- Email: [email protected]
Incidence of Disability in Frail Older Persons With or Without Slow Walking Speed
JAMDA 16 (2015) 690e696
JAMDA journal homepage: www.jamda.com
Original Study
Incidence of Disability in Frail Older Persons With or Without Slow Walking Speed Hiroyuki Shimada PhD a, *, Hyuma Makizako PhD a, Takehiko Doi PhD a, Kota Tsutsumimoto PhD a, Takao Suzuki PhD b a b
Department of Preventive Gerontology, Center for Gerontology and Social Science, National Center for Geriatrics and Gerontology, Obu, Japan National Center for Geriatrics and Gerontology, Obu, Japan
a b s t r a c t Keywords: Frailty physical performance gait activities of daily living aged
Objective: To identify the differences of incidence of disability between frail older persons with and without slow walking speed. Design: Prospective cohort study. Setting: Japanese community. Participants: A total of l4,081 older adults aged 65 years living in the community, participated in a baseline assessment and were followed for incidence of disability for 29.5 months. Measurements: Care-needs certification in the national long-term care insurance system of Japan, physical frailty (slow walking speed, muscle weakness, exhaustion, low activity, weight loss), adjusted for several potential confounders such as demographic characteristics; Kaplan-Meier survival curves for incident disability by physical frailty with and without slow walking speed. Results: During the follow-up period, 198 participants (4.9%) were certified as requiring long-term care insurance in accordance with incident disability. Participants who had prefrailty without slow walking speed (hazard ratio 1.86, 95% confidence interval 1.19e2.92), prefrailty with slow walking speed (3.62, 2.19e5.96), frailty without slow walking speed (4.33, 2.00e9.39), and frailty with slow walking speed (4.68, 2.72e8.05) at the baseline assessment had an increased risk of incident disability compared with nonfrail participants. In stratified analyses, frail older men and frail participants with low cognitive performance had the highest risk of incidence of disability. Conclusion: The presence of frailty or even prefrailty when older adults showed slow walking speed increased the risk of future disability in community-dwelling older adults. Ó 2015 AMDA e The Society for Post-Acute and Long-Term Care Medicine.
Japan implemented the national social long-term care insurance (LTCI) system on April 1, 2000. Every Japanese person aged 65 and older is eligible for benefits based strictly on physical and mental frailty or disability.1 Physical frailty increases with advancing age and is a major risk factor for dependency, institutionalization, and mortality in older people.2e4 People with a disability incur higher health This work received financial support from Health and Labor Sciences Research Grants (Comprehensive Research on Aging and Health, grant number H24-ChojuIppan-004), a Grant-in-Aid for Scientific Research (B) (grant number 23300205), and Research Funding for Longevity Sciences from the National Center for Geriatrics and Gerontology (grant number 22-16), Japan. No support was received from industry. The funding source played no role in the design or conduct of the study; collection, management, analysis, or interpretation of the data; or preparation, review, or approval of the manuscript. The authors declare no conflicts of interest. * Address correspondence to Hiroyuki Shimada, PhD, Department of Preventive Gerontology, Center for Gerontology and Social Science, National Center for Geriatrics and Gerontology, 7-430 Morioka-cho, Obu, Aichi 474-8511, Japan. E-mail address: [email protected] (H. Shimada). http://dx.doi.org/10.1016/j.jamda.2015.03.019 1525-8610/Ó 2015 AMDA e The Society for Post-Acute and Long-Term Care Medicine.
care costs compared with those without a disability.5 For the purpose of targeting risk factors for future frailty, adequate assessment of individuals is necessary. A feasible and valid screening tool available for research and clinical settings is required to identify frailty in the community. The well-known frailty phenotype introduced by Fried et al,6 which classifies people into categories of robust, prefrail, or frail, fits within this physiological approach to frailty. The frailty phenotype postulates that 5 indicators (weight loss, exhaustion, slow walking speed, low grip strength, and low physical activity) are related to each other in a cycle of frailty. A person with none of the indicators is robust, a person with 1 or 2 indicators is prefrail, and a person with 3 or more indicators is frail. Older people who are frail according to the phenotype have a higher risk of disability.7e9 The Interventions on Frailty Working Group developed recommendations to screen, recruit, evaluate, and retain frail older persons in clinical trials.10 They reported that most researchers focus on the following domains for the identification of physical frailty: mobility, such as lower-extremity
H. Shimada et al. / JAMDA 16 (2015) 690e696
performance and gait abnormalities; muscle weakness; poor exercise tolerance; unstable balance; and factors related to body composition, such as weight loss, malnutrition, and muscle loss.10 In an effort to select tailored preventive programs in the Japanese LTCI system, those at high risk for subsequent disability are identified with a basic functional status questionnaire. Although the questionnaire is relatively quick to administer, a performance-based assessment could determine actual physical capacity and might more accurately predict subsequent physical disability in community-living older people. Guralnik et al11 reported that measures of physical performance may identify older persons with a preclinical stage of disability who may benefit from interventions to prevent the development of frank disability. Walking speed has been consistently reported to differentiate between participants with and those without personal care, with frail older persons walking significantly slower,12,13 and has proved to be a strong predictor of adverse events, such as disability,14e20 mortality,15,16,21,22 hospitalization,15,16,18,23 falls,23,24 and dementia.25 We generally agree with the concept of the frailty model and its components to identify frailty including slowness, weakness, exhaustion, low activity, and weight loss. However, previous studies suggest that the separate components do not have equal impacts on the onset of disability in older adults. We hypothesize that slow walking speed has a greater impact on the incidence of disability than the other components of frailty. The purpose of this study was to identify the differences of incidence of disability between frail older persons with and without slow walking speed. Methods Participants This prospective cohort study involved 5104 community-dwelling older adults (65 years) enrolled in the Obu Study of Health Promotion for the Elderly (OSHPE).26 OSHPE participants were recruited from Obu, a residential suburb of Nagoya, Japan. Inclusion criteria were an age of 65 years at examination in 2011 or 2012, Obu residency, and no previous participation in other studies. Exclusion criteria were the need for support or care certified by the Japanese public LTCI system, disability in basic activities of daily living, and inability to undergo performance-based assessments.26 Between August 2011 and February 2012, 5104 community-dwelling older people participated in a baseline OSHPE assessment including a faceto-face interview and measures of physical and cognitive function. Participants were followed monthly and monitored for certification of LTCI for at least 2 years. In this longitudinal study, we included participants who completed baseline assessments and follow-up assessments of disability by the LTIC system. We excluded participants with a history of Parkinson disease, stroke, depression, and dementia, Mini-Mental State Examination (MMSE)27 scores of <20, or having a disability based on the LTIC system at baseline. Participants who died or who moved to another city during the follow-up period were also excluded. Of 5104 participants who completed a baseline assessment, 1023 older adults were excluded from the present study. The remaining 4081 participants of average age 71.7 5.3 years (women 51.6%) were included in the following analyses. Informed consent was obtained from all participants prior to their inclusion in the study, and the Ethics Committee of the National Center for Gerontology and Geriatrics approved the study protocol. Operationalization of Physical Frailty We considered the frailty phenotype to be characterized by limitations in 3 or more of the following 5 conditions based on those used in Fried’s original studies6: slow walking speed, weakness,
691
