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. 2025 Oct 6;25:3362. doi: 10.1186/s12889-025-24618-8

The role of sunlight in sleep regulation: analysis of morning, evening and late exposure

Luiz Antônio Alves de Menezes-Júnior 1,2,3,4,✉, Thais da Silva Sabião 1,2, Júlia Cristina Cardoso Carraro 1,2, George Luiz Lins Machado-Coelho 1,5, Adriana Lúcia Meireles 1,2,3
PMCID: PMC12502225  PMID: 41053799

Abstract

Background

Recent lifestyle changes have reduced sunlight exposure, impacting circadian rhythms and sleep regulation. This study investigates how sunlight exposure at different times of the day affects sleep parameters.

Methods

This cross-sectional study included 1,762 adults from the Iron Quadrilateral region, Brazil, and was conducted between October and December 2020. Sunlight exposure was self-reported for three periods: before 10 a.m., between 10 a.m. and 3 p.m., and after 3 p.m. Sleep quality was assessed using the Pittsburgh Sleep Quality Index (PSQI), which also provided information on total sleep time (total minutes slept), sleep latency (time to fall asleep), sleep efficiency (ratio of time spent asleep to time in bed), and the midpoint of sleep (the halfway point between sleep onset and wake-up time, indicative of circadian rhythm alignment). Associations between sunlight exposure and sleep outcomes were evaluated using linear regression models, adjusted for sociodemographic and behavioral variables.

Results

The midpoint of sleep was the most affected sleep parameter, showing significant associations with sunlight exposure across all timeframes, particularly in the morning. Every 30-minute increment of morning sun exposure (before 10 a.m.) was associated with a 23-minute reduction in the midpoint of sleep (-0:23 hh: mm; 95%CI: -0:36, -0:10; beta: -0.387; 95%CI: -0.607, -0.166). Sunlight exposure after 3 p.m. also reduced the midpoint of sleep but to a lesser extent (-0:19 hh: mm; 95%CI: -0:36, -0:03; beta: -0.325; 95%CI: -0.600, -0.051). Furthermore, an increase in morning sun exposure was significantly associated with a lower PSQI total score, improving sleep quality (beta: -0.184; 95%CI: -0.362, -0.006). No significant associations were observed between sunlight exposure and total sleep time, latency, or efficiency.

Conclusion

Morning sunlight exposure influences the regulation of the sleep midpoint and overall sleep quality. These findings highlight the potential role of morning sun exposure in aligning circadian rhythms and improving sleep health.

Keywords: Sunlight, Circadian rhythm, Sleep quality, Midpoint of sleep, Sun exposure

Introduction

Sunlight plays a fundamental role in the regulation of various physiological processes, particularly through its influence on the circadian rhythm. This rhythm is primarily regulated by the light-dark cycle, which in turn affects the secretion of melatonin, a hormone critical for sleep onset and quality [1]. Exposure to natural light, especially sunlight, can modulate sleep patterns by affecting the synchronization of the circadian clock with the external environment. Research has shown that light exposure during the day, particularly in the morning, is linked to improved sleep outcomes, including better sleep quality, faster sleep onset, and longer sleep duration [2].

Circadian rhythms are governed by the suprachiasmatic nucleus (SCN) located in the hypothalamus, which serves as the central clock of the human body. The SCN responds to light signals received by the retina, influencing the production of melatonin and other neurochemicals associated with alertness and sleep [3]. Exposure to light is an important clue for the circadian clock, aligning it with the solar day and promoting an earlier start to sleep. This may help individuals who experience delayed sleep phase syndrome or suffer from social jetlag, defined as the misalignment between circadian and social clocks that often results in chronic sleep loss [4] due to irregular sleep patterns [5, 6].

Beyond the characteristic variables relating to sleep quality, such as total sleep time, sleep latency, and efficiency, one of the sleep variables often little examined is the midpoint of sleep, which refers to the intermediate point between sleep onset and awakening. The midpoint of sleep is closely linked to circadian rhythms and can provide information about the timing of sleep, particularly in the context of social jetlag or delayed sleep phase syndrome [4]. Sun exposure, especially in the morning, can shift the midpoint of sleep earlier, promoting more synchronized and restorative sleep. On the contrary, insufficient exposure to natural light can delay this midpoint, leading to misalignment between biological and social time, often resulting in chronic sleep deficits [7–9].

Exposure to sunlight has decreased due to work activities and sedentary lifestyles in front of screens, reducing exposure to natural light during the day [10]. The COVID-19 pandemic has worsened this situation, drastically changing daily routines around the world, with lockdowns and social restrictions limiting opportunities for outdoor activities and exposure to natural sunlight [11]. Many individuals shifted to indoor environments for extended periods due to remote work or unemployment, significantly reducing their exposure to morning and afternoon sunlight [12]. This sudden lifestyle change likely disrupted sleep patterns for many, exacerbating issues like delayed sleep phases, increased sleep latency, and reduced sleep efficiency. Even after the pandemic, we believe that altered routines, driven by social isolation measures, remote work, and other containment strategies, may have led to long-term reductions in sunlight exposure [13, 14]. During the pandemic, many individuals adopted new habits that limited outdoor activities [13], and those who continued working from home [14] or maintained pandemic-related routines may have experienced ongoing sleep disruptions due to reduced natural light exposure. This diminished sunlight exposure could affect circadian regulation and overall sleep quality.

Therefore, understanding the relationship between the timing of sunlight exposure and its interfaces with sleep parameters is crucial, particularly in light of how the pandemic may have long-lasting effects on sun exposure habits. This study aims to evaluate how sun exposure at different times of the day affects various sleep parameters, including total sleep time, sleep latency, sleep efficiency, midpoint of sleep, and sleep quality. The hypothesis is that exposure to the sun at different times of the day affects sleep quality differently, with morning exposure having a greater impact.

Methods

Study design

This is a population-based, cross-sectional survey that was carried out between October and December 2020 in two medium-sized cities (Ouro Preto and Mariana) in the south-central Minas Gerais region, also known as the Iron Quadrangle, which is one of the largest iron ore producing regions in Brazil [15]. The survey was conducted using stratified, multistage probability cluster sampling.

The sample design was executed by the conglomerate in three phases: census sector, household and resident. The National Household Sample Survey (PNAD) [16], the Family Budget Survey (POF) (IBGE 2020), the “Saúde em Beagá” survey [17], and, more recently, the “EPICOVID19” study[18] served as the foundation for this design. Consequently, the census sectors were regarded as primary sampling units in the study design, chosen with a probability proportional to the number of households, and the number of households was used as a measure of size, derived from the summary of the 2010 census of population. To reduce the possibility of selecting a sample from non-representative sectors, prior stratification was carried out before selecting the primary units. This involved taking into account the average income based on information from the Brazilian Institute of Geography and Statistics (IBGE) 2010 demographic census. Consequently, the final sample was guaranteed to be representative of the three socioeconomic strata (wages less than one, wages between one and three, and wages greater than four).

