of light exposure using wearables may provide novel insights.

Supplementary material

The supplementary material for this article can be found at http://doi.org/10.1192/j.eurpsy.2025.20.

Data availability statement

Access to AGDS data is restricted due to the ethical guidelines governing the study but may be accessible following ethical review and data transfer agreements. Please contact Nicholas Martin () with queries related to accessing AGDS data.

Acknowledgements

We are indebted to the participants for giving their time to contribute to this study. We thank all the people who helped in the conception, implementation, beta testing, media campaign, and data cleaning. The study protocol used by 23andMe, Inc., was approved by an external AAHRPP-accredited institutional review board. We would like to thank the research participants and employees of 23andMe, Inc., for making this work possible. The GWAS summary statistics from Howard et al. [Reference Chang45] include those provided by 23andMe, Inc.

Financial support

The Australian Genetics of Depression Study was funded by grant 108663 from the Australian National Health and Medical Research Council (NHMRC). This work was supported by NHMRC Investigator Grants to JJC (2008196); BLM (2017176); SEM (1172917); NRW (1173790); NGM (1172990); and IBH (2016346); funding from PRE-EMPT NHMRC Centre for Research Excellence and the University of Queensland Health Research Accelerator Program to EMB; philanthropic donations in the form of the Trevor Scott PhD Scholarship to ZdH; Breakthrough Mental Health Research Foundation Fellowship to ET; and donations from families affected by mental illness, who would like to be left anonymous.

Competing interest

JJC, SHP, BLM, EMB, SEM, JS, ZdH, ET, FI, NRW, and NGM have nothing to disclose. IBH has previously led community-based and pharmaceutical industry-supported (Wyeth, Eli Lily, Servier, Pfizer, AstraZeneca) projects focused on the identification and better management of anxiety and depression. He is the Chief Scientific Advisor to, and a 3.2% equity shareholder in, InnoWell Pty Ltd. InnoWell was formed by the University of Sydney (45% equity) and PwC (Australia; 45% equity) to deliver the $30M Australian Government-funded Project Synergy (2017-20) and to lead transformation of mental health services internationally through the use of innovative technologies.

