Sex Differences in the Association between Sleep Quality and Excess Weight: Exploring Lifestyle and Health-Related Mediators

Abstract

Objective

To compare the associations between sleep quality and body mass index (BMI), as well as excess weight status, in male and female subjects, while exploring potential mediating factors, including lifestyle and health-related variables.

Materials and Methods

The present cross-sectional study analyzed data from 5,260 (29.7% male and 70.3% female) Brazilian adults collected through a virtual survey applied from 2023 to 2024. Sleep quality and duration were assessed using the Pittsburgh Sleep Quality Index (PSQI). The BMI was derived from self-reported weight and height, with excess weight defined as BMI > 24.9 kg/m². Associations were explored using multiple linear and logistic regression models, marginal probabilities for being overweight, and restricted cubic splines. Potential mediating variables were identified through mediation analysis.

Results

Among the female subjects, poor sleep quality (β = 0.46; 95%CI: 0.15–0.77) and short sleep duration (β = 0.62; 95%CI: 0.27–0.97) were associated with higher BMI and 21% of increased odds of excess weight after adjusting for all covariates (age, depression, chronic conditions, level of schooling, marriage status, smoking, alcohol consumption, screen time before bed, physical activity, diet quality, and whether dinner is the largest meal of the day). The mediation analysis showed that chronic conditions (20%), depression (16%), and diet quality (14%) mediated the total effect of poor sleep quality on BMI in female participants. Screen time before bed was the only significant mediator for short sleep duration, accounting for ∼ 5% of the total effect. No significant associations were found in male subjects.

Conclusion

Our findings highlight sex differences in the relationship between sleep and BMI, emphasizing the need for sex-specific approaches to sleep and weight management, focusing on health and lifestyle improvements.

 

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Keywords

body mass index - overweight - sleep - sleep quality - lifestyle

Introduction

Obesity remains one of the most persistent global epidemics, presenting significant challenges in terms of prevention, control, and treatment. Projections from the World Obesity Federation[1] indicate that the prevalence of overweight and obesity will rise from 38% of the global population in 2020 to more than 50% by 2035. In Brazil, this trend is equally concerning, with estimates suggesting that, by 2030, 68.1% of the population will be overweight or obese.[2] This significant public health issue has paralleled a decline in both sleep duration and quality, alongside a growing prevalence of sleep disorders. Approximately one-third of the global population is estimated to suffer from insufficient sleep.[3] This trend is expected to worsen in the coming years, driven by the increasing role of technology and the pressures of a fast-paced, 24-hour modern lifestyle.[4]

Research increasingly highlights the interconnection between the rising prevalence of excess weight and sleep-related problems, suggesting that these trends are not merely coincidental.[5] While a U-shaped association between sleep duration and body weight has been observed,[6] with both short and long sleep linked to increased body weight, insufficient sleep remains the primary concern in modern society. Beyond its role in obesity,[6] [7] insufficient sleep has also been identified[8] as a significant risk factor for several other non-communicable chronic diseases, including cardiovascular diseases, osteoporosis, stroke, and type-2 diabetes.

Although the precise mechanisms linking short sleep duration to obesity are not fully understood, three key pathways have been proposed[9] to explain how insufficient sleep disrupts energy balance and contributes to weight gain: first, sleep restriction alters metabolic hormones, increasing ghrelin levels while reducing those of leptin, which together stimulate appetite; second, inadequate sleep impairs glucose metabolism, further intensifying hunger; lastly, the fatigue resulting from insufficient sleep reduces overall energy expenditure, contributing to a more sedentary lifestyle.

Building on this understanding of sleep's role in metabolic health, recent research[10] highlights notable sex differences in sleep quality, with female subjects reporting poorer sleep and more frequent disturbances compared with male subjects. These disparities are influenced by a range of social, biological, and hormonal factors, particularly during key life stages such as menstruation, pregnancy, and menopause, which can further disrupt respiration, mood, and body temperature.[11] For instance, Fan et al.[12] found that female subjects with short sleep duration had a 62% higher likelihood of developing general obesity and a 22% higher likelihood of developing visceral obesity, whereas no such association was observed in male subjects. These findings, alongside those of other studies,[7] [13] [14] [15] suggest that the relationship between sleep and body composition may be more pronounced in female than in male subjects.