The updated list of current household units in the primary sampling units (selected census sectors) was used to systematically select the households that made up the secondary sampling units. Private households with occupants make up the household units. Following the census sectors’ selection, the household selection interval (k) for the systematic sampling was computed using the following formula: k = Ni/(xi/ni), where Ni is the total number of households in the census sector, xi is the sample size, and ni is the number of households to be selected. In this manner, the entire geographic area was covered and a proportionate number of homes per sector was obtained. After choosing the first household in the census sector based on IBGE indications, the next household was systematically sampled by the household selection interval (k).

The individuals, chosen through a random sampling procedure, constituted the tertiary sampling units. A list of every adult resident in the chosen home was created, and one resident was chosen at random to take part in the study. The sample calculation indicated that 1,464 people would be the minimum sample size.

We assessed 1,762 people who represented adult residents of the two cities’ urban areas throughout the data collection process. The interviewer used an electronic form to conduct in-person interviews with the residents while adhering to national protocols to stop the coronavirus from spreading, like wearing personal protective equipment. The questionnaire was broken down into sections based on general health conditions, living habits, sleep quality, and sociodemographic and economic factors.

Outcome variables: Sleep parameters

The Pittsburgh Sleep Quality Index (PSQI), a validated questionnaire intended to measure sleep quality and disturbances over 1 month, was used to assess sleep parameters [19]. The 19 items that make up the PSQI are divided into seven categories: (1) subjective quality of sleep; (2) sleep latency; (3) duration of sleep; (4) habitual sleep efficiency; (5) disturbances of sleep; (6) use of sleeping medication; and (7) dysfunction during the day. Every element is assigned a number between 0 and 3, with higher numbers denoting lower-quality sleep. The seven components add up to the total PSQI score, which is a number between 0 and 21. Higher scores denote lower overall quality of sleep. Sleep quality was classified as “good” (PSQI score ≤< 5) or “poor” (PSQI score > 5) in this study, and used the PSQI total score as a continuous outcome variable [20].

In addition to the total PSQI score, the following specific sleep parameters were used as outcome variables: Total sleep time represents the amount of sleep, in minutes, reported by participants each night. It was measured through the PSQI’s sleep duration component. Sleep latency, in minutes, it takes for participants to fall asleep after going to bed. Sleep efficiency represents the percentage of time spent asleep relative to the total time spent in bed, which is calculated as: (time spent in bed/total sleep time)​×100. The midpoint of sleep reflects the halfway point between the time the participant falls asleep and the time they wake up, which is a crucial indicator of circadian phase alignment. It was calculated using the following formula: sleep onset time + (total sleep time/2​). Sleep onset time was determined based on self-reported bedtimes, and wake-up times were recorded in the PSQI. A delayed midpoint of sleep suggests a misalignment of the circadian rhythm.

The decision to focus on these specific parameters, among the seven evaluated by the PSQI, was made due to their strong association with physiological markers of sleep quality and their implications for metabolic and circadian health. Other components, such as subjective sleep quality or sleep disturbances, though informative, were not included in this analysis as they do not provide direct quantifiable data on sleep duration or circadian timing, which are central to the objectives of this study.

Exposure variable: Sunlight exposure

Sunlight exposure was evaluated based on participant self-report. Each participant was asked to report how often and for how long they were exposed to sunlight at different times of the day: before 10 a.m., between 10 a.m. and 3 p.m., and after 3 p.m. For each period, the following questions were asked: “From Monday to Sunday, how many days per week are you exposed to sunlight before 10 a.m.?” and “On those days, how long (in minutes per day) are you exposed to sunlight?“. Similar questions were asked for the time intervals between 10 a.m. and 3 p.m. and after 3 p.m. Based on these responses, the total amount of time exposed to sunlight for each period was calculated. The daily average sunlight exposure for each period was computed using the formula: [weekly frequency of sunlight (0 to 7 days) x daily time of sunlight (minutes)/7]. This provided the average daily sunlight exposure for the three distinct periods: morning (before 10 a.m.), midday (10 a.m. to 3 p.m.), and evening (after 3 p.m.). For the statistical models, sunlight exposure was treated as a continuous variable and analyzed in increments of both 10 and 30 min to assess its impact on sleep parameters. The use of different increments allowed for a more detailed understanding of the dose-response relationship between sunlight exposure and sleep outcomes.

Covariates

The variables for potential confounding controls in the analysis of the relationship between sunlight exposure and sleep parameters were included in the questionnaire. Sex (male or female), age group (18–34 years; 35–59 years; ≥ 60 years), marital status (single or married); living status (living alone or not living alone); education level ( 0 to 8 years; 9 to 11 years or > 12 years of study); employment status during the pandemic (employed or not employed); and work-from-home schedule (percentage of active workers who were working from home) were the sociodemographic and economic variables evaluated. The IBGE categories for self-reported race and skin color were used for evaluation [21]. The participants were categorized into white, black, brown, indigenous, and yellow.

The following health conditions and lifestyle variables were assessed. Chronic diseases were assessed by self-reporting the following diseases: hypertension, diabetes, asthma, lung disease, chronic kidney disease, cancer, heart disease, or thyroid disease, which were dichotomized into morbidity (reporting at least one disease) and without morbidity (no disease). In addition, the following lifestyle variables were evaluated: whether or not they currently smoke and whether or not they currently drink alcoholic beverages.

Movement behavior, physical activity, and sedentary behavior were also assessed. Sedentary behavior was measured by the total time spent sitting during weekdays and weekends. The measurement was carried out using the following question: ‘Currently, how much time on average do you spend sitting per day? (Include time used for cell phone, TV, computer, tablet, books, car, and bus)’ and classified considering a cut-off point of ≥ 9hours. Physical activity during leisure time was evaluated based on the VIGITEL questionnaire and classified according to the degree of activity (active when meeting the recommended weekly minimum of 150–300 min of moderate-intensity aerobic physical activity or 75–150 min of vigorous-intensity aerobic physical activity otherwise). Participants who mainly engaged in light physical activity, those who did not meet these thresholds, were classified as “inactive in leisure time”. Light physical activity was considered insufficient to meet the recommended guidelines, in line with current public health recommendations [22].

Self-reported weight (kg) and height (cm) were used to calculate the body mass index (BMI). The BMI classifications were as follows: underweight (BMI < 18.5 kg/m2 if < 60 years old; BMI < 23.0 kg/m2 if ≥ 60 years old), eutrophic (BMI 18.5–24.9 kg/m2 if < 60 years old; BMI 23.0–28.0 kg/m2 if ≥ 60 years old), and overweight (BMI ≥ 25.0 kg/m2 if < 60 years old; BMI ≥ 28.0 kg/m2 if ≥ 60 years old). For adults and the elderly, respectively, according to WHO [23] and PAHO [24]. The Generalized Anxiety Disorder Scale (GAD-7) and the Patient Health Questionnaire (PHQ-9) scales were used to assess the symptoms of depression and anxiety, respectively. Scores of 10 or higher on both scales were taken into consideration when determining the presence of symptoms of depression and anxiety [25, 26].

