OBJECTIVELY MEASURED PHYSICAL ACTIVITY BALANCE AMONG U.S. ADULTS PAUL D. LOPRINZI1
AND
AND
JOSEPH A. BROSKY JR2
1
Department of Exercise Science, Donna and Allan Lansing School of Nursing and Health Sciences, Bellarmine University, Louisville, Kentucky; and 2Physical Therapy Program, Donna and Allan Lansing School of Nursing and Health Sciences, Bellarmine University, Louisville, Kentucky ABSTRACT
Loprinzi, PD and Brosky Jr, JA. Objectively measured physical activity and balance among U.S. adults. J Strength Cond Res 28(8): 2290–2296, 2014—The purpose of this study was to examine the association between objectively measured physical activity (PA) and balance in a nationally representative sample of U.S. adults 40 years of age and older. Data from the 2003–2004 National Health and Nutrition Examination Survey were used. Physical activity was measured over a 7-day period using accelerometry, and balance was assessed using the Romberg test. Participants completed a questionnaire regarding their subjective views on difficulty with falling in the past 12 months. For every 60-minute increase in light-intensity PA, participants were 10% (p = 0.04) more likely to have functional balance. Similarly, for every 1-minute increase in log-transformed moderate-to-vigorous physical activity, participants were 23% (p = 0.04) more likely to have functional balance. Regular PA, regardless of intensity, may have health benefits for older adults and is associated with functional balance.
KEY WORDS accelerometry, light-intensity physical activity, functional balance INTRODUCTION
balance are more likely to fall and tend to be less active (21,15), with this inactivity increasing the incidence of falls through various mechanisms, such as muscle atrophy, muscle weakness, reduced flexibility, and gait impairments (2,4,11,17). As a result, regular participation in safe forms of physical activity may help to prevent falls and reduce societal costs associated with injurious falls (22). Most balance and fall prevention interventions have been conducted in community, institutional, residential care, or other clinic-based populations (14,23). To our knowledge, no population-based studies have examined the association between objectively measured physical activity and balance in a nationally representative sample. As a result, previous studies may lack generalizability and are limited by the use of self-report physical activity methodology, which is prone to considerable measurement error (20). Therefore, the primary purpose of this study was to examine the association between objectively measured physical activity (i.e., accelerometry technology) and balance in a nationally representative sample of U.S. adults. A particular interest of this study was to examine whether light-intensity physical activity, in addition to moderate-to-vigorous physical activity (MVPA), is associated with balance. If so, this may have strong practical implications for strength and conditioning professionals as light-intensity physical activity may be a more palatable and feasible intensity to promote among older individuals at risk of falls and/or have a fear of falling.
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METHODS
Address correspondence to Paul D. Loprinzi, [email protected].
Subjects
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Among participants in the 2003–2004 NHANES, 3,299 were eligible for the accelerometry and balance component (age range: 40-85 yrs). After excluding those with missing balance
alls, particularly in older adults, are associated with significant morbidity and mortality (12). Several factors are associated with increased incidence of falls, such as older age (19), gender (5), raceethnicity (13), medications (18), comorbid illness (9), visual acuity (8), and hearing sensitivity (25). Fear of falling is also an important risk factor for falls and has been linked to adverse psychological, physical and functional changes in older adults (3). Falls and fear of falling are complex and interrelated constructs with each being considered a risk factor for the other. Additionally, individuals with dysfunctional
Journal of Strength and Conditioning Research Ó 2014 National Strength and Conditioning Association
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Experimental Approach to the Problem
Data from the 2003–2004 National Health and Nutrition Examination Survey (NHANES) were used, as this is the only NHANES cycle with both accelerometry and balance data. National Health and Nutrition Examination Survey is an ongoing survey conducted by the National Center for Health Statistics, which evaluates a representative sample of non-institutionalized U.S. civilians, selected by a complex, multistage probability design.
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Among these, 575 were excluded because of insufficient accelerTABLE 1. Unweighted characteristics of the analyzed participants (n = 1,831) ometry data (i.e., ,4 days of 10 compared to excluded participants (n = 575), NHANES 2003–2004.*† + hours per day of monitoring), Mean/proportion (95% CI) which left an analytic sample of 1,831 adult participants. Previous Variable Analyzed sample Excluded sample p studies have also shown a high number of missing/incomplete Age (y) 61.3 (60.7–61.9) 60.4 (59.2–61.6) 0.16 % Male 50.6 (48.3–52.9) 50.9 (46.8–55.0) 0.89 accelerometry data and have 28.2 (28.0–28.5) 28.3 (27.8–28.7) 0.77 Body mass index (kg$m22) demonstrated potential biases Race-ethnicity 0.12 associated with incomplete data % Non-Hispanic white 58.1 (55.8–60.3) 54.4 (50.3–58.5) (10). As a result, Table 1 shows % Other 41.8 (39.6–44.1) 45.5 (41.4–49.6) characteristics of the analyzed Education 0.002 % High school or less 53.7 (51.5–56.0) 61.2 (57.2–65.2) sample (n = 1,831) compared % Some college or more 46.2 (43.9–48.4) 38.7 (34.7–42.7) with those excluded because of Comorbidity index 0.60 insufficient accelerometry data % 0 comorbidities 31.4 (29.2–33.5) 30.2 (26.5–34.0) (n = 585). Overall, and in this % 1+ comorbidity 68.5 (66.4–70.7) 69.7 (65.9–73.4) sample, those included in the Vision 0.004 % Excellent 22.5 (20.5–24.4) 18.2 (15.0–21.4) analyses were similar to those % Good 35.8 (33.6–38.0) 32.5 (28.6–36.3) excluded with the exception of % Fair or worse 41.6 (39.3–43.8) 49.2 (45.1–53.3) education and vision. Excluded Hearing 0.86 participants were less educated % Good 64.8 (62.6–67.0) 65.7 (61.8–69.6) (38.7 vs. 46.2% had some college % A little trouble 28.0 (26.0–30.1) 26.9 (23.3–30.5) % A lot of trouble/deaf 7.0 (5.8–8.2) 7.3 (5.1–9.4) or more) and had worse vision Balance problems from medication 0.10 (49.2 vs. 41.6% had fair or worse % Yes 3.8 (2.9–4.7) 5.3 (3.5–7.2) vision). % No 96.1 (95.2–97.0) 94.6 (92.7–96.4) All procedures for data col*NHANES = National Health and Nutrition Examination Survey; CI = confidence interval. lection were approved by the †Independent t-tests were used to examine differences for continuous variables, and x2 National Center for Health tests were used for categorical variables. Statistics Ethics Review Board (no university institutional review board approval is needed), and all participants and covariate data (i.e., age, gender, education, raceprovided written informed consent before data collection. ethnicity, body mass index (BMI), comorbidity index, vision, National Health and Nutrition Examination Survey data are hearing, and medication use), 2,406 participants remained. publically accessible online (http://www.cdc.gov/nchs/). Procedures
Figure 1. Mean light-intensity physical activity (LPA) and moderate-to-vigorous physical activity (MVPA) estimates across balance status. Error bars represent standard errors.
