Abstract
Background
Financial strain is a well-established risk factor for poor sleep, yet few studies have examined whether this association varies by race-ethnicity, particularly using longitudinal data. This study investigates the relationship between financial strain and multiple dimensions of sleep, including sleep quality, sleep disturbance, daytime dysfunction, sleep latency, and sleep medication use, across White, Black, Mixed, Asian, and Other racial-ethnic groups.
Methods
Data were drawn from Waves 4, 7, 10, and 13 of the UK Household Longitudinal Study (N = 36,544 individuals; 114,164 person-years). Fixed-effects regression models were applied to assess within-person changes over time.
Results
Financial strain was associated with poorer outcomes across all sleep dimensions. These associations varied by race-ethnicity: they were stronger among Asian individuals than among their White counterparts. Further disaggregation revealed that this pattern was particularly pronounced among Pakistani individuals but not among other Asian subgroups.
Conclusions
By revealing subgroup-specific disparities, particularly among Pakistani individuals, this study underscores the importance of tailoring public health strategies to address the combined effects of financial strain and racial-ethnic identity, thereby advancing equity in sleep health and overall well-being.
Keywords: Financial strain, Sleep problems, Race-ethnicity
Background
Sleep problems are highly prevalent and increasingly recognized as a pressing concern for individual well-being [1]. In the UK, recent survey data indicate that most adults report experiencing sleep difficulties, with many describing problems lasting more than six years [2]. Prior research has shown that sleep problems, including poor sleep quality and difficulty falling or staying asleep, are associated with adverse mental health conditions such as depression, anxiety, and suicidal behaviors [3, 4], as well as an increased risk of chronic physical illnesses, including cardiovascular disease, diabetes, and obesity [5–7]. Beyond these health consequences, sleep problems also create substantial burdens for healthcare systems [8] and contribute to economic costs associated with treatment and lost productivity [9]. Taken together, these patterns highlight the urgency of identifying predictors of sleep problems, as doing so is essential for informing public health prevention and intervention efforts.
One predictor that has received growing attention is financial strain, which reflects individuals’ subjective experiences of hardship. Financial strain encompasses struggles to meet essential household expenses, difficulties paying monthly bills, or feelings that one’s financial resources are inadequate to meet daily needs [10]. While it is correlated with objective indicators such as income or assets, financial strain represents a distinct construct [11]. For instance, individuals with similar income levels may experience differing degrees of financial strain depending on competing demands on their resources, such as housing, transportation, or healthcare costs [12]. This variation underscores that financial strain captures the lived pressures of household demands, not just income alone. At a broader level, the distinctiveness of financial strain is further illustrated by evidence from the aftermath of the 2007–2009 Great Recession, when older adults in the U.S. reported lower levels of financial strain and depression in 2010 than in 2006, even though their assets had declined during that period [13]. This pattern shows that perceptions of financial strain are shaped by shifting social and economic contexts, such as evaluating one’s finances relative to others who are similarly affected [13] and may diverge meaningfully from objective conditions. Together, these examples reinforce the need to examine perceived financial strain as a distinct and consequential determinant of well-being [11].
The stress process model offers a useful framework for understanding why financial strain may undermine sleep [14]. Financial strain is often conceptualized as a chronic stressor that generates ongoing insecurity and worry, particularly when such perceptions persist over time [10, 14]. Such chronic stress can impose a continuous cognitive burden, heighten physiological arousal, and make it more difficult for individuals to disengage from ongoing concerns at night [15, 16]. These stress-related responses may interfere with sleep initiation, sleep continuity, and overall sleep quality. In this sense, sleep may be understood as one important health-related outcome through which the effects of chronic financial strain become expressed. Empirical research has increasingly linked financial strain to sleep problems [1, 17–19]. For instance, Gaston and colleagues [1], using nationally representative cross-sectional data from the U.S., found that individuals reporting greater financial strain were more likely to experience sleep problems. A similar pattern has been reported in Switzerland [19]. Chai and Lu [17], using longitudinal data from the UK, also found that financial strain was associated with poorer sleep outcomes over time.
While the stress process model posits that social status shapes individuals’ exposure to stressors, with those situated within systems of social and structural disadvantage facing a greater number and intensity of stressors [14], this framework has primarily been used to explain why racial and ethnic minority groups experience greater health disparities through heightened stress exposure [20]. In contrast, recent research points to the potential moderating role of race-ethnicity in shaping responses to stressors such as financial strain [1]. The minority stress perspective offers a valuable framework for understanding this dynamic [21]. Although originally developed to examine health disparities among sexual minority groups [21], it has since been extended to racially minoritized populations [22]. This framework highlights how racialized populations experience chronic, identity-based stressors, such as discrimination and marginalization [21, 22], that can intensify the effects of general life stressors like financial strain. For these populations, financial strain may be experienced not only as a material burden but also as a psychological stressor that interacts with ongoing racialized stress. This compound burden reflects stress amplification [14], where chronic identity-based stress heightens vulnerability to other stressors such as financial strain, ultimately undermining cognitive and emotional regulation and contributing to greater sleep problems.
Although empirical evidence remains limited, one study supports this line of reasoning. Gaston and colleagues [1] found that financial strain was associated with greater sleep problems overall and that this association was stronger among Black/African American adults than among White adults. While this study highlights important racial disparities, its reliance on cross-sectional data limits the ability to assess changes over time and is also limited to the U.S. context. By using longitudinal data from the UK Household Longitudinal Study (UKHLS) and fixed-effects models that focus on within-person changes across survey waves, the present study extends this work by examining whether the within-individual associations between financial strain and multiple dimensions of sleep differ across diverse racial-ethnic groups. The UK context provides a meaningful setting for this analysis given its ethnically diverse population, distinct welfare and healthcare systems compared to the U.S. [23], and ongoing concerns regarding racism and racialized inequalities.
Based on the stress process and minority stress frameworks, and in light of the limited empirical research to date, this study proposes two hypotheses. First, financial strain will be associated with worsening sleep outcomes over time (Hypothesis 1). Second, the adverse association between financial strain and sleep will be more pronounced among minoritized racial-ethnic groups than among White individuals (Hypothesis 2).
Methods
Data
This study draws on data from the United Kingdom Household Longitudinal Study (UKHLS), a large, nationally representative panel survey that has followed individuals and households across the UK since 2009. Using a stratified and clustered sampling strategy, the UKHLS collects extensive information on social, economic, and health-related topics from approximately 30,000 households [24]. Detailed documentation is available at https://www.understandingsociety.ac.uk.
The current analysis focuses on four waves of data: Wave 4 (2012–2014), Wave 7 (2015–2017), Wave 10 (2018–2020), and Wave 13 (2021–2023), selected because these are the only waves to consistently include key measures of sleep, financial strain, and other variables central to the present study. The analytical sample was restricted to adults aged 18 and older. Missing values were low, with the highest percentage being 7.00% for the second sleep disturbance item, as described below. After removing missing values, the final sample included 36,544 individuals and 114,164 person-years. Person-years refer to the total number of observations contributed by individuals across survey waves, with each person contributing one observation per wave in which they participated. The present study uses a publicly available version of the dataset, which does not require ethics board approval.
Measures
Perceived financial strain was assessed using the item: “How well would you say you are managing financially these days?” Respondents selected from five options: “living comfortably” (1), “doing alright” (2), “just about getting by” (3), “finding it quite difficult” (4), and “finding it very difficult” (5). Responses were coded from 1 (least strain) to 5 (most strain), with higher values indicating greater financial strain. This variable was treated as continuous, consistent with prior research practices [17, 25, 26].
Sleep problems were measured using six items derived from the Pittsburgh Sleep Quality Index (PSQI), capturing respondents’ experiences over the past month [27, 28]. Sleep quality was assessed with the question: “How would you rate your sleep quality overall?” Responses ranged from “very good” (1), “fairly good” (2), “fairly bad” (3), to “very bad” (4). Sleep disturbance was measured using two questions: “How often have you had trouble sleeping because you wake up in the middle of the night or early in the morning?” and “How often have you had trouble sleeping because you cough or snore loudly?” Each item was coded from “not during the past month” (1), “less than once a week” (2), “once or twice a week” (3), “three or more times a week” (4), to “more than once most nights” (5), then recoded to a 0–4 scale. The two items were summed to form an index ranging from 0 to 8.
Daytime dysfunction was measured by asking: “How often have you had trouble staying awake while driving, eating meals, or engaging in social activities?” Responses ranged from “not during the past month” (1), “less than once a week” (2), “once or twice a week” (3), to “three or more times a week” (4). Sleep latency was assessed with the question: “How often have you had trouble sleeping because you cannot get to sleep within 30 minutes?” Responses were coded from “not during the past month” (1), “less than once a week” (2), “once or twice a week” (3), “three or more times a week” (4), to “more than once most nights” (5). Sleep medication use was measured by asking: “How often have you taken medicine (prescribed or over-the-counter) to help you sleep?” Responses were coded from “not during the past month” (1), “less than once a week” (2), “once or twice a week” (3), to “three or more times a week” (4). Given the conceptual distinctiveness of these five indicators, they were analyzed as separate continuous outcomes. This analytical strategy follows established practices in the sleep literature [17, 27].
Race-ethnicity was categorized into five groups based on previous studies using the UKHLS [29, 30]: White (British, English, Scottish, Welsh, Northern Irish, Irish, or any other White background), Mixed (White and Black Caribbean, White and Black African, White and Asian, or any other mixed background), Asian (Indian, Pakistani, Bangladeshi, Chinese, or any other Asian background), Black (Caribbean, African, or any other Black background), and Other (Arab or other unspecified background).
Several sociodemographic characteristics were included as control variables. Marital status was dichotomized as “married” versus “nonmarried.” Number of children under age 15 was treated as a continuous variable. Education was coded as “degree,” “other higher degree,” “A-level,” “general certificate of secondary education (GCSE),” “other qualification,” and “no qualification.” Employment status was classified as “employed” or “other.” Household monthly income (£) was included as a continuous variable. Geographic location was recoded into England and the rest of the UK, with the latter including Wales, Scotland, and Northern Ireland. All analyses accounted for survey wave, with data coded to reflect Wave 4 (2012–2014), Wave 7 (2015–2017), Wave 10 (2018–2020), and Wave 13 (2021–2023). Pooled descriptive statistics based on person-years for all primary variables are presented in Table 1 and more detailed descriptive statistics across each racial-ethnic group are presented in Appendix 1.
Table 1.
Descriptive statistics
| Mean or % | S.D. | |
|---|---|---|
| Financial strain | 2.07 | 0.97 |
| Sleep problems | ||
| Sleep quality | 2.03 | 0.78 |
| Sleep disturbance | 2.51 | 2.11 |
| Daytime dysfunction | 1.19 | 0.58 |
| Sleep latency | 2.20 | 1.36 |
| Sleep medication use | 1.22 | 0.73 |
| Race-ethnicity | ||
| White | 86.07 | |
| Mixed | ||
| White & Black Caribbean | 0.63 | |
| White & Black African | 0.20 | |
| White & Asian | 0.43 | |
| Any other mixed | 0.33 | |
| Asian | ||
| Indian | 3.25 | |
| Pakistani | 2.60 | |
| Bangladeshi | 1.42 | |
| Chinese | 0.40 | |
| Any other Asian | 0.83 | |
| Black | ||
| Caribbean | 1.58 | |
| African | 1.55 | |
| Any other Black | 0.13 | |
| Other | ||
| Arab | 0.25 | |
| Any other race-ethnicity | 0.34 | |
| Age | 51.35 | 17.25 |
| Marital status | ||
| Married | 56.63 | |
| Unmarried | 43.37 | |
| Number of children | 0.27 | 0.71 |
| Education | ||
| Degree | 28.77 | |
| Other higher | 12.84 | |
| A-level | 20.30 | |
| GCSE | 18.83 | |
| Other qualification | 8.91 | |
| No qualification | 10.34 | |
| Employment status | ||
| Employed | 57.30 | |
| Other | 42.70 | |
| Household income (£) | 4043.19 | 2904.37 |
| Geographic location | ||
| England | 77.76 | |
| Rest of the UK | 22.24 | |
| N (individuals) | 36,544 | |
| N (person-years) | 114,164 |
Note: Descriptive information on age is reported in response to a reviewer’s request. However, age was not included as a control variable, as survey wave was controlled for instead
Appendix 1.
Descriptive statistics across racial-ethnic groups
| White | White & | White & | White & | Other | p-value | ||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| Caribbean | African | Asian | Mixed | ||||||||
| Mean/% | S.D. | Mean/% | S.D. | Mean/% | S.D. | Mean/% | S.D. | Mean/% | S.D. | ||
| Financial strain | 2.00 | 0.94 | 2.57 | 1.04 | 2.48 | 1.05 | 2.14 | 1.01 | 2.33 | 0.96 | *** |
| Sleep problems | |||||||||||
| Sleep quality | 2.03 | 0.78 | 2.25 | 0.82 | 2.09 | 0.78 | 2.12 | 0.69 | 2.15 | 0.79 | *** |
| Sleep disturbance | 2.56 | 2.10 | 2.62 | 2.19 | 2.08 | 1.97 | 2.38 | 2.06 | 2.31 | 2.02 | *** |
| Daytime dysfunction | 1.18 | 0.57 | 1.27 | 0.70 | 1.26 | 0.65 | 1.24 | 0.62 | 1.25 | 0.58 | *** |
| Sleep latency | 2.20 | 1.35 | 2.62 | 1.45 | 2.32 | 1.36 | 2.35 | 1.39 | 2.47 | 1.40 | *** |
| Sleep medication use | 1.22 | 0.73 | 1.31 | 0.83 | 1.21 | 0.71 | 1.30 | 0.82 | 1.32 | 0.87 | *** |
| Age | 52.56 | 17.26 | 41.00 | 15.63 | 43.39 | 14.91 | 42.23 | 15.09 | 42.77 | 15.15 | *** |
| Marital status | *** | ||||||||||
| Married | 56.39 | 26.43 | 33.62 | 46.30 | 47.88 | ||||||
| Unmarried | 43.61 | 73.57 | 66.38 | 53.70 | 52.12 | ||||||
| Number of children | 0.24 | 0.66 | 0.37 | 0.80 | 0.43 | 0.86 | 0.35 | 0.77 | 0.33 | 0.73 | *** |
| Education | *** | ||||||||||
| Degree | 27.52 | 30.77 | 37.55 | 46.50 | 47.09 | ||||||
| Other higher | 13.00 | 11.47 | 14.41 | 16.05 | 16.14 | ||||||
| A-level | 20.48 | 24.34 | 24.89 | 22.02 | 22.49 | ||||||
| GCSE | 19.52 | 21.12 | 17.90 | 8.64 | 8.20 | ||||||
| Other qualification | 9.07 | 6.43 | 4.37 | 3.70 | 5.56 | ||||||
| No qualification | 10.40 | 5.87 | 0.87 | 3.09 | 0.53 | ||||||
| Employment status | *** | ||||||||||
| Employed | 56.78 | 62.24 | 68.56 | 69.55 | 70.63 | ||||||
| Other | 43.22 | 37.76 | 31.44 | 30.45 | 29.37 | ||||||
| Household income (£) | 4002.79 | 2843.09 | 3891.84 | 3203.82 | 4111.64 | 2975.84 | 4732.73 | 3373.08 | 5052.95 | 3702.96 | *** |
| Geographic location | *** | ||||||||||
| England | 74.64 | 96.36 | 96.51 | 94.65 | 89.68 | ||||||
| Rest of the UK | 25.36 | 3.64 | 3.49 | 5.35 | 10.32 | ||||||
| N (individuals) | 30,877 | 248 | 83 | 164 | 135 | ||||||
| N (person-years) | 98,258 | 715 | 229 | 486 | 378 | ||||||
| Indian | Pakistani | Bangladeshi | Chinese | Other | |||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| Asian | |||||||||||
| Mean/% | S.D. | Mean/% | S.D. | Mean/% | S.D. | Mean/% | S.D. | Mean/% | S.D. | ||
| Financial strain | 2.24 | 1.00 | 2.52 | 1.04 | 2.64 | 1.06 | 2.00 | 0.86 | 2.36 | 1.06 | |
| Sleep problems | |||||||||||
| Sleep quality | 1.94 | 0.75 | 2.05 | 0.80 | 2.06 | 0.76 | 1.91 | 0.73 | 1.89 | 0.70 | |
| Sleep disturbance | 2.10 | 2.16 | 2.20 | 2.23 | 2.20 | 2.19 | 2.04 | 1.99 | 1.89 | 2.01 | |
| Daytime dysfunction | 1.19 | 0.58 | 1.28 | 0.71 | 1.26 | 0.66 | 1.18 | 0.53 | 1.14 | 0.47 | |
| Sleep latency | 2.14 | 1.37 | 2.34 | 1.48 | 2.40 | 1.48 | 1.97 | 1.24 | 2.05 | 1.27 | |
| Sleep medication use | 1.20 | 0.69 | 1.26 | 0.76 | 1.22 | 0.72 | 1.14 | 0.56 | 1.18 | 0.63 | |
| Age | 45.50 | 14.89 | 40.03 | 14.09 | 39.12 | 13.24 | 45.44 | 13.17 | 45.68 | 14.05 | |
| Marital status | |||||||||||
| Married | 72.07 | 69.55 | 68.91 | 65.13 | 69.02 | ||||||
| Unmarried | 27.93 | 30.45 | 31.09 | 34.87 | 30.98 | ||||||
| Number of children | 0.35 | 0.77 | 0.62 | 1.15 | 0.58 | 1.01 | 0.31 | 0.66 | 0.38 | 0.77 | |
| Education | |||||||||||
| Degree | 41.45 | 28.16 | 26.22 | 71.27 | 42.15 | ||||||
| Other higher | 10.44 | 8.51 | 6.48 | 8.11 | 13.59 | ||||||
| A-level | 18.27 | 20.46 | 22.15 | 8.33 | 18.12 | ||||||
| GCSE | 13.28 | 18.10 | 17.71 | 4.61 | 10.75 | ||||||
| Other qualification | 7.26 | 8.51 | 6.97 | 3.51 | 9.80 | ||||||
| No qualification | 9.31 | 16.25 | 20.48 | 4.17 | 5.58 | ||||||
| Employment status | |||||||||||
| Employed | 65.30 | 49.93 | 50.71 | 77.19 | 66.39 | ||||||
| Other | 34.70 | 50.07 | 49.29 | 22.81 | 33.61 | ||||||
| Household income (£) | 5086.27 | 3649.35 | 3863.23 | 2862.98 | 4207.72 | 2955.61 | 5156.10 | 3619.14 | 4404.02 | 3058.78 | |
| Geographic location | |||||||||||
| England | 97.76 | 97.64 | 98.52 | 95.61 | 93.05 | ||||||
| Rest of the UK | 2.24 | 2.36 | 1.48 | 4.39 | 6.95 | ||||||
| N (individuals) | 1292 | 1085 | 598 | 144 | 336 | ||||||
| N (person-years) | 3706 | 2972 | 1621 | 456 | 949 | ||||||
| Caribbean | African | Other | Arab | Other | |||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| Black | Race/ethnicity | ||||||||||
| Mean/% | S.D. | Mean/% | S.D. | Mean/% | S.D. | Mean/% | S.D. | Mean/% | S.D. | ||
| Financial strain | 2.62 | 1.03 | 2.78 | 1.04 | 2.58 | 1.02 | 2.48 | 1.05 | 2.48 | 1.12 | |
| Sleep problems | |||||||||||
| Sleep quality | 2.11 | 0.84 | 1.93 | 0.77 | 2.24 | 0.87 | 2.13 | 0.81 | 2.08 | 0.79 | |
| Sleep disturbance | 2.40 | 2.22 | 2.05 | 2.08 | 2.58 | 2.19 | 2.24 | 2.16 | 2.45 | 2.19 | |
| Daytime dysfunction | 1.25 | 0.69 | 1.24 | 0.66 | 1.24 | 0.65 | 1.27 | 0.72 | 1.24 | 0.63 | |
| Sleep latency | 2.28 | 1.48 | 2.19 | 1.41 | 2.47 | 1.51 | 2.36 | 1.44 | 2.28 | 1.43 | |
| Sleep medication use | 1.20 | 0.69 | 1.19 | 0.66 | 1.21 | 0.71 | 1.18 | 0.64 | 1.24 | 0.74 | |
| Age | 51.62 | 16.21 | 43.44 | 14.70 | 42.44 | 14.95 | 41.83 | 14.49 | 47.27 | 15.60 | |
| Marital status | |||||||||||
| Married | 30.46 | 44.58 | 28.95 | 56.29 | 58.66 | ||||||
| Unmarried | 69.54 | 55.42 | 71.05 | 43.71 | 41.34 | ||||||
| Number of children | 0.29 | 0.71 | 0.57 | 1.06 | 0.41 | 0.78 | 0.46 | 0.97 | 0.47 | 0.87 | |
| Education | |||||||||||
| Degree | 25.51 | 41.86 | 36.84 | 52.10 | 42.38 | ||||||
| Other higher | 17.12 | 17.34 | 19.08 | 8.39 | 13.95 | ||||||
| A-level | 17.01 | 19.49 | 26.97 | 16.43 | 9.56 | ||||||
| GCSE | 19.46 | 10.17 | 6.58 | 8.74 | 11.89 | ||||||
| Other qualification | 11.95 | 6.95 | 7.24 | 8.04 | 11.89 | ||||||
| No qualification | 8.95 | 4.18 | 3.29 | 6.29 | 10.34 | ||||||
| Employment status | |||||||||||
| Employed | 59.76 | 65.71 | 67.11 | 55.24 | 53.49 | ||||||
| Other | 40.24 | 34.29 | 32.89 | 44.76 | 46.51 | ||||||
| Household income (£) | 3630.77 | 2977.90 | 3864.72 | 3059.99 | 4158.94 | 2989.14 | 4151.69 | 3563.82 | 3803.85 | 2565.96 | |
| Geographic location | |||||||||||
| England | 99.22 | 98.19 | 99.34 | 93.01 | 90.96 | ||||||
| Rest of the UK | 0.78 | 1.81 | 0.66 | 6.99 | 9.04 | ||||||
| N (individuals) | 635 | 654 | 54 | 105 | 134 | ||||||
| N (person-years) | 1799 | 1770 | 152 | 286 | 387 | ||||||
Note: ANOVA and chi-square tests were used to assess statistically significant differences across racial-ethnic groups. Descriptive information on age is reported in response to a reviewer’s request. However, age was not included as a control variable, as survey wave was controlled for instead
Analytical strategy
To assess the association between financial strain and sleep outcomes, and to examine whether this relationship varies by race-ethnicity, fixed effects regression models were employed. This modeling approach is well-suited for analyzing within-person change over time, as it controls for all time-invariant individual characteristics (e.g., personality traits) that may otherwise confound the association [31]. By focusing on intra-individual variation, fixed effects models help isolate the effect of changes in financial strain on changes in sleep, net of stable individual characteristics. To verify the appropriateness of this approach, Hausman tests were conducted to compare fixed effects and random effects estimates. The statistically significant results indicated that random effects models would likely yield biased estimates due to correlations between unobserved individual characteristics and the independent variables. Therefore, fixed effects models were retained as the primary analytic strategy.
The fixed-effects model can be expressed as follows: 
where 
represents each sleep outcome for individual i at survey wave t. 
is the time-varying measure of financial strain. 
represents the set of time-varying control variables. 
represents survey wave controls, 
captures individual fixed effects, and 
is the error term.
To assess moderation by race-ethnicity, interaction terms between financial strain and race-ethnicity group membership (i.e., financial strain ⋅ race-ethnicity) were constructed for each sleep outcome. These interaction terms were included in the fixed effects models to evaluate whether the strength or direction of the association between financial strain and sleep differs across racial-ethnic groups. All statistical analyses were conducted using Stata version 18.
Results
As shown in Table 2, financial strain was associated with increases across all five dimensions of sleep problems in both the models without covariates and the fully adjusted models. In the adjusted models, within-individual increases in financial strain over time predicted poorer sleep quality (b = 0.078, p < 0.001), more frequent sleep disturbance (b = 0.160, p < 0.001), greater daytime dysfunction (b = 0.030, p < 0.001), longer sleep latency (b = 0.098, p < 0.001), and more frequent use of sleep medication (b = 0.023, p < 0.001). The corresponding coefficients in the unadjusted models were similar in magnitude. These findings provide robust support for Hypothesis 1, demonstrating that financial strain is a significant within-person predictor of worsening sleep outcomes.
Table 2.
Fixed-effects regression predicting greater sleep problems
| Model 1a | Model 1b | Model 2a | Model 2b | Model 3a | Model 3b | Model 4a | Model 4b | Model 5a | Model 5b | |
|---|---|---|---|---|---|---|---|---|---|---|
| Sleep | Sleep | Sleep | Sleep | Daytime | Daytime | Sleep | Sleep | Sleep | Sleep | |
| Quality | Quality | Disturbance | Disturbance | Dysfunction | Dysfunction | Latency | Latency | Medication | Medication | |
| Financial strain | 0.070*** | 0.078*** | 0.149*** | 0.160*** | 0.027*** | 0.030*** | 0.095*** | 0.098*** | 0.020*** | 0.023*** |
| (0.003) | (0.003) | (0.009) | (0.009) | (0.003) | (0.003) | (0.005) | (0.006) | (0.003) | (0.003) | |
| Marital status | ||||||||||
| Married | 0.028** | 0.178*** | 0.008 | -0.031 | -0.007 | |||||
| (0.010) | (0.029) | (0.009) | (0.018) | (0.011) | ||||||
| Nonmarried (ref.) | ||||||||||
| Number of children | 0.019** | -0.118*** | -0.007 | -0.056*** | -0.022*** | |||||
| (0.006) | (0.018) | (0.006) | (0.011) | (0.006) | ||||||
| Education | ||||||||||
| Degree (ref.) | ||||||||||
| Other higher | 0.019 | 0.073 | 0.018 | 0.045 | -0.048 | |||||
| (0.030) | (0.089) | (0.028) | (0.054) | (0.032) | ||||||
| A-level | -0.028 | -0.002 | -0.015 | -0.010 | -0.040 | |||||
| (0.020) | (0.058) | (0.018) | (0.035) | (0.021) | ||||||
| GCSE | -0.040 | -0.087 | -0.005 | -0.105 | -0.110** | |||||
| (0.033) | (0.097) | (0.030) | (0.059) | (0.035) | ||||||
| Other | -0.021 | 0.104 | 0.020 | -0.007 | -0.198*** | |||||
| (0.053) | (0.156) | (0.049) | (0.095) | (0.056) | ||||||
| No qualification | -0.134* | 0.190 | 0.002 | -0.139 | -0.233*** | |||||
| (0.059) | (0.173) | (0.054) | (0.105) | (0.062) | ||||||
| Employment status | ||||||||||
| Employed | -0.029*** | -0.151*** | 0.013* | -0.092*** | -0.031*** | |||||
| (0.007) | (0.022) | (0.007) | (0.013) | (0.008) | ||||||
| Other (ref.) | ||||||||||
| Household income (logged) | 0.009** | 0.003 | 0.006* | -0.008 | 0.000 | |||||
| (0.003) | (0.010) | (0.003) | (0.006) | (0.003) | ||||||
| Geographic location | ||||||||||
| England (ref.) | ||||||||||
| Rest of the UK | -0.032 | -0.068 | 0.023 | -0.053 | -0.036 | |||||
| (0.037) | (0.109) | (0.034) | (0.066) | (0.039) | ||||||
| Wave | ||||||||||
| Wave 1 (ref.) | ||||||||||
| Wave 2 | 0.053*** | -0.206*** | 0.017*** | 0.012 | 0.016*** | |||||
| (0.005) | (0.014) | (0.005) | (0.008) | (0.005) | ||||||
| Wave 3 | 0.108*** | 0.725*** | 0.040*** | 0.127*** | 0.107*** | |||||
| (0.005) | (0.015) | (0.005) | (0.009) | (0.005) | ||||||
| Wave 4 | 0.150*** | 1.048*** | 0.018*** | 0.174*** | 0.102*** | |||||
| (0.006) | (0.016) | (0.005) | (0.010) | (0.006) | ||||||
| N (individuals) | 36,544 | 36,544 | 36,544 | 36,544 | 36,544 | 36,544 | 36,544 | 36,544 | 36,544 | 36,544 |
| N (person-years) | 114,164 | 114,164 | 114,164 | 114,164 | 114,164 | 114,164 | 114,164 | 114,164 | 114,164 | 114,164 |
Note: Standard errors are in parentheses. ***p < 0.001; **p < 0.01; *p < 0.05
Table 3 presents models testing whether the relationship between financial strain and sleep varies by broad racial-ethnic group. Several significant interactions emerged. The adverse association between financial strain and sleep quality was stronger among Asian individuals (b = 0.036, p < 0.001) and weaker among Black individuals (b = -0.044, p < 0.01) than among White adults. For sleep disturbance, the interaction was again significant for Asian respondents (b = 0.097, p < 0.001). Asian respondents also showed significantly stronger associations for daytime dysfunction (b = 0.039, p < 0.001), sleep latency (b = 0.084, p < 0.001), and sleep medication use (b = 0.030, p < 0.01). In addition, the association between financial strain and daytime dysfunction was weaker among Black individuals than among White adults (b = -0.029, p < 0.05), while the association with sleep latency was stronger among respondents in the Other racial-ethnic category (b = 0.136, p < 0.05). No significant interactions were observed for individuals identified as Mixed race-ethnicity.
Table 3.
Fixed-effects regression models predicting greater sleep problems
| Model 1 | Model 2 | Model 3 | Model 4 | Model 5 | |
|---|---|---|---|---|---|
| Sleep | Sleep | Daytime | Sleep | Sleep | |
| Quality | Disturbance | Dysfunction | Latency | Medication | |
| Financial strain (FS) | 0.077*** | 0.150*** | 0.026*** | 0.086*** | 0.020*** |
| (0.003) | (0.010) | (0.003) | (0.006) | (0.004) | |
| Interaction | |||||
| FS X Mixed | -0.038 | -0.034 | 0.010 | 0.032 | 0.001 |
| (0.023) | (0.068) | (0.021) | (0.041) | (0.024) | |
| FS X Asian | 0.036*** | 0.097*** | 0.039*** | 0.084*** | 0.030** |
| (0.010) | (0.029) | (0.009) | (0.018) | (0.010) | |
| FS X Black | -0.044** | -0.023 | -0.029* | 0.037 | 0.011 |
| (0.015) | (0.045) | (0.014) | (0.027) | (0.016) | |
| FS X Other | -0.002 | 0.077 | 0.045 | 0.136* | -0.006 |
| (0.035) | (0.103) | (0.032) | (0.063) | (0.037) | |
| N (individuals) | 36,544 | 36,544 | 36,544 | 36,544 | 36,544 |
| N (person-years) | 114,164 | 114,164 | 114,164 | 114,164 | 114,164 |
Note: All models include the full set of control variables: marital status, number of children, education, employment status, household income, geographic location, and survey wave. Standard errors are in parentheses. ***p < 0.001; **p < 0.01; *p < 0.05
Table 4 disaggregates the broad racial-ethnic categories into specific subgroups to provide a more nuanced understanding of variation in the relationship between financial strain and sleep. The most consistent pattern emerged for Pakistani individuals, who showed significantly stronger associations across all five dimensions of sleep compared to White individuals: sleep quality (b = 0.069, p < 0.001), sleep disturbance (b = 0.187, p < 0.001), daytime dysfunction (b = 0.080, p < 0.001), sleep latency (b = 0.133, p < 0.001), and sleep medication use (b = 0.069, p < 0.001).
Table 4.
Fixed-effects regression models predicting greater sleep problems
| Model 1 | Model 2 | Model 3 | Model 4 | Model 5 | |
|---|---|---|---|---|---|
| Sleep | Sleep | Daytime | Sleep | Sleep | |
| Quality | Disturbance | Dysfunction | Latency | Medication | |
| Financial strain (FS) | 0.077*** | 0.150*** | 0.026*** | 0.086*** | 0.020*** |
| (0.003) | (0.010) | (0.003) | (0.006) | (0.004) | |
| Interaction | |||||
| FS X White & Caribbean | -0.051 | -0.053 | 0.073* | -0.004 | 0.007 |
| (0.036) | (0.106) | (0.033) | (0.064) | (0.038) | |
| FS X White & African | -0.082 | 0.026 | 0.023 | 0.039 | 0.041 |
| (0.072) | (0.210) | (0.066) | (0.128) | (0.075) | |
| FS X White & Asian | -0.046 | -0.070 | -0.013 | -0.030 | -0.008 |
| (0.044) | (0.130) | (0.041) | (0.079) | (0.047) | |
| FS X Any other mixed | 0.016 | 0.019 | -0.084 | 0.171* | -0.019 |
| (0.049) | (0.144) | (0.045) | (0.087) | (0.051) | |
| FS X Indian | 0.026 | 0.065 | 0.023 | 0.077** | 0.004 |
| (0.016) | (0.048) | (0.015) | (0.029) | (0.017) | |
| FS X Pakistani | 0.069*** | 0.187*** | 0.080*** | 0.133*** | 0.069*** |
| (0.016) | (0.047) | (0.015) | (0.029) | (0.017) | |
| FS X Bangladeshi | -0.012 | 0.064 | -0.011 | 0.066 | 0.012 |
| (0.022) | (0.065) | (0.020) | (0.040) | (0.023) | |
| FS X Chinese | 0.042 | 0.014 | -0.007 | -0.062 | 0.033 |
| (0.055) | (0.160) | (0.050) | (0.097) | (0.057) | |
| FS X Any other Asian | 0.039 | -0.038 | 0.051 | 0.002 | 0.009 |
| (0.031) | (0.091) | (0.028) | (0.055) | (0.032) | |
| FS X Caribbean | -0.063** | -0.033 | -0.062** | 0.037 | 0.008 |
| (0.023) | (0.067) | (0.021) | (0.041) | (0.024) | |
| FS X African | -0.033 | 0.003 | 0.015 | 0.043 | 0.015 |
| (0.021) | (0.062) | (0.019) | (0.038) | (0.022) | |
| FS X Any other Black | 0.013 | -0.241 | -0.230*** | -0.045 | -0.011 |
| (0.073) | (0.214) | (0.067) | (0.130) | (0.077) | |
| FS X Arab | 0.107 | 0.114 | 0.116* | 0.170 | 0.036 |
| (0.057) | (0.168) | (0.052) | (0.102) | (0.060) | |
| FS X Any other ethnic | -0.068 | 0.055 | 0.003 | 0.116 | -0.031 |
| (0.044) | (0.130) | (0.041) | (0.079) | (0.047) | |
| N (individuals) | 36,544 | 36,544 | 36,544 | 36,544 | 36,544 |
| N (person-years) | 114,164 | 114,164 | 114,164 | 114,164 | 114,164 |
Note: All models include the full set of control variables: marital status, number of children, education, employment status, household income, geographic location, and survey wave. Standard errors are in parentheses. ***p < 0.001; **p < 0.01; *p < 0.05
By contrast, Caribbean respondents demonstrated weaker associations between financial strain and both sleep quality (b = -0.063, p < 0.01) and daytime dysfunction (b = -0.062, p < 0.01) than White adults. Indian individuals showed a stronger association for sleep latency (b = 0.077, p < 0.01), while Arab individuals showed a stronger association for daytime dysfunction (b = 0.116, p < 0.05). However, no other sleep dimensions showed significant moderation effects for these groups. No significant interaction effects were found for Bangladeshi, Chinese, or African subgroups across any of the sleep outcomes. These subgroup-specific findings offer partial support for Hypothesis 2, highlighting the heterogeneous ways in which financial strain contributes to sleep problems across racial-ethnic groups relative to White adults.
Discussion
This study addressed key gaps in the literature by examining how financial strain relates to multiple dimensions of sleep, including sleep quality, disturbance, daytime dysfunction, latency, and sleep medication use, over time, and whether these associations vary across racial-ethnic groups. Although prior research has linked financial strain to adverse sleep outcomes [1, 17–19], few studies have used longitudinal data or explored potential variations by race-ethnicity. Even fewer have conceptualized sleep as a multidimensional construct. By leveraging repeated measures from a large and nationally representative UK sample and employing fixed-effects models, this study offers new insights into how sleep is socially patterned and how perceptions of economic hardship may manifest differently across population subgroups.
Consistent with the first hypothesis, results revealed that financial strain was associated with poorer outcomes across all sleep dimensions. These findings align with prior research suggesting that financial stress may interfere with sleep through pathways such as stress-related physiological arousal, worry, and emotional exhaustion [1, 10, 15, 16]. From the perspective of the stress process model [14], financial strain can function as a chronic stressor, making it more difficult to mentally disengage during sleep. The consistency of these associations underscores the broad reach of perceived financial hardship in shaping individuals’ ability to rest and recover.
Findings related to the second hypothesis were more nuanced. When examining broader categorizations of race-ethnicity, financial strain was more strongly associated with sleep problems among Asian individuals than among White individuals, while associations among Black individuals were generally comparable to, or slightly weaker than, those observed among White respondents, particularly for sleep quality and daytime dysfunction. These results partially align with the minority stress perspective [22], which holds that the cumulative burden of identity-related stressors may intensify the effects of financial strain for racialized groups. In particular, the stronger associations among Asian individuals challenge the model minority myth, which frames Asians as uniformly successful and resilient, and instead point to cultural and structural pressures that may contribute to vulnerability [32, 33]. While most often discussed in U.S. contexts, this stereotype is also present in the UK [34], where extended-family financial responsibilities, high parental expectations, and achievement-related stress have been linked to shame, social isolation, and low uptake of mental health supports among some Asian communities [35, 36]. These pressures may heighten susceptibility to financial strain, making its adverse effects more pronounced among Asian individuals than among White individuals.
By contrast, the somewhat weaker association between financial strain and both sleep quality and daytime dysfunction among Black individuals in our study differs from the U.S. study [1], which reported a stronger association between financial strain and sleep problems among Black/African American adults than among White adults. This divergence suggests that the moderating role of race-ethnicity in the association between financial strain and sleep may be shaped by national and institutional contexts [23]. In the UK context, the weaker associations observed here should not be interpreted as indicating reduced exposure to hardship or stressors compared to White individuals [20]. Rather, they may reflect the presence of resilience mechanisms, such as strong social networks, affirmation of racial identity, and culturally grounded coping strategies, that have been shown to buffer psychological distress among Black British populations [37, 38]. These interpretations remain speculative and highlight the need for qualitative research to better understand how individuals from different groups interpret, navigate, and respond to financial strain in ways that shape their sleep and well-being.
Subgroup analyses revealed substantial within-group variation, particularly among Asian respondents. Pakistani individuals consistently experienced stronger associations between financial strain and all sleep dimensions, suggesting a distinct profile of risk. These patterns may reflect the particular salience, in this subgroup, of structural inequities, such as poverty rates, employment discrimination, and housing precarity, as well as cultural expectations around financial responsibilities within extended families [39, 40]. Although these pressures are not unique to Pakistani individuals, they may be especially relevant for interpreting the stronger association observed in this group. In contrast, Caribbean individuals showed weaker associations for certain outcomes, including sleep quality and daytime dysfunction. This may reflect the presence of protective factors, such as strong community ties and culturally embedded values of self-reliance [41]. Taken together, these divergent patterns reinforce the importance of moving beyond broad racial-ethnic categories to identify more targeted avenues for support. Nevertheless, subgroup findings should be interpreted with caution, particularly given the smaller sample sizes for some groups.
Implications from a public health perspective
These findings have important implications for public health. First, they underscore that financial strain is not only an economic challenge but also a public health issue with far-reaching implications for sleep and, by extension, mental and physical health. Because sleep problems are linked to chronic disease, productivity loss, and elevated healthcare costs [5–9], addressing financial strain has the potential to produce broad population-level health benefits. Second, the results demonstrate that the relationship between financial strain and sleep is not uniform across racial-ethnic groups. Public health strategies therefore need to move beyond “one-size-fits-all” approaches and instead prioritize culturally tailored interventions. For instance, prevention and intervention programs could incorporate community-based approaches that consider extended-family financial obligations and reduce barriers to accessing mental health and sleep-related services. Third, these findings point to the structural drivers underlying both financial strain and sleep disparities. Policies that reduce income precarity, strengthen employment protections, and address housing insecurity are essential components of a comprehensive public health strategy. Without tackling these upstream determinants, downstream interventions such as stress management programs or sleep hygiene education will have limited impact on reducing health inequities [42, 43]. Overall, this study reinforces the need for integrated public health policies that address both the material and psychosocial aspects of financial strain, while simultaneously advancing equity by tailoring solutions based on the unique challenges faced by diverse communities.
Limitations
Several limitations should be acknowledged. First, although fixed effects models offer advantages over cross-sectional analyses by controlling for all time-invariant individual characteristics and focusing on within-person change, they do not establish causality. The observed associations should therefore be interpreted as suggestive rather than definitive. Second, sleep duration was not examined as a focal outcome. Although short sleep duration is a commonly used indicator [28], it was excluded due to modeling constraints: treating it as a binary outcome within a fixed-effects framework would have substantially reduced the analytic sample, as only respondents who experienced within-person changes in sleep duration could be retained. Third, all key measures, including financial strain and sleep outcomes, were based on self-reports. While these are appropriate and often necessary for capturing subjective experiences, they may be vulnerable to recall bias, social desirability, or varying interpretations across respondents. Finally, while the use of single items from the PSQI is common in large-scale surveys, these items may not fully capture the clinical complexity of sleep problems.
Despite these limitations, the study contributes to the growing literature on economic hardship and sleep by using longitudinal data, a large and diverse UK sample, and rigorous analytic techniques to examine how financial strain relates to sleep across and within racial-ethnic groups. The findings offer a more differentiated understanding of how perceived financial strain intersects with social identity, reinforcing the need for more inclusive, culturally grounded, and structurally informed approaches to addressing the consequences of economic stress.
Acknowledgements
NA.
Author contributions
LC conceptualized the study, performed the analysis, and drafted the manuscript. CC contributed feedback and revisions. KT contributed further feedback and revisions.
Funding
This research is supported by the Seed Fund for Basic Research for New Staff, the University of Hong Kong (PI: Lei Chai).
Data availability
This study used publicly available the United Kingdom Household Longitudinal Study (UKHLS) data, accessible via the UKHLS website (https://www.understandingsociety.ac.uk) with registration. Study materials for this study are available upon request.
Declarations
Ethics approval and consent to participate
The present study uses a publicly available file of the UK household longitudinal study, which does not need ethics board approval.
Consent for publication
NA.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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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
This study used publicly available the United Kingdom Household Longitudinal Study (UKHLS) data, accessible via the UKHLS website (https://www.understandingsociety.ac.uk) with registration. Study materials for this study are available upon request.