Statistical analysis

The sample weight was calculated to adjust the natural weights of the design and/or rectify issues arising from the absence or refusal to respond. This was accomplished by assigning distinct weights to the sample elements, which corresponded to the inverse of the product from the probabilities included in the various selection stages [27]. When calculating the sample weights, we considered the probabilities of inclusion of the sample elements in the three stages: (1) probability of the census sector being randomized; (2) probability of the household being randomized; and (3) probability of the individual over 18 years old being randomized. The adjustment was implemented to compensate for the non-response loss of interviews and to calibrate the sample weight for the population totals by sex and age group to align with the population projections for 2019. Therefore, all statistical analyses were conducted using the “svy” package in Stata® version 15.0, taking into account the complex survey design and sample weighting factors. A significance level of 0.05 was set for all analyses.

Moreover, using the online program Dagitty, version 3.2, a theoretical causality model based on a directed acyclic graph (DAG) was created by the exposure variable (sunlight), outcome (sleep parameters), and covariates. There were established causal relationships between the variables, shown by arrows (Fig. 1). Every variable in the DAG was represented by a rectangle, and each color had a distinct meaning: the response variable was blue, circled by black, and the exposure variable was green. Additionally, variables thought to be potential confounders were included: the outcome variable’s antecedents are in blue, and the exposure and outcome variables’ antecedents are in red. A minimum set of confounding variables was chosen to fit the analyses using the backdoor criterion to prevent needless adjustment, erroneous associations, and estimation errors [28]. The model was adjusted by the following minimum and sufficient set of variables: age (continuous variable), sex (male or female), education (0 to 8 years; 9 to 11 years or > 12 years), employment status (not workers or active workers), presence of chronic diseases (no or yes), and movement behaviors (physical activity, minutes day of moderate to vigorous physical activity; and sedentary behavior, hours/day of total sitting time).

Fig. 1.

Fig. 1

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Direct acyclic graph (DAG) of association with sunlight exposure and sleep quality, with covariates. COVID-Inconfidentes Study, 2020 Note: The variables in green with the symbol “►” inside the rectangle are explanatory; the variable in blue with the letter “I” inside the rectangle is the outcome variable; the variables in red without the symbol “►” inside the rectangle are the antecedents of the outcome and exposure variables. Variables in white are the collider variables

For the variables defined by the DAG, both unadjusted and adjusted multivariable linear regression models were employed to assess the relationship between sunlight exposure and sleep parameters. The outcome variables included were total sleep time (minutes), sleep latency (minutes), sleep efficiency (percentage), midpoint of sleep (hours), and the PSQI total score (range: 0–21). Sunlight exposure was analyzed in increments of 10 and 30 min for three different periods: before 10 a.m., between 10 a.m. and 3 p.m., and after 3 p.m. To generate these exposure increments, a formula was applied where the average daily exposure time was divided by 10 or 30, allowing for the assessment of the impact of incremental increases in sunlight exposure on sleep outcomes.

Before running the regression models, assumptions of linear regression were tested. Linearity between the independent and dependent variables was checked, and residual plots were examined to confirm homoscedasticity. The normality of residuals was assessed using the Shapiro-Wilk test and Q-Q plots, while the Durbin-Watson test was applied to ensure independence of residuals. Multicollinearity was evaluated through the variance inflation factor (VIF), with all values indicating no significant collinearity between explanatory variables. Finally, Cook’s distance and leverage statistics were used to identify any potential outliers or influential data points, which were found to be within acceptable limits. All assumptions were satisfied, ensuring the validity of the regression models used in the analysis.

In regression analyses, beta coefficients (β) for sleep outcomes measured in hours were converted to minutes to facilitate interpretation. This conversion was calculated by multiplying the beta coefficient by 60 (minutes/hour).

Results

Characteristics of study participants

The sociodemographic, health, and behavioral characteristics of the study population are presented in Table 1, which also displays the midpoint of sleep and sun exposure across different periods of the day. The sample comprised 1,762 study participants, the majority (74.4%) of whom self-reported their skin color as brown, black, yellow, or indigenous; more than half of them (53.2%) were married; and had more than eight years of education (70.8%). The participants’ ages ranged from 35 to 59 years (45.6%), 51.9% were women, and 39.7% had nine to eleven years of education. The participants’ monthly family income was less than two minimum wages (41.1%) and they had no chronic diseases (60.2%). As far as their occupation went, 52.5% of them were employed. Regarding their habits, 58.2% of them consumed alcohol, and 17% of them smoked at the moment. In addition, 15.3% engaged in sedentary behavior and 69.2% were physically inactive in leisure time. Furthermore, 52.5% of participants had trouble sleeping, and 23.4% and 15.8% of participants, respectively, reported having symptoms of anxiety and depression (Table 1).

Table 1.

Sociodemographic, health, and behavioral characteristics according to midpoint of sleep and sunlight exposure, during the COVID-19 pandemic.COVID-Inconfidentes study, 2020

