Abstract
Background
As the leading cause of injury and injury-related death for older adults in the United States, falls can be consequential for function and mortality but are preventable. Sleep may be a modifiable risk factor for falls.
Methods
Data from 1795 female participants of the Study of Women’s Health Across the Nation (SWAN) were analyzed to examine whether frequent insomnia symptoms and shorter sleep duration are associated with an increased risk of falls or fall burden. At visit 12, assessed insomnia symptoms included frequency of restless sleep, trouble falling asleep, and waking early. Sleep duration was self-reported hours of sleep, dichotomized as fewer than <6 h/night and ≥6 h/night. At visit 15, participants reported the falls in the year prior. Log-binomial and multinomial logistic regression models were adjusted for demographic, health, and socioeconomic factors.
Results
Women who reported frequent (5+ times/week) trouble falling asleep and frequent waking at baseline had a 30% increased risk (aRR = 1.30, 95% CI, 1.04-1.62) and 24% increased risk (aRR = 1.24, 95% CI, 1.03-1.49), respectively, of having fallen in the year prior at follow-up. Frequent trouble falling asleep and short sleep duration (<6 h) were both associated with higher odds of falling 3 or more times vs once or never prior to follow-up (aOR = 2.42, 95% CI, 1.26-4.63; aOR = 1.77, 95% CI, 1.08-2.93), respectively.
Conclusions
Multiple indicators of poor sleep, including trouble falling asleep, frequent waking, and short sleep duration, were associated with an increased risk of falling and odds of higher fall burden in older adult women. Promoting adequate, high-quality sleep may be an essential component in fall prevention.
Keywords: Sleep, Sleep duration, Insomnia, Older adults, Falls
Introduction
Falls are consequential for older adults because they are associated with injury, hospitalization, temporary or permanent loss of function, diminished resilience, and mortality.1,2 Age-related changes in musculature, gait, and sensory function can increase fall risk.3 In the United States, there are approximately 36 million falls in older adults per year, with 1 in 4 adults 65 years or older reporting at least 1 fall per year.4 Of those, nearly 20% of older adults experience a resulting fall-related serious injury, such as a head injury or fracture.5 For those 75 years and older, falls are among the top 10 causes of disability-adjusted life years worldwide.6 Most concerning is the growing burden of falls; between 2018 and 2022, the number of fall-related deaths among older adults in the United States rose by 26%, and provisional data from 2023 suggests that this number continues to rise.7 This, compounded by the overall aging of the US population, suggests that, without effective and prevalent fall prevention measures, the number of falls in older adults will continue to increase.8
Several risk factors for falls among older adults have been studied. Fall prevalence increases with age, and older adult women are more likely to fall than older adult men.9,10 Frailty, slow processing speed, and decreased executive function predict future falls.11–13 Sleep, a health indicator which is now understood to be a major predictor of many age-related health outcomes, may be an important risk factor for falls. In cross-sectional studies, short sleep duration, poor sleep quality, excessive daytime sleepiness, and insomnia symptoms are associated with falls in older adults.14–20
The design of cross-sectional studies, however, precludes consideration of sleep as a predictor of fall risk. Thus, prospective studies are important to understand whether sleep predicts falls or is merely a co-occurring measure of health. Existing prospective studies relating sleep to fall risk indicate a possible association, though more work is needed. In the Health and Retirement Study (HRS), an increasing number of insomnia symptoms predicted falls 2 years later, even after adjustment for known risk factors for falls, including physical functioning.21 In the Women’s Health Initiative (WHI), both short sleep duration and clinically significant insomnia symptoms predicted recurrent falls over a 12-year follow-up.22 In the Study of Osteoporotic Fractures, Stone et al. found that short sleep duration and poor sleep efficiency were associated with fall risk in the subsequent year.23 However, in the National Health and Aging Study, there was no association between insomnia symptoms and fall risk at 2- or 4-year follow-ups in covariate-adjusted analyses.24
Sleep is a multi-dimensional factor, critical to many health maintenance tasks of the body, and a lack of quality sleep can lead to short- and long-term changes in cognition, physical function, and overall health.25–29 Sleep can be assessed through several features, including quality, duration, timing, and efficiency.30,31 Aging has been associated with shorter sleep duration and more frequent awakenings during sleep.32,33 Women are more vulnerable to experiencing subjectively poor sleep with aging relative to men; across the lifespan, there is a progressively widening gap in the risk for insomnia in women vs men, and among older adults, women are 73% more likely than men to report insomnia symptoms.34
Given the female sex disparity in both sleep and falls, examining the predictive nature of poor sleep on fall risk among women is of high importance, particularly because some aspects of sleep are modifiable. In this paper, we explored the relationship between sleep duration and insomnia symptoms and risk of falls in a cohort of women.
Methods
Study design and participants
The Study of Women’s Health Across the Nation (SWAN) is a multi-ethnic, community-based cohort study of the menopause transition and aging. At baseline in 1996, 3302 premenopausal women aged 42-52 years were enrolled at 7 study sites.35 Near-annual study visits provided clinical and behavioral data. Data for these analyses come from visit 12, conducted in 2010-2011, considered the baseline visit for these analyses; visit 13, conducted in 2012-2013; and visit 15, conducted in 2015-2016, considered the follow-up visit.