Assessment of Balance. Participants aged 40 years and older were eligible for the balance assessment. For safety precautions, participants were excluded if they felt unable to stand on their own, had current symptoms of dizziness or lightheadedness, weighed more than 275 lbs, could not fit into the standardized safety gait belt, required a leg brace to stand unassisted, or had a foot or leg amputation. Before balance testing, participants completed a questionnaire regarding their subjective views
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Physical Activity and Balance Balance testing consisted of using the modified Romberg Test of Standing Balance on firm and compliant support surfaces. This test evaluated the participants ability to stand unassisted under 4 different conditions (ordered in increasing levels of difficulty) designed to test sensory inputs from the vestibular system, vision, and proprioception: test 1—eyes open, firm surface; test 2—eyes closed, firm surface; test 3—eyes open, compliant surface; and test 4—eyes closed, compliant surface. ParticFigure 2. Mean light-intensity physical activity (LPA) and moderate-to-vigorous physical activity (MVPA) estimates across difficulty with falls. Error bars represent standard errors. ipants were allowed 2 trials for each condition. For all tests, balance was scored as pass or fail. on difficulty with falling in the past 12 months. Specifically, With tests 1 and 2 lasting 15 seconds and tests 3 and 4 lasting they were asked, “During the past 12 months, have you had 30 seconds, test failure was defined as the participant needing difficulty with falling?” The questionnaire contained no items to open their eyes, moving their arms or feet to increase staon actual falls or fall frequency. bility, or beginning to fall or requiring assistance to maintain
TABLE 2. Multivariable logistic regression examining the association between physical activity and odds (95% CI) of having functional balance, NHANES 2003–2004 (n = 1,831).* Odds ratio (95% CI) for functional balance†
Physical activity Covariates Age (y) 50–59 vs. 40–49 60–69 vs. 40–49 70+ vs. 40–49 Women vs. men Some college or more vs. high school or less Non-hispanic white vs. other BMI, 1 kg$m22 higher Presence of comorbidities (1+) vs. none Vision Good vs. excellent Fair or worse vs. excellent Hearing A little trouble vs. good A lot trouble/deaf vs. good Balance problems from medication No vs. yes
LPAz
p
MVPAz
p
1.10 (1.00–1.21)
0.04
1.24 (1.00–1.53)
0.04
0.31 0.25 0.10 0.99 1.75 1.15 1.04 0.75
(0.17–0.56) (0.13–0.49) (0.06–0.16) (0.78–1.25) (1.39–2.21) (0.93–1.42) (1.02–1.07) (0.49–1.14)
0.001 ,0.001 ,0.001 0.94 ,0.001 0.17 0.001 0.17
0.32 0.27 0.11 1.11 1.66 1.14 1.05 0.77
(0.18–0.57) (0.14–0.53) (0.06–0.21) (0.89–1.38) (1.35–2.03) (0.91–1.42) (1.02–1.07) (0.50–1.18)
0.001 0.001 ,0.001 0.29 ,0.001 0.22 0.001 0.22
0.81 (0.53–1.24) 0.64 (0.42–0.98)
0.32 0.04
0.82 (0.53–1.26) 0.65 (0.42–0.98)
0.35 0.04
0.83 (0.62–1.11) 0.65 (0.40–1.06)
0.19 0.08
0.82 (0.61–1.11) 0.66 (0.39–1.09)
0.18 0.10
1.89 (0.88–4.06)
0.09
1.85 (0.88–3.89)
0.09
*CI = confidence interval; LPA = light-intensity physical activity; MVPA = moderate-to-vigorous physical activity; BMI = body mass index; NHANES = National Health and Nutrition Examination Survey. †Referent group was dysfunctional balance (i.e., failed one of the 4 test conditions). Two multivariable logistic regression models were computed: 1 for LPA and the other for MVPA. zLPA expressed as a 60-minute interval change; MVPA log-transformed to improve normality and expressed as a 1-unit, log-transformed interval change.
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Journal of Strength and Conditioning Research balance. For this study, and similar to others (1), participants were classified as having dysfunctional balance if they failed any of the 4 test conditions. Functional balance, in contrast, was operationally defined as successful completion of all 4 of the test conditions. Assessment of Physical Activity. Participants who were able to walk were asked to wear an ActiGraph 7164 accelerometer on their right hip for 7 days. Accelerometers were affixed to an elastic belt that was worn around the participant’s waist near the iliac crest. Participants were asked to wear the accelerometer during all activities except water-based activities and sleeping. The accelerometer measured the frequency, intensity, and duration of physical activity by generating an activity count proportional to the measured acceleration. Estimates for physical activity were summarized in 1-minute time intervals. Minutes with activity counts $2,020 were classified as MVPA (24), with light-intensity physical activity defined as activity counts between 100 and 2019. Only those participants with at least 4 days of 10 or more hours per day of accelerometer wear time were included in the analyses to make sure that data adequately captured habitual physical activity patterns (24). To monitor the amount of time the device was worn, nonwear was