Characteristics Frequency
% (CI95%)		 The midpoint of sleep (hours) Sun exposure
< 10 a.m		 Sun exposure
10–15 a.m		 Sun exposure
> 15 a.m
Sex
Male 48.1 (41.0-55.3) 4.9 (4.6–6.3)a 59.6 (46.2–73.0)b 76.5 (48.2-104.7)a 8.5 (5.1–11.9)a
Female 51.9 (44.7–59.0) 5.4 (4.0-5.8)a 18.8 (13.5–24.0)a 20.7 (11.1–30.3)b 32.4 (22.3–42.6)b
Age
18–34 years 35.6 (31.2–40.3) 6.5 (5.4–7.6)a 43.8 (29.9–57.7)a 62.2 (40.2–84.1)a 22.0 (16.1–27.9)a
35–59 years 45.6 (41.1–50.2) 4.3 (3.6-5.0)b 35.5 (25.9–45.2)a 45.1 (24.6–65.5)b 21.5 (11.0–32.0)a
≥ 60 years 18.8 (15.4–22.6) 4.8 (3.8–5.9)b 34.7 (25.2–44.1)a 24.1 (16.4–31.7)c 11.9 (5.6–18.2)b
Skin color
White 25.6 (20.8–31.1) 4.8 (4.1–5.6)a 31.9 (19.9–43.9)a 31.9 (16.6–47.3)a 17.2 (10.6–23.9)a
Black, Brown, Indigenous and Yellow 74.4 (68.9–79.2) 5.3 (4.5–6.1)a 40.5 (29.8–51.2)a 52.6 (31.3–73.9)a 20.8 (13.2–28.4)a
Marital status 1
Married 53.3 (47.3–59.2) 4.6 (3.8–5.4)a 44.1 (30.3–57.9)a 51.3 (22.7–79.9)a 21.9 (12.2–31.6)a
Not married 46.7 (40.8–52.7) 5.9 (4.9–6.8)b 31.7 (23.9–39.6)a 42.7 (28.2–57.2)a 17.6 (11.9–23.2)a
Education
0–8 years 31.1 (26.7–36.0) 4.0 (3.1-5.0)a 44.6 (31.1–58.0)a 52.9 (24.4–81.3)a 23.3 (8.7–37.9)a
9–11 years 39.7 (35.6–44.0) 4.9 (4.1–5.8)b 49.5 (37.6–61.4)a 65.7 (44.9–86.4)b 25.4 (18.7–32.2)a
≥ 12 years 29.1 (23.8–35.1) 6.7 (5.7–7.8)c 16.6 (13.6–19.5)b 16.6 (11.6–21.5)c 8.9 (6.3–11.4)a
Family income 2
≤ 2 MW 45.6 (40.6–50.7) 5.4 (4.3–6.5)a 38.5 (27.1–50.0)a 54.5 (31.6–77.3)a 25.3 (14.3–36.3)a
>2 a ≤ 4 MW 29.5 (25.0-34.5) 4.7 (4.0-5.4)a 42.9 (32.1–53.8)a 48.3 (35.8–60.8)a 19.1 (13.5–24.7)a
>4 MW 24.9 (20.3–30.1) 5.3 (4.1–6.6)a 32.5 (16.0-49.1)a 33.1 (2.7–63.4)b 11.2 (6.5–15.9)b
Worker 3
No 48.2 (43.5–52.9) 5.5 (4.6–6.4)a 26.8 (17.3–36.2)a 29.2 (9.16–49.3)a 15.4 (5.2–25.5)a
Yes 51.8 (47.1–56.5) 4.9 (4.1–5.6)a 49.1 (39.4–58.8)b 60.0 (46.9–81.2)b 24.2 (18.2–30.1)a
Smoking
No 83.1 (78.7–86.7) 5.3 (4.5-6.0)a 37.4 (27.4–47.5)a 47.2 (26.9–67.5)a 19.6 (12.4–26.8)a
Yes 16.9 (13.3–21.3) 4.7 (3.7–5.7)a 42.5 (27.8–57.2)a 47.6 (35.3–59.8)a 21.4 (14.2–28.5)a
Alcohol consumption
No 41.7 (35.9–47.8) 4.6 (3.9–5.3)a 34.2 (26.8–41.5)a 40.7 (28.8–52.6)a 18.3 (11.4–25.1)a
Yes 58.3 (52.2–64.1) 5.6 (4.7–6.4)a 41.3 (28.1–54.6)a 52.2 (25.5–78.9)a 21.2 (12.1–30.2)a
Physical activity in leisure time4
Active 30.8 (26.3–35.8) 5.3 (4.2–6.5)a 43.0 (31.0-54.9)a 43.9 (27.2–60.5)a 17.3 (11.5–23.1)a
Inactive 69.2 (64.2–73.7) 5.2 (4.5–5.9)a 37.1 (25.5–48.6)a 49.6 (26.3–72.9)a 21.4 (13.3–29.6)a
Sedentary behavior 5
< 9 h 84.7 (81.0-87.8) 5.0 (4.3–5.8)a 43.0 (33.0-53.1)a 55.1 (34.9–75.3)a 22.4 (15.0-29.9)a
≥ 9 h 15.3 (12.2–18.9) 6.3 (4.7–7.9)a 17.9 (12.1–23.9)b 20.4 (12.9–27.9)b 11.1 (6.8–15.4)b
Sleep quality 6
Good 47.5 (43.6–51.4) 4.9 (3.9–5.8)a 43.0 (32.1–53.9)a 50.6 (33.4–67.8)a 22.4 (17.1–27.7)a
Poor 52.5 (48.6–56.4) 5.5 (4.7–6.3)a 34.0 (25.0–43.0)a 44.3 (25.1–63.4)a 16.7 (7.6–27.7)a
Anxiety 7
No 64.9 (61.0-68.6) 4.9 (4.3–5.4)a 39.8 (32.9–46.8)a 48.3 (34.3–62.4)a 21.6 (14.5–28.6)a
Yes 35.1 (31.4–39.0) 6.2 (4.8–7.6)b 33.4 (15.4–51.3)a 44.0 (12.8–72.3)a 14.6 (8.4–20.8)b
Depression 7
No 75.3 (70.8–79.2) 5.0 (4.3–5.7)a 40.9 (31.0-50.7)a 49.3 (29.7–68.8)a 20.7 (13.8–27.6)a
Yes 24.7 (20.8–29.2) 6.1 (4.8–7.5)a 24.8 (11.2–38.4)a 37.0 (13.5–60.4)a 15.7 (6.1–25.3)a
MW Minimum wage.
Equal letters in the column indicate no statistically significant differences between the groups (p > 0.05). Different letters indicate statistically significant differences (p < 0.05).
1 Not married: Widowed, divorced, single.
2 Minimum wage value: BRL 1045.00 z USD 194.25 (1 USD ¼ 5.3797 BRL).
 3 Not workers: Unemployed, pensioner, retiree.
 4 Physically inactive (< 150 min/week of moderate PA or < 75 min/week of vigorous activity).
 5 Sedentary behavior was measured by total time spent sitting.
 6 Sleep quality assessed by the Pittsburgh Sleep Quality Index.
7 Anxiety and depression were evaluated by symptoms with GAD-7 and PHQ-9, self-reported medical diagnosis, and use of medications.
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No significant differences were observed for the midpoint of sleep across sexes, with both men and women averaging 4:48 h. However, men had significantly more sunlight exposure before 10 a.m. and after 3 p.m. than women. Age was also a factor, with older individuals (≥ 60 years) having an earlier midpoint of sleep and greater sunlight exposure in the morning than younger participants (18–34 years). Married participants had earlier midpoints and more morning sunlight exposure than unmarried individuals. Other factors, such as education and health behaviors, showed distinct patterns. Participants with higher education had more midday sun exposure, while those who were physically active also reported higher exposure, particularly in the early morning. Mental health indicators, such as anxiety and depression, were associated with delayed midpoints of sleep and lower sunlight exposure, particularly in the morning and late afternoon. Sleep quality also played a role, with those reporting poor sleep quality having a later midpoint and lower sun exposure (Table 1).

Exposure to sunlight and sleep parameters

The most commonly observed bedtime was between 9:00 p.m. and 11:00 p.m. (61.6%), while the most frequent wake-up time was between 5:00 a.m. and 7:00 a.m. (62.5%). The midpoint of sleep was 5:18 a.m. (95% CI: 4:54 a.m. – 5:83 a.m.), with the highest frequencies occurring between 1:00 a.m. and 3:00 a.m. (49.4%) (Fig. 2). The mean total sleep time was 7.01h per day (95%CI: 6.81h – 7.21h), with an average sleep latency of 47.00 min (95%CI: 41.42–58.59), and a sleep efficiency of 84.28% (95%CI: 82.83–85.74). The mean PSQI total score was 6.33 (95%CI: 6.03–6.62). Regarding sunlight exposure, participants were exposed to an average of 53.60 min before 10 a.m. (95%CI: 40.58–66.63), 65.09 min between 10 and 15 a.m. (95%CI: 44.36–85.83), and 31.27 min after 3 p.m. (95%CI: 21.70-40.84) (Table 2).

Fig. 2.

Fig. 2

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Circular histogram of sleep onset time, awake time, and midpoint of sleep frequency in adults. COVID-Inconfidentes, 2020 Legend: (A) Circular histogram of sleep start times. (B) Circular histogram of wake up times. (C) Circular histogram of sleep midpoint. Sleep midpoint reflects the halfway point between the time the participant falls asleep and the time they wake up: sleep onset time + (total sleep time/2​)

Table 2.

