Abstract
Objectives
A comfortable walking speed is a suitable measurement of functional status in older adults. In addition to assessing their comfortable walking speed, two complex walking tests were administered to a cohort of older people, assuming that these tests would be a more sensitive predictor of the incident long-term care needs than comfortable walking speed.
Design
A prospective observational study was conducted to collect data.
Setting and Participants
Among the initial 5,563 community-dwelling independent older adults (aged ≥ 65 years), 935 were excluded and the data of 4,628 (mean age, 73.9 ± 5.5 years, 65–94 years; 2,052 men, 2,576 women) older adults were finally analyzed.
Methods
Three walking tasks were administered: comfortable, complicated balance, and Go-stop walking. Complicated balance walking was measured under comfortable walking conditions, with participants having to walk with their hands crossed at the shoulder joint at 90°. For the Go-stop walking test, the time taken to walk 2 meters was measured using a stopwatch. For two years following baseline assessments, participants received monthly follow-ups for incident certification of the need for care under the long-term care insurance (LTCI) system.
Results
Low performance in comfortable, complicated balance, and Go-stop walking were 29.8%, 37.7%, and 35.1%, respectively. During the 24-month follow-up period, 246 participants (5.3%) required LTCI certification. The Youden Index was used to determine the cut-points of the incident long-term care needs in the comfortable, complicated balance, and Go-stop walking conditions, which were 1.055 m/s, 0.936 m/s, and 3.205 seconds, respectively. Participants classified as exhibiting low performance included 1,381 (29.8%) under comfortable walking, 1,746 (37.7%) under complicated balance walking, and 1,623 (35.1%) under the Go-stop walking tests. The C-indices of the comfortable, complicated balance, and Go-stop walking tests were 0.72 (95% confidence interval (CI) 0.69–0.76), 0.71 (95% CI 0.67–0.74), and 0.65 (95% CI 0.61–0.69), respectively. Cox proportional hazards regression model revealed significant relationships between the incident long-term care needs and the comfortable (hazard ratio (HR) 2.14, 95% CI 1.62–2.84), complicated balance (1.81, 1.36–2.41), and Go-stop (1.46, 1.12–1.91) walking conditions.
Conclusions and Implications
The findings suggest that slow walking speed has a considerably greater impact on the incident long-term care needs in older adults. However, the complex walking task did not improve the predictive performance. Comfortable walking speed tests, which can easily be measured to predict the future incident long-term care needs, are effective tools in community health promotion and primary care.
Key words: Incident long-term care needs, older adults, gait, walking speed, community
Introduction
Japan is the world's most aged country, with 29.1% of its population aged ≥65 years in 2021. An important public health concern for a country with an aging population is whether an increase in life expectancy will be accompanied by morbidity, disability, and the subsequent need for long-term care services (1). A long-term care insurance (LTCI) system was introduced in 2000 as part of universal health coverage. It assures citizens that they will receive care and support from Japanese society as a whole (2). The LTCI services are provided based on a care plan designed by a care manager; this plan includes home-visit services, day services, short-stay services, residential services, and in-facility services. These services are determined based on the level of disability and the needs of the care recipient (3).
Measures of physical performance can help identify older adults with early stages of disability, who may benefit from interventions to prevent disability incidence (4). Walking speed is a reliable, valid, sensitive, inexpensive, safe, quick, and simple way to assess physical performance. People who have personal care needs and frail older adults may have slower walking speeds than others (5, 6). Walking speed is a strong predictor of adverse events such as disability (7, 8, 9, 10, 11, 12, 13, 14), mortality (8, 9, 15), hospitalization (8, 9, 11, 16), falls (16, 17), and dementia (18). Mobility is a vital aspect of life, and an energy shift away from walking occurs when other vital activities are threatened (19). Walking speed cutoff at a comfortable pace of 1.0 m/s is crucial for predicting future functional decline in older adults living in the community (8, 9, 11, 12, 13). It is also the most important measure to determine personal care demand among older adults (20). Walking speed is an outcome of intervention effectiveness and a predictor of future prognosis. A minimal detectable change of 0.14 m/s is necessary to establish the intervention effect on community-dwelling older adults (21). Based on these scientific pieces of evidence, walking speed is considered an appropriate measure of functional status in older adults.
Gait regulation involves multiple cognitive functions, particularly under complex conditions. Prospective studies indicate that baseline dual-task gait performance among those aged 65 years and older is associated with the risk of falls (22) and cognitive decline (23). Central motor control stress tests can be achieved with complex walking tests that challenge the overlearned task of walking. Previous studies suggest that brain regions involved in central motor control and cognitive decline have overlapping functions (24, 25, 26). Consequently, complex walking tests may reveal latent pathology that increases the risk of cognitive and functional decline. However, the impact of latent brain pathology on disability development is not clear, and whether complex walking tests are useful to predict disability development remains a debated topic.
We conducted a cohort study of older adults and administered two complex walking tests in addition to measuring their comfortable walking speed. We assumed that the complex walking tests would be more sensitive than comfortable walking speed in predicting the occurrence of LTCI certification. To the best of our knowledge, this study is the first to demonstrate differences in the predictive validity of various walking tests for the occurrence of LTCI certification, focusing on older adults from a national cohort database.
Materials and Methods
Participants
We conducted a prospective cohort study involving 5,563 older adults (aged ≥ 65 years) living independently in the community. Participants were enrolled in the National Center for Geriatric and Gerontology-Study of Geriatric Syndromes (NCGG-SGS) (27), which included residents of Tokai city, Japan. To be included in the study, participants had to be aged ≥65 years and living in Tokai city at the time of examination.
We excluded some participants who had conditions that could be mistaken for disability. Specifically, we excluded individuals with a history of stroke (n = 341), Parkinson's disease (n = 16), depression (n = 100), dementia (n = 9), and those with Mini-Mental State Examination (28) (MMSE) scores <20 (n = 35), which suggests moderate dementia (29). We also excluded participants with other brain diseases (n = 158), those who used a cane to walk (n = 20), those who were outliers in the walking assessments (n = 27), and those with missing values in the outcome of the LTCI certification and exclusion variables (n = 229) (Fig. 1). Of the initial 5,563 participants, 935 were excluded, leaving us with data from 4,628 older adults (mean age: 73.9 ± 5.5 years, age range: 65–94 years; 2,052 men, 2,576 women) for analysis. All participants were informed of the objective of the study and provided written informed consent before being included in the study. The study protocol was approved by the ethics committee of the NCGG (registration numbers: 1067-3).