exhaustion, low activity, and weight loss. Participants having none of these components were considered to be nonfrail, and those having 1 or 2 components were considered to be in prefrail. Participants with prefrailty and frailty were divided into the following 4 groups according to frailty status and walking speed (cutoff <1.0 m/s): prefrailty without slow walking speed, prefrailty with slow walking speed, frailty without slow walking speed, and frailty with slow walking speed. Walking speed was measured in seconds using a stopwatch. Participants were asked to walk on a flat and straight surface at a comfortable walking speed. Two markers were used to indicate the start and end of a 2.4-m walk path, with a 2-m section to be traversed before passing the start marker so that participants were walking at a comfortable pace by the time they reached the timed path. Participants were asked to continue walking for an additional 2 m past the end of the path to ensure a consistent walking pace while on the timed path. Slowness was established according to a cutoff (<1.0 m/ s).26 Weakness was defined using maximum grip strength. Grip strength was measured in kilograms using a Smedley-type handheld dynamometer (GRIP-D; Takei Ltd, Niigata, Japan). Weakness was established according to a sex-specific cutoff (<26 kg for men and <18 kg for women).28 Exhaustion was considered present if the participant responded “yes” to the following question, which included the Kihon-Checklist, a self-reported comprehensive health checklist that was developed by the Japanese Ministry of Health, Labor, and Welfare29: “In the last 2 weeks, have you felt tired without a reason?” We evaluated the role of physical activity by asking the following questions about time spent engaged in sports and exercise: (1) “Do you engage in moderate levels of physical exercise or sports aimed at health?” and (2) “Do you engage in low levels of physical exercise aimed at health?” If participants answered “no” to both of these questions, we considered them to be low activity.26 Weight loss was assessed by a response of “yes” to the question, “Have you lost 2 kg or more in the past 6 months?”29 Investigation for Incidence of Disability Participants were followed monthly for incident certification of need of care in the national LTCI system during the 2 years following the baseline assessment. We defined onset of disability as the point when a participant was certified as needing care by the LTCI. Every Japanese person aged 65 and older is eligible for benefits (institutional and community-based services, but not cash) based strictly on physical and mental disability. The computer-aided standardized needs-assessment system categorizes people into 7 levels of need. The LTCI certifies a person as “support level 1 or 2” if they need support for daily activities or “care level 1, 2, 3, 4, or 5” if they need continuous care.1 In this study, the outcome of disability was defined as a new certification of the LTCI service at any level. Potential Confounding Factors of Activities of Daily Living With reference to the review article by Stuck et al30 and a previous longitudinal study by Ishizaki et al,31 we selected 2 demographic variables, 3 physiological variables, 4 primary diseases or geriatric syndromes, and 6 psychosocial variables as possible confounding factors of activities of daily living (ADL) limitations (Table 1). The physiological variables “overweight” and “underweight” were determined by measuring body mass index, and the cut points of overweight and underweight were set at 27.5 kg/m2 and <18.5 kg/m2, respectively.32 Measurements of the ability to walk continuously for 15 minutes and knee and lumbar pain (“yes” or “no”) were recorded from a self-report collected through the interview survey. The nurses who identified the chronic condition from the interview survey
692
H. Shimada et al. / JAMDA 16 (2015) 690e696
Table 1 Characteristics of the Participants, Mean SD or %
Demographic variables Age, years Sex, female Physiological variables Overweight, BMI 27.5 kg/m2, yes Underweight, BMI <18.5 kg/m2, yes Knee pain, yes Primary disease Heart disease, yes Pulmonary disease, yes Osteoarthritis, yes Diabetes, yes Psychological and social variables MMSE score GDS-15, score Going outdoors by bus and train, no Shopping of daily necessaries, no Visiting the homes of friends, no Being called on for advice, no
Overall (n ¼ 4081)
Nonfrailty (n ¼ 1805)
Prefrailty Without Slow Walking Speed (n ¼ 1648)
Prefrailty With Slow Walking Speed (n ¼ 371)
Frailty Without Slow Walking Speed (n ¼ 65)
Frailty With Slow Walking Speed (n ¼ 192)
P
71.7 5.3 51.6
70.7 4.3 52.1
71.3 5.0 50.3
74.8 6.3 50.7
74.6 5.3 52.3
78.1 6.7 60.4
<.001 .117
4.4 8.7 22.6
3.4 7.5 17.1
4.4 8.7 24.2
3.2 12.1 31.8
16.9 4.6 29.2
10.9 14.1 40.6
<.001 .002 <.001
15.4 10.6 13.6 13.0
13.1 9.1 12.1 10.0
16.4 12.0 13.2 13.6
17.3 8.9 19.4 18.9
13.8 13.8 12.3 26.2
24.5 14.6 21.9 19.8
<.001 .015 <.001 <.001
26.4 2.5 2.7 2.5 8.8 3.1 12.7 7.7
26.8 2.3 2.0 1.9 6.0 1.7 9.1 4.4
26.4 2.5 3.0 2.6 8.7 3.1 13.8 9.2
25.7 2.6 3.2 2.4 14.6 7.0 17.5 10.0
25.5 2.8 4.6 3.5 16.9 4.6 21.5 12.3
25.0 2.7 4.9 2.8 21.4 8.3 25.0 20.8
<.001 <.001 <.001 <.001 <.001 <.001
BMI, body mass index; SD, standard deviation.
recorded primary diseases or geriatric syndromes. The following diseases and geriatric syndromes were included in the analysis: heart disease, pulmonary disease, osteoarthritis, cancer, diabetes, falls, incontinence, and medications. The psychological and social measurements were assessed from a self-report collected through the interview survey. The Mini-Mental State Examination (MMSE) was used to assess global cognitive function.27 Depressive symptoms were measured using the 15-item Geriatric Depression Scale (GDS). 33 Instrumental activities and social roles were assessed using the subscales of the Kihon-Checklist (“yes” or “no”).29
walking speed were 40.4%, 9.1%, 1.6%, and 4.7%, respectively. During the follow-up period, 198 participants (4.9%) were determined as needing certification based on the LTCI system because of incident disability. Table 1 shows possible confounding factors of ADL limitations for each subtype of frailty. All measurements except sex exhibited significant differences. Participants who developed disability were older, more often women, had a higher prevalence of being overweight, knee pain, and heart disease, lower MMSE score, higher GDS score, and lower instrumental and social activities compared with those who remained independent (Table 2).
Statistical Analyses Student t-test and Pearson c2 test were used to test differences in the baseline characteristics between frailty statuses and between participants with and without incidence of disability. We calculated the cumulative incident of disability during followup according to baseline frailty status with the Kaplan-Meier curves. Intergroup differences were estimated by the log-rank test. Cox proportional hazards regression models were used to analyze associations between frailty and incidence of disability. The first model (model 1) was adjusted for age and sex. We then used a multiple adjustment model adjusted for 2 demographic variables, 3 physiological variables, 4 primary diseases or geriatric syndromes, and 6 psychosocial variables as possible confounding factors (model 2). We estimated adjusted hazard ratios (HRs) for incidence of disability and their 95% confidence intervals (CIs). Stratified analyses were performed to examine the relationship between frailty and incidence of disability in different subgroups defined by sex, age (74/75 years), cognitive function (MMSE 23/24 scores), and depressive symptoms (GDS 5/6 scores).34 Adjusted HRs for incidence of disability and their 95% CIs were also estimated in the stratified analyses. All analyses were done using IBM SPSS Statistics 20.0 (IBM Japan, Tokyo, Japan). The level of statistical significance was set at P < .05. Results Prevalence rates of each subtype of physical frailty including prefrailty without slow walking speed, prefrailty with slow walking speed, frailty without slow walking speed, and frailty with slow
Table 2 Comparisons With Confounding Factors Between the Participants Without and With Incident of Disability, Mean SD or % Participants Without Disability (n ¼ 3883) Demographic variables Age, years 71.4 5.0 Sex, female/male 94.2/96.2 Physiological variables Overweight, BMI 27.5 90.4/95.4 kg/m2, yes/no 95.3/93.2 Underweight, BMI <18.5 kg/m2, yes/no Knee pain, yes/no 93.3/95.7 Primary disease Heart disease, yes/no 92.3/95.7 Pulmonary disease, yes/no 93.5/95.3 Osteoarthritis, yes/no 93.7/95.4 Diabetes, yes/no 94.0/95.3 Psychological and social variables MMSE, score 26.5 2.4 GDS-15, score 2.6 2.5 Going outdoors by bus 90.5/95.6 and train, no/yes Shopping of daily 92.9/95.2 necessaries, no/yes Visiting the homes of 91.1/95.7 friends, no/yes Being called on for 90.2/95.6 advice, no/yes BMI, body mass index; SD, standard deviation.
Participants With Disability (n ¼ 198)
P
78.1 6.2 5.8/3.8
<.001 .002
9.6/4.6
.003
6.8/4.7
.080
6.7/4.3
.003
7.7/4.3 6.5/4.7 6.3/4.6 6.0/4.7
<.001 .095 .091 .173
24.9 2.7 3.8 2.7 9.5/4.4
<.001 <.001 <.001
7.1/4.8
.224
8.9/4.3
<.001
9.8/4.4
<.001
H. Shimada et al. / JAMDA 16 (2015) 690e696
693
Fig. 1. Estimates of survival rate on incident of disability according to frailty status. (1) Nonfrailty group. (2) Prefrailty without slow walking speed group. (3) Prefrailty with slow walking speed group, (4) Frailty without slow walking speed group. (5) Frailty with slow walking speed group. Left panel shows survival curves on incident of disability adjusted for age and sex, and right panel shows fully adjusted model.