Descriptive statistics of sleep parameters and sunlight exposure during the COVID-19 pandemic. COVID-Inconfidentes study, 2020

Mean 95%CI Linearized Std. Err.
Lower bound Upper bound
Sleep parameters
Total sleep time (hour/day) 7.01 6.81 7.21 0.99
Sleep latency (minutes) 47.00 41.42 58.59 2.81
Sleep efficiency (%) 84.28 82.83 85.74 0.73
The midpoint of sleep (hour) 5.18 4.54 5.83 0.32
PSQI total score (points 0–21) 6.33 6.03 6.62 0.15
Sunlight exposure
< 10 a.m (minutes/day) 53.60 40.58 66.63 6.57
10–15 a.m (minutes/day) 65.09 44.36 85.83 10.44
>15 a.m (minutes/day) 31.27 21.70 40.84 4.82
This table presents the mean, 95% confidence intervals (95% CI), and linearized standard error for sleep parameters and sunlight exposure in the study population.
Total sleep time represents the average number of hours participants sleep per day. Sleep latency represents the average time (in minutes) it takes participants to fall asleep. Sleep efficiency represents the ratio of time spent asleep to time spent in bed, expressed as a percentage. The midpoint of sleep represents the time point halfway between sleep onset and wake time, measured in hours. PSQI total score are a measure of overall sleep quality, ranging from 0 (better sleep) to 21 (worse sleep). Sunlight exposure represents the average time (minutes/day) participants were exposed to sunlight per day, categorized into three groups based on the time of exposure (< 10 a.m., 10–15 a.m., and > 15 a.m.).
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In the linear regression analysis, the midpoint of sleep showed the strongest association with sunlight exposure, being significantly affected by exposure before 10 a.m. and after 3 p.m. However, no significant association was observed for sunlight exposure between 10 a.m. and 3 p.m. In the multivariate model, for every 30-minute increase in sunlight exposure before 10 a.m., there was a 23-minute reduction in the midpoint of sleep (-0:23 hh:mm; 95%CI: -0:39, -0:14; beta: -0.387; 95%CI: -0.644, -0.233). Sunlight exposure after 3 p.m. also exhibited an association, though less pronounced (-0:19 hh:mm; 95%CI: -0:36, -0:03; beta: -0.325; 95%CI: -0.600, -0.051). Furthermore, the total PSQI score was significantly associated with sunlight exposure before 10 a.m., with a 0.184-point reduction in the score for every 30-minute increase (beta: -0.184; 95%CI: -0.362, -0.006). Other sleep parameters, such as total sleep time, latency, and efficiency, did not show significant associations with the various sunlight exposure timeframes (Table 3).

Table 3.

Linear regression results for association of sunlight exposure with sleep parameters during the COVID-19 pandemic. COVID-Inconfidentes study, 2020

Sun exposure < 10 a.m Sun exposure 10 a.m – 3 p.m Sun exposure > 3 p.m
Increments of 10 min Increments of 30 min Increments of 10 min Increments of 30 min Increments of 10 min Increments of 30 min
Total sleep time
Crude -0.012 (-0.040;0.015) -0.037 (-0.120;0.046) -0.019 (-0.046;0.008) -0.056 (-0.138;0.025) -0.004 (-0.052;0.043) -0.014 (-0.158;0.129)
Adjusted -1.111 (-3.107;0.884) -3.335 (-9.323;2.653) -0.918 (-2.855;1.018) -2.754 (-8.565;3.055) 0.252 (-2.7603.265) 0.757 (-8.282;9.797)
Sleep latency
Crude -0.832 (-1.72;0.061) -2.495 (-5.172;0.182) 0.440 (-0.988;1.867) 1.320 (-2.963;5.604) -0.419 (-1.738;0.901) -1.257 (-5.215; 2.702)
Adjusted -0.420 (-1.411;0.570) -1.261 (-4.234;1.711) 0.700 (-0.756;2.156) 2.100 (-2.269;6.469) -0.256 (-1.679;1.167) -0.767 (-5.037; 3.502)
Sleep efficiency
Crude 0.077 (-0.216;0.370) 0.231 (-0.649;1.110) -0.068 (-0.400;0.264) -0.205 (-1.201;0.792) 0.082 (-0.425; 0.590) 0.247 (-1.275;1.769)
Adjusted -0.013 (-0.310;0.284) -0.039 (-0.932;0.852) -0.160 (-0.518;0.197) -0.482 (-1.556;0.592) 0.087 (-0.338;0.513) 0.262 (-1.016;1.540)
Midpoint of sleep
Crude -0.146 (-0.215; -0.078) * -0.439 (-0.644;-0.233) * -0.012 (-0.073;-0.048) * -0.038 (-0.219;0.143) * -0.093(−0.185; −0.001) * -0.279(-0.556;-0.002) *
Adjusted -0.129 (-0.202; -0.055) * -0.387 (-0.607;-0.166) * -0.040 (-0.105; 0.024) -0.121 (-0.316;0.074) -0.108 (-0.200; −0.016) * -0.325 (-0.600;-0.051) *
PSQI total score
Crude -0.058 (-0.113; -0.002) * -0.173 (-0.339; -0.007) * 0.013 (-0.027;0.054) 0.040 (-0.082;0.162) -0.037 (-0.121;0.047) -0.111 (-0.362;0.141)
Adjusted -0.061 (-0.120; -0.002) * -0.184 (-0.362; -0.006) * 0.023 (-0.020;0.067) 0.069 (-0.062;0.202) -0.031 (-0.106;0.044) -0.093 (-0.319;0.133)
Sleep parameters: Total sleep time (hours/day), sleep latency (minutes), sleep efficiency (%), midpoint of sleep (hours), and PSQI total score (points, 0–21). The table reports beta coefficients (β), representing the change in each sleep parameter for a 10 or 30-minute increase in sunlight exposure, along with 95% confidence intervals (95% CI)
Values in bold and with * represent statistically significant values (p-value < 0.05)
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Discussion

The results of this study highlight the importance of morning sun exposure for regulating sleep midpoint and overall sleep quality. The significant reduction in sleep midpoint with morning exposure suggests that sunlight may play a crucial role in adjusting circadian rhythms. Our findings indicate that early morning light exposure can help align the internal circadian clock, contributing to healthier sleep patterns.

Adequate sleep is essential for overall health and well-being, influencing both mental and physical health. Among the various sleep parameters, the midpoint of sleep is a key indicator of circadian rhythm alignment. The midpoint, which marks the halfway point between sleep onset and waking, reflects an individual’s internal biological clock. Research suggests that deviations in the sleep midpoint, particularly delayed sleep, can lead to adverse health outcomes, such as metabolic disorders, cardiovascular disease, and psychological disorders [29–32]. Furthermore, alterations in the timing of sleep midpoint have been strongly associated with metabolic dysfunction. Recent studies indicate that a later sleep midpoint is linked to changes in several metabolic pathways, particularly those involved in inflammation and oxidative stress [33]. For instance, research by [33] found that delayed midpoints are associated with metabolites like erythrulose, a product of advanced glycation, which may contribute to higher risks of diabetes and cardiovascular diseases. Additionally, delayed midpoints were inversely related to metabolites in the γ-glutamyl pathway, which plays a critical role in amino acid metabolism and antioxidant defense mechanisms. This suggests that disruptions in sleep timing could exacerbate metabolic stress and oxidative damage. Therefore, maintaining an early average sleep time can act as a protective factor, promoting healthier metabolic function and reducing the risk of related diseases [33].