Figure 1.
Flow chart of participant enrollment
Measurements
Walking speed
Walking speed was measured in seconds using a stopwatch. The study included three walking tasks: comfortable, complicated balance, and Go-stop walking. These tasks were designed to be easily performed as a screening test for the occurrence of LTCI certification in clinical or community settings. To measure comfortable walking, participants were instructed to walk on a flat, straight surface at a comfortable pace. Two markers were used to indicate the start and end of a 2.4-meter walk path. Participants were required to walk a 2-meter section before passing the start marker to ensure that they were walking at a steady pace when they reached the timed section. As the NCGG-SGS walking speed measurement protocol involves a short distance of 2.4 meters, we conducted a preliminary investigation to determine the correlation between walking speed on a 10-meter walking path and the 2.4-meter path. The results exhibited an extremely high correlation (r = 0.989, p < 0.01) (30). Complicated balance walking was measured under comfortable walking conditions and the walking time was measured when the participants walked with their hands crossed at 90° at their shoulder joint. For the Go-stop walking test, the time taken to walk for 2 meters was measured using a stopwatch. The examiner started the stopwatch at the start signal and recorded the time until the subject stood stationary in front of the marker position 2 meters away. Significantly, complicated balance walking is loaded on balancing while walking by restricting upper body movement. In Go-stop walking, 1.0–1.5 m is required to reach a steady speed from a static standing position, and stopping at a landmark 2 m away requires rapid deceleration from acceleration. To perform this complex task smoothly, the physical functions to cope with rapid acceleration and deceleration and the cognitive functions to recognize the goal and plan the action are thought to require. We considered complicated balance and Go-stop walking to be complex tests because they require more effort than a simple walking task.
Occurrence of LTCI certification
We followed up with participants every month for two years after their baseline assessments to certify any incidents of the need for care in the LTCI system. The occurrence of LTCI certification was defined as the point at which participants were certified as requiring care by LTCI. In Japan, all individuals aged ≥65 years are eligible to receive benefits, such as institutional and community-based services, based solely on their physical and mental disability status. A computer-aided standardized needs assessment system categorizes people into seven levels of needs. LTCI certifies a person as requiring “Support Level 1 or 2” if they require support sometimes to perform activities of daily living (ADL) or as requiring “Care Levels 1, 2, 3, 4, or 5” if they require continuous care (2).
Potential confounding factors
According to Stuck et al. (31) and Ishizaki et al. (32), two demographic variables, six medical and general conditions, two cognitive and psychosocial variables, and one activity variable were identified as possible confounding factors of ADL limitations (Table 1). Nurses recorded the presence of primary diseases and medication use after identifying chronic conditions from the interview survey. The analysis also included osteoarthritis, heart disease, pulmonary disease, and diabetes. Data on falls were collected by asking the following question: “During the past 12 months, have you had any falls where you have landed on the ground or floor?” A faller was defined as an individual who had at least one fall in the past 12 months. MMSE was used to assess global cognitive function (28). The study assistants underwent training to learn the proper protocols for administering assessment measures before the start of the study. The 15-item Geriatric Depression Scale (GDS-15) was used to measure depressive symptoms (33). Activity status (“yes” or “no”) was measured using self-reported data collected through an interview survey. To determine activity status, the study measured several parameters such as going outdoors via public transportation, shopping for daily necessities, handling cash and banking, participating in outdoor activities at least once a week, and any reduction in outdoor activities from the previous year. These measurements were carried out in accordance with pre-established methods (34, 35).
Table 1.
Demographic characteristics of the study participants stratified by incident long-term care needs status
| Participants without incident long-term care needs | Participants with incident long-term care needs | P value | |
|---|---|---|---|
| Age, years | 73.5 (5.3) | 79.6 (5.2) | <0.01 |
| Sex, women* | 2432 (55.5) | 144 (58.5) | 0.351 |
| Osteoarthritis, yes* | 768 (17.5) | 66 (26.8) | <0.01 |
| Heart disease, yes* | 788 (18.0) | 64 (26.0) | <0.01 |
| Pulmonary disease, yes* | 464 (10.6) | 36 (14.6) | 0.047 |
| Diabetes, yes* | 588 (13.4) | 41 (16.7) | 0.148 |
| Faller, yes* | 718 (16.4) | 71 (28.9) | <0.01 |
| Medications, n | 3.0 (2.7) | 4.2 (3.2) | <0.01 |
| MMSE, point | 27.5 (2.3) | 25.9 (2.8) | <0.01 |
| GDS, point | 2.4 (2.3) | 3.7 (3.1) | <0.01 |
| Physical inactivity, yes* | 959 (21.9) | 71 (28.9) | 0.01 |
| Comfortable walking speed, m/s | 1.168 (0.200) | 0.998 (0.214) | <0.01 |
| Complicated balance walking speed, m/s | 1.006 (0.199) | 0.848 (0.208) | <0.01 |
| Go-stop walking time, s | 3.037 (0.614) | 3.446 (0.849) | <0.01 |
: n (%), MMSE: mini-mental state examination, GDS: geriatric depression scale
Statistical analyses
Receiver operating characteristic curves were inspected to determine cut-points for the walking tests that best discriminated between those with and without the occurrence of LTCI certification. The Youden Index was used to determine cut points that maximized the sensitivity and specificity of each test (36). We calculated the concordance index (C-index), sensitivity, specificity, positive and negative likelihood ratios, positive and negative predictive values, and accuracy at the cut points for the occurrence of LTCI certification in the three walking tests. Participants were classified into high and low-performance groups based on the cut-points of three walking tests. Student's t-tests and Pearson's chi-squared tests were performed to determine differences in baseline characteristics between the walking speed groups and between participants with and without the occurrence of LTCI certification. Pearson's chi-squared tests were also performed to test for differences in the occurrence of LTCI certification between the walking speed groups. We used Kaplan-Meier curves to calculate the cumulative occurrence of LTCI certification over the follow-up period based on baseline walking speed status. Intergroup differences were estimated by performing the logrank test, and the associations between the types of walking tests and the occurrence of LTCI certification were analyzed using Cox proportional hazards regression models. We used a multiple adjustment model to adjust for possible confounding factors and estimated adjusted hazard ratios (HRs) for the occurrence of LTCI certification and their 95% confidence intervals (CIs). An assessment was made to determine whether the measures could be included as covariates in subsequent statistical models without violating assumptions of multicollinearity. An acceptable level of multicollinearity was considered to be a variance inflation factor (VIF) < 5 for the covariate variables (37). All analyses were performed using SPSS Statistics, version 25.0 (IBM, Armonk, NY). P-values <0.05 were considered statistically significant.
