Abstract
Background
Depression is a common and debilitating psychiatric disorder worldwide. Recognizing the relationships between depression-related factors can play a significant role in depression management. However, no study has yet used path analysis to examine the mediating role of physical activity, morning wake-up time, and sleep-inducing medication in the relationship between age and depression.
Methods
This path analysis study utilized data from the baseline phase of the Ravansar non-communicable disease cohort study. This study was conducted on people aged 35 to 65 years using sociodemographic, clinical, and the PERSIAN cohort’s standard physical activity questionnaires. These analyses were done using SPSS (version 22) and MPLUS (version 8.3). Path analysis was applied to evaluate the direct, indirect, and total effects of age on depression.
Results
The results indicated that increasing age was associated with an increase in depression through a decrease in physical activity and an increase in sleep-inducing medication use. In addition, an increase in age was significantly related to a reduced incidence of depression via an earlier morning wake-up time.
Discussion
We found evidence for a mediational effect of age on depression, as the biopsychosocial model of mental illness implies. The findings of this study can add to the existing body of knowledge on depression management and help clarify the mechanisms of the effect of age on depression.
Keywords: Physical activity, Sleep aids, Age, Depression, PERSIAN
Introduction
Depression is a widespread and debilitating mental health condition characterized by persistent sadness, loss of interest in activities, low mood, sleep disturbances, fatigue, feelings of guilt, and difficulty concentrating. In severe cases, depression can lead to suicidal thoughts or attempts [1, 2]. Depression is a widespread concern, affecting approximately 300 million individuals worldwide [3]. Research has indicated that within Iran, around 7 million people grapple with various mental health disorders, with roughly 15–20% of the populace experiencing symptoms ranging from mild to severe depression [4]. Depression stands as the most prevalent source of emotional distress, detrimentally impacting the quality of life, elevating the risk of mortality and cardiovascular disease, and correlating with overall poor health and suicidal tendencies [4].
Recognizing the risk factors for depression and their relationship to depression can play a significant role in preventing or treating the symptoms of this disease [5]. Studies have shown that some risk factors for depression are age [6], physical activity [6], time to wake up in the morning [7], and sleep-inducing medication use [8].
One of the factors that may increase the risk of depression is age [6]. With advancing age, physiological changes in the body can exacerbate depressive symptoms. For instance, increased levels of neurotransmitters such as serotonin and dopamine, elevated levels of stress hormones such as cortisol, and decreased levels of thyroid hormones can have negative effects on people’s moods and emotions and increase the incidence of depression [9, 10].
Research highlights that physical activity, a significant predictor of depression [11–13], tends to decrease with age. However, it contributes to improved memory, cognition, quality of life, and a reduction in depressive symptoms [14–16]. For instance, Kim et al. (2017) reported that physical activity is linked to lower depression levels and enhanced quality of life in individuals over 60 [17]. Considering its low-cost and non-pharmacological nature, investigating the relationship between physical activity and depression remains essential [18, 19].
Morning wake-up time is another factor linked to depression [7, 20]. Both early and delayed wake-up times can contribute to depression, often due to disruptions in the body’s circadian rhythm and hormone levels [21, 22]. While irregular waking times—either too early or too late—can contribute to or worsen depressive symptoms, depression itself can cause disturbances in sleep-wake patterns [21, 23]. In addition, wake-up time is significantly influenced by age, with research indicating that older adults tend to wake up earlier than younger individuals. This shift toward early waking in older adults is largely attributed to age-related changes in circadian rhythms and sleep-regulating hormone levels, such as reduced melatonin secretion and alterations in cortisol patterns [24, 25]. These physiological changes commonly lead older individuals to adopt earlier bedtimes and, consequently, earlier wake-up times [26].
Another risk factor for depression is sleep-inducing medication use, which may also be a predictor of depression [8]. Furthermore, there is an association between the increase in the consumption of sleeping pills and increasing age, as shown in several studies [27–29]. With age, changes occur in a person’s sleep and wakefulness patterns, leading to sleep and wakefulness problems. One of the ways used to improve the quality of sleep in people is through sleep-inducing medication use [30]. Although sleeping pills can be used as an effective solution to improve people’s sleep, their long-term use may be associated with some problems and side effects [27], including the aggravation of the symptoms of depression [31].