Of the 3302 women enrolled at SWAN baseline, 1915 women contributed data for all 5 sleep variables at V12 and had participated in V15. Of those, 1914 women contributed prior-year fall data at V15. We additionally excluded 119 women missing covariates related to peripheral neuropathy and chronic pain, which were assessed at only 1 visit, resulting in a final analytic sample of 1795 women.
Study variables
Exposure: self-reported sleep characteristics
Four sleep variables self-reported at analytic baseline were included in analyses. Insomnia symptoms were assessed with 3 questions: “In the past two weeks, did you have trouble falling asleep?”; “In the past two weeks, did you wake up several times a night?”; and “In the past two weeks, did you wake up earlier than you had planned to, and were unable to fall asleep again?” Participants could report the frequency of these symptoms as “No, not in the past 2 weeks,” “Yes, less than once a week,” “Yes, 1 or 2 times a week,” “Yes, 3 or 4 times per week,” or “Yes, 5 or more times a week.” Insomnia symptom responses were collapsed into less than once a week, 1-4 times a week, and 5 or more times a week.
To assess sleep duration, participants were asked, “During the past month, how many hours of actual sleep did you get at night? (This may be different than the number of hours you spend in bed.)” Sleep duration was categorized as <6 h (“short sleep duration”) or ≥6 h.
Outcome: self-reported falls
At visit 15 (2015-2016, analytic follow-up), participants were asked, “In the past year, have you fallen and landed on the floor or ground (or fallen and hit an object like a table or stair)?” If participants answered “Yes,” they were asked, “How many times have you fallen in the past year?” Number of falls was trichotomized into 0-1, 2, or 3+ falls. Women with zero falls and 1 fall were grouped together for analyses, as recurrent fallers are demonstrably different than women who fall once.36
Covariates
Demographic variables included age, self-reported race/ethnicity (Black, Chinese, Hispanic, Japanese, or White), and difficulty paying for basics (not at all, somewhat, or very difficult). Height and weight were measured at annual visits, and body mass index (BMI) was calculated as weight in kilograms divided by height in meters squared. Physical activity was assessed using the Kaiser Physical Activity Survey, summing across 3 indices (household/caregiving, sports, and non-sports leisure), with total possible scores ranging from 3 to 15.37,38 Participants self-reported a diagnosis of osteoarthritis since the previous study visit. Peripheral neuropathy was indicated by a Michigan Neuropathy Screening Instrument (MNSI) score of 4 or more. Urge incontinence was assessed via the question, “In the last month, have you lost any urine, even a small amount, beyond your control when you have the urge to urinate and can’t get to the toilet fast enough?” Participants could choose “Never,” “Rarely,” “A few times per month,” “A few times per week,” or “Daily.” Perceived stress was assessed via the four-item Cohen’s Perceived Stress Scale, with higher scores indicating greater stress.39 Depressive symptoms (yes/no) were defined as ≥16 on the Center for Epidemiologic Studies Depression Scale (CES-D).40 Chronic pain (yes/no) was defined as reporting body site pain frequency at least some of the time with a severity greater than 3 on a scale from 0 (no pain) to 10 (worst pain) in any of 8 locations (head, face, neck/shoulders, back, arms/hands, knee/legs/feet, chest, or abdomen/pelvis/hip).41 Participants were asked whether they had a knee replacement since the last study visit.
Most covariates were collected at visit 13 (2012-2013); if covariates were missing at visit 13, values at the closest available visit between visits 10 (2006-2007) and 15 were used. In the small number of cases where there were no data available regarding BMI and/or physical activity between visits 10-15, missing values were imputed using linear regression. A sensitivity analysis was conducted excluding these participants from the analysis; results were consistent with results from the full sample, so we only present results from the full sample. Figure 1 visualizes the timing of exposure, covariate, and outcome assessment.
Figure 1.
Diagram of timing of exposure, covariate, and outcome assessment. MNSI, Michigan Neuropathy Screening Instrument.
Statistical analyses
Means and standard deviations (SDs) for continuous variables and frequencies for categorical variables were calculated overall and by fall status. To assess differences in means and proportions, t-tests and Chi-square tests, respectively, were employed.
A log-binomial regression model was utilized to examine the relative risk of having fallen in the past year (yes/no), and multinomial logistic models were utilized to estimate the odds of fall burden (0-1, 2, or 3+ falls). Separate regression models were considered for each sleep variable (trouble falling asleep, waking several times, waking early, and sleep duration).
Unadjusted models (model 1) were compared with models adjusting for covariates as follows: model 2 was adjusted for demographic variables and health behaviors (age, BMI, race/ethnicity, difficulty paying for basics, and physical activity), and model 3 was additionally adjusted for medical conditions (osteoarthritis and peripheral neuropathy). Additional analyses were conducted to consider urge incontinence and chronic pain as potential mediators. As depressive symptoms may be a risk factor for falls,24 models stratified by depressive symptoms were examined. Because those with knee replacements may experience different gait patterns, a sensitivity analysis was conducted, excluding women who had undergone a knee replacement in the past year; results from the sensitivity analysis were consistent with results from the full analysis, so only the full results are presented.