Results
During the 24-month follow-up period, 246 participants (5.3%) required LTCI certification. The occurrence of LTCI certification in the high and low-performance groups for comfortable, complicated balance, and Go-stop walking was 2.7% and 11.4%, 2.8% and 9.5%, and 3.4% and 8.9%, respectively (P < 0.01).
Table 1 presents the possible confounding factors associated with ADL limitations in the high and low-performance groups. All measures except sex and diabetes exhibited significant differences between participants with and without the occurrence of LTCI certification. Participants with LTCI certification were significantly older; had a higher prevalence of osteoarthritis, heart disease, pulmonary disease, and falls; took a larger number of medications; and had lower cognitive function status, higher GDS scores, and lower activity levels than those without the occurrence of LTCI certification. Participants who developed disabilities also showed significantly lower performance in the three walking tests (Table 1).
The Youden Index was used to determine the cut-points of the occurrence of LTCI certification in the comfortable, complicated balance, and Go-stop walking tests, which were 1.055 m/s, 0.936 m/s, and 3.205 seconds, respectively (Supplementary Figure 1). The number of participants in the low-performance group was 1,381 (29.8%) for comfortable walking, 1,746 (37.7%) for complicated balance walking, and 1,623 (35.1%) for Go-stop walking tests (Fig. 1). The C-indices in the comfortable, complicated balance, and Go-stop walking tests were 0.72 (95% CI 0.69–0.76), 0.71 (95% CI 0.67–0.74), and 0.65 (95% CI 0.61–0.69), respectively (Table 2). Sensitivity and specificity for the occurrence of LTCI certification were similar for the three walking tests, ranging from 58.9 to 64.2% for sensitivity and 63.9 to 72.1% for specificity. With regard to the occurrence of LTCI certification, values ranged from 1.8 to 2.3 for positive likelihood ratio, 0.5 to 0.6 for negative likelihood ratio, 8.9 to 11.4 for positive predictive value, 96.6 to 97.3 for negative predictive value, and 64.1 to 71.7 for accuracy (Table 2).
Table 2.
Predictive values of the walking tests
| Comfortable walking | Complicated balance walking | Go-stop walking | |
|---|---|---|---|
| C-index | 0.72 (0.69–0.76) | 0.71 (0.67–0.74) | 0.65 (0.61–0.69) |
| Sensitivity, % | 64.2 (57.9–70.2) | 67.5 (61.2–73.3) | 58.9 (52.5–65.2) |
| Specificity, % | 72.1 (70.7–73.4) | 63.9 (62.5–65.4) | 66.3 (64.9–67.7) |
| Positive likelihood ratio | 2.3 (2.1–2.6) | 1.9 (1.7–2.1) | 1.8 (1.6–2.0) |
| Negative likelihood ratio | 0.5 (0.4–0.6) | 0.5 (0.4–0.6) | 0.6 (0.5–0.7) |
| Positive predictive value, % | 11.4 (10.4–12.6) | 9.5 (8.7–10.4) | 8.9 (8.1–9.9) |
| Negative predictive value, % | 97.3 (96.8–97.7) | 97.2 (96.7–97.7) | 96.6 (96.1–97.1) |
| Accuracy, % | 71.7 (70.4–73.0) | 64.1 (62.7–65.5) | 65.9 (64.5–67.3) |
Participants in the low-performance group of the comfortable walking test were older; had a higher prevalence of osteoarthritis, heart disease, pulmonary disease, diabetes, and falls; took a higher number of medications; and had lower cognitive function status, higher GDS scores, and lower activity levels than those who had high performance. Similar results were observed for the complicated balance and Go-stop walking tests. In the former test, participants in the low-performance group were significantly older and men; had a higher prevalence of osteoarthritis, heart disease, pulmonary disease, diabetes, and falls; took a larger number of medications; and had lower cognitive function status, higher GDS score, and lower activity levels than those in the highperformance group. In the latter test, participants in the low-performance group were significantly older; had a higher prevalence of osteoarthritis, diabetes, and falls; took a larger number of medications; and had lower cognitive function status, higher GDS score, and lower activity levels than those in the high-performance group. Walking speed or time taken for the comfortable, complicated balance, and Go-stop walking tests showed significant differences between high and low-performance groups (Table 4).
Table 4.
