Abstract
OBJECTIVES
To test the hypothesis that subjective and objective sleep disturbances are associated with an increased risk of incident falls in older men.
DESIGN
The prospective observational MrOS Sleep Study.
SETTING
6 sites in the United States.
PARTICIPANTS
3101 community-dwelling men ≥ 67 years of age (mean, 76 years).
MEASUREMENTS
Subjective sleep measurements included daytime sleepiness [Epworth Sleepiness Scale (ESS)], sleep quality [Pittsburgh Sleep Quality Index (PSQI)] and total sleep time (TST). Objective sleep measurements included actigraphic TST and sleep efficiency (an index of fragmentation) and sleep disordered breathing (measured using in-home polysomnography). Fall frequency during the subsequent year was ascertained by tri-annual questionnaires. Recurrent falling was defined as having ≥2 falls in the subsequent year.
RESULTS
In multivariable-adjusted models, those with excessive daytime sleepiness (ESS > 10) but not poor subjective sleep quality (PSQI > 5) had an elevated odds of experiencing ≥2 falls in the subsequent year (OR=1.52 95% CI 1.14-2.03). Based on actigraphic recordings, the odds of having recurrent falls was elevated for men who slept ≤ 5 hours (OR=1.79; 1.22 – 2.60) relative to the referent group (> 7 to 8 hours). Actigraphically measured sleep efficiency was also associated with increased risk of falls, as was nocturnal hypoxemia (≥ 10% of sleep time with SaO2 < 90% OR=1.62; 1.17-2.24), but not the apnea hypopnea index.
CONCLUSION
Both subjective and objective sleep disturbances were associated with an increased risk of falls in older men, independent of confounders.
Keywords: falls, sleep, sleep disordered breathing, hypoxemia
INTRODUCTION
Falls pose a major health risk among older adults and are a leading cause of mortality, morbidity and premature nursing home placement,1 posing a significant burden to the healthcare system.2 It is estimated that falls occur yearly in a third of persons over the age of 65.3
As many as 50% of older adults report sleep problems.4 A few studies have examined the relationship between falls and sleep problems.5-12 All but one8 have focused on subjective sleep data. Most studies have had limitations including retrospective assessment of falls,5-7,10-12 or incomplete collection of covariate information.
To our knowledge there is only one previous study of sleep apnea and falls.12 No studies have examined the association of less severe levels of sleep disordered breathing (SDB) and falls.
To test the hypothesis that subjective and objective sleep disturbances are associated with an increased falls risk in older men, we measured sleep parameters in the Outcomes of Sleep Disorders in Older Men (MrOS Sleep) Study and collected information about incident falls. This study provides a unique opportunity to examine this question in a large cohort of community-dwelling older men. Comprehensive sleep measurements allow for assessment of the independent contributions of sleep duration, sleep fragmentation, daytime sleepiness and SDB to risk of falls.
METHODS
Participants
During the baseline examination (2000 to 2002), 5994 community-dwelling men 65 years or older were enrolled at 6 sites in the United States: Birmingham, Alabama; Minneapolis, Minnesota; Palo Alto, California; the Monongahela Valley near Pittsburgh, Pennsylvania; Portland, Oregon; and San Diego, California.13,14 The men had to be able to walk unassisted and must not have had a bilateral hip replacement.
The MrOS Sleep Study (2003 to 2005), an ancillary study of the MrOS cohort, recruited 3135 of these participants (56% of active survivors, >100% of recruitment goal) for a comprehensive sleep assessment. Of these, 3101 had data available for incident falls within the first year after the visit.
All men provided written informed consent, and the study was approved by the Institutional Review Board at each site.
Sleep Parameters
Self-Reported Sleep Parameters
Participants completed the Pittsburgh Sleep Quality Index (PSQI, range 0-21), a validated measure of subjective sleep quality over a one-month time period. A standard cut-off of >5 is indicative of poor sleep quality.15
Participants also completed the Epworth Sleepiness Scale (ESS, range 0-24), a self-administered questionnaire which classifies daytime sleepiness. An ESS score >10 indicates excessive daytime sleepiness.16
Participants were asked about total sleep time (sTST), which was categorized as ≤5, >5 to 7, >7 to 8, and >8 hours.