Figure 1 shows the survival rates of disability based on frailty status and by non-frailty and subtype of frailty. Survival analyses using the Kaplan-Meier log-rank tests showed that the probability of incidence of disability was significantly higher in participants with prefrailty without slow walking speed, prefrailty with slow walking speed, frailty without slow walking speed, and frailty with slow walking speed compared with the nonfrail older group (P < .001). Also, there were significant differences in the incidence of disability between the subtypes of prefrailty and frailty, although there was no significant difference between prefrailty with slow walking speed and frailty without slow walking speed (P ¼ .785). Cox proportional hazards regression models were used to analyze associations between frailty and incidence of disability (Table 3). In
Table 3 HRs for Incident of Disability According to Frailty Status and Confounding Factors Model 1
Age, years Sex, female/male Overweight, BMI 27.5 kg/m2, yes Underweight, BMI <18.5 kg/m2, yes Knee pain, yes Heart disease, yes Pulmonary disease, yes Osteoarthritis, yes Diabetes, yes MMSE, score GDS-15, score Going outdoors by bus and train, yes Shopping of daily necessaries, yes Visiting the homes of friends, yes Being called on for advice, yes Physical frailty Nonfrailty Prefrailty without slow walking speed Prefrailty with slow walking speed Frailty without slow walking speed Frailty with slow walking speed
Model 2
HR (95% CI)
P
HR (95% CI)
P
1.13 (1.11e1.16) 0.68 (0.51e0.91)
<.001 .009
1.12 (1.10e1.15) 0.64 (0.47e0.87) 1.07 (0.63e1.82)
<.001 .004 .795
1.33 (0.85e2.07)
.214
1.05 1.43 1.18 0.89 1.08 0.89 1.01 1.23
(0.75e1.48) (1.02e2.00) (0.78e1.77) (0.59e1.33) (0.74e1.59) (0.84e0.94) (0.96e1.07) (0.80e1.88)
.773 .037 .432 .571 .683 <.001 .649 .343
0.64 (0.31e1.34)
.236
1.30 (0.87e1.94)
.194
1.10 (0.70e1.73)
.690
<.001 1 2.10 (1.35e3.26)
<.001
.001
1 1.86 (1.19e2.92)
.007
4.38 (2.70e7.10)
<.001
3.62 (2.19e5.96)
<.001
5.72 (2.70e12.16)
<.001
4.33 (2.00e9.39)
<.001
6.46 (3.90e10.70)
<.001
4.68 (2.72e8.05)
<.001
BMI, body mass index; SD, standard deviation.
the first model (model 1) that was adjusted for age and sex, the following HRs were determined for participants who had prefrailty without slow walking speed (HR 2.10, 95% CI 1.35e3.26), prefrailty with slow walking speed (4.38, 2.70e7.10), frailty without slow walking speed (5.72, 2.70e12.16), and frailty with slow walking speed (6.46, 3.90e10.70) compared with nonfrail participants. Age and sex were significantly associated with increased risk of disability. In the fully adjusted model (model 2), the following HRs were determined for prefrailty without slow walking speed (HR 1.86, 95% CI 1.19e2.92), prefrailty with slow walking speed (3.62, 2.19e5.96), frailty without slow walking speed (4.33, 2.00e9.39), and frailty with slow walking speed (4.68, 2.72e8.05) compared with nonfrail participants. In model 2, age, sex, heart disease, and MMSE score were related to increased risk of incident disability. Figure 2 shows the results of stratified analyses, which were divided by sex, age, MMSE, and depressive symptoms. The older men with frailty without and with slow walking speed showed higher HRs compared with the older women (men; 7.2e8.6, women; 3.0e3.2). Frail older adults who had low cognitive function (ie, MMSE <24) showed relatively higher HRs compared with those with high cognitive function (MMSE <24; 11.1e13.0, MMSE 24; 3.3e3.8). There were no marked differences between older adults aged 65 to 74 years and 75 and over, and between those with and without depressive symptoms.
Discussion The prevalence rates of physical frailty and prefrailty in this study were 6.3% and 49.5%, respectively, which is consistent with large studies in other countries.6,35e39 For instance, in the American Cardiovascular Health Study, the prevalence of frailty among 5317 community-dwelling men and women aged 65 years was 6.9%, and frailty was associated with older age, male gender, being African American, having lower education and income, poorer health, and higher rates of comorbid chronic disease and disability.6 The French Three-City Study demonstrated a frailty prevalence of 7% among 6078 community-dwelling men and women aged 65 years and older, and frailty was associated with older age, female gender, lower education, lower income, a poorer self-reported health status, and more chronic disease in addition to incident disability.6 The Hertfordshire Cohort Study in the United Kingdom, reported that the prevalence of frailty, as defined by Fried, among 638 community-dwelling participants aged 64e74 years was 8.5% for women and 4.1% for men.39 Only 18% of prefrail older adults exhibited slow walking speed, although 75% of frail older adults had slow walking speed. These results suggest that
694
H. Shimada et al. / JAMDA 16 (2015) 690e696
Fig. 2. HRs estimate relative risk of incident of disability in subgroups defined by sex, age, cognitive function, and depressive symptoms in stratified analyses.
the older adults with multiple frailty components tend to be associated with the presence of slow walking speed. Slow walking speed is widely used in geriatric assessment, and has become a good single estimator of frailty and its outcomes.15,40 A walking speed greater than 1.2 m/s suggests high life expectancy, whereas speeds lower than 1 m/s predict frailty and have been associated with disability, hospitalization and decreased survival.15,22,41 Based on these previous studies, we hypothesized that whether a person is classed as prefrailty or frailty has a different impact on the future incidence of disability depending on whether they have slow walking speed or not. Log-rank tests showed significant differences in the incidence of disability between subtypes in prefrailty and frailty, whereas there was no significant difference between prefrailty with slow walking speed and frailty without slow walking speed. The results indicate that slow walking speed has a major impact on the incidence of disability in prefrailty status. Our 4-frailty classification (ie, prefrailty with or without slow walking
speed and frailty with or without slow walking speed) may be a useful indicator to identify future risk of disability in older adults. Our frailty classification showed significant relationships exploring the incidence of disability in Cox proportional hazard models. In the fully adjusted model, HRs in prefrailty with slow walking speed, frailty without slow walking speed, and frailty with slow walking speed were similar values (HRs 3.62e4.68) in contrast to the HR of prefrailty with slow walking speed group (HR 1.86). These results suggest that healthcare providers should consider the necessity of interventions to prevent the incidence of disability in frail older persons as well as prefrail older adults with slow walking speed. In the stratified analyses, older men who were classified in the frailty group with and without slow walking speed showed higher HRs compared with older women. A greater age-associated decline in aerobic capacity in men, compared with women, has been reported in previous studies.42,43 Moreover, a previous study of sedentary older
H. Shimada et al. / JAMDA 16 (2015) 690e696
adults reported that very old participants who had mild frailty and physical inactivity showed greater decline in aerobic capacity than their younger counterparts.44 The rapid decrease of physiological capacity in men might influence the incidence of disability. Frail older adults who had low cognitive function (ie, MMSE <24) showed relatively higher risk of disability compared with those with high cognitive function. Previous studies have shown a strong association between cognitive impairment and subsequent ADL disability.45,46 The stratified analysis of cognitive performance in the present study showed similar findings to previous investigations. To reduce the risk of incidence of disability in older adults with cognitive decline, positive prevention programs such as high-intensive multicomponent exercise programs47,48 should be implemented in clinical and community-care settings. A major strength of this study is the application of a monthly follow-up of disability using a mandatory social LTCI, which included a population-based large sample of Japanese older adults. An important limitation of our study is that participants were not recruited randomly in the community. This may lead to an underestimation of the prevalence of frailty, as the participants were relatively healthy elderly persons who were able to access a health checkup from their homes. Second, for some participants, we were not able to contact an informant, such as a family member, to verify medical records, lifestyle information, and asymptomatic aberrant behavior. Third, our follow-up period was too short to identify how the incidence of disability developed with advancing age. In summary, the results of this prospective cohort study identified that frailty has a strong impact on the increased risk of disability. In particular, frailty or even prefrailty when older adults show slow walking speed increases the risk of future disability in communitydwelling older adults.
Acknowledgment The authors would like to thank the Obu City Office for assistance with participant recruitment.