Comparisons between high and low walking performances
| Comfortable walking |
Complicated balance walking |
Go-stop walking |
|||||||
|---|---|---|---|---|---|---|---|---|---|
| High performance | Low performance | P value | High performance | Low performance | P value | High performance | Low performance | P value | |
| Age, years | 72.9 (5.0) | 76.0 (5.9) | <0.01 | 72.9 (5.0) | 75.4 (5.9) | <0.01 | 73.0 (5.1) | 75.4 (5.9) | <0.01 |
| Sex, women* | 1824 (56.2) | 752 (54.5) | 0.281 | 1661 (57.6) | 915 (52.4) | <0.01 | 1664 (55.4) | 912 (56.2) | 0.593 |
| Osteoarthritis, yes* | 502 (15.5) | 332 (24.0) | <0.01 | 459 (15.9) | 375 (21.5) | <0.01 | 512 (17.0) | 322 (19.8) | 0.018 |
| Heart disease, yes* | 560 (17.2) | 292 (21.1) | <0.01 | 481 (16.7) | 371 (21.2) | <0.01 | 532 (17.7) | 320 (19.7) | 0.092 |
| Pulmonary disease, yes* | 326 (10.0) | 174 (12.6) | 0.01 | 284 (9.9) | 216 (12.4) | <0.01 | 305 (10.1) | 195 (12.0) | 0.051 |
| Diabetes, yes* | 396 (12.2) | 233 (16.9) | <0.01 | 346 (12.0) | 283 (16.2) | <0.01 | 372 (12.4) | 257 (15.8) | <0.01 |
| Faller, yes* | 486 (15.0) | 303 (21.9) | <0.01 | 428 (14.9) | 361 (20.7) | <0.01 | 466 (15.5) | 323 (19.9) | <0.01 |
| Medications, n | 2.7 (2.6) | 3.8 (3.1) | <0.01 | 2.7 (2.5) | 3.6 (3.1) | <0.01 | 2.8 (2.6) | 3.4 (3.1) | <0.01 |
| Mini-mental state examination, point | 27.6 (2.2) | 26.8 (2.5) | <0.01 | 27.7 (2.2) | 26.9 (2.5) | <0.01 | 27.7 (2.2) | 26.9 (2.5) | <0.01 |
| Geriatric depression scale, point | 2.2 (2.2) | 3.0 (2.7) | <0.01 | 2.2 (2.2) | 2.9 (2.6) | <0.01 | 2.2 (2.2) | 2.8 (2.6) | <0.01 |
| Physical inactivity, yes* | 611 (18.8) | 419 (30.3) | <0.01 | 557 (19.3) | 473 (27.1) | <0.01 | 592 (19.7) | 438 (27.0) | <0.01 |
| Comfortable walking speed, m/s | 1.260 (0.140) | 0.920 (0.111) | <0.01 | 1.260 (0.156) | 0.993 (0.160) | <0.01 | 1.234 (0.173) | 1.019 (0.183) | <0.01 |
| Complicated balance walking speed, m/s | 1.080 (0.164) | 0.806 (0.145) | <0.01 | 1.122 (0.133) | 0.793 (0.110) | <0.01 | 1.064 (0.182) | 0.877 (0.180) | <0.01 |
| Go-stop walking time, s | 2.846 (0.484) | 3.559 (0.666) | <0.01 | 2.832 (0.495) | 3.433 (0.663) | <0.01 | 2.696 (0.343) | 3.730 (0.486) | <0.01 |
: n (%)
In survival analyses using the log-rank test, the probability of occurrence of LTCI certification was found to be significantly higher among participants with low performances in the comfortable, complicated balance, and Go-stop walking tests (P < 0.01) than among those with high performances in terms of walking speed. Cox proportional hazards regression models adjusted for possible confounding factors were used to identify predictors of the occurrence of LTCI certification in the low and high-performance groups. Assumptions of multicollinearity were not violated (i.e., VIF <5 in all variables); thus, we included all measurements in the statistical models. Cox proportional hazards regression model revealed significance in terms of age (HR 1.14, 95% CI 1.11–1.17), sex (0.76, 0.58–0.99), faller (1.37, 1.03–1.81), MMSE (0.89, 0.84–0.93), GDS (1.10, 1.05–1.15), and low walking performance (2.14, 1.62–2.84) in comfortable walking. Similar results were observed in the complicated balance and Go-stop walking tests. In the former, age (1.14, 1.12–1.17), sex (0.74, 0.57–0.96), faller (1.36, 1.03–1.80), MMSE (0.89, 0.84–0.93), GDS (1.10, 1.05–1.15), and low walking performance (1.81, 1.36–2.41) were identified as significant variables. In the latter, age (1.15, 1.12–1.18), sex (0.75, 0.57–0.98), faller (1.43, 1.08–1.89), MMSE (0.88, 0.84–0.93), GDS (1.11, 1.06–1.15), and low walking performance (1.46, 1.12–1.91) were identified as significant variables (Table 3). Osteoarthritis, heart disease, pulmonary disease, diabetes, medications, and physical inactivity were not associated with the occurrence of LTCI certification.
Table 3.
Hazard ratios for the incident long-term care needs
| Comfortable walking |
Complicated balance walking |
Go-stop walking |
||||
|---|---|---|---|---|---|---|
| Hazard ratio (95% CI) | P value | Hazard ratio (95% CI) | P value | Hazard ratio (95% CI) | P value | |
| Age, years | 1.14 (1.11–1.17) | <0.01 | 1.14 (1.12–1.17) | <0.01 | 1.15 (1.12–1.18) | <0.01 |
| Sex, women | 0.76 (0.58–0.99) | 0.04 | 0.74 (0.57–0.96) | 0.03 | 0.75 (0.57–0.98) | 0.03 |
| Osteoarthritis, yes | 1.15 (0.85–1.54) | 0.37 | 1.20 (0.89–1.62) | 0.23 | 1.22 (0.91–1.64) | 0.19 |
| Heart disease, yes | 1.16 (0.85–1.57) | 0.35 | 1.15 (0.85–1.56) | 0.36 | 1.15 (0.85–1.56) | 0.37 |
| Pulmonary disease, yes | 1.21 (0.85–1.74) | 0.30 | 1.22 (0.85–1.75) | 0.28 | 1.19 (0.83–1.71) | 0.34 |
| Diabetes, yes | 1.12 (0.79–1.60) | 0.51 | 1.13 (0.79–1.60) | 0.51 | 1.15 (0.81–1.63) | 0.44 |
| Faller, yes | 1.37 (1.03–1.81) | 0.03 | 1.36 (1.03–1.80) | 0.03 | 1.43 (1.08–1.89) | 0.01 |
| Medications, n | 1.01 (0.96–1.05) | 0.72 | 1.01 (0.97–1.06) | 0.66 | 1.02 (0.97–1.06) | 0.50 |
| Mini-mental state examination, point | 0.89 (0.84–0.93) | <0.01 | 0.89 (0.84–0.93) | <0.01 | 0.88 (0.84–0.93) | <0.01 |
| Geriatric depression scale, point | 1.10 (1.05–1.15) | <0.01 | 1.10 (1.05–1.15) | <0.01 | 1.11 (1.06–1.15) | <0.01 |
| Physical inactivity, yes | 1.10 (0.84–1.46) | 0.49 | 1.16 (0.88–1.53) | 0.30 | 1.18 (0.89–1.56) | 0.25 |
| Low comfortable walking speed, yes | 2.14 (1.62–2.84) | <0.01 | ||||
| Low complicated balance walking speed, yes | 1.81 (1.36–2.41) | <0.01 | ||||
| High Go-stop walking time, yes | 1.46 (1.12–1.91) | <0.01 | ||||
Discussion
Changes in the unique sequential gait pattern of humans reveal key information about the status and progression of many underlying health problems, from neurological and musculoskeletal conditions to age-related ambulatory dysfunction and trauma. Accurate and reliable identification of gait patterns and characteristics in clinical and community settings, and their monitoring and assessment over time, will enable effective tailored interventions, predictive outcome assessments, and improved health promotion practices. In this study, two complex walking tests were conducted in addition to the comfortable walking test based on the hypothesis that walking assessments with additional complex conditions would provide a more sensitive test of future LTCI certification occurrence to detect abnormalities at an earlier stage among older people, which could then lead to successful intervention. In older adults, some complex walking tasks have been used to challenge the overlearned motor task of walking in the clinic or laboratory (38). These tasks tap into attentional networks (39) and provide a stress test of the central motor control system, which may reveal deficits earlier than would be observed at comfortable walking speeds. Previous studies have used cognitive dual-task walking as a complex task, but it may be difficult to clinically standardize owing to differences in the nature of secondary cognitive tasks (40) and in the consideration of task prioritization (41). Therefore, physically demanding tasks and responses to verbal instructions were used in this study to assess walking performance in a complex task.
