Abstract
Background
This study provides the first longitudinal evidence on whether anticipatory financial strain, defined as worrying about one’s future finances, is associated with loneliness and sleep problems, whether loneliness mediates the association between anticipatory financial strain and sleep problems, and whether these relationships vary by gender.
Methods
Using Waves 10 and 13 of the United Kingdom Household Longitudinal Study, corresponding to fieldwork conducted in 2018–2020 and 2021–2023, respectively (20,355 individuals; 40,710 person-wave observations), fixed-effects regression models were estimated to assess within-person changes over time. Sleep problems were measured using five PSQI-derived indicators. Mediation was assessed using the Karlson–Holm–Breen method, and moderation was examined using interaction terms.
Results
Anticipatory financial strain–expecting to be worse off rather than the same or better off–was associated with higher loneliness and worse outcomes on specific sleep dimensions, including poorer sleep quality and greater sleep disturbance. Loneliness partially mediated the association between anticipatory financial strain and these sleep outcomes. In contrast, anticipatory financial strain was not significantly associated with sleep latency, daytime dysfunction, or sleep medication use. Gender did not moderate the association between anticipatory financial strain and loneliness or most sleep dimensions, with the exception of sleep medication use.
Conclusions
Interventions and policies aimed at reducing financial uncertainty, alongside strategies that strengthen social connections and reduce loneliness, may help mitigate sleep problems among individuals experiencing heightened economic worry.
Keywords: Anticipatory financial strain, Sleep problems, Loneliness
Introduction
Sleep is a fundamental physiological need [1], occupying roughly one-third of human life [2] and playing a central role in physical restoration, cognitive functioning, and emotional regulation [1, 3]. Beyond simply the number of hours slept, sleep health is increasingly understood as a multidimensional construct encompassing features such as quality, efficiency, timing, and daytime alertness [1, 4]. Sleep problems are also common in the United Kingdom. For instance, a recent study using UK Biobank data found that 29% of participants reported frequent insomnia symptoms [5], underscoring the high prevalence of sleep difficulties in the UK context. These problems are consequential, as sleep difficulties have been linked to a range of adverse outcomes, including an increased risk of accidents, poorer mental health, premature mortality, and chronic physical conditions such as cardiovascular disease, diabetes, and hypertension [2–3, 6]. Given the wide-ranging consequences of sleep problems for both individual well-being and population health, identifying the determinants of sleep difficulties is an important public health priority.
Financial strain and sleep
One important determinant of sleep difficulties is financial strain. Financial strain refers to the perceived difficulty of meeting basic needs and covering everyday expenses [7–8]. Within the stress process model [9], it can be understood as a chronic stressor because difficulties making ends meet are often persistent rather than short-lived, requiring ongoing adaptation to financial hardship over time. As such, financial strain may undermine well-being by generating continual worry, cognitive burden, and a sustained sense of insecurity [8]. Consistent with this perspective, a substantial body of research has documented a robust association between financial strain and poorer mental health in both cross-sectional and longitudinal studies (e.g., [8, 10–11]. By comparison, the relationship between financial strain and sleep has received less attention, although a growing literature suggests that financial strain is associated with multiple dimensions of sleep problems, including poorer sleep quality, lower sleep efficiency, and greater sleep disturbance [3, 12–14].
Anticipatory financial strain and sleep
Despite these valuable insights, most existing research has focused on current financial strain, whereas concerns about one’s future financial situation may also matter for sleep. Current financial strain and anticipatory financial strain are related but conceptually distinct. Whereas current financial strain captures perceived difficulties in one’s present financial situation, anticipatory financial strain captures expected future deterioration, uncertainty, and worry about one’s financial situation. This distinction is important because individuals may worry about becoming worse off even when they do not report current financial strain while others who report current financial strain may expect their circumstances to improve.
As a future-oriented form of economic stress, anticipatory financial strain aligns with work on anticipatory stressors, which refer to worries about adverse events or hardships that may occur in the future or may never occur at all [15]. From the perspective of the stress process model [9], anticipatory financial strain may be especially important because stress is not limited to present financial difficulty; it can also arise from the expectation of future difficulty. In this sense, anticipatory financial strain may function as a chronic or recurring stressor, as individuals may repeatedly dwell on the possibility of being unable to meet expenses, maintain their standard of living, or manage future obligations. Even when these feared circumstances do not fully materialize, the persistent anticipation of economic insecurity may still generate ongoing vigilance, mental preoccupation, and emotional strain that undermine health and well-being [15].
These ideas are especially relevant for sleep. Sleep requires the ability to disengage from worry and reduce cognitive and emotional arousal at night [16–17]. Anticipatory financial strain may therefore disrupt sleep by increasing rumination, arousal, and difficulty mentally “switching off” at night [18–19]. Yet, compared with the literature on current financial strain and mental health [8, 10–11], research on anticipatory financial strain is still limited, and its relationship with sleep has received little empirical attention. To our knowledge, longitudinal evidence on whether changes in anticipatory financial strain are associated with changes in multiple dimensions of sleep problems remains especially scarce. This gap is important because the stress process perspective suggests that future-oriented economic worries, much like current financial strain, may have meaningful consequences for health [9], including sleep.
Mediating role of loneliness
Within the stress process model, stressors may affect health not only directly but also indirectly through secondary stressors that emerge from the initial strain and transmit its consequences to later outcomes [9]. In the present study, loneliness is proposed as one such secondary stressor. Loneliness refers to “the distressing feeling that one’s social connections are insufficient in terms of quantity or quality” [20]. It is a particularly relevant mediator because financial worries are unlikely to remain confined to the economic domain alone; instead, they may reshape how individuals engage with others, interpret their social circumstances, and experience connection and support. Thus, anticipatory financial strain may function not only as a direct stressor, but also as a source of loneliness, which may in turn undermine sleep.
Although empirical research has not yet directly examined the association between anticipatory financial strain and loneliness, there are theoretical reasons to expect such a relationship. For instance, individuals who worry about future financial difficulty may withdraw from social activities because of anticipated costs [21], embarrassment about their circumstances [22], or a desire to avoid situations that highlight their financial insecurity [23–24]. In this way, anticipatory financial strain may erode subjective social integration, increasing the likelihood of loneliness.
A large body of research has shown that loneliness is associated with poorer sleep across a range of indicators, with evidence from cross-sectional and longitudinal studies [25–27]. Although some research suggests that the association between loneliness and sleep may be bidirectional [28], there are still theoretical grounds for treating loneliness as a predictor of sleep problems in the present context. Loneliness may disrupt sleep by heightening vigilance, emotional distress, and feelings of social insecurity, thereby making it more difficult to initiate and maintain restful sleep [29–30]. Moreover, feeling socially disconnected may weaken emotional regulation and increase vulnerability to nocturnal arousal and sleep disruption [17, 31]. Taken together, loneliness can be understood as a secondary stressor that emerges from anticipatory financial strain and helps explain why future-oriented economic worry may become embodied in sleep problems.
Moderating role of gender
Although loneliness may help explain how anticipatory financial strain becomes linked to sleep problems, this process may not unfold in the same way for men and women. Focusing on gender is theoretically important because competing perspectives suggest different possibilities regarding who may be more vulnerable to the consequences of anticipatory financial strain. Although direct empirical evidence is lacking, existing theory points to two contrasting expectations. From a gender socialization perspective [32], women and men are socialized into different roles, responsibilities, and expectations, which may shape how they experience and respond to anticipatory financial strain. Women are often more strongly socialized into relational and caregiving roles and may rely more heavily on maintaining supportive ties beyond the household [33–37]. As such, worries about future financial strain may be especially consequential for women if they reduce the time, emotional resources, and opportunities available for sustaining social participation and relational connection, particularly when women feel compelled to prioritize household responsibilities and obligations [36–37]. These pressures may, in turn, heighten women’s risk of loneliness. A similar argument may apply to sleep. Anticipatory financial strain may be especially disruptive for women if future-oriented economic worries intensify role overload, mental preoccupation, and the strain of balancing household and relational responsibilities, thereby increasing vulnerability to sleep difficulties.
An alternative expectation follows from gender relations and provider-role perspectives. Traditional breadwinner norms position men as principal economic providers, which means that anticipated financial strain may be especially threatening for men when it signals difficulty fulfilling socially valued expectations of masculinity [38–39]. Research on gender social stress suggests that financial setbacks can carry symbolic costs for men when they threaten the masculine ideal of being a family provider, and that men’s failure to meet provider expectations may generate stress, particularly in contexts where male-breadwinner norms remain salient [38–39]. Broader scholarship on masculinity further suggests that norms emphasizing self-reliance, invulnerability, and emotional restraint can restrict men’s willingness to seek or mobilize support, while also increasing vulnerability to loneliness and insufficient social connection [40–41]. From this perspective, anticipatory financial strain may be more strongly associated with loneliness among men because future-oriented economic worry may carry greater stigma, threaten provider identities, and be less likely to be buffered through supportive coping [42]. A similar argument may apply to sleep. If anticipatory financial strain threatens men’s provider role while also discouraging emotional disclosure or support-seeking [38–41], it may generate more unbuffered worry and nocturnal arousal, thereby increasing vulnerability to sleep difficulties. Given these competing theoretical arguments, the moderating role of gender remains an open empirical question.
The present study
Taken together, the existing literature leaves several important gaps. First, although financial strain has been linked to poorer mental health and, to a lesser extent, sleep problems, most research has focused on current financial strain, leaving the relationship between anticipatory financial strain and multiple dimensions of sleep problems, particularly in longitudinal research, poorly understood. Second, although loneliness is a theoretically plausible pathway linking anticipatory financial strain to sleep problems, this mediating role has not been examined directly. Finally, despite strong theoretical reasons to expect gender differences, it remains unclear whether the associations between anticipatory financial strain, loneliness, and sleep problems differ for women and men.
To address these gaps, the present study uses longitudinal data from the United Kingdom to examine whether anticipatory financial strain is associated with multiple dimensions of sleep problems, whether loneliness mediates this association, and whether gender moderates the association between anticipatory financial strain and loneliness and the association between anticipatory financial strain and sleep problems. Figure 1 presents the conceptual framework.
Fig. 1.