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defined by a period of a minimum of 60 consecutive minutes of zero activity counts, with the allowance of 1–2 minutes of activity counts between 0 and 100 (24). Measurement of Covariates. Information about age (binned by decade), gender, race/ethnicity, and education were obtained from questionnaires. Participants were classified as having 0 or 1+ comorbidities based on self-report of having a history of coronary heart disease, stroke, cancer, arthritis, diabetes, kidney disease, or hypertension. Participants self-reported their vision and hearing function, with vision categorized as excellent, good, fair, poor, or very poor, with hearing function defined as good, a little trouble, lot of trouble, or deaf. Participants also indicated whether any potential balance problems were a result of medication they were taking. Lastly, BMI was calculated from measured weight and height (kg$m22). Statistical Analyses
All statistical analyses were performed using procedures from sample survey data using STATA (StataCorp., version 12.0, College Station, TX) to account for the complex survey design used in NHANES. To account for oversampling, non-response, non-coverage, and to provide nationally representative
TABLE 3. Multivariable logistic regression examining the association between physical activity and odds of having difficulty with falls in the past 12 months, NHANES 2003–2004 (n = 1,831).* Odds ratio (95% CI) for having difficulty with falling in past 12 mo†
Physical activity Covariates Age (y) 50–59 vs. 40–49 60–69 vs. 40–49 70+ vs. 40–49 Women vs. men Some college or more vs. high school or less Non-Hispanic white vs. other BMI, 1 kg$m22 higher Presence of comorbidities (1+) vs. none Vision Good vs. Excellent Fair or worse vs. excellent Hearing A little trouble or worse vs. good Balance problems from medication No vs. yes
LPAz
p
MVPAz
p
0.78 (0.65–1.00)
0.06
0.59 (0.40–0.85)
0.009
2.81 2.20 3.76 2.48 1.13 1.55 1.00 1.73
0.06 0.20 0.02 0.02 0.66 0.15 0.97 0.32
2.41 1.74 2.33 1.92 1.27 1.52 0.99 1.64
0.14 0.43 0.19 0.10 0.38 0.16 0.84 0.36
1.50 (0.71–3.16) 4.06 (1.51–10.92)
0.26 0.008
1.39 (0.65–2.96) 3.75 (1.28–10.99)
0.35 0.01
2.21 (1.39–3.52)
0.002
2.27 (1.43–3.62)
0.002
0.16 (0.07–0.36)
,0.001
0.16 (0.06–0.41)
0.001
(0.93–8.47) (0.61–7.93) (1.24–11.36) (1.16–5.27) (0.62–2.07) (0.83–2.87) (0.95–1.04) (0.55–5.40)
(0.70–8.23) (0.40–7.51) (0.61–8.88) (0.85–4.35) (0.72–2.24) (0.81–2.83) (0.94–1.04) (0.53–5.05)
*LPA = light-intensity physical activity; MVPA = moderate-to-vigorous physical activity; BMI = body mass index; NHANES = National Health and Nutrition Examination Survey. †Referent group was not having difficulty with falling. Two multivariable logistic regression models were computed: 1 for LPA and the other for MVPA. zLPA expressed as a 60-minute interval change; MVPA log-transformed to improve normality and expressed as a 1-unit, logtransformed interval change.
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Physical Activity and Balance estimates, all analyses included the use of appropriate survey sample weights, clustering and primary sampling units. In an effort to maintain nationally representative estimates, the sample weights for those with 4 or more days of valid accelerometry data were ratio-adjusted to maintain the age, sex, and race-ethnicity distribution of the full sample. Means were computed for continuous variables and proportions were computed for categorical variables. To examine the association between physical activity (independent variable) and balance status, a multivariable logistic regression model was computed. Similarly, a multivariable logistic regression was used with self-reported difficulty with falling serving as the outcome variable. Moderate-to-vigorous physical activity was log-transformed because it failed tests of normality. Model covariates included age, gender, education, race-ethnicity, BMI, comorbidity index, vision, hearing, and medication use. All covariates were entered into the model at the same time as there was no evidence of multicollinearity based on correlations ,0.8 between covariate pairs (max observed correlation ,0.40), mean variance inflation factor ,6 (observed mean = 1.1), individual variance inflation factors ,10 (highest observed = 1.7), and tolerance statistics .0.1 (all observed to be .0.50). Statistical significance was established p # 0.05.
RESULTS Physical activity estimates across balance status and difficulty with falls, respectively, are shown in Figures 1 and 2. Participants with dysfunctional balance engaged in less lightintensity physical activity than those with functional balance (319.8 vs. 352.5 min$d21; p # 0.05) (Figure 1). Similarly, those with dysfunctional balance engaged in less MVPA than those with functional balance (16.1 vs. 22.8 min$d21; p # 0.05) (Figure 1). Participants who had difficulty with falling within the past 12 months engaged in less light-intensity physical activity than those who self-reported not having difficulty with falling (295.0 vs. 341.0 min$d21; p # 0.05). Similarly, participants who had difficulty with falling within the past 12 months engaged in less MVPA than those without difficulty (9.0 vs. 20.5 min$d21; p # 0.05). Table 2 shows the odds of having functional balance based on physical activity levels. For every 60-minute increase in light-intensity physical activity, participants were 10% (p = 0.04) more likely to have functional balance. Similarly, for every 1-minute increase in log-transformed MVPA, participants were 24% (p = 0.04) more likely to have functional balance. Table 3 shows the odds of having difficulty with falling in the past 12 months based on physical activity levels. Lightintensity physical activity was not significant (p = 0.06); however, for every 1-minute increase in log-transformed MVPA, participants were 41% (p = 0.009) less likely to have difficulty with falling in the past 12 months.