Contrary to expectations, the highest HR, as a predictor of future LTCI certification occurrence, was observed in the comfortable walking test. Disability risk is related to age, poor health, cognitive impairment, low activity, and poor physical performance (42, 43). Walking speed is a simple performance measure that predicts disability risk (7, 8, 9, 10, 11, 12, 13, 14, 44). A complex interplay of body function, proprioception (45), muscle performance (46), proactive and reactive postural control (47), aerobic capacity (48), vision (49), sensory and perceptual function (50), and psychological, social, or environmental factors, cognitive status (51), motivation and mental health (52, 53), and habitual activity level (54) may influence walking speed. Comfortable walking speed, as a result of complex factors, may contribute to the occurrence of LTCI certification. However, the complicated balance and Go-stop walking tests, which are more complex tasks than the highly automated comfortable walking test, did not provide any predictive ability for the occurrence of LTCI certification compared with the comfortable walking test. The complex tasks used may not have been challenging enough for the participants, which could explain these results. Importantly, despite the low-performance group in the comfortable walking task, there were 237 (5%) participants in the high-performance group for the complicated balance walking test and 455 (10%) in the highperformance group for the Go-stop walking test, suggesting that the complex walking task was not sufficiently demanding for these participants.
Although all walking tests were associated with the occurrence of LTCI certification, the values of diagnostic tests, such as sensitivity and specificity, were not clinically critical (Table 2). One reason for this finding may be that the occurrence of LTCI certification is influenced by multiple factors, limiting the ability of walking speed alone to predict disability (15, 44). Disability is a complex, multifactorial phenomenon that may be influenced by a variety of factors, including age, chronic health conditions, physical activity level, social support, and access to healthcare (55). Therefore, using walking speed alone as a predictor of the occurrence of LTCI certification may not be sufficient; other measures may need to be taken into consideration to accurately assess an individual's risk in this regard.
For the validity of the cutoff values, a walking speed cutoff point at a comfortable pace of 1.0 m/s is critical for predicting a future functional decline in community-dwelling older adults (8, 9, 11, 12, 13). The results of this study are consistent with those of previous studies. The validity of the cutoff values for the complicated balance and the Go-stop walking tests is difficult to compare with previous studies, as they showed similar predictive accuracy to the comfortable walking speed. Therefore, the cutoff points of these tests are generally considered to have reasonable validity as measures of walking ability and predictors of adverse outcomes in older adults.
A major strength of this study was the monthly follow-up, which allowed for the assessment of mandatory social LTCI among a large population-based sample of older Japanese adults. However, this study has some important limitations. First, the non-random community recruitment of participants could lead to an underestimation of the occurrence of LTCI certification, as the participants were relatively healthy older adults who could access health check-ups at home. Second, considering that LTCI certification is given if care is needed even in the absence of functional impairment, the occurrence of LTCI certification may have been overestimated. Furthermore, since LTCI certification is given only to those older adults who apply for it, there may have been older adults with mild functional impairment who did not apply for it, and the occurrence of LTCI certification may have been underestimated. Third, for some participants, informants such as family members could not be contacted to verify medical records and lifestyle information. This could have led to inaccurate or biased information. Fourth, the follow-up period was too short to identify how the occurrence of LTCI certification increases with advancing age. Finally, information on other behavioral factors such as diet, which have been previously shown to be associated with the occurrence of LTCI certification, was unavailable.
Conclusions and Implications
The findings of this study suggest that slow comfortable walking speed has a considerably greater impact than complex walking task on the occurrence of LTCI certification among community-dwelling older adults. Contrary to the hypothesis, the complex walking task did not improve predictive performance regarding the occurrence of LTCI certification. Comfortable walking speed showed the highest HR for the occurrence of LTCI certification, indicating that it was the strongest predictor of LTCI certification among the walking tests. Comfortable walking speed tests, which can be easily measured to predict the development of future LTCI certification, are effective tools in community health promotion and primary care. We believe that confirming the significance of the comfortable walking speed measure is clinically meaningful to predict the occurrence of LTCI certification among older adults. However, the predictive accuracy of these walking tests was insufficient to predict the development of future LTCI certification occurrence as a stand-alone test. Future research is needed to investigate walking tasks, beyond the comfortable walking speed test, that have predictive ability.
Acknowledgement
We would like to thank the Tokai City offices for assistance with participant recruitment. We are also grateful to Dr. Park Hyuntae, Dr. Hyuma Makizako, Dr. Daisuke Yoshida, Dr. Ryo Hotta, Mr. Yuya Anan, Dr. SungChul Lee, Dr. Kazuhiro Harada, Dr. Kazuki Uemura, Dr. Hideaki Ishii, Dr. Ippei Chiba, Mr. Yohei Shinkai, and Dr. Osamu Katayama for their contributions to data collection in this study.
Author Contributions
HS planned the study concept and design, performed analysis and interpretation of data, wrote the first draft of the manuscript, and coordinated the review and editing process leading to the final manuscript. TD participated in the design of the study and wrote the paper. TD, KT, KM, and KH acquired the data and contributed to the editorial process and review of the manuscript. HA supervised the study, suggested several ideas that have been pursued in this research, and participated in the planning, editorial, and review processes that led to the final manuscript.