References

Walker, WH et al. Circadian rhythm disruption and mental health. Transl Psychiatry. 2020;10(1):28.10.1038/s41398-020-0694-0CrossRefGoogle ScholarPubMed
Carpenter, JS et al. Circadian depression: a mood disorder phenotype. Neurosci Biobehav Rev. 2021;126:79–101.10.1016/j.neubiorev.2021.02.045CrossRefGoogle ScholarPubMed
Crouse, JJ et al. Circadian rhythm sleep–wake disturbances and depression in young people: Implications for prevention and early intervention. Lancet Psychiatr. 2021;8(9):813–23.10.1016/S2215-0366(21)00034-1CrossRefGoogle Scholar
Crouse, JJ, Park, SH, Byrne, EM, Mitchell, BL, Chan, K, Scott, J, Medland, SE, Martin, NG, Wray, NR, Hickie, IB. Evening Chronotypes With Depression Report Poorer Outcomes of Selective Serotonin Reuptake Inhibitors: A Survey-Based Study of Self-Ratings. Biol Psychiatry. 2024;96(1):4–14.10.1016/j.biopsych.2023.12.023CrossRefGoogle ScholarPubMed
Fishbein, AB, Knutson, KL, Zee, PC. Circadian disruption and human health. J Clin Invest. 2021;131(19).10.1172/JCI148286CrossRefGoogle ScholarPubMed
Karatsoreos, IN. Effects of circadian disruption on mental and physical health. Curr Neurol Neurosci Rep. 2012;12(2):218–25.10.1007/s11910-012-0252-0CrossRefGoogle ScholarPubMed
Hickie, IB et al. Manipulating the sleep-wake cycle and circadian rhythms to improve clinical management of major depression. BMC Med. 2013;11(1):79.10.1186/1741-7015-11-79CrossRefGoogle ScholarPubMed
Mure, LS et al. Diurnal transcriptome atlas of a primate across major neural and peripheral tissues. Science. 2018;359(6381):eaao0318.10.1126/science.aao0318CrossRefGoogle ScholarPubMed
Reppert, SM, Weaver, DR. Coordination of circadian timing in mammals. Nature. 2002;418(6901): 935–41.10.1038/nature00965CrossRefGoogle ScholarPubMed
Schmidt, C et al. A time to think: circadian rhythms in human cognition. Cogn Neuropsychol. 2007;24(7):755–89.10.1080/02643290701754158CrossRefGoogle ScholarPubMed
Boivin, DB et al. Dose-response relationships for resetting of human circadian clock by light. Nature. 1996;379(6565):540–2.10.1038/379540a0CrossRefGoogle ScholarPubMed
Deacon, S, Arendt, J. Melatonin-induced temperature suppression and its acute phase-shifting effects correlate in a dose-dependent manner in humans. Brain Res. 1995;688(1-2):77–85.10.1016/0006-8993(95)96872-ICrossRefGoogle Scholar
Lockley, SW, Brainard, GC, Czeisler, CA. High sensitivity of the human circadian melatonin rhythm to resetting by short wavelength light. J Clin Endocrinol Metabol. 2003;88(9):4502–5.10.1210/jc.2003-030570CrossRefGoogle ScholarPubMed
Roenneberg, T, Merrow, M. The network of time: understanding the molecular circadian system. Curr Biol. 2003;13(5):R198–207.10.1016/S0960-9822(03)00124-6CrossRefGoogle Scholar
Warman, VL et al. Phase advancing human circadian rhythms with short wavelength light. Neurosci Lett. 2003;342(1–2):37–40.10.1016/S0304-3940(03)00223-4CrossRefGoogle ScholarPubMed
Zaidi, FH, et al. Short-wavelength light sensitivity of circadian, pupillary, and visual awareness in humans lacking an outer retina. Curr Biol. 2007;17(24): 2122–8.10.1016/j.cub.2007.11.034CrossRefGoogle ScholarPubMed
Zeitzer, JM et al. Sensitivity of the human circadian pacemaker to nocturnal light: melatonin phase resetting and suppression. J Physiol. 2000;526(Pt 3):695–702.10.1111/j.1469-7793.2000.00695.xCrossRefGoogle Scholar
Stevens, RG, Zhu, Y. Electric light, particularly at night, disrupts human circadian rhythmicity: Is that a problem? Philos Trans R Soc Lond Ser B Biol Sci. 2015;370(1667).Google ScholarPubMed
Powell, AL. Today’s trends in lighting. Electr Eng. 1936;55(10):1100–10.10.1109/EE.1936.6539190CrossRefGoogle Scholar
Burns, AC et al. Time spent in outdoor light is associated with mood, sleep, and circadian rhythm-related outcomes: a cross-sectional and longitudinal study in over 400,000 UK Biobank participants. J Affect Disord. 2021;295:347–52.10.1016/j.jad.2021.08.056CrossRefGoogle Scholar
Klepeis, NE et al. The national human activity pattern survey (NHAPS): a resource for assessing exposure to environmental pollutants. J Expo Sci Environ Epidemiol. 2001;11(3):231–52.10.1038/sj.jea.7500165CrossRefGoogle ScholarPubMed
Hickie, IB, Crouse, JJ. Sleep and circadian rhythm disturbances: plausible pathways to major mental disorders? World Psychiatry. 2024;23(1):150–151.10.1002/wps.21154CrossRefGoogle ScholarPubMed
Wang, R et al. Cross-sectional associations between long-term exposure to particulate matter and depression in China: the mediating effects of sunlight, physical activity, and neighborly reciprocity. J Affect Disord. 2019;249:8–14.10.1016/j.jad.2019.02.007CrossRefGoogle Scholar
O’Hare, C, et al. Seasonal and meteorological associations with depressive symptoms in older adults: A geo-epidemiological study. J Affect Disord. 2016;191:172–9.10.1016/j.jad.2015.11.029CrossRefGoogle ScholarPubMed
Brown, MJ, Jacobs, DE. Residential light and risk for depression and falls: results from the LARES study of eight European cities. Public Health Rep. 2011;126(Suppl 1):131–40.10.1177/00333549111260S117CrossRefGoogle ScholarPubMed
Burns, AC et al. Day and night light exposure are associated with psychiatric disorders: an objective light study in >85,000 people. Nat Mental Health. 2023;1(11):853–62.10.1038/s44220-023-00135-8CrossRefGoogle Scholar