Actigraphic Parameters of Sleep-Wake Activity
Actigraphic characteristics of sleep-wake activity were estimated using an actigraph (SleepWatch-O, Ambulatory Monitoring, Inc., Ardsley, NY), a device used to detect movement that is similar in appearance to a wristwatch. Details of the data collection and sleep scoring algorithms utilized in the study have been published elsewhere.17,18
Participants were instructed to wear the actigraph on the non-dominant wrist for a minimum of 5 nights (5.2 ± 0.9 nights). Participants completed sleep diaries which were used in editing the data files as described previously.17
Variables used in this analysis included: 1) total sleep time (TST): the hours spent sleeping after “lights off”; 2) sleep efficiency (an index of sleep fragmentation): the percentage of time spent sleeping after “lights off”; 3) nap time: minutes scored as sleep for blocks of ≥5 minutes before “lights off”. All variables reflect average daily experience. Variables were categorized as: TST ≤5, >5 to 7, >7 to 8, >8 hours; sleep efficiency <70% vs. ≥70%; napping ≥2 vs. <2 hours.
Polysomnographic Parameters of Sleep Disordered Breathing
In-home sleep studies were completed using unattended, portable polysomnography (Safiro, Compumedics, Inc.®, Melbourne, AU). The recording montage included: C3/A2 and C4/A1 electroencephalograms, bilateral electrooculograms and a bipolar submental electromyogram; thoracic and abdominal respiratory inductance plethysmography; airflow (by nasal-oral thermocouple and nasal pressure cannula); finger pulse oximetry; lead I EKG; body position (mercury switch sensor); and bilateral tibialis leg movements (piezoelectric sensors). Centrally-trained, certified staff performed home visits using a protocol similar to that used in the Sleep Heart Health Study.19 The studies were scored at the Sleep Reading Center (Case Western Reserve University).
SDB was measured with the apnea hypopnea index (AHI). Nocturnal hypoxemia was defined with the percent of sleep time with arterial oxygen saturation (SaO2) below 90% (% of sleep time with SaO2 <90%). Apnea was defined as complete or near complete cessation of airflow for >10 seconds. Hypopneas were scored if clear reductions in breathing amplitude (at least 30% below baseline breathing) occurred, and lasted >10 seconds.20 Only apneas and hypopneas with a desaturation of ≥3% were included. AHI was calculated as the number of apneas and hypopneas per hour of sleep. Variables were categorized as: AHI <5, 5 to <15, 15 to <30 and ≥30; percent of sleep time with SaO2 <90% ≥10% vs. <10%.
Ascertainment of Incident Falls
Participants were contacted by postcard or telephone every four months to ascertain self-reported incident falls. Response rates exceeded 99%.
Falls reported on the first three tri-annual postcards returned after the Sleep Visit (covering 0.99 ± 0.02 years) were included in this analysis. The outcome for this analysis was recurrent falls, defined as ≥2 falls (versus ≤1 fall) in the year after the sleep assessment.
Other Measurements
Participants completed questionnaires about demographics, medical history, physical activity, smoking and alcohol use. Participants reported how often their sleep was disturbed by having to go to the bathroom. Lower urinary tract symptoms (LUTS) was assessed using the American Urological Association Symptom Index (AUA-SI).21 Medications used within the preceding 30 days were matched to their ingredient(s) based on the Iowa Drug Information Service (IDIS) Drug Vocabulary (College of Pharmacy, University of Iowa, Iowa City, IA).22 Depression was defined as ≥6 symptoms on the Geriatric Depression Scale (GDS).23 Anxiety was assessed with the Goldberg Anxiety Scale,24 activity using the Physical Activity Scale for the Elderly (PASE),25 and cognitive function using the Modified Mini-Mental State Exam (3MS).26 Body weight and height measurements were used to calculate the body mass index (BMI). Functional status was assessed for 5 instrumental activities of daily living (IADL).27,28 Physical function was measured by walking speed (time to walk 6 meters at usual pace).