DISCUSSION The primary purpose of this study was to examine the association between objectively measured physical activity
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and balance in a nationally representative sample of U.S. adults. Findings of this study confirm intervention studies in that individuals engaging in higher levels of accelerometerassessed physical activity (both light-intensity and MVPA) were more likely to have functional balance. Additionally, higher levels of MVPA were also associated with a reduced likelihood of having difficulty with falling. Many older adults may limit their regular physical activity for a host of reasons that might include but are not limited to fatigue, fear of falling, and the development of age related conditions such as osteoarthritis. Balance is an important attribute required for safe mobility and is dependent on a number of interrelated mechanisms and interdependent systems. Although this study does not confirm preventive effects of physical activity on the number or frequency of actual falls, successful performance by individuals (i.e., receiving a “pass” score) on the balance tests performed in this study does appear to provide an objective relationship between regular physical activity and balance performance. A recent Cochrane Review (6) on interventions for preventing falls in community dwelling older people concluded that there was no evidence of any effects for cognitive behavioral interventions on rate of falls or risk of falling. However, this same Cochrane Review did report benefits of group and home-based exercise programs by reducing the rate of falls and risks of falling. Educational programs developed to increase knowledge of risk factors and promote positive behaviors have been reported to have only marginal benefits, and the findings are inconclusive at best for actually preventing falls. The benefits of conducting standardized individual risk assessments, like the balance tests performed in this study, may be a critical component in helping individuals truly understand their functional abilities and limitations when they are provided information specific to age and gender related normative values. Additionally, physical performance assessments using standardized tests such as the Timed Up and Go, Timed Sit to Stand, and Four Square Step Test, can also establish baseline function, increase individual awareness of deficits, and guide effective intervention strategies. One program that has demonstrated effectiveness is the Matter of Balance intervention program (7). The Matter of Balance Program (7) incorporates light-intensity physical activity exercises and balance training, a cognitivebehavior intervention and social support. The 8-session program includes specific elements that promote the view that falls and fear of falling are controllable, sets realistic goals for gradually increasing activity, addresses environmental factors to reduce falls risk and promotes light-intensity physical activity to increase strength and balance. A limitation of this study was the inability to determine number of actual falls or fall frequency. Another limitation includes the cross-sectional study design, rendering causal relationships not possible. It is possible that individuals with better balance engaged in more physical activity because of enhanced functional capacity and possibly having greater
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Journal of Strength and Conditioning Research confidence in participating in instrumental activities of daily living; however, it is plausible to suggest that increasing activity behavior may play a causal role in increasing balance and reducing falls, as previous intervention studies have shown such an effect and have also demonstrated potential mechanisms (e.g., improve gait function, general mobility, and increase lower limb strength) to elucidate this relationship. Despite these noted limitations, major strengths of this study include the use of an objective measure of physical activity, an objective measure of balance, and using a nationally representative sample to examine this relationship. In conclusion, the results of this study contribute to the existing literature on the important relationship between falls risk and physical activity levels. Regular physical activity, regardless of intensity, may have health benefits for older adults and is associated with functional balance. Falls are a major public health problem and have serious implications on health care costs associated with falls, on quality of life and may threaten community dwelling older adults’ independence. As the risk of falls generally increases with advancing age, strategies are needed to identify the risk factors and incorporate effective interventions aimed at recognition and prevention. Important to any prevention program is the regular, simple, and cost effective methods of screening balance. Many of the balance measures such as the Romberg test described in this study meet these criteria, but as Pardasaney et al. (16) reported in a recent systematic analysis study, these balance measures may not adequately assess the postural control demands in daily activities that involve dynamic stability, changing environments, multi-tasking, object interaction, or obstacle negotiation. Future studies are needed to further examine performance indicators on individual balance measures and their relationship with physical activity levels.
PRACTICAL APPLICATIONS Findings from this study demonstrate that both lightintensity physical activity and MVPA were associated with better functional balance among a nationally representative sample of U.S. adults. Our finding that light-intensity physical activity is particularly important as light-intensity physical activity, compared with MVPA, may be a more feasible and palatable approach to promoting physical activity among individuals at risk for falling (e.g., elderly). This is an encouraging finding as most physical activity related research has focused on MVPA, with less research demonstrating a beneficial effect of light-intensity physical activity. Based on these findings, health care professionals should encourage light-intensity physical activity, and when appropriate, MVPA, to help reduce injurious falls. Other forms of exercise, such as strength training, should be promoted to augment the effects of ambulatory activity.
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REFERENCES 1. Agrawal, Y, Carey, JP, Hoffman, HJ, Sklare, DA, and Schubert, MC. The modified Romberg Balance Test: Normative data in U.S. adults. Otol Neurotol 32: 1309–1311, 2011. 2. Barnett, A, Smith, B, Lord, SR, Williams, M, and Baumand, A. Community-based group exercise improves balance and reduces falls in at-risk older people: A randomised controlled trial. Age Ageing 32: 407–414, 2003. 3. Boyd, R and Stevens, JA. Falls and fear of falling: Burden, beliefs and behaviours. Age Ageing 38: 423–428, 2009. 4. Buchner, DM, Cress, ME, de Lateur, BJ, Esselman, PC, Margherita, AJ, Price, R, and Wagner, EH. The effect of strength and endurance training on gait, balance, fall risk, and health services use in community-living older adults. J Gerontol A Biol Sci Med Sci 52: M218–M224, 1997. 5. Campbell, AJ, Spears, GF, and Borrie, MJ. Examination by logistic regression modelling of the variables which increase the relative risk of elderly women falling compared to elderly men. J Clin Epidemiol 43: 1415–1420, 1990. 6. Gillespie, LD, Robertson, MC, and Gillespie, WJ, et al. Interventions for preventing falls in older people living in the community. Cochrane Database Syst Rev 9: CD007146, 2012. 7. Healy, TC, Peng, C, Haynes, MS, McMahon, EM, Botler, JL, and Gross, L. The feasibility and effectiveness of translating “A Matter of Balance” into a volunteer lay leader model. J Appl Gerontol 27: 34– 51, 2008. 8. Jack, CI, Smith, T, Neoh, C, Lye, M, and McGalliard, JN. Prevalence of low vision in elderly patients admitted to an acute geriatric unit in Liverpool: Elderly people who fall are more likely to have low vision. Gerontology 41: 280–285, 1995. 9. Lawlor, DA, Patel, R, and Ebrahim, S. Association between falls in elderly women and chronic diseases and drug use: Cross sectional study. BMJ 327: 712–717, 2003. 10. Loprinzi, PD, Cardinal, BJ, Crespo, CJ, Brodowicz, GR, Andersen, RE, and Smit, E. Differences in demographic, behavioral, and biological variables between those with valid and invalid accelerometry data: Implications for generalizability. J Phys Act Health 10: 79–84, 2013. 11. Lord, SR, Ward, JA, Williams, P, and Strudwick, M. The effect of a 12-month exercise trial on balance, strength, and falls in older women: A randomized controlled trial. J Am Geriatr Soc 43: 1198– 1206, 1995. 12. Masud, T and Morris, RO. Epidemiology of falls. Age Ageing 30(Suppl 4): 3–7, 2001. 13. Nevitt, MC, Cummings, SR, Kidd, S, and Black, D. Risk factors for recurrent nonsyncopal falls. A prospective study. JAMA 261: 2663– 2668, 1989. 14. Nyman, SR and Victor, CR. Older people’s participation in and engagement with falls prevention interventions in community settings: An augment to the Cochrane systematic review. Age Ageing 41: 16–23, 2012. 15. O’Loughlin, JL, Robitaille, Y, Boivin, JF, and Suissa, S. Incidence of and risk factors for falls and injurious falls among the communitydwelling elderly. Am J Epidemiol 137: 342–354, 1993. 16. Pardasaney, PK, Slavin, MD, Wagenaar, RC, Latham, NK, Ni, P, and Jette, AM. Conceptual limitations of balance measures for community-dwelling older adults. Phys Ther 93: 1351–1368, 2013.
ACKNOWLEDGMENTS
17. Province, MA, Hadley, EC, Hornbrook, MC, Lipsitz, LA, Miller, JP, Mulrow, CD, Ory, MG, Sattin, RW, Tinetti, ME, and Wolf, SL. The effects of exercise on falls in elderly patients. A preplanned metaanalysis of the FICSIT Trials. Frailty and Injuries: Cooperative Studies of Intervention Techniques. JAMA 273: 1341–1347, 1995.
