Abstract
Objectives
Sleep and pain are co-occurring issues in adulthood. “Pain reactivity to worse sleep” refers to person-specific changes in daily pain following nights of shorter-than-usual sleep duration or poorer-than-usual sleep quality. We examined the cross-sectional and longitudinal relationships between sleep-related pain reactivity with psychological distress and the number of chronic conditions.
Methods
Data were obtained from the Work, Family, and Health Study, which collected data from workers (n = 311; Mage = 41.38 years), who completed eight days of daily diary. We controlled for sociodemographic and health covariates in a series of multilevel structural equation models and growth curve models in Mplus. Sensitivity analyses adjusted for sleep and pain medications and stratified analyses by age.
Results
Those with higher pain severity reactivity to shorter sleep duration (B = 0.54, p < .001) and higher pain severity reactivity to poorer sleep quality (B = 0.58, p < .001) had more concurrent distress. In the growth curve models, pain reactivity to worse sleep was not associated with a change in distress or the number of chronic conditions. After controlling for sleep and pain medication use, higher pain reactivity to poorer sleep quality was associated with more concurrent distress, and contrary to hypotheses, higher pain location reactivity to poorer sleep quality was associated with a decrease in distress and fewer chronic conditions over time.
Discussion
Higher pain reactivity to worse sleep was associated with more concurrent distress but not distress over time or risk of chronic conditions. Future directions are to examine the influence of pain reactivity to worse sleep in larger and more diverse samples across longer follow-up.
Keywords: Pain severity, Reactivity, Psychological distress, Chronic conditions, Person-centered analysis
In midlife and later life, 33% of United States adults report that they do not obtain sufficient sleep, and 21% experience chronic pain (Centers for Disease Control and Prevention, 2024; Mansfield et al., 2016). Sleep and pain are dynamic processes that have shared mechanisms and significant variations between people (interindividual variability) and within individuals across days (intraindividual variability) (Babiloni et al., 2020; Haack et al., 2020; Mun et al., 2019; O’Brien et al., 2011). This study seeks to characterize the interplay between daily sleep and pain by evaluating the effects of pain reactivity to worse sleep, which models experiences of pain after shorter-than-usual sleep duration or poorer-than-usual sleep quality, and its associations with psychological distress and the number of chronic conditions in middle-aged and older adults.
Several mechanisms may explain the bidirectional relationships between poor sleep and more pain. Daily diary designs have revealed that poorer sleep quality, more insomnia symptoms, lower sleep efficiency, and shorter sleep duration are associated with more frequent and severe pain, and vice versa (Alsaadi et al., 2014; Anderson & Holliday, 2023; Edwards et al., 2008; Lücke et al., 2023; Mu & Lee, 2024). Systematic reviews have surmised that sleep problems often precede pain (Afolalu et al., 2018; Goossens et al., 2025; Koffel et al., 2019; Whibley et al., 2019). Several explanatory pathways have been proposed, including compromised central pain-modulatory dopaminergic signaling, hypothalamus-pituitary-adrenal axis, immune system, and psychological factors such as negative affect and fatigue (Babiloni et al., 2020; Goossens et al., 2025; Haack et al., 2020; Whibley et al., 2019). When examined independently, poor sleep and pain are associated with worse physical and mental health outcomes, including more depressive symptoms, anxiety symptoms, and chronic conditions (Curtis et al., 2021; DeVon et al., 2014; Gordon, 2015; Knutson et al., 2017; Lee et al., 2022).
The Middle-Range Theory of Unpleasant Symptoms originated from observations made in clinical practice and describes how symptoms are likely to co-occur and catalyze one another (Lenz et al., 1997). One example of the theory is the co-occurrence of pain and fatigue among those with chronic obstructive pulmonary disease (Lenz et al., 1997). Reactivity, which describes interindividual variability in responses to stimuli, is one of the ways to measure the co-occurrence of symptoms that are also interrelated (McEwen, 1998; Sliwinski et al., 2009; Smyth et al., 2023). Studies have explored the effects of reactivity to poorer-than-usual sleep. Higher sleep reactivity to stressors (i.e., difficulty sleeping after experiencing a stressful event) is associated with a greater risk of insomnia disorder and anxiety/depressive symptoms (Drake et al., 2014; Kalmbach et al., 2018). Greater stressor reactivity to insufficient sleep (i.e., experiencing a greater number of stressors and stressor severity in response to insufficient sleep) is associated with a higher risk of obesity (Vigoureux et al., 2020). Higher negative affective reactivity to stressors (i.e., greater negative affect after experiencing stressful events) is associated with more inflammation and increased risks of affective disorders and chronic health conditions 10 years later (Charles et al., 2013; Piazza et al., 2013; Sin et al., 2015). Researchers posit that the negative health consequences of reactivity may be explained by the cumulative buildup and wear-and-tear of stress, resulting in poorer health over time (Charles et al., 2013; McEwen, 1998; Piazza et al., 2013).
When compared to those with mild pain and no insomnia, moderate to severe pain paired with clinical insomnia is associated with higher insomnia severity; greater opioid, tricyclic antidepressant, and non-tricyclic antidepressant use; and healthcare utilization (i.e., more medical office visits, longer length of stay at hospital, hip/knee replacement, and higher outpatient and inpatient medical expenses) over time (Liu et al., 2018, 2019). Greater pain intensity combined with lower sleep efficiency is associated with worse inductive reasoning and working memory overall (Curtis et al., 2021). While these studies capture the joint effects of sleep and pain on health outcomes, they do not fully capture the dynamic and interrelated nature of sleep and pain on health. Pain can fluctuate daily in response to nightly sleep, with potential individual differences in the degree of reactivity. Building on the reactivity literature, we modeled pain reactivity to shorter-than-usual sleep and poorer-than-usual sleep to study its influence on psychological distress and the number of chronic conditions. Person-specific approaches allow researchers to capture dynamic changes within individuals, which moves beyond the summation of variables over time (Bergman & Trost, 2006). Knowledge of pain reactivity to worse sleep can help identify at-risk individuals of poor health outcomes as a result of their sleep and pain experiences.
Pain reactivity to worse sleep is a person-specific approach for modeling the joint associations of sleep and pain. By modeling slopes representing a series of daily pain experiences in relation to the previous night’s sleep duration, this approach for quantifying individual risk advances the literature beyond traditional variable-centered approaches, such as interaction effects or stratified analyses, which aggregate the joint associations of sleep and pain across individuals. Using data from the Work, Family, and Health Study (WFHS), we hypothesized that those who report more frequent pain and severe pain following shorter-than-usual sleep duration or poorer-than-usual sleep quality (higher pain reactivity to worse sleep) will have more psychological distress and chronic conditions over time.
Methods
Participants
WFHS was a group-randomized field experiment that collected data from extended-care workers and information technology workers. For extended-care workers, data were collected primarily from the Northeastern region of the United States. For information technology workers, participants were recruited nationwide, but a majority were recruited from Colorado and Ohio. Contractors and temporary workers were excluded from the study. Enrollment data were collected between 2009 and 2011, with participants followed 6, 12, and 18 months post-enrollment. A subset of 313 participants completed eight days of daily diary data via telephone interviews, which measured daily sleep and pain data. Sociodemographic data were collected at enrollment, and health outcomes were collected at each visit using surveys. Due to the missingness on the outcomes, the analytic sample included 310 participants for psychological distress and 311 participants for chronic conditions. We conducted power analyses in G*Power 3.1 using two-tailed tests with a significance of p < .05 and a sample size of 311 participants. The regression analyses with all predictors and covariates will detect minimum effect sizes of f2 of 0.03. See the WFHS participant flowchart in Supplementary Figure 1. This study was exempt from IRB review because the data were secondary and de-identified. The study protocol was published elsewhere (Bray et al., 2013), and additional information can be found online (https://workfamilyhealthnetwork.org). This study was not preregistered. The data management code and analyses can be found online: https://osf.io/8tbp9/?view_only=c9c7152428af4f28891ec40de5b8478f. at enrollment,
Measures
Sleep
For eight consecutive days, participants were asked to report on their previous night’s sleep duration with the question, “How many hours did you sleep (last night)?” Responses were presented in continuous decimal hours. Sleep quality was measured using a single item from the Pittsburgh Sleep Quality Index. Participants were asked, “How would you rate (your last night’s) sleep quality overall?” Response options were very badly (=1), badly (=2), well (=3), and very well (=4). For interpretability of results, both sleep duration and sleep quality were reverse-coded, so that higher values indicated shorter sleep duration or worse sleep quality than the person’s average.
Pain
For eight consecutive days, participants were asked if they experienced any headaches; back, neck, or shoulder pain; leg or foot pain; or finger, hand, or wrist pain within a given day. Each pain item was dichotomized (1 = yes, 0 = no), and all pain locations were summed, ranging from 0 (no pain locations) to 4 (all pain locations). If participants reported any pain, they were then prompted to rate their pain severity from 1 (very mild) to 10 (very severe). The average of all pain severity items was taken to represent overall pain severity. If participants reported having no pain locations, then their pain severity scores were considered 0.
Physical and mental health outcomes
Psychological distress.
Psychological distress was a composite sum asking participants to reflect on the past 30 days (e.g., feelings of sadness, worthlessness). Response options were originally coded from 1 (all of the time) to 5 (none of the time). All items were reverse-coded so that higher scores indicated higher psychological distress. The possible sum of the psychological distress ranged from 6 to 30 (Cronbach’s alpha = .82).
Chronic conditions.
The number of chronic conditions was the sum of the following physician-diagnosed conditions: high blood pressure, heart attack, diabetes, stroke, and cancer.
Covariates
Covariates were selected based on established associations with sleep, pain, and health outcomes (Grandner, 2019; O’Neill et al., 2018). This study controlled for age, sex (0 = female, 1 = male), race/ethnicity (0 = non-Hispanic White individuals, 1 = Hispanics and People of Color), education, household income (0 = less than $60,000, 1 = equal to or greater than $60,000), marital status (0 = married/partnered, 1 = unmarried, single, divorced, separated, or widowed), work shift (0 = day-shift, 1 = shift work), extended-care vs. information technology, intervention, health covariates (i.e., body mass index [BMI, kg/m2], smoking status, having an alcohol consumption, and physical activity), and average between-person sleep. Age and BMI were centered at the sample mean. After the initial enrollment assessment, the parent study conducted group-randomized field experiments aimed at increasing employee schedule control, fostering more workplace support, and improving health. Thus, in the longitudinal analyses, the present study controlled for the effects of the workplace intervention. All covariates were assessed at enrollment.
For education, three categories were created: less than or equivalent to high school education, some college education, or college graduate or more. The “college graduate or more” category was used as the reference category. Participants responded to two questions on alcohol consumption. They were asked, “On average, in 1 week, how many days do you drink any type of alcohol?” and “On days you do drink alcohol, how many drinks do you have on the average day?” Guidelines from the National Center for Health Promotion and Disease Prevention were used to code sex-specific drinking behaviors (Centers for Disease Control and Prevention, 2022). For women, the cut point was 7 drinks per week (0 = less than or equal to 7 drinks/week, 1 = greater than 7 drinks/week—not recommended), and for men, the cut point was 14 drinks per week (0 = less than or equal to 14 drinks/week, 1 = greater than 14 drinks/week—not recommended).
For physical activity, participants reported the number of times they exercised for at least 20 minutes, causing them to break out into a sweat over the past 4 weeks. The American College of Sports Medicine and the American Heart Association recommend at least 3 days of physical activity each week for a minimum of 20 minutes per session (Haskell et al., 2007). Guided by this recommendation, this study used a crude cut point of 12 sessions per month, which is similar to an average of 3 days of physical activity per week. Less than 12 sessions of physical activity were coded as 1 (lower physical activity), and equal to or more than 12 sessions were coded as 0 (higher physical activity).
Sensitivity analyses controlled for using sleep and pain medications. Participants shared information on the types and dosages of the medications they took over the daily diary data collection. Using the Anatomical Therapeutic Chemical Classification codes, medications with the prefixes starting with N05, N05C, N05CA, NO5CB, N0CC, N05CD, N05CE, N05CH, N05CJ, N05CM, and N05CX were classified as sleep medication (Ernstsen et al., 2023; Pahor et al., 1994). Medications with the prefix starting with N02, NO2A, NO2B, NO2C, and M02 were classified as pain medication (Scholz et al., 2024). Additional sensitivity analyses controlled for both between-person level sleep quality and sleep duration in the longitudinal models.
Statistical analyses
Analyses included 2,208 daily observations nested within 311 participants. Multilevel structural equation modeling (ML-SEM) was used to calculate parameter estimates (Brose et al., 2022). The within-person and between-person aspects of sleep and pain were decomposed manually in the data management step. Compared to previous modeling approaches for reactivity (ie, extracting person-specific slopes), ML-SEM models estimate closer to the true association, whereby the individual slope estimates are treated as estimated values rather than observed values and ultimately reduce the risk of Type I error (Brose et al., 2022). ML-SEM was used to examine associations with concurrent outcomes, and growth curve models with Bayesian methods were used for longitudinal analyses. Figure 1 displays the path diagram of the longitudinal associations between pain reactivity to poorer-than-usual sleep and health outcomes. Figure 2 displays spaghetti plots of each participant’s reactivity slopes (random slopes) and the sample-level average of the reactivity slope. Data management was conducted in SAS 9.4, and analyses were conducted in Mplus 8.11. Both unadjusted models and fully adjusted models were tested.
Figure 1.
Path diagram of the longitudinal associations between pain reactivity to worse sleep and health outcomes (i.e., psychological distress or chronic conditions), accounting for state dynamics and trait change using ML-SEM. Level 1 depicts the within-person associations modeling daily sleep and next-day pain. Level 2 illustrates the between-person associations between pain reactivity to worse sleep and health outcomes, controlling for covariates. pmc = person-mean centered; pain = number of pain locations or pain severity; T1i–T4i represents the time in between the four waves of data collection: T1 = At enrollment/Time 1, T2 = Time 2 (6 months), T3 = Time 3 (12 months), T4 = Time 4 (18 months). ML-SEM = Multilevel structural equation modeling.
Figure 2.
Spaghetti plots of pain reactivity to worse sleep random slopes. Each colored line represents an individual’s reactivity slopes. The sample average slope is the within-person (daily) estimates.
Results
The sample descriptives are presented in Table 1. The mean age was 41 years, and the sample was predominantly female (74%) and non-Hispanic White (66%). Bivariate correlations of key variables are presented in Supplementary Table 1. Intraclass correlations (ICCs) display the amount of variance attributed to between-person variability. The ICCs for daily sleep duration, sleep quality, number of pain locations, and pain severity were 0.30, 0.31, 0.50, and 0.39, respectively. Using two-part modeling to examine the presence of pain and pain severity simultaneously (Ruf et al., 2021), the empty model, which does not include any predictors but only the intercepts, revealed that individuals report an average of 4.39 on pain severity, and the mean probability of having no pain was 58.6%. Supplementary Figure 2 displays the frequency of daily pain occurrences and the pain severity ratings throughout the study.
Table 1.
Descriptive statistics of WFHS analytic sample (n = 311).
| Variable | Mean (SD) | n (%) |
|---|---|---|
| Age (Range = 21–63 years) | 41.38 (7.11) | |
| Male | 80 (25.72) | |
| Non-Hispanic White | 205 (65.92) | |
| Hispanic & People of Color | 106 (34.08) | |
| Education | ||
| Less Than High School Education & High School Education or Equivalent | 67 (21.54) | |
| Some College Education | 124 (39.87) | |
| College Graduate or More | 120 (38.59) | |
| Household Income (≥$60,000/year) | 195 (62.70) | |
| Unmarried | 78 (25.08) | |
| Nightshift Worker | 102 (32.80) | |
| Healthcare Worker | 180 (57.88) | |
| BMI (kg/m2) | 30.03 (7.32) | |
| Currently Smokes | 56 (18.01) | |
| Alcohol Problem | 12 (3.86) | |
| Physical Activity | 112 (36.01) | |
| Intervention Group | 159 (51.13) | |
| Sleep and Pain Variables | ||
| Sleep Duration (in hours, higher = longer) | 6.28 (1.40) | |
| Sleep Quality (higher=better) | 2.95 (0.77) | |
| Number of Pain Locations (Range = 0–4) | 1.02 (1.02) | |
| Pain Severity (Range = 1–10) | 4.18 (1.90) | |
| Outcome Variables | ||
| Has Chronic Condition(s) | 101 (32.48) | |
| Distress (Range = 6–28) | 11.97 (4.03) |
Note. WFHS = Work, Family, and Health Study.
Psychological distress
Tables 2 and 3 display the fully adjusted associations between pain reactivity to worse sleep and psychological distress. Supplementary Table 2 displays the unadjusted findings with psychological distress as the outcome. In the fully covariate adjusted model, higher pain severity reactivity to shorter sleep duration (B = 0.54, SE = 0.13, p < .001) and higher pain severity reactivity to worse sleep quality (B = 0.58, SE = 0.16, p < .001) were associated with more concurrent distress. In the growth curve models, pain reactivity to worse sleep was not associated with distress over time. However, the association between pain location reactivity to poorer sleep quality and distress (intercept: Path Est. = 1.42, posterior SD = 0.92, p = .051; change in distress: Path Est. = -0.63, posterior SD = 0.41, p = .057), as well as the association between pain severity reactivity to poorer sleep quality and the distress intercept reached trend-level statistical significance (intercept: Path Est. = 0.65, posterior SD = 0.32, p = .055).
Table 2.
Adjusted models for pain reactivity to sleep and psychological distress.
| Variable | Distress at enrollment |
Distress at 6 months |
Distress at 12 months |
Distress at 18 months |
||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Est. | SE | p | Est. | SE | p | Est. | SE | p | Est. | SE | p | |
| Pain Location Reactivity to Shorter Sleep Duration | ||||||||||||
| Pain reactivity | 0.87 | 45.67 | .985 | 0.52 | 0.77 | .497 | −0.15 | 0.79 | .848 | 0.10 | 0.89 | .906 |
| T2 on T1 Distress | — | — | — | 0.53 | 0.05 | <.001 | 0.55 | 0.06 | <.001 | 0.38 | 0.06 | <.001 |
| Pain location | 2.52 | 17.03 | .882 | 0.17 | 0.30 | .565 | 0.12 | 0.30 | .685 | 0.23 | 0.34 | .493 |
| Mean pain reactivity | 0.08 | 0.02 | <.001 | 0.09 | 0.02 | <.001 | 0.09 | 0.02 | <.001 | 0.09 | 0.02 | <.001 |
| Variance pain reactivity | 0.07 | 0.008 | <.001 | 0.09 | 0.009 | <.001 | 0.09 | 0.009 | <.001 | 0.09 | 0.009 | <.001 |
| Pain Location Reactivity to Poorer Sleep Quality | ||||||||||||
| Pain reactivity | 1.05 | 54.83 | .985 | 0.39 | 0.50 | .432 | −0.03 | 0.49 | .954 | −0.21 | 0.58 | .716 |
| T2 on T1 Distress | — | — | — | 0.52 | 0.06 | <.001 | 0.54 | 0.06 | <.001 | 0.37 | 0.06 | <.001 |
| Pain location | 2.21 | 33.75 | .948 | 0.12 | 0.31 | .708 | 0.09 | 0.31 | .763 | 0.21 | 0.34 | .532 |
| Mean pain reactivity | 0.16 | 0.03 | <.001 | 0.19 | 0.04 | <.001 | 0.19 | 0.04 | <.001 | 0.19 | 0.04 | <.001 |
| Variance pain reactivity | 0.20 | 0.02 | <.001 | 0.23 | 0.03 | <.001 | 0.23 | 0.03 | <.001 | 0.23 | 0.03 | <.001 |
| Pain Severity Reactivity to Shorter Sleep Duration | ||||||||||||
| Pain reactivity | 0.54 | 0.13 | <.001 | 0.19 | 0.25 | .449 | −0.04 | 0.25 | .880 | 0.09 | 0.28 | .756 |
| T2 on T1 Distress | — | — | — | 0.52 | 0.06 | <.001 | 0.55 | 0.06 | <.001 | 0.37 | 0.06 | <.001 |
| Pain severity | 1.21 | 0.23 | <.001 | 0.17 | 0.14 | .228 | −0.009 | 0.14 | .948 | 0.08 | 0.15 | .597 |
| Mean pain reactivity | 0.18 | 0.06 | .001 | 0.21 | 0.06 | .001 | 0.21 | 0.06 | .001 | 0.21 | 0.06 | .001 |
| Variance pain reactivity | 0.74 | 0.08 | <.001 | 0.85 | 0.09 | <.001 | 0.85 | 0.09 | <.001 | 0.85 | 0.09 | <.001 |
| Pain Severity Reactivity to Poorer Sleep Quality | ||||||||||||
| Pain reactivity | 0.58 | 0.16 | <.001 | 0.08 | 0.17 | .641 | 0.04 | 0.16 | .813 | −0.02 | 0.19 | .937 |
| T2 on T1 Distress | — | — | — | 0.51 | 0.06 | <.001 | 0.54 | 0.06 | <.001 | 0.37 | 0.07 | <.001 |
| Pain severity | 1.15 | 0.21 | <.001 | 0.17 | 0.14 | .233 | −0.01 | 0.14 | .942 | 0.07 | 0.16 | .636 |
| Mean pain reactivity | 0.40 | 0.09 | <.001 | 0.46 | 0.11 | <.001 | 0.46 | 0.11 | <.001 | 0.46 | 0.11 | <.001 |
| Variance pain reactivity | 1.87 | 0.20 | <.001 | 2.12 | 0.24 | <.001 | 2.12 | 0.24 | <.001 | 2.12 | 0.24 | <.001 |
Note. Est. = Path Estimate, SE = Standard Error. Statistically significant associations are bolded (p < .05).
Table 3.
Parameter estimates from the latent growth curve modeling analyses of linear change in psychological distress over time.
| Variables | Path Est. | Posterior SD | p |
|---|---|---|---|
| Pain Location Reactivity to Shorter Sleep Duration | |||
| Reactivity on Distress Intercept | −0.74 | 1.59 | .285 |
| Reactivity on Change in Distress | 0.27 | 0.66 | .327 |
| Average Pain Location on Distress Intercept | 0.70 | 0.53 | .115 |
| Average Pain Location on Change in Distress | −0.18 | 0.24 | .220 |
| Covariance (Intercept, Slope) | −0.35 | 0.53 | .243 |
| Pain Location Reactivity to Poorer Sleep Quality | |||
| Reactivity on Distress Intercept | 1.42 | 0.92 | .051 |
| Reactivity on Change in Distress | −0.63 | 0.41 | .057 |
| Average Pain Location on Distress Intercept | 0.16 | 0.55 | .389 |
| Average Pain Location on Change in Distress | −0.005 | 0.24 | .491 |
| Covariance (Intercept, Slope) | −0.002 | 0.67 | .497 |
| Pain Severity Reactivity to Shorter Sleep Duration | |||
| Reactivity on Distress Intercept | 0.09 | 0.50 | .440 |
| Reactivity on Change in Distress | −0.02 | 0.21 | .477 |
| Average Pain Location on Distress Intercept | 0.46 | 0.18 | .003 |
| Average Pain Location on Change in Distress | −0.10 | 0.09 | .120 |
| Covariance (Intercept, Slope) | −0.17 | 0.49 | .383 |
| Pain Severity Reactivity to Poorer Sleep Quality | |||
| Reactivity on Distress Intercept | 0.65 | 0.32 | .055 |
| Reactivity on Change in Distress | −0.14 | 0.15 | .180 |
| Average Pain Location on Distress Intercept | 0.42 | 0.21 | .025 |
| Average Pain Location on Change in Distress | −0.10 | 0.09 | .165 |
| Covariance (Intercept, Slope) | −0.54 | 0.60 | .140 |
Note. Est. = Path Estimate, SE = Standard Error. Statistically significant associations are bolded (p < .05).
Chronic conditions
Tables 4 and 5 display the fully adjusted associations between pain reactivity to worse sleep and the number of chronic conditions. Supplementary Table 3 displays the unadjusted findings with the number of chronic conditions as the outcome. There were no statistically significant associations with chronic conditions as the outcome.
Table 4.
Adjusted models of pain reactivity to sleep reactivity to continuous chronic conditions.
| Variables | Chronic Conditions at Enrollment |
Chronic Conditions at 6 months |
Chronic Conditions at 12 months |
Chronic Conditions at 18 months |
||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Est. | SE | p | Est. | SE | p | Est. | SE | p | Est. | SE | p | |
| Pain Location Reactivity to Shorter Sleep Duration | ||||||||||||
| Pain reactivity | 0.10 | 0.15 | .506 | −0.02 | 0.06 | .686 | −0.05 | 0.06 | .454 | 0.03 | 0.07 | .714 |
| T2 on T1 Chronic Conditions | — | — | — | 0.94 | 0.02 | <.001 | 1.00 | 0.03 | <.001 | 1.01 | 0.03 | <.001 |
| Pain location | 0.07 | 0.06 | .200 | −0.007 | 0.02 | .757 | 0.007 | 0.02 | .747 | −0.01 | 0.03 | .629 |
| Mean pain reactivity | 0.08 | 0.02 | <.001 | 0.08 | 0.02 | <.001 | 0.08 | 0.02 | <.001 | 0.08 | 0.02 | <.001 |
| Variance pain reactivity | 0.07 | 0.008 | <.001 | 0.07 | 0.008 | <.001 | 0.07 | 0.008 | <.001 | 0.07 | 0.008 | <.001 |
| Pain Location Reactivity to Poorer Sleep Quality | ||||||||||||
| Pain reactivity | 0.004 | 0.10 | .967 | 0.06 | 0.04 | .120 | 0.02 | 0.04 | .582 | 0.01 | 0.04 | .742 |
| T2 on T1 Chronic Conditions | — | — | — | 0.94 | 0.02 | <.001 | 1.00 | 0.03 | <.001 | 1.01 | 0.03 | <.001 |
| Pain location | 0.10 | 0.06 | .085 | 0.004 | 0.02 | .878 | 0.02 | 0.02 | .426 | 0.01 | 0.03 | .668 |
| Mean pain reactivity | 0.16 | 0.03 | <.001 | 0.16 | 0.03 | <.001 | 0.16 | 0.03 | <.001 | 0.16 | 0.03 | <.001 |
| Variance pain reactivity | 0.20 | 0.02 | <.001 | 0.20 | 0.02 | <.001 | 0.20 | 0.02 | <.001 | 0.20 | 0.02 | <.001 |
| Pain Severity Reactivity to Shorter Sleep Duration | ||||||||||||
| Pain reactivity a | −0.09 | 33.28 | .998 | −0.01 | 0.02 | .536 | −0.02 | 0.02 | .339 | −0.006 | 0.02 | .776 |
| T2 on T1 Chronic Conditions | — | — | — | 0.94 | 0.02 | <.001 | 1.00 | 0.03 | <.001 | 1.01 | 0.03 | <.001 |
| Pain severity | −0.16 | 15.96 | .992 | 0.004 | 0.009 | .685 | 0.006 | 0.009 | .507 | −0.006 | 0.01 | .602 |
| Mean pain reactivity | 0.19 | 0.06 | .001 | 0.18 | 0.06 | .001 | 0.18 | 0.06 | .001 | 0.18 | 0.06 | .001 |
| Variance pain reactivity | 0.74 | 0.08 | <.001 | 0.74 | 0.08 | <.001 | 0.74 | 0.08 | <.001 | 0.74 | 0.08 | <.001 |
| Pain Severity Reactivity to Poorer Sleep Quality | ||||||||||||
| Pain reactivity | Model did not converge | 0.006 | 0.01 | .623 | −0.001 | 0.01 | .957 | 0.01 | 0.01 | .410 | ||
| T2 on T1 Chronic Conditions | 0.94 | 0.02 | <.001 | 1.00 | 0.03 | <.001 | 1.01 | 0.03 | <.001 | |||
| Pain severity | 0.01 | 0.01 | .310 | 0.01 | 0.01 | .214 | 0.003 | 0.01 | .800 | |||
| Mean pain reactivity | 0.40 | 0.09 | <.001 | 0.40 | 0.09 | <.001 | 0.40 | 0.09 | <.001 | |||
| Variance pain reactivity | 1.89 | 0.20 | <.001 | 1.89 | 0.20 | <.001 | 1.89 | 0.20 | <.001 | |||
Note. Est. = Path Estimate, SE = Standard Error. Statistically significant associations are bolded (p < .05).
Due to convergence issues, these models removed the covariance statements between the chronic conditions with pain and the slope.
Table 5.
Parameter estimates from the latent growth curve modeling analyses of linear change in chronic conditions over time.
| Variable | Path Est. | Posterior SD | p |
|---|---|---|---|
| Pain Location Reactivity to Shorter Sleep Duration | |||
| Reactivity on Chronic Conditions Intercept | 0.006 | 0.16 | .483 |
| Reactivity on Change in Chronic Conditions | −0.007 | 0.03 | .407 |
| Average Pain Location on Chronic Conditions Intercept | 0.06 | 0.06 | .161 |
| Average Pain Location on Change in Chronic Conditions | 0.004 | 0.01 | .351 |
| Covariance (Intercept, Slope) | −0.003 | 0.007 | .307 |
| Pain Location Reactivity to Poorer Sleep Quality | |||
| Reactivity on Chronic Conditions Intercept | 0.04 | 0.10 | .334 |
| Reactivity on Change in Chronic Conditions | −0.01 | 0.03 | .356 |
| Average Pain Location on Chronic Conditions Intercept | 0.09 | 0.06 | .066 |
| Average Pain Location on Change in Chronic Conditions | −0.002 | 0.02 | .456 |
| Covariance (Intercept, Slope) | −0.02 | 0.007 | .010 |
| Pain Severity Reactivity to Shorter Sleep Duration | |||
| Reactivity on Chronic Conditions Intercept | −0.04 | 0.06 | .267 |
| Reactivity on Change in Chronic Conditions | 0.007 | 0.01 | .313 |
| Average Pain Location on Chronic Conditions Intercept | 0.04 | 0.02 | .060 |
| Average Pain Location on Change in Chronic Conditions | −0.004 | 0.007 | .277 |
| Covariance (Intercept, Slope) | −0.02 | 0.007 | .001 |
| Pain Severity Reactivity to Poorer Sleep Quality | |||
| Reactivity on Chronic Conditions Intercept | −0.006 | 0.03 | .426 |
| Reactivity on Change in Chronic Conditions | 0.001 | 0.006 | .420 |
| Average Pain Location on Chronic Conditions Intercept | 0.04 | 0.03 | .065 |
| Average Pain Location on Change in Chronic Conditions | 0.003 | 0.005 | .252 |
| Covariance (Intercept, Slope) | 0.00 | 0.006 | .493 |
Note. Est. = Path Estimate, SE = Standard Error. Statistically significant associations are bolded (p < .05).
Sensitivity analyses
Supplementary Table 4 displays the sensitivity analyses controlling for all covariates, sleep medication, and pain medication use. For cross-sectional associations at enrollment, higher pain severity to poorer sleep quality was associated with more distress (B = 0.69, SE = 0.16, p < .001), and higher pain severity reactivity to shorter sleep duration was associated with fewer concurrent chronic conditions (B = −0.05, SE = 0.03, p = .039). In the growth curve models, higher pain location reactivity to poorer sleep quality was associated with higher concurrent distress and a decrease in distress over time (intercept: Path Est. = 2.13, posterior SD = 0.91, p = .004; change in distress: Path Est. = −0.85, posterior SD = 0.41, p = .018). Higher pain location reactivity to poorer sleep quality was associated with fewer chronic conditions (change in distress: Path Est. = −0.04, posterior SD = 0.02, p = .017). Higher pain severity reactivity to poorer sleep quality was associated with higher concurrent distress (intercept: Path Est. = 0.64, posterior SD = 0.30, p = .024).
Supplementary Tables 5 and 6 display additional stratified analyses where associations are stratified by those equal to or less than 40 years of age compared to those over the age of 40 years. The cut point was close to the mean (41.4 years) and median (42 years) of the sample. Fewer statistically significant associations were found, which may be caused by the reduced sample sizes when exploring stratified analyses by age; however, similar patterns were detected. For adults aged 40 years or younger, higher pain severity reactivity to poorer sleep quality was associated with more distress (Path Est. = 0.96, posterior SD = 0.56, p = .036). For adults over the age of 40 years, higher pain location reactivity to shorter sleep duration (B = 1.16, SE = 0.14, p < .001) and higher pain location reactivity to poorer sleep quality were associated with higher concurrent distress (B = 0.99, SE = 0.14, p < .001). Among those 40 years or older, there were no statistically significant longitudinal associations detected.
Supplementary Table 7 displays the results from fully covariate-adjusted models controlling for between-person sleep duration and sleep quality simultaneously. In the growth curve model, higher pain severity reactivity to poorer sleep quality was associated with more concurrent distress (Path Est. = 0.56, posterior SD = 0.27, p < .001). No other associations were statistically significant.
Discussion
This study captured the consequences of the sleep and pain interplay by modeling the effects of pain reactivity to worse sleep on health outcomes. Higher pain reactivity to poorer sleep quality was associated with a greater level of concurrent distress. There were no associations between pain reactivity to worse sleep and change in distress or the number of chronic conditions. However, after controlling for sleep and pain medication use, contrary to expectations, higher pain location reactivity to poorer sleep quality was associated with a decrease in distress and fewer chronic conditions over time. These findings offer some support for the Middle-Range Theory of Unpleasant Symptoms, which postulates that overlapping and co-occurring symptoms have synergistic effects beyond their independent effects (Lenz et al., 1997).
Before examining the main associations between pain reactivity to worse sleep and health outcomes, this study calculated the amount of variability in sleep and pain over time, as well as tested associations between sleep and pain. ICCs of daily sleep duration, sleep quality, number of pain locations, and pain severity suggest that there is significant within-person variability over time. At the between-person and within-person levels, shorter sleep duration and poorer sleep quality were associated with more pain locations and greater pain severity (see Figure 2 for within-person estimates). By determining the amount of variance at the within-person level and testing independent associations of sleep and pain with each other and health outcomes, this study displayed consistent evidence that sleep and pain are associated and change over time (Alsaadi et al., 2014; Anderson & Holliday, 2023; Edwards et al., 2008; Lücke et al., 2023; Mu & Lee, 2024; Mun et al., 2019).
Consistent with previous literature on sleep reactivity to stress, stressor reactivity to shorter sleep, and negative affective reactivity to stress (Drake et al., 2014; Piazza et al., 2013; Vigoureux et al., 2020), we found that higher pain severity reactivity to shorter sleep was associated with more concurrent psychological distress. Higher pain reactivity to worse sleep suggests that an individual is eliciting a greater pain response to shorter or poorer sleep, which may trigger physiological increases in stress-related hormones like inflammatory biomarkers (e.g., C-reactive protein), cortisol, and substance P (neuropeptide responsible for the transmission of pain) (DeVon et al., 2014; Hannibal & Bishop, 2014). The allostatic load theory suggests that repeated exposure to chronic stressors (e.g., short sleep and pain) adversely influences overall health (McEwen, 1998). Stress and inflammatory pathways may be underlying the association between pain reactivity to worse sleep and greater psychological distress.
Contrary to expectations, after controlling for sleep and pain medication use, this study also found that higher pain location reactivity to poorer sleep quality was associated with decreases in distress and fewer chronic conditions over time. These findings may be driven by unaccounted confounders. For instance, individuals with higher pain location reactivity may engage in coping strategies that help them manage their levels of distress and improve their health over time. Alternatively, these findings may have emerged due to loss of follow-up in the longitudinal analyses or influenced by the general trend that distress levels were reduced over the study. After controlling for both between-person sleep duration and sleep quality in each model, this finding was no longer statistically significant (see Supplementary Table 7).
Unlike the associations found with psychological distress, there was a lack of associations between pain reactivity to shorter sleep and the number of chronic conditions. WFHS assessed more everyday pain instances that, although common among working middle-aged and older adults, are also more trivial when determining long-term health consequences. Other pain locations, such as chest or joint pain, which reflect major and common age-related pain regions, are linked to serious chronic conditions, and are associated with a higher risk of disability, may yield different results (Haasenritter et al., 2015; Malik et al., 2018). Future directions are to examine the influence of pain reactivity to worse sleep health using a longer follow-up time and assessing more comprehensive measures of sleep and pain.
Strengths and implications
WFHS was selected for this study because it offers rich daily diary data. Daily life has important implications for long-term health and well-being. Daily sleep, affect, and stress responses are associated with long-term health outcomes, such as chronic conditions, physical symptoms, inflammation, and mental health disorders (Lee, 2022; Piazza et al., 2013; Sliwinski et al., 2009; Sun et al., 2023; Vigoureux et al., 2020). By studying daily experiences and long-term outcomes, researchers may identify points of intervention that could prevent or delay the onset of more serious health conditions that increase with age.
Both sleep and pain problems have been at the forefront of public policy initiatives like Healthy People 2030 and the National Institutes of Health: The Helping to End Addiction Long-term Initiative (NIH HEAL). Healthy People seeks to improve sleep health among adults, and the NIH HEAL focuses on addressing the opioid crisis through research and community (Healthy People, 2020; National Institutes of Health, 2024). In line with these missions, this preliminary work may inform and inspire future work and interventions focused on identifying high-risk individuals and creating personalized interventions that concurrently treat sleep and pain.
Limitations and future directions
Despite the strengths, this study has limitations that can guide future research. First, studying the interplay between sleep and pain is challenging to model because of its dynamic nature and the spontaneity of pain. Although eight consecutive days of daily diary is a significant strength of this study, the covariance between daily sleep and pain was fairly weak within this sample. Future studies may want to explore longer data collection periods to increase statistical power and allow for more opportunities for sleep and pain to emerge.
Psychological distress is closely related to and a potential mediator of sleep and pain (Anderson & Holliday, 2023). In this study, we examined psychological distress as an outcome. Without multiple data points, we are unable to untangle whether there was reverse causality between sleep, pain, and psychological distress. Future directions of this work are to examine temporal pathways and potential mediators connecting pain reactivity to health and well-being outcomes (e.g., stress pathways).
Finally, our sample was relatively healthy. Healthy volunteer bias describes how participants tend to be healthier and more health-conscious because there may be fewer barriers to their participation. The consequence of pain reactivity to shorter sleep duration may not be a prevalent issue among relatively healthy adults. Future research may find greater variability in pain reactivity among clinical samples who frequently encounter sleep and pain problems. More research on pain reactivity to sleep over time and across diverse populations is needed to better understand this phenomenon.
Conclusion
A contribution of this study is the exploration of pain reactivity to worse sleep and its association with psychological distress and the number of chronic conditions. This study focused on a person-specific approach, which diverges from the current traditional variable-centered approaches for studying the interplay between sleep and pain. Higher pain reactivity to worse sleep, specifically higher pain severity reactivity to shorter sleep duration and higher pain severity reactivity to poorer sleep quality, was associated with more concurrent psychological distress but not distress over time. After controlling for sleep and pain medication use, results remained consistent except contrary to expectations, higher pain location reactivity to poorer sleep quality was also associated with a decrease in distress and fewer chronic conditions over time. Future directions of this research are to examine multidimensional measures of sleep health and pain, explore potential causal pathways between sleep and pain on health outcomes over time, and include more diverse samples who frequently experience sleep and pain problems.
Supplementary Material
Acknowledgments
The authors would like to thank the Work, Family and Health Network study team and its participants for their contributions to the research. Thank you to the anonymous reviewers and editor of the journal for improving the quality and rigor of this manuscript.
Contributor Information
Christina X Mu, Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, California, United States.
Brent J Small, School of Nursing, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States.
Christina S McCrae, Sleep and Health Innovations in Neurobehavioral (SHINE) Science Center, College of Nursing, University of South Florida, Tampa, Florida, United States.
Lindsay J Peterson, School of Aging Studies, University of South Florida, Tampa, Florida, United States.
Ross Andel, Edson College of Nursing and Health Innovation, Arizona State University, Tempe, Arizona, United States; Memory Clinic, Department of Neurology, Charles University, Second Faculty of Medicine and Motol University Hospital, Prague, Czech Republic.
Katie L Stone, Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, California, United States; Research Institute, California Pacific Medical Center, San Francisco, California, United States.
Soomi Lee, Department of Human Development and Family Studies, Center for Healthy Aging, The Pennsylvania State University, University Park, Pennsylvania, United States.
Supplementary material
Supplementary data are available at The Journals of Gerontology, Series B: Psychological Sciences and Social Sciences online.
Funding
This work was supported by the National Heart, Lung, and Blood Institute (F31HL165898 to C.X.M.) and the National Institute on Aging (T32AG049663 to C.X.M.) of the National Institutes of Health (NIH). Outside of the current work, S.L. received grants from NIH (R56AG065251, R01HL163226). The content is solely the responsibility of the authors and does not necessarily represent the official views of NIH. Work, Family and Health Network (WFHN; www.WorkFamilyHealthNetwork.org) is funded by a cooperative agreement through the National Institutes of Health and the Centers for Disease Control and Prevention: Eunice Kennedy Shriver National Institute of Child Health and Human Development (Grant # U01HD051217, U01HD051218, U01HD051256, U01HD051276); National Institute on Aging (Grant # U01AG027669); the National Heart, Lung and Blood Institute (R01HL107240); Office of Behavioral and Science Sciences Research, and National Institute for Occupational Safety and Health (Grant # U01OH008788, U01HD059773). Grants from the William T. Grant Foundation, Alfred P Sloan Foundation, and the Administration for Children and Families have provided additional funding. The contents of this publication are solely the responsibility of the authors and do not necessarily represent the official views of these institutes and offices.
Conflict of interest
None declared.
Data availability
Data from the Work, Family and Health Study are made publicly available online: https://workfamilyhealthnetwork.org. This study was not preregistered.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
Data from the Work, Family and Health Study are made publicly available online: https://workfamilyhealthnetwork.org. This study was not preregistered.