Statistical Analyses
The sleep predictor variables were expressed categorically, defined similarly to previous publications for comparability or using standard cutpoints.
Characteristics of participants were compared across categories of actigraphic TST using ANOVA for normally distributed continuous variables, Kruskal-Wallis tests for skewed continuous variables, and chi-square tests for categorical variables. Similar comparisons were performed across categories of the other sleep predictors (data not shown).
The association between a given sleep parameter and risk of recurrent falls was examined using logistic regression, presented as odds ratios (OR) with 95% confidence intervals (CI). Models were minimally adjusted for age, ethnicity and clinic. Additional covariates were included in a multivariable model if they were related to recurrent falls and ≥1 sleep parameter in univariate analyses at P <.10.
Secondary analyses were performed for models with SDB predictors removing men from the analyses who reported using CPAP at the beginning of the study (n=55) or during follow-up (n=64). Multivariable models including the predictors of actigraphic TST and sleep fragmentation, excessive daytime sleepiness, and nocturnal hypoxemia were examined to determine if associations to recurrent falls were independent.
All significance levels reported were two-sided. All analyses were conducted using SAS version 9.2 (SAS Institute, Inc., Cary, NC).
RESULTS
Of the 3101 men in our analyses, 440 (14.2%) suffered ≥2 falls during the year after the sleep assessments. The mean age of the analysis cohort was 76.4 ± 5.5 years and 89.9% were Caucasian. Mean actigraphic TST and sTST were similar (6.4 ± 1.2 hours and 6.9 ± 1.2 hours, respectively), with approximately 12% of men with short sleep duration (≤5 hours; self-reported 11.7%, actigraphic 12.3%) and about 6% with long sleep duration (>8 hours; self-reported 5.6%, actigraphic 7.1%). On average, participants had a sleep efficiency of 78.1% (± 12.0%) and napped about an hour a day. About half of the men (43.2%) had SDB (AHI ≥15), and 12.3% spent ≥10% of sleep time with SaO2 <90%. In addition, 12.8% reported excessively daytime sleepiness, and 44.1% rated their sleep quality as poor.
Many characteristics differed significantly across categories of actigraphically measured TST: age, BMI, cognitive function, physical activity, presence of ADL/IADL impairments, race, antidepressant use, level of alcohol use, smoking status, walking speed, resting SaO2 level, and a history of comorbid conditions (Table 1). Of the 42 men on trazadone, 32 (76%) reported taking it to help them sleep. Similarly, of the men taking the following medications, the percentage who reported taking them to help sleep was: long-acting benzodiazepines 50%; short-acting benzodiazepines 73%; antidepressants 22%.
Table 1.
Characteristics of 3024 Men by Actigraphic Total Sleep Time Category.
| Total Sleep Time, hours |
|||||
|---|---|---|---|---|---|
| Characteristic | < 5 (N= 372) | > 5 to 7 (N= 1698) | > 7 to 8 (N= 738) | > 8 (N= 216) | P Valuea |
| Age, mean +/- SD, y | 76.5 ± 5.7 | 76.1 ± 5.3 | 76.5 ± 5.7 | 77.7 ± 5.9 | <.001 |
| Body mass index, kg/m2, mean ± SD | 29.3 ± 4.8 | 27.1 ± 3.6 | 26.5 ± 3.5 | 26.8 ± 3.5 | <.001 |
| 3MS score (range 0-100), mean ± SD | 92.5 ± 6.2 | 93.0 ± 5.9 | 92.7 ± 6.0 | 91.1 ± 6.7 | <.001 |
| PASE physical activity score, mean ± SD | 140.2 ± 74.8 | 150.6 ± 71.9 | 140.6 ± 68.8 | 139.2 ± 69.4 | <.001 |
| One or more ADL/IADL impairments, n (%) | 110 (29.6) | 340 (20.0) | 139 (18.8) | 48 (22.2) | <.001 |
| One or more ADL impairmentb, n (%) | 94 (25.3) | 249 (14.7) | 105 (14.2) | 34 (15.7) | <.001 |
| One or more IADL impairmentc, n (%) | 59 (16.0) | 198 (11.7) | 69 (9.4) | 32 (14.8) | .007 |
| Depression, GDS score ≥ 6, n (%) | 31 (8.3) | 98 (5.8) | 53 (7.2) | 17 (7.9) | .20 |
| Race/Ethnicity, n (%) | |||||
| Caucasian | 322 (86.6) | 1525 (89.8) | 677 (91.7) | 198 (91.7) | .002 |
| African-American | 28 (7.5) | 60 (3.5) | 18 (2.4) | 6 (2.8) | |
| Other | 22 (5.9) | 113 (6.7) | 43 (5.8) | 12 (5.6) | |
| Current benzodiazepine use, n (%) | 19 (5.1) | 67 (4.0) | 37 (5.0) | 15 (7.0) | .18 |
| Current short-acting benzodiazepine used | 14 (3.8) | 47 (2.8) | 20 (2.7) | 9 (4.2) | .51 |
| Current long-acting benzodiazepine used | 5 (1.3) | 22 (1.3) | 17 (2.3) | 6 (2.8) | .16 |
| Current antidepressant use, n (%) | 34 (9.1) | 120 (7.1) | 52 (7.1) | 31 (14.4) | .001 |
| Current non-benzodiazepine anxiolytic/hypnotic use, n (%) | 9 (2.4) | 36 (2.1) | 15 (2.0) | 2 (0.9) | .65 |
| Goldberg anxiety score (range 0-9), mean ± SD | 1.2 ± 2.2 | 0.9 ± 1.8 | 1.0± 1.9 | 0.9 ± 1.8 | .26 |
| Alcohol intake, drinks/week, n (%) | |||||
| 0-2 | 224 (60.7) | 1024 (60.6) | 411 (55.8) | 125 (58.7) | .04 |
| 3-13 | 122 (33.1) | 585 (34.6) | 281 (38.1) | 68 (31.9) | |
| 14+ | 23 (6.2) | 82 (4.9) | 45 (6.1) | 20 (9.4) | |
| Smoking, n (%) | |||||
| Never | 123 (33.1) | 683 (40.2) | 296 (40.1) | 90 (41.7) | .002 |
| Past | 231 (62.1) | 985 (58.0) | 429 (58.1) | 124 (57.4) | |
| Current | 18 (4.8) | 30 (1.8) | 13 (1.8) | 2 (0.9) | |
| Have trouble sleeping due to having to get up to use bathroom, n (%) | |||||
| None | 30 (8.1) | 143 (8.4) | 49 (6.6) | 16 (7.4) | .21 |
| <1/week | 36 (9.7) | 116 (6.8) | 49 (6.6) | 13 (6.0) | |
| 1-2/week | 45 (12.1) | 192 (11.3) | 83 (11.3) | 15 (6.9) | |
| 3+/week | 261 (70.2) | 1246 (73.4) | 557 (75.5) | 172 (79.6) | |
| Lower Urinary Tract Symptoms, n (%) | |||||
| Mild (AUA-SI 0-7) | 166 (45.1) | 811 (47.9) | 322 (43.8) | 112 (52.1) | .11 |
| Moderate (AUA-SI 8-19) | 178 (48.4) | 736 (43.5) | 338 (46.0) | 87 (40.5) | |
| Severe (AUA-SI 20-35) | 24 (6.5) | 146 (8.6) | 75 (10.2) | 16 (7.4) | |
| ≥ 1 selected comorbid conditions, n (%) | 198 (53.4) | 749 (44.2) | 322 (43.6) | 98 (45.4) | .01 |
| Stroke | 17 (4.6) | 55 (3.2) | 30 (4.1) | 12 (5.6) | .26 |
| Diabetes | 67 (18.0) | 213 (12.6) | 92 (12.5) | 29 (13.4) | .04 |
| Parkinsons disease | 6 (1.6) | 16 (0.9) | 11 (1.5) | 5 (2.3) | .26 |
| COPD | 29 (7.8) | 91 (5.4) | 27 (3.7) | 10 (4.6) | .03 |
| Cardiovascular diseasee | 144 (38.8) | 549 (32.4) | 236 (32.0) | 68 (31.5) | .09 |
| Walking speed, m/s, mean ± SD | 1.1 ± 0.2 | 1.2 ± 0.2 | 1.2 ± 0.2 | 1.1 ± 0.2 | <.001 |
P-values for continuous variables from ANOVA for normally distributed data, a Kruskal-Wallis test for skewed data. P-values for categorical data from a chi-square test for homogeneity.
ADLs include difficulty walking 2-3 blocks or climbing 10 steps.
IADLs include difficulty preparing meals, doing heaving housework, or shopping.
Short-acting benzodiazepines include estazolam, alprazolam, temazepam, lorazepam, triazolam, oxazepam, and midazolam. Long-acting benzodiazepines include chlordiazepoxide, clorazepate, diazepam, flurazepam, and clonazepam.
Cardiovascular disease includes myocardial infarction, angina, congestive heart failure, bypass surgery, angioplasty, and pacemaker placement.
SD= standard deviation; 3MS= Modified Mini-Mental State Exam; PASE= Physical Activity Scale for the Elderly; IADL= instrumental activities of daily living; GDS= Geriatric Depression Scale; COPD=chronic obstructive pulmonary disease.
Associations Between Self-Reported Sleep Parameters and Recurrent Falls
After minimal adjustment, poor sleep quality and excessive daytime sleepiness were significantly associated with a greater odds of having recurrent falls (Table 2). After further adjustment for multiple confounders the association between excessive daytime sleepiness and recurrent falls remained significant but was attenuated in size, and the association between poor sleep quality and falls was no longer significant. Both short (≤5 hours) and long (>8 hours) sTST were associated with a significant 1.7-fold increased odds of having recurrent falls after minimal adjustment when compared to those who slept >7 to 8 hours. These associations were no longer significant after multivariable adjustment.
Table 2.
Self-Reported and Actigraphically Measured Sleep Parameters and Risk of Recurrent Fallsa During 1 Year of Follow-up.
| Odds Ratio (95% Confidence Interval) | |||
|---|---|---|---|
| Predictor | N (%) of Recurrent Falls | Minimally Adjustedb | Multivariable Adjustedc |
| Subjective Sleep Parameters | |||
| Pittsburgh Sleep Quality Index | |||
| ≤ 5 (n= 1734) | 204 (11.8) | 1 (Reference) | 1 (Reference) |
| > 5 (n= 1365) | 235 (17.2) | 1.53 (1.24, 1.88) | 1.06 (0.84, 1.34) |
| Epworth Sleepiness Scale | |||
| ≤ 10 (n = 2702) | 354 (13.1) | 1 (Reference) | 1 (Reference) |
| > 10 (n = 398) | 85 (21.4) | 1.78 (1.36, 2.33) | 1.52 (1.14, 2.03) |
| Self-Reported Total Sleep Time | |||
| ≤ 5 hours (n = 363) | 69 (19.0) | 1.73 (1.23, 2.44) | 1.34 (0.93, 1.95) |
| > 5 to 7 hours (n = 1760) | 232 (13.2) | 1.10 (0.86, 1.42) | 1.07 (0.82, 1.39) |
| > 7 to 8 hours (n = 805) | 102 (12.7) | 1 (Reference) | 1 (Reference) |
| > 8 hours (n = 172) | 36 (20.9) | 1.67 (1.09, 2.57) | 1.52 (0.96, 2.39) |
| Actigraphic Sleep Parameters | |||
| Total Sleep Time | |||
| > 5 to 7 hours (n = 1698) | 243 (14.3) | 1.41 (1.07, 1.84) | 1.42 (1.08, 1.89) |
| > 7 to 8 hours (n = 738) | 83 (11.3) | 1 (Reference) | 1 (Reference) |
| > 8 hours (n = 216) | 33 (15.3) | 1.33 (0.86, 2.07) | 1.28 (0.80, 2.04) |
| Sleep Efficiency | |||
| < 70% (n = 2448) | 111 (19.3) | 1.56 (1.23, 1.99) | 1.32 (1.01, 1.72) |
| ≥ 70% (n = 576) | 320 (13.1) | 1 (Reference) | 1 (Reference) |
| Nap Time | |||
| < 2 hours (n = 2570) | 345 (13.4) | 1 (Reference) | 1 (Reference) |
| ≥ 2 hours (n = 438) | 81 (18.5) | 1.35 (1.03, 1.77) | 1.34 (0.96, 1.87) |
examination.
Adjusted for age, race, and clinic.
Adjusted for age, race, clinic, body mass index, cognitive function, physical activity, lower urinary tract symptoms, instrumental activities of daily living, depression, long- and short-acting benzodiazepine use, antidepressant use, anxiety, comorbidities, and walking speed.
Associations Between Actigraphic Sleep-Wake Activity and Recurrent Falls
After minimal adjustment, participants with levels of actigraphically measured TST ≤5 hours experienced a two-fold increase in odds of recurrent falls, whereas those who slept > 5 to 7 hours had a significant 1.4-fold increase in risk of falls compared to the reference group (>7 to 8 hours; Table 2). These associations remained significant after multivariable adjustment. After minimal adjustment, compared to those with sleep efficiency ≥70%, those with actigraphic sleep efficiency <70% had a 56% increase in risk of recurrent falls. After multivariable adjustment the association between sleep efficiency and recurrent falls was somewhat attenuated but remained significant. More napping was weakly associated to recurrent falls in minimally adjusted models, but lost significance after further adjustment.
Associations Between Sleep-Disordered Breathing and Recurrent Falls
AHI was not associated with risk of falls. After multivariable adjustment, nocturnal hypoxemia (≥ 10% of sleep time spent with SaO2 <90%) was associated with a significant 1.6-fold increased odds of recurrent falls when compared to those who spent <10% of sleep time with SaO2 <90% (Figure 1). Results were similar after removing 119 men who reported use of CPAP at the start of the study or during follow-up.
Figure 1.
Sleep Disordered Breathing and Hypoxemia and Risk of Recurrent Fallsa During 1 Year of Follow-upb
aRecurrent falls are defined as 2 or more falls in the subsequent year after the examination.
bAdjusted for age, race, clinic, body mass index, cognitive function, physical activity, lower urinary tract symptoms, instrumental activities of daily living, depression, long- and short-acting benzodiazepine use, antidepressant use, anxiety, comorbidities, and walking speed. SaO2=arterial oxygen saturation.
Secondary Analyses
When examining predictors of actigraphic TST and sleep efficiency, hypoxemia and excessive daytime sleepiness in the same fully adjusted model the associations to recurrent falls remained unchanged for excessive daytime sleepiness and nocturnal hypoxemia but were no longer significant for sleep efficiency and TST [OR (95%CI) TST ≤5 hours: 1.44 (0.90 – 2.33); sleep efficiency<70%: 1.14 (0.81 – 1.61); % of sleep time with SaO2 <90% ≥10%: 1.60 (1.15 – 2.23); ESS >10: 1.50 (1.11 – 2.03)].
We explored different cutpoints for the PSQI (> 8 vs. ≤ 8) and AHI (≥ 15 vs. < 15; ≥ 30 vs. < 30), and also performed analysis for the AHI defined using events associated with ≥4% oxygen desaturation (rather than ≥3%). Results were very similar and are not presented.
To explore whether associations between nocturnal SaO2 and risk of falls could be explained by SaO2 levels during wakefulness, we further adjusted these analyses for resting SaO2 levels. Results were similar.
DISCUSSION
Older men with actigraphically measured short sleep duration and lower sleep efficiency; and more sleep time spent with SaO2<90% were at increased risk of recurrent falls, as were men with self-reported excessive daytime sleepiness. When considering short sleep duration, sleep efficiency, daytime sleepiness and nocturnal hypoxemia together, daytime sleepiness and nocturnal hypoxemia were independently associated with recurrent falls, suggesting there may be multiple pathways by which disturbed sleep may increase risk of falls.
Among the subjective sleep parameters examined, daytime sleepiness was strongly related to recurrent falls, and this association was also independent of other sleep parameters. There were no adjusted associations between sleep quality or sTST and falls. Other studies have examined this association, with conflicting results. 6,10-12 The significance of the association even after adjusting for other sleep parameters suggest that this self-reported symptom may provide a clinically useful measure for identifying individuals at increased fall risk. Few studies have examined self-reported sleep duration and risk of falls, finding either no association with falls,6,10 or an association with falls among women but not men.5,7
Actigraphic short sleep duration and lower sleep efficiency were also associated with recurrent falls. There were no associations seen between actigraphic napping or long sleep duration and risk of falls. Similar analyses performed in the Study of Osteoporotic Fractures (SOF) among elderly women,8 yielding similar results. Discrepancies between the associations of the self-reported and actigraphically measured TST may be due to misclassification by self-report, 29 possibly reflecting diminished ability of older adults to accurately report sleep duration.
We found no association with SDB and risk of falls, but did find an association between nocturnal hypoxemia and falls risk that was independent of TST, sleep fragmentation, and daytime sleepiness. To our knowledge, there is only one other study of the relationship of SDB and risk of falls,12 which defined sleep apnea by self-report of a diagnosis based on polysomnography. This previous study found a doubling of likelihood of falls among apneics. In the current study there were no significant associations between AHI and risk of falls, suggesting that in older adults fall risk is more strongly associated with nocturnal hypoxemia than respiratory disturbances.
The association between poor sleep and risk of falls could be mediated through many mechanisms, including impaired cognitive function, depression, balance problems, use of medications, or other factors. Adjustment for many potential explanatory variables suggests that these factors may explain some, but not all of the relationship between poor sleep and risk of falls.
This study has many strengths, including the large sample size and prospective ascertainment of falls. The collection of data on numerous sleep traits is unique to this study, allowing for a combined analysis. There were also limitations. Results are not generalizable to other groups. Actigraphy cannot reliably separate daytime napping from periods of extreme inactivity. We cannot account for the fact that sleep patterns could have changed during the year, and may not accurately reflect the participants’ sleep at the time the fall occurred. The time and circumstances of falling were not collected, which may have provided information on potential mechanisms for associations. Finally, we did not use a validated insomnia questionnaire in our study. While several of our sleep exposures likely correlate with insomnia, we are unable to replicate findings from other studies which reported significant associations between insomnia and risk of falls30.
In conclusion, actigraphic measures of sleep duration and fragmentation were associated with greater risk of recurrent falls among older men. Excessive daytime sleepiness and nocturnal hypoxemia were also related to greater fall risk, independent of each other, TST and sleep fragmentation. Future studies using comprehensive objective measures of sleep should confirm the interrelationships between sleep characteristics to determine if these contribute independently towards risk of falls.
ACKNOWLEDGMENTS
This work was previously presented at the 2006 meeting of the Associated Professional Sleep Societies (APSS)
Funding:
The Osteoporotic Fractures in Men (MrOS) Study is supported by National Institutes of Health funding. The following institutes provide support: the National Institute of Arthritis and Musculoskeletal and Skin Diseases (NIAMS), the National Institute on Aging (NIA), the National Cancer Institute (NCI), the National Center for Research Resources (NCRR) and NIH Roadmap for Medical Research under the following grant numbers: U01 AR45580, U01 AR45614, U01 AR45632, U01 AR45647, U01 AR45654, U01 AR45583, U01 AG18197, U01-AG027810, UL1 RR024140.
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.
Dr. Ancoli-Israel is also supported by NIA grant AG08415.
All co-authors have received grant support from the NIH (and supporting agencies) grant as listed under Funding Sources on the title page.
Dr. Ancoli-Israel has consulted for or is on the scientific advisory board of Astra Zeneca, Ferring Pharmaceuticals Inc., GlaxoSmithKline, Hypnocore, Johnson & Johnson, Merck, NeuroVigil, Inc., Orphagen Pharmaceuticals, Pfizer, Philips, Purdue Pharma LP, sanofiaventis,
Dr. Redline is the incumbent of an endowed chair professorship donated to Harvard Medical School by Dr. Peter Farrell, the founder and board chairman of ResMed, Inc, has received research support from Dymedix, Inc, and has received equipment for use in research from Philips Respironics.
Dr. Ensrud serves as a consultant on a Data Monitoring Committee for Merck Sharpe & Dohme.
Sponsor's Role:
The funding agencies had no direct role in the conduct of the study; the collection, management, analyses and interpretation of the data; or preparation or approval of the manuscript.
Investigators in the Outcomes of Sleep Disorders in Older Men study (MrOS Sleep):
Coordinating Center (California Pacific Medical Center Research Institute and University of California, San Francisco): K.L. Stone (Principal Investigator), D.C. Bauer (co-Investigator), S.R. Cummings (co-Investigator), N. Goldschlager (co-Investigator), G. Tranah (co-Investigator), P. Varosy (co-Investigator), K. Yaffe (co-Investigator), P.M. Cawthon (co-Investigator), R. Fullman (Project Director), R. Benard, T. Blackwell, L. Concepcion, J. Diehl, S. Ewing, C. Fox, M. Jaime-Chavez, E. Kwan, S. Litwack, W. Liu, L.Y. Lui, K. Peters, W. Sauer, J. Schneider, R. Scott, D. Tanaka, J. Ziarno; Administrative Center (Oregon Health & Sciences University): E. Orwoll (Principal Investigator), C. Lee (co-Investigator), C. Pedersen (Project Director), M. Abrahamson, L Masterfield; University of Alabama, Birmingham: C.E. Lewis (Principal Investigator), J. Shikany (co-Investigator), P. Johnson (Project Director), M. Young, S. House, N. Webb, S. Felder, J. King, T. Johnsey, C. Collier, K. Hardy, J. Smith, H. Dwivedi; University of Minnesota: K. Ensrud (Principal Investigator), S. Diem (co-Investigator), H. Fink (co-Investigator), N. Nelson (Clinic Coordinator), R. Andrews, S. Fillhouer, M. Forseth, K Jacobson, S. Luthi, K. Moen, M. Paudel, P. Van Coevering, S. Ziesche; Stanford University: M. Stefanick (Principal Investigator), A. Hoffman (co-Investigator), K. Kent, N. Ellsworth, S. Belding, A. Krauss; University of Pittsburgh: J. Cauley (Principal Investigator), J. Zmuda (co-Investigator), M. Danielson (Study Administrator), L. Harper (Project Director), L. Buck (Clinic Coordinator), D. Cusick, M. Gorecki, C. Newman; University of California, San Diego: E. Barrett-Connor (Principal Investigator), S. Ancoli-Israel (co-Investigator), T. Dam (co-Investigator), ML Carrion-Petersen (Project Director), D. Claflin, N. Kamantigue, K. Marksbury Jappe, P. Miller, M. Stephens; Brigham and Women's Hospital Sleep Reading Center: S. Redline (Principal Investigator), S. Surovec (Project Administrator), D. Mobley (Chief Polysomnologist), M. Rueschman (Programmer Analyst), M. Morrical (Polysomnologist), J. Arnold (Polysomnologist), R. Nawabit (Polysomnologist).
Footnotes
Author Contributions:
Katie L. Stone, PhD- study concept and design, acquisition of data, interpretation of data, preparation of manuscript
Terri L. Blackwell, MA – analysis and interpretation of data, preparation of manuscript
Sonia Ancoli-Israel, PhD- study concept and design, interpretation of data, critical review of manuscript
Jane A. Cauley, DrPH- study concept and design, acquisition of data, interpretation of data, critical review of manuscript
Susan Redline, MD, MPH- study concept and design, acquisition of data, interpretation of data, critical review of manuscript
Lynn M. Marshall, ScD- study concept and design, interpretation of data, critical review of manuscript
Kristine E. Ensrud, MD, MPH - study concept and design, acquisition of data, interpretation of data, critical review of manuscript
Conflicts of Interest:
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