References 1. Tsutsui T, Muramatsu N. Care-needs certification in the long-term care insurance system of Japan. J Am Geriatr Soc 2005;53:522e527. 2. Fried LP, Ettinger WH, Lind B, et al. Physical disability in older adults: A physiological approach. Cardiovascular Health Study Research Group. J Clin Epidemiol 1994;47:747e760. 3. Ensrud KE, Ewing SK, Taylor BC, et al. Frailty and risk of falls, fracture, and mortality in older women: The study of osteoporotic fractures. J Gerontol A Biol Sci Med Sci 2007;62:744e751. 4. Woods NF, LaCroix AZ, Gray SL, et al. Frailty: Emergence and consequences in women aged 65 and older in the Women’s Health Initiative Observational Study. J Am Geriatr Soc 2005;53:1321e1330. 5. Guralnik JM, Fried LP, Simonsick EM, et al. The women’s health and aging study: Health and social characteristics of older women with disability. Bethesda, MD: National Institute of Aging; 1995. NIH Pub. No: 95e4009. 6. Fried LP, Tangen CM, Walston J, et al. Frailty in older adults: Evidence for a phenotype. J Gerontol A Biol Sci Med Sci 2001;56:M146eM156. 7. Boyd CM, Xue QL, Simpson CF, et al. Frailty, hospitalization, and progression of disability in a cohort of disabled older women. Am J Med 2005;118: 1225e1231. 8. Al Snih S, Graham JE, Ray LA, et al. Frailty and incidence of activities of daily living disability among older Mexican Americans. J Rehabil Med 2009;41: 892e897. 9. Ensrud KE, Ewing SK, Cawthon PM, et al. A comparison of frailty indexes for the prediction of falls, disability, fractures, and mortality in older men. J Am Geriatr Soc 2009;57:492e498. 10. Ferrucci L, Guralnik JM, Studenski S, et al. Designing randomized, controlled trials aimed at preventing or delaying functional decline and disability in frail, older persons: A consensus report. J Am Geriatr Soc 2004;52:625e634. 11. Guralnik JM, Ferrucci L, Simonsick EM, et al. Lower-extremity function in persons over the age of 70 years as a predictor of subsequent disability. N Engl J Med 1995;332:556e561.
695
12. Shimada H, Suzukawa M, Tiedemann A, et al. Which neuromuscular or cognitive test is the optimal screening tool to predict falls in frail communitydwelling older people? Gerontology 2009;55:532e538. 13. Tiedemann A, Shimada H, Sherrington C, et al. The comparative ability of eight functional mobility tests for predicting falls in community-dwelling older people. Age Ageing 2008;37:430e435. 14. Guralnik JM, Ferrucci L, Pieper CF, et al. Lower extremity function and subsequent disability: Consistency across studies, predictive models, and value of gait speed alone compared with the short physical performance battery. J Gerontol A Biol Sci Med Sci 2000;55:M221eM231. 15. Cesari M, Kritchevsky SB, Penninx BW, et al. Prognostic value of usual gait speed in well-functioning older peopledResults from the Health, Aging and Body Composition Study. J Am Geriatr Soc 2005;53:1675e1680. 16. Cesari M, Kritchevsky SB, Newman AB, et al. Added value of physical performance measures in predicting adverse health-related events: Results from the Health, Aging and Body Composition Study. J Am Geriatr Soc 2009;57: 251e259. 17. Onder G, Penninx BW, Ferrucci L, et al. Measures of physical performance and risk for progressive and catastrophic disability: Results from the Women’s Health and Aging Study. J Gerontol A Biol Sci Med Sci 2005;60:74e79. 18. Studenski S, Perera S, Wallace D, et al. Physical performance measures in the clinical setting. J Am Geriatr Soc 2003;51:314e322. 19. Simonsick EM, Newman AB, Visser M, et al. Mobility limitation in selfdescribed well-functioning older adults: Importance of endurance walk testing. J Gerontol A Biol Sci Med Sci 2008;63:841e847. 20. Shinkai S, Watanabe S, Kumagai S, et al. Walking speed as a good predictor for the onset of functional dependence in a Japanese rural community population. Age Ageing 2000;29:441e446. 21. Markides KS, Black SA, Ostir GV, et al. Lower body function and mortality in Mexican American elderly people. J Gerontol A Biol Sci Med Sci 2001;56: M243eM247. 22. Studenski S, Perera S, Patel K, et al. Gait speed and survival in older adults. JAMA 2011;305:50e58. 23. Montero-Odasso M, Schapira M, Soriano ER, et al. Gait velocity as a single predictor of adverse events in healthy seniors aged 75 years and older. J Gerontol A Biol Sci Med Sci 2005;60:1304e1309. 24. Dargent-Molina P, Favier F, Grandjean H, et al. Fall-related factors and risk of hip fracture: The EPIDOS prospective study. Lancet 1996;348:145e149. 25. Abellan van Kan G, Rolland Y, Gillette-Guyonnet S, et al. Gait speed, body composition, and dementia. The EPIDOS-Toulouse cohort. J Gerontol A Biol Sci Med Sci 2012;67:425e432. 26. Shimada H, Makizako H, Doi T, et al. Combined prevalence of frailty and mild cognitive impairment in a population of elderly Japanese people. J Am Med Dir Assoc 2013;14:518e524. 27. Folstein MF, Folstein SE, McHugh PR. Mini-Mental State.” A practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res 1975; 12:189e198. 28. Chen LK, Liu LK, Woo J, et al. Sarcopenia in Asia: Consensus report of the Asian Working Group for Sarcopenia. J Am Med Dir Assoc 2014;15:95e101. 29. Fukutomi E, Okumiya K, Wada T, et al. Relationships between each category of 25-item frailty risk assessment (Kihon Checklist) and newly certified older adults under long-term care insurance: A 24-month follow-up study in a rural community in Japan. Geriatr Gerontol Int 2015;15:864e871. 30. Stuck AE, Walthert JM, Nikolaus T, et al. Risk factors for functional status decline in community-living elderly people: A systematic literature review. Soc Sci Med 1999;48:445e469. 31. Ishizaki T, Watanabe S, Suzuki T, et al. Predictors for functional decline among nondisabled older Japanese living in a community during a 3-year follow-up. J Am Geriatr Soc 2000;48:1424e1429. 32. WHO Expert Consultation. Appropriate body-mass index for Asian populations and its implications for policy and intervention strategies. Lancet 2004;363:157e163. 33. Yesavage JA. Geriatric Depression Scale. Psychopharmacol Bull 1988;24: 709e711. 34. Friedman B, Heisel MJ, Delavan RL. Psychometric properties of the 15-item geriatric depression scale in functionally impaired, cognitively intact, community-dwelling elderly primary care patients. J Am Geriatr Soc 2005;53: 1570e1576. 35. Cesari M, Leeuwenburgh C, Lauretani F, et al. Frailty syndrome and skeletal muscle: Results from the Invecchiare in Chianti study. Am J Clin Nutr 2006;83: 1142e1148. 36. Avila-Funes JA, Helmer C, Amieva H, et al. Frailty among community-dwelling elderly people in France: The three-city study. J Gerontol A Biol Sci Med Sci 2008;63:1089e1096. 37. Garcia-Garcia FJ, Gutierrez Avila G, Alfaro-Acha A, et al. The prevalence of frailty syndrome in an older population from Spain. The Toledo Study for Healthy Aging. J Nutr Health Aging 2011;15:852e856. 38. Castell MV, Sanchez M, Julian R, et al. Frailty prevalence and slow walking speed in persons age 65 and older: Implications for primary care. BMC Fam Pract 2013;14:86. 39. Syddall H, Roberts HC, Evandrou M, et al. Prevalence and correlates of frailty among community-dwelling older men and women: Findings from the Hertfordshire Cohort Study. Age Ageing 2010;39:197e203. 40. Afilalo J. Frailty in patients with cardiovascular disease: Why, when, and how to measure. Curr Cardiovasc Risk Rep 2011;5:467e472.
696
H. Shimada et al. / JAMDA 16 (2015) 690e696
41. Shimada H, Suzuki T, Suzukawa M, et al. Performance-based assessments and demand for personal care in older Japanese people: A cross-sectional study. BMJ Open 2013 Apr 10;3(4). pii: e002424. 42. Hossack KF, Bruce RA. Maximal cardiac function in sedentary normal men and women: Comparison of age-related changes. J Appl Physiol Respir Environ Exerc Physiol 1982;53:799e804. 43. Toth MJ, Gardner AW, Ades PA, Poehlman ET. Contribution of body composition and physical activity to age-related decline in peak VO2 in men and women. J Appl Physiol (1985) 1994;77:647e652. 44. Weiss EP, Spina RJ, Holloszy JO, Ehsani AA. Gender differences in the decline in aerobic capacity and its physiological determinants during the later decades of life. J Appl Physiol (1985) 2006;101:938e944.
45. Kempen GI, Ormel J. The impact of physical performance and cognitive status on subsequent ADL disability in low-functioning older adults. Int J Geriatr Psychiatry 1998;13:480e483. 46. Dodge HH, Kadowaki T, Hayakawa T, et al. Cognitive impairment as a strong predictor of incident disability in specific ADL-IADL tasks among communitydwelling elders: The Azuchi Study. Gerontologist 2005;45:222e230. 47. Daniels R, van Rossum E, de Witte L, et al. Interventions to prevent disability in frail community-dwelling elderly: A systematic review. BMC Health Serv Res 2008;8:278. 48. Suzuki T, Shimada H, Makizako H, et al. A randomized controlled trial of multicomponent exercise in older adults with mild cognitive impairment. PLoS One 2013;8:e61483.