All authors disclose no conflicts of interest. No funding was used to prepare this manuscript.
18. Ray, WA, Thapa, PB, and Gideon, P. Benzodiazepines and the risk of falls in nursing home residents. J Am Geriatr Soc 48: 682–685, 2000. VOLUME 28 | NUMBER 8 | AUGUST 2014 |
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Physical Activity and Balance 19. Robbins, AS, Rubenstein, LZ, Josephson, KR, Schulman, BL, Osterweil, D, and Fine, G. Predictors of falls among elderly people. Results of two population-based studies. Arch Intern Med 149: 1628– 1633, 1989. 20. Shephard, RJ. Limits to the measurement of habitual physical activity by questionnaires. Br J Sports Med 37: 197–206, 2003; discussion 206. 21. Skelton, DA. Effects of physical activity on postural stability. Age Ageing 30(Suppl 4): 33–39, 2001. 22. Stevens, JA, Corso, PS, Finkelstein, EA, and Miller, TR. The costs of fatal and non-fatal falls among older adults. Inj Prev 12: 290–295, 2006.
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23. Todd, C and Skelton, D. What are the Main Risk Factors for Falls Among Older People and What Are the Most Effective Interventions to Prevent These Falls? Copenhagen, Denmark: WHO Regional Office for Europe; Health Evidence Network report, 2004. Available at: http://www.euro.who.int/__data/assets/pdf_file/0018/74700/ E82552.pdf. 24. Troiano, RP, Berrigan, D, Dodd, KW, Maˆsse, LC, Tilert, T, and McDowell, M. Physical activity in the United States measured by accelerometer. Med Sci Sports Exerc 40: 181–188, 2008. 25. Viljanen, A, Kaprio, J, Pyykko, I, Sorri, M, Pajala, S, Kauppinen, M, Koskenvuo, M, and Rantanen, T. Hearing as a predictor of falls and postural balance in older female twins. J Gerontol A Biol Sci Med Sci 64: 312–317, 2009.
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AND
AND
JOSEPH A. BROSKY JR2
1
Department of Exercise Science, Donna and Allan Lansing School of Nursing and Health Sciences, Bellarmine University, Louisville, Kentucky; and 2Physical Therapy Program, Donna and Allan Lansing School of Nursing and Health Sciences, Bellarmine University, Louisville, Kentucky ABSTRACT
Loprinzi, PD and Brosky Jr, JA. Objectively measured physical activity and balance among U.S. adults. J Strength Cond Res 28(8): 2290–2296, 2014—The purpose of this study was to examine the association between objectively measured physical activity (PA) and balance in a nationally representative sample of U.S. adults 40 years of age and older. Data from the 2003–2004 National Health and Nutrition Examination Survey were used. Physical activity was measured over a 7-day period using accelerometry, and balance was assessed using the Romberg test. Participants completed a questionnaire regarding their subjective views on difficulty with falling in the past 12 months. For every 60-minute increase in light-intensity PA, participants were 10% (p = 0.04) more likely to have functional balance. Similarly, for every 1-minute increase in log-transformed moderate-to-vigorous physical activity, participants were 23% (p = 0.04) more likely to have functional balance. Regular PA, regardless of intensity, may have health benefits for older adults and is associated with functional balance.
KEY WORDS accelerometry, light-intensity physical activity, functional balance INTRODUCTION
balance are more likely to fall and tend to be less active (21,15), with this inactivity increasing the incidence of falls through various mechanisms, such as muscle atrophy, muscle weakness, reduced flexibility, and gait impairments (2,4,11,17). As a result, regular participation in safe forms of physical activity may help to prevent falls and reduce societal costs associated with injurious falls (22). Most balance and fall prevention interventions have been conducted in community, institutional, residential care, or other clinic-based populations (14,23). To our knowledge, no population-based studies have examined the association between objectively measured physical activity and balance in a nationally representative sample. As a result, previous studies may lack generalizability and are limited by the use of self-report physical activity methodology, which is prone to considerable measurement error (20). Therefore, the primary purpose of this study was to examine the association between objectively measured physical activity (i.e., accelerometry technology) and balance in a nationally representative sample of U.S. adults. A particular interest of this study was to examine whether light-intensity physical activity, in addition to moderate-to-vigorous physical activity (MVPA), is associated with balance. If so, this may have strong practical implications for strength and conditioning professionals as light-intensity physical activity may be a more palatable and feasible intensity to promote among older individuals at risk of falls and/or have a fear of falling.
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METHODS
Address correspondence to Paul D. Loprinzi, [email protected].
Subjects
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Among participants in the 2003–2004 NHANES, 3,299 were eligible for the accelerometry and balance component (age range: 40-85 yrs). After excluding those with missing balance
alls, particularly in older adults, are associated with significant morbidity and mortality (12). Several factors are associated with increased incidence of falls, such as older age (19), gender (5), raceethnicity (13), medications (18), comorbid illness (9), visual acuity (8), and hearing sensitivity (25). Fear of falling is also an important risk factor for falls and has been linked to adverse psychological, physical and functional changes in older adults (3). Falls and fear of falling are complex and interrelated constructs with each being considered a risk factor for the other. Additionally, individuals with dysfunctional
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Experimental Approach to the Problem
Data from the 2003–2004 National Health and Nutrition Examination Survey (NHANES) were used, as this is the only NHANES cycle with both accelerometry and balance data. National Health and Nutrition Examination Survey is an ongoing survey conducted by the National Center for Health Statistics, which evaluates a representative sample of non-institutionalized U.S. civilians, selected by a complex, multistage probability design.