Funding Sources
This work received financial support of the Research Funding for Longevity Sciences (22, 23, 24, 25, 26, 27, 28, 29, 30) from the National Center for Geriatrics and Gerontology, Research and Development Grants (15dk0207019h0001, 18dk0110021h0003, 18le0110004h0002) of the Japan Agency for Medical Research and Development, Japan. No support was received from the industry. The funding source played no role in the study design or procedure; collection, management, analysis, or interpretation of the data; or preparation, review, or approval of the manuscript.
Statement of Ethics
Written informed consent was obtained from all participants before their inclusion in the study, and the ethics committee of the National Center for Geriatrics and Gerontology approved the study protocol (registration number: 1067-3).
Conflict of Interest Statement
The authors have no conflicts of interest to declare.
Electronic Supplementary Material
Supplementary material is available in the online version of this article at https://doi.org/10.1007/s12603-023-1978-x.
Supplementary material, approximately 86.7 KB.
References
- 1.Guzman-Castillo M, Ahmadi-Abhari S, Bandosz P, Capewell S, Steptoe A, Singh-Manoux A, Kivimaki M, Shipley MJ, Brunner EJ, O'Flaherry M. Forecasted trends in disability and life expectancy in England and Wales up to 2025: a modelling study. Lancet Public Health. 2017;2(7):e307–ee13. doi: 10.1016/S2468-2667(17)30091-9. 28736759 5500313 10.1016/S2468-2667(17)30091-9 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Tsutsui T, Muramatsu N. Care-needs certification in the long-term care insurance system of Japan. J Am Geriatr Soc. 2005;53(3):522–527. doi: 10.1111/j.1532-5415.2005.53175.x. 15743300 10.1111/j.1532-5415.2005.53175.x [DOI] [PubMed] [Google Scholar]
- 3.Shimada H, Nitta J, Sasaki H, Watanabe T, Sakamoto T, Komoto S, Arai H. Japan's long-term care issues: construction and adoption of the LIFE database for establishing evidence-based care practice. J Am Med Dir Assoc. 2022;23(8):1433–1434. doi: 10.1016/j.jamda.2022.05.010. 35659886 10.1016/j.jamda.2022.05.010 [DOI] [PubMed] [Google Scholar]
- 4.Guralnik JM, Ferrucci L, Simonsick EM, Salive ME, Wallace RB. Lower-extremity function in persons over the age of 70 years as a predictor of subsequent disability. N Engl J Med. 1995;332(9):556–561. doi: 10.1056/NEJM199503023320902. 7838189 9828188 10.1056/NEJM199503023320902 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Shimada H, Suzukawa M, Tiedemann A, Kobayashi K, Yoshida H, Suzuki T. Which neuromuscular or cognitive test is the optimal screening tool to predict falls in frail community-dwelling older people? Gerontology. 2009;55(5):532–538. doi: 10.1159/000236364. 19776609 10.1159/000236364 [DOI] [PubMed] [Google Scholar]
- 6.Tiedemann A, Shimada H, Sherrington C, Murray S, Lord S. The comparative ability of eight functional mobility tests for predicting falls in community-dwelling older people. Age Ageing. 2008;37(4):430–435. doi: 10.1093/ageing/afn100. 18487264 10.1093/ageing/afn100 [DOI] [PubMed] [Google Scholar]
- 7.Guralnik JM, Ferrucci L, Pieper CF, Leveille SG, Markides KS, Ostir GV, Studenski S, Berkman LF, Wallace RB. 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(4):M221–M231. doi: 10.1093/gerona/55.4.m221. 10811152 10.1093/gerona/55.4.M221 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Cesari M, Kritchevsky S B, Penninx B W, Nicklas B J, Simonsick E M, Newman A B, Tylavsky F A, Brach J S, Satterfield S, Bauer D C, Visser M, Rubin S M, Harris T B, Pahor M. Prognostic value of usual gait speed in well-functioning older people—results from the Health, Aging and Body Composition Study. J Am Geriatr Soc. 2005;53(10):1675–1680. doi: 10.1111/j.1532-5415.2005.53501.x. 16181165 10.1111/j.1532-5415.2005.53501.x [DOI] [PubMed] [Google Scholar]
- 9.Cesari M, Kritchevsky S B, Newman A B, Simonsick E M, Harris T B, Penninx B W, Brach J S, Tylavsky F A, Satterfield S, Bauer D C, Rubin S M, Visser M, Pahor M, Health, Aging and Body Composition Study 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(2):251–259. doi: 10.1111/j.1532-5415.2008.02126.x. 19207142 2695653 10.1111/j.1532-5415.2008.02126.x [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Onder G, Penninx BW, Ferrucci L, Fried LP, Guralnik JM, Pahor M. 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(1):74–79. doi: 10.1093/gerona/60.1.74. 15741286 10.1093/gerona/60.1.74 [DOI] [PubMed] [Google Scholar]
- 11.Studenski S, Perera S, Wallace D, Chandler JM, Duncan PW, Rooney E, Fox M, Guralnik JM. Physical performance measures in the clinical setting. J Am Geriatr Soc. 2003;51(3):314–322. doi: 10.1046/j.1532-5415.2003.51104.x. 12588574 10.1046/j.1532-5415.2003.51104.x [DOI] [PubMed] [Google Scholar]
- 12.Simonsick EM, Newman AB, Visser M, Goodpaster B, Kritchevsky SB, Rubin S, Nevitt MC, Harris TB. Mobility limitation in self-described well-functioning older adults: importance of endurance walk testing. J Gerontol A Biol Sci Med Sci. 2008;63(8):841–847. doi: 10.1093/gerona/63.8.841. 18772472 5722013 10.1093/gerona/63.8.841 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Shinkai S, Watanabe S, Kumagai S, Fujiwara Y, Amano H, Yoshida H, Ishizaki T, Yukawa H, Suzuki T, Shibata H. Walking speed as a good predictor for the onset of functional dependence in a Japanese rural community population. Age Ageing. 2000;29(5):441–446. doi: 10.1093/ageing/29.5.441. 11108417 10.1093/ageing/29.5.441 [DOI] [PubMed] [Google Scholar]