JAMDA journal homepage: www.jamda.com
Original Study
Incidence of Disability in Frail Older Persons With or Without Slow Walking Speed Hiroyuki Shimada PhD a, *, Hyuma Makizako PhD a, Takehiko Doi PhD a, Kota Tsutsumimoto PhD a, Takao Suzuki PhD b a b
Department of Preventive Gerontology, Center for Gerontology and Social Science, National Center for Geriatrics and Gerontology, Obu, Japan National Center for Geriatrics and Gerontology, Obu, Japan
a b s t r a c t Keywords: Frailty physical performance gait activities of daily living aged
Objective: To identify the differences of incidence of disability between frail older persons with and without slow walking speed. Design: Prospective cohort study. Setting: Japanese community. Participants: A total of l4,081 older adults aged 65 years living in the community, participated in a baseline assessment and were followed for incidence of disability for 29.5 months. Measurements: Care-needs certification in the national long-term care insurance system of Japan, physical frailty (slow walking speed, muscle weakness, exhaustion, low activity, weight loss), adjusted for several potential confounders such as demographic characteristics; Kaplan-Meier survival curves for incident disability by physical frailty with and without slow walking speed. Results: During the follow-up period, 198 participants (4.9%) were certified as requiring long-term care insurance in accordance with incident disability. Participants who had prefrailty without slow walking speed (hazard ratio 1.86, 95% confidence interval 1.19e2.92), prefrailty with slow walking speed (3.62, 2.19e5.96), frailty without slow walking speed (4.33, 2.00e9.39), and frailty with slow walking speed (4.68, 2.72e8.05) at the baseline assessment had an increased risk of incident disability compared with nonfrail participants. In stratified analyses, frail older men and frail participants with low cognitive performance had the highest risk of incidence of disability. Conclusion: The presence of frailty or even prefrailty when older adults showed slow walking speed increased the risk of future disability in community-dwelling older adults. Ó 2015 AMDA e The Society for Post-Acute and Long-Term Care Medicine.
Japan implemented the national social long-term care insurance (LTCI) system on April 1, 2000. Every Japanese person aged 65 and older is eligible for benefits based strictly on physical and mental frailty or disability.1 Physical frailty increases with advancing age and is a major risk factor for dependency, institutionalization, and mortality in older people.2e4 People with a disability incur higher health This work received financial support from Health and Labor Sciences Research Grants (Comprehensive Research on Aging and Health, grant number H24-ChojuIppan-004), a Grant-in-Aid for Scientific Research (B) (grant number 23300205), and Research Funding for Longevity Sciences from the National Center for Geriatrics and Gerontology (grant number 22-16), Japan. No support was received from industry. The funding source played no role in the design or conduct of the study; collection, management, analysis, or interpretation of the data; or preparation, review, or approval of the manuscript. The authors declare no conflicts of interest. * Address correspondence to Hiroyuki Shimada, PhD, Department of Preventive Gerontology, Center for Gerontology and Social Science, National Center for Geriatrics and Gerontology, 7-430 Morioka-cho, Obu, Aichi 474-8511, Japan. E-mail address: [email protected] (H. Shimada). http://dx.doi.org/10.1016/j.jamda.2015.03.019 1525-8610/Ó 2015 AMDA e The Society for Post-Acute and Long-Term Care Medicine.
care costs compared with those without a disability.5 For the purpose of targeting risk factors for future frailty, adequate assessment of individuals is necessary. A feasible and valid screening tool available for research and clinical settings is required to identify frailty in the community. The well-known frailty phenotype introduced by Fried et al,6 which classifies people into categories of robust, prefrail, or frail, fits within this physiological approach to frailty. The frailty phenotype postulates that 5 indicators (weight loss, exhaustion, slow walking speed, low grip strength, and low physical activity) are related to each other in a cycle of frailty. A person with none of the indicators is robust, a person with 1 or 2 indicators is prefrail, and a person with 3 or more indicators is frail. Older people who are frail according to the phenotype have a higher risk of disability.7e9 The Interventions on Frailty Working Group developed recommendations to screen, recruit, evaluate, and retain frail older persons in clinical trials.10 They reported that most researchers focus on the following domains for the identification of physical frailty: mobility, such as lower-extremity
H. Shimada et al. / JAMDA 16 (2015) 690e696
performance and gait abnormalities; muscle weakness; poor exercise tolerance; unstable balance; and factors related to body composition, such as weight loss, malnutrition, and muscle loss.10 In an effort to select tailored preventive programs in the Japanese LTCI system, those at high risk for subsequent disability are identified with a basic functional status questionnaire. Although the questionnaire is relatively quick to administer, a performance-based assessment could determine actual physical capacity and might more accurately predict subsequent physical disability in community-living older people. Guralnik et al11 reported that measures of physical performance may identify older persons with a preclinical stage of disability who may benefit from interventions to prevent the development of frank disability. Walking speed has been consistently reported to differentiate between participants with and those without personal care, with frail older persons walking significantly slower,12,13 and has proved to be a strong predictor of adverse events, such as disability,14e20 mortality,15,16,21,22 hospitalization,15,16,18,23 falls,23,24 and dementia.25 We generally agree with the concept of the frailty model and its components to identify frailty including slowness, weakness, exhaustion, low activity, and weight loss. However, previous studies suggest that the separate components do not have equal impacts on the onset of disability in older adults. We hypothesize that slow walking speed has a greater impact on the incidence of disability than the other components of frailty. The purpose of this study was to identify the differences of incidence of disability between frail older persons with and without slow walking speed. Methods Participants This prospective cohort study involved 5104 community-dwelling older adults (65 years) enrolled in the Obu Study of Health Promotion for the Elderly (OSHPE).26 OSHPE participants were recruited from Obu, a residential suburb of Nagoya, Japan. Inclusion criteria were an age of 65 years at examination in 2011 or 2012, Obu residency, and no previous participation in other studies. Exclusion criteria were the need for support or care certified by the Japanese public LTCI system, disability in basic activities of daily living, and inability to undergo performance-based assessments.26 Between August 2011 and February 2012, 5104 community-dwelling older people participated in a baseline OSHPE assessment including a faceto-face interview and measures of physical and cognitive function. Participants were followed monthly and monitored for certification of LTCI for at least 2 years. In this longitudinal study, we included participants who completed baseline assessments and follow-up assessments of disability by the LTIC system. We excluded participants with a history of Parkinson disease, stroke, depression, and dementia, Mini-Mental State Examination (MMSE)27 scores of <20, or having a disability based on the LTIC system at baseline. Participants who died or who moved to another city during the follow-up period were also excluded. Of 5104 participants who completed a baseline assessment, 1023 older adults were excluded from the present study. The remaining 4081 participants of average age 71.7 5.3 years (women 51.6%) were included in the following analyses. Informed consent was obtained from all participants prior to their inclusion in the study, and the Ethics Committee of the National Center for Gerontology and Geriatrics approved the study protocol. Operationalization of Physical Frailty We considered the frailty phenotype to be characterized by limitations in 3 or more of the following 5 conditions based on those used in Fried’s original studies6: slow walking speed, weakness,
691