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Among these, 575 were excluded because of insufficient accelerTABLE 1. Unweighted characteristics of the analyzed participants (n = 1,831) ometry data (i.e., ,4 days of 10 compared to excluded participants (n = 575), NHANES 2003–2004.*† + hours per day of monitoring), Mean/proportion (95% CI) which left an analytic sample of 1,831 adult participants. Previous Variable Analyzed sample Excluded sample p studies have also shown a high number of missing/incomplete Age (y) 61.3 (60.7–61.9) 60.4 (59.2–61.6) 0.16 % Male 50.6 (48.3–52.9) 50.9 (46.8–55.0) 0.89 accelerometry data and have 28.2 (28.0–28.5) 28.3 (27.8–28.7) 0.77 Body mass index (kg$m22) demonstrated potential biases Race-ethnicity 0.12 associated with incomplete data % Non-Hispanic white 58.1 (55.8–60.3) 54.4 (50.3–58.5) (10). As a result, Table 1 shows % Other 41.8 (39.6–44.1) 45.5 (41.4–49.6) characteristics of the analyzed Education 0.002 % High school or less 53.7 (51.5–56.0) 61.2 (57.2–65.2) sample (n = 1,831) compared % Some college or more 46.2 (43.9–48.4) 38.7 (34.7–42.7) with those excluded because of Comorbidity index 0.60 insufficient accelerometry data % 0 comorbidities 31.4 (29.2–33.5) 30.2 (26.5–34.0) (n = 585). Overall, and in this % 1+ comorbidity 68.5 (66.4–70.7) 69.7 (65.9–73.4) sample, those included in the Vision 0.004 % Excellent 22.5 (20.5–24.4) 18.2 (15.0–21.4) analyses were similar to those % Good 35.8 (33.6–38.0) 32.5 (28.6–36.3) excluded with the exception of % Fair or worse 41.6 (39.3–43.8) 49.2 (45.1–53.3) education and vision. Excluded Hearing 0.86 participants were less educated % Good 64.8 (62.6–67.0) 65.7 (61.8–69.6) (38.7 vs. 46.2% had some college % A little trouble 28.0 (26.0–30.1) 26.9 (23.3–30.5) % A lot of trouble/deaf 7.0 (5.8–8.2) 7.3 (5.1–9.4) or more) and had worse vision Balance problems from medication 0.10 (49.2 vs. 41.6% had fair or worse % Yes 3.8 (2.9–4.7) 5.3 (3.5–7.2) vision). % No 96.1 (95.2–97.0) 94.6 (92.7–96.4) All procedures for data col*NHANES = National Health and Nutrition Examination Survey; CI = confidence interval. lection were approved by the †Independent t-tests were used to examine differences for continuous variables, and x2 National Center for Health tests were used for categorical variables. Statistics Ethics Review Board (no university institutional review board approval is needed), and all participants and covariate data (i.e., age, gender, education, raceprovided written informed consent before data collection. ethnicity, body mass index (BMI), comorbidity index, vision, National Health and Nutrition Examination Survey data are hearing, and medication use), 2,406 participants remained. publically accessible online (http://www.cdc.gov/nchs/). Procedures
Figure 1. Mean light-intensity physical activity (LPA) and moderate-to-vigorous physical activity (MVPA) estimates across balance status. Error bars represent standard errors.
Assessment of Balance. Participants aged 40 years and older were eligible for the balance assessment. For safety precautions, participants were excluded if they felt unable to stand on their own, had current symptoms of dizziness or lightheadedness, weighed more than 275 lbs, could not fit into the standardized safety gait belt, required a leg brace to stand unassisted, or had a foot or leg amputation. Before balance testing, participants completed a questionnaire regarding their subjective views
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Physical Activity and Balance Balance testing consisted of using the modified Romberg Test of Standing Balance on firm and compliant support surfaces. This test evaluated the participants ability to stand unassisted under 4 different conditions (ordered in increasing levels of difficulty) designed to test sensory inputs from the vestibular system, vision, and proprioception: test 1—eyes open, firm surface; test 2—eyes closed, firm surface; test 3—eyes open, compliant surface; and test 4—eyes closed, compliant surface. ParticFigure 2. Mean light-intensity physical activity (LPA) and moderate-to-vigorous physical activity (MVPA) estimates across difficulty with falls. Error bars represent standard errors. ipants were allowed 2 trials for each condition. For all tests, balance was scored as pass or fail. on difficulty with falling in the past 12 months. Specifically, With tests 1 and 2 lasting 15 seconds and tests 3 and 4 lasting they were asked, “During the past 12 months, have you had 30 seconds, test failure was defined as the participant needing difficulty with falling?” The questionnaire contained no items to open their eyes, moving their arms or feet to increase staon actual falls or fall frequency. bility, or beginning to fall or requiring assistance to maintain
TABLE 2. Multivariable logistic regression examining the association between physical activity and odds (95% CI) of having functional balance, NHANES 2003–2004 (n = 1,831).* Odds ratio (95% CI) for functional balance†
Physical activity Covariates Age (y) 50–59 vs. 40–49 60–69 vs. 40–49 70+ vs. 40–49 Women vs. men Some college or more vs. high school or less Non-hispanic white vs. other BMI, 1 kg$m22 higher Presence of comorbidities (1+) vs. none Vision Good vs. excellent Fair or worse vs. excellent Hearing A little trouble vs. good A lot trouble/deaf vs. good Balance problems from medication No vs. yes
LPAz
p
MVPAz
p
1.10 (1.00–1.21)
0.04
1.24 (1.00–1.53)
0.04
0.31 0.25 0.10 0.99 1.75 1.15 1.04 0.75
(0.17–0.56) (0.13–0.49) (0.06–0.16) (0.78–1.25) (1.39–2.21) (0.93–1.42) (1.02–1.07) (0.49–1.14)
0.001 ,0.001 ,0.001 0.94 ,0.001 0.17 0.001 0.17
0.32 0.27 0.11 1.11 1.66 1.14 1.05 0.77
(0.18–0.57) (0.14–0.53) (0.06–0.21) (0.89–1.38) (1.35–2.03) (0.91–1.42) (1.02–1.07) (0.50–1.18)
0.001 0.001 ,0.001 0.29 ,0.001 0.22 0.001 0.22
0.81 (0.53–1.24) 0.64 (0.42–0.98)
0.32 0.04
0.82 (0.53–1.26) 0.65 (0.42–0.98)
0.35 0.04
0.83 (0.62–1.11) 0.65 (0.40–1.06)
0.19 0.08
0.82 (0.61–1.11) 0.66 (0.39–1.09)
0.18 0.10
1.89 (0.88–4.06)
0.09
1.85 (0.88–3.89)
0.09
*CI = confidence interval; LPA = light-intensity physical activity; MVPA = moderate-to-vigorous physical activity; BMI = body mass index; NHANES = National Health and Nutrition Examination Survey. †Referent group was dysfunctional balance (i.e., failed one of the 4 test conditions). Two multivariable logistic regression models were computed: 1 for LPA and the other for MVPA. zLPA expressed as a 60-minute interval change; MVPA log-transformed to improve normality and expressed as a 1-unit, log-transformed interval change.
