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Article

Depression Severity and Its Predictors: Findings from a Nationally Representative Canadian Sample

by
Eric D. Tessier
,
Geoffrey S. Rachor
,
Blake A. E. Boehme
,
Braeden Hysuick-Weik
and
Gordon J. G. Asmundson
*
Department of Psychology, University of Regina, Regina, SK S4S A02, Canada
*
Author to whom correspondence should be addressed.
Psychiatry Int. 2025, 6(3), 114; https://doi.org/10.3390/psychiatryint6030114
Submission received: 3 June 2025 / Revised: 6 July 2025 / Accepted: 12 September 2025 / Published: 16 September 2025
Versions Notes

Abstract

Depression is a major global health issue that significantly contributes to the burden of disease. Despite the wealth of existing research on depression, several key aspects remain underexplored, including factors that predict the onset, severity, and recurrence of depressive symptoms. The purpose of the current study was to assess the sociodemographic correlates and risk and protective factors of depression using a representative sample of the Canadian population. The data were drawn from the 2017–2018 Canadian Community Health Survey (CCHS), a cross-sectional survey with a sample size greater than 113,000. Results from regression analyses identified sleep quality, social support, and perceived life satisfaction as protective factors for depression severity, while a current, self-reported diagnosis of an anxiety- or mood-related disorder was identified as a risk factor. Being younger emerged as the only pertinent sociodemographic risk factor for depression. Contrary to expectations, vigorous physical activity and sedentary behaviour did not significantly predict depression severity. Taken together, the results underscore the importance of identifying modifiable risk and protective factors to inform population-level mental health strategies (e.g., campaigns seeking to raise awareness regarding the importance of sleep, social support) to guide the development of targeted, evidence-based interventions.

1. Introduction

Depression is a major global health issue that significantly contributes to the burden of disease [1]. Depression affects approximately 280 million people worldwide, representing roughly 5% of adults at any given time [2]. The global lifetime prevalence of major depressive disorder (MDD) is approximately 10%, while the 12-month prevalence is 4–5% [3]. Evidence indicates a negative relationship between depression and physical health (e.g., cardiovascular disease, neurotransmitter dysregulation) and psychological functioning (e.g., distress tolerance, rumination) while increasing the risk of all-cause mortality by 52% [4,5,6,7]. Productivity losses associated with mental illness are estimated to cost the global economy USD 5 trillion annually [8], with depression accounting for a substantial portion of this cost due to its high prevalence and widespread adverse health effects [9].
Approximately 75% of Canadians who access mental health services do so because of anxiety- or mood-related disorders [10]. However, among Canadians who express the need to access mental healthcare, only 56% report that their needs are fully met, 22% report that their needs are partially met, and 21% report that their mental healthcare needs remain unmet [11]. These Canadian findings suggest that to better tailor interventions and translate findings into effective public health strategies, more research efforts are required to understand the correlates and relevant predictors of depression.
Despite the wealth of existing research on depression, several key aspects remain underexplored, including factors that predict the onset, severity, and recurrence of depressive symptoms [12]. Clarity on these matters is essential for healthcare professionals to identify relevant variables (e.g., age, sex, modifiable protective factors), enhance sensitive screening practices, and deliver effective treatments [13]. Investigating the risk factors, protective factors, and sociodemographic correlates of depression may provide vital information to guide clinical decision making and improve mental health outcomes. Below, theoretically significant sociodemographic, behavioural, and clinical correlates of depression are reviewed to support their inclusion in the present study.
Sedentary behaviour, poor sleep, lack of social support, and insufficient physical exercise are known risk factors for depression [14,15,16,17]. A recent population-representative study in Norwegian adults (n = 28,047) found all of these factors to be associated with psychological distress [18]. A cross-sectional sample of older Chinese adults (n = 49,317) demonstrated that people who are more sedentary and sleep less report more depressive symptoms [19]. Similarly, statistically significant relationships were established between risk and protective factors (i.e., physical activity, sedentary behaviour, sleep) and mental well-being indicators (i.e., depression, stress, life satisfaction, self-efficacy) in a cross-sectional sample of Polish adolescents [20]. Across the lifespan, the less satisfied a person is with their life, the more they tend to report depressive symptoms [21,22]. However, bidirectional effects (i.e., depression reducing perceived life satisfaction, greater life satisfaction buffering against depression) are likely present among risk and protective factors [23,24,25].
Physical exercise, both aerobic and anaerobic, is an effective intervention for depression, whether used as an adjunct to psychotherapy or pharmacotherapy [26] or as a standalone treatment [27]. A recent four-year study of older European adults demonstrated that engaging in moderate or vigorous physical activity for at least one hour per week is associated with lower depression severity [28]. Similarly, approximately 15 min of vigorous physical activity per day has been associated with a reduced likelihood of depressive symptoms [29]. While physical activity typically improves mood, sedentary behaviour is consistently associated with more severe depressive symptoms in both clinical and non-clinical populations [30]. Consequently, reducing sedentary behaviour is important for both treatment and prevention [31,32].
Sleep quality has also been identified as a reliable statistical predictor of mental health, and sleep dysfunction is an important transdiagnostic symptom of psychiatric diagnoses [33]. Poor sleep quality and short sleep duration have demonstrated consistent associations with symptoms of stress, anxiety, depression, and rumination [34,35,36,37]. A recent meta-analysis highlighted a dose-dependent relationship, with better sleep quality leading to greater improvement of depressive symptoms [36].
Anxiety and depression are highly co-occurring disorders, with approximately 46% of people diagnosed with MDD experiencing an anxiety disorder in their lifetime [38]. People diagnosed with both anxiety and depressive disorders are more likely to face greater impairment in their daily lives [39], which increases the probability of experiencing worsening symptoms [40]. Therefore, comorbid anxiety- or mood-related disorders represent a theoretically significant risk factor for depression severity. Similarly, more social support, defined as access to a community that fulfills a person’s need to feel cared for and loved, is moderately correlated with positive mental health outcomes and inversely associated with psychological distress [41,42]. In the context of depression, social support is a well-documented protective factor that prevents onset and reduces severity in clinical and non-clinical populations [43].