exhaustion, low activity, and weight loss. Participants having none of these components were considered to be nonfrail, and those having 1 or 2 components were considered to be in prefrail. Participants with prefrailty and frailty were divided into the following 4 groups according to frailty status and walking speed (cutoff <1.0 m/s): prefrailty without slow walking speed, prefrailty with slow walking speed, frailty without slow walking speed, and frailty with slow walking speed. Walking speed was measured in seconds using a stopwatch. Participants were asked to walk on a flat and straight surface at a comfortable walking speed. Two markers were used to indicate the start and end of a 2.4-m walk path, with a 2-m section to be traversed before passing the start marker so that participants were walking at a comfortable pace by the time they reached the timed path. Participants were asked to continue walking for an additional 2 m past the end of the path to ensure a consistent walking pace while on the timed path. Slowness was established according to a cutoff (<1.0 m/ s).26 Weakness was defined using maximum grip strength. Grip strength was measured in kilograms using a Smedley-type handheld dynamometer (GRIP-D; Takei Ltd, Niigata, Japan). Weakness was established according to a sex-specific cutoff (<26 kg for men and <18 kg for women).28 Exhaustion was considered present if the participant responded “yes” to the following question, which included the Kihon-Checklist, a self-reported comprehensive health checklist that was developed by the Japanese Ministry of Health, Labor, and Welfare29: “In the last 2 weeks, have you felt tired without a reason?” We evaluated the role of physical activity by asking the following questions about time spent engaged in sports and exercise: (1) “Do you engage in moderate levels of physical exercise or sports aimed at health?” and (2) “Do you engage in low levels of physical exercise aimed at health?” If participants answered “no” to both of these questions, we considered them to be low activity.26 Weight loss was assessed by a response of “yes” to the question, “Have you lost 2 kg or more in the past 6 months?”29 Investigation for Incidence of Disability Participants were followed monthly for incident certification of need of care in the national LTCI system during the 2 years following the baseline assessment. We defined onset of disability as the point when a participant was certified as needing care by the LTCI. Every Japanese person aged 65 and older is eligible for benefits (institutional and community-based services, but not cash) based strictly on physical and mental disability. The computer-aided standardized needs-assessment system categorizes people into 7 levels of need. The LTCI certifies a person as “support level 1 or 2” if they need support for daily activities or “care level 1, 2, 3, 4, or 5” if they need continuous care.1 In this study, the outcome of disability was defined as a new certification of the LTCI service at any level. Potential Confounding Factors of Activities of Daily Living With reference to the review article by Stuck et al30 and a previous longitudinal study by Ishizaki et al,31 we selected 2 demographic variables, 3 physiological variables, 4 primary diseases or geriatric syndromes, and 6 psychosocial variables as possible confounding factors of activities of daily living (ADL) limitations (Table 1). The physiological variables “overweight” and “underweight” were determined by measuring body mass index, and the cut points of overweight and underweight were set at 27.5 kg/m2 and <18.5 kg/m2, respectively.32 Measurements of the ability to walk continuously for 15 minutes and knee and lumbar pain (“yes” or “no”) were recorded from a self-report collected through the interview survey. The nurses who identified the chronic condition from the interview survey
692
H. Shimada et al. / JAMDA 16 (2015) 690e696
Table 1 Characteristics of the Participants, Mean SD or %
Demographic variables Age, years Sex, female Physiological variables Overweight, BMI 27.5 kg/m2, yes Underweight, BMI <18.5 kg/m2, yes Knee pain, yes Primary disease Heart disease, yes Pulmonary disease, yes Osteoarthritis, yes Diabetes, yes Psychological and social variables MMSE score GDS-15, score Going outdoors by bus and train, no Shopping of daily necessaries, no Visiting the homes of friends, no Being called on for advice, no
Overall (n ¼ 4081)
Nonfrailty (n ¼ 1805)
Prefrailty Without Slow Walking Speed (n ¼ 1648)
Prefrailty With Slow Walking Speed (n ¼ 371)
Frailty Without Slow Walking Speed (n ¼ 65)
Frailty With Slow Walking Speed (n ¼ 192)
P
71.7 5.3 51.6
70.7 4.3 52.1
71.3 5.0 50.3
74.8 6.3 50.7
74.6 5.3 52.3
78.1 6.7 60.4
<.001 .117
4.4 8.7 22.6
3.4 7.5 17.1
4.4 8.7 24.2
3.2 12.1 31.8
16.9 4.6 29.2
10.9 14.1 40.6
<.001 .002 <.001
15.4 10.6 13.6 13.0
13.1 9.1 12.1 10.0
16.4 12.0 13.2 13.6
17.3 8.9 19.4 18.9
13.8 13.8 12.3 26.2
24.5 14.6 21.9 19.8
<.001 .015 <.001 <.001
26.4 2.5 2.7 2.5 8.8 3.1 12.7 7.7
26.8 2.3 2.0 1.9 6.0 1.7 9.1 4.4
26.4 2.5 3.0 2.6 8.7 3.1 13.8 9.2
25.7 2.6 3.2 2.4 14.6 7.0 17.5 10.0
25.5 2.8 4.6 3.5 16.9 4.6 21.5 12.3
25.0 2.7 4.9 2.8 21.4 8.3 25.0 20.8
<.001 <.001 <.001 <.001 <.001 <.001
BMI, body mass index; SD, standard deviation.
recorded primary diseases or geriatric syndromes. The following diseases and geriatric syndromes were included in the analysis: heart disease, pulmonary disease, osteoarthritis, cancer, diabetes, falls, incontinence, and medications. The psychological and social measurements were assessed from a self-report collected through the interview survey. The Mini-Mental State Examination (MMSE) was used to assess global cognitive function.27 Depressive symptoms were measured using the 15-item Geriatric Depression Scale (GDS). 33 Instrumental activities and social roles were assessed using the subscales of the Kihon-Checklist (“yes” or “no”).29
walking speed were 40.4%, 9.1%, 1.6%, and 4.7%, respectively. During the follow-up period, 198 participants (4.9%) were determined as needing certification based on the LTCI system because of incident disability. Table 1 shows possible confounding factors of ADL limitations for each subtype of frailty. All measurements except sex exhibited significant differences. Participants who developed disability were older, more often women, had a higher prevalence of being overweight, knee pain, and heart disease, lower MMSE score, higher GDS score, and lower instrumental and social activities compared with those who remained independent (Table 2).
Statistical Analyses Student t-test and Pearson c2 test were used to test differences in the baseline characteristics between frailty statuses and between participants with and without incidence of disability. We calculated the cumulative incident of disability during followup according to baseline frailty status with the Kaplan-Meier curves. Intergroup differences were estimated by the log-rank test. Cox proportional hazards regression models were used to analyze associations between frailty and incidence of disability. The first model (model 1) was adjusted for age and sex. We then used a multiple adjustment model adjusted for 2 demographic variables, 3 physiological variables, 4 primary diseases or geriatric syndromes, and 6 psychosocial variables as possible confounding factors (model 2). We estimated adjusted hazard ratios (HRs) for incidence of disability and their 95% confidence intervals (CIs). Stratified analyses were performed to examine the relationship between frailty and incidence of disability in different subgroups defined by sex, age (74/75 years), cognitive function (MMSE 23/24 scores), and depressive symptoms (GDS 5/6 scores).34 Adjusted HRs for incidence of disability and their 95% CIs were also estimated in the stratified analyses. All analyses were done using IBM SPSS Statistics 20.0 (IBM Japan, Tokyo, Japan). The level of statistical significance was set at P < .05. Results Prevalence rates of each subtype of physical frailty including prefrailty without slow walking speed, prefrailty with slow walking speed, frailty without slow walking speed, and frailty with slow
Table 2 Comparisons With Confounding Factors Between the Participants Without and With Incident of Disability, Mean SD or % Participants Without Disability (n ¼ 3883) Demographic variables Age, years 71.4 5.0 Sex, female/male 94.2/96.2 Physiological variables Overweight, BMI 27.5 90.4/95.4 kg/m2, yes/no 95.3/93.2 Underweight, BMI <18.5 kg/m2, yes/no Knee pain, yes/no 93.3/95.7 Primary disease Heart disease, yes/no 92.3/95.7 Pulmonary disease, yes/no 93.5/95.3 Osteoarthritis, yes/no 93.7/95.4 Diabetes, yes/no 94.0/95.3 Psychological and social variables MMSE, score 26.5 2.4 GDS-15, score 2.6 2.5 Going outdoors by bus 90.5/95.6 and train, no/yes Shopping of daily 92.9/95.2 necessaries, no/yes Visiting the homes of 91.1/95.7 friends, no/yes Being called on for 90.2/95.6 advice, no/yes BMI, body mass index; SD, standard deviation.
Participants With Disability (n ¼ 198)
P
78.1 6.2 5.8/3.8
<.001 .002
9.6/4.6
.003
6.8/4.7
.080
6.7/4.3
.003
7.7/4.3 6.5/4.7 6.3/4.6 6.0/4.7
<.001 .095 .091 .173
24.9 2.7 3.8 2.7 9.5/4.4
<.001 <.001 <.001
7.1/4.8
.224
8.9/4.3
<.001
9.8/4.4
<.001
H. Shimada et al. / JAMDA 16 (2015) 690e696
693
Fig. 1. Estimates of survival rate on incident of disability according to frailty status. (1) Nonfrailty group. (2) Prefrailty without slow walking speed group. (3) Prefrailty with slow walking speed group, (4) Frailty without slow walking speed group. (5) Frailty with slow walking speed group. Left panel shows survival curves on incident of disability adjusted for age and sex, and right panel shows fully adjusted model.