- 14.Cummings SR, Studenski S, Ferrucci L. A diagnosis of dismobility—giving mobility clinical visibility: a Mobility Working Group recommendation. JAMA. 2014;311(20):2061–2062. doi: 10.1001/jama.2014.3033. 24763978 5012417 10.1001/jama.2014.3033 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Studenski S, Perera S, Patel K, Rosano C, Faulkner K, Inzitari M, Brach J, Chandler J, Cawthon P, Connor E B, Nevitt M, Visser M, Kritchevsky S, Badinelli S, Harris T, Newman A B, Cauley J, Ferrucci L, Guralnik J. Gait speed and survival in older adults. JAMA. 2011;305(1):50–58. doi: 10.1001/jama.2010.1923. 21205966 3080184 10.1001/jama.2010.1923 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Montero-Odasso M, Schapira M, Soriano ER, Varela M, Kaplan R, Camera LA, Mayorga LM. 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(10):1304–1309. doi: 10.1093/gerona/60.10.1304. 16282564 10.1093/gerona/60.10.1304 [DOI] [PubMed] [Google Scholar]
- 17.Dargent-Molina P, Favier F, Grandjean H, Baudoin C, Schott AM, Hausherr E, Meunier PJ, Breart G. Fall-related factors and risk of hip fracture: the EPIDOS prospective study. Lancet. 1996;348(9021):145–149. doi: 10.1016/s0140-6736(96)01440-7. 8684153 10.1016/S0140-6736(96)01440-7 [DOI] [PubMed] [Google Scholar]
- 18.Abellan van Kan G, Rolland Y, Gillette-Guyonnet S, Gardette V, Annweiler C, Beauchet O, Andrieu S, Vellas B. Gait speed, body composition, and dementia. The EPIDOS-Toulouse cohort. Journals of Gerontology Series A: Biomedical Sciences and Medical Sciences. 2012;67(4):425–432. doi: 10.1093/gerona/glr177. 10.1093/gerona/glr177 [DOI] [PubMed] [Google Scholar]
- 19.Wang L, Larson EB, Bowen JD, van Belle G. Performance-based physical function and future dementia in older people. Arch Intern Med. 2006;166(10):1115–1120. doi: 10.1001/archinte.166.10.1115. 16717174 10.1001/archinte.166.10.1115 [DOI] [PubMed] [Google Scholar]
- 20.Shimada H, Suzuki T, Suzukawa M, Makizako H, Doi T, Yoshida D, Tsutsumimoto K, Anan Y, Uemura K, Ito T, Lee S, Park H. Performance-based assessments and demand for personal care in older Japanese people: a cross-sectional study. BMJ Open. 2013;3(4) doi: 10.1136/bmjopen-2012-002424. 23578683 3641481 10.1136/bmjopen-2012-002424 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Goldberg A, Schepens S. Measurement error and minimum detectable change in 4-meter gait speed in older adults. Aging Clin Exp Res. 2011;23(5–6):406–412. doi: 10.1007/BF03325236. 22526072 10.1007/BF03325236 [DOI] [PubMed] [Google Scholar]
- 22.Muir-Hunter SW, Wittwer JE. Dual-task testing to predict falls in community-dwelling older adults: a systematic review. Physiotherapy. 2016;102(1):29–40. doi: 10.1016/j.physio.2015.04.011. 26390824 10.1016/j.physio.2015.04.011 [DOI] [PubMed] [Google Scholar]
- 23.Montero-Odasso M M, Sarquis-Adamson Y, Speechley M, Borrie M J, Hachinski V C, Wells J, Riccio P M, Schapira M, Sejdic E, Camicioli R M, Bartha R, McIlroy W E, Muir-Hunter S. Association of dual-task gait with incident dementia in mild cognitive impairment: results from the Gait and Brain Study. JAMA Neurol. 2017;74(7):857–865. doi: 10.1001/jamaneurol.2017.0643. 28505243 5710533 10.1001/jamaneurol.2017.0643 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Rosso AL, Verghese J, Metti AL, Boudreau RM, Aizenstein HJ, Kritchevsky S, Harris T, Yaffe K, Satterfield S, Studenski S, Rosano C. Slowing gait and risk for cognitive impairment: the hippocampus as a shared neural substrate. Neurology. 2017;89(4):336–342. doi: 10.1212/WNL.0000000000004153. 28659421 5574674 10.1212/WNL.0000000000004153 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.Holtzer R, Epstein N, Mahoney JR, Izzetoglu M, Blumen HM. Neuroimaging of mobility in aging: a targeted review. J Gerontol A Biol Sci Med Sci. 2014;69(11):1375–1388. doi: 10.1093/gerona/glu052. 24739495 4204614 10.1093/gerona/glu052 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Rosso A L, Studenski S A, Chen W G, Aizenstein H J, Alexander N B, Bennett D A, Black S E, Camicioli R, Carlson M C, Ferrucci L, Guralnik J M, Hausdorff J M, Kaye J, Launer L J, Lipsitz L A, Verghese J, Rosano C. Aging, the central nervous system, and mobility. J Gerontol A Biol Sci Med Sci. 2013;68(11):1379–1386. doi: 10.1093/gerona/glt089. 23843270 3805295 10.1093/gerona/glt089 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Shimada H, Makizako H, Lee S, Doi T, Lee S, Tsutsumimoto K, Harada K, Hotta R, Bae S, Nakakubo S, Harada K, Suzuki T. Impact of cognitive frailty on daily activities in older persons. J Nutr Health Aging. 2016;20(7):729–735. doi: 10.1007/s12603-016-0685-2. 27499306 10.1007/s12603-016-0685-2 [DOI] [PubMed] [Google Scholar]
- 28.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(3):189–198. doi: 10.1016/0022-3956(75)90026-6. 1202204 10.1016/0022-3956(75)90026-6 [DOI] [PubMed] [Google Scholar]
- 29.National Institute for Health and Care Excellence: Alzheimer's disease - donepezil, galantamine, rivastigmine and memantine (TA217). In: NICE technology appraisal guidance 2011.