Kerr, DC, et al. Two longterm studies of seasonal variation in depressive symptoms among community participants. J Affect Disord. 2013;51(3):837–42.10.1016/j.jad.2013.07.019CrossRefGoogle Scholar
Henríquez-Sánchez, P, et al. Geographical and climatic factors and depression risk in the SUN project. Eur J Pub Health. 2014;24(4):626–31.10.1093/eurpub/cku008CrossRefGoogle ScholarPubMed
Wirz-Justice, A, et al. Natural’ light treatment of seasonal affective disorder. J Affect Disord. 1996;37(2–3):109–20.10.1016/0165-0327(95)00081-XCrossRefGoogle ScholarPubMed
Sarran, C et al. Meteorological analysis of symptom data for people with seasonal affective disorder. Psychiatry Res. 2017;257:501–5.10.1016/j.psychres.2017.08.019CrossRefGoogle ScholarPubMed
Couvy-Duchesne, B et al. Validation and psychometric properties of the Somatic and Psychological HEalth REport (SPHERE) in a young Australian-based population sample using non-parametric item response theory. BMC Psychiatr. 2017;17(1):279.10.1186/s12888-017-1420-1CrossRefGoogle Scholar
Komulainen, K et al. Associations of long-term solar insolation with specific depressive symptoms: evidence from a prospective cohort study. J Psychiatr Res. 2022;151:606–10.10.1016/j.jpsychires.2022.05.038CrossRefGoogle ScholarPubMed
Esaki, Y et al. Association between light exposure at night and manic symptoms in bipolar disorder: cross-sectional analysis of the APPLE cohort. Chronobiol Int. 2020;37(6):887–96.10.1080/07420528.2020.1746799CrossRefGoogle ScholarPubMed
Esaki, Y et al. Effect of nighttime bedroom light exposure on mood episode relapses in bipolar disorder. Acta Psychiatr Scand. 2022;146(1):64–73.10.1111/acps.13422CrossRefGoogle ScholarPubMed
Roguski, A, Ritter, P, Smith, DJ. Sensitivity to light in bipolar disorder: Implications for research and clinical practice. Br J Psychiatr. 2024;224(5):143–6.10.1192/bjp.2023.150CrossRefGoogle ScholarPubMed
Byrne, EM et al. Cohort profile: the Australian genetics of depression study. BMJ Open. 2020;10(5):e032580.10.1136/bmjopen-2019-032580CrossRefGoogle ScholarPubMed
Gomez, L et al. Phenotypic and genetic factors associated with donation of DNA and consent to record linkage for prescription history in the Australian Genetics of Depression Study. Eur Arch Psychiatr Clin Neurosci. 2023;273(6):1359–68.10.1007/s00406-022-01527-0CrossRefGoogle ScholarPubMed
Hickie, IB et al. Development of a simple screening tool for common mental disorders in general practice. Med J Aust. 2001;175(S1):S10–7.Google ScholarPubMed
Altman, EG, et al. The Altman self-rating mania scale. Biol Psychiatr. 1997;42(10):948–55.10.1016/S0006-3223(96)00548-3CrossRefGoogle ScholarPubMed
Konings, M et al. Validity and reliability of the CAPE: a self-report instrument for the measurement of psychotic experiences in the general population. Acta Psychiatr Scand. 2006;114(1):55–61.10.1111/j.1600-0447.2005.00741.xCrossRefGoogle Scholar
Morin, CM, et al. The insomnia severity index: psychometric indicators to detect insomnia cases and evaluate treatment response. Sleep. 2011;34(5):601–8.10.1093/sleep/34.5.601CrossRefGoogle ScholarPubMed
Johns, MW. A new method for measuring daytime sleepiness: the Epworth sleepiness scale. Sleep. 1991;14(6):540–5.10.1093/sleep/14.6.540CrossRefGoogle ScholarPubMed
Abecasis, GR, et al. A map of human genome variation from population-scale sequencing. Nature. 2010;467(7319):1061–73.Google ScholarPubMed
Purcell, S et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet. 2007;81(3):559–75.10.1086/519795CrossRefGoogle Scholar
Chang, CC et al. Second-generation PLINK: rising to the challenge of larger and richer datasets. Gigascience. 2015;4:7.10.1186/s13742-015-0047-8CrossRefGoogle Scholar
National Academies of Sciences, Engineering, and Medicine. Using population descriptors in genetics and genomics research: a new framework for an evolving field. Washington, DC; 2023.Google Scholar
Haplotype Reference Consortium. A reference panel of 64,976 haplotypes for genotype imputation. Nat Genet. 2016;48(10):1279–83.10.1038/ng.3643CrossRefGoogle Scholar
Howard, DM et al. Genome-wide meta-analysis of depression identifies 102 independent variants and highlights the importance of the prefrontal brain regions. Nat Neurosci. 2019;22(3):343–52.10.1038/s41593-018-0326-7CrossRefGoogle ScholarPubMed
Stahl, EA et al. Genome-wide association study identifies 30 loci associated with bipolar disorder. Nat Genet. 2019;51(5):793–803.10.1038/s41588-019-0397-8CrossRefGoogle ScholarPubMed
Pardiñas, AF et al. Common schizophrenia alleles are enriched in mutation-intolerant genes and in regions under strong background selection. Nat Genet. 2018;50(3):381–9.10.1038/s41588-018-0059-2CrossRefGoogle ScholarPubMed
Zheng, Z et al. Leveraging functional genomic annotations and genome coverage to improve polygenic prediction of complex traits within and between ancestries. Nat Genet. 2024;56(5):767–77.10.1038/s41588-024-01704-yCrossRefGoogle ScholarPubMed
Team, RC. R: A language and environment for statistical computing. Vienna: R Foundation for Statistical Computing; 2013. Available online at https://www.R-project.org/.Google Scholar
