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Participation in Older Adult Physical Activity Programs and Risk for Falls Requiring Medical Care, Washington State, 2005-2011

Mikael Anne Greenwood-Hickman, MPH; Dori E. Rosenberg, PhD, MPH; Elizabeth A. Phelan, MD, MS; Annette L. Fitzpatrick, PhD, MA

Suggested citation for this article: Greenwood-Hickman MA, Rosenberg DE, Phelan EA, Fitzpatrick AL. Participation in Older Adult Physical Activity Programs and Risk for Falls Requiring Medical Care, Washington State, 2005–2011. Prev Chronic Dis 2015;12:140574. DOI: http://dx.doi.org/10.5888/pcd12.140574.

PEER REVIEWED

Abstract

Introduction
Physical activity is known to prevent falls; however, use of widely available exercise programs for older adults, including EnhanceFitness and Silver Sneakers, has not been examined in relation to effects on falls among program participants. We aimed to determine whether participation in EnhanceFitness or Silver Sneakers is associated with a reduced risk of falls resulting in medical care.

Methods
A retrospective cohort study examined a demographically representative sample from a Washington State integrated health system. Health plan members aged 65 or older, including 2,095 EnhanceFitness users, 13,576 Silver Sneakers users, and 55,127 nonusers from 2005 through 2011, were classified as consistent users (used a program ≥2 times in all years they were enrolled in the health plan during the study period); intermittent users (used a program ≥2 times in 1 or more years enrolled but not all years), or nonusers of EnhanceFitness or Silver Sneakers. The main outcome was measured as time-to-first-fall requiring inpatient or out-of-hospital medical treatment based on the International Classification of Diseases, 9th Revision, Clinical Modification, Sixth Edition and E-codes.

Results
In fully adjusted Cox proportional hazards models, consistent (hazard ratio [HR], 0.74; 95% confidence interval [CI], 0.63–0.88) and intermittent (HR, 0.87; 95% CI, 0.8–0.94) EnhanceFitness participation were both associated with a reduced risk of falls resulting in medical care. Intermittent Silver Sneakers participation showed a reduced risk (HR, 0.93; 95% CI, 0.90–0.97).

Conclusion
Participation in widely available community-based exercise programs geared toward older adults (but not specific to fall prevention) reduced the risk of medical falls. Structured programs that include balance and strength exercise, as EnhanceFitness does, may be effective in reducing fall risk.

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Introduction

Falls affect 30% to 40% of community-living adults over age 65 every year. Half of falls result in some type of injury, and falls may contribute to declining function, loss of independence, illness, and death (1). Fall-related health care costs the United States nearly $30 billion annually (2). For many older adults, fear of falls and their physical and psychological aftermath are a serious concern, often leading to self-imposed restrictions on activity (3,4).

Fall prevention research has found that regular physical activity incorporating strength and balance exercise can reduce falls, fall-related injuries, and falls resulting in medical care (5–7). Community-based programs intended to enhance older adults’ access to age-appropriate exercise may be beneficial in preventing falls. In a large integrated health system in Washington State, Group Health Cooperative (GHC), Medicare-qualifying adult members aged 65 or older are eligible to participate in 2 nationally disseminated exercise programs at no additional cost. EnhanceFitness (EF), an evidence-based intervention funded by the Centers for Disease Control and Prevention, offers community-based group exercise classes led by qualified instructors. Another program, Silver Sneakers (SS), provides full membership to participating fitness centers nationwide in more than 10,000 locations (8,9). Previous investigations have suggested that specific fall prevention exercise programs can be effective in preventing falls among community-dwelling older adults (10,11). However, these programs targeted fall prevention and were not as widely disseminated as EF and SS are. Although general exercise programs for older adults reach a much wider audience, their impact on fall prevention has not been studied. Although EF participation has been shown to improve physical function (12,13), no previous studies have examined associations between participation in either EF or SS and fall-related outcomes (14). Preventing falls and the costs and system burden they create is a relevant outcome from both the societal and health plan perspectives.

Our objective was to examine the relationship between participation in EF or SS and risk for a fall requiring medical treatment (termed “medical fall” hereafter), in a sample of GHC Medicare plan enrollees. On the basis of existing evidence that physical activity plays an important role in fall prevention, we hypothesized that consistent users of either program would have lower risks of a medical fall compared with nonusers.

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Methods

Study design and sample

A retrospective cohort study was conducted among a demographically representative sample of the Seattle-area population of older adults. The exposure of interest was defined as participation in either the EF or SS program and the outcome was the first occurrence of a medical fall during the study period (2005–2011). Participants were members older than age 65 of GHC, which serves patients throughout the states of Washington and Northern Idaho. Participants were selected by using the following eligibility criteria: Integrated Group Practice members (receiving medical care primarily within the GHC system), continuous enrollment for at least 1 year, aged 65 to 98, and eligible for the Medicare EF and SS programs for some portion of 2005 through 2011. Individuals were excluded if they met any of the following specific criteria: residing in long-term care or nursing home setting, receiving hospice care (International Classification of Diseases, 9th Revision, Clinical Modification [ICD-9-CM] code V66.7), wheelchair-bound (V46 or V53.8), aged 99 or older, or having a diagnosis of a serious mental health or substance use disorder (290–319.99, not including depression [296.2, 296.3, 300.4, 311] anxiety [300.02], or dementia [290]). All demographic, health, and medical record data were extracted from GHC electronic health records and merged with participation data supplied by the EF and SS programs. GHC has complete records of health care use and costs, and these data have been validated and are frequently used for research (15). This research was approved by the institutional review boards of both Group Health Research Institute and the University of Washington.

EF, a nationally disseminated, evidence-based exercise program (16) for older adults, offers group-based exercise classes in community settings. Each class lasts 1 hour and follows a set format, including exercises targeting cardiovascular endurance (20–25 min), strength (20 min), and balance and flexibility (10 min), all of which are adaptable to individual ability level (8,17). SS is a benefit offered to Medicare Advantage enrollees that allows access to more than 10,000 fitness facilities nationwide. People who enroll in SS are granted access to exercise equipment and group exercise classes offered through selected fitness centers. SS also offers older adult fitness classes through these facilities that participants may choose to attend (9).

Exposure and outcome assessments

Participation in either program was defined as documentation of attendance at either the EF or SS programs at least twice in a given year. Because specific attendance counts were not available in the data set, participation in the EF and SS programs was stratified into 3 levels in an effort to approximate regular exposure to the programs. These strata were 1) consistent users, who participated every year they were enrolled in GHC during the study period (2005–2011); 2) intermittent users, who participated at least 1 but not all years they were enrolled; and 3) nonusers, who never participated in either program while enrolled. Mean duration of enrollment in GHC for this sample was 4.7 (standard deviation, 2.3) years.

Falls requiring medical treatment were identified through the use of both inpatient and outpatient recording of ICD-9-CM codes (805–829: fractures, including hip fracture; 830–839: joint dislocations; and 800–804 and 850–854: intracranial injury) and E-codes (880–888: accidental fall injury) indicating medical treatment of an injury related to having had a fall (18). The presence of an E-code or one of the listed ICD-9-CM codes was sufficient to define a study outcome consistent with definitions developed in previous research (2,19).

Covariates

The following covariates were extracted from electronic health record data of both inpatient and outpatient treatment in the GHC system and were assessed as potential confounders to the relationship of interest (ie, the relationship between program participation and risk of a medical fall): age (continuous), sex (male/female), race/ethnicity (white, black, Asian, Hispanic, Native American), body mass index (BMI, kg/m2, calculated from most recent height and weight for each year; continuous), smoking status (yes/no), and Charlson Comorbidity Index score (20), a general measure of comorbidity based on the presence or absence of 19 conditions weighted for severity (continuous). Small amounts of missing data were noted in the variables for race/ethnicity (6.1%), BMI (19.7%), and smoking (2.3%); models including these variables excluded individuals with missing data. Because of their strong association with increased fall risk, pharmacy data were used to indicate whether participants used sedatives or sleeping medications (2 fills within 90 days for benzodiazepines or prescription sleep medications; yes/no).

The following comorbidities were identified through ICD-9 codes in the medical record and were also included in the analysis to account for their potentially confounding relationship with program participation and risk of a medical fall: diabetes status (249–251; yes/no); diagnosis of dementia (290, 294.1, 294.2, 331.0, 331.1, 331.82, 331.83; yes/no), walking disorder (719.7: difficulty walking; 781.2: abnormal gait; and 728.87: generalized weakness; yes/no), osteoarthritis (715, 721.0–721.9; yes/no), osteoporosis (733; yes/no), musculoskeletal conditions (712–719: arthropathy, rheumatoid arthritis, joint derangement; yes/no), and visual impairment (365: glaucoma; 366: cataract; 362.50–362.53, 362.55, 362.63: macular degeneration; and 362.01–362.03, 362.10, 362.11, 362.2, 363.31: retinopathy; yes/no). All comorbidity diagnosis variables were time-varying and constructed to reference a diagnosis for the given condition in the year before the year of interest.

Data analysis

Initially, Kaplan–Meier survival curves were fit to the data to depict the time-to-medical-fall for each group. Time-to-event analyses were then conducted using days from entry into the study to the date of a fall, loss to follow-up, or the end of the study period (December 31, 2011). Individuals were censored if loss to follow-up occurred because of death or withdrawal from the GHC system. Specific dates for these events were unavailable and therefore defined as June 30 of the last year an individual appeared in the data set if that year was before 2011. A series of Cox Proportional Hazard models compared time-to-fall of nonusers to that of consistent and intermittent users of each program. Several models were constructed: a crude model (no adjustment for confounders), a demographic model (adjusted for age, race, and sex), and a full model (adjusted for age, race, sex, BMI, smoking, and the following comorbidities: dementia, walking disorder, osteoarthritis, osteoporosis, musculoskeletal conditions, and visual impairment). In fully adjusted models, individuals with missing values for a covariate were dropped from the analysis. All analyses were conducted using Stata 13 (StataCorp, LP).

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Results

Compared with nonusers, consistent and intermittent users of either program were more likely to be female, less likely to smoke, and had lower Charlson comorbidity scores ( Tables 1 and 2). However, all users of either EF or SS were more likely to have had a diagnosis for osteoarthritis, osteoporosis, visual impairment, and musculoskeletal conditions during the study period, whereas consistent users of either EF or SS were less likely to use sedatives or sleeping medications compared with nonusers and intermittent users of either program. EF users had a mean attendance of 65 times in the year (median of 67), while SS users’ mean attendance was 51 times in 2011 (median of 33). Survival curves (Figure) suggest that consistent EF users had the greatest proportion of the sample remaining without a medical fall at the end of the study period, followed by intermittent EF users, and, finally, nonusers. Both consistent and intermittent SS groups had similar curves, with both showing reduced time to falls compared with the nonuser group.

  Kaplan–Meier survival curves of time to first medical fall for EnhanceFitness and Silver Sneakers users

Figure. Kaplan–Meier survival curves of time to first medical fall for EnhanceFitness and Silver Sneakers users among enrollees of the Group Health Integrated Group Practice (Seattle, Washington), by consistent, intermittent, and nonusers. [A text description of this figure is also available.]

In hierarchically adjusted Cox regression models ( Table 3), a decreased risk of medical fall was found for both consistent and intermittent users of EF compared with nonusers, after adjusting for demographics. The same pattern remained in the fully adjusted model, showing consistent EF users to have a 26% decreased risk of a medical fall compared with nonusers, whereas intermittent EF users had a 13% decreased risk. The demographic model for SS use indicated a significant reduction for consistent and intermittent users. However, the full model yielded similar results for intermittent users (7% decrease in fall risk) but not for consistent SS users.

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Discussion

To our knowledge, this is the first study to directly investigate the impact on the risk of medical falls of 2 exercise programs not specific to fall prevention. Consistent use of EF, our proxy for regular participation in the program over several years, was associated with the greatest reduction in risk of a medical fall, lowering risk by 20% to 30%. However, even intermittent use of the EF program also decreased the risk of medical falls. Although this finding corroborates previous evidence suggesting that strength and balance exercises, major components of the EF program, are essential to reducing fall and fall injury risk in older adults (5,21,22), further investigation involving more precise measure and categorization of participation is needed.

The results were less clear for the impact of the SS program. Unadjusted findings were suggestive of moderate risk reduction, but full adjustment yielded significant reduction only for intermittent users. This risk reduction was smaller than those seen for EF but was in the range of a 10% to 15% reduction in medical falls. However, the finding of significant impacts only for intermittent users runs counter to established findings in the literature, which suggest that consistent physical activity yields the strongest health impacts (5–7). This result makes drawing conclusions challenging and suggests that misclassification may be obscuring accurate interpretation of SS analyses.

Despite these limitations in the SS analysis, the magnitude of the association observed for SS participants was smaller than that seen with EF, a more structured program that includes balance and strength exercises that research suggests are critical to reducing fall risk (23). Because of the unstructured nature of the SS program in which participants can use whatever gym equipment or attend any classes they wish, we know very little about the type or intensity of exercise that participants engaged in or whether balance or strength exercises were performed. It is likely that balance exercises were not routinely practiced, as evidence suggests that most older adults do not engage in balance exercise (24,25). This likelihood may be part of the reason we did not see stronger associations between SS use and fall prevention.

EF has routinely scheduled classes multiple times per week and a strong social environment, promoting regular attendance at the EF program over many years. Users of the more free-form and independently driven SS program may be more likely to vary in the amount of use. On the basis of data supplied by each program, aggregate use statistics for this sample in 2011 support this differential attendance pattern. This finding suggests not only that EF users tend to use the program more frequently but also that the use pattern in the sample is more normally distributed (meaning that SS may have few very frequent users skewing the mean use statistic). This differential usage pattern may make the consistent and intermittent categories created for our analyses more problematic for SS, as the consistent category would be less likely to strongly parallel regular use. In short, although these results suggest that the EF program is more successful in reducing the risk of medical falls for this population, the nature of SS as an independently driven program with more sporadic attendance patterns lends itself more to misclassification under the participation definitions used in these analyses and makes conclusions about the program’s association with fall-related outcomes less robust.

This study has several limitations. First, it focuses only on falls resulting in medical care, as these appear in the inpatient or outpatient medical record, making it possible to measure without the use of self-report. This outcome definition excludes fall cases for which people do not seek medical attention and any injury truly due to a fall but not reported (and thus not coded) as such in the medical record. Despite this limitation, falls resulting in medical care are of high priority for risk reduction efforts given their adverse personal and societal effects (26,27).

Although missing data were not an issue for the outcome of interest, some data were missing for certain covariates, primarily race and BMI. Though these missing data resulted in small reductions in sample size for full adjustment models, they are not believed to have limited power. Participation in EF and SS is voluntary and, therefore, inherently self-selected. People who choose to participate in these programs may be systematically different from those who do not in ways that may affect their fall risk. Therefore, the potential for residual confounding remains. No information about engagement in physical activity outside either program was available for users or nonusers. The inability to adjust for baseline physical activity level in either group is a limitation; however, only 6% to 25% of older adults are estimated to regularly engage in the balance training and muscle-strengthening exercises requisite to fall risk reduction (28,29), suggesting that the impact of outside physical activity is likely low. Additionally, the threshold for participation in a given year was only 2 uses in that year, which is not indicative of regular physical activity through these programs. Although this finding limits our conclusions to the impact of 2 or more program uses per year, the low cut point used may have minimized the exposure of the group as a whole. This type of exposure misclassification would be expected to attenuate any association, rather than inflate it, so our results may be conservative. Future investigations should aim to use a continuous measure of participation to address this issue.

Despite these limitations, this study has several strengths. First, all outcomes and comorbidities were based on ICD-9-CM codes in the medical record rather than self-report. This procedure greatly reduces the potential for misclassification of comorbidity and outcome status, lending itself to a higher degree of accuracy in risk estimates. Furthermore, the use of this administrative data allowed for adjustment of many fall-related comorbidities that can be difficult to capture, including a history of gait and balance problems. Additionally, these analyses were based on a large, demographically representative sample, increasing power to detect associations and maximizing the generalizability of findings.

The results of this analysis provide evidence that participation in EF is associated with a reduced risk of medical falls. Furthermore, as hypothesized, this relationship shows a consistent pattern in which the strongest protective association was for consistent users of the program. Participation in SS may provide a moderate degree of fall protection, although findings were inconclusive. Overall, results suggest that evidence-based physical activity programs, particularly EF, should be more widely disseminated into communities not only for their general effects on fitness but also for their likely benefits on prevention of fall-related health care use, an important personal and societal outcome.

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Acknowledgments

There was no financial support available for this work. Analyses were performed on an existing data set within the Group Health Research Institute.

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Author Information

Corresponding Author: Mikael Anne Greenwood-Hickman, University of North Carolina, Radiology, 130 Mason Farm Road, Room 3126, Chapel Hill, NC 27514. Telephone: 765-586-3323. Email: mikgreen@umail.iu.edu.

Author Affiliations: Dori E. Rosenberg, Group Health Research Institute, Seattle, Washington; Elizabeth A. Phelan, Department of Medicine, School of Public Health and Community Medicine, University of Washington, Seattle, Washington; Annette L. Fitzpatrick, University of Washington, Seattle, Washington. At the time of this study, Ms Greenwood-Hickman was affiliated with the University of Washington, Seattle, Washington, and Group Health Research Institute, Seattle, Washington.

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References

  1. Hausdorff JM, Rios DA, Edelberg HK. Gait variability and fall risk in community-living older adults: a 1-year prospective study. Arch Phys Med Rehabil 2001;82(8):1050–6. CrossRef PubMed
  2. Bohl AA, Fishman PA, Ciol MA, Williams B, Logerfo J, Phelan EA. A longitudinal analysis of total 3-year healthcare costs for older adults who experience a fall requiring medical care. J Am Geriatr Soc 2010;58(5):853–60. CrossRef PubMed
  3. Bethancourt HJ, Rosenberg DE, Beatty T, Arterburn DE. Barriers to and facilitators of physical activity program use among older adults. Clin Med Res 2014;12(1–2):10–20. CrossRef PubMed
  4. Yardley L, Smith H. A prospective study of the relationship between feared consequences of falling and avoidance of activity in community-living older people. Gerontologist 2002;42(1):17–23. CrossRef PubMed
  5. El-Khoury F, Cassou B, Charles MA, Dargent-Molina P. The effect of fall prevention exercise programmes on fall induced injuries in community dwelling older adults: systematic review and meta-analysis of randomised controlled trials. BMJ 2013;347(oct28 9):f6234. CrossRef PubMed
  6. Gillespie LD, Robertson MC, Gillespie WJ, Sherrington C, Gates S, Clemson LM, et al. Interventions for preventing falls in older people living in the community. Cochrane Database Syst Rev 2012;9:CD007146. PubMed
  7. Robertson MC, Gillespie LD. Fall prevention in community-dwelling older adults. JAMA 2013;309(13):1406–7. CrossRef PubMed
  8. Enhance: What is EnhanceFitness? 2013. http://www.projectenhance.org/enhancefitness.aspx. Accessed February 14, 2014.
  9. Sneakers HS. What is the Silver Sneakers fitness program? 2014. https://www.silversneakers.com/home. Accessed February 19, 2014.
  10. Suzuki T, Kim H, Yoshida H, Ishizaki T. Randomized controlled trial of exercise intervention for the prevention of falls in community-dwelling elderly Japanese women. J Bone Miner Metab 2004;22(6):602–11. CrossRef PubMed
  11. Franco MR, Pereira LS, Ferreira PH. Exercise interventions for preventing falls in older people living in the community. Br J Sports Med 2014;48(10):867–8. CrossRef PubMed
  12. Belza B, Shumway-Cook A, Phelan EA, Williams B. The effects of a community-based exercise program on function and health in older adults: the EnhanceFitness program. J Appl Gerontol 2006;25(4):291–306. CrossRef
  13. Tomioka M, Sugihara N, Braun KL. Replicating the EnhanceFitness physical activity program in Hawai‘i’s multicultural population, 2007-2010. Prev Chronic Dis 2012;9:E74. PubMed
  14. Yoon PW, Bastian B, Anderson RN, Collins JL, Jaffe HW; Centers for Disease Control and Prevention (CDC). Potentially preventable deaths from the five leading causes of death — United States, 2008–2010. MMWR Morb Mortal Wkly Rep 2014;63(17):369–74. PubMed
  15. Saunders KW, Davis RL, Stergachis A. Group health cooperative. In: Strom BL, editor. Pharmacoepidemiology. Hoboken (NJ): John Wiley and Sons; 2005. p. 223–40.
  16. Wallace JI, Buchner DM, Grothaus L, Leveille S, Tyll L, LaCroix AZ, et al. Implementation and effectiveness of a community-based health promotion program for older adults. J Gerontol A Biol Sci Med Sci 1998;53(4):M301–6. CrossRef PubMed
  17. Belza B, Snyder S, Thompson M, LoGerfo J, Lewis CB. From research to practice: EnhanceFitness, an innovative community-based senior exercise program. Top Geriatr Rehabil 2010;26(4):299–309. CrossRef
  18. Tinetti ME, Baker DI, King M, Gottschalk M, Murphy TE, Acampora D, et al. Effect of dissemination of evidence in reducing injuries from falls. N Engl J Med 2008;359(3):252–61. CrossRef PubMed
  19. Bohl AA, Phelan EA, Fishman PA, Harris JR. How are the costs of care for medical falls distributed? The costs of medical falls by component of cost, timing, and injury severity. Gerontologist 2012;52(5):664–75. CrossRef PubMed
  20. Charlson ME, Pompei P, Ales KL, MacKenzie CR. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis 1987;40(5):373–83. CrossRef PubMed
  21. Chodzko-Zajko WJ, Proctor DN, Fiatarone Singh MA, Minson CT, Nigg CR, Salem GJ, et al. American College of Sports Medicine position stand. Exercise and physical activity for older adults. Med Sci Sports Exerc 2009;41(7):1510–30. CrossRef PubMed
  22. Bird ML, Pittaway JK, Cuisick I, Rattray M, Ahuja KD. Age-related changes in physical fall risk factors: results from a 3 year follow-up of community dwelling older adults in Tasmania, Australia. Int J Environ Res Public Health 2013;10(11):5989–97. CrossRef PubMed
  23. Administration on Aging. Evidence-based Disease and Disability Prevention Program. Washington (DC): US Department of Health and Human Services, Administration on Aging; 2010.
  24. Merom D, Pye V, Macniven R, van der Ploeg H, Milat A, Sherrington C, et al. Prevalence and correlates of participation in fall prevention exercise/physical activity by older adults. Prev Med 2012;55(6):613–7. CrossRef PubMed
  25. Centers for Disease Control and Prevention (CDC). Adult participation in aerobic and muscle-strengthening physical activities — United States, 2011. MMWR Morb Mortal Wkly Rep 2013;62(17):326–30. PubMed
  26. Hartholt KA, van Beeck EF, Polinder S, van der Velde N, van Lieshout EM, Panneman MJ, et al. Societal consequences of falls in the older population: injuries, healthcare costs, and long-term reduced quality of life. J Trauma 2011;71(3):748–53. CrossRef PubMed
  27. Gill TM, Murphy TE, Gahbauer EA, Allore HG. The course of disability before and after a serious fall injury. JAMA Intern Med 2013;173(19):1780–6. CrossRef PubMed
  28. Loustalot F, Carlson SA, Kruger J, Buchner DM, Fulton JE. Muscle-strengthening activities and participation among adults in the United States. Res Q Exerc Sport 2013;84(1):30–8. CrossRef PubMed
  29. Centers for Disease Control and Prevention (CDC). Adult participation in aerobic and muscle-strengthening physical activities — United States, 2011. MMWR Morb Mortal Wkly Rep 2013;62(17):326–30. PubMed

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Tables

Return to your place in the textTable 1. Demographic Characteristics of EnhanceFitness Users and Nonusers, Washington State, United States, 2005–2011
TraitaNonusers, n = 55,127Consistent, n = 517Intermittent, n = 1,578P Valueb
n (%)
Mean age, y (range)74.1 (65–98)73.7 (65–95)75.0 (65–97)<.001
Female30,640 (55.6)381 (73.7)1,169 (74.1)<.001
Racec
White45,471 (90.6)436 (85.5)1,356 (88.6)<.001
Black1418 (2.8)21 (4.2)60 (3.9)
Asian2,420 (4.8)40 (7.9)96 (6.3)
Other880 (1.8)7 (1.4)19 (1.2)
Mean BMI,c kg/m2 (range)28.3 (7.4–77.1)26.9 (16.3–45.7)27.3 (14.9–49.4)<.001
<18.5531 (1.0)4 (0.8)12 (0.8)<.001
18.5–24.98,851 (16.1)142 (27.1)367 (24.0)
25.0–29.911,142 (20.2)145 (28.1)428 (27.1)
>30.034,603 (62.8)228 (44.1)771 (48.9)
Smokerc4,359 (7.9)22 (4.3)59 (3.7)<.001
Charlson scored0.92 (0-18)0.63 (0-10)0.60 (0-10)<.001
Diagnosis in study period
Diabetes10,984 (19.9)87 (16.8)278 (17.6)<.001
Dementia1,965 (3.6)9 (1.7)63 (4.0).002
Walking disordere624 (1.1)9 (1.7)37 (2.3).09
Osteoarthritis22,801 (41.4)277 (53.6)990 (62.7)<.001
Osteoporosis873 (1.6)22 (4.3)63 (4.0)<.001
Musculoskeletal conditionf27,254 (49.4)330 (63.8)1,151 (72.9)<.001
Visual impairmentg31,894 (57.9)371 (71.8)1,271 (80.5)<.001
Coronary heart disease11,265 (20.4)99 (19.2)371 (23.5)<.001
Hypertension28,598 (51.9)306 (59.2)1,053 (66.7)<.001
Use of sedatives or sleeping medication7,106 (12.9)43 (8.3)208 (13.2)<.001
Fall resulting in medical treatment16,834 (30.5)146 (28.2)672 (42.6)<.001

Abbreviation: BMI, body mass index; EF, EnhanceFitness.
a Unless otherwise specified, traits are described at first enrollment. Analysis of variance (for continuous covariates) comparing trends in the covariate across levels of either EF or SS participation.
b P values correspond to a Pearson’s χ2 analysis (for categorical covariates) or a 1-wayanalysis of variance (for continuous covariates) comparing trends in the covariate: race (6.1%), BMI (19.7%), and smoking (2.3%).
c The following variables have missing values for some individuals: race (6.1%), BMI (19.7%), and smoking (2.3%). Percentages are approximate.
d Charlson comorbidity score measures comorbidity based on the presence or absence of 19 conditions weighted for severity (continuous).
e Includes difficulty walking (719.7), abnormal gait (781.2), and generalized weakness (728.87).
f Includes arthropathy, rheumatoid arthritis, and joint derangement (712–719).
g Includes glaucoma (365), cataract (366), macular degeneration (362.50–362.53, 362.55, 362.63), and retinopathy (362.01–362.03, 362.10, 362.11, 362.2, 363.31).

 

Return to your place in the textTable 2. Demographic Characteristics of Silver Sneakers Users and Nonusers, Washington State, United States, 2005–2011
TraitaNonusers, n = 55,127Consistent, n = 3,953Intermittent, n = 9,623P Valueb
n (%)
Mean age, y (range)74.1 (65–98)70.0 (65–95)71.5 (65–96)<.001
Female30,640 (55.6)2,377 (60.1)5,879 (61.1)<.001
Racec
White45,471 (90.6)3,462 (91.6)8,571 (92.4)<.001
Black1,418 (2.8)65 (1.7)210 (2.3)
Asian2,420 (4.8)202 (5.4)388 (4.2)
Other880 (1.8)49 (1.3)109 (1.2)
Mean BMI,c kg/m2 (range)28.3 (7.4–77.1)27.9 (16.2–51.7)28.6 (10.0–111.0)<.001
<18.5531 (1.0)20 (0.5)31 (0.3)<.001
18.5–24.98,851 (16.1)862 (21.8)1,565 (16.3)
25.0–29.911,142 (20.2)1,145 (29.0)2,253 (23.4)
>30.034,603 (62.8)1,926 (48.7)5,774 (60.0)
Smokerc4,359 (7.9)118 (3.0)485 (5.0)<.001
Charlson score (range)d0.92 (0–18)0.57 (0–10)0.61 (0–10)<.001
Diagnosis in study period
Diabetes10,984 (19.9)572 (14.5)1,968 (20.5)<.001
Dementia1,965 (3.6)25 (0.6)214 (2.2)<.001
Walking disordere624 (1.1)34 (0.9)130 (1.4).09
Osteoarthritis22,801 (41.4)1,854 (46.9)6,034 (62.7)<.001
Osteoporosis873 (1.6)108 (2.7)265 (2.8)<.001
Musculoskeletal conditionf27,254 (49.4)2,189 (55.4)6,893 (71.6)<.001
Visual impairmentg31,894 (57.9)2,446 (61.9)7,453 (77.5)<.001
Coronary heart disease11,265 (20.4)561 (14.2)2,226 (23.1)<.001
Hypertension28,598 (51.9)1850 (46.8)5,985 (62.2)<.001
Use of sedatives or sleeping medication7,106 (12.9)344 (8.7)1,357 (14.1)<.001
Fall resulting in medical treatment16,834 (30.5)861 (21.8)3,563 (37.0)<.001

Abbreviation: BMI, body mass index; SS, Silver Sneakers.
a Unless otherwise specified, traits are described at first enrollment. Values are presented as n (%) unless otherwise indicated.
b P values correspond to a Pearson’s χ2 analysis (for categorical covariates) or a 1-way analysis of variance (for continuous covariates) comparing trends in the covariate across levels of program participation.
c The following variables have missing values for some individuals: race (6.1%), BMI (19.7%), and smoking (2.3%). Percentages are approximate.
d Charlson comorbidity score measures comorbidity based on the presence or absence of 19 conditions weighted for severity (continuous).
e Includes difficulty walking (719.7), abnormal gait (781.2), and generalized weakness (728.87).
f Includes arthropathy, rheumatoid arthritis, and joint derangement (712–719).
g Includes glaucoma (365), cataract (366), macular degeneration (362.50–362.53, 362.55, 362.63), and retinopathy (362.01–362.03, 362.10, 362.11, 362.2, 363.31).

 

Return to your place in the textTable 3. Risk of a Medical Fall Among EF/SS Users Compared With Nonusers, Washington State, United States, 2005–2011
GroupNCrude Model, HR (95% CI)P ValueDemographic Model,a HR (95% CI)P ValueFull Model,b HR (95% CI)P Value
EFNonusers55,1271 [Reference]1 [Reference]1 [Reference]
Intermittent1,5780.90 (0.84–0.98).0090.84 (0.77–0.91)<.0010.87 (0.80–0.94).001
Consistent5170.73 (0.62–0.86)<.0010.71 (0.60–0.84)<.0010.74 (0.63–0.88)<.001
SSNonusers55,1271 [Reference]1 [Reference]1 [Reference]
Intermittent9,6230.85 (0.82–0.88)<.0010.92 (0.88–0.95)<.0010.93 (0.90–0.97)<.001
Consistent3,9530.83 (0.78–0.89)<.0010.93 (0.87–0.99).030.95 (0.89–1.02).18

Abbreviations: CI, confidence interval; EF, EnhanceFitness; HR, hazard ratio; SS, Silver Sneakers.
a Model adjusted for age, sex, and race.
b Model adjusted for age, sex, race, and all covariates outlined in Tables 1 and 2.

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