Abstract
Objectives
To identify the relationships between physical and/or cognitive frailty and instrumental activities of daily living (IADL) functioning in community living older persons.
Design
Cross sectional observation study.
Setting
Data extracted from the 2011–2013 of the National Center for Geriatrics and Gerontology–Study of Geriatric Syndromes (NCGG-SGS) database.
Participants
A total of 8,864 older adults aged ≥ 65 years who were enrolled in the NCGG-SGS.
Measurements
We characterized physical frailty as limitations in three or more of the following five domains: slow walking speed, muscle weakness, exhaustion, low activity and weight loss. To screen for cognitive impairment, we used the National Center for Geriatrics and Gerontology-Functional Assessment Tool (NCGG-FAT) which included tests of word list memory, attention and executive function (tablet version of the Trail Making Test, part A and B), and processing speed (tablet version of the Digit Symbol Substitution Test). Two or more cognitive impairments indicated by an ageadjusted score of at least 1.5 standard deviations below the reference threshold was characterized as cognitive impairment. Each participant reported on their IADL status (use of public transportation, shopping, management of finances, and housekeeping) and several potential confounders such as demographic characteristics.
Results
The overall prevalence of physical frailty, cognitive impairment, and cognitive frailty, i.e. co-occurrence of frailty and cognitive impairment, was 7.2%, 5.2%, and 1.2%, respectively. We found significant relationships between IADL limitations and physical frailty (Odds Ratio (OR) 1.24, 95% confidence interval (95% CI) 1.01 to 1.52), cognitive impairment (OR 1.71, 95% CI 1.39 to 2.11), and cognitive frailty (OR 2.63, 95% CI 1.74 to 3.97).
Conclusion
Using the NCGG-SGS frailty criteria, we found more participants with physical frailty than with cognitive frailty. The individuals with cognitive frailty had the highest risks of IADL limitations. Future investigation is necessary to determine whether this population is at increased risk for incidence of disability or mortality.
Keywords: Frailty, prevalence, cognitive impairment, IADL, aged
Introduction
According to The World Health Organization, more than a billion people are estimated to live with some form of disability, representing approximately 15% of the world's population based on 2010 estimates. The number of people with disabilities is growing because the world's population is aging and there is a rise in prevalence of chronic health conditions associated with disability, such as diabetes, cardiovascular diseases, and mental illness (1). In older people, frailty increases with advancing age and poses a higher risk of activities of daily living (ADL) disability compared to non-frail older people (2., 3., 4.).
The concept of frailty has focused principally on the physical domain. The well-known frailty phenotype introduced by Fried et al. (5), which classifies people into categories of robust, pre-frail or frail, fits within this physiological approach to frailty. The frailty phenotype postulates that five indicators (weight loss, exhaustion, slow walking speed, low grip strength and low physical activity) are related to each other in a cycle of frailty. Older people who are frail according to the phenotype have a higher risk of disability (2, 4, 6).
In contrast, there is an insufficient evidence regarding the operational definition, rationale, and validity of cognitive frailty (7). An International Consensus Group on “Cognitive Frailty” was organized by the International Academy on Nutrition and Aging (IANA) and the International Association of Gerontology and Geriatrics (IAGG) in 2013 and provides the first definition of a “Cognitive Frailty” condition in older adults (8). The consensus group proposed the identification of the so-called “cognitive frailty” as a heterogeneous clinical manifestation characterized by the simultaneous presence of both physical frailty and cognitive impairment. The following four categories were established; robust older individuals, physically frail older adults with normal cognitive function, non-physically frail older adults with cognitive impairment, and physically frail older adults with cognitive impairment (8).
Previously we reported the combined prevalence of physical frailty and mild cognitive impairment (MCI) and the relationships between physical frailty and MCI in older Japanese adults (9). The overall prevalence of physical frailty, MCI, and combined physical frailty and MCI was 11.3%, 18.8%, and 2.7%, respectively. We found significant relationships between physical frailty and MCI (9). The aims of the present study were to identify the prevalence of cognitive frailty defined by IANA and IAGG using a database of the National Center for Geriatrics and Gerontology–Study of Geriatric Syndromes (NCGG-SGS) which was a Japanese national cohort study (10). We also aimed to determine whether the subtypes of frailty, such as with or without cognitive impairment, were associated with daily functioning. Decline of instrumental activities of daily living (IADL) which require highly complex neuropsychological organization, appears with advancing age and precedes a decline of basic ADL (11, 12) and incident dementia (13, 14). We used IADL measurements as a measure of daily activity in the present study. We hypothesized that physically frail older adults with cognitive impairment would experience a greater impact on their IADL than the other subgroup of frailty and robust older individuals.
Methods
Participants
The present prospective cohort study involved 10,885 community-dwelling older adults (= 65 years) enrolled in the NCGG–SGS (10). The NCGG–SGS participants were recruited from either Nagoya or Obu, Japan. Our inclusion criteria were as follows: all participants resided either in Obu or Nagoya city and participants from Obu were 65 years or older (August 2011–February 2012, June 2013), and those from Nagoya were 70 years or older (July 2013–December 2013) at the time of examination. Exclusion criteria included the need for support or care certified by the Japanese public long-term care insurance system, disability in basic activities of daily living, and inability to undergo performance-based assessments (9). We also excluded participants with a history of Parkinson's disease, stroke, depression, or dementia. All participants whose Mini-Mental State Examination (MMSE) (15) scores were less than 21 were also excluded as the participants suspected of moderate dementia (16). Of the initial 10,885 participants, 2,021 were excluded such that data from 8,864 older adults (mean age 73.4 ± 5.4 years, 65–96 years; 4,258 men, 4,606 women) were analyzed in the current study. Informed consent was obtained from all participants prior to their inclusion in the study, and the Ethics Committee of the National Center for Geriatrics and Gerontology approved the study protocol.
Operationalization of cognitive frailty
We considered physical frailty to be characterized by limitations in three or more of the following five conditions based on those used in Fried's original studies (5): slow walking speed, weakness, exhaustion, low activity, and weight loss. Participants with none of these components were considered non-frail, and those with one or two components were considered to be in pre-frailty. 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/after 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) (9, 17). 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) (18). Exhaustion was considered to be 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, Labour and Welfare (19): “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 engage in low levels of activity (9). Weight loss was assessed by a response of “yes” to the question, “Have you lost 2 kg or more in the past 6 months?” (19).
Cognitive screening was conducted using the National Center for Geriatrics and Gerontology-Functional Assessment Tool (NCGG-FAT) (20). The NCGG-FAT consists of 4 domains including memory (word list memory-I (immediate recognition) and word list memory-II (delayed recall)), attention (an electronic tablet version of the Trail Making Test, TMT-part A), executive function (an electronic tablet version of the TMT-part B), and processing speed (an electronic tablet version of the Digit Symbol Substitution Test). The participants were given about 20 minutes to complete the battery. High test-retest reliability and moderate to high validity of the NCGG-FAT have been confirmed in community-dwelling older adults (21). Well-trained study assistants conducted assessments of cognitive functions in the community, such as community hall. Prior to commencing the study, all staff received training from the authors in the correct protocols for administering the assessment measures. All tests used in this study had established standardized thresholds for the definition of impairment in the corresponding domain (score <1.5 SDs below the age and education-specific means) for a population-based cohort consisting of community-dwelling older adults. We considered major cognitive impairment to be characterized by deficits on two or more of the tests in the NCGG-FAT. Participants without deficits on these tests were considered cognitively intact, and those with one deficit was considered to have minor cognitive impairment. We did not include minor impairment as cognitive impairment in the NCGG-SGS cognitive frailty criteria.
The participants were categorized according to these four groups; 1) robust older individuals who had no physical frailty and cognitive impairment (robust group), 2) physically frail older adults without cognitive impairment (physical frailty group), 3) non-physically frail older adults with major cognitive impairment (cognitive impairment group), and 4) physically frail older adults with cognitive impairment (cognitive frailty group) (8).
Measurements of daily functioning
Ability to perform IADL was assessed using the following items: using a bus or a train, grocery shopping, management of finances, housekeeping, and telephone use. The items of IADL were based on the Lawton and Brody IADL scales (22). Subjects were asked whether they had performed each activity during the past month; responses were ‘yes (did)' or ‘no (did not)'. In addition, we totaled the number of activities for which each participant answered ‘yes' for IADL.
Potential confounding factors of daily functioning
Functional decline results from many factors co-occurring over a long period of time. Previous studies have reported that demographic variables (23., 24., 25.), chronic medical conditions (23, 25., 26., 27.), life-styles (23, 28., 29., 30.), and psychosocial factors (23, 31, 32) are associated with functional decline in older adults as well as physical performance (24, 33), and cognitive function (23, 34., 35., 36.). We selected three demographic variables, seven chronic medical conditions, three life-styles, and two psychosocial factors as possible confounding factors of IADL performance (Table 1). Age, sex and education were assessed as demographic variables. The nurses who identified the chronic medical condition from the interview survey recorded primary diseases or medications. The following items were included in the analyses: hypertension, heart disease, pulmonary disease, osteoarthritis, diabetes, medications, and hospitalization. The life-styles and psychosocial measurements were assessed using self-reported information collected through the interview survey. Current status of hobbies, alcohol use, smoking and living alone were assessed using dichotomized yes/no responses. Depressive symptoms were measured using the 15-item Geriatric Depression Scale (GDS) (37).
Table 1.
Participant characteristics with mean (SD) or number (%)
| Participants with IADL limitations (n=2366) | Participants without IADL limitations (n=6498) | P value | |
|---|---|---|---|
| Demographic variables | |||
| Age, years* | 73.1 (5.9) | 73.5 (5.3) | 0.003 |
| Sex, male | 1669 (70.5) | 2589 (39.8) | <0.001 |
| Education, years* | 11.7 (2.7) | 11.8 (2.5) | 0.043 |
| Chronic medical conditions | |||
| Hypertension, yes | 1134 (47.9) | 2876 (44.3) | 0.002 |
| Heart disease, yes | 450 (19.0) | 1038 (16.0) | 0.001 |
| Pulmonary disease, yes | 340 (14.4) | 904 (13.9) | 0.583 |
| Osteoarthritis, yes | 351 (14.8) | 1200 (18.5) | <0.001 |
| Diabetes, yes | 374 (15.8) | 765 (11.8) | <0.001 |
| Medications, number* | 2.7 (2.6) | 2.5 (2.5) | 0.001 |
| Hospitalization during 3 months, yes | 46 (1.9) | 101 (1.6) | 0.204 |
| Life-styles | |||
| Hobby and/or sports activity, no | 685 (29.0) | 1195 (18.4) | <0.001 |
| Alcohol use, no | 1136 (48.0) | 3772 (58.0) | <0.001 |
| Smoking, yes | 324 (13.7) | 415 (6.4) | <0.001 |
| Psychosocial factors | |||
| Geriatric Depression Scale-15, score* | 3.3 (2.8) | 2.6 (2.5) | <0.001 |
| Live alone, yes |
133 (5.6) |
933 (14.4) |
<0.001 |
Mean (SD)
Statistical analyses
Student's t-test and Pearson's chi-squared test were used to test differences in the baseline characteristics between frailty statuses and between participants with and without IADL limitations. Individuals who selected “no” in one or more of the four IADL items were defined as having an IADL limitation. To identify whether there was a significant impact of cognitive frailty on IADL limitation using chi-square, we used adjusted standardized residuals. The adjusted standardized residuals followed the t distribution, with > 1.96, P < 0.05 and > 2.56, P < 0.01.
Multivariate logistic regression analyses with forced-entry were used to determine adjusted odds ratios (ORs) and 95% confidence intervals (95% CIs), and to assess independent associations between cognitive frailty and IADL limitations and each of the IADL items. The first model (Model 1) was adjusted for age and sex. We then used a multiple adjustment model adjusted for two demographic variables, three physiological variables, four primary diseases or geriatric syndromes, and six psychosocial variables as possible confounding factors (Model 2). We estimated adjusted odds ratios (ORs) for IADL limitations and their 95% confidence intervals (95% CIs). All analyses were performed using IBM SPSS Statistics 20.0 (IBM Japan Tokyo). The level of statistical significance was set at P < 0.05.
Results
Prevalence rates of each subtype of cognitive frailty including the robust group, physical frailty group, cognitive impairment group, and cognitive frailty group were 86.4%, 7.2%, 5.2%, and 1.2%, respectively. A total of 2,366 participants (26.7%) had IADL limitations for at least one component.
Table 1 shows possible confounding factors of ADL limitations for each participant with and without IADL disease and hospitalization exhibited significant differences between the participants with and without IADL limitations (Table 1). There were significant differences on all IADL items and all confounding factors except smoking among the subgroups of cognitive frailty (Table 2). In the residual analyses, the cognitive frailty group included many participants who had statistically significant IADL limitations (p < 0.01); using a bus or a train (p < 0.01), grocery shopping (p < 0.01), management of finances (p < 0.01), and housekeeping (p < 0.01) but in the finding for telephone use was not significant.
Table 2.
Comparisons with IADL limitations and confounding factors between the participants who were robust, or had physical frailty, cognitive impairment, or cognitive frailty with mean (SD) or number (%)
| Robust (n=7661) | Physical frailty (n=634) | Cognitive impairment (n=460) | Cognitive frailty (n=109) | P value | |
|---|---|---|---|---|---|
| IADL items | |||||
| IADL decline, yes | 1935 (25.3) | 203 (32.0) | 175 (38.0) | 53 (48.6) | <0.001 |
| Using a bus or a train, no | 585 (7.6) | 83 (13.1) | 51 (11.1) | 25 (22.9) | <0.001 |
| Grocery shopping, no | 286 (3.7) | 49 (7.7) | 34 (7.4) | 20 (18.3) | <0.001 |
| Management of finances, no | 704 (9.2) | 60 (9.5) | 71 (15.4) | 25 (22.9) | <0.001 |
| Housekeeping, no | 707 (9.2) | 87 (13.7) | 63 (13.7) | 19 (17.4) | <0.001 |
| Telephone use, no | 468 (6.1) | 43 (6.8) | 53 (11.5) | 11 (10.1) | <0.001 |
| Demographic variables | |||||
| Age, years* | 72.9 (5.2) | 78.2 (5.8) | 73.9 (5.1) | 77.6 (6.1) | <0.001 |
| Sex, male | 3719 (48.5) | 267 (42.1) | 228 (49.6) | 44 (40.4) | 0.005 |
| Education, years* | 11.9 (2.5) | 11.0 (2.7) | 11.1 (2.4) | 10.3 (2.4) | <0.001 |
| Chronic medical conditions | |||||
| Hypertension, yes | 3390 (44.3) | 342 (53.9) | 228 (49.6) | 50 (45.9) | <0.001 |
| Heart disease, yes | 1231 (16.1) | 167 (26.3) | 64 (13.9) | 26 (23.9) | <0.001 |
| Pulmonary disease, yes | 1069 (14.0) | 117 (18.5) | 43 (9.3) | 15 (13.8) | <0.001 |
| Osteoarthritis, yes | 1280 (16.7) | 180 (28.4) | 73 (15.9) | 18 (16.5) | <0.001 |
| Diabetes, yes | 903 (11.8) | 136 (21.5) | 76 (16.5) | 24 (22.0) | <0.001 |
| Medications, number* | 2.4 (2.4) | 4.1 (3.3) | 2.6 (2.5) | 3.9 (3.0) | <0.001 |
| Hospitalization during 3 months, yes | 108 (1.4) | 21 (3.3) | 8 (1.7) | 10 (9.2) | <0.001 |
| Life-styles | |||||
| Hobby and/or sports activity, no | 1400 (18.3) | 287 (45.3) | 136 (29.6) | 57 (52.3) | <0.001 |
| Alcohol use, no | 4135 (54.0) | 421 (66.4) | 273 (59.3) | 79 (72.5) | <0.001 |
| Smoking, yes | 630 (8.2) | 48 (7.6) | 52 (11.3) | 9 (8.3) | 0.116 |
| Psychosocial factors | |||||
| Geriatric Depression Scale-15, score* | 2.5 (2.4) | 4.8 (3.2) | 3.3 (2.7) | 5.0 (3.2) | <0.001 |
| Live alone, yes |
878 (11.5) |
105 (16.6) |
65 (14.1) |
18 (16.5) |
<0.001 |
Multiple logistic regression models were used to analyze associations between cognitive frailty and IADL limitations. In the first model (Model 1), the following odds ratios were determined for participants who had physical frailty (odds ratio (OR) 1.83, 95% confidence interval (95% CI) 1.51 to 2.21), cognitive impairment (OR 1.95, 95% CI 1.59 to 2.40), and cognitive frailty (OR 4.01, 95% CI 2.68 to 5.99) compared with robust participants. In the fully adjusted model (Model 2), the following ORs were determined for physical frailty (OR 1.24, 95% CI 1.01 to 1.52), cognitive impairment (OR 1.71, 95% CI 1.39 to 2.11), and cognitive frailty (OR 2.63, 95% CI 1.74 to 3.97) compared with robust participants.
Figure 1 shows a forest plot of ORs and 95% CI for each of the IADL items. The cognitive frailty group showed the highest odds ratios for all IADL items except telephone use. The participants with cognitive frailty showed higher ORs for using a bus or a train (OR 2.91, 95% CI 1.79 to 4.72), grocery shopping (OR 4.67, 95% CI 2.67 to 8.16), management of finances (OR 3.01, 95% CI 1.80 to 5.03), and house keeping (OR 2.35, 95% CI 1.33 to 4.15) compared with robust participants. There was no significant OR for telephone use. The cognitive impairment group showed higher ORs for intellectual activity such as managing finances and telephone use than the physical frailty group. In contrast, the physical frailty group indicated a higher OR for using a bus or a train than the cognitive impairment group (Figure 1).
Figure 1.
Odds ratios for instrumental activities of daily living limitations according to cognitive frailty status
Discussion
The International Consensus Group on “Cognitive Frailty” proposed the identification of the so-called “cognitive frailty” as a clinical manifestation characterized by the simultaneous presence of both physical frailty and cognitive impairment (8). In the NCGG-SGS cohort, prevalence rates of each subtype of cognitive frailty including physical frailty, cognitive impairment, and cognitive frailty were 7.2%, 5.2%, and 1.2%, respectively. We reported previously that overall prevalence of physical frailty, MCI, and frailty and MCI combined was 11.3%, 18.8%, and 2.7%, respectively in the OSHPE cohort (9). The prevalence rates of physical frailty in this study were 7.2%, which is consistent with large studies performed in other countries (5, 41., 42., 43., 44., 45.). For instance, in the American Cardiovascular Health Study, the prevalence of frailty among 5,317 community-dwelling men and women aged 65 years was 6.9%. Furthermore, frailty was associated with older age, male sex, being African American, having lower education and income, poorer health, and higher rates of comorbid chronic disease and disability (5). The French Three-City Study demonstrated a frailty prevalence of 7% among 6,078 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 selfreported health status, and more chronic disease in addition to incident disability (5). There was a major difference between cognitive impairment in the NCGG-SGS cohort and MCI in the OSHPE cohort. In the NCGG-SGS cohort, the prevalence of major cognitive impairment was characterized by deficits on two or more of the tests in the NCGG-FAT. Major cognitive impairment corresponded with multiple-domain MCI. The prevalence of individuals with multiple-domain MCI was similar (6.1%, unpublished data) in the OSHPE cohort (9). The prevalence of MCI varies between studies as a result of different diagnostic criteria, as well as different sampling and assessment procedures (46). Despite some methodological differences, most previous studies report prevalence figures for MCI or for cognitive impairment without dementia ranging from 11%–23%. The Women Cognitive Impairment Study found the prevalence of MCI or cognitive impairment without dementia to be 23.2% in a sample of 1,299 participants aged 85 years and over (47). The Mayo Clinic Study of Aging diagnosed 329 of 1,969 study participants (16.7%) with MCI or cognitive impairment without dementia using the Clinical Dementia Rating Scale and neuropsychological testing (48). A study from Leipzig, Germany found the overall prevalence of MCI or cognitive impairment without dementia to be 19.2% in participants aged 75 years and older (49). The Cardiovascular Health Study found the overall rate of MCI or cognitive impairment without dementia to be 19% in participants aged 75 years and older (50). In the Aging, Demographics, and Memory Study, an estimated 5.4 million people (22.2% of the total population of the country) in the United States aged 71 years or older were found to have cognitive impairment without dementia (51). In a Japanese study, MCI was diagnosed in 271 of 1,433 study participants (18.9%) (52). The wide variation in the published overall prevalence estimates may be partially because of differences in the age distributions of the populations studied (46, 53). The prevalence of multipledomain MCI in the Japanese community cohort study with similar age populations was 5.8%, which is similar to present study that used multiple cognitive tests to detect MCI.
Our cognitive frailty classification based on the International Consensus Group on “Cognitive Frailty” showed significant relationships exploring IADL limitations in multiple logistic regression models. In the fully adjusted model, ORs for cognitive frailty were higher than for physical frailty and cognitive impairment. These results suggest that healthcare providers should consider the necessity of interventions to avoid IADL limitations especially in cognitively frail older persons. In the stratified analyses for each of the IADL items, the cognitive frailty group showed the highest ORs for all IADL items except telephone use. Frail older adults who had low cognitive function showed a 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 as well as physical frailty (54, 55). Furthermore, older adults with cognitive impairment had difficulty in IADL, particularly in activities that were cognitively demanding (56). Although the cognitive impairment group showed higher ORs for intellectual activity than the physical frailty group, the physical frailty group indicated a higher odds ratio for physical activity than the cognitive impairment group. A better understanding of the mechanisms that underlie IADL skills may be useful in the development of compensatory and intervention strategies designed to improve IADL performance in older adults.
There are several limitations to this study. First, participants were not recruited randomly in the community. This may lead to an underestimation of the prevalence of cognitive frailty, as the participants were relatively healthy elderly persons who were able to receive to health checkups 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, the information collected on medical conditions and comorbidities was based on self-reports. We did not have access to medical records to confirm subject self-reports; however, a good agreement between self-reported medical conditions and actual medical diagnoses has been reported (57). Finally, we did not confirm predictive validity because the design of the present study was cross-sectional. Thus, the predictive validity of cognitive frailty should be confirmed using longitudinally monitored older populations.
The implications of our findings for medical care are several fold. Our findings are supported by comprehensive geriatric assessments intended to identify frailty and cognitive impairments. We also included a wide range of covariates related to ADL performance. To our knowledge, this is first study to reveal the relationships between cognitive frailty and IADL performance using a large population-based sample. Individuals with a co-occurrence of frailty and cognitive impairment may face a higher risk of IADL limitations than robust older adults or older adults with either frailty or cognitive impairment. The Hispanic Established Populations for the Epidemiologic Study of the Elderly demonstrated that frailty and cognitive impairment affect mortality differently when they occur independently compared with when they are present together. In particular, individuals with cognitive frailty and cognitive impairment have been found to have a higher mortality compared with individuals with either frailty or cognitive impairment (58). Further longitudinal study is needed to clarify whether cognitive frailty might predict incidence of IADL decline among older adults.
In summary, the results of this cross-sectional cohort study identified that physical frailty, cognitive impairment and cognitive frailty have an impact on IADL performance in older adults. In particular, the older adults with comorbid frailty and cognitive impairment had an increased risk of IADL limitations compared with robust older adults or older adults with either frailty or cognitive impairment.
Acknowledgements
We would like to thank the healthcare staff members who were involved in this study, Dr. Daisuke Yoshida, and Mr. Yuya Anan for assistance with the assessments. Hyuma Makizako, PhD, PT1, Sangyoon Lee, PhD1, Takehiko Doi, PhD, PT1, SungChul Lee, PhD1, Kota Tsutsumimoto, PhD, PT1, Kazuhiro Harada, PhD1, Ryo Hotta, PhD1, SeongRyu Bae, MSc1, Sho Nakakubo, MSc, PT1, Kenji Harada, MSc1, Takao Suzuki, PhD, MD2
Statement of author's contributions to manuscript
Shimada planned the study and wrote the first draft of the manuscript, and coordinated the review and editing process leading to the final manuscript. Makizako, Sangyoon Lee, and Doi participated in the design of the study and wrote the paper. SungChul Lee, Tsutsumimoto, Kazuhiro Harada, Hotta, Bae, Nakakubo, and Kenji Harada corrected data and contributed to the editorial process and review of the manuscript. Suzuki supervised the study and suggested many of the ideas that have been pursued in this research, and participated in the planning, editorial, and review processes that led to the final manuscript.
Ethical standards
Ethical standards for epidemiological study were adhered to according to guidelines from the Ministry of Health, Labour and Welfare, Japan.
Sources of Financial Support
This work received financial support from the Health and Labor Sciences Research Grants (Comprehensive Research on Aging and Health), the Research Institute of Science and Technology for Society (RISTEX) from the Japan Science and Technology Agency (JST) for redesigning communities for aged society in 2012, a Grant-in-Aid for Scientific Research (B) (grant number 23300205), and Research Funding for Longevity Sciences (22) from the National Center for Geriatrics and Gerontology, Japan. No support was received from industry.
Conflicts of interest
There are no conflicts of interest.
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