The midpoint of sleep is particularly sensitive to disruptions in light exposure, with delayed midpoint timing being a marker of circadian misalignment [34]. This misalignment can contribute to poor sleep quality, difficulty waking up, and even daytime fatigue. In alignment with existing literature, our study confirms that morning sunlight exposure, especially before 10 a.m., is linked to earlier sleep timing and an earlier midpoint of sleep. Studies have demonstrated that exposure to morning sunlight, particularly before 10 a.m., is associated with earlier sleep onset and a more aligned circadian rhythm. This highlights the critical role of morning light as an external cue (zeitgeber), which helps to reset the internal biological clock and mitigate the effects of social jetlag and circadian misalignment [35]. Despite its importance, limited epidemiological data is evaluating the direct impact of sunlight exposure on sleep parameters. This study highlights the substantial influence of sunlight exposure on the midpoint of sleep, with morning light exposure being the most impactful.

Morning sunlight, in particular, helps regulate the secretion of melatonin, a hormone crucial for sleep regulation, thereby improving sleep onset and sleep quality. Increased sunlight exposure also correlates with lower levels of daytime sleepiness and improved alertness during the day. In contrast, reduced exposure to sunlight, especially early in the morning, can have profound effects on the circadian system, delaying the onset of sleep and prolonging the midpoint of sleep. This delay creates a cascade of negative effects, including poorer sleep quality, increased daytime fatigue, and decreased cognitive performance [35, 36]. Our data showed that individuals exposed to sunlight before 10 a.m. had significantly lower PSQI scores, reflecting better overall sleep quality.

While the association between afternoon light exposure (after 3 p.m.) and sleep was weaker, we observed a significant reduction in the midpoint of sleep with greater late-afternoon exposure. This could potentially be explained by two mechanisms. First, afternoon sunlight exposure might signal the approaching end of the daylight period, reinforcing the circadian cycle. Alternatively, increased physical activity during afternoon sunlight exposure may contribute to improved sleep parameters, as physical activity is known to positively impact sleep efficiency and quality [37]. Although our study did not find a direct association between sunlight exposure and sleep efficiency, these factors may interact to influence overall sleep health.

Another important pathway through which sunlight impacts sleep is through its role in stimulating vitamin D production in the skin. Vitamin D plays a vital role in various physiological processes, including the regulation of sleep [38, 39]. Studies suggest that adequate levels of vitamin D are associated with improved sleep quality [38, 40–42] Menezes-Júnior and Sabião [10, 43], potentially due to its role in modulating inflammatory pathways and regulating mood [44–46]. Thus, sunlight exposure may enhance sleep both directly by aligning circadian rhythms and indirectly by boosting vitamin D synthesis, which in turn contributes to better sleep quality and overall health.

The robust methodology employed in our study, which included face-to-face interviews conducted during the COVID-19 pandemic, a household survey, and a representative random sample of residents from various socioeconomic strata, strengthens the findings. To develop theories that can bolster the analysis’s underlying presumptions, the hypotheses were also meticulously defined by recent scientific literature and expressed in terms of counterfactuals.

A key limitation of this study is the assessment of the sleep midpoint as an overall measure, without distinguishing between the midpoint of weekdays and weekends. This distinction could have provided more detailed indicators of the variability caused by social jetlag, which typically arises from differing sleep schedules on workdays versus free days. However, the overall midpoint used in this study still offers valuable information, representing the average sleep behavior over the entire week. This broader measure can capture general circadian alignment and habitual sleep timing, making it a useful indicator for long-term health outcomes. Another limitation is that exposure to artificial light, including screen time, was not controlled in this study. Light exposure, especially from screens in the evening, could influence both sleep onset and the circadian rhythm, potentially affecting the sleep midpoint and its interpretation. Therefore, future research should not only explore the specific impacts of weekday versus weekend midpoint differences but also control for environmental factors like artificial light exposure and screen time, which may influence these relationships. Additionally, sleep efficiency was estimated using the Pittsburgh Sleep Quality Index (PSQI), which does not directly measure total sleep duration or nocturnal awakenings, as it relies on self-reported bedtime, wake-up time, and sleep latency. More precise assessments of sleep efficiency require objective measures such as actigraphy or polysomnography, which were not feasible in this study due to data collection limitations during the COVID-19 pandemic. Future research should incorporate these objective methods to improve the accuracy of sleep efficiency assessments.

Conclusion

This study underscores the significant influence of sunlight exposure, particularly in the early morning, on the regulation of sleep, specifically the midpoint of sleep and sleep quality. Given the increasing prevalence of circadian misalignment in modern society, promoting greater exposure to natural sunlight, especially before 10 a.m. can serve as a practical, non-pharmacological intervention to improve sleep, and reduce the negative effects of social jetlag and related health risks.

Acknowledgements

The authors acknowledge the support of the Federal University of Ouro Preto (UFOP) and the Group for Research and Education in Nutrition and Collective Health (GPENSC) for their support and encouragement, and the support of the Municipal Health Secretariats of the municipalities was evaluated in the study.

Abbreviations

BMI

Body mass index

IBGE

Brazilian Institute of Geography and Statistics

DAG

Directed acyclic graph

POF

Family Budget Survey

GAD

7-Generalized Anxiety Disorder Scale

PNAD

National Household Sample Survey

PHQ

9-Patient Health Questionnaire

PSQI

Pittsburgh Sleep Quality Index

SCN

Suprachiasmatic nucleus

VIF

Variance inflation factor

VIGITEL

Risk and protection factors for chronic non communicable diseases by telephone survey

WHO

World Health Organization

PAHO

Pan American Health Organization

Authors’ contributions

LAAMJ: conception and study design, data collection, analysis and interpretation of data, writing the manuscript, critical review, and final approval. TSS: data collection, critical review, and final approval. JCCC: conception and study design; critical review, and final approval. GLLMC: conception and coordination of data collection, management of financial resources, critical review, and final approval. ALM: conception and coordination of data collection, management of financial resources, critical review, and final approval.

Funding

The Coordination for the Improvement of Higher Education Personnel-Brazil (CAPES) [9/2020; nº88887.504994/2020e00], the Foundation for Research Support of the State of Minas Gerais (FAPEMIG) [nº001/2021; APQ-02445-21], the Brazilian Council for Scientific and Technological Development (CNPq), and finance code 001 for Ph.D. student scholarship all supported this study.

Data availability

The datasets generated and/or analyzed as part of the current study are not publicly available due to confidentiality agreements with subjects. However, they can be made available solely for the purpose of review and not for the purpose of publication from the corresponding author upon reasonable request.