Australian Institute of Health and Welfare. Health of young people. 2024.Google Scholar
Australian Bureau of Statistics. National Study of Mental Health and Wellbeing. 2020–2022.Google Scholar
Freeman, D et al. The effects of improving sleep on mental health (OASIS): a randomised controlled trial with mediation analysis. Lancet Psychiatry. 2017;4(10):749–58.10.1016/S2215-0366(17)30328-0CrossRefGoogle ScholarPubMed
Harvey, AG et al. Treating insomnia improves mood state, sleep, and functioning in bipolar disorder: a pilot randomized controlled trial. J Consult Clin Psychol. 2015;83(3):564–77.10.1037/a0038655CrossRefGoogle ScholarPubMed
Freeman, D et al. Sleep disturbance and psychiatric disorders. Lancet Psychiatry. 2020;7(7):628–37.10.1016/S2215-0366(20)30136-XCrossRefGoogle ScholarPubMed
Brown, J, Makker, HK. An approach to excessive daytime sleepiness in adults. BMJ. 2020;368:m1047.10.1136/bmj.m1047CrossRefGoogle ScholarPubMed
Wong, TR, et al. Dynamic modelling of chronotype and hypo/manic and depressive symptoms in young people with emerging mental disorders. Chronobiology Int. 2023:1–11.Google Scholar
Bajaj, A et al. Validation of a light questionnaire with real-life photopic illuminance measurements: the Harvard light exposure assessment questionnaire. Cancer Epidemiol Biomarker Prev. 2011;20(7):1341–9.10.1158/1055-9965.EPI-11-0204CrossRefGoogle ScholarPubMed
Paksarian, D, et al. Association of outdoor artificial light at night with mental disorders and sleep patterns among US adolescents. JAMA Psychiatr. 2020.10.1001/jamapsychiatry.2020.1935CrossRefGoogle ScholarPubMed
Hickie, IB, Merikangas, KR, Carpenter, JS, Iorfino, F, Scott, EM, Scott, J, Crouse, JJ. Does circadian dysrhythmia drive the switch into high- or low-activation states in bipolar I disorder? Bipolar Disord. 2023;25(3):191–199.10.1111/bdi.13304CrossRefGoogle ScholarPubMed
Schweizer, C et al. Indoor time–microenvironment–activity patterns in seven regions of Europe. J Expo Sci Environ Epidemiol. 2007;17(2):170–81.10.1038/sj.jes.7500490CrossRefGoogle ScholarPubMed
Munafò, MR et al. Collider scope: when selection bias can substantially influence observed associations. Int J Epidemiol. 2018;47(1):226–35.10.1093/ije/dyx206CrossRefGoogle ScholarPubMed
McGlashan, EM et al. The SSRI citalopram increases the sensitivity of the human circadian system to light in an acute dose. Psychopharmacology. 2018;235(11):3201–9.10.1007/s00213-018-5019-0CrossRefGoogle Scholar
Fernandez, DC et al. Light affects mood and learning through distinct retina-brain pathways. Cell. 2018;175(1):71–84.e18.10.1016/j.cell.2018.08.004CrossRefGoogle ScholarPubMed
LeGates, TA, et al. Aberrant light directly impairs mood and learning through melanopsin-expressing neurons. Nature. 2012;491(7425):594–8.10.1038/nature11673CrossRefGoogle ScholarPubMed
LeGates, TA, Fernandez, DC, Hattar, S. Light as a central modulator of circadian rhythms, sleep and affect. Nat Rev Neurosci. 2014;15(7):443–54.10.1038/nrn3743CrossRefGoogle ScholarPubMed
LeGates, TA, Kvarta, MD. Illuminating a path from light to depression. Nat Neurosci. 2020;23(7):785–7.10.1038/s41593-020-0659-xCrossRefGoogle ScholarPubMed
Weil, T et al. Daily changes in light influence mood via inhibitory networks within the thalamic perihabenular nucleus. Sci Adv. 2022;8(23):eabn3567.10.1126/sciadv.abn3567CrossRefGoogle ScholarPubMed
Wright, KP Jr, et al. Entrainment of the human circadian clock to the natural light-dark cycle. Curr Biol, 2013;23(16):1554–8.10.1016/j.cub.2013.06.039CrossRefGoogle Scholar
Blume, C, Garbazza, C, Spitschan, M. Effects of light on human circadian rhythms, sleep and mood. Somnologie (Berl). 2019;23(3):147–56.10.1007/s11818-019-00215-xCrossRefGoogle ScholarPubMed
Lowe, H et al. Does exercise improve sleep for adults with insomnia? A systematic review with quality appraisal. Clin Psychol Rev. 2019;68:1–12.10.1016/j.cpr.2018.11.002CrossRefGoogle ScholarPubMed
Fried, EI, Nesse, RM. The impact of individual depressive symptoms on impairment of psychosocial functioning. PLoS One. 2014;9(2):e90311.10.1371/journal.pone.0090311CrossRefGoogle ScholarPubMed
Cain, SW et al. Evening home lighting adversely impacts the circadian system and sleep. Sci Rep. 2020;10(1):1–10.10.1038/s41598-020-75622-4CrossRefGoogle Scholar
Scheuermaier, K, Laffan, AM, Duffy, JF. Light exposure patterns in healthy older and young adults. J Biol Rhythm. 2010;25(2):113–22.10.1177/0748730410361916CrossRefGoogle ScholarPubMed
McGorry, PD et al. The lancet psychiatry commission on youth mental health. Lancet Psychiatry. 2024;11(9):731–74.10.1016/S2215-0366(24)00163-9CrossRefGoogle ScholarPubMed
Patel, V, et al. Mental health of young people: a global public-health challenge. Lancet. 2007;369(9569):1302–13.10.1016/S0140-6736(07)60368-7CrossRefGoogle ScholarPubMed
Herrman, H, et al. Time for united action on depression: a Lancet-World Psychiatric Association Commission. Lancet. 2022.10.1016/S0140-6736(21)02141-3CrossRefGoogle ScholarPubMed
Hickie, IB, Crouse, JJ. Sleep and circadian rhythm disturbances: plausible pathways to major mental disorders? World Psychiatry. 2024;23(1):150–151.10.1002/wps.21154CrossRefGoogle ScholarPubMed
Figure 0

Table 1. Socio-demographic characteristics of the sample (N = 13,480)

Figure 1

Table 2. Mental health and sleep characteristics of the sample (N = 13,480)

Figure 2

Figure 1. Self-reported hours of natural daylight exposure on free days and workdays in adults with depression (N = 13,480). Note: black vertical line represents the median.

Figure 3

Figure 2. Self-reported number of hours of daylight exposure on free days and workdays and dimensions of mental and sleep health. Note: Coefficients to the left of the broken line indicate that more outdoor daylight exposure is associated with a lower level of symptoms. Adjusted models include covariates of age, sex, shift worker status, employment status, educational attainment, and season. SPHERE-12 = Somatic and Psychological Health Report; CAPE = Community Assessment of Psychic Experiences; ASRMS = Altman Self-Rating Mania Scale; ISI = Insomnia Severity Index; ESS = Epworth Sleepiness Scale.

Figure 4

Table 3. Differences in light exposure and mental health and sleep ratings across age groups

Figure 5

Figure 3. Exploratory analyses of self-reported hours of daylight exposure on free days and workdays and sleep and mental health across age-groups. Note: Models are adjusted for age, sex, shift worker status, employment status, educational attainment, and season. We illustrate unadjusted associations in Supplementary Figure 2. SPHERE-12 = Somatic and Psychological Health Report; CAPE = Community Assessment of Psychic Experiences; ASRMS = Altman Self-Rating Mania Scale; ISI = Insomnia Severity Index; ESS=Epworth Sleepiness Scale.

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