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Journal of Strength and Conditioning Research balance. For this study, and similar to others (1), participants were classified as having dysfunctional balance if they failed any of the 4 test conditions. Functional balance, in contrast, was operationally defined as successful completion of all 4 of the test conditions. Assessment of Physical Activity. Participants who were able to walk were asked to wear an ActiGraph 7164 accelerometer on their right hip for 7 days. Accelerometers were affixed to an elastic belt that was worn around the participant’s waist near the iliac crest. Participants were asked to wear the accelerometer during all activities except water-based activities and sleeping. The accelerometer measured the frequency, intensity, and duration of physical activity by generating an activity count proportional to the measured acceleration. Estimates for physical activity were summarized in 1-minute time intervals. Minutes with activity counts $2,020 were classified as MVPA (24), with light-intensity physical activity defined as activity counts between 100 and 2019. Only those participants with at least 4 days of 10 or more hours per day of accelerometer wear time were included in the analyses to make sure that data adequately captured habitual physical activity patterns (24). To monitor the amount of time the device was worn, nonwear was
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defined by a period of a minimum of 60 consecutive minutes of zero activity counts, with the allowance of 1–2 minutes of activity counts between 0 and 100 (24). Measurement of Covariates. Information about age (binned by decade), gender, race/ethnicity, and education were obtained from questionnaires. Participants were classified as having 0 or 1+ comorbidities based on self-report of having a history of coronary heart disease, stroke, cancer, arthritis, diabetes, kidney disease, or hypertension. Participants self-reported their vision and hearing function, with vision categorized as excellent, good, fair, poor, or very poor, with hearing function defined as good, a little trouble, lot of trouble, or deaf. Participants also indicated whether any potential balance problems were a result of medication they were taking. Lastly, BMI was calculated from measured weight and height (kg$m22). Statistical Analyses
All statistical analyses were performed using procedures from sample survey data using STATA (StataCorp., version 12.0, College Station, TX) to account for the complex survey design used in NHANES. To account for oversampling, non-response, non-coverage, and to provide nationally representative
TABLE 3. Multivariable logistic regression examining the association between physical activity and odds of having difficulty with falls in the past 12 months, NHANES 2003–2004 (n = 1,831).* Odds ratio (95% CI) for having difficulty with falling in past 12 mo†
Physical activity Covariates Age (y) 50–59 vs. 40–49 60–69 vs. 40–49 70+ vs. 40–49 Women vs. men Some college or more vs. high school or less Non-Hispanic white vs. other BMI, 1 kg$m22 higher Presence of comorbidities (1+) vs. none Vision Good vs. Excellent Fair or worse vs. excellent Hearing A little trouble or worse vs. good Balance problems from medication No vs. yes
LPAz
p
MVPAz
p
0.78 (0.65–1.00)
0.06
0.59 (0.40–0.85)
0.009
2.81 2.20 3.76 2.48 1.13 1.55 1.00 1.73
0.06 0.20 0.02 0.02 0.66 0.15 0.97 0.32
2.41 1.74 2.33 1.92 1.27 1.52 0.99 1.64
0.14 0.43 0.19 0.10 0.38 0.16 0.84 0.36
1.50 (0.71–3.16) 4.06 (1.51–10.92)
0.26 0.008
1.39 (0.65–2.96) 3.75 (1.28–10.99)
0.35 0.01
2.21 (1.39–3.52)
0.002
2.27 (1.43–3.62)
0.002
0.16 (0.07–0.36)
,0.001
0.16 (0.06–0.41)
0.001
(0.93–8.47) (0.61–7.93) (1.24–11.36) (1.16–5.27) (0.62–2.07) (0.83–2.87) (0.95–1.04) (0.55–5.40)
(0.70–8.23) (0.40–7.51) (0.61–8.88) (0.85–4.35) (0.72–2.24) (0.81–2.83) (0.94–1.04) (0.53–5.05)
*LPA = light-intensity physical activity; MVPA = moderate-to-vigorous physical activity; BMI = body mass index; NHANES = National Health and Nutrition Examination Survey. †Referent group was not having difficulty with falling. Two multivariable logistic regression models were computed: 1 for LPA and the other for MVPA. zLPA expressed as a 60-minute interval change; MVPA log-transformed to improve normality and expressed as a 1-unit, logtransformed interval change.
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Physical Activity and Balance estimates, all analyses included the use of appropriate survey sample weights, clustering and primary sampling units. In an effort to maintain nationally representative estimates, the sample weights for those with 4 or more days of valid accelerometry data were ratio-adjusted to maintain the age, sex, and race-ethnicity distribution of the full sample. Means were computed for continuous variables and proportions were computed for categorical variables. To examine the association between physical activity (independent variable) and balance status, a multivariable logistic regression model was computed. Similarly, a multivariable logistic regression was used with self-reported difficulty with falling serving as the outcome variable. Moderate-to-vigorous physical activity was log-transformed because it failed tests of normality. Model covariates included age, gender, education, race-ethnicity, BMI, comorbidity index, vision, hearing, and medication use. All covariates were entered into the model at the same time as there was no evidence of multicollinearity based on correlations ,0.8 between covariate pairs (max observed correlation ,0.40), mean variance inflation factor ,6 (observed mean = 1.1), individual variance inflation factors ,10 (highest observed = 1.7), and tolerance statistics .0.1 (all observed to be .0.50). Statistical significance was established p # 0.05.
RESULTS Physical activity estimates across balance status and difficulty with falls, respectively, are shown in Figures 1 and 2. Participants with dysfunctional balance engaged in less lightintensity physical activity than those with functional balance (319.8 vs. 352.5 min$d21; p # 0.05) (Figure 1). Similarly, those with dysfunctional balance engaged in less MVPA than those with functional balance (16.1 vs. 22.8 min$d21; p # 0.05) (Figure 1). Participants who had difficulty with falling within the past 12 months engaged in less light-intensity physical activity than those who self-reported not having difficulty with falling (295.0 vs. 341.0 min$d21; p # 0.05). Similarly, participants who had difficulty with falling within the past 12 months engaged in less MVPA than those without difficulty (9.0 vs. 20.5 min$d21; p # 0.05). Table 2 shows the odds of having functional balance based on physical activity levels. For every 60-minute increase in light-intensity physical activity, participants were 10% (p = 0.04) more likely to have functional balance. Similarly, for every 1-minute increase in log-transformed MVPA, participants were 24% (p = 0.04) more likely to have functional balance. Table 3 shows the odds of having difficulty with falling in the past 12 months based on physical activity levels. Lightintensity physical activity was not significant (p = 0.06); however, for every 1-minute increase in log-transformed MVPA, participants were 41% (p = 0.009) less likely to have difficulty with falling in the past 12 months.