Results
Table 1 presents the characteristics of the 1795 SWAN participants included in analyses. At the analytic follow-up, participants were, on average, 65.6 years old (SD = 2.7). Just over one-quarter of the sample (25.6%) were Black, 48.7% were White, 10.3% were Japanese, 10.4% were Chinese, and 5.1% were Hispanic. Most (80%) women attended some college or more, and 26% of women reported difficulty paying for basic necessities. Considering comorbidities, 31.9% had osteoarthritis, and 15.4% scored a 4 or higher on the MNSI, indicating symptoms of peripheral neuropathy. A quarter of participants reported symptoms of urge incontinence at least a few times per week, 13.9% had depressive symptoms, and nearly two-thirds had chronic pain (Table 1).
Table 1.
Population characteristics overall and by fallen in the past year in the Study of Women’s Health Across the Nation.
| Fallen in past year |
||||
|---|---|---|---|---|
| Overall (N = 1795) | No (N = 1260) | Yes (N = 535) | ||
| Variable | Mean (SD) | Mean (SD) | Mean (SD) | p-Valuea |
| Age (years) | 65.6 (2.7) | 65.6 (2.7) | 65.6 (2.7) | .637 |
| Body mass index (kg/m2) | 29.1 (7.3) | 29.0 (7.3) | 29.5 (7.3) | .113 |
| Physical activity score | 7.6 (1.8) | 7.6 (1.8) | 7.6 (1.9) | .829 |
| SF-36 physical function score | 80.4 (22.7) | 81.7 (21.9) | 77.2 (24.3) | <.001 |
| Perceived stress | 7.0 (2.8) | 6.8 (2.7) | 7.3 (3.0) | <.001 |
| N (%) | N (%) | N (%) | p-Valueb | |
| Race/ethnicity | .047 | |||
| White | 874 (48.7) | 588 (46.7) | 286 (53.5) | |
| Black | 459 (25.6) | 335 (26.6) | 124 (23.2) | |
| Chinese | 186 (10.4) | 138 (11.0) | 48 (9.0) | |
| Japanese | 185 (10.3) | 139 (11.0) | 46 (8.6) | |
| Hispanic | 91 (5.1) | 60 (4.8) | 31 (5.8) | |
| Education level | .076 | |||
| Less than high school | 85 (4.8) | 59 (4.7) | 26 (4.9) | |
| High school | 271 (15.2) | 196 (15.7) | 75 (14.1) | |
| Some college | 568 (31.9) | 411 (32.9) | 157 (29.6) | |
| College degree | 390 (21.9) | 280 (22.4) | 110 (20.7) | |
| Post-college | 465 (26.1) | 302 (24.2) | 163 (30.7) | |
| Missing | 16 | 12 | 4 | |
| Difficult to pay for basic necessities | .029 | |||
| Very | 82 (4.6) | 50 (4.0) | 32 (6.0) | |
| Somewhat | 382 (21.4) | 257 (20.4) | 128 (23.9) | |
| Not at all | 1328 (74.0) | 953 (75.6) | 375 (70.1) | |
| Knee replacement | .120 | |||
| Yes | 21 (1.2) | 11 (0.9) | 10 (1.9) | |
| Osteoarthritis | <.001 | |||
| Yes | 572 (31.9) | 362 (28.7) | 210 (39.3) | |
| Urge incontinence | .214 | |||
| Never | 373 (20.8) | 268 (21.3) | 105 (19.6) | |
| Rarely | 677 (37.7) | 491 (39.0) | 186 (34.8) | |
| Few times/month | 303 (16.9) | 203 (16.1) | 100 (18.7) | |
| Few times/week | 282 (15.7) | 187 (14.8) | 95 (17.8) | |
| Daily | 160 (8.9) | 111 (8.8) | 49 (9.2) | |
| MNSI | <.001 | |||
| Score ≥4 | 276 (15.4) | 156 (12.4) | 120 (22.4) | |
| Depressive symptoms (CES-D ≥ 16) | .010 | |||
| Yes | 2549 (13.9) | 157 (12.5) | 92 (17.2) | |
| Chronic pain | .005 | |||
| Yes | 1141 (63.6) | 774 (61.4) | 367 (68.6) | |
Abbreviations: CES-D, Center for Epidemiological Studies Depression Scale; MNSI, Michigan Neuropathy Screening Instrument; SF-36, Medical Outcomes Study Questionnaire Short Form 36 Health Survey; Physical Function Subscale.55
T-test.
Chi-square test.
In unadjusted comparisons, women who fell had higher perceived stress scores (7.3 vs 6.8, p < .001) and lower physical function scores (77.2 vs 81.7, p < .001) but were comparable in age, BMI, and physical activity scores. Fallers were more likely to report symptoms of peripheral neuropathy (22.4% of fallers vs 12.4% of non-fallers, p < .001) and have difficulty paying for basic necessities (29.9% vs 24.4%, p < .05).
One-third (33.9%) of women reported having trouble falling asleep at least once per week; of those, 138 women, or 7.7% of the total sample, reported trouble falling asleep 5 times a week or more. Over 60% of women reported waking up several times a night at least once a week, and nearly one-quarter of women reported this symptom 5 or more times per week. Waking up earlier than intended and being unable to fall asleep more than 5 times per week was reported by 8.4% of the sample. Past-month sleep duration of fewer than 6 h was reported by 18.8% of the sample.
At the analytic follow-up, 535 participants (29.8%) reported falling in the prior year. Of women who reported any fall, 224 reported 2 or more falls (Table 2).
Table 2.
Bivariate associations between sleep variables and fall variables in the Study of Women’s Health Across the Nation.
| Sleep variables | Overall | Fallen |
Number of falls |
|||||
|---|---|---|---|---|---|---|---|---|
| No | Yes | 0-1 | 2 | 3+ | ||||
| (N = 1795) | (N = 1260) | (N = 535) | (N = 1571) | (N = 134) | (N = 90) | |||
| N (%) | N (%) | N (%) | p-valuea | N (%) | N (%) | N (%) | p-Valuea | |
| Trouble falling asleep, past 2 weeks | .021 | <.001 | ||||||
| None/less than once | 1187 (66.1) | 849 (67.4) | 338 (63.2) | 1058 (67.3) | 84 (62.7) | 45 (50.0) | ||
| 1-4 times per week | 470 (26.2) | 328 (26.0) | 142 (26.5) | 404 (25.7) | 38 (28.4) | 28 (31.1) | ||
| 5+ times per week | 138 (7.7) | 83 (6.6) | 55 (10.3) | 109 (6.9) | 12 (9.0) | 17 (18.9) | ||
| Waking up several times, past 2 weeks | .003 | .058 | ||||||
| None/less than once | 660 (36.8) | 490 (38.9) | 170 (31.8) | 591 (37.6) | 46 (34.3) | 23 (25.6) | ||
| 1-4 times per week | 700 (39.0) | 489 (38.8) | 211 (39.4) | 614 (39.1) | 47 (35.1) | 39 (43.3) | ||
| 5+ times per week | 435 (24.2) | 281 (22.3) | 154 (28.8) | 366 (23.3) | 41 (30.6) | 28 (31.1) | ||
| Waking up early, past 2 weeks | .240 | .052 | ||||||
| None/less than once | 1153 (64.2) | 825 (65.5) | 328 (61.3) | 1023 (65.1) | 81 (60.4) | 49 (54.4) | ||
| 1-4 times per week | 492 (27.4) | 334 (26.5) | 158 (29.5) | 419 (26.7) | 45 (33.6) | 28 (31.1) | ||
| 5+ times per week | 150 (8.4) | 101 (8.0) | 49 (9.2) | 129 (8.2) | 8 (6.0) | 13 (14.4) | ||
| Sleep duration | .024 | .001 | ||||||
| <6 h | 337 (18.8) | 219 (17.4) | 118 (22.1) | 282 (18.0) | 25 (18.7) | 30 (33.3) | ||
| ≥6 h | 1458 (81.2) | 1041 (82.6) | 417 (77.9) | 1289 (82.0) | 109 (81.3) | 60 (66.7) | ||
Chi-square test.
In bivariate analyses, women who reported trouble falling asleep 5 or more times per week were more likely to have fallen at follow-up than women who had less frequent or no trouble falling asleep (10.3% of fallers vs 6.6% of non-fallers, p < .05). Similarly, women who reported waking up several times throughout sleep were more likely to have fallen (28.8% vs. 22.3%, p < .05). There was no statistically significant difference between fallers and non-fallers in waking up early. Women who slept fewer than 6 hours per night were more likely to fall than women who slept 6 or more hours per night (22.1% vs. 17.4%, p < .05).
Both women with frequent trouble falling asleep and women who slept fewer than 6 hours per night at baseline also had higher odds of falling 3 or more times at follow-up compared to women with less frequent or no trouble falling asleep and who slept 6 hours or more. Women who did not report frequent waking several times per night or frequent waking early did not have increased odds of a higher fall burden.
Insomnia symptoms, sleep duration, and risk of falling
Table 3 shows results examining the association between sleep characteristics at baseline and reporting at least 1 fall at follow-up. In log-binomial models adjusted for demographic characteristics, health conditions, and health behaviors, women who reported frequent (ie, 5 times or more per week) trouble falling asleep and frequent waking in the middle of the night at baseline had, respectively, a 30% and 24% increased risk of falling compared to women who did not report these issues (aRR = 1.30, 95% CI, 1.04-1.62; aRR = 1.24, 95% CI, 1.03-1.49). There was a marginally significant association between sleep duration and risk of falling at follow-up (aRR = 1.18, 95% CI, 1.00-1.40).
Table 3.
Unadjusted and adjusted risk ratios of fallen in past year for sleep predictors.
| Model 1 | Model 2 | Model 3 | Model 3 + urge incontinence | Model 3 + chronic pain | |
|---|---|---|---|---|---|
| Sleep predictor | RR (95% CI) | RR (95% CI) | RR (95% CI) | RR (95% CI) | RR (95% CI) |
| Trouble falling asleep | |||||
| <1/week | Ref | Ref | Ref | Ref | Ref |
| 1-4 times/week | 1.06 (0.90-1.25) | 1.05 (0.89-1.23) | 1.00 (0.85-1.18) | 0.99 (0.84-1.16) | 0.99 (0.84-1.17) |
| 5+ times/week | 1.40 (1.12-1.75)** | 1.36 (1.09-1.71)** | 1.30 (1.04-1.62)* | 1.30 (1.04-1.63)* | 1.28 (1.02-1.60)* |
| Waking several times | |||||
| <1/week | Ref | Ref | Ref | Ref | Ref |
| 1-4 times/week | 1.17 (0.99-1.39) | 1.15 (0.97-1.36) | 1.11 (0.94-1.32) | 1.11 (0.93-1.32) | 1.11 (0.93-1.31) |
| 5+ times/week | 1.37 (1.15-1.65)*** | 1.31 (1.09-1.58)** | 1.24 (1.03-1.49)* | 1.23 (1.02-1.48)* | 1.23 (1.02-1.48)* |
| Waking up early | |||||
| <1/week | Ref | Ref | Ref | Ref | Ref |
| 1-4 times/week | 1.13 (0.96-1.32) | 1.11 (0.95-1.30) | 1.05 (0.90-1.23) | 1.05 (0.89-1.22) | 1.05 (0.89-1.22) |
| 5+ times/week | 1.15 (0.90-1.47) | 1.12 (0.87-1.43) | 1.05 (0.82-1.35) | 1.05 (0.82-1.35) | 1.05 (0.82-1.34) |
| Sleep duration | |||||
| <6 h | 1.22 (1.04, 1.45)* | 1.24 (1.05, 1.47)* | 1.18 (1.00, 1.40)* | 1.19 (1.00, 1.40)* | 1.18 (0.99, 1.39) |
| ≥6 h | Ref | Ref | Ref | Ref | Ref |
Model 1: unadjusted. Model 2: adjusted for age, body mass index, race/ethnicity, difficulty paying for basic necessities, and physical activity. Model 3: adjusted for variables in model 2 plus osteoarthritis and peripheral neuropathy. Abbreviations: Ref, reference group; RR, risk ratio.
<.05.
<.01.
<.001.
Insomnia symptoms, sleep duration, and fall burden
Table 4 displays covariate-adjusted results regarding the association between sleep characteristics and number of incident falls at follow-up. Having trouble falling asleep 5 or more times per week was associated with higher odds of falling 3 or more times (aOR = 2.42, 95% CI, 1.26-4.63). Short sleep duration (<6 h) was associated with 77% higher odds of falling 3 or more times (aOR = 1.77, 95% CI, 1.08-2.93).
Table 4.
Unadjusted and adjusted multinomial logistic models of fallen 2 or 3+ times in past year for sleep predictors.
| Sleep predictor | Number of falls | Model 1 | Model 2 | Model 3 | Model 3 + urge incontinence | Model 3 + chronic pain |
|---|---|---|---|---|---|---|
| OR (95% CI) | OR (95% CI) | OR (95% CI) | OR (95% CI) | OR (95% CI) | ||
| Trouble falling asleep | ||||||
| <1/week | Ref | Ref | Ref | Ref | Ref | Ref |
| 1-4 times/week | 2 | 1.18 (0.79-1.77) | 1.17 (0.78-1.76) | 1.09 (0.72-1.64) | 1.09 (0.72-1.65) | 1.07 (0.71-1.62) |
| 3+ | 1.63 (1.00-2.65)* | 1.52 (0.93-2.49) | 1.36 (0.82-2.24) | 1.34 (0.81-2.21) | 1.30 (0.78-2.15) | |
| 5+ times/week | 2 | 1.39 (0.73-2.62) | 1.41 (0.74-2.71) | 1.20 (0.62-2.33) | 1.21 (0.62-2.35) | 1.18 (0.61-2.29) |
| 3+ | 3.67 (2.03-6.63)*** | 3.10 (1.66-5.81)*** | 2.42 (1.26-4.63)** | 2.40 (1.25-4.60)** | 2.29 (1.20-4.38)* | |
| Waking several times | ||||||
| <1/week | Ref | Ref | Ref | Ref | Ref | Ref |
| 1-4 times/week | 2 | 0.98 (0.64-1.50) | 0.98 (0.64-1.50) | 0.94 (0.61-1.44) | 0.94 (0.61-1.44) | 0.93 (0.61-1.43) |
| 3+ | 1.63 (0.96-2.77) | 1.56 (0.91-2.67) | 1.45 (0.84-2.50) | 1.42 (0.82-2.45) | 1.41 (0.82-2.43) | |
| 5+ times/week | 2 | 1.44 (0.93-2.24) | 1.40 (0.89-2.19) | 1.26 (0.80-1.99) | 1.26 (0.80-2.01) | 1.24 (0.78-1.96) |
| 3+ | 1.97 (1.12, 3.46)* | 1.77 (0.98, 3.18) | 1.41 (0.77, 2.58) | 1.39 (0.76, 2.55) | 1.34 (0.73, 2.46) | |
| Waking up early | ||||||
| <1/week | Ref | Ref | Ref | Ref | Ref | Ref |
| 1-4 times/week | 2 | 1.36 (0.93-1.99) | 1.35 (0.92-1.99) | 1.30 (0.88-1.92) | 1.30 (0.88-1.91) | 1.29 (0.87-1.90) |
| 3+ | 1.40 (0.87-2.25) | 1.36 (0.83-2.20) | 1.24 (0.76-2.03) | 1.24 (0.75-2.03) | 1.20 (0.73-1.96) | |
| 5+ times/week | 2 | 0.78 (0.37-1.66) | 0.76 (0.36-1.63) | 0.66 (0.31-1.43) | 0.67 (0.31-1.45) | 0.65 (0.30-1.41) |
| 3+ | 2.10 (1.11-3.98)* | 1.78 (0.92-3.46) | 1.38 (0.69-2.75) | 1.34 (0.67-2.69) | 1.35 (0.68-2.67) | |
| Sleep duration | ||||||
| <6 h | 2 | 1.05 (0.67-1.65) | 1.10 (0.69-1.76) | 1.01 (0.63-1.63) | 1.01 (0.63-1.62) | 1.00 (0.62-1.61) |
| 3+ | 2.29 (1.45-3.61)*** | 2.06 (1.26-3.34)** | 1.77 (1.08-2.93)* | 1.71 (1.03-2.82)* | 1.73 (1.05-2.85)* | |
| ≥6 h | Ref | Ref | Ref | Ref | Ref | Ref |
Model 1: unadjusted. Model 2: adjusted for age, body mass index, race/ethnicity, difficulty paying for basic necessities, and physical activity. Model 3: adjusted for variables in model 2 plus osteoarthritis and peripheral neuropathy. Abbreviations: OR, odds ratio; Ref, reference.
<.05.
<.01.
<.001.
Inclusion of urge incontinence, chronic pain, and depressive symptoms
The inclusion of urge incontinence or chronic pain in the final models had minimal effects (ie, <10% impact on point estimates). Women with depressive symptoms were more likely to have had frequent insomnia symptoms and more likely to have fallen than women without depressive symptoms (Table S1). In bivariate analyses, among women with frequent trouble falling asleep, the presence of depressive symptoms was associated with an increased risk of falling; nearly half (47.6%, 20/42) of women with depressive symptoms fell as compared to about one-third (36.5%, 35/96) among women without depressive symptoms. A similar association was observed with frequent waking during the night. Of the women with depressive symptoms who experienced frequent waking 5 or more times per week, 40.5% experienced a fall, compared to 34.2% of women without depressive symptoms who experienced frequent waking (Table S2).
Discussion
This study examined the association between insomnia symptoms and sleep duration with subsequent risk of falls and recurrent falls in a community-based, multi-ethnic cohort of post-menopausal women. Because aging influences sleep and can increase the risk of insomnia, understanding relationships with major age-related, potentially debilitating events such as falls is of utmost importance. Further, because sleep is potentially modifiable, it is critical to understand how to foster support for the health and well-being of women as they age.
This study found that frequent trouble falling asleep and nighttime awakenings were predictive of fall risk several years later, even when adjusting for demographic factors, health behaviors, and health conditions. Short sleep duration was marginally associated with fall risk and was significantly associated with fall burden (ie, falling 3 or more times). This suggests that poor and/or insufficient sleep may be associated with both falling and a greater burden of falls. As falls can be immensely consequential for health, addressing risk factors for falls, especially a modifiable one like sleep, is imperative. These results suggest that frequent insomnia symptoms and short sleep duration may increase an older adult woman’s future risk of falling, and frequent trouble falling asleep and short sleep duration may be linked to the risk of recurrent falls. As such, sleep health should be considered as an important modifiable factor to address fall risk in populations of older adult women.
Further research is needed to fully disentangle why we observed differential associations for sleep symptoms and fall risk. One possibility is that trouble falling asleep and waking frequently during the night may be differentially associated with sleep duration. Previous literature42 has shown a stronger association between trouble falling asleep and short duration of sleep compared to waking frequently during the night and short sleep duration. Nevertheless, we note that sleep treatment approaches may vary depending on the primary sleep symptom contributing to fall risk. If trouble falling asleep and short sleep duration are caused by chronic insomnia, cognitive-behavioral therapy for insomnia is indicated43 and, when appropriate, circadian-based and sleep scheduling interventions to increase sleep duration.44 In contrast, frequent nighttime awakenings may arise from physiological and/or environmental factors (ie, nocturia, noise, bed partner, reflux, pain) rather than insomnia per se. In such cases, interventions should focus on targeting these contributors (ie, managing nocturia, minimizing noise and light exposure during the night, treating pain or reflux, and adjusting sleeping arrangements if a bed partner or pet is causing awakenings).
The findings from this analysis align with previous research connecting insomnia symptoms and/or short sleep duration to fall risk in older adults. Similar to our study, the relationship between insomnia symptoms and future fall risk has also been reported by Chen et al.21 in the HRS and Cauley et al.22 in the WHI.
Data from the HRS indicated that for each additional insomnia symptom reported, participants had 5% increased odds of reporting a fall at follow-up, a finding that also aligns with the direction of the present study’s findings.21 In terms of specific symptoms, having frequent trouble falling asleep or nighttime awakening was associated with 8% and 11% increased odds of a later fall, though these results were not statistically significant. However, our results are not directly comparable, as HRS participants reported higher symptom frequency as “most of the time” or “always,” a frequency which, though similar, cannot be directly compared with the frequency used in the present study (“five or more times per week”). Furthermore, in the HRS, results were not stratified by sex, obscuring any distinctions between men and women in the association between sleep and fall risk. It is possible that the relationship between insomnia symptoms and fall risk is stronger in older adult women than in men, which would explain the tempered association in HRS data compared to our findings.
In the WHI, women with a score of 9 or greater on the WHI Insomnia Rating Scale (WHIIRS), characterized as a summary score based on the number and frequency of insomnia symptoms along with sleep quality, had 18% higher odds of 2 or more falls in the prior year.22 The magnitude of this effect is smaller than the reported odds of recurrent falls for frequent trouble falling asleep in the present analyses (OR = 2.42, 95% CI, 1.26-4.63). However, the WHIIRS combines insomnia symptoms, precluding direct comparison of specific symptoms. The same study looked at the relationship between short sleep duration and increased odds of recurrent falls. Our study also demonstrated a relationship between short sleep duration and increased odds of recurrent falls in older adult women, similar to the study from Cauley et al. using data from the WHI. However, while the SWAN and WHI women were approximately the same age at analytic baseline, the magnitude of the association between short sleep duration and recurrent falls was much stronger in our analysis from SWAN (77% higher odds) as compared to findings from WHI (27% higher odds). This may be due to a much longer follow-up period for incident falls in WHI compared to SWAN and may suggest that the importance of insomnia symptoms and short sleep duration as a fall risk factor declines with time.
Our findings relating sleep and fall risk also build upon previous work within SWAN. In SWAN, Kline et al.45 reported associations between trajectories of sleep duration and insomnia symptoms and physical function. Women with consistently low insomnia symptoms had faster 40-foot walk speeds and 4-m walk speeds than women with consistently high insomnia symptoms. Thus, insomnia symptoms may contribute to slower gait speed, an important hallmark of physical functioning and a well-known predictor of increased fall risk.46–48 Thus, physical functioning may mediate the relationship between poor sleep and fall risk, though more research is needed to examine this hypothesis, which will be the focus of future work within SWAN.
Because urge incontinence and chronic pain are known correlates of both poor sleep and fall risk,49–52 we considered whether they may be mediators (ie, variable on the causal pathway) of the relationship between sleep and fall risk. However, inclusion of these variables in the multivariable models had only a minimal impact on point estimates. Thus, while urge incontinence and/or chronic pain may be risk factors for falls and may be associated with poor sleep, our results suggest that they do not mediate the relationship between sleep and falls, though conducting a formal mediation analysis was beyond the scope of this work. Future research may investigate whether these variables are mediators or whether they function as upstream variables, or predictors of poor sleep, as has been previously reported.49,53 If so, both urge incontinence and chronic pain may serve as important targets to improve sleep. There are, however, likely many pathways linking poor sleep and fall risk. For example, women with insomnia symptoms may be more likely to be out of bed in the middle of the night and may trip over something in the dark, resulting in a fall.
Results from bivariate analyses indicate that the relationship between sleep and fall risk may be stronger among women with depressive symptoms compared to those without depressive symptoms. These initial findings offer an interesting extension of the original question; further research in a cohort with adequate cell sizes (ie, enough participants with and without depressive symptoms in each level of insomnia symptom frequency) and where timing of depressive symptom onset is considered can better articulate the role of depressive symptoms in this association.
Our study contributes to the literature due to its numerous strengths. First, data span several years, thereby enabling us to establish temporality between sleep measures and future falls, thus avoiding reverse causation wherein a fall may have influenced sleep characteristics. Next, the data come from a community-based, racially and ethnically diverse sample of older adult women who have been followed since 1996, thereby allowing us to evaluate several important covariates in our analyses. Finally, measurement of sleep is captured through multiple constructs, including insomnia symptoms and duration, thereby providing more nuanced findings regarding sleep and falls. There appears to be a consistent link between poorer sleep and fall risks, even across the different dimensions of sleep, lending credibility to the idea that worse sleep may increase the risk of falling.
Despite these many strengths, there are important limitations to consider. First, sleep and falls were self-reported. While most sleep characteristics were subject to a relatively short window of recall, women may not accurately portray their recent sleep characteristics. This may lead to both over- and under-reporting of sleep problems. However, we also note that insomnia symptoms are frequently defined by self-report.54 Additionally, while a fall is often a notable outcome, some participants may not have remembered minor falls. Thus, we may be capturing a measure of more serious falls and an under-estimated number of falls. If this misclassification of falls is related to poor sleep, the relationship between sleep and falls may be underestimated. Additionally, long sleep duration, often defined as 9 or more hours of sleep/night, was not explored in analyses, as few women in this sample reported long sleep duration. Furthermore, SWAN did not capture experiences of falls until after sleep symptoms were assessed at V12; therefore, we were unable to exclude women with a fall prior to sleep symptom assessment. If available, this information may have added information about the role of sleep as an independent risk factor for overall falls.
Conclusions
Participants who frequently experienced trouble falling asleep or frequently woke in the middle of the night were more likely to report an incident fall. Furthermore, short sleep duration and frequent trouble falling asleep were associated with multiple incident falls. Consistent and sufficient quality sleep is one of the most important ways an older adult can support their health. As falls pose a significant risk factor for declines in health, including death, promoting good sleep may be an essential component in fall prevention.
Supplementary Material
Acknowledgments
Clinical Centers:
University of Michigan, Ann Arbor—Carrie Karvonen-Gutierrez, PI 2021-present, Siobán Harlow, PI 2011-2021, MaryFran Sowers, PI 1994-2011; Massachusetts General Hospital, Boston, MA—Sherri‐Ann Burnett‐Bowie, PI 2020-Present; Joel Finkelstein, PI 1999-2020; Robert Neer, PI 1994-1999; Rush University, Rush University Medical Center, Chicago, IL—Imke Janssen, PI 2020-Present; Howard Kravitz, PI 2009-2020; Lynda Powell, PI 1994-2009; University of California, Davis/Kaiser—Elaine Waetjen and Monique Hedderson, PIs 2020-Present; Ellen Gold, PI 1994-2020; University of California, Los Angeles—Arun Karlamangla, PI 2020-Present; Gail Greendale, PI 1994-2020; Albert Einstein College of Medicine, Bronx, NY—Carol Derby, PI 2011-present, Rachel Wildman, PI 2010-2011; Nanette Santoro, PI 2004-2010; University of Medicine and Dentistry—New Jersey Medical School, Newark—Gerson Weiss, PI 1994-2004; and the University of Pittsburgh, Pittsburgh, PA—Rebecca Thurston, PI 2020-Present; Karen Matthews, PI 1994-2020.
NIH program office: National Institute on Aging, Bethesda, MD—Rosaly Correa-de-Araujo 2020-present; Chhanda Dutta 2016-present; Winifred Rossi 2012-2016; Sherry Sherman 1994—2012; Marcia Ory 1994–2001; National Institute of Nursing Research, Bethesda, MD—Program Officers.
Central Laboratory: University of Michigan, Ann Arbor—Daniel McConnell (Central Ligand Assay Satellite Services).
Coordinating center: University of Pittsburgh, Pittsburgh, PA—Maria Mori Brooks, PI 2012-present; Kim Sutton-Tyrrell, PI 2001-2012; New England Research Institutes, Watertown, MA—Sonja McKinlay, PI 1995–2001.
Steering committee: Susan Johnson, Current Chair; Chris Gallagher, Former Chair.
We thank the study staff at each site and all the women who participated in SWAN.
Contributor Information
Jillian S Baker, Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, Michigan, United States.
Michelle M Hood, Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, Michigan, United States.
Leslie M Swanson, Department of Psychiatry, University of Michigan, Ann Arbor, Michigan, United States.
Christopher E Kline, Department of Health and Human Development, University of Pittsburgh, Pittsburgh, Pennsylvania, United States.
Kelly R Ylitalo, Department of Public Health, Robbins College of Health and Human Sciences, Baylor University, Waco, Texas, United States.
Jane A Cauley, Department of Epidemiology, School of Public Health, University of Pittsburgh, Pittsburgh, Pennsylvania, United States.
Robin R Green, Saul R. Korey Department of Neurology, Albert Einstein College of Medicine, Bronx, New York, United States.
Carrie A Karvonen-Gutierrez, Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, Michigan, United States.
Supplementary material
Supplementary data are available at The Journals of Gerontology, Series A: Biological Sciences and Medical Sciences online.
Funding
The Study of Women’s Health Across the Nation (SWAN) has grant support from the National Institutes of Health (NIH), DHHS, through the National Institute on Aging (NIA), the National Institute of Nursing Research (NINR), and the NIH Office of Research on Women’s Health (ORWH) (Grants U01NR004061; U01AG012505, U01AG012535, U01AG012531, U01AG012539, U01AG012546, U01AG012553, U01AG012554, U01AG012495, and U19AG063720). J.S.B. was supported by the National Institute on Aging (T32AG027708, PI: Kobayashi, Mezuk). The content of this article is solely the responsibility of the authors and does not necessarily represent the official views of the NIA, NINR, ORWH, or the NIH.
Conflict of interest
None declared.
Author contributions
Jillian S. Baker led writing, review, and editing for the manuscript. Michelle M. Hood led formal analysis. Jillian S. Baker and Michelle M. Hood supported methodology and visualization. Leslie M. Swanson, Christopher E. Kline, and Kelly R. Ylitalo further contributed to methodology. Jane A. Cauley, Robin R. Green, and Carrie A. Karvonen-Gutierrez contributed to funding acquisition. Carrie A. Karvonen-Gutierrez led conceptualization and supervision. All authors contributed to review and editing.
Ethics approval
Study activities were approved by the Institutional Review Board at each SWAN Study Site: University of Michigan IRB #00000245; Partners Healthcare IRB#: 1999P006353/MGH; Rush University Medical Center IRB#: 13021201-IRB01-AM04; University of California, Davis IRB# 260339-17; UCLA Office of the Human Research Protection Program IRB#11-002274-AM-00009; Albert Einstein American College of Medicine of Yeshiva University IRB#: 2005-012; University of Pittsburgh IRB #: REN16020248/IRB0402168; SWAN Coordinating Center IRB at University of Pittsburgh IRB#: REN15070236/IRB0709006.
Consent to participate
Informed consent was collected at each study visit, written in the case of in-person visits and verbal in the case of phone visits.
Data availability
SWAN provides access to public use datasets that include data from SWAN screening, the baseline visit, and follow-up visits (https://agingresearchbiobank.nia.nih.gov/). To preserve participant confidentiality, some, but not all, of the data used for this article are contained in the public use datasets. A link to the public-use datasets is also located on the SWAN website: http://www.swanstudy.org/swan-research/data-access/. Investigators who require assistance accessing the public use dataset may contact the SWAN Coordinating Center at the following email address: swanaccess@edc.pitt.edu.
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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
SWAN provides access to public use datasets that include data from SWAN screening, the baseline visit, and follow-up visits (https://agingresearchbiobank.nia.nih.gov/). To preserve participant confidentiality, some, but not all, of the data used for this article are contained in the public use datasets. A link to the public-use datasets is also located on the SWAN website: http://www.swanstudy.org/swan-research/data-access/. Investigators who require assistance accessing the public use dataset may contact the SWAN Coordinating Center at the following email address: swanaccess@edc.pitt.edu.