Declarations

Ethics approval and consent to participate

The Research Ethics Committee of the Federal University of Minas Gerais approved all procedures, which were carried out by Brazilian guidelines and standards for human subjects research, as outlined in the Declaration of Helsinki (Ethics Submission Certificate no. 32815620.0.1001.5149). Informed consent was obtained from all participants.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Footnotes

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

References

  • 1.Tähkämö L, Partonen T, Pesonen AK. Systematic review of light exposure impact on human circadian rhythm. Chronobiol Int. 2019;36(2):151–70. 10.1080/07420528.2018.1527773. [DOI] [PubMed] [Google Scholar]
  • 2.Stothard ER, McHill AW, Depner CM, Birks BR, Moehlman TM, Ritchie HK, et al. Circadian entrainment to the natural light-dark cycle across seasons and the weekend. Curr Biol. 2017;27(4):508–13. 10.1016/j.cub.2016.12.041. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 3.Phillips AJK, Vidafar P, Burns AC, McGlashan EM, Anderson C, Rajaratnam SMW, et al. High sensitivity and interindividual variability in the response of the human circadian system to evening light. Proc Natl Acad Sci USA. 2019;116(24):12019–24. 10.1073/pnas.1901824116. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 4.Roenneberg T. The human sleep project. Nat 2013. 2013;498(7455):427–8. 10.1038/498427a. 498. [DOI] [PubMed] [Google Scholar]
  • 5.Brown TM, Brainard GC, Cajochen C, Czeisler CA, Hanifin JP, Lockley SW, Lucas RJ, Münch M, OHagan JB, Peirson SN, Price LLA, Roenneberg T, Schlangen LJM, Skene DJ, Spitschan M, Vetter C, Zee PC, Wright KP. Recommendations for daytime, evening, and nighttime indoor light exposure to best support physiology, sleep, and wakefulness in healthy adults. PLoS Biol. 2022;20(3):e3001571. 10.1371/JOURNAL.PBIO.3001571. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 6.Münch M, Bromundt V. Light and chronobiology: implications for health and disease. Dialogues Clin Neurosci. 2012;14(4):448. 10.31887/DCNS.2012.14.4/MMUENCH. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 7.Crowley SJ, Eastman CI. Phase advancing human circadian rhythms with morning bright light, afternoon melatonin, and gradually shifted sleep: can we reduce morning bright-light duration? Sleep Med. 2015;16(2):288–97. 10.1016/j.sleep.2014.12.004. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8.Roenneberg T, Pilz LK, Zerbini G, Winnebeck EC. Chronotype and social jetlag: A (Self-. Crit Rev Biology. 2019;8(3):54. 10.3390/biology8030054. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 9.Wright KP, McHill AW, Birks BR, Griffin BR, Rusterholz T, Chinoy ED. Entrainment of the human circadian clock to the natural light-dark cycle. Curr Biol. 2013;23(16):1554–8. 10.1016/j.cub.2013.06.039. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10.de Menezes-Júnior LAA, de Moura SS, Miranda AG, Andrade AC, de Machado-Coelho S, G. L. L., Meireles AL. The mediating role of sun exposure on the association between sedentary behavior and sleep quality: A population-based cross-sectional study. Sleep Med. 2023a;108:98–9. 10.1016/j.sleep.2023.05.024. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11.Sharun K, Tiwari R, Dhama K. COVID-19 and sunlight: impact on SARS-CoV-2 transmissibility, morbidity, and mortality. Ann Med Surg. 2021;66:102419. 10.1016/j.amsu.2021.102419. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12.Roecklein KA, Franzen PL, Wescott DL, Hasler BP, Miller MA, Donofry SD, DuPont CM, Gratzmiller SM, Drexler SP, Wood-Vasey WM, Gamlin PD. Melanopsin-driven pupil response in summer and winter in unipolar seasonal affective disorder. J Affect Disord. 2021;291(November 2020):93–101. 10.1016/j.jad.2021.04.084. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13.Chen X, Chong WF, Feng R, Zhang L. Pandemic risk management: resources contingency planning and allocation. IET Intell Transp Syst. 2021;101:359–83. 10.1016/j.insmatheco.2021.08.001. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14.Silver H. Working from home: before and after the pandemic. Contexts. 2023;22(1):66–70. 10.1177/15365042221142839. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15.Meireles AL, Machado-Coelho GLL, Lourenção LG, de Júnior LAA M., Coletro HN. COVID-Inconfidentes - SARS-CoV-2 Seroprevalence in two Brazilian urban areas during the pandemic first wave: study protocol and initial results. Población Y Salud En Mesoamérica. 2023. 10.15517/psm.v21i1.53127. [Google Scholar]
  • 16.Silva PL, do N, Pessoa DGC, Lila MF. Análise estatística de Dados Da PNAD: incorporando a estrutura do Plano Amostral. Ciênc Saúde Coletiva. 2002;7(4):659–70. 10.1590/S1413-81232002000400005. [Google Scholar]
  • 17.Meireles AL, Xavier CC, Proietti FA, Caiaffa WT. Influence of individual and socio-environmental factors on self-rated health in adolescents. Revista Brasileira De Epidemiologia = Brazilian J Epidemiol. 2015;18(3):538–51. 10.1590/1980-5497201500030002. [DOI] [PubMed] [Google Scholar]
  • 18.Hallal PC, Hartwig FP, Horta BL, Silveira MF, Struchiner CJ, Vidaletti LP, Neumann NA, Pellanda LC, Dellagostin OA, Burattini MN, Victora GD, Menezes AMBB, Barros FC, Barros AJDD, Victora CG. SARS-CoV-2 antibody prevalence in brazil: results from two successive nationwide serological household surveys. Lancet Global Health. 2020;8(11):e1390–8. 10.1016/S2214-109X(20)30387-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 19.Buysse DJ, Reynolds CF, Monk TH, Berman SR, Kupfer DJ. The Pittsburgh sleep quality index: a new instrument for psychiatric practice and research. Psychiatry Res. 1989;28(2):193–213. 10.1016/0165-1781(89)90047-4. [DOI] [PubMed] [Google Scholar]
  • 20.Bertolazi AN, Fagondes SC, Hoff LS, Dartora EG, da Silva Miozzo IC, de Barba MEF, Barreto M, S. S. Validation of the Brazilian Portuguese version of the Pittsburgh sleep quality index. Sleep Med. 2011;12(1):70–5. 10.1016/j.sleep.2010.04.020. [DOI] [PubMed] [Google Scholar]
  • 21.Kabad JF, Bastos JL, Santos RV. Raça, Cor e Etnia Em estudos epidemiológicos sobre populações brasileiras: Revisão Sistemática Na base pubmed. Physis: Revista De Saúde Coletiva. 2012;22(3):895–918. 10.1590/S0103-73312012000300004. [Google Scholar]
  • 22.Bull FC, Al-Ansari SS, Biddle S, Borodulin K, Buman MP, Cardon G, et al. World health organization 2020 guidelines on physical activity and sedentary behaviour. Br J Sports Med. 2020;54(24):1451–62. 10.1136/bjsports-2020-102955. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23.WHO. (1995). Physical Status: The Use and Interpretation of Anthropometry. Technical Report Series No. 854. [PubMed]
  • 24.PAHO PAHO. (2001). XXXVI Reunión del Comitê Asesor de Investigaciones en Salud. [PubMed]
  • 25.Kroenke K, Spitzer RL, Williams JBW. The PHQ-9. J Gen Intern Med. 2001;16(9):606–13. 10.1046/j.1525-1497.2001.016009606.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26.Spitzer RL, Kroenke K, Williams JBW, Löwe B. A brief measure for assessing generalized anxiety disorder: the GAD-7. Arch Intern Med. 2006;166(10):1092–7. 10.1001/archinte.166.10.1092. [DOI] [PubMed] [Google Scholar]
  • 27.Szwarcwald CL, Damacena GN. Amostras complexas Em inquéritos populacionais: Planejamento e implicações Na análise estatística Dos Dados. Rev Bras Epidemiol. 2008;11(1):38–45. 10.1590/s1415-790x2008000500004. [Google Scholar]
  • 28.Cortes TR, Faerstein E, Struchiner CJ. Use of causal diagrams in epidemiology: application to a situation with confounding. Cadernos De Saude Publica. 2016;32(8):e00103115. 10.1590/0102-311X00103115. [DOI] [PubMed] [Google Scholar]
  • 29.Juda M, Vetter C, Roenneberg T. The Munich chronotype questionnaire for shift-workers (MCTQ shift). J Biol Rhythm. 2013;28(2):130–40. 10.1177/0748730412475041. [DOI] [PubMed] [Google Scholar]
  • 30.Maghsoudipour M, Allison MA, Patel SR, Talavera GA, Daviglus M, Zee PC, et al. Associations of chronotype and sleep patterns with metabolic syndrome in the Hispanic community health study/study of Latinos. Chronobiol Int. 2022;39(8):1087–99. 10.1080/07420528.2022.2069030. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 31.Sauers SC, Toedebusch CD, Richardson R, Spira AP, Morris JC, Holtzman DM, et al. Midpoint of sleep is associated with sleep quality in older adults with and without symptomatic Alzheimer’s disease. Sleep Advances. 2024. 10.1093/sleepadvances/zpae023. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32.Yan B, Li R, Li J, Jin X, Gao F, Gao Y, et al. Sleep timing may predict congestive heart failure: a community-based cohort study. J Am Heart Assoc. 2021. 10.1161/JAHA.120.018385. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 33.Gordon-Dseagu VLZ, Derkach A, Xiao Q, Williams I, Sampson J, Stolzenberg-Solomon RZ. The association of sleep with metabolic pathways and metabolites: evidence from the dietary approaches to stop hypertension (DASH)—sodium feeding study. Metabolomics. 2019;15(4):48. 10.1007/s11306-019-1472-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 34.Randler C, Vollmer C, Kalb N, Itzek-Greulich H. Breakpoints of time in bed, midpoint of sleep, and social jetlag from infancy to early adulthood. Sleep Med. 2019;57:80–6. 10.1016/j.sleep.2019.01.023. [DOI] [PubMed] [Google Scholar]
  • 35.Anderson AR, Ostermiller L, Lastrapes M, Hales L. Does sunlight exposure predict next-night sleep? A daily diary study among U.S. adults. J Health Psychol. 2024. 10.1177/13591053241262643. [DOI] [PubMed] [Google Scholar]
  • 36.Smith S, Trinder J. Morning sunlight can phase advance the circadian rhythm of young adults. Sleep Biol Rhythms. 2005;3(1):39–41. 10.1111/j.1479-8425.2005.00153.x. [Google Scholar]
  • 37.Dolezal BA, Neufeld EV, Boland DM, Martin JL, Cooper CB. Interrelationship between sleep and exercise: A systematic review. Adv Prev Med. 2017;2017:1–14. 10.1155/2017/1364387. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 38.Abboud M. Vitamin D supplementation and sleep: a systematic review and meta-analysis of intervention studies. Nutrients. 2022;14(5):1076. 10.3390/nu14051076. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 39.Hossein-nezhad A, Holick MF. Vitamin D for health: a global perspective. Mayo Clin Proc. 2013;88(7):720–55. 10.1016/j.mayocp.2013.05.011. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 40.de Menezes Júnior LAA, Fajardo VC, de Freitas SN, Machado-Coelho GLL, de Oliveira FLP, do Nascimento Neto RM, et al. Rotating shift workers with vitamin D deficiency have a higher risk of obstructive sleep apnea. Sleep Breath. 2022a. 10.1007/s11325-022-02603-4. [DOI] [PubMed] [Google Scholar]
  • 41.de Menezes Júnior LAA, Fajardo VC, do Nascimento Neto RM, de Freitas SN, de Oliveira FLP, Pimenta FAP, Machado-Coelho GLL, Meireles AL, Menezes-Júnior LAA, Fajardo VC, do Nascimento Neto RM, de Freitas SN, de Oliveira FLP, Pimenta FAP, Machado-Coelho GLL, Meireles AL. Association of hypovitaminosis D with sleep parameters in rotating shift worker drivers. Sleep Sci. 2022b;84–91. 10.1055/s-0043-1767748.
  • 42.de Menezes-Júnior LAA, Sabião TdaS, de Moura SS, Batista AP, de Menezes MC, Carraro JCC, Machado-Coelho GLL, Meireles AL. The role of interaction between vitamin D and VDR FokI gene polymorphism (rs2228570) in sleep quality of adults. Sci Rep 2024. 2024;14:1(1):1–11. 10.1038/s41598-024-58561-2. 14. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 43.Menezes-Júnior LAA, Sabião TdaS, de Moura SS, Batista AP, de Menezes MC, Carraro JCC, Andrade ACdeS, Machado-Coelho GLL, Meireles AL. Influence of sunlight on the association between 25-hydroxyvitamin D levels and sleep quality in Brazilian adults: A population-based study. Nutrition. 2023b;110. 10.1016/j.nut.2023.112008. [DOI] [PMC free article] [PubMed]
  • 44.da Silva Sabião T, Alves de Menezes-Júnior LA, Batista AP, Silva de Moura S, Meireles AL, de Menezes C, Machado-Coelho ML, G. L., Cardoso Carraro JC. Interaction between Fokl polymorphism and vitamin D deficiency in the symptoms of mental disorders in adults: a population-based study. Sci Rep. 2024a;14(1):6925. 10.1038/s41598-024-57558-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 45.Muscogiuri G, Barrea L, Scannapieco M, Di Somma C, Scacchi M, Aimaretti G, et al. The lullaby of the sun: the role of vitamin D in sleep disturbance. Sleep Med. 2019;54:262–5. 10.1016/j.sleep.2018.10.033. [DOI] [PubMed] [Google Scholar]
  • 46.Silva Sabião T, da, Valer-Martínez A, Sayon-Orea C, del M, Sánchez-Villegas A, Pons JJ, Carraro JCC, Martínez-González MÁ, Bes Rastrollo M. Predicted vitamin D levels and risk of depression in the SUN study: A prospective cohort study. J Psychiatr Res. 2024b. 10.1016/j.jpsychires.2024.09.034. [DOI] [PubMed] [Google Scholar]

Associated Data

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

Data Availability Statement

The datasets generated and/or analyzed as part of the current study are not publicly available due to confidentiality agreements with subjects. However, they can be made available solely for the purpose of review and not for the purpose of publication from the corresponding author upon reasonable request.

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