Despite these findings, research on sex differences regarding the relationship between nutritional status and sleep remains limited, emphasizing the need for further investigation. To our knowledge, no studies have examined this comparison in Latin or South America. To address this gap, the primary objective of the current study is to examine the associations of sleep quality and duration with the body mass index (BMI) and excess weight among male and female participants, hypothesizing that these associations will be stronger in female than in male subjects. For the identified associations, we will further investigate potential mediators, including a range of lifestyle and health-related factors, which may influence these relationships.

 

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Materials and Methods

Participants

The present cross-sectional study used data from the third cycle of the Sonar-Brazil Survey, which was conducted between May 2023 and May 2024[38]. The survey is virtual, exploratory, and population-based, aiming to investigate chronobiological aspects related to sleep, food, and nutrition among Brazilian adults.

The inclusion criteria for Sonar-Brazil were: being Brazilian, residing in Brazil, being aged between 18 and 65 years, and not being pregnant. Much effort was made to ensure the sample would be representative of the general population. The survey link was emailed to all public and private higher education institutions registered with the Brazilian Ministry of Education, as well as to regional nutritionist, dental, and medical councils; health units; electronic, printed, and televised newspapers; sleep and nutrition congresses; and scientific events. The survey was also widely promoted on social media platforms and web sites, with a request for the link to be shared with eligible contacts. Dedicated social media pages were established to enhance the survey's visibility nationwide.

A total of 5,580 individuals filled out the Sonar-Brazil questionnaire. A total of 60 responses were excluded from the analysis: 39 did not meet the age criteria, 12 had missing or inconsistent data on weight and/or height (such as responses of ‘zero’ or ‘I don't know’), and 9 provided inconsistent data regarding daily routines. Therefore, the final sample for Sonar-Brazil comprised 5,520 Brazilian adults. Night and shift workers were also excluded from the present study due to their distinct circadian rhythms and irregular sleep patterns, which could potentially confound the analysis of sleep behaviors and their predictors (n = 260). Consequently, the current analysis was conducted with a sample of 5,260 Brazilian adults.

To estimate population proportions with a 95% confidence level and a margin of error of 5%, an initial minimum sample size of 385 valid questionnaires was determined. However, the sample size was not fixed, and additional efforts were made to increase it as much as possible, thereby reducing the margin of error. The final sample size of 5,260 provides proportion estimates with a 95% confidence level and a margin of error lower than 4%, enhancing the robustness of the findings.

Brazil is geographically divided into five macroregions—North, Northeast, Midwest, Southeast, and South—each characterized by distinct climate, economic, and cultural characteristics. According to the 2022 Brazilian Demographic Census, conducted by Instituto Brasileiro de Geografia e Estatística (IBGE)[39], Brazil's total population is of 207,750,291 inhabitants, with regional distributions as follows: North – 8.6%; Northeast – 26.7%; Southeast – 42.0%), South – 14.8%; and Midwest – 7.9%). The final sample of the current reflects this demographic composition: North – 9.1%; Northeast – 27.5%; Southeast – 41.2%; South – 14.3%; and Midwest – 7.9%).

Sex: Female and Male Subjects

In the present study, we opted to use the term sex rather than gender because sex is appropriate when focusing on physiological differences between male and female individuals, such as hormonal profiles, reproductive anatomy, and chromosomal distinctions. Since our aim was to analyze these biological variables, sex was the most precise term to describe the distinctions captured in our data, ensuring clarity and scientific accuracy in the context of our research.

 

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Data Collection

The participants filled out an online structured questionnaire after reviewing and agreeing to sign the informed consent form. The questionnaire covered a range of sociodemographic variables and lifestyle and health-related factors.

All data collection procedures were conducted in accordance with the principles outlined in the Declaration of Helsinki and received approval from the institutional Ethics in Research Committee.