- 30.Shimada H, Tsutsumimoto K, Doi T, Lee S, Bae S, Nakakubo S, Makino K, Arai H. Effect of sarcopenia status on disability incidence among Japanese older adults. J Am Med Dir Assoc. 2020 doi: 10.1016/j.jamda.2020.10.036. [DOI] [PubMed] [Google Scholar]
- 31.Stuck AE, Walthert JM, Nikolaus T, Bula CJ, Hohmann C, Beck JC. Risk factors for functional status decline in community-living elderly people: a systematic literature review. Soc Sci Med. 1999;48(4):445–469. doi: 10.1016/s0277-9536(98)00370-0. 10075171 10.1016/S0277-9536(98)00370-0 [DOI] [PubMed] [Google Scholar]
- 32.Ishizaki T, Watanabe S, Suzuki T, Shibata H, Haga H. Predictors for functional decline among nondisabled older Japanese living in a community during a 3-year follow-up. J Am Geriatr Soc. 2000;48(11):1424–1429. doi: 10.1111/j.1532-5415.2000.tb02632.x. 11083318 10.1111/j.1532-5415.2000.tb02632.x [DOI] [PubMed] [Google Scholar]
- 33.Yesavage JA. Geriatric Depression Scale. Psychopharmacol Bull. 1988;24(4):709–711. 3249773 [PubMed] [Google Scholar]
- 34.Shimada H, Makizako H, Doi T, Tsutsumimoto K, Suzuki T. Incidence of disability in frail older persons with or without slow walking speed. J Am Med Dir Assoc. 2015 doi: 10.1016/j.jamda.2015.03.019. [DOI] [PubMed] [Google Scholar]
- 35.Fukutomi E, Okumiya K, Wada T, Sakamoto R, Ishimoto Y, Kimura Y, Chen WL, Imai H, Kasahara Y, Fujisawa M, Otsuka K, Matsubayashi K. 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. 2014;15:864–871. doi: 10.1111/ggi.12360. 25316532 10.1111/ggi.12360 [DOI] [PubMed] [Google Scholar]
- 36.Perkins NJ, Schisterman EF. The inconsistency of “optimal” cutpoints obtained using two criteria based on the receiver operating characteristic curve. Am J Epidemiol. 2006;163(7):670–675. doi: 10.1093/aje/kwj063. 16410346 10.1093/aje/kwj063 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 37.Akinwande M, Dikko H, Samson A. Variance inflation factor: As a condition for the inclusion of suppressor variable(s) in regression analysis. Open J Stat. 2015;5:754–767. 10.4236/ojs.2015.57075 [Google Scholar]
- 38.Rosano C, Snitz BE. Predicting dementia from decline in gait speed: are we there yet? J Am Geriatr Soc. 2018;66(9):1659–1660. doi: 10.1111/jgs.15368. 29608781 6156941 10.1111/jgs.15368 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 39.Seidler RD, Bernard JA, Burutolu TB, Fling BW, Gordon MT, Gwin JT, Kwak Y, Lipps DB. Motor control and aging: links to age-related brain structural, functional, and biochemical effects. Neurosci Biobehav Rev. 2010;34(5):721–733. doi: 10.1016/j.neubiorev.2009.10.005. 19850077 10.1016/j.neubiorev.2009.10.005 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 40.Menant JC, Sturnieks DL, Brodie MA, Smith ST, Lord SR. Visuospatial tasks affect locomotor control more than nonspatial tasks in older people. PLoS One. 2014;9(10) doi: 10.1371/journal.pone.0109802. 25285913 4186860 10.1371/journal.pone.0109802 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 41.Verghese J, Kuslansky G, Holtzer R, Katz M, Xue X, Buschke H, Pahor M. Walking while talking: effect of task prioritization in the elderly. Arch Phys Med Rehabil. 2007;88(1):50–53. doi: 10.1016/j.apmr.2006.10.007. 17207675 1894901 10.1016/j.apmr.2006.10.007 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 42.Guralnik JM, Fried LP, Salive ME. Disability as a public health outcome in the aging population. Annu Rev Public Health. 1996;17:25–46. doi: 10.1146/annurev.pu.17.050196.000325. 8724214 10.1146/annurev.pu.17.050196.000325 [DOI] [PubMed] [Google Scholar]
- 43.Gill TM. Assessment of function and disability in longitudinal studies. J Am Geriatr Soc. 2010;58(Suppl2):S308–S312. doi: 10.1111/j.1532-5415.2010.02914.x. 21029059 2992435 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 44.Guralnik JM, Ferrucci L, Pieper CF, Leveille SG, Markides KS, Ostir GV, Studenski S, Berkman LF, Wallace RB. 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(4):M221–M231. doi: 10.1093/gerona/55.4.m221. 10811152 10.1093/gerona/55.4.M221 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 45.Park YH, Kim YM, Lee BH. An ankle proprioceptive control program improves balance, gait ability of chronic stroke patients. J Phys Ther Sci. 2013;25(10):1321–1324. doi: 10.1589/jpts.25.1321. 24259785 3820201 10.1589/jpts.25.1321 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 46.Bohannon RW. Comfortable and maximum walking speed of adults aged 20–79 years: reference values and determinants. Age Ageing. 1997;26(1):15–19. doi: 10.1093/ageing/26.1.15. 9143432 10.1093/ageing/26.1.15 [DOI] [PubMed] [Google Scholar]
- 47.Woollacott MH, Tang PF. Balance control during walking in the older adult: research and its implications. Phys Ther. 1997;77(6):646–660. doi: 10.1093/ptj/77.6.646. 9184689 10.1093/ptj/77.6.646 [DOI] [PubMed] [Google Scholar]
- 48.Fiser WM, Hays NP, Rogers SC, Kajkenova O, Williams AE, Evans CM, Evans WJ. Energetics of walking in elderly people: factors related to gait speed. J Gerontol A Biol Sci Med Sci. 2010;65(12):1332–1337. doi: 10.1093/gerona/glq137. 20679072 10.1093/gerona/glq137 [DOI] [PubMed] [Google Scholar]
- 49.Aartolahti E, Hakkinen A, Lonnroos E, Kautiainen H, Sulkava R, Hartikainen S. Relationship between functional vision and balance and mobility performance in community-dwelling older adults. Aging Clin Exp Res. 2013;25(5):545–552. doi: 10.1007/s40520-013-0120-z. 24002802 10.1007/s40520-013-0120-z [DOI] [PubMed] [Google Scholar]
- 50.te VA, Savelsbergh GJ, Barela JA, van der Kamp J. Safety in road crossing of children with cