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. 2025 Oct 11;9(11):igaf113. doi: 10.1093/geroni/igaf113

Back pain precedes sleep problems in older men

Soomi Lee 1,✉, T Muhammad 2, Eric J Roseen 3, David T McNaughton 4, Christina X Mu 5, Cecilie Krage Øverås 6,7, Hazel Jenkins 8, Casper Nim 9,10, James J Young 11,12, Howard A Fink 13,14, Kristine E Ensrud 15, David M Almeida 16, Brent J Small 17, Peggy M Cawthon 18,19, Katie L Stone 20,21
Editor: Michelle Putnam
PMCID: PMC12659964  PMID: 41322105

Abstract

Background and Objectives

While cross-sectional associations between any pain and sleep problems have been established, longitudinal studies examining the temporal relationship between back pain and multidimensional sleep health remain limited. We evaluated whether the association between back pain and sleep problems was bidirectional in older men aged 65 years and above.

Research Design and Methods

Data came from the Osteoporotic Fractures in Men Study with a sample of 1,055 older men who completed 2 clinical sleep visits. A composite sleep problems score was created using self-report and actigraphy data reflecting irregularity, dissatisfaction, lack of daytime alertness, suboptimal timing, inefficiency, and suboptimal duration. Participants were queried by mail about back pain every 4 months, and we calculated the prevalence of any, frequent, severe, and activity-limiting back pain around their 2 sleep visits. Cross-lagged panel models estimated bidirectional associations between sleep problems and subsequent back pain, and vice versa, over 6 years.

Results

Multivariable-adjusted results showed that having any back pain, frequent back pain, severe back pain, and activity-limiting back pain predicted 12%–25% greater sleep problems 6 years later (Exp(β) = 1.12; 95% confidence interval [CI] = 1.03–1.21 to Exp(β) = 1.25; 95% CI = 1.05–1.48), but sleep problems did not predict subsequent back pain.

Discussion and Implications

This study highlights the long-term temporal directionality of the association between back pain and sleep problems in older men. Back pain preceded more sleep problems, but an inverse association was not observed. Our findings suggest that interventions targeting back pain may help decrease sleep problems in older men and warrant further investigation into potential mechanisms.

Keywords: Sleep health composite, Chronic pain, Temporal directionality

Innovation and Translational Significance:

In a U.S. community-dwelling sample of older men, back pain preceded sleep problems but not vice versa. Having back pain (any, frequent, severe, or activity-limiting) predicted a 12%–25% greater sleep problems 6 years later, independent of other risk factors and baseline sleep problems. The sleep problems predicted by prior back pain were mainly related to poor satisfaction with sleep and suboptimal sleep timing. Given the well-established links between sleep problems, adverse aging outcomes, and increased mortality risk, interventions aimed at managing back pain in older men may also help improve sleep health.

Back pain is the leading cause of disability globally (Ferreira et al., 2023; Hartvigsen et al., 2018). Approximately 40% of adults aged 18 years and over report experiencing back pain in the past 3 months, and the prevalence increases with advancing age (Lucas et al., 2021; McNaughton et al., 2024). Back pain is conceptualized within a biopsychosocial model in that the symptom experience may be associated with several biological, psychological, and social factors (Otero-Ketterer et al., 2022). Further salient factors reflecting core lifestyle behaviors, such as physical activity, nutrition, and sleep, play an important role in the expression and severity of back pain (Chang et al., 2022; Mahdavi et al., 2021; Silva et al., 2024). In particular, sleep problems commonly co-occur with back pain in late adulthood (Alsaadi et al., 2011; Roseen et al., 2020). Thus, understanding the direction and strength of the associations between sleep and back pain is of clinical importance.

While the association between sleep and any pain has been documented cross-sectionally, a lack of longitudinal studies limits our understanding of temporal relationships between pain and poor sleep in older adults (T.-Y. Chen et al., 2019; Morelhão et al., 2022; Runge et al., 2022). If sleep problems contribute to the subsequent onset of back pain and worsening symptoms, early sleep interventions may help reduce the risk of back pain. Conversely, if back pain precedes and contributes to the onset of sleep problems, effective pain management may be critical for maintaining good sleep and preventing broader functional decline in older adults.

Furthermore, older adults may experience diverse sleep problems, but most studies assess only a limited set of sleep dimensions (e.g., sleep duration alone or sleep quality alone). Even in the absence of a clinical sleep disorder, individuals may still experience sleep problems, and optimal to suboptimal sleep health should be understood across multiple dimensions encompassing regularity, satisfaction, alertness, timing, efficiency, and duration (Ru-SATED) (Buysse, 2014). This multidimensional sleep health framework is important for capturing the full range of sleep problems in older adults and for identifying strategies to reduce related health risks, such as back pain.

Finally, while back pain is prevalent in older adults, little is known about the specific manifestations of back pain in this population and their associations with sleep. Examining multiple measures of back pain (e.g., presence, frequency, severity, and activity limitations) may enhance the sensitivity of detecting associations with sleep, which may differ in both the size and direction of the effect. For example, the onset of new back pain may subsequently increase sleep problems, such as poor sleep quality and short or long sleep duration (Amtmann et al., 2020; Runge et al., 2022). Alternatively, the accrual of more sleep problems may increase the frequency and likelihood of developing disabling or activity-limiting back pain (Finan et al., 2013).

The current study sought to overcome the identified issues by examining the temporal associations between different measures of back pain and a multidimensional sleep health composite in a community-dwelling, longitudinal sample of older men. We hypothesized bidirectional relationships; more baseline back pain would predict more sleep problems at follow-up, while more baseline sleep problems would predict more back pain at follow-up approximately 6 years later. We explored potential differences in these associations based on specific back pain measures, including any back pain, frequent back pain, and severe or activity-limiting back pain.

Method

Data

Data originated from the Osteoporotic Fractures in Men Study (MrOS), a prospective cohort study of older men (age ≥ 65 years) in the United States. The MrOS study was designed predominantly to study risk factors for fractures and osteoporosis in men (see Orwoll et al., 2005 for more details of the design of the study). At the MrOS baseline (2000–2002), 5,994 older men were recruited from six geographically dispersed clinical centers (Birmingham, AL; Minneapolis, MN; Palo Alto, CA; Pittsburgh, PA; Portland, OR; San Diego, CA). Eligibility required participants to walk independently (with or without a gait aid) and not have a bilateral hip replacement. Participants were invited to complete an ancillary sleep visit between 2003 and 2005 (target n = 3,000). Non-participants in this ancillary sleep study were excluded based on death, withdrawal from the parent study, ineligibility, recruitment targets having been met, or refusal to participate. The details of the inclusion/exclusion criteria for sleep visit 1 have been published previously (Blackwell et al., 2011; Blank et al., 2005).

During sleep visit 1, participants completed questionnaires assessing perceived sleep quality and sleepiness. Participants were also asked to wear an actigraph on their nondominant wrist for a minimum of five consecutive 24-hr periods following the visit. The actigraph (SleepWatch-O, Ambulatory Monitoring, Inc., Ardsley, NY) detects movement via a piezoelectric bimorph-ceramic cantilever beam, with voltages recorded and summarized into 1-min epochs. Participants completed sleep diaries, which provided information on bedtimes, wake times, and when the actigraph was removed. This data were used to cross-check the actigraphy files, set sleep intervals, and exclude non-wear periods (Blackwell et al., 2011). Participants from sleep visit 1 (n = 3,135) were re-contacted and invited for a second sleep study visit between 2009 and 2012 if they had complete polysomnography and actigraphy data from the initial visit (target n = 1,000), and a total of 1,055 participants returned for the second sleep visit.

Participants were surveyed by mail tri-annually (every 4 months) about back pain throughout the MrOS study. For the current study, the timepoints for back pain data were chosen to align with an approximately 6-year gap between the participants’ two clinical sleep visits (see Supplementary Figure 1). Ninety-two participants were excluded due to missing data on back pain (n = 56), sleep (n = 9), and/or other covariates (n = 27), leaving a total analytical sample of 963 older men. The selection of participants for sleep visits and the sample selection process for this study are illustrated in Figure 1.

Figure 1.

This figure presents the study sample selection for MrOS. It shows the initial recruited cohort, the sequential exclusion of participants based on eligibility criteria, and the final analytic sample included in the study.

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Study sample and exclusion flow.

Measures

Back pain

We utilized four binary measures to capture back pain: any, frequent, severe, and activity-limiting back pain. In each triannual questionnaire, participants were asked, “Have you experienced any back pain in the past four months?” Responses were coded as yes or no. Having any back pain was defined as any report of back pain in the triannual questionnaires within 1 year, while frequent back pain was defined as three consecutive reports of back pain. For severe back pain or activity-limiting back pain, participants were asked, “In the past four months, how many days did you have severe back pain?” and “In the past four months, how many days did you cut down on things you usually do because of back pain?” Responses were 0 days (=0), 1–7 days (=1), 8–14 days (=2), and more than 14 days (=3). We aggregated the triannual responses to yearly reports of severe or activity-limiting back pain, imputing a value of 0 for missing responses from participants who did not report any back pain during the year. Consequently, possible scores of severe back pain and activity-limiting back pain each year ranged from 0 to 9. To aid in the clinical interpretation of our results, we further dichotomized the scores following previous research (Dworkin et al., 2008), such that those who scored 2 or more points were classified as having severe back pain or activity-limiting back pain within the 1-year period. Additionally, we employed a cut-off score of 4 or more for conducting sensitivity analysis.

Sleep problems

We assessed the number of sleep problems using the Ru-SATED dimensions of sleep health, as defined in Table 1. Regularity, timing, efficiency, and duration dimensions were measured by actigraphy, while satisfaction and alertness were measured by self-report using standardized questionnaires. We constructed binary indicators of poor sleep in each dimension, that is, sleep irregularity (greater than 1 SD of average sleep midpoint [the time halfway between the sleep onset and the sleep offset]) (Lee & Lawson, 2021), sleep dissatisfaction (captured through either bad/very bad sleep quality or having sleep disturbances at least once a week over the past month from the Pittsburgh Sleep Quality Index) (Buysse et al., 1989), poor daytime alertness (over the past month, measured by Epworth Sleepiness Scale, >10) (Johns, 1991), suboptimal sleep timing (average sleep midpoint ≤2 a.m. or >4 a.m.) (Wallace et al., 2018), inefficient sleep (wake after sleep onset ≥90 min/night; with supplemental analyses using sleep efficiency <85%, defined as total sleep time divided by time in bed) (Smagula et al., 2014), and suboptimal sleep duration (<6 hr or >9 hr, with supplemental analyses excluding long sleep duration due to its low prevalence in this sample) (Lee & Lawson, 2021). Then we summed the scores of six binary indicators (0 = not having the condition, 1 = having the poor sleep condition), and a higher score represented more sleep problems (range 0–6).

Table 1.

The Ru-SATED sleep health dimensions, definition, and cut-point.

Dimension Description Cut-point
Irregular sleep (Lee & Lawson, 2021) Higher SD of average sleep midpoint (the time midway between sleep onset and waking) > mean + 1SD
Sleep dissatisfaction (Buysse et al., 1989) Pittsburgh Sleep Quality Index, items on poor sleep quality or sleep disturbances over the past month Bad/very bad sleep quality or having sleep disturbances at least once a week
Poor daytime alertness (Johns, 1991) Higher score on Epworth Sleepiness Scale (range 0–24) >10
Suboptimal sleep timing (Wallace et al., 2018) Early or late average sleep midpoint (averaged across 7-days) ≤2 a.m. or >4 a.m.
Inefficient sleep (Smagula et al., 2014) Long wake after sleep onset ≥90 min/night
Suboptimal sleep duration (Lee & Lawson, 2021) Short or long total sleep time average (averaged across 7 days) <6 hr or >9 hr
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Note. Ru-SATED = Regularity, Satisfaction, Alertness, Timing, Efficiency and Duration; SD = standard deviation.

Covariates

We included sociodemographic and health-related covariates based on the existing literature linking physical pain to sleep problems and vice versa (Afolalu et al., 2018; Canever et al., 2023; T.-Y. Chen et al., 2019; Morelhao et al., 2020; Tsai et al., 2022). Most covariates were time-varying and measured at each sleep visit, except race/ethnicity, education, and marital status obtained from the main baseline MrOS visit. Beginning with sociodemographic covariates, age was treated as a continuous variable, measured in years. Two categories were created for race/ethnicity: non-Hispanic White individuals and Hispanic and non-White individuals (e.g., Black/African American and Asian participants). Education level was categorized into college graduate versus less. Current marital status was grouped into men who reported being currently married versus others (widowed, separated, divorced, or never married). Enrollment site (Birmingham, Minneapolis, Pittsburgh, Portland, Palo Alto, and San Diego) was also included.

For health-related covariates, body mass index was calculated as weight in kg divided by height in square meters. Smoking history was categorized into ever (current or past) and never. Current alcohol consumption was categorized into 0, 1–3, and >3 drinks per week. Self-reported physical activity was measured using the Physical Activity Scale for the Elderly (PASE) questionnaire (Washburn et al., 1993), which includes 12 items covering various domains of physical activity. Each activity was weighted, and the activities were summed to compute the total PASE score, ranging from 0 to 400. Several chronic conditions were measured based on self-reported physician diagnoses of seven chronic conditions (hypertension, diabetes, heart attack, congestive heart failure, chronic obstructive pulmonary disease, Parkinson’s disease, and stroke); we analyzed this as a count variable. Depressive symptoms were measured using the Geriatric Depression Scale-15 (Almeida & Almeida, 1999; Sheikh & Yesavage, 2014) (≤2 as minimal depressive symptoms, 3–5 as some depressive symptoms, and ≥6 as clinically relevant depressive symptoms [Paudel et al., 2008]). Cognitive function was not an exclusion criterion in MrOS; however, it was assessed using the Modified Mini-Mental State Examination (3MS) to characterize the study population. The scores range from 0 to 100, with higher scores representing better cognitive functioning (Teng & Chui, 1987). Our sample had high cognitive functioning, on average (see Table 2). Fall history was measured using self-reports, where participants indicated their experience of a fall in the past year during the sleep visits. Participants were also asked to bring all the medications they had taken in the past 30 days. Both prescription and over-the-counter drugs were recorded in an electronic database, with each medication’s ingredients matched using the Iowa Drug Information Service Drug Vocabulary from the College of Pharmacy, University of Iowa, Iowa City, IA (Pahor et al., 1994). The recorded medications were categorized into antidepressants, benzodiazepines, sedatives/hypnotics, and sleep medications.

Table 2.

Baseline characteristics of older men by back pain measures.

Variables Total sample (N = 963) Any back pain (n = 453, 47.04%) Frequent back pain (n = 209, 21.70%) Severe back pain (n = 299, 31.05%) Activity-limiting back pain (n = 280, 29.08%)
Sleep problems composite score (0–6), M (SD) 1.47 (1.20) 1.62 (1.22) 1.69 (1.23) 1.70 (1.25) 1.66 (1.23)
Irregular sleep, n (%) 293 (30.43) 137 (30.24) 67 (32.06) 93 (31.1) 88 (31.43)
Sleep dissatisfaction, n (%) 135 (14.02) 72 (15.89) 35 (16.75) 52 (17.39) 44 (15.71)
Poor daytime alertness, n (%) 247 (25.65) 112 (24.72) 52 (24.88) 78 (26.09) 71 (25.36)
Suboptimal sleep timing, n (%) 244 (25.34) 116 (25.61) 56 (26.79) 80 (26.76) 73 (26.07)
Inefficient sleep, n (%) 131 (13.6) 76 (16.78) 34 (16.27) 50 (16.72) 49 (17.5)
Suboptimal sleep duration, n (%) 364 (37.8) 220 (48.57) 109 (52.15) 155 (51.84) 140 (50)
Age at sleep visit 1, M (SD) 74.53 (4.6) 74.32 (4.41) 73.87 (4.21) 73.86 (4.15) 73.86 (4.22)
Race/ethnicity (non-Hispanic White), n (%) 852 (88.47) 405 (89.4) 187 (89.47) 269 (89.97) 252 (90)
Education level (college-graduate), n (%) 594 (61.68) 279 (61.59) 116 (55.5) 174 (58.19) 160 (57.14)
Marital status (currently married), n (%) 844 (87.64) 395 (87.2) 183 (87.56) 265 (88.63) 250 (89.29)
Body mass index (kg/m2), M (SD) 27.2 (3.72) 27.47 (3.93) 27.21 (3.56) 27.61 (3.91) 27.53 (3.84)
Smoking status, current/past (yes), n (%) 553 (57.42) 255 (56.29) 135 (64.59) 182 (60.87) 175 (62.5)
Current alcohol consumption, n (%)
 0 drink/week 292 (30.32) 133 (29.36) 68 (32.54) 90 (30.1) 89 (31.79)
 1–2 drinks/week 273 (28.35) 124 (27.37) 53 (25.36) 78 (26.09) 67 (23.93)
 3+ drinks/week 398 (41.33) 196 (43.27) 88 (42.11) 131 (43.81) 124 (44.29)
Physical activity (PASE score), M (SD) 157.36 (68.71) 155.86 (64.76) 151.15 (63.44) 155.39 (63.91) 155.62 (64.4)
Number of chronic conditions,a  M (SD) 0.8 (0.83) 0.83 (0.85) 0.85 (0.8) 0.86 (0.86) 0.86 (0.87)
Depressive symptoms (GDS-15), n (%)
 Minimal depressive symptoms (≤2) 798 (82.87) 351 (77.48) 155 (74.16) 224 (74.92) 211 (75.36)
 Some depressive symptoms (3–5) 126 (13.08) 77 (17) 39 (18.66) 56 (18.73) 53 (18.93)
 Clinically significant depressive symptoms (≥6) 39 (4.05) 25 (5.52) 15 (7.18) 19 (6.35) 16 (5.71)
Cognitive function (3MS, 0–100), M (SD) 94.09 (4.71) 94.01 (4.84) 93.84 (4.82) 93.95 (4.62) 93.94 (4.58)
Fall history (yes), n (%) 254 (26.38) 149 (32.89) 73 (34.93) 100 (33.44) 95 (33.93)
Medication use, n (%)
 Antidepressantsb 65 (6.75) 37 (8.17) 18 (8.61) 28 (9.36) 25 (8.93)
 Benzodiazepines 34 (3.53) 22 (4.86) 10 (4.78) 13 (4.35) 12 (4.29)
 Sedatives/hypnotics 18 (1.87) 11 (2.43) 3 (1.44) 8 (2.68) 6 (2.14)
 Sleep medications 99 (10.28) 52 (11.48) 20 (9.57) 36 (12.04) 32 (11.43)
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Note. Any back pain refers to having back pain in any of the triannual questionnaires; frequent back pain refers to three consecutive reports of back pain across the tri-annual questionnaires administered within 1 year prior to baseline sleep visit; severe back pain refers to having clinically significant severe back pain (a score of 2+ on a scale of 0–9); activity-limiting back pain refers to having clinically significant activity-limiting back pain (a score of 2+ on a scale of 0–9).

a

Self-reported physician diagnosis of chronic conditions that include hypertension, diabetes, heart attack, congestive heart failure, chronic obstructive pulmonary disease, Parkinson’s disease, stroke.

b

Antidepressants include tricyclics, selective serotonin reuptake inhibitors, or monoamine oxidase inhibitors.

SD = standard deviation; PASE = Physical Activity Scale for the Elderly; GDS = Geriatric Depression Scale; 3MS = Modified Mini-Mental State Examination.

Statistical analysis

We used descriptive statistics to summarize the baseline characteristics of the sample. An autoregressive cross-lagged panel model (CLPM) (Selig & Little, 2012) approach was used to estimate bidirectional prospective associations between a composite sleep problems score and several dichotomized back pain measures (any, frequent, severe, activity-limiting) over a 6-year follow-up period. Sociodemographics, body mass index, smoking status, alcohol consumption, physical activity, chronic conditions, depressive symptoms, cognitive function, fall history, and relevant medication use were added as exogenous variables to both sleep problems and back pain variables at sleep visit 1. We used fully-adjusted models to account for the potential influence of the covariates in the sleep problems–back pain relationship. Additionally, to examine whether observed associations between back pain measures and subsequent sleep problems were also present for individual sleep dimensions, we conducted further analyses using separate CLPMs. In these analyses, individual binary variables representing poor sleep for each sleep health dimension were used as predictors or outcomes for different back pain variables. Model fit was evaluated using well-established criteria: nonsignificant chi-square value, comparative fit index ≥0.95, root mean square error of approximation ≤0.05, and the standardized root mean square residual ≤0.08 (Kline, 2010). Detailed fit indices are reported in Supplementary Table 3. Stata version 18.0 was used for statistical analyses.

Results

Descriptive results

Table 2 displays baseline characteristics of our sample by back pain measures. The mean ± standard deviation age was 74.53 ± 4.6 years. Approximately 88% were non-Hispanic White. Forty-seven percent of participants reported having any back pain during the previous year, with 22% reporting frequent back pain, 31% reporting severe back pain, and 29% reporting activity-limiting back pain. Over 30% of the sample had irregular sleep, 14% expressed dissatisfaction with their sleep, 26% experienced poor daytime alertness, and 25% had either early or late sleep timing. Additionally, 14% had inefficient sleep, and 38% had either short or long sleep duration (only 0.61% slept >9 hr). Overall, the mean sleep problems score was higher among individuals who reported any (M = 1.62, SD = 1.2), frequent (M = 1.69, SD = 1.2), severe (M = 1.70, SD = 1.3), or activity-limiting back pain (M = 1.66, SD = 1.2).

Prospective associations between back pain and sleep problems

Figure 2A–D illustrates the estimates from the fully-adjusted CLPMs examining the associations between back pain and sleep problems across the two timepoints over 6 years. All models demonstrated a good fit according to the model fit criteria.

Figure 2.

This figure illustrates cross-lagged panel models testing bidirectional associations between sleep problems and four back pain measures (any, frequent, severe, and activity-limiting).

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Illustration of cross-lagged panel models of the relationships between sleep problems and back pain measures.

Note. (A) For any back pain; (B) frequent back pain; (C) severe pain; (D) activity-limiting back pain. Solid arrows indicate significant paths, and dotted arrows indicate nonsignificant paths; Models are adjusted for sociodemographics, body mass index, smoking, alcohol, physical activity, chronic conditions, depressive symptoms, cognitive function, fall history, and relevant medication use; any back pain refers to having back pain in any of the triannual questionnaires; frequent back pain refers to three consecutive reports of back pain across the tri-annual questionnaires; severe back pain refers to having clinically significant severe back pain (a score of 2+ on a scale of 0-9); activity-limiting back pain refers to having clinically significant activity-limiting back pain (a score of 2+ on a scale of 0–9); T1, 2003–2005; T2, 2009–2012. *p < .05. **p < .01. ***p < .001.

Any back pain

For any back pain (Figure 2A), the autoregressive paths (see the parallel arrows) were statistically significant. That is, having any back pain at sleep visit 1 (β = 0.62, 95% confidence interval [CI]: 0.56, 0.67; Exp(β) = 1.86) was positively associated with any back pain at sleep visit 2, and having more sleep problems at sleep visit 1 (β = 0.43, 95% CI: 0.37, 0.48; Exp(β) = 1.54) was also positively associated with more sleep problems at sleep visit 2.

Our main interest was the cross-lagged paths (see the diagonal arrows), and these indicated that the path linking any back pain at sleep visit 1 and sleep problems at sleep visit 2 was statistically significant. Participants reporting any back pain at sleep visit 1 showed a 12% increase in sleep problems 6 years later (β = 0.11, 95% CI: 0.03, 0.19; Exp(β) = 1.12). The other cross-lagged path linking sleep problems at sleep visit 1 and any back pain at sleep visit 2 was not statistically significant, as indicated by the dotted diagonal arrow (β = 0.02, 95% CI: −0.02, 0.05). Supplementary Table 1 shows the effects of all covariates.

Frequent back pain

Frequent back pain (Figure 2B) at sleep visit 1 was significantly associated with more sleep problems at sleep visit 2 (β = 0.16, 95% CI: 0.01, 0.32; Exp(β) = 1.17). That is, consecutive reports of back pain every 4 months throughout a year were associated with a 17% increase in sleep problems 6 years later. However, the opposite temporal direction linking sleep problems at sleep visit 1 to frequent back pain at sleep visit 2 was not statistically significant (β = 0.01, 95% CI: −0.01, 0.03).

Severe and activity-limiting back pain

In further analyses, we utilized measures of severe back pain and activity-limiting back pain (Figure 2C,D). Results were consistent with those of any back pain and frequent back pain. Older men with severe back pain had a 19% increase in sleep problems 6 years later (β = 0.17, 95% CI: 0.02, 0.32; Exp(β) = 1.19). Moreover, older men with activity-limiting back pain at sleep visit 1 had a 25% increase in sleep problems 6 years later (β = 0.22, 95% CI: 0.05, 0.39; Exp(β) = 1.25). However, sleep problems at sleep visit 1 did not predict subsequent severe back pain (β = −0.01, 95% CI: −0.02, 0.01) or subsequent activity-limiting back pain (β = −0.01, 95% CI: −0.02, 0.02). Supplementary Figure 2C,D presents the estimates from a sensitivity analysis of severe back pain and activity-limiting back pain using an increased cut-off score of four, with results remaining consistent and showing increased effect sizes.

Table 3 displays the associations between back pain measures and individual sleep dimensions, obtained from separately conducted CLPM models. The observed association between back pain and subsequent sleep problems was mostly related to sleep dissatisfaction and too early or too late sleep timing. Older men reporting any back pain (β  = 0.10, 95% CI: 0.07, 0.14; Exp(β) = 1.11), frequent back pain (β = 0.18, 95% CI: 0.12, 0.25; Exp(β) = 1.20), severe back pain (β = 0.12, 95% CI: 0.05, 0.20; Exp(β) = 1.13), or activity-limiting back pain (β = 0.13, 95% CI: 0.06, 0.19; Exp(β) = 1.14) exhibited a significantly higher risk of sleep dissatisfaction 6 years later. Additionally, older men with severe back pain (β = 0.06, 95% CI: 0.01, 0.12; Exp(β) = 1.06) or activity-limiting back pain (β = 0.09, 95% CI: 0.03, 0.16; Exp(β) = 1.09) at sleep visit 1 showed a higher risk of disruption in sleep timing (either too early or too late) 6 years later. Supplemental analyses (Supplementary Table 2 and Supplementary Figure 3) using sleep efficiency instead of wake after sleep onset and short sleep duration (<6 hr) versus 6–9 hr (excluding long sleepers) yielded results consistent with the main analyses.

Table 3.

Cross-lagged effects between individual Ru-SATED sleep dimensions and back pain measures.

Cross-lagged effects Any back pain
 Frequent back pain
 Severe back pain
 Activity-limiting back pain
β 95% CI β 95% CI β 95% CI β 95% CI
Back pain (T1) -> Irregular sleep (T2) −0.01 −0.04, 0.03 0.01 −0.06, 0.07 −0.03 −0.07, 0.02 −0.01 −0.06, 0.05
Back pain (T1) -> Sleep dissatisfaction (T2) 0.10*** 0.07, 0.14 0.18*** 0.12, 0.25 0.12*** 0.05, 0.20 0.13*** 0.06, 0.19
Back pain (T1) -> Poor daytime alertness (T2) 0.03 −0.01, 0.05 0.04 −0.02, 0.09 0.02 −0.03, 0.07 0.01 −0.05, 0.07
Back pain (T1) -> Suboptimal sleep timing (T2) 0.03 −0.01, 0.06 0.03 −0.04, 0.09 0.06* 0.01, 0.12 0.09* 0.03, 0.16
Back pain (T1) -> Inefficient sleep (T2) −0.03 −0.05, 0.01 −0.05 −0.11, 0.01 0.04 −0.03, 0.10 0.03 −0.04, 0.10
Back pain (T1) -> Suboptimal sleep duration (T2) −0.01 −0.04, 0.03 −0.01 −0.08, 0.05 −0.02 −0.08, 0.05 −0.02 −0.09, 0.06
Irregular sleep (T1) -> Back pain (T2) 0.03 −0.06, 0.13 0.02 −0.03, 0.08 0.03 −0.04, 0.11 0.05 −0.01, 0.12
Sleep dissatisfaction (T1) -> Back pain (T2) 0.02 −0.07, 0.11 0.03 −0.02, 0.08 0.02 −0.04, 0.07 0.05 −0.01, 0.09
Poor daytime alertness (T1) -> Back pain (T2) 0.01 −0.13, 0.12 0.03 −0.04, 0.10 −0.05 −0.13, 0.02 −0.02 −0.08, 0.05
Suboptimal sleep timing (T1) -> Back pain (T2) 0.07 −0.02, 0.17 0.04 −0.02, 0.09 0.04 −0.01, 0.10 0.06 −0.01, 0.12
Inefficient sleep (T1) -> Back pain (T2) 0.03 −0.09, 0.15 0.02 −0.05, 0.09 0.01 −0.05, 0.06 −0.03 −0.08, 0.02
Suboptimal sleep duration (T1) -> Back pain (T2) −0.03 −0.13, 0.06 −0.04 −0.09, 0.01 −0.01 −0.07, 0.04 −0.02 −0.06, 0.03
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Note. Autoregressive paths were significant in all the models and are not presented in this table. For each sleep dimension, the two directions were tested simultaneously in one model; all the models are adjusted for sociodemographics, body mass index, smoking, alcohol, physical activity, chronic conditions, depressive symptoms, cognitive function, fall history, and relevant medication use.

*

p < .05.

**

p < .01.

***

p < .001.

Discussion

This study contributes to our understanding of the temporal directionality between back pain and sleep problems, two distressing issues in older adults (Alsaadi et al., 2011; Roseen et al., 2020). While limited longitudinal studies reported bidirectional associations between pain and sleep (T.-Y. Chen et al., 2019; Morelhão et al., 2022), in our study sample of older men, back pain consistently preceded more sleep problems, but the inverse association was not observed. This temporal directionality was supported across different back pain measures (i.e., any back pain, frequent back pain, severe back pain, and activity-limiting back pain) and across the multidimensional sleep health composite and individual sleep dimensions.

The few longitudinal studies reporting bidirectional associations between pain and sleep (T.-Y. Chen et al., 2019; Morelhão et al., 2022) have primarily focused on limited sleep characteristics (self-reported sleep only, not assessing a variety of sleep health dimensions) or overall bodily pain rather than back pain specifically. To our knowledge, no previous studies have examined how the relationships evolve over time in older men. Our findings reveal that in older men, back pain precedes subsequent sleep problems but not the reverse. We can speculate on two potential explanations for this finding. First, sleep may be disrupted in older men who have difficulty finding a comfortable sleep position because of back pain. Similarly, older adults with back pain often have other pain sites in the upper or lower extremities that may cause further discomfort or inflammation that disrupts sleep (Lee et al., 2020; Roseen et al., 2020; Rundell et al., 2019). Second, sleep problems may be indirectly, rather than directly, linked to subsequent back pain through mechanisms unaddressed in this study. For example, given the reciprocal relationships between poor sleep, stress, and pain (Lee & Lawson, 2021; Martire et al., 2020; Miaskowski et al., 2020), the effect of sleep problems on later back pain could be mediated by psychological mechanisms, such as stress and depression.

While our main results indicate that older men may have more sleep problems if they experience back pain, our supplemental results additionally inform us which specific sleep health dimensions are more likely to be affected by the experience of back pain. The sleep problems predicted by prior back pain were mainly related to poor satisfaction with sleep and suboptimal sleep timing, such as too early or too late clock time between sleep onset and offset (Table 3). Moreover, having severe back pain and activity-limiting back pain at sleep visit 1 were associated with suboptimal sleep timing. Notably, we found no significant association between back pain and subsequent sleep duration, the most widely studied sleep dimension. Previous cross-sectional studies reported significant associations between sleep duration and pain and pain-related conditions, suggesting that both short sleep (<6 hr) and long sleep (>9 hr) are associated with more pain (W. Chen et al., 2022; Park et al., 2019). However, these findings did not specifically focus on back pain or older men. Overall, findings from our longitudinal study focused on back pain in older men show that this condition may particularly degrade satisfaction and timing dimensions of sleep health.

Strengths and limitations

This study has several strengths that address the limitations of previous research. First, we assessed the temporal directionality between back pain and sleep problems using longitudinal data tracking the same older men participants over time. Second, we examined specific measures of back pain in a population of older men, enhancing our confidence that different back pain indicators are similarly and robustly associated with sleep health. Finally, we used a comprehensive measure of sleep health, incorporating both actigraphy and self-report, to assess potential issues across the Ru-SATED dimensions (Buysse, 2014), thereby increasing the validity of the findings. However, this study also has limitations that may guide future research. Although our study results imply that back pain increased sleep problems at follow-up, we used observational data, and thus causality cannot be drawn. Despite our extensive adjustment for sociodemographic and health-related factors, we may have failed to consider other confounding factors, such as stress and social isolation. Furthermore, results from this study are based on a sample of community-dwelling, mostly White older men recruited from six clinical centers. While the six centers were located across geographically dispersed areas, our sample is not representative of all older U.S. men. In addition, results may not be generalizable to older women, younger adults, and other populations. Also, reference time frames for key variables differed, that is, back pain was assessed over the past year, whereas sleep problems were measured or reported for the past week or month, potentially introducing temporal misalignment in the analysis. Finally, since only two waves of data are available in this study, the Random Intercept CLPM (RI-CLPM), which accounts for within-person variations, cannot be identified, making the standard CLPM the only applicable model (Orth et al., 2021; Usami et al., 2019).

Future directions and clinical and research implications

Further analyses could shed light on the temporal relationship between back pain and sleep problems. For instance, future studies should investigate whether back pain, or worsening back pain, increases the risk of developing sleep problems at follow-up among participants without baseline sleep problems. Addressing this question may require a carefully designed study that minimizes participant attrition, as those who develop sleep problems may be more likely to drop out. Higher attrition in longitudinal studies could result in a healthier sample, potentially obscuring the true relationship between back pain and sleep problems. Additionally, the directionality between back pain and sleep may vary depending on the population of interest and the study follow-up period. Therefore, it is important to note that our findings are based on a community sample of older men living in the United States with a follow-up period of approximately 6 years. Given that both back pain and sleep problems can fluctuate over shorter timeframes—months, weeks, or even days—it is essential to observe these conditions within a shorter time window with more frequent measurements. Studies employing varying follow-up periods could provide further insights.

Overall, these findings may have important implications for future preventive intervention efforts as our data suggest that worsening back pain may increase sleep problems. There are existing intervention programs designed to improve back pain symptoms, and such programs may help address sleep problems. For example, an 8-week mind-body group intervention improved short-term function and long-term severity of pain in older adults with chronic low back pain (Morone et al., 2016). The intervention was modeled on the Mindfulness-Based Stress Reduction program, which is also known to help address sleep problems (Carlson & Garland, 2005). Cognitive behavioral therapy (CBT) has been found effective to address chronic pain (e.g., CBT for chronic pain; CBT-CP) as well as sleep problems like insomnia (e.g., CBT for insomnia; CBT-I). A brief CBT-CP delivered online is an effective and accessible treatment for older adults with chronic noncancer pain (Martinson et al., 2024). Although our results may not support the idea that improving sleep health will also lead to positive outcomes for back pain—at least in older men, programs such as CBT-I may still be effective, as they have been found to enhance both sleep and pain symptoms in older adults with comorbid insomnia and osteoarthritis (Vitiello et al., 2009). Furthermore, lifestyle interventions targeting physical inactivity, excessive alcohol consumption, smoking, and an unhealthy diet could improve spinal health and alleviate back pain, which may indirectly and positively influence sleep health (e.g., Nordstoga et al., 2024). These behavioral interventions are safe and provide long-term, sustained results, without side effects often found in pharmacological treatments for pain and sleep (Taylor et al., 2019).

Conclusion

In a community-dwelling sample of older men in the United States, back pain preceded sleep problems but not vice versa. Having any back pain, frequent back pain, severe back pain, and activity-limiting back pain predicted a 12%–25% greater sleep problems 6 years later, independent of other well-known risk factors and sleep problems at baseline. More research is needed to fully understand the dynamic interplay between back pain and sleep problems across different time scales and populations. Overall, our results point to the need to develop and study interventions to improve back pain in older men that may also help improve sleep health.

Supplementary Material

igaf113_Supplementary_Data
igaf113_supplementary_data.zip (483.9KB, zip)

Acknowledgments

The Osteoporotic Fractures in Men (MrOS) Study is supported by the National Institutes of Health. The following institutes provide support: the National Institute on Aging (NIA), the National Institute of Arthritis and Musculoskeletal and Skin Diseases (NIAMS), the National Center for Advancing Translational Sciences (NCATS), and NIH Roadmap for Medical Research under the following grant numbers: U01 AG027810, U01 AG042124, U01 AG042139, U01 AG042140, U01 AG042143, U01 AG042145, U01 AG042168, U01 AR066160, R01 AG066671, and UL1 TR002369. The National Heart, Lung, and Blood Institute (NHLBI) provides funding for the MrOS Sleep ancillary study “Outcomes of Sleep Disorders in Older Men” under the following grant numbers: R01 HL071194, R01 HL070848, R01 HL070847, R01 HL070842, R01 HL070841, R01 HL070837, R01 HL070838, and R01 HL070839.

Contributor Information

Soomi Lee, Department of Human Development and Family Studies, Center for Healthy Aging, The Pennsylvania State University, University Park, Pennsylvania, United States.

T Muhammad, Department of Human Development and Family Studies, Center for Healthy Aging, The Pennsylvania State University, University Park, Pennsylvania, United States.

Eric J Roseen, Section of General Internal Medicine, Department of Medicine, Boston University Chobanian & Avedisian School of Medicine and Boston Medical Center, Boston, Massachusetts, United States.

David T McNaughton, College of Medical, Health and Applied Sciences, CQUniversity, Brisbane, Australia.

Christina X Mu, Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, California, United States.

Cecilie Krage Øverås, Department of Public Health and Nursing, Norwegian University of Science and Technology - NTNU, Trondheim, Norway; The Norwegian Chiropractors’ Research Foundation, Et Liv i Bevegelse (ELiB), Oslo, Norway.

Hazel Jenkins, Department of Chiropractic, Macquarie University, Sydney, Australia.

Casper Nim, Medical Spinal Research Unit, Spine Centre of Southern Denmark, University Hospital of Southern Denmark, Odense, Denmark; Department of Regional Health Research, University of Southern Denmark, Odense, Denmark.

James J Young, Centre for Muscle and Joint Health, Department of Sports Science and Clinical Biomechanics, University of Southern Denmark, Odense, Denmark; Schroeder Arthritis Institute, Krembil Research Institute, University Health Network, Toronto, Ontario, Canada.

Howard A Fink, Department of Medicine and Division of Epidemiology and Community Health, University of Minnesota, Minneapolis, Minnesota, United States; Geriatric Research Education and Clinical Center, Veterans Affairs Health Care System, Minneapolis, Minnesota, United States.

Kristine E Ensrud, Department of Medicine and Division of Epidemiology and Community Health, University of Minnesota, Minneapolis, Minnesota, United States.

David M Almeida, Department of Human Development and Family Studies, Center for Healthy Aging, The Pennsylvania State University, University Park, Pennsylvania, United States.

Brent J Small, School of Nursing, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States.

Peggy M Cawthon, 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.

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.

Supplementary material

Supplementary data are available at Innovation in Aging online.

Funding

This study was supported by the National Institutes of Health (R01HL163226; PI: S.L.). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Conflict of interest

The authors declare no conflict of interest relevant to the current work. Outside of the current work, K.L.S. served as a consultant for Axsome Therapeutics and has grant funding from Eli Lilly.

Data availability

The study used publicly available data, accessible through https://mrosonline.ucsf.edu/. 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

igaf113_Supplementary_Data
igaf113_supplementary_data.zip (483.9KB, zip)

Data Availability Statement

The study used publicly available data, accessible through https://mrosonline.ucsf.edu/. This study was not preregistered.

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