Conceptual framework
This study makes several contributions. Conceptually, it extends research on financial stress and sleep by shifting attention from current financial strain to anticipatory financial strain, a future-oriented stressor that has received far less scholarly attention. It also advances the literature by identifying loneliness as a potential psychosocial pathway through which anticipatory financial strain may become linked to sleep problems. Methodologically, by using longitudinal data and examining multiple dimensions of sleep, the study moves beyond the predominantly cross-sectional literature and offers a more nuanced understanding of how future-oriented economic worries may shape sleep over time. More broadly, the findings may have implications for intervention by suggesting that efforts to improve sleep should consider future-oriented financial worry alongside current financial strain and the social disconnection that such worries may generate.
Methods
Data
This study uses data from Waves 10 and 13 of the UK Household Longitudinal Study (UKHLS), which correspond to fieldwork conducted in 2018–2020 and 2021–2023, respectively. These are the only waves that currently contain comparable measures of both loneliness and sleep problems. The UKHLS is a nationally representative longitudinal study of people living in the United Kingdom. At baseline, it included members of approximately 40,000 households and has followed participating households annually to capture changes in household and individual circumstances over time, although fieldwork for a given wave may extend across more than one calendar year [43]. The study was designed using a stratified, clustered, and equal-probability sample of the general population and also includes boost samples to improve representation of minority and immigrant groups. Data are collected through interviewer-administered surveys and self-completion questionnaires, with adult interviews conducted among respondents aged 16 and older. Designed to provide high-quality longitudinal information on topics such as health, income, work, family life, and social well-being, the UKHLS is well suited for examining how changing social and economic circumstances are associated with sleep over time.
The analytical sample was restricted to respondents aged 18 and older. Item nonresponse was low for study variables, with missing data below 5% for each variable included in the analysis. Accordingly, we used complete-case person-wave observations (i.e., listwise deletion), excluding observations with missing data on analysis variables. The final analytical dataset included 20,355 individuals contributing 40,710 person-wave observations. All participants provided informed consent, and UKHLS data were anonymized prior to public release. Because this study relies on de-identified, publicly available data, additional ethical review was not required.
Measures
Loneliness was assessed using the three-item UCLA Loneliness Scale [44]. Respondents indicated how often they felt they lacked companionship, felt left out, and felt isolated from others. Each item was scored on a three-point scale ranging from 1 (“hardly ever or never”), 2 (“some of the time”), to 3 (“often”). Responses were summed to form a total score ranging from 3 to 9, with higher scores indicating greater loneliness (Cronbach’s alpha = 0.86).
Anticipatory financial strain was measured with the item: “Looking ahead, how do you think you will be financially a year from now, will you be…” Responses included “better off,” “about the same,” and “worse off than now.” For the analyses, this variable was coded dichotomously, with respondents who expected to be worse off coded as 1 and those who expected to be better off or about the same coded as 0.
Sleep problems were measured using five indicators drawn from the Pittsburgh Sleep Quality Index (PSQI) [45–46], all referring to experiences during the past month. Overall sleep quality was based on respondents’ rating of their sleep, with responses ranging from 1 (“very good”), 2 (“fairly good”), 3 (“fairly bad”) to 4 (“very bad”). Sleep disturbance was derived from two items asking how often respondents had trouble sleeping because they woke during the night or too early in the morning, and how often they had trouble sleeping because they coughed or snored loudly. Each item was scored from 1 (“not during the past month”), 2 (“less than once a week”), 3 (“once or twice a week”), 4 (“three or more times a week”), to 5 (“more than once most nights”), recoded from 0 to 4 and summed to create an index ranging from 0 to 8. Daytime dysfunction was measured by how often respondents had difficulty staying awake while driving, eating meals, or engaging in social activities, with responses coded from 1 (“not during the past month”), 2 (“less than once a week”), 3 (“once or twice a week”), to 4 (“three or more times a week”). Sleep latency was assessed by asking how often respondents had trouble falling asleep within 30 min, with responses ranging from 1 (“not during the past month”), 2 (“less than once a week”), 3 (“once or twice a week”), 4 (“three or more times a week”), to 5 (“more than once most nights”). Sleep medication use captured how often respondents had taken prescribed or over-the-counter medication to help them sleep, coded from 1 (“not during the past month”), 2 (“less than once a week”), 3 (“once or twice a week”), to 4 (“three or more times a week”). Because these five indicators capture distinct aspects of sleep, they were examined separately, consistent with prior research (e.g., [46–48]).
Gender was coded as male or female.
The analyses also adjusted for several sociodemographic characteristics. Marital status was coded as married versus not married. The number of children younger than age 15 was included as a continuous measure. Educational attainment was categorized into six categories: degree, other higher degree, A-level, GCSE, other qualification, and no qualification. Current economic activity was classified as employed, retired, or other. Total net monthly personal income was included as a continuous variable. Survey wave was represented by Wave 10 and Wave 13.
Analytic strategy
All analyses were conducted using fixed-effects regression models to estimate within-person changes in anticipatory financial strain, loneliness, and sleep problems. By focusing on within-person change, fixed-effects models account for all time-invariant respondent characteristics, such as personality traits that could otherwise confound the associations among financial worry, loneliness, and sleep. To determine the most appropriate panel specification, we conducted Hausman tests comparing fixed- and random-effects estimates; the results supported the use of fixed-effects models [49]. All models adjusted for time-varying covariates, including marital status, number of children under age 15, educational attainment, current economic activity, logged monthly personal income, and survey wave.
To assess whether loneliness mediates the association between anticipatory financial strain and sleep problems, we estimated a series of fixed-effects models shown in Table 2. Model 1 regressed loneliness on anticipatory financial strain and the covariates. Models 2a through 6a then examined the association between anticipatory financial strain and each sleep outcome separately: sleep quality (Model 2a), sleep disturbance (Model 3a), daytime dysfunction (Model 4a), sleep latency (Model 5a), and sleep medication use (Model 6a). Models 2b through 6b added loneliness to the corresponding sleep models to assess whether the coefficient for anticipatory financial strain was attenuated after accounting for loneliness. We formally tested mediation using the Karlson–Holm–Breen (KHB) method, which decomposes the total association into direct and indirect components and provides an estimate of the proportion mediated [50].
Table 2.
Fixed-effects regression models predicting loneliness and sleep problems
| Model 1 | Model 2a | Model 2b | Model 3a | Model 3b | Model 4a | Model 4b | Model 5a | Model 5b | Model 6a | Model 6b | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| Loneliness | Sleep Quality | Sleep Quality | Sleep Disturbance | Sleep Disturbance | Daytime Dysfunction | Daytime Dysfunction | Sleep Latency | Sleep Latency | Sleep Medication | Sleep Medication | |
| Financial strain (ref: Better off/same) | |||||||||||
| Worse off than now | 0.110*** | 0.048*** | 0.042*** | 0.204*** | 0.195*** | 0.015 | 0.012 | 0.036 | 0.028 | 0.025 | 0.023 |
| (0.023) | (0.011) | (0.011) | (0.033) | (0.033) | (0.010) | (0.010) | (0.020) | (0.020) | (0.013) | (0.013) | |
| Loneliness | 0.049*** | 0.086*** | 0.027*** | 0.074*** | 0.017*** | ||||||
| (0.003) | (0.010) | (0.003) | (0.006) | (0.004) | |||||||
| Marital status (ref: Married) | |||||||||||
| Not married | 0.358*** | −0.008 | −0.026 | −0.073 | −0.103 | 0.016 | 0.006 | 0.048 | 0.021 | 0.022 | 0.016 |
| (0.049) | (0.023) | (0.023) | (0.071) | (0.071) | (0.022) | (0.022) | (0.042) | (0.042) | (0.029) | (0.029) | |
| Number of children | 0.099** | 0.029 | 0.024 | −0.157*** | −0.166*** | −0.004 | −0.007 | −0.052 | −0.059* | −0.025 | −0.027 |
| (0.032) | (0.015) | (0.015) | (0.047) | (0.047) | (0.014) | (0.014) | (0.028) | (0.028) | (0.019) | (0.019) | |
| Education (ref: Degree) | |||||||||||
| Other higher degree | −0.146 | 0.050 | 0.057 | −0.051 | −0.038 | 0.019 | 0.023 | 0.142 | 0.153 | −0.006 | −0.003 |
| (0.147) | (0.069) | (0.069) | (0.214) | (0.213) | (0.065) | (0.065) | (0.127) | (0.126) | (0.086) | (0.086) | |
| A-level | −0.071 | −0.034 | −0.031 | −0.149 | −0.143 | 0.026 | 0.028 | −0.015 | −0.010 | −0.037 | −0.036 |
| (0.084) | (0.040) | (0.040) | (0.123) | (0.122) | (0.037) | (0.037) | (0.073) | (0.072) | (0.049) | (0.049) | |
| GCSE | 0.160 | 0.145* | 0.137* | −0.142 | −0.156 | 0.071 | 0.066 | 0.162 | 0.150 | −0.100 | −0.103 |
| (0.149) | (0.070) | (0.070) | (0.217) | (0.216) | (0.066) | (0.066) | (0.128) | (0.128) | (0.087) | (0.087) | |
| Other qualification | 0.047 | 0.135 | 0.133 | −0.895 | −0.899 | −0.244 | −0.245 | 0.006 | 0.002 | −0.591** | −0.592** |
| (0.326) | (0.154) | (0.153) | (0.474) | (0.473) | (0.144) | (0.144) | (0.280) | (0.279) | (0.191) | (0.191) | |
| No qualification | −0.035 | 0.163 | 0.164 | −1.145* | −1.142* | −0.277 | −0.276 | −0.054 | −0.051 | −0.469* | −0.468* |
| (0.336) | (0.158) | (0.158) | (0.487) | (0.487) | (0.149) | (0.148) | (0.289) | (0.288) | (0.197) | (0.197) | |
| Current economic activity (ref: Employed) | |||||||||||
| Retired | 0.039 | −0.008 | −0.009 | −0.001 | −0.004 | 0.001 | -0.000 | 0.026 | 0.023 | 0.023 | 0.023 |
| (0.046) | (0.022) | (0.021) | (0.066) | (0.066) | (0.020) | (0.020) | (0.039) | (0.039) | (0.027) | (0.027) | |
| Other | 0.220*** | 0.135*** | 0.124*** | 0.351*** | 0.332*** | 0.006 | -0.000 | 0.167*** | 0.151*** | 0.066** | 0.062** |
| (0.036) | (0.017) | (0.017) | (0.053) | (0.053) | (0.016) | (0.016) | (0.031) | (0.031) | (0.021) | (0.021) | |
| Log income | 0.002 | 0.005 | 0.005 | 0.012 | 0.012 | 0.001 | 0.001 | 0.008 | 0.008 | −0.004 | −0.004 |
| (0.006) | (0.003) | (0.003) | (0.008) | (0.008) | (0.002) | (0.002) | (0.005) | (0.005) | (0.003) | (0.003) | |
| Wave (ref: Wave 10) | |||||||||||
| Wave 13 | 0.086*** | 0.042*** | 0.038*** | 0.284*** | 0.277*** | −0.022*** | −0.025*** | 0.044*** | 0.037*** | −0.010 | −0.011 |
| (0.011) | (0.005) | (0.005) | (0.016) | (0.016) | (0.005) | (0.005) | (0.010) | (0.010) | (0.007) | (0.007) | |
| N (person-wave observations) | 40,710 | 40,710 | 40,710 | 40,710 | 40,710 | 40,710 | 40,710 | 40,710 | 40,710 | 40,710 | 40,710 |
| N (individuals) | 20,355 | 20,355 | 20,355 | 20,355 | 20,355 | 20,355 | 20,355 | 20,355 | 20,355 | 20,355 | 20,355 |
Note: Standard errors are in parentheses. * p < 0.05, ** p < 0.01, *** p < 0.001
To assess whether associations differed by gender, we estimated additional fixed-effects models including an interaction term between anticipatory financial strain and gender (Table 3). Model 1 examined whether gender moderated the association between anticipatory financial strain and loneliness. Models 2 through 6 examined whether gender moderated the association between anticipatory financial strain and each sleep outcome. Because the interaction between anticipatory financial strain and gender was statistically significant only for sleep medication use, we also estimated gender-stratified fixed-effects models for sleep medication use. These stratified models are presented as Models 7a and 7b in Table 3. All analyses were conducted in Stata 19.
Table 3.
Fixed-effects regression models with the interaction of financial strain and gender
| Model 1 | Model 2 | Model 3 | Model 4 | Model 5 | Model 6 | Model 7a | Model 7b | |
|---|---|---|---|---|---|---|---|---|
| Loneliness | Sleep Quality | Sleep Disturbance | Daytime Dysfunction | Sleep Latency | Sleep Medication | Sleep Medication | Sleep Medication | |
| Financial strain (ref: Better off/same) | ||||||||
| Worse off than now | 0.133*** | 0.028* | 0.151*** | 0.021 | 0.022 | −0.013 | 0.073*** | −0.014 |
| (0.030) | (0.014) | (0.044) | (0.013) | (0.026) | (0.018) | (0.019) | (0.018) | |
| Financial strain × Gender | ||||||||
| Worse off × Male | −0.053 | 0.032 | 0.102 | −0.020 | 0.013 | 0.082** | ||
| (0.045) | (0.021) | (0.066) | (0.020) | (0.039) | (0.027) | |||
| Loneliness | 0.049*** | 0.086*** | 0.027*** | 0.074*** | 0.018*** | 0.018** | 0.017** | |
| (0.003) | (0.010) | (0.003) | (0.006) | (0.004) | (0.006) | (0.005) | ||
| Marital status (ref: Married) | ||||||||
| Not married | 0.357*** | −0.025 | −0.102 | 0.006 | 0.022 | 0.017 | 0.033 | 0.005 |
| (0.049) | (0.023) | (0.071) | (0.022) | (0.042) | (0.029) | (0.043) | (0.039) | |
| Number of children | 0.100** | 0.023 | −0.167*** | −0.007 | −0.060* | −0.028 | 0.003 | −0.028 |
| (0.032) | (0.015) | (0.047) | (0.014) | (0.028) | (0.019) | (0.079) | (0.020) | |
| Education (ref: Degree) | ||||||||
| Other higher degree | −0.146 | 0.058 | −0.038 | 0.022 | 0.153 | −0.003 | −0.118 | 0.062 |
| (0.147) | (0.069) | (0.213) | (0.065) | (0.126) | (0.086) | (0.145) | (0.109) | |
| A-level | −0.071 | −0.031 | −0.143 | 0.028 | −0.010 | −0.036 | −0.097 | −0.009 |
| (0.084) | (0.040) | (0.122) | (0.037) | (0.072) | (0.049) | (0.082) | (0.063) | |
| GCSE | 0.160 | 0.137* | −0.156 | 0.066 | 0.150 | −0.103 | −0.226 | −0.033 |
| (0.149) | (0.070) | (0.216) | (0.066) | (0.128) | (0.087) | (0.139) | (0.114) | |
| Other qualification | 0.049 | 0.132 | −0.902 | −0.245 | 0.002 | −0.594** | −0.890*** | −0.386 |
| (0.326) | (0.153) | (0.473) | (0.144) | (0.279) | (0.191) | (0.276) | (0.265) | |
| No qualification | −0.035 | 0.165 | −1.141* | −0.276 | −0.051 | −0.468* | −0.534 | −0.394 |
| (0.336) | (0.158) | (0.487) | (0.148) | (0.288) | (0.196) | (0.275) | (0.280) | |
| Current economic activity (ref: Employed) | ||||||||
| Retired | 0.038 | −0.009 | −0.002 | −0.001 | 0.023 | 0.024 | 0.051 | 0.001 |
| (0.046) | (0.021) | (0.066) | (0.020) | (0.039) | (0.027) | (0.037) | (0.038) | |
| Other | 0.219*** | 0.124*** | 0.332*** | −0.000 | 0.151*** | 0.062** | 0.110** | 0.037 |
| (0.036) | (0.017) | (0.053) | (0.016) | (0.031) | (0.021) | (0.035) | (0.027) | |
| Log income | 0.002 | 0.005 | 0.012 | 0.001 | 0.008 | −0.004 | −0.000 | −0.006 |
| (0.006) | (0.003) | (0.008) | (0.002) | (0.005) | (0.003) | (0.005) | (0.005) | |
| Wave (ref: Wave 10) | ||||||||
| Wave 13 | 0.087*** | 0.038*** | 0.277*** | −0.025*** | 0.037*** | −0.012 | −0.019* | −0.006 |
| (0.011) | (0.005) | (0.016) | (0.005) | (0.010) | (0.007) | (0.009) | (0.009) | |
| N (person-wave observations) | 40,710 | 40,710 | 40,710 | 40,710 | 40,710 | 40,710 | 17,804 | 22,906 |
| N (individuals) | 20,355 | 20,355 | 20,355 | 20,355 | 20,355 | 20,355 | 8,902 | 11,453 |
Note: Standard errors are in parentheses. * p< 0.05, ** p < 0.01, *** p < 0.001
Results
Table 1 presents descriptive statistics for the analytic sample based on person-wave observations. The mean loneliness score was 4.35 on a scale ranging from 3 to 9. Most person-wave observations indicated that respondents expected their financial situation to remain stable or improve over the coming year (82.93%), whereas 17.07% reported anticipatory financial strain by expecting to be worse off than they were currently. To further describe individual-level change in anticipatory financial strain across the two-wave balanced panel, we also examined transitions into anticipatory financial strain between Wave 10 and Wave 13. Among respondents in the analytic sample, 3,167 individuals, or 15.56%, moved from not expecting to be worse off financially at Wave 10 to expecting to be worse off financially at Wave 13 (results not shown but available upon request). Across the five sleep dimensions, the mean score for overall sleep quality was 2.06 on a 1–4 scale, the mean sleep disturbance score was 3.05 on a 0–8 scale, the mean daytime dysfunction score was 1.19 on a 1–4 scale, the mean sleep latency score was 2.26 on a 1–5 scale, and the mean medication use score was 1.25 on a 1–4 scale. The sample was composed of 43.73% male and 56.27% female respondents.
Table 1.
Descriptive statistics of the analytical sample
| Variable | Percentage/Mean (SE) |
|---|---|
| Loneliness (range: 3–9) | 4.35 (0.008) |
| Anticipatory financial strain (%) | |
| Better off/about the same | 82.93% |
| Worse off than now | 17.07% |
| Overall sleep quality (range: 1–4) | 2.06 (0.004) |
| Sleep disturbance (range: 0–8) | 3.05 (0.011) |
| Daytime dysfunction (range: 1–4) | 1.19 (0.003) |
| Sleep latency (range: 1–5) | 2.26 (0.007) |
| Sleep medication use (range: 1–4) | 1.25 (0.004) |
| Gender (%) | |
| Male | 43.73% |
| Female | 56.27% |
| Marital status (%) | |
| Married | 59.11% |
| Not married | 40.89% |
| Number of children under 15 (range: 0–7) | 0.23 (0.003) |
| Educational attainment (%) | |
| Degree | 33.88% |
| Other higher degree | 13.54% |
| A-level | 20.54% |
| GCSE | 18.15% |
| Other qualification | 7.39% |
| No qualification | 6.49% |
| Current economic activity (%) | |
| Employed | 59.01% |
| Retired | 27.71% |
| Other | 13.28% |
| Total net monthly personal income (£) | 1806.53 (10.24) |
| N (person-wave observations) | 40,710 |
| N (individuals) | 20,355 |
Table 2 presents fixed-effects regression results predicting loneliness and sleep problems. Compared with person-waves in which respondents expected their financial situation to be better or about the same one year later, person-waves in which respondents expected to be worse off were associated with an increase in loneliness (b = 0.110, p < 0.001; Model 1). Anticipatory financial strain was also associated with poorer outcomes for some, but not all, sleep dimensions. Specifically, expecting to be worse off financially was associated with poorer overall sleep quality (b = 0.048, p < 0.001; Model 2a) and greater sleep disturbance (b = 0.204, p < 0.001; Model 3a). In contrast, anticipatory financial strain was not significantly associated with daytime dysfunction (b = 0.015, p > 0.05; Model 4a), sleep latency (b = 0.036, p > 0.05; Model 5a), or sleep medication use (b = 0.025, p > 0.05; Model 6a). Given the absence of significant associations between anticipatory financial strain and these three sleep outcomes, the mediation analysis focused on overall sleep quality and sleep disturbance.
When loneliness was added to the sleep models, the coefficients for anticipatory financial strain were modestly attenuated but remained statistically significant for sleep quality and sleep disturbance. The coefficient for sleep quality declined from 0.048 to 0.042 (Models 2a–2b), while the coefficient for sleep disturbance declined from 0.204 to 0.195 (Models 3a–3b). Loneliness itself was positively associated with all five sleep outcomes, including poorer sleep quality (b = 0.049, p < 0.001), greater sleep disturbance (b = 0.086, p < 0.001), greater daytime dysfunction (b = 0.027, p < 0.001), longer sleep latency (b = 0.074, p < 0.001), and greater sleep medication use (b = 0.017, p < 0.001).
Formal mediation tests using the Karlson–Holm–Breen method showed that loneliness partially mediated the association between anticipatory financial strain and the two sleep dimensions for which anticipatory financial strain was significantly associated with the outcome: sleep quality and sleep disturbance. For sleep quality, the indirect effect through loneliness accounted for 11.45% of the total association (p < 0.001). For sleep disturbance, the indirect effect accounted for 4.63% of the total association (p < 0.001). Taken together, these findings suggest that reporting anticipatory financial strain is associated with greater loneliness, and that loneliness helps explain part of its association with poorer sleep quality and greater sleep disturbance.
Table 3 presents the fixed-effects models testing whether gender moderates the association between anticipatory financial strain and loneliness and the association between anticipatory financial strain and each sleep outcome. Overall, there was little evidence that these associations differed by gender. The interaction between anticipatory financial strain and gender was not statistically significant for loneliness (b = -0.053, p > 0.05), sleep quality (b = 0.032, p > 0.05), sleep disturbance (b = 0.102, p > 0.05), daytime dysfunction (b = -0.020, p > 0.05), or sleep latency (b = 0.013, p > 0.05). These findings suggest that the associations between anticipatory financial strain and these outcomes were broadly similar for women and men.
The only significant interaction emerged for sleep medication use. In Model 6, the interaction between anticipatory financial strain and gender was positive and statistically significant (b = 0.082, p < 0.01), indicating that the association between anticipatory financial strain and sleep medication use was stronger for men than for women. The gender-stratified models further supported this pattern. In Model 7a, anticipatory financial strain was positively associated with sleep medication use among men (b = 0.073, p < 0.001), whereas in Model 7b, the association was not statistically significant among women (b = -0.014, p > 0.05). Figure 2 illustrates this interaction, showing higher predicted sleep medication use among men reporting anticipatory financial strain, but little difference among women by anticipatory financial strain status.
Fig. 2.
Predicted values of sleep medication use (varying by anticipatory financial strain status and gender)
Discussion
Sleep problems are common and consequential, yet less is known about how future-oriented economic worries may shape sleep than how current financial strain affects mental health and well-being. Drawing on the stress process model [9], this study examined whether anticipatory financial strain was associated with multiple dimensions of sleep problems, whether loneliness helped explain these associations, and whether these patterns differed by gender. Using longitudinal data from the United Kingdom, the findings showed that anticipatory financial strain was associated with greater loneliness, poorer sleep quality, and greater sleep disturbance, but not with daytime dysfunction, sleep latency, or sleep medication use. Loneliness partially mediated the associations between anticipatory financial strain and both sleep quality and sleep disturbance. Gender differences were limited overall, with moderation emerging only for sleep medication use. Taken together, these findings extend prior research by showing that worries about future financial difficulty, not only present financial strain, are relevant for understanding sleep and related psychosocial processes over time.
The positive association between anticipatory financial strain and loneliness is noteworthy both empirically and theoretically. Although previous work has paid little direct attention to this relationship, the findings are consistent with the idea that future-oriented economic worry may spill over beyond the financial domain and erode subjective social integration. Anticipatory financial strain may lead individuals to withdraw from social activities, either because they expect such participation to become harder to afford or because financial worry produces embarrassment, shame, or a desire to avoid situations that make insecurity visible [21–23]. In this sense, the findings support the view that anticipatory financial strain is not merely a cognitive appraisal of future difficulty, but also a stress with interpersonal implications. This is important because it suggests that future-oriented financial worries may reshape how people experience connection, belonging, and support before any anticipated financial difficulty has fully materialized.
The results also show that anticipatory financial strain was associated with some, but not all, dimensions of sleep problems. Specifically, it predicted poorer overall sleep quality and greater sleep disturbance, but not daytime dysfunction, sleep latency, or sleep medication use in the main models. One possible explanation is that anticipatory financial strain operates primarily through persistent mental preoccupation and diffuse arousal, which may be more likely to affect how people evaluate their sleep overall and whether they experience restless or interrupted sleep across the night. By contrast, daytime dysfunction may reflect a broader set of influences, including work schedules, caregiving demands, physical health, and cumulative sleep debt, which may dilute the role of anticipatory financial strain. The null finding for sleep latency is somewhat more surprising given the hypothesized role of pre-sleep worry, but it may be that future-oriented financial concerns do not consistently translate into difficulty falling asleep within 30 min, particularly once stable unobserved differences are accounted for. Similarly, sleep medication use may depend not only on sleep difficulty itself, but also on treatment preferences, health care access, symptom recognition, and willingness to use pharmacological coping strategies. Because the present analysis was not designed to directly examine the mechanisms underlying these dimension-specific patterns, these interpretations remain speculative. Future research, including qualitative work, could provide further insight into how individuals experience anticipatory financial strain in ways that shape different aspects of sleep.
The mediation findings further clarify how anticipatory financial strain may become linked to sleep. Loneliness partially mediated the associations with sleep quality and sleep disturbance, indicating that one reason future-oriented economic worry may undermine sleep is because it is associated with greater subjective social disconnection. This pattern is consistent with a stress proliferation perspective, in which an initial stressor gives rise to secondary strains that help transmit its effects to health outcomes [9]. At the same time, mediation was only observed for the sleep dimensions that were directly associated with financial strain in the first place. This likely helps explain why loneliness did not mediate the association with daytime dysfunction, sleep latency, or sleep medication use: where no direct association was evident, there was less of a relationship to explain through an intervening pathway. The results also reinforce the broader loneliness–sleep literature by showing that loneliness was positively associated with all five sleep outcomes. This suggests that loneliness may be a fairly general correlate of poor sleep, even if it only mediates certain pathways from anticipatory financial strain. Theoretically, loneliness may matter because it heightens vigilance, emotional unease, and social insecurity [29–30], all of which may interfere with restful sleep. The mediation results therefore suggest that loneliness is one meaningful, but not exclusive, pathway through which anticipatory financial strain becomes embodied in sleep problems. Future research could further examine whether health behaviors, physical health, and psychological symptoms, including anxious and depressive symptoms, also serve as mediating mechanisms in these processes.
The gender moderation findings were more limited than the competing theoretical perspectives may have suggested. Contrary to expectations derived from both gender socialization [32] and provider-role [38–39] arguments, the associations between anticipatory financial strain and loneliness, sleep quality, sleep disturbance, daytime dysfunction, and sleep latency did not differ significantly between women and men. One interpretation is that future-oriented financial worry may be sufficiently salient and stressful that its implications for loneliness and many dimensions of sleep are broadly similar across gender. It is also possible that the competing gendered processes outlined in the introduction offset one another to some extent. For instance, women may be more vulnerable because financial worries interfere with relational participation and role management [34, 36–37], whereas men may be more vulnerable because such worries threaten provider identities and may be less likely to be buffered through support-seeking [38–41]. These countervailing mechanisms could result in relatively similar net associations overall.
The only significant moderation emerged for sleep medication use, with anticipatory financial strain showing a stronger association among men. This result should be interpreted cautiously, especially given the absence of moderation for the other sleep outcomes, but one possible explanation is that when financial worry threatens provider identities, men may be less likely to externalize distress through emotional disclosure or social coping and more likely to adopt private, instrumental responses to sleep problems, including medication use [51].
From a public health perspective, these findings suggest that sleep difficulties may be shaped not only by current financial strain, but also by worries about future financial decline. This has practical implications for screening, prevention, and intervention. Clinicians, public health practitioners, and community-based services may benefit from paying closer attention to future-oriented economic stress alongside current financial strain. The results also suggest that interventions targeting sleep may benefit from incorporating social and relational dimensions, particularly loneliness. Efforts to strengthen social connection, reduce isolation, and improve access to supportive resources may help mitigate some of the sleep-related consequences of anticipatory financial strain. More broadly, the findings point to the value of integrated approaches that connect economic insecurity, psychosocial well-being, and sleep health, rather than treating these as separate domains.
Several limitations should be noted. First, although the study uses longitudinal data and fixed-effects models, the analyses cannot establish causality with certainty, and reciprocal relationships remain possible, particularly between loneliness and sleep [28]. Second, anticipatory financial strain was measured using a single item and dichotomized, which may not capture the full range or intensity of future-oriented economic worry. Third, all measures were self-reported and may be subject to reporting bias. Finally, although fixed-effects models adjust for all stable respondent characteristics, time-varying unobserved confounders may still influence the observed associations.
In conclusion, this study shows that anticipatory financial strain is linked to greater loneliness, poorer sleep quality, and greater sleep disturbance over time, and that loneliness helps explain part of the association between future-oriented financial worry and sleep. The findings extend existing research by showing that concerns about future financial insecurity, not only current financial strain, matter for understanding sleep and psychosocial well-being. They also highlight loneliness as an important pathway through which future-oriented economic stress may affect health. Overall, the study underscores the need to consider how anticipatory financial strain, social disconnection, and sleep are intertwined in everyday life.
Author contributions
L.C. conceptualized the study, conducted the analysis, and drafted the manuscript. X.D. provided feedback and rewrote some sections. Both L.C. and X.D. approved the final version of the manuscript.
Funding
L.C. and X.D. received seed fund for basic research for new staff at the University of Hong Kong.
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. This study was not preregistered. Study materials for this study are available upon request.
Declarations
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. This study was not preregistered. Study materials for this study are available upon request.