DISCUSSION The primary purpose of this study was to examine the association between objectively measured physical activity
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and balance in a nationally representative sample of U.S. adults. Findings of this study confirm intervention studies in that individuals engaging in higher levels of accelerometerassessed physical activity (both light-intensity and MVPA) were more likely to have functional balance. Additionally, higher levels of MVPA were also associated with a reduced likelihood of having difficulty with falling. Many older adults may limit their regular physical activity for a host of reasons that might include but are not limited to fatigue, fear of falling, and the development of age related conditions such as osteoarthritis. Balance is an important attribute required for safe mobility and is dependent on a number of interrelated mechanisms and interdependent systems. Although this study does not confirm preventive effects of physical activity on the number or frequency of actual falls, successful performance by individuals (i.e., receiving a “pass” score) on the balance tests performed in this study does appear to provide an objective relationship between regular physical activity and balance performance. A recent Cochrane Review (6) on interventions for preventing falls in community dwelling older people concluded that there was no evidence of any effects for cognitive behavioral interventions on rate of falls or risk of falling. However, this same Cochrane Review did report benefits of group and home-based exercise programs by reducing the rate of falls and risks of falling. Educational programs developed to increase knowledge of risk factors and promote positive behaviors have been reported to have only marginal benefits, and the findings are inconclusive at best for actually preventing falls. The benefits of conducting standardized individual risk assessments, like the balance tests performed in this study, may be a critical component in helping individuals truly understand their functional abilities and limitations when they are provided information specific to age and gender related normative values. Additionally, physical performance assessments using standardized tests such as the Timed Up and Go, Timed Sit to Stand, and Four Square Step Test, can also establish baseline function, increase individual awareness of deficits, and guide effective intervention strategies. One program that has demonstrated effectiveness is the Matter of Balance intervention program (7). The Matter of Balance Program (7) incorporates light-intensity physical activity exercises and balance training, a cognitivebehavior intervention and social support. The 8-session program includes specific elements that promote the view that falls and fear of falling are controllable, sets realistic goals for gradually increasing activity, addresses environmental factors to reduce falls risk and promotes light-intensity physical activity to increase strength and balance. A limitation of this study was the inability to determine number of actual falls or fall frequency. Another limitation includes the cross-sectional study design, rendering causal relationships not possible. It is possible that individuals with better balance engaged in more physical activity because of enhanced functional capacity and possibly having greater
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Journal of Strength and Conditioning Research confidence in participating in instrumental activities of daily living; however, it is plausible to suggest that increasing activity behavior may play a causal role in increasing balance and reducing falls, as previous intervention studies have shown such an effect and have also demonstrated potential mechanisms (e.g., improve gait function, general mobility, and increase lower limb strength) to elucidate this relationship. Despite these noted limitations, major strengths of this study include the use of an objective measure of physical activity, an objective measure of balance, and using a nationally representative sample to examine this relationship. In conclusion, the results of this study contribute to the existing literature on the important relationship between falls risk and physical activity levels. Regular physical activity, regardless of intensity, may have health benefits for older adults and is associated with functional balance. Falls are a major public health problem and have serious implications on health care costs associated with falls, on quality of life and may threaten community dwelling older adults’ independence. As the risk of falls generally increases with advancing age, strategies are needed to identify the risk factors and incorporate effective interventions aimed at recognition and prevention. Important to any prevention program is the regular, simple, and cost effective methods of screening balance. Many of the balance measures such as the Romberg test described in this study meet these criteria, but as Pardasaney et al. (16) reported in a recent systematic analysis study, these balance measures may not adequately assess the postural control demands in daily activities that involve dynamic stability, changing environments, multi-tasking, object interaction, or obstacle negotiation. Future studies are needed to further examine performance indicators on individual balance measures and their relationship with physical activity levels.
PRACTICAL APPLICATIONS Findings from this study demonstrate that both lightintensity physical activity and MVPA were associated with better functional balance among a nationally representative sample of U.S. adults. Our finding that light-intensity physical activity is particularly important as light-intensity physical activity, compared with MVPA, may be a more feasible and palatable approach to promoting physical activity among individuals at risk for falling (e.g., elderly). This is an encouraging finding as most physical activity related research has focused on MVPA, with less research demonstrating a beneficial effect of light-intensity physical activity. Based on these findings, health care professionals should encourage light-intensity physical activity, and when appropriate, MVPA, to help reduce injurious falls. Other forms of exercise, such as strength training, should be promoted to augment the effects of ambulatory activity.
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ACKNOWLEDGMENTS
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All authors disclose no conflicts of interest. No funding was used to prepare this manuscript.
18. Ray, WA, Thapa, PB, and Gideon, P. Benzodiazepines and the risk of falls in nursing home residents. J Am Geriatr Soc 48: 682–685, 2000. VOLUME 28 | NUMBER 8 | AUGUST 2014 |
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Physical Activity and Balance 19. Robbins, AS, Rubenstein, LZ, Josephson, KR, Schulman, BL, Osterweil, D, and Fine, G. Predictors of falls among elderly people. Results of two population-based studies. Arch Intern Med 149: 1628– 1633, 1989. 20. Shephard, RJ. Limits to the measurement of habitual physical activity by questionnaires. Br J Sports Med 37: 197–206, 2003; discussion 206. 21. Skelton, DA. Effects of physical activity on postural stability. Age Ageing 30(Suppl 4): 33–39, 2001. 22. Stevens, JA, Corso, PS, Finkelstein, EA, and Miller, TR. The costs of fatal and non-fatal falls among older adults. Inj Prev 12: 290–295, 2006.
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23. Todd, C and Skelton, D. What are the Main Risk Factors for Falls Among Older People and What Are the Most Effective Interventions to Prevent These Falls? Copenhagen, Denmark: WHO Regional Office for Europe; Health Evidence Network report, 2004. Available at: http://www.euro.who.int/__data/assets/pdf_file/0018/74700/ E82552.pdf. 24. Troiano, RP, Berrigan, D, Dodd, KW, Maˆsse, LC, Tilert, T, and McDowell, M. Physical activity in the United States measured by accelerometer. Med Sci Sports Exerc 40: 181–188, 2008. 25. Viljanen, A, Kaprio, J, Pyykko, I, Sorri, M, Pajala, S, Kauppinen, M, Koskenvuo, M, and Rantanen, T. Hearing as a predictor of falls and postural balance in older female twins. J Gerontol A Biol Sci Med Sci 64: 312–317, 2009.
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