Previous research has explored the relationships between age and depression [9, 32, 33], age and physical activity [14], morning wake-up time [20], and sleep-inducing medication use [30], as well as the associations between physical activity [13], morning wake-up time [34], and sleep-inducing medication use with depression [8]. However, to the best of our knowledge, no study has yet used path analysis to examine the mediating role of physical activity, morning wake-up time, and sleep-inducing medication use in the relationship between age and depression. Investigating the mediating role of these variables can provide useful insights into the impact of age on depression, help to better recognize and manage the risk factors for depression, and add to the body of knowledge on depression management by developing an effective model using suitable research and data analysis approaches such as path analysis. Therefore, this study aims to identify how physical activity, morning wake-up time, and sleep-inducing medication use mediate the relationship between age and depression, using path analysis to better understand these connections. This study hypothesized that there was a significant relationship between age and depression, mediated by physical activity, morning wake-up time, and the use of sleep-inducing medications.
Theoretical framework
The theoretical foundation of this study is based on the Biopsychosocial Model of Mental Illness, which suggests that mental health outcomes are shaped by the complex interaction of biological, psychological, and social factors [35]. This model provides a framework for understanding how these factors might influence the relationship between age and depression.
Materials and methods
This mediation analysis study utilized data from the baseline phase of the Ravansar Non-Communicable Disease (RaNCD) cohort study, which is a component of the extensive PERSIAN (Prospective Epidemiological Research in Iran) study. Ravansar is located in the Kermanshah province in the west of Iran, near the Iraq border. The region comprises both urban and rural areas, with a population of around 50,000 Kurdish residents.
All individuals between the ages of 35 and 65 who participated in the first phase of the cohort study were included in this study. The RaNCD cohort study had specific exclusion criteria, including reluctance to participate in the study, living in Ravansar for less than nine months per year, being a new resident (less than one year), and being unable to attend the cohort center or communicate with interviewers due to physical disability, acute psychological disorder, blindness, deafness, or muteness. To ensure the feasibility of the study, a representative sample was recruited from both urban and rural areas, with the sample size proportional to the population served by each health center [36].
This research involved the analysis of information gathered from a cohort of more than 10,000 individuals, all between the ages of 35 and 65, who willingly participated in the study and granted informed consent. The study was launched in November 2014 and is currently ongoing. The data from the recruitment stage have been compiled, with the primary sample collection during the baseline phase conducted from March 2015 to February 2017. Further details on the study design can be found in the cohort protocol [36–38].
For data collection, a qualified specialist explained to eligible individuals about the study’s objectives and design. Those who expressed a willingness to participate in the cohort study were then invited to visit the study center. Subsequently, all chosen participants underwent individual, in-person interviews in a private setting, adhering to the procedures outlined in the cohort protocol.
Before initiating data collection, written consent was obtained from each participant. The gathered data were reviewed by the center supervisor and meticulously recorded on the same day, with a thorough check for accuracy. In case of any discrepancies or concerns, follow-up procedures were established, and participants were invited to revisit the cohort center to complete the questionnaire again if necessary. It is important to note that the study employed online versions of established questionnaires in accordance with the guidelines outlined in the PERSIAN cohort protocol. However, it is worth mentioning that these questionnaires underwent a revision process following a preliminary pilot phase to enhance their overall validity and reliability [36, 38].
Sociodemographic and clinical features, such as age, gender, educational level, marital status, residence type, and medical history, were gathered using digital questionnaires administered by a trained interviewer. The digital questionnaire also included questions regarding personal habits, such as physical activity and morning wake-up time (in hours). The study design and rationale have been previously published and are available for reference [36, 38].