Current Study

The purpose of the current study was to identify factors associated with depression severity using a recent population-representative sample of Canadians. Given the rising burden of mental health challenges in Canada and other countries, including growing service gaps and the lasting impact of the COVID-19 pandemic on population well-being [44], understanding the predictors of depression severity is both timely and essential. A substantial body of international research has identified a range of risk and protective factors for, as well as sociodemographic correlates of, depression. However, these findings have not been sufficiently replicated within the Canadian context. At its core, the current study seeks to better understand which factors are currently associated with depression in Canadians. Therefore, the current study addresses gaps in the literature by examining how key sociodemographic, behavioural, and clinical factors relate to the severity of depressive symptoms in a nationally representative sample. Generating context-specific evidence of these associations is critical for informing mental health policy (e.g., how time, staff, and resources are allocated), guiding targeted prevention strategies (e.g., psychoeducation regarding modifiable protective factors), and supporting the effective allocation of resources to reduce the prevalence and impact of depression, across Canada and elsewhere.
Several hypotheses were tested in the current study. First, it was hypothesized that sleep quality would significantly and negatively predict depression severity, serving as a protective factor that buffers against depressive symptoms [45]. Second, vigorous physical activity was hypothesized to predict depression severity significantly and negatively, as supported by previous research [28,29]. Third, it was hypothesized that social support would significantly and negatively predict depression severity in the Canadian population [43,46]. Fourth, sedentary behaviour (excluding work or school activities) was hypothesized to significantly and positively predict depression severity, representing a potential risk factor for depression in this sample [47]. Fifth, a current, self-reported diagnosis of an anxiety- or mood-related disorder was hypothesized to be a significant positive predictor of depression severity [39,40,48] due to the increased burden of comorbid symptoms. Finally, age and sex were hypothesized to significantly predict depression severity [49]. Gender was not explored in this study, as it was not reported in the current Statistics Canada survey [50].

2. Method

The current study used self-reported, cross-sectional, correlational data from the 2017–2018 Canadian Community Health Survey (CCHS), which was combined into a two-year data file [51]. Utilizing the CCHS, Statistics Canada collects annual health information of Canadians, including health status, healthcare utilization, and determinants of health (e.g., physical activity duration, frequency), employing population-representative sampling methods [50]. The CCHS was selected for use in the current study due to its broad coverage of health behaviours and inclusion of validated psychological measures.
Participants (n = 113,290) were recruited by Statistics Canada using multistage stratified cluster sampling [50]. Inclusion criteria required participants to be (1) at least 12 years of age and (2) living in private dwellings in Canada. Participants were excluded if they were (1) living on reserves or Crown land, (2) institutionalized, or (3) serving in the Canadian Armed Forces. Additionally, for the purposes of the present research, participants were excluded if they did not complete measures relating to the variables of interest presented herein. Participants were initially contacted via letter mail, followed by a telephone call to schedule the interview and obtain informed consent. Data collection for the 2017–2018 CCHS was conducted using computer-assisted telephone interviewing (75%) and computer-assisted in-person interviewing (25%), with a total response rate of 58.8%. The current study received an ethics exemption from the Research Ethics Board at the University of Regina (REB# 462), as the data used were made publicly available by Statistics Canada [50].

2.1. Measures

Depression severity was measured using the severity indices of the Patient Health Questionnaire (PHQ-9 [52]). The PHQ-9 is a nine-item self-report measure used to assess depression severity based on the diagnostic criteria for MDD. Items are scored on a four-point Likert scale ranging from (0) not at all to (3) nearly every day. Total scores range from 0 to 27 and are treated as continuous, with higher scores indicating more severe depression. Cut-off scores of 5, 10, 15, and 20 correspond to the lower limits of mild, moderate, moderately severe, and severe depression, respectively. The 2017–2018 CCHS did not provide item-level scores for the PHQ-9; therefore, internal consistency was not measured. However, the PHQ-9 has demonstrated adequate construct validity and internal consistency in previous studies [53].
Social support was measured using the Social Provisions Scale (SPS-10 [54]). The SPS-10 is used to measure perceived social support across five domains (i.e., attachment, social integration, reassurance of worth, sense of reliable alliance, guidance), with items rated on a four-point Likert scale ranging from (1) strongly disagree to (4) strongly agree. Total scores range from 10 to 40 and are treated as continuous, with higher scores indicating greater perceived support. The SPS-10 has demonstrated strong psychometric properties across various sample populations [55]. In the present study, internal consistency was excellent (McDonald’s ω = 0.93).
Physical activity was measured based on participants’ self-reported frequency and duration of vigorous physical activity over the previous seven days. As part of the survey, participants were provided with examples of activities classified as vigorous to support accurate reporting. Frequency data were treated as continuous in the current study. Participants were asked “In the last 7 days, how much time in total did you spend doing vigorous activities that caused you to be out of breath?” Activities that did not meet the vigorous intensity threshold or that were less than 10 min in duration were excluded from the calculation of total activity. This measure of physical activity is part of the Public Health Agency of Canada’s physical activity surveillance system (PASS) framework, which includes measures of sleep and sedentary behaviour in addition to physical activity [56].
Sedentary behaviour was assessed based on participants’ self-reported average time sitting, lying down, or reclining while awake, not attributable to work or school activities. Responses were recorded on a five-point Likert scale, ranging from (1) ≤ 2 h per day to (5) ≥ 8 h per day, and were considered ordinal, with higher scores indicating greater sedentary behaviour. The current single-item measure of sedentary behaviour is included in the PASS framework [56].
Sleep quality was assessed using a single-item measure evaluating the perceived frequency of experiencing refreshing sleep over the previous seven days, consistent with the PASS framework [56]. Responses were rated on a five-point Likert scale ranging from (1) never to (5) all of the time and considered ordinal, with higher scores indicating greater perceived sleep quality. Single-item sleep measures are commonly used in both clinical and population research to reduce participant burden and have demonstrated comparable psychometric properties to longer self-report sleep measures [57,58].
Life satisfaction was assessed using a single-item self-report measure that rated perceived life satisfaction on a five-point Likert scale from (1) very satisfied to (5) very dissatisfied. Lower scores reflected greater perceived life satisfaction and were treated as ordinal. Single-item measures of perceived life satisfaction are considered a valid and reliable indicator of subjective well-being in population-level research [59].
Current, self-reported anxiety- and mood-related disorder diagnosis was assessed using two single-item self-report measures from the CCHS. These items were not meant as diagnostic tools but were rather used to determine if participants self-reported previously being diagnosed with an anxiety- or mood-related disorder. Participants were asked (1) “Do you have a mood disorder such as depression, bipolar disorder, mania, or dysthymia?” and (2) “Do you have an anxiety disorder such as a phobia, obsessive-compulsive disorder, or panic disorder?” Responses to each item were initially recorded as binary, (1) yes or (2) no. These items reflect self-reported, clinician-diagnosed conditions and are widely used in national health surveillance to estimate the prevalence of internalizing mental disorders [50]. To represent the full range of self-reported combinations, binary responses were combined into a four-level categorical variable with no diagnosis as the reference category: (0) no current diagnosis, (1) anxiety disorder only, (2) mood disorder only, and (3) comorbid anxiety and mood disorders.
Participant age was recorded using ordinal age groups, ranging from (1) ages 18–19 years to (14) ages 80 and older. Sex was recorded as a binary variable, with participants indicating either (1) male or (2) female. In this case, male was used as the reference category.

2.2. Complex Sampling

To ensure that the results of the present study were representative of the Canadian population, scaled weight adjustment with bootstrap estimation was applied to account for potential sampling biases [46,50,60]. Bootstrap estimation was used to generate more accurate standard errors and confidence intervals, as it accounts for the complex survey design and variability introduced through stratification and clustering [61]. Only weight-adjusted results were presented to protect participant confidentiality and reduce the risk of de-anonymization based on unweighted data [50].

2.3. Data Analysis

Analyses were conducted using STATA version 18 [62]. Sample characteristics are presented in the supplemental material (see Table S1). Descriptive statistics were used to calculate means, standard errors, confidence intervals, and frequencies for all variables of interest (see Table S2). Per the central limit theorem, population-representative samples are generally robust concerning violations of the assumption of normality. The assumptions of linearity and heteroscedasticity were evaluated visually through scatterplots and residual plots. Multicollinearity among predictors was assessed by calculating the variance inflation factor (VIF) and comparing it to the overall model threshold. A VIF greater than 1/(1 − R2) was considered indicative of problematic multicollinearity, which could bias parameter estimates [63]. Outliers were addressed post-estimation using the coefficient of variation (CV) for all variables of interest. Variables with a CV lower than 35% were considered sufficiently stable and appropriate for inclusion in regression models [50].
Two weight-adjusted linear regression models were constructed to examine risk factors, protective factors, and sociodemographic correlates as statistical predictors of depression severity. The initial model included all theoretically relevant variables of interest, while the final weighted regression model retained only statistically significant predictors within allowable CV limits. Adjusted R2 values are not typically reported for survey-weighted models, as conventional definitions of sample size and degrees of freedom do not directly apply in the context of complex sampling and bootstrap estimation [64]. Unadjusted R2 values are presented herein to provide an estimate of overall model fit.

3. Results

The average participant in the current study was middle-aged (Mage = 50 to 55, SE = ± 0.115), female (52.4%), white (94.4%), and married (57.7%), with a certificate, diploma, university degree or higher (64%) and a total yearly household income of CAD 80,000 or greater (46.5%). On average, participants reported sometimes experiencing refreshing sleep (M = 3.37, SE = ± 0.037). Participants engaged in approximately 0.5 h of vigorous physical activity daily (M = 0.518, SE ± 0.062) and approximately 2 h of sedentary behaviour daily (M = 2.037, SE ± 0.036). Participants were, on average, satisfied with life in general (M = 1.661, SE ± 0.022), reported high levels of social support (M = 35.909, SE ± 0.141), and did not self-report the presence of an anxiety- or mood-related disorder (M = 0.269, SE ± 0.024). Participant depression severity is best characterized as falling between the threshold of none and mild (M = 2.582, SE ± 0.118), which is consistent with the current sample being population-representative.
In the initial regression model (see Table 1), several variables emerged as statistically significant predictors of depression severity. Notably, sleep quality with β = −0.925 (95% CI: −1.107 to −0.743, p < 0.001), social support with β = −0.074 (95% CI: −0.119 to −0.029, p = 0.001), perceived life satisfaction with β = 1.523 (95% CI: 1.159 to 1.886, p < 0.001), and a current, self-reported diagnosis of an anxiety- or mood-related disorder with β = 1.668 (95% CI: 1.264 to 2.071, p < 0.001) were significant, with CV values well within limits. Sedentary behaviour with β = 0.140 (95% CI: −0.062 to 0.342, p = 0.174) was not significant, and a high CV (73.49%) indicated an unstable estimate. Vigorous physical activity with β = 0.081 (95% CI: −0.001 to 0.164, p = 0.054) showed a marginal positive association with depression severity, which, while counterintuitive, may point to reverse causality or measurement limitations. This predictor was removed due to an unacceptably high CV (51.83%). Among sociodemographic factors, age with β = −0.074 (95% CI: −0.120 to −0.028, p = 0.002) was a statistically significant negative predictor of depression severity, whereas sex with β = 0.293 (95% CI: −0.053 to 0.639, p = 0.097) was not a statistically significant predictor. Predictors in the initial regression model explained approximately 42.7% of the variance in depression severity.
In the final regression model (see Table 2), excluding unstable and non-significant predictors of depression severity had a negligible impact on overall fit, with independent variable predictors explaining 42.2% of the variance in depression severity in the current sample. Perceived life satisfaction with β = 1.558 (95% CI: 1.204 to 1.912, p < 0.001) remained the strongest negative predictor, followed by sleep quality with β = −0.945 (95% CI: −1.126 to −0.764, p < 0.001) and social support with β = −0.074 (95% CI: −0.119 to −0.029, p = 0.001). Being older with β = −0.078 (95% CI: −0.124 to −0.032, p = 0.001) and the presence of a current, self-reported anxiety- or mood-related disorder with β = 1.707 (95% CI: 1.315 to 2.099, p < 0.001) remained positive predictors of depression severity. All CV estimates in the final model were below 35%, indicating relatively stable parameter estimates. These findings underscore the centrality of psychosocial variables, particularly sleep quality and life satisfaction, in understanding depression severity among Canadian adults.

4. Discussion

The goal of the current study was to further identify and explore risk factors, protective factors, and sociodemographic correlates of depression severity in a recent population-representative sample of Canadians. Consistent with prior research findings [20,28,29,43,45,46], it was hypothesized that sleep quality, physical activity, social support, and perceived life satisfaction would negatively predict depression severity, serving as theoretically supported protective factors. In contrast, sedentary behaviour (excluding work or school activities) and a current, self-reported anxiety- or mood-related disorder diagnosis were hypothesized to positively predict depression severity, representing potential risk factors [39,40,47,48]. Sociodemographic correlates (i.e., age, sex) were hypothesized to significantly predict depression severity [49,65,66]. The hypothesized relationships between risk and protective factors and depression severity were supported, with some exceptions. Specifically, variables corresponding to vigorous physical activity and sedentary behaviour did not emerge as significant predictors in the final regression model. Moreover, sex was not a significant sociodemographic predictor of depression severity.

4.1. Physical Activity

One question arising from the results is the incongruence with previous studies of physical activity and depression severity [26,27,67,68], as well as studies specifically focusing on the mental health benefits of vigorous physical activity [28,29]. Physical activity is typically associated with lower depression severity and a decreased likelihood of a depressive episode occurring [26,27]. Similarly, the effectiveness of physical activity as a primary or adjunct intervention for depression has been well-established [27,68]. Issues with operationalizing vigorous physical activity in the current version of CCHS may have contributed to the null results. In the current study, physical activity that fell below the intensity threshold of increased breathing or heart rate, and activity that lasted less than 10 min, was excluded. Therefore, high-volume, low-intensity exercise and brief-duration, high-intensity exercise may have been overlooked, potentially contributing to non-significant results.
The results show that, on average, Canadian adults in the current sample are engaging in approximately 30 min of vigorous physical activity per day and limiting sedentary time to approximately 2 h per day, which meets or exceeds the Canadian 24-h Movement Guidelines [69]. However, an issue with using self-reporting to measure physical activity is an observable inflation of physical activity estimates when compared to objective measures, such as accelerometry [70,71,72]. Comparable differences (i.e., underreporting) have also been observed in the measurement of sedentary behaviour [72,73]. It is, therefore, encouraging that the sixth cycle of the Canadian Health Measures Survey (CHMS) in 2018–2019 included objective measures of physical activity and sedentary behaviour using accelerometry. Unfortunately, the CHMS does not include clinical or psychological measures, making the objective measures of physical activity and sedentary behaviour less useful for clinicians or for analyses of the type employed in this study. In the future, including similar objective measures of physical activity and sedentary behaviour in the CCHS, even for a small subset of participants, would significantly bolster the utility of the survey overall from a research and public health perspective. With objective measures of physical activity and sedentary behaviour added to the CCHS, researchers could, for example, examine the relationship between activity and mental health outcomes across specific ages, demographics, and contexts to better target future policy initiatives (e.g., reducing sedentary behaviour in adolescents, increasing physical activity in older adults). Objective measures, such as accelerometry, would also make the shift towards brief longitudinal surveying techniques (e.g., ecological momentary assessment) more feasible and effective.

4.2. Life Satisfaction

The consistent association between perceived life satisfaction and depression severity emphasizes the importance of broader subjective well-being in mental health. Life satisfaction reflects a person’s overall evaluation of life quality and is shaped by factors like relationships, achievement, autonomy, and security [59]. When people feel generally satisfied with life, they may have more psychological resources to cope with stress and regulate their emotions. Clinically, this finding supports using interventions that promote meaning-making and value-driven action, such as Acceptance and Commitment Therapy, which has been shown to enhance psychological flexibility and overall well-being [74]. From a public health perspective, policies that reduce inequality and improve access to housing, education, and employment may contribute to improved mental health outcomes by enhancing life satisfaction and psychosocial stability [75].

4.3. Self-Reported Anxiety- or Mood-Related Disorder

People with a current, self-reported diagnosis of an anxiety- or mood-related disorder experienced greater depression severity in the current study, consistent with the high rates of co-occurrence between these conditions [40,48]. Anxiety and depression often reinforce one another through shared cognitive, emotional, and neurobiological pathways [39]. The results of the current study support the importance of early detection and long-term support for those with a history of anxiety- or mood-related disorders. In both clinical and community contexts, adopting a preventative care model, in which individuals receive support even when symptoms are subthreshold, could help reduce relapse rates and improve long-term outcomes [76]. Public health efforts should also address structural barriers to care, ensuring that individuals with co-occurring mental health conditions have access to timely, coordinated, and integrated services [77].

4.4. Age

In the current study, younger individuals reported greater depression severity, aligning with national trends indicating that adolescents and young adults in Canada are experiencing increasing psychological distress [78]. Contributing factors may include financial strain, academic pressures, and social disconnection [79,80]. These findings highlight the need for youth-centered prevention strategies, including digital mental health resources, peer-led initiatives, and accessible services within schools and universities.

4.5. Sex

Sex did not emerge as a significant factor in the current study. While the existing literature often reports sex differences in depression prevalence, with typically higher rates in women [81], these differences may not always translate into meaningful clinical predictors when considered alongside behavioural and psychosocial variables (e.g., sleep quality, social support). Recent research has questioned the utility of relying on sociodemographic variables, such as age and sex, to predict depression severity, particularly when more modifiable and proximal factors (e.g., sleep, social support, psychological distress) demonstrate greater explanatory power [57,82]. From both a clinical and public health standpoint, this suggests a shift in focus toward addressing modifiable behavioural and contextual risk factors, which may yield more actionable insights for prevention and intervention. For example, the Public Health Agency of Canada has made efforts to implement a physical activity surveillance system (PASS) for monitoring national trends related to sleep, sedentary behaviour, and physical activity.

4.6. Sleep and Social Support

Sleep quality and perceived social support were both associated with reduced depression severity in the current study, consistent with a vast body of literature [15,36,43]. Sleep disturbances are among the most commonly reported symptoms across mental health conditions and often precede depressive episodes [33]. Similarly, strong social relationships are protective against the onset and worsening of depression, offering practical support and a sense of belonging [43]. The current findings underscore the importance of developing and implementing low-cost, low-barrier, internet-delivered sleep interventions [83] and structured social support interventions like group therapy and peer networks [84] as clinical priorities. From a population health perspective, fostering community engagement and improving access to sleep psychoeducation may promote resilience and reduce the overall mental health burden. Recommendations from the current study may raise awareness of the importance of modifiable factors like sleep and social support and improve the likelihood of increased support from governmental agencies.

5. Strengths and Limitations

The present study has several notable strengths. The use of a robust sampling methodology (i.e., multistage stratified cluster sampling) and a large, population-representative sample, as provided by Statistics Canada [50], enhances the generalizability of the current findings to the Canadian population. Additionally, because the CCHS is conducted annually, there is sufficient opportunity for researchers to replicate or extend these results, providing further clarity regarding the risk and protective factors associated with depression severity in Canada. The public availability of the CCHS also facilitates external corroboration of the present findings by independent research teams. Importantly, this study examined modifiable risk and protective factors—such as sleep quality, physical activity, and sedentary behaviour—that are highly relevant to the daily lives of Canadians and have been included in the PASS framework for monitoring at a national level. As such, the results have direct implications for the population health strategies previously discussed and are well-suited to public dissemination and policy discourse.
Despite its strengths, several limitations of the current study must be acknowledged. The CCHS comprises self-report measures, which lack the precision of objective measures, particularly for constructs like physical activity, sedentary behaviour, and sleep. Relatedly, several key variables, including sleep quality, life satisfaction, and self-reported psychiatric diagnoses (e.g., mood disorders, anxiety disorders), were assessed using single-item indicators, potentially attenuating construct validity, although there is considerable evidence in favour of single-item measures in the context of population research [50,57,58,59]. The cross-sectional, correlational design of the study precludes inference regarding causality and raises the possibility of reverse causality. For example, individuals with more severe depressive symptoms may engage in less physical activity or report poorer sleep, rather than these behaviours being risk factors.
While the use of a population-representative sample enhances national relevance, generalizability may be limited in clinical populations or for individuals outside of Canada. Moreover, although Statistics Canada applies weighting adjustments for sampling design and nonresponse, individuals with more severe depression may still be underrepresented due to low mood or lack of motivation to participate [85], potentially resulting in an underestimation of depression severity overall. Finally, while the CCHS offers broad coverage of health behaviours and perceptions, this breadth comes at the expense of measurement depth. Future research would benefit from using item-level data from the PHQ-9 or other comprehensive clinical measures to examine the somatic, cognitive, and affective dimensions of depression, as well as impacts on daily functioning and associations with modifiable risk and protective factors.

6. Conclusions

The current study is the first to comprehensively assess risk and protective factors as statistical predictors of depression severity in a recent population-representative sample of Canadians. The results collectively indicate that sleep quality, social support, and perceived life satisfaction are protective factors for depression severity, while having a current, self-reported anxiety- or mood-related disorder diagnosis is a risk factor that predicts greater depression severity. Respondent age (e.g., being younger was associated with more severe depressive symptoms) was also identified as a pertinent sociodemographic predictor. Contrary to established findings, physical activity and sedentary behaviour did not emerge as significant predictors of depression severity in the Canadian population [26,27,28,29,30,31,32]. Based on the unexpected results of this study, there is a clear need to (1) replicate the results using newer, soon to be available versions of the CCHS and (2) extend the breadth of these outcomes using different population datasets to determine if the current results can be replicated or extended. Conducting longitudinal research on the effects of risk and protective factors as they relate to depression could improve existing treatments and potentially inform the development of new interventions for depression.
The findings of the present study provide valuable insights into the health behaviours of Canadians as they relate to depression severity. Given the current unmet need for mental health services in Canada [11,44,78,86,87], research that identifies risk and protective factors in a recent, population-representative sample is essential for informing public health policy (e.g., allocation of staffing and resources) and clinical practice (e.g., assessment, intervention). These results provide valuable information for policymakers and healthcare providers to inform effective mental health messaging campaigns and guide future research priorities. Findings from the current study may also support community programs seeking to improve mental health by providing evidence for modifiable factors (i.e., sleep, social support) as well as recommendations for low-cost, low-barrier interventions. By identifying risk and protective factors, the findings of the current study may inform the development of targeted psychoeducational programs and interventions, potentially helping to reduce the burden on primary care and the Canadian healthcare system.

Supplementary Materials

The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/psychiatryint6030114/s1, Table S1: Sample characteristics; Table S2: Means, standard errors, and confidence intervals for variables of interest.

Author Contributions

Conceptualization, E.D.T., G.S.R. and G.J.G.A.; methodology, E.D.T., G.S.R. and G.J.G.A.; formal analysis, E.D.T.; supervision, G.S.R. and G.J.G.A.; validation, E.D.T., G.S.R. and B.A.E.B.; writing, review, and editing, E.D.T., G.S.R., B.A.E.B., B.H.-W. and G.J.G.A. The current manuscript was originally formatted as a thesis to partially fulfill requirements for a Bachelor of Arts (Honours) in psychology by E.D.T., which was supervised by G.S.R. and G.J.G.A. The original manuscript is available at the University of Regina repository in a pre-print version but has not been previously published or peer reviewed. All authors have read and agreed to the published version of the manuscript.

Funding

The Canadian Institutes of Health Research partially supported the primary researcher and associated research team during the conceptualization, execution, and dissemination of the current research project. Gordon Asmundson’s research is partially funded by his appointment as the President’s Research Chair in Adult Mental Health at the University of Regina. The APC for the current article was waived by the publisher.

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki, and received an ethics exception from the Research Ethics Board at the UNIVERSITY OF REGINA (REB# 462, 12 January 2024), for studies involving humans. Ethical review and approval were waived for this study as the data were made publicly available by Statistics Canada.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study by Statistics Canada. Written informed consent was obtained from the study participants to publish research associated with the publicly available data analyzed herein.

Data Availability Statement

To access the study data under the Statistics Canada open license, see: https://hdl.handle.net/11272.1/AB2/SEB16A (accessed on 27 August 2025).

Conflicts of Interest

The authors of this study declare no conflicts of interest.

References

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Table 1. Initial weight-adjusted linear regression model—depression severity.
Table 1. Initial weight-adjusted linear regression model—depression severity.
Depression SeverityβLower 95% CIUpper 95% CISig.ZStd. ErrorCV (%)
 Sleep Quality−0.925−1.107−0.743<0.001−9.970.09310.03
 Physical Activity0.081−0.0010.1640.0541.930.04251.83 †
 Sedentary Behaviour0.140−0.0620.3420.1741.360.10373.49 †
 Social Support−0.074−0.119−0.0290.001−3.250.02330.79
 Age−0.074−0.120−0.0280.002−3.170.02331.53
 Sex0.293−0.0530.6390.0971.660.17660.21 †
 Anxiety/Mood Disorder1.6681.2642.071<0.0018.100.20612.34
 Life Satisfaction1.5231.1591.886<0.0018.220.18512.17
N-observations 105,737
R-squared0.427
Note. Coefficient of variation (CV) < 35% allowable, † exceeds acceptable CV limit. N-observations were adjusted using bootstrap estimation.
Table 2. Final weight-adjusted linear regression model—depression severity.
Table 2. Final weight-adjusted linear regression model—depression severity.
Depression SeverityβLower 95% CIUpper 95% CISig.ZStd. ErrorCV (%)
 Sleep Quality−0.945−1.126−0.764<0.001−10.240.0929.76
 Social Support−0.074−0.119−0.0290.001−3.250.02330.78
 Age−0.078−0.124−0.0320.001−3.300.02430.28
 Anxiety/Mood Disorder1.7071.3152.099<0.0018.540.20011.71
 Life Satisfaction1.5581.2041.912<0.0018.630.18011.58
N-observations 105,737
R-squared0.422
Note. Coefficient of variation (CV) < 35% allowable. N-observations were adjusted using bootstrap estimation.
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Tessier, E.D.; Rachor, G.S.; Boehme, B.A.E.; Hysuick-Weik, B.; Asmundson, G.J.G. Depression Severity and Its Predictors: Findings from a Nationally Representative Canadian Sample. Psychiatry Int. 2025, 6, 114. https://doi.org/10.3390/psychiatryint6030114

AMA Style

Tessier ED, Rachor GS, Boehme BAE, Hysuick-Weik B, Asmundson GJG. Depression Severity and Its Predictors: Findings from a Nationally Representative Canadian Sample. Psychiatry International. 2025; 6(3):114. https://doi.org/10.3390/psychiatryint6030114

Chicago/Turabian Style

Tessier, Eric D., Geoffrey S. Rachor, Blake A. E. Boehme, Braeden Hysuick-Weik, and Gordon J. G. Asmundson. 2025. "Depression Severity and Its Predictors: Findings from a Nationally Representative Canadian Sample" Psychiatry International 6, no. 3: 114. https://doi.org/10.3390/psychiatryint6030114

APA Style

Tessier, E. D., Rachor, G. S., Boehme, B. A. E., Hysuick-Weik, B., & Asmundson, G. J. G. (2025). Depression Severity and Its Predictors: Findings from a Nationally Representative Canadian Sample. Psychiatry International, 6(3), 114. https://doi.org/10.3390/psychiatryint6030114

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