Figure 1 shows the survival rates of disability based on frailty status and by non-frailty and subtype of frailty. Survival analyses using the Kaplan-Meier log-rank tests showed that the probability of incidence of disability was significantly higher in participants with prefrailty without slow walking speed, prefrailty with slow walking speed, frailty without slow walking speed, and frailty with slow walking speed compared with the nonfrail older group (P < .001). Also, there were significant differences in the incidence of disability between the subtypes of prefrailty and frailty, although there was no significant difference between prefrailty with slow walking speed and frailty without slow walking speed (P ¼ .785). Cox proportional hazards regression models were used to analyze associations between frailty and incidence of disability (Table 3). In
Table 3 HRs for Incident of Disability According to Frailty Status and Confounding Factors Model 1
Age, years Sex, female/male Overweight, BMI 27.5 kg/m2, yes Underweight, BMI <18.5 kg/m2, yes Knee pain, yes Heart disease, yes Pulmonary disease, yes Osteoarthritis, yes Diabetes, yes MMSE, score GDS-15, score Going outdoors by bus and train, yes Shopping of daily necessaries, yes Visiting the homes of friends, yes Being called on for advice, yes Physical frailty Nonfrailty Prefrailty without slow walking speed Prefrailty with slow walking speed Frailty without slow walking speed Frailty with slow walking speed
Model 2
HR (95% CI)
P
HR (95% CI)
P
1.13 (1.11e1.16) 0.68 (0.51e0.91)
<.001 .009
1.12 (1.10e1.15) 0.64 (0.47e0.87) 1.07 (0.63e1.82)
<.001 .004 .795
1.33 (0.85e2.07)
.214
1.05 1.43 1.18 0.89 1.08 0.89 1.01 1.23
(0.75e1.48) (1.02e2.00) (0.78e1.77) (0.59e1.33) (0.74e1.59) (0.84e0.94) (0.96e1.07) (0.80e1.88)
.773 .037 .432 .571 .683 <.001 .649 .343
0.64 (0.31e1.34)
.236
1.30 (0.87e1.94)
.194
1.10 (0.70e1.73)
.690
<.001 1 2.10 (1.35e3.26)
<.001
.001
1 1.86 (1.19e2.92)
.007
4.38 (2.70e7.10)
<.001
3.62 (2.19e5.96)
<.001
5.72 (2.70e12.16)
<.001
4.33 (2.00e9.39)
<.001
6.46 (3.90e10.70)
<.001
4.68 (2.72e8.05)
<.001
BMI, body mass index; SD, standard deviation.
the first model (model 1) that was adjusted for age and sex, the following HRs were determined for participants who had prefrailty without slow walking speed (HR 2.10, 95% CI 1.35e3.26), prefrailty with slow walking speed (4.38, 2.70e7.10), frailty without slow walking speed (5.72, 2.70e12.16), and frailty with slow walking speed (6.46, 3.90e10.70) compared with nonfrail participants. Age and sex were significantly associated with increased risk of disability. In the fully adjusted model (model 2), the following HRs were determined for prefrailty without slow walking speed (HR 1.86, 95% CI 1.19e2.92), prefrailty with slow walking speed (3.62, 2.19e5.96), frailty without slow walking speed (4.33, 2.00e9.39), and frailty with slow walking speed (4.68, 2.72e8.05) compared with nonfrail participants. In model 2, age, sex, heart disease, and MMSE score were related to increased risk of incident disability. Figure 2 shows the results of stratified analyses, which were divided by sex, age, MMSE, and depressive symptoms. The older men with frailty without and with slow walking speed showed higher HRs compared with the older women (men; 7.2e8.6, women; 3.0e3.2). Frail older adults who had low cognitive function (ie, MMSE <24) showed relatively higher HRs compared with those with high cognitive function (MMSE <24; 11.1e13.0, MMSE 24; 3.3e3.8). There were no marked differences between older adults aged 65 to 74 years and 75 and over, and between those with and without depressive symptoms.
Discussion The prevalence rates of physical frailty and prefrailty in this study were 6.3% and 49.5%, respectively, which is consistent with large studies in other countries.6,35e39 For instance, in the American Cardiovascular Health Study, the prevalence of frailty among 5317 community-dwelling men and women aged 65 years was 6.9%, and frailty was associated with older age, male gender, being African American, having lower education and income, poorer health, and higher rates of comorbid chronic disease and disability.6 The French Three-City Study demonstrated a frailty prevalence of 7% among 6078 community-dwelling men and women aged 65 years and older, and frailty was associated with older age, female gender, lower education, lower income, a poorer self-reported health status, and more chronic disease in addition to incident disability.6 The Hertfordshire Cohort Study in the United Kingdom, reported that the prevalence of frailty, as defined by Fried, among 638 community-dwelling participants aged 64e74 years was 8.5% for women and 4.1% for men.39 Only 18% of prefrail older adults exhibited slow walking speed, although 75% of frail older adults had slow walking speed. These results suggest that
694
H. Shimada et al. / JAMDA 16 (2015) 690e696
Fig. 2. HRs estimate relative risk of incident of disability in subgroups defined by sex, age, cognitive function, and depressive symptoms in stratified analyses.
the older adults with multiple frailty components tend to be associated with the presence of slow walking speed. Slow walking speed is widely used in geriatric assessment, and has become a good single estimator of frailty and its outcomes.15,40 A walking speed greater than 1.2 m/s suggests high life expectancy, whereas speeds lower than 1 m/s predict frailty and have been associated with disability, hospitalization and decreased survival.15,22,41 Based on these previous studies, we hypothesized that whether a person is classed as prefrailty or frailty has a different impact on the future incidence of disability depending on whether they have slow walking speed or not. Log-rank tests showed significant differences in the incidence of disability between subtypes in prefrailty and frailty, whereas there was no significant difference between prefrailty with slow walking speed and frailty without slow walking speed. The results indicate that slow walking speed has a major impact on the incidence of disability in prefrailty status. Our 4-frailty classification (ie, prefrailty with or without slow walking
speed and frailty with or without slow walking speed) may be a useful indicator to identify future risk of disability in older adults. Our frailty classification showed significant relationships exploring the incidence of disability in Cox proportional hazard models. In the fully adjusted model, HRs in prefrailty with slow walking speed, frailty without slow walking speed, and frailty with slow walking speed were similar values (HRs 3.62e4.68) in contrast to the HR of prefrailty with slow walking speed group (HR 1.86). These results suggest that healthcare providers should consider the necessity of interventions to prevent the incidence of disability in frail older persons as well as prefrail older adults with slow walking speed. In the stratified analyses, older men who were classified in the frailty group with and without slow walking speed showed higher HRs compared with older women. A greater age-associated decline in aerobic capacity in men, compared with women, has been reported in previous studies.42,43 Moreover, a previous study of sedentary older
H. Shimada et al. / JAMDA 16 (2015) 690e696
adults reported that very old participants who had mild frailty and physical inactivity showed greater decline in aerobic capacity than their younger counterparts.44 The rapid decrease of physiological capacity in men might influence the incidence of disability. Frail older adults who had low cognitive function (ie, MMSE <24) showed relatively higher risk of disability compared with those with high cognitive function. Previous studies have shown a strong association between cognitive impairment and subsequent ADL disability.45,46 The stratified analysis of cognitive performance in the present study showed similar findings to previous investigations. To reduce the risk of incidence of disability in older adults with cognitive decline, positive prevention programs such as high-intensive multicomponent exercise programs47,48 should be implemented in clinical and community-care settings. A major strength of this study is the application of a monthly follow-up of disability using a mandatory social LTCI, which included a population-based large sample of Japanese older adults. An important limitation of our study is that participants were not recruited randomly in the community. This may lead to an underestimation of the prevalence of frailty, as the participants were relatively healthy elderly persons who were able to access a health checkup from their homes. Second, for some participants, we were not able to contact an informant, such as a family member, to verify medical records, lifestyle information, and asymptomatic aberrant behavior. Third, our follow-up period was too short to identify how the incidence of disability developed with advancing age. In summary, the results of this prospective cohort study identified that frailty has a strong impact on the increased risk of disability. In particular, frailty or even prefrailty when older adults show slow walking speed increases the risk of future disability in communitydwelling older adults.
Acknowledgment The authors would like to thank the Obu City Office for assistance with participant recruitment.
References 1. Tsutsui T, Muramatsu N. Care-needs certification in the long-term care insurance system of Japan. J Am Geriatr Soc 2005;53:522e527. 2. Fried LP, Ettinger WH, Lind B, et al. Physical disability in older adults: A physiological approach. Cardiovascular Health Study Research Group. J Clin Epidemiol 1994;47:747e760. 3. Ensrud KE, Ewing SK, Taylor BC, et al. Frailty and risk of falls, fracture, and mortality in older women: The study of osteoporotic fractures. J Gerontol A Biol Sci Med Sci 2007;62:744e751. 4. Woods NF, LaCroix AZ, Gray SL, et al. Frailty: Emergence and consequences in women aged 65 and older in the Women’s Health Initiative Observational Study. J Am Geriatr Soc 2005;53:1321e1330. 5. Guralnik JM, Fried LP, Simonsick EM, et al. The women’s health and aging study: Health and social characteristics of older women with disability. Bethesda, MD: National Institute of Aging; 1995. NIH Pub. No: 95e4009. 6. Fried LP, Tangen CM, Walston J, et al. Frailty in older adults: Evidence for a phenotype. J Gerontol A Biol Sci Med Sci 2001;56:M146eM156. 7. Boyd CM, Xue QL, Simpson CF, et al. Frailty, hospitalization, and progression of disability in a cohort of disabled older women. Am J Med 2005;118: 1225e1231. 8. Al Snih S, Graham JE, Ray LA, et al. Frailty and incidence of activities of daily living disability among older Mexican Americans. J Rehabil Med 2009;41: 892e897. 9. Ensrud KE, Ewing SK, Cawthon PM, et al. A comparison of frailty indexes for the prediction of falls, disability, fractures, and mortality in older men. J Am Geriatr Soc 2009;57:492e498. 10. Ferrucci L, Guralnik JM, Studenski S, et al. Designing randomized, controlled trials aimed at preventing or delaying functional decline and disability in frail, older persons: A consensus report. J Am Geriatr Soc 2004;52:625e634. 11. Guralnik JM, Ferrucci L, Simonsick EM, et al. Lower-extremity function in persons over the age of 70 years as a predictor of subsequent disability. N Engl J Med 1995;332:556e561.
695
12. Shimada H, Suzukawa M, Tiedemann A, et al. Which neuromuscular or cognitive test is the optimal screening tool to predict falls in frail communitydwelling older people? Gerontology 2009;55:532e538. 13. Tiedemann A, Shimada H, Sherrington C, et al. The comparative ability of eight functional mobility tests for predicting falls in community-dwelling older people. Age Ageing 2008;37:430e435. 14. Guralnik JM, Ferrucci L, Pieper CF, et al. Lower extremity function and subsequent disability: Consistency across studies, predictive models, and value of gait speed alone compared with the short physical performance battery. J Gerontol A Biol Sci Med Sci 2000;55:M221eM231. 15. Cesari M, Kritchevsky SB, Penninx BW, et al. Prognostic value of usual gait speed in well-functioning older peopledResults from the Health, Aging and Body Composition Study. J Am Geriatr Soc 2005;53:1675e1680. 16. Cesari M, Kritchevsky SB, Newman AB, et al. Added value of physical performance measures in predicting adverse health-related events: Results from the Health, Aging and Body Composition Study. J Am Geriatr Soc 2009;57: 251e259. 17. Onder G, Penninx BW, Ferrucci L, et al. Measures of physical performance and risk for progressive and catastrophic disability: Results from the Women’s Health and Aging Study. J Gerontol A Biol Sci Med Sci 2005;60:74e79. 18. Studenski S, Perera S, Wallace D, et al. Physical performance measures in the clinical setting. J Am Geriatr Soc 2003;51:314e322. 19. Simonsick EM, Newman AB, Visser M, et al. Mobility limitation in selfdescribed well-functioning older adults: Importance of endurance walk testing. J Gerontol A Biol Sci Med Sci 2008;63:841e847. 20. Shinkai S, Watanabe S, Kumagai S, et al. Walking speed as a good predictor for the onset of functional dependence in a Japanese rural community population. Age Ageing 2000;29:441e446. 21. Markides KS, Black SA, Ostir GV, et al. Lower body function and mortality in Mexican American elderly people. J Gerontol A Biol Sci Med Sci 2001;56: M243eM247. 22. Studenski S, Perera S, Patel K, et al. Gait speed and survival in older adults. JAMA 2011;305:50e58. 23. Montero-Odasso M, Schapira M, Soriano ER, et al. Gait velocity as a single predictor of adverse events in healthy seniors aged 75 years and older. J Gerontol A Biol Sci Med Sci 2005;60:1304e1309. 24. Dargent-Molina P, Favier F, Grandjean H, et al. Fall-related factors and risk of hip fracture: The EPIDOS prospective study. Lancet 1996;348:145e149. 25. Abellan van Kan G, Rolland Y, Gillette-Guyonnet S, et al. Gait speed, body composition, and dementia. The EPIDOS-Toulouse cohort. J Gerontol A Biol Sci Med Sci 2012;67:425e432. 26. Shimada H, Makizako H, Doi T, et al. Combined prevalence of frailty and mild cognitive impairment in a population of elderly Japanese people. J Am Med Dir Assoc 2013;14:518e524. 27. Folstein MF, Folstein SE, McHugh PR. Mini-Mental State.” A practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res 1975; 12:189e198. 28. Chen LK, Liu LK, Woo J, et al. Sarcopenia in Asia: Consensus report of the Asian Working Group for Sarcopenia. J Am Med Dir Assoc 2014;15:95e101. 29. Fukutomi E, Okumiya K, Wada T, et al. Relationships between each category of 25-item frailty risk assessment (Kihon Checklist) and newly certified older adults under long-term care insurance: A 24-month follow-up study in a rural community in Japan. Geriatr Gerontol Int 2015;15:864e871. 30. Stuck AE, Walthert JM, Nikolaus T, et al. Risk factors for functional status decline in community-living elderly people: A systematic literature review. Soc Sci Med 1999;48:445e469. 31. Ishizaki T, Watanabe S, Suzuki T, et al. Predictors for functional decline among nondisabled older Japanese living in a community during a 3-year follow-up. J Am Geriatr Soc 2000;48:1424e1429. 32. WHO Expert Consultation. Appropriate body-mass index for Asian populations and its implications for policy and intervention strategies. Lancet 2004;363:157e163. 33. Yesavage JA. Geriatric Depression Scale. Psychopharmacol Bull 1988;24: 709e711. 34. Friedman B, Heisel MJ, Delavan RL. Psychometric properties of the 15-item geriatric depression scale in functionally impaired, cognitively intact, community-dwelling elderly primary care patients. J Am Geriatr Soc 2005;53: 1570e1576. 35. Cesari M, Leeuwenburgh C, Lauretani F, et al. Frailty syndrome and skeletal muscle: Results from the Invecchiare in Chianti study. Am J Clin Nutr 2006;83: 1142e1148. 36. Avila-Funes JA, Helmer C, Amieva H, et al. Frailty among community-dwelling elderly people in France: The three-city study. J Gerontol A Biol Sci Med Sci 2008;63:1089e1096. 37. Garcia-Garcia FJ, Gutierrez Avila G, Alfaro-Acha A, et al. The prevalence of frailty syndrome in an older population from Spain. The Toledo Study for Healthy Aging. J Nutr Health Aging 2011;15:852e856. 38. Castell MV, Sanchez M, Julian R, et al. Frailty prevalence and slow walking speed in persons age 65 and older: Implications for primary care. BMC Fam Pract 2013;14:86. 39. Syddall H, Roberts HC, Evandrou M, et al. Prevalence and correlates of frailty among community-dwelling older men and women: Findings from the Hertfordshire Cohort Study. Age Ageing 2010;39:197e203. 40. Afilalo J. Frailty in patients with cardiovascular disease: Why, when, and how to measure. Curr Cardiovasc Risk Rep 2011;5:467e472.
696
H. Shimada et al. / JAMDA 16 (2015) 690e696
41. Shimada H, Suzuki T, Suzukawa M, et al. Performance-based assessments and demand for personal care in older Japanese people: A cross-sectional study. BMJ Open 2013 Apr 10;3(4). pii: e002424. 42. Hossack KF, Bruce RA. Maximal cardiac function in sedentary normal men and women: Comparison of age-related changes. J Appl Physiol Respir Environ Exerc Physiol 1982;53:799e804. 43. Toth MJ, Gardner AW, Ades PA, Poehlman ET. Contribution of body composition and physical activity to age-related decline in peak VO2 in men and women. J Appl Physiol (1985) 1994;77:647e652. 44. Weiss EP, Spina RJ, Holloszy JO, Ehsani AA. Gender differences in the decline in aerobic capacity and its physiological determinants during the later decades of life. J Appl Physiol (1985) 2006;101:938e944.
45. Kempen GI, Ormel J. The impact of physical performance and cognitive status on subsequent ADL disability in low-functioning older adults. Int J Geriatr Psychiatry 1998;13:480e483. 46. Dodge HH, Kadowaki T, Hayakawa T, et al. Cognitive impairment as a strong predictor of incident disability in specific ADL-IADL tasks among communitydwelling elders: The Azuchi Study. Gerontologist 2005;45:222e230. 47. Daniels R, van Rossum E, de Witte L, et al. Interventions to prevent disability in frail community-dwelling elderly: A systematic review. BMC Health Serv Res 2008;8:278. 48. Suzuki T, Shimada H, Makizako H, et al. A randomized controlled trial of multicomponent exercise in older adults with mild cognitive impairment. PLoS One 2013;8:e61483.