Participants’ physical activity levels were assessed using the standardized physical activity questionnaire from the PERSIAN cohort. This questionnaire comprised 22 inquiries regarding an individual’s daily activities. Participants were classified based on their daily metabolic equivalents (METs) and the corresponding cutoff values. Those with a weekly physical activity level ranging from 24 to 36.5 METs-hours were designated as having low physical activity, while those falling within the range of 36.6 to 44.9 METs-hours were classified as having moderate physical activity. Participants with a weekly physical activity level of 45 METs-hours or higher were categorized as having a high level of physical activity, as detailed in reference [36].
The diagnosis of depression, whether symptomatic or non-symptomatic, was determined by a psychologist based on DSM-5 criteria and through a self-reported history of taking medication for depression. Self-reported data were also used to assess participants’ morning wake-up time (in hours). The evaluation of the use of medication for sleep induction was based on participants’ self-reported and documented history of regularly taking such medication.
Statistical analyses
To depict the sociodemographic attributes of the participants, descriptive statistics, which encompass measures like means, standard deviations, and frequencies, were employed. Additionally, we assessed various assumptions, such as the normal distribution of data, handling of missing data, identification of outlier values, and examination of variable collinearity. The normal distribution of variables was evaluated using skewness and kurtosis tests, with values between − 2 and + 2 for skewness and between − 7 and + 7 for kurtosis indicating normal data [39, 40]. Outliers were identified based on a constant score of 3.29. Notably, there were no instances of missing data reported in this study. To assess potential multicollinearity among predictor variables, Pearson’s correlation coefficient and the variance inflation factor (VIF) were computed. These analyses were conducted using SPSS version 22 (SPSS, Inc., Chicago IL, USA), and a significance level of P < 0.05 was established for all statistical tests.
To assess the effects of age on depression in the context of physical activity, morning wake-up time, and sleep-inducing medication use, path analysis was performed. It enabled a comprehensive evaluation of both direct and indirect effects.
To conduct the path analysis, the maximum likelihood estimation method was utilized. The mediation effect’s significance was determined using the 5,000 bootstrap method, which generated 95% bias-corrected confidence intervals. Following Hayes’ approach, an indirect effect was considered statistically significant if the value of 0 was outside the 95% confidence interval [41, 42]. To assess the model fit, various fit indices were taken into consideration, including the normed χ2, Comparative Fit Index (CFI), Root Mean Square Error of Approximation (RMSEA), Tucker-Lewis Index (TLI), and Standardized Root Mean Square Residual (SRMR). A model was deemed to have a good fit when CFI and TLI values exceeded 0.90, RMSEA was less than 0.05, and SRMR was less than 0.08 [43]. The path model was executed using MPlus version 8.3.
In addition to path analysis, multiple regression analysis was conducted to examine the predictive role of age, physical activity, morning wake-up time, and sleep-inducing medication use on depression. The regression analysis was performed using SPSS version 22, with a significance level of P < 0.05.
Ethical considerations
This study was conducted in strict accordance with the principles set forth in the Declaration of Helsinki and received ethical approval from the ethics committee of Kermanshah University of Medical Sciences, under the reference code No. IR. KUMS. REC.1394.318. It is important to note that participation in this study was entirely voluntary, and we secured written consent from all participants involved.
Results
The study included 10,047 participants aged 35 to 65 years, selected through an available sampling method from the first phase of the cohort study. The mean age of the participants was 47.33 ± 8.29 years. The majority of participants were female (52.58%), married (90.23%), illiterate (45.90%), and lived in urban areas (59.69%) (Table 1). The weekly physical activity level of participants was 41 ± 8.22 METs-hours, which was classified as moderate physical activity. The mean morning wake-up time was 6.63 a.m. ± 1.20.
Table 1.
The sociodemographic characteristics of the sample (n = 10047)
| Variables | Female | Male | Total |
|---|---|---|---|
| Age Mean (SD)* | 47.61 (8.46) | 47.01 (8.08) | 47.33 (8.29) |
| Morning wake-up time Mean (SD) | 6.79 (1.19) | 6.45 (1.19) | 6.63 (1.20) |
| Physical activity level Mean (SD) | 39.33 (4.52) | 42.86 (1.06) | 41 (8.22) |
| Marital status N (%)** | |||
|
Single Married Widowed Divorced |
325 (6.2) 4436 (84) 430 (8.1) 91 (1.7) |
95 (2) 4629 (97.2) 8 (0.1) 33 (0.7) |
420 (4.18) 9065 (90.23) 438 (4.36) 124 (1.23) |
| Educational level N (%) | |||
|
Illiterate Elementary school Middle school High school diploma Associate degree Bachelor’s degree Master’s degree PhD. degree |
3316 (62.8) 0 (0) 1289 (24.4) 319 (6) 216 (4.1) 47 (0.9) 87 (1.6) 9 (0.2) |
1297 (27.2) 3 (0.1) 1330 (27.9) 759 (15.9) 755 (15.8) 169 (3.5) 380 (8) 71 (1.5) |
4613 (45.90) 3 (0.02) 2619 (26.06) 1078 (10.72) 971 (9.65) 216 (2.14) 476 (4.72) 80 (0.79) |
| Residence type N (%) | |||
|
Urban Rural |
3043 (57.6) 2240 (42.4) |
2954 (62) 1810 (38) |
5997 (59.69) 4050 (40.31) |
| Depression N (%) | |||
|
Yes No |
222 (4.2) 5061 (95.8) |
99 (2.1) 4665 (97.9) |
321 (3.19) 9726 (96.81) |
| Sleep-inducing medication use N (%) | |||
|
Yes No |
144 (2.7) 5139 (97.3) |
73 (1.5) 4691 (98.5) |
217 (2.16) 9830 (97.84) |
*M (SD): Mean (Standard Deviation)
**N (%): Number (Percent)
Before commencing with the path analysis, a thorough examination of the data was carried out to assess normality, identify outlier values, and investigate multicollinearity. There were no substantial indications of deviations from normality or the presence of outliers in the data. Furthermore, the Pearson correlation analysis revealed that the correlation coefficients among the variables were all below 0.44 (Table 2), and the VIF values for all independent variables fell within the range of 1 to 1.11. This indicates the absence of noteworthy multicollinearity concerns in the dataset.
Table 2.
Bivariate correlations among the variables under study
| Variables | (1) | (2) | (3) | (4) | (5) | (6) | (7) | (8) | (9) |
|---|---|---|---|---|---|---|---|---|---|
| (1) Age | 1 | ||||||||
| (2) Gender | − 0.04*** | 1 | |||||||
| (3) Residence type | − 0.08*** | 0.04*** | 1 | ||||||
| (4) Marital status | 0.09*** | − 0.021*** | − 0.08*** | 1 | |||||
| (5) Educational level | 0.44*** | -0.38*** | − 0.15** | 0.11*** | 1 | ||||
| (6) Physical activity | − 0.04*** | 0.21*** | − 0.1*** | − 0.08*** | 0.06*** | 1 | |||
| (7) Morning wake up hour | − 0.22*** | − 0.14*** | 0.2*** | 0.05*** | − 0.14*** | − 0.21*** | 1 | ||
| (8) Sleep-inducing medication use | 0.05*** | − 0.04*** | − 0.02* | 0.02 | 0.04*** | − 0.03** | 0.04*** | 1 | |
| (9) Depression | 0.02* | − 0.06*** | 0.0 | 0.01 | 0.03** | − 0.05*** | 0.08*** | 0.38*** | 1 |
***p < 0.001, ** p < 0.01, * p < 0.05
Bivariate correlations between the research variables are displayed in Table 2. According to the results of the Pearson correlation analysis, gender and educational level were significantly associated with age, physical activity, morning wake-up time, sleep-inducing medication use, and depression (P-value < 0.01). Moreover, marital status and residence type were significantly associated with age, physical activity, and morning wake-up time (P-values < 0.01). In addition, residence type was significantly associated with sleep-inducing medication use (P-values < 0.05). As a result, gender, marital status, type of residence, and education level were considered as covariates in the mediation analyses.
Path analysis results
The path model was designed to explore the relationships between age and depression mediated by physical activity, morning wake-up time, and sleep-inducing medication use. The path model fitted well. Table 3 shows the parameter estimation for this model.
Table 3.
Fit statistics of the estimated model
| Statistic | Model |
|---|---|
| χ2 | 32.34 |
| df | 4 |
| CFI | 0.99 |
| TLI | 0.97 |
| SRMR | 0.008 |
| RMSEA | 0.02 |
| AIC | 150727.48 |
| BIC | 151073.80 |
Note: N = 10,047 participants. χ2: Chi-square test statistic; df: degree of freedom; CFI: comparative fit index; TLI: Tucker-Lewis index; SRMR: standardized root mean square residual; RMSEA: root-mean-square error of approximation; AIC: Akaike’s information criterion; BIC: Bayesian information criterion
The direct paths from age to physical activity (β = -0.089, 95% CI = -0.108 to -0.069), morning wake-up time (β = -0.224, 95% CI = -0.242 to -0.205), and sleep-inducing medication use (β = 0.051, 95% CI = 0.032 to 0.071) were significant. Also, the direct paths from physical activity (β = -0.031, 95% CI = -0.050 to -0.013), morning wake-up time (β = 0.058, 95% CI = 0.039 to 0.077), and sleep-inducing medication use (β = 0.374, 95% CI = 0.357 to 0.391) to depression were significant. The direct path from age to depression was non-significant (β = 0.017, 95% CI = -0.002 to 0.035). The morning wake-up time was significantly associated with physical activity (β = -0.233, 95% CI = -0.252 to -0.214). In addition, the effect of physical activity on sleep-inducing medication use was significant (β = -0.024, 95% CI = -0.044 to -0.005). The path coefficients of direct effects between variables in this model are shown in Table 4 (Table 4; Fig. 1).
Table 4.
Path coefficients of direct effects among variables in the estimated model (N = 10047)
| Effect | Independent variable | Dependent variable | B | β | SE | 95% CI |
|---|---|---|---|---|---|---|
| Direct effect | Age | Physical activity | -0.088*** | -0.089 | 0.010 | [-0.108, -0.069] |
| Age | Morning wake up hour | -0.032*** | -0.224 | 0.009 | [-0.242, -0.205] | |
| Age | Sleep-inducing medication use | 0.001*** | 0.051 | 0.010 | [0.032, 0.071] | |
| Morning wake up hour | Physical activity | -1.593*** | -0.233 | 0.010 | [-0.252, -0.214] | |
| Physical activity | Sleep-inducing medication use | 0.000* | -0.024 | 0.010 | [-0.044, -0.005] | |
| Physical activity | Depression | -0.001** | -0.031 | 0.009 | [-0.050, -0.013] | |
| Morning wake up hour | Depression | 0.008*** | 0.058 | 0.010 | [0.039, 0.077] | |
| Sleep-inducing medication use | Depression | 0.452*** | 0.374 | 0.009 | [0.357, 0.391] | |
| Age | Depression | 0.000 | 0.017 | 0.010 | [-0.002, 0.035] |
Note: B = Unstandardized coefficient; β = Standardized coefficient; CI = 95% Confidence interval; SE = Standard error; ***P < 0.001; **P ≤ 0.01 *P < 0.05. Gender, residence type, marital status and educational level were controlled in the model
Fig. 1.
The mediation role of physical activity, morning wake-up time, and sleep-inducing medication use in the relationship between age and depression. Gender, residence type, marital status, and educational level were controlled in the model. Note: ***P < 0.001; **P ≤ 0.01 *P < 0.05
The results of this model showed that the indirect paths from age to depression through physical activity (β = 0.003, 95% CI = 0.001 to 0.005), morning wake-up time (β = -0.013, 95% CI = -0.017 to -0.009), and sleep-inducing medication use (β = 0.019, 95% CI = 0.012 to 0.027) were significant (Table 5).
Table 5.
Path coefficients of indirect effects among variables in the estimated model (N = 10047)
| Effect | Independent variable | Mediating variable | Dependent variable | B | β | SE | 95% CI |
|---|---|---|---|---|---|---|---|
| Indirect effect | Age | Physical activity | Depression | 0.000** | 0.003 | 0.001 | [0.001, 0.005] |
| Morning wake up hour | 0.000*** | -0.013 | 0.002 | [-0.017, -0.009] | |||
| Sleep-inducing medication use | 0.000*** | 0.019 | 0.004 | [0.012, 0.027] | |||
| Total indirect | 0.000 | 0.008 | 0.004 | [-0.001, 0.016] | |||
| Total | 0.001* | 0.024 | 0.010 | [0.005, 0.044] |
Note: B = Unstandardized coefficient; β = Standardized coefficient; CI = 95% Confidence interval; SE = Standard error; ***P < 0.001; **P ≤ 0.01 *P < 0.05. Gender, residence type, marital status and educational level were controlled in the model
The total effect of age on depression was significant (β = 0.024, 95% CI = 0.005 to 0.044), but the direct effect of age on depression was non-significant (β = 0.017, 95% CI = -0.002 to 0.035) after controlling for the indirect effects through the mediators. These results suggest that the relationship between age and depression is partially explained by the effects of mediators.
In this study, in addition to the path analysis, a multiple regression analysis was conducted to examine the direct effects of various predictors on depression.
Multiple regression analysis results
In this study, general characteristics such as gender, marital status, residence type, last education level, and age, as well as specific predictors including physical activity, morning wake-up time, and use of sleep-inducing medication, were included as predictors of depression. A multiple regression analysis was conducted to examine the predictors of depression. The model was found to be statistically significant, F(8,10038) = 219.681,p < 0.001. Significant predictors of depression included the use of sleep-inducing medication (β = 0.373, 95% CI = 0.429 to 0.473), morning wake-up time (β = 0.055, 95% CI = 0.005 to 0.011), and physical activity (β = -0.030, 95% CI = -0.001 to -0.0002). However, the direct effect of age on depression was not significant (β = 0.009, 95% CI = 0.000 to 0.001), suggesting that age did not directly influence depression after controlling for the other variables. The results of the multiple regression analysis predicting depression are summarized in Table 6.
Table 6.
Multiple regression analysis results for predictors of Depression
| Variable | Unstandardized coefficients | Standardized coefficients | t-value | P-value | Lower Bound of 95% CI (for Beta) | Upper Bound of 95% CI (for Beta) | |
|---|---|---|---|---|---|---|---|
| Coefficient | Std. Error | Beta | |||||
| Constant | − 0.014 | 0.025 | − 0.548 | 0.584 | − 0.062 | 0.035 | |
| Gender | 0.008 | 0.004 | 0.024 | 2.270 | 0.023 | 0.001 | 0.016 |
| Residence type | − 0.0004 | 0.003 | − 0.001 | − 0.113 | 0.910 | − 0.007 | 0.006 |
| Marital status | − 0.004 | 0.005 | − 0.008 | − 0.892 | 0.373 | − 0.013 | 0.005 |
| Educational level | − 0.002 | 0.001 | − 0.019 | -1.639 | 0.101 | − 0.005 | 0.0004 |
| Age | 0002 | 0.0002 | 0.009 | 0.882 | 0.378 | − 0.0002 | 0.001 |
| Physical activity | − 0.001 | 0.0002 | − 0.030 | -2.956 | 0.003 | − 0.001 | − 0.0002 |
| Morning wake up hour | 0.008 | 0.001 | 0.055 | 5.489 | P < 0.001 | 0.005 | 0.011 |
| Sleep-inducing medication use | 0.451 | 0.011 | 0.373 | 40.361 | P < 0.001 | 0.429 | 0.473 |
Discussion
This study investigated the mediating role of physical activity, morning wake-up time, and sleep-inducing medication use in the relationship between age and depression through path analysis. The findings indicated that while age did not directly correlate with depression, it was indirectly associated with higher depression via decreased physical activity and increased use of sleep-inducing medications. Conversely, older age was linked to reduced depression through earlier wake-up times. The results were supported by multiple regression analysis, emphasizing the significant predictive value of these mediators. These findings underscore the importance of considering indirect pathways to fully understand the age-depression relationship.
This study did not find a significant direct relationship between age and depression, which contrasts with previous research suggesting that age may serve as a risk factor for depression [6, 9, 44]. For instance, Dziurkowska (2021) indicated that physiological changes associated with aging could exacerbate depression symptoms [9], and Zenebe et al. (2021) reported a significant association between age and the prevalence of depression [44]. Additionally, other studies have demonstrated that older adults tend to experience depression-related complications [32, 33, 45]. The discrepancy between this study’s findings and prior research may be attributed to individual and environmental factors influencing the age-depression relationship. Despite the lack of studies exploring the mediating role of physical activity, morning wake-up time, and sleep-inducing medication use, the current study underscores their significant influence. Although the direct effect of age on depression was not significant, the combined direct and indirect effects through these mediators were found to be significant, emphasizing their crucial role in understanding the relationship between age and depression.
The present study found that increasing age was indirectly linked to higher depression through decreased physical activity. Previous research supports age as a factor influencing physical activity levels [46, 47]. Studies by Kim et al. (2009) and Lee et al. (2022) similarly reported a negative relationship between age and engagement in daily activities, work, and leisure [47, 48]. The reduction in physical activity with age, partly due to declining muscle mass and strength, limits participation in routine activities [49]. This study also confirmed the negative association between physical activity and depression, aligning with findings that higher physical activity reduces depression severity, improves mood, and enhances energy and sleep quality [50–52]. Several studies have also shown that increased physical activity is associated with fewer depressive symptoms [53–56]. Furthermore, research suggests that physical activity serves as a valuable complementary treatment for reducing residual depression symptoms and preventing relapse [57–59]. Several factors contribute to the relationship between physical activity, aging, and depression. Aging is often accompanied by physical limitations such as arthritis, heart disease, reduced muscle mass, and decreased lung function, which hinder engagement in physical activities. Additionally, psychological factors play a significant role; for instance, older adults may experience social isolation or loneliness, diminishing their motivation for physical activity and increasing their risk of depression. Thus, implementing strategies to facilitate age-appropriate physical activities, including daily tasks and exercise, alongside mental health support, is crucial for preventing and managing depressive symptoms.
The present study identified a positive link between increasing age and higher depression incidence through increased sleep-inducing medication use. Consistent with these findings, Leite et al. (2022) reported higher prevalence rates of sleep-inducing drug use in older adults, particularly women aged 40–59 [60]. Kodaira and Silva (2017) found that older Brazilian adults, and people with depressive symptoms, frequently used sleeping pills [28]. Age-related changes in sleep patterns often lead to reliance on such medications, which, while effective, may have long-term side effects [30, 61]. The study also confirmed the significant relationship between sleep medication use and depression, supporting previous research [31, 62]. Although medications like benzodiazepines can alleviate depression-related sleep issues, they may also exacerbate symptoms in some cases [63]. Thus, considering the available and low-cost treatments that improve the quality of sleep can play a significant role in reducing a person’s need to take sleeping pills. Encouragingly, this study found that physical activity was negatively related to sleep-inducing medication use. Previous research supports physical activity as an effective non-pharmaceutical intervention for improving sleep and reducing sleep-inducing medication use [64]. Therefore, as people age, promoting physical activity can be an effective, non-pharmaceutical approach to improving sleep quality, reducing dependence on sleep-inducing medications, and supporting better mental health by alleviating depression.
The present study found that increasing age was linked to reduced depression through earlier wake-up times. Consistent with these findings, prior research has shown that as individuals age, they tend to follow an earlier sleep-wake schedule, going to bed and waking up approximately 1 to 2 h earlier [25, 65]. This shift may be explained by reduced sleep needs in older adults [66]. Additionally, the current study highlighted a direct and significant link between early wake-up times and lower depression levels. In line with these results, Daghlas et al. (2021) found that waking up one hour earlier can reduce the risk of major depression by 23% [67]. It is also indicated that people who woke up earlier were less susceptible to depression compared to people who woke up later [68]. Moreover, this study revealed that early wake-up times were significantly and negatively associated with physical activity. This suggests that early risers might engage more in daily activities and maintain an active lifestyle, contributing to reduced depression. These insights underscore the potential mental health benefits of a regular sleep schedule and earlier wake-up times as practical strategies for reducing depression risk.
The study’s outcomes align with the Biopsychosocial Theory, highlighting the complex interplay of biological, psychological, and social factors in the relationship between age and depression. Although no direct link between age and depression was found, our results reveal complex indirect pathways. Increasing age was associated with heightened depression risk via reduced physical activity and increased sleep-inducing medication use. Conversely, higher age is related to lowered depression risk through earlier wake-up times, aligning with circadian rhythm theories. These findings underscore the complexity of age-related depression dynamics and imply potential interventions targeting physical activity, sleep patterns, and circadian alignment for effective mitigation of age-related depression risks.
The results reported in this study should be interpreted with a focus on its limitations. One of the limitations of this study is the absence of a standardized tool for measuring depression scores. In this study, diagnoses were made by a psychologist based on DSM-5 criteria and/or through a self-reported history of medication use, which may compromise the validity of the results. Therefore, future studies should utilize standardized tools to enhance reliability. Another limitation is the unidirectional analysis of depression in relation to physical activity, sleep medication, and wake-up time. Future research should explore bidirectional relationships to better understand these factors as potential consequences of depression. Moreover, in this study, the morning wake-up time and sleep-inducing medication use were evaluated based on self-reported criteria at a point in time. Thus, future studies need to perform more objective evaluations at different points in time. Furthermore, similar studies can be replicated in different societies and in other age groups to increase the generalizability of the findings.
Conclusion
Following the findings from the present study, increasing age was related to an increase in the incidence of depression through a decrease in physical activity and an increase in sleep-inducing medication use, and there is a negative and significant association between physical activity and sleep-inducing medication use. Moreover, increasing age was associated with a reduced incidence of depression via earlier waking up.
This study also showed that morning wake-up time had a significant and negative relationship with physical activity. The findings of this study can add to the existing body of knowledge on depression management and help clarify the mechanisms of the effect of age on depression. Following these findings, healthcare providers and psychotherapists can improve the management of depression during the aging process by taking measures to promote the level of physical activity, adjusting the number of sleeping pills, and adjusting the morning wake-up time.
Acknowledgements
The authors thank the PERSIAN cohort Study collaborators and of Kermanshah University of Medical Sciences. The Iranian Ministry of Health and Medical Education has also contributed to the funding used in the PERSIAN Cohort through Grant no 700/534.
Abbreviations
- RaNCD
Ravansar Non-Communicable Disease
- PERSIAN
Prospective Epidemiological Re-Search in IrAN
- MET
Metabolic Equivalent of Task
- SPSS
Statistical Package for Social Sciences
- EFA
Explanatory Factor Analysis
- CFA
Confirmatory Factor Analysis
- KMO
Kaiser-Meyer-Olkin Index
- AVE
Average Variance Extracted
- CR
Composite Reliability
- WLSMV
Weighted least squares
- TLI
Tucker-Lewis Index
- CFI
Comparative Fit Index
- RMSEA
Root Mean Square Error of Approximation
- SRMR
Standardized Root Mean Square Residual
Author contributions
FA.H, SB, MM and FN were responsible for the study conception and design; FA.H, CCM performed the data analysis; FA.H, SB, CCM, MM and FN were responsible for the drafting of the manuscript; FA.H, SB, CCM, MM and FN made critical revisions to the paper for important intellectual content. All authors have read and approved the manuscript.
Funding/Support
This research was supported by the Kermanshah University of Medical Sciences (grant number: 92472).
Data availability
The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.
Declarations
Ethics approval and consent to participate
The Ethics Committee of Kermanshah University of Medical Sciences approved the study (KUMS.REC.1394.318). All methods were carried out in accordance with relevant guidelines and regulations. All the participants were provided oral and written informed consent.
Consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing interests.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.