 

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Dependent Variable: BMI

The participants reported their weight and height in the questionnaire, and these data were used to calculate the BMI, which was analyzed as a continuous and as a categorical variable. We considered individuals with overweight or obesity (BMI ≥ 25 kg/m²) as having excess weight.[1]

 

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Focal Independent Variables: Sleep Quality and Sleep Duration

We used the validated Brazilian Portuguese version of the Pittsburgh Sleep Quality Index[16] (PSQI) to evaluate sleep quality. The questionnaire consists of 19 self-rated questions organized into 7 components: subjective sleep quality, sleep latency, sleep duration, habitual sleep efficiency, sleep disturbances, use of sleeping medication, and daytime dysfunction. Each component is scored from 0 to 3, and the global score, derived from the sum of these component scores, ranges from 0 to 21. Higher scores indicate poorer sleep quality. The global score was categorized as good (≤ 5) or poor (> 5) sleep quality.

Sleep duration (in hours) was calculated by subtracting usual wake time from bedtime, regarding typical weekdays and weekends, with responses recorded in 30-minute intervals. Weekly averages of waketime, bedtime and sleep duration were computed using the formula: [(5 × weekdays value) + (2 × weekend value)] / 7.

Although sleep duration is one of the components of the PSQI, we opted to analyze this variable separately as well, because it is an important and frequently studied indicator of sleep quality. Based on the cutoff points established by the United States National Sleep Foundation,[17] short sleep duration was defined as less than 7 hours per night.

 

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Focal Mediating Variables

We hypothesized several lifestyle and health-related mediators, including: duration of weekly physical exercise (measured in hours based on reported days and duration), tobacco smoking (yes or no), alcohol intake (assessed by the total number of drinks and frequency of consumption per week), screen time before bedtime (measured in minutes, it includes the use of various devices, such as cell phones, televisions, computers, and tablets), diagnosed depression and chronic conditions (including diabetes, hypertension, hypercholesterolemia, heart disease, hypertriglyceridemia, and/or metabolic syndrome).

Another hypothesized mediator was diet quality, assessed using a food frequency questionnaire that encompassed 17 categories. The participants reported their typical consumption days from Monday to Sunday, specifying frequency and time. Each food group received positive scores for healthy choices (such as, fresh fruits, vegetables, dairy) and negative scores for unhealthy options (such as, sweets, fried snacks, sugary drinks). The total score, ranging from 19 to 77, reflected diet quality and was categorized into tertiles: low – 19 to 45; intermediate – 46 to 54/ and high – 55 to 77.

Lastly, we examined whether having dinner as the largest meal of the day acted as a mediator. The participants identified their largest meal, defined as the one with the highest caloric intake, with possible responses of breakfast, lunch, dinner, or none.

 

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Statistical Analysis

All analyses were performed using the Stata software, version 18 (StataCorp LLC). The characteristics of the participants divided by sex were compared using the student's t-test for continuous variables and the Chi-squared test for categorical variables.

We estimated the adjusted marginal probabilities of excess weight across sleep quality groups, defined by PSQI scores < 5 (good), from 5 to 10 (poor), and > 10 (very poor), for all participants, as well as separately for female and male subjects. Adjustments were made for age, level of schooling, and marriage status. Additionally, restricted cubic splines, adjusted for the same variables, were used to illustrate the association between sleep duration and BMI for all participants, as well as for female and male subjects separately.

To examine the association involving poor sleep quality, short sleep duration, and both BMI and excess weight, we employed multiple linear and logistic regression models. In the linear regression, BMI was treated as a continuous outcome, yielding β coefficients and 95% CIs. Logistic regression was used to assess the odds of having excess weight, providing odds ratios (ORs) and 95%CIs. Model 1 was adjusted for physiological factors (age, depression, and chronic conditions); model 2 included adjustments for socioeconomic and lifestyle factors (level of schooling, marriage status, smoking, alcohol intake, and screen time before bed) in addition to the variables in model 1; and model 3 included adjustments for dietary and physical activity factors (physical exercise, diet quality, and dinner as the largest meal of the day). Statistical significance as p ≤ 0.05.

As a preparatory step for the mediation analysis, we first examined the unadjusted regression coefficients to investigate the relationships regarding each hypothesized mediator and the predictors (poor sleep quality and short sleep duration), as well as the outcome (BMI). Given that we did not find associations involving short sleep duration (β = 0.22; 95%CI: -0.24 01500.69; p = 0.348) or poor sleep quality (β = 0.37; 95%CI: -0.08–0.82; p = 0.109) and BMI among male subjects, the analysis of mediating factors was conducted exclusively for the group of female participants.

We then conducted univariate analyses among female participants to test the associations of each mediator with the outcomes and predictors, as shown in [Supplementary Table S1] (online only). As a result of this analysis, the mediators tested for the relationship between poor sleep quality and BMI (all with p < 0.05 for both predictor and outcome) included: depression (percentage), chronic conditions (percentage), smoking (percentage), alcohol intake (times per week), screen time before bedtime (minutes), physical exercise (hours per week), diet quality (score), and dinner as the largest meal of the day (percentage). For short sleep duration, the mediators examined were depression (percentage), chronic conditions (percentage), screen time before bedtime (minutes), and dinner as the largest meal of the day (percentage).

We used the Stata “medeff” package for the mediation analysis, calculating estimates for the average causal mediation effect (ACME), which represents the portion of the effect of the independent variable on the dependent variable mediated by the hypothesized mediator. We also calculated the average direct effect, which reflects the independent variable's effect on the dependent variable without mediation, and the percentage of the effect mediated, indicating the proportion of the total effect explained by the mediator. These calculations were performed using the quasi-Bayesian Monte Carlo method based on a normal approximation with 5 thousand simulations. Statistical significance was determined based on the 95%CI, with significant values highlighted when the interval did not include zero. Variables with a significant ACME were considered mediators.

 

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Results

A total of 5,260 adults (aged 18 to 65 years; 70% of female subjects) participated in the current study. Significant sex-based differences (p ≤ 0.01) are detailed in [Table 1]. In the comparison between male and female subjects, the female participants had a higher prevalence of higher education (73% versus 67%) and depression (15% versus 10%) respectively, while the male subjects were more likely to be married or in stable unions (46% versus 42% of female participants). The male subjects presented higher mean BMI, a greater prevalence of excess weight (61% versus 44%), and higher rates of tobacco smoking (8% versus 5%), alcohol consumption (2.51 versus 1.59 times per week), and physical activity (0.68 additional hours per week). They also presented a higher percentage in the lowest diet quality tertile (39% versus 32%), were more likely to report dinner as their largest meal (8.52% versus 8.33%) and presented later bedtimes (23:53 versus 23:25). In contrast, the female subjects presented poorer sleep quality (PSQI > 5: 53.39% versus 45.74%) and longer sleep durations (7.54 versus 7.28 hours).

Table 1 Participant characteristics by sex

Variables	All (n = 5,260)	Female subjects (n = 3,699)	Male subjects (n = 1,561)	p-value[a]
Mean age (years)	36.39 ± 12.37	36.32 ± 12.48	36.55 ± 12.40	0.54
Age groups (years): %
18–29	36.18	36.09	36.39	0.94
30–49 years	46.16	46.31	45.80
50–65	17.66	17.60	17.81
Level of schooling: %
Lower education	28.71	26.93	32.93	< 0.001
Higher education	71.29	73.07	67.07
Marriage status: %
Single/Divorced/Widowed	56.60	57.75	53.88	0.010
Married/Living with partner	43.40	42.25	46.12
Mean BMI (kg/m2)	25.66 ± 4.93	25.27 ± 4.97	26.60 ± 4.69	< 0.001
Excess weight (BMI > 24.9kg/m2), %	49.18	44.23	60.92	< 0.001
Depression: yes (%)	13.57	15.11	9.93	< 0.001
Chronic conditions: yes (%)	26.16	25.63	27.42	0.17
Tobacco smoking: yes (%)	5.82	4.84	8.14	< 0.001
Mean alcohol intake (times/week)	1.86 ± 2.83	1.59 ± 2.44	2.51 ± 3.51	< 0.001
Mean screen time before bed (minutes)	3.10 ± 1.01	3.10 ± 1.02	3.12 ± 0.99	0.38
Mean physical exercise (hours/week)	4.15 ± 3.78	3.95 ± 3.64	4.63 ± 4.05	< 0.001
Mean diet quality score	49.17 ± 9.13	49.64 ± 9.04	48.06 ± 9.24	< 0.001
Low (1st tertile): %	34.30	32.22	39.21	< 0.001
Intermediate (2nd tertile): %	35.68	35.98	34.98
Good (3rd tertile): %	130.02	31.79	25.82
Largest meal (%)
Breakfast	5.80	6.84	3.33	< 0.001
Lunch	80.13	78.43	84.18
Dinner	8.38	8.33	8.52
None/Other	5.68	6.41	3.97
Wake time (decimal time b )	6.79 ± 1.18	6.79 ± 1.17	6.81 ± 1.23	0.49
Bedtime (decimal time b )	23.33 ± 1.21	23.25 ± 1.18	23.53 ± 1.26	< 0.001
Sleep duration (hours b )	7.46 ± 1.11	7.54 ± 1.11	7.28 ± 1.11	< 0.001
Short (< 7): %	29.94	27.66	35.36	< 0.001
Normal (7–9): %	64.16	65.34	61.37
Long (> 9): %	5.89	7.00	3.27
Sleep Quality (PSQI score)	6.06 ± 3.07	6.24 ± 3.12	5.62 ± 2.91	< 0.001
Poor (> 5): %	51.12	53.39	45.74	< 0.001

Abbreviations: BMI, Body Mass Index; PSQI, Pittsburgh Sleep Quality Index.

Notes:

^a The p-values are derived from the student's t-test (for continuous variables) and from the Chi-squared test (for categorical variables). ^bWeekly average: ([weekday value * 5] + [weekend value * 2])/7. Significant associations (p-values < 0.05) are shown in bold.

[Fig. 1] illustrates that the adjusted marginal probability of having excess weight (BMI > 24.9 kg/m²) increases as sleep quality declines, among all participants (PSQI < 5: 46%; PSQI from 5–10: 50%; and PSQI > 10: 54%) and among the female subjects (PSQI < 5: 39%; PSQI from 5–10: 46%; and PSQI > 10: 53%) (all p < 0.001). However, among the male subjects, the differences were minimal and not statistically significant (PSQI < 5: 61%; PSQI from 5–10: 61%; and PSQI > 10: 60%; p > 0.05).

Fig. 1 Marginal probability of excess weight (Body Mass Index [BMI] > 24.9Kg/m²) by sleep quality groups. Models are adjusted for age, schooling, and marriage status (al l= 5,260; female subjects = 3,699; male subjects= 1,561).

[Fig. 2] illustrates similar findings, depicting the dose–response relationships between sleep duration and BMI using restricted cubic splines. Among all participants and specifically the female subjects, lower BMI values were associated with sleep durations of approximately 8 hours, while the highest BMI values corresponded to shorter sleep durations. In contrast, the association among males wass weaker and not statistically significant ([Fig. 2]).

Fig. 2 Sleep duration and BMI. The red lines plot the predicted BMI values with 95% CIs (color fill). Models are adjusted for age, schooling and marriage status (all = 5,260; female subjects = 3,699; male subjects = 1,561).

[Tables 2], [3] present the associations involving short sleep duration and poor sleep quality with BMI and excess weight respectively. Significant associations were observed across all multiple regression models for both the overall sample and the female participants, whereas no significant relationships were identified for the male participants. Among the female subjects, short sleep duration was associated with an increase in BMI (model 3: β = 0.62; [95%CI: 0.27–0.97]; p = 0.001) and 21% higher odds of excess weight (model 3: 95%CI = 1.04–1.41; p = 0.014). Similarly, poor sleep quality was significantly associated with BMI for the overall sample (model 3: β = 0.35 [95%CI: 0.10–0.61]; p = 0.007) and specifically for the female subjects (model 3: β = 0.46 [95%CI: 0.15–0.77]; p = 0.004). Additionally, the female participants with poor sleep quality had a 21% greater likelihood of being classified as having excess weight (model 3: 95% CI = 1.05–1.39; p = 0.01).

Table 2 Multiple linear regression models: association involving short sleep duration, poor sleep quality, and Body Mass Index by sex

	All (n = 5,260)		Female subjects (n = 3,699)		Male subjects (n = 1,561)
	β (95%CI)	p-value	β (95%CI)	p-value	β (95%CI)	p-value
Short sleep duration
Model 1	0.62 (0.33–0.90)	< 0.001	0.66 (0.31–1.02)	< 0.001	0.22 (-0.24–0.69)	0.34
Model 2	0.64 (0.36–0.93)	< 0.001	0.72 (0.36–1.08)	< 0.001	0.26 (-0.21–0.73)	0.27
Model 3	0.56 (0.28–0.84)	< 0.001	0.62 (0.27–0.97)	0.001	0.22 (-0.25–0.69)	0.35
Poor sleep quality
Model 1	0.49 (0.23–0.75)	< 0.001	0.65 (0.34–0.96)	< 0.001	0 0.37 (-0.08–0.82)	0.10
Model 2	0.46 (0.20–0.72)	0.001	0.59 (0.27–0.91)	< 0.001	0.43 (-0.03–0.89)	0.06
Model 3	0.35 (0.10–0.61)	0.007	0.46 (0.15–0.77))	0.004	0.36 (-0.10–0.81)	0.12

Notes: Short sleep duration is defined as < 7 hours, with ≥ 7 hours as the reference. Poor sleep quality is defined as a Pittsburgh Sleep Quality Index score > 5, with a score ≤ 5 as the reference. Model 1 was adjusted for age, depression, and chronic conditions; model 2 was adjusted for schooling, marriage status, smoke, alcohol intake, and screen time before bed, in addition to model 1; model 3 was adjusted for physical exercise, diet quality, and dinner as the largest meal of the day, in addition to model 2. Significant associations (p-values < 0.05) are shown in bold.

Table 3 Multiple logistic regression models: association involving short sleep duration, poor sleep quality, and excess weight (BMI > 24.9 kg/m²) by sex

	All (n = 5,260)		Female subjects (n = 3,699)		Male subjects (n = 1,561)
	Odds ratio (95%CI)	p-value*	Odds ratio (95%CI)	p-value*	Odds ratio (95%CI)	p-value*
Short sleep duration
Model 1	1.24 (1.10–1.40)	0.001	1.22 (1.06–1.42)	0.007	1.90 (0.87–1.36)	0.44
Model 2	1.25 (1.11–1.42)	< 0.001	1.25 (1.07–1.45)	0.004	1.12 (0.90–1.40)	0.32
Model 3	1.22 (1.08–1.38)	0.002	1.21 (1.04–1.41)	0.014	1.12 (0.89–1.40)	0.34
Poor sleep quality
Model 1	1.15 (1.02–1.29)	0.019	1.28 (1.17–1.47)	< 0.001	1.03 (0.83–1.28)	0.79
Model 2	1.14 (1.01–1.28)	0.032	1.26 (1.09–1.45)	0.001	1.05 (0.84–1.32)	0.64
Model 3	1.10 (0.98–1.24)	0.10	1.21 (1.05–1.39)	0.01	1.05 (0.84–1.32)	0.67

Notes: Excess weight is defined as Body Mass index > 24.9kg/m². Short sleep duration is defined as < 7 hours, with ≥ 7 hours as the reference. Poor sleep quality is defined as a Pittsburgh Sleep Quality Index score > 5, with a score ≤ 5 as the reference. Model 1 was adjusted for age, depression, and chronic conditions; model 2 was adjusted for schooling, marriage status, smoke, alcohol intake, and screen time before bed, in addition to model 1; model 3 was adjusted for physical exercise, diet quality, and dinner as the largest meal of the day, in addition to model 2. Significant associations (p-values < 0.05) are shown in bold.

Since we only found significant associations between sleep and BMI among the female subjects, we conducted mediation analysis solely in the females group, and the results are presented in [Fig. 3] and [Supplementary Tables S2] and [S3] [online only], detailing the direct effect, the ACME, the total effect, and the percentage of the total effect mediated by each lifestyle factor.

Fig. 3 Mediated total effect of poor sleep quality and short sleep duration with BMI among female subjects. Models are adjusted for age, schooling and marriage status (n = 3,699). Abbreviation: ACME, Average Causal Mediated Effect. Notes: Short sleep duration is defined as < 7 hours, with ≥ 7 hours as the reference. Poor sleep Quality is defined as a PSQI score > 5, with a score ≤ 5 as the reference. ** Statistically significant, as indicated by confidence intervals that do not include zero. All models adjusted for age, schooling, and marriage status.

The results indicate significant direct and total effects of poor sleep quality on BMI (direct effect: 0.46; 95%CI: 0.15–0.78). Several lifestyle and health-related factors significantly mediate this relationship among female participants. Chronic conditions demonstrated the highest mediation effect, accounting for 19.54% (95%CI: 12.48–44.76) of the total effect, with an ACME of 0.11 (95% CI: 0.05–0.18). Following this, depression mediated 15.96% (95% CI: 10.12–36.11) of the total effect, with an ACME of 0.09 (95%CI: 0.01–0.16). Diet quality accounted for 14.34% (95%CI: 8.90–34.59), with an ACME of 0.08 (95%CI: 0.04–0.12). Screen time before bed mediated 11.30% (95%CI: 7.00–28.06), with an ACME of 0.06 (95%CI: 0.02–0.10). Lastly, dinner as the largest meal of the day showed a mediation effect of 2.97% (95%CI: 1.77–8.59), with an ACME of 0.01 (95%CI: 0.00–0.03) ([Fig. 3] and [Supplementary Table S2] [online only]).

Similarly, the direct and total effects of short sleep duration on BMI were significant, with a direct effect of 0.62 (95%CI: 0.27–0.96). However, only screen time before bed showed a significant ACME, of 0.03 (95%CI: 0.01–0.06), accounting for 4.93% of the proportion mediated (95%CI: 3.23–10.61) ([Fig. 3] and [Supplementary Table S3] [online only]).

 

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Discussion

As far as we are aware, the present is the first study to investigate sex differences in the relationships between sleep and body weight status in Latin America, making it pioneering in exploring the mediating factors involved in this relationship.

Our findings indicate that, among female subjects, poor sleep quality and short sleep duration are significantly associated with higher BMI and increased odds of presenting excess weight. In contrast, no significant associations were found among male participants, supporting our hypothesis that the effects of sleep on BMI differ according to sex. The mediation analysis further highlighted that chronic conditions, depression, and diet quality play a key role in this relationship, with chronic conditions accounting for 20%, depression, for 16%, and diet quality, for 14%. For short sleep duration, screen time before bed was identified as the sole significant mediator, explaining 5% of the effect.

These findings are particularly significant in the context of the rising rates of excess weight in Brazil. It is anticipated[2] that, by 2030, 68.1% of adults will be affected, with women expected to exhibit a higher prevalence. This scenario is exacerbated by the growing economic impact of overweight and obesity, which is projected to rise from US$1.96 trillion in 2020 to over US$4 trillion by 2035–an economic burden comparable to the impact of the coronavirus disease 2019 (COVID-19) pandemic in 2020.[1] Given these alarming trends, we hope the present study underscores the urgency of developing comprehensive public health strategies that not only address traditional risk factors but also integrate sleep hygiene and mental health interventions, with particular attention to women in the prevention and management of excess weight.

Our results are consistent with those of previous research, such as the one by St-Onge et al.,[13] who concluded, in their analysis of the Coronary Artery Risk Development in Young Adults (CARDIA) study, that the associations between self-reported sleep duration and body composition measures may be stronger in female than in male subjects. Similarly, sex differences in the relationship between sleep duration and BMI were demonstrated by Ren et al.[14] among Chinese adults: although ordinary least squares regression revealed no significant associations for either sex after controlling for confounders, quantile regression identified a U-shaped relationship for female subjects, indicating the lowest BMI at ∼ 9 hours of sleep; in contrast, male subjects exhibited an inverse U-shaped relationship, with BMI peaking at around 7 hours of sleep.

Despite previous studies[7] [12] [13] [14] [15] suggesting sex differences in the relationship between sleep and body weight, contrasting results were found in a recent meta-analysis of 6 longitudinal cohort studies[7] with at least 12 months of follow-up, which showed that short sleep duration significantly increased the risk of obesity in both male (OR = 1.26) and female subjects (OR = 1.36), with no significant difference between the sexes.

Physiological and sociocultural factors are commonly mentioned to explain the significant disparities in sleep quality and duration between male and female subjects.[18] [19] [20] Female subjects experience unique life stages–such as puberty, pregnancy, and menopause–that heighten their vulnerability to sleep disorders due to hormonal fluctuations,[21] particularly sex steroids,[11] which influence sleep physiology, including reduced rapid eye movement (REM) sleep during periods of elevated progesterone levels.[22] In addition to the more rapid accumulation of sleep debt,[23] women–particularly those with children–experience greater sleep disruptions, often due to caregiving responsibilities.[5] Furthermore, they exhibit heightened vulnerability to psychosocial stressors and are more predisposed to insomnia and nightmares following traumatic events.[24] [25]

The current study supports these findings, showing that female subjects not only experienced significantly poorer sleep quality but also exhibited a higher prevalence of depression compared with the male participants. Notably, depression emerged as one of the strongest mediators in the relationship between sleep quality and BMI. Depression can significantly impact eating behaviors and has a well-established bidirectional relationship with obesity[26] and sleep disturbances.[27] This cyclical link is likely driven by neurophysiological mechanisms, including neurotransmitter dysregulation—particularly serotonin, norepinephrine, and dopamine—and alterations in the hypothalamic-pituitary-adrenal axis, which is closely tied to the circadian system.[28] [29]

The additional mediators identified in the current study, such as diet quality, screen time, and chronic conditions, are also well-documented in the literature as being intricately linked to sleep and body weight through various interconnected pathways. A poor-quality diet disrupts sleep and, in turn, poor sleep increases appetite, alters energy expenditure, and affects hormonal regulation, raising the risk of excess weight.[30] [31] [32] Taheri et al.[33] found that habitual 5-hour sleep was associated with 15.5% lower leptin and 14.9% higher ghrelin levels compared with 8-hour sleep, regardless of the BMI. Chronic conditions impair sleep, disrupt metabolism, and cause hormonal imbalances, inflammation, and stress, all contributing to weight gain.[34] Lastly, excessive screen time before bed negatively impacts sleep quality by suppressing melatonin production due to blue light exposure, which not only affects the sleep-wake cycle but also leads to appetite dysregulation and weight gain.[35] Engaging with stimulating content on social media can further delay sleep onset and reduce sleep duration.[36] Additionally, chronic blue light exposure over time may disrupt circadian rhythms and metabolism, increasing the risk of obesity.[35]

 

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Strengths and Limitations

A strength of the current study is that the participants provided responses based on their routines throughout the previous month, ensuring a genuine representation of their lifestyle habits. Additionally, the survey also distinguished between weekdays (workdays) and weekends (free days), calculating weighted aggregate scores to more accurately reflect weekly eating and sleep patterns.

However, the present study has several limitations. First, the reliance on self-reported questionnaires introduces the potential for underreporting or misreporting. Despite this limitation, BMI calculated from self-reported data has been validated as a reliable measure across various sociodemographic groups.[37] Although the PSQI is a validated and widely-recognized tool in epidemiological sleep research, its subjective nature, relying on self-reports, presents a limitation to the current study, as it lacks the objective sleep assessments provided by methods such as polysomnography or actigraphy. Additionally, the cross-sectional design of the study limits our ability to establish causality between sleep patterns and body weight, highlighting the need for longitudinal research to further explore these relationships.

 

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Conclusion

The current study revealed that poor sleep quality and short sleep duration are significantly associated with higher BMI and increased odds of excess weight among female subjects, while no association was found among the male participants. Chronic conditions, depression, and diet quality mediated the effect of sleep quality on BMI, whereas screen time before bed emerged as the sole significant mediator of the effect of short sleep duration on BMI.

These findings highlight the importance of addressing sleep quality in interventions aimed at managing BMI, particularly in female subjects. Future research should explore the mechanisms behind these sex differences and mediators, using longitudinal designs to clarify causal pathways and develop effective interventions.

 

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Conflict of Interests

The authors have no conflict of interests to declare.

Data Visualization

Data described in the manuscript will be made available upon request pending approval.

Ethics Approval

The present study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Ethics in Research Committee of Universidade Federal de Alagoas (CAAE: 48689221.3.0000.5013).

Conflict of Interest

The authors have no relevant financial or non-financial interests to disclose.

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Publication History

Received: 07 November 2024

Accepted: 07 March 2025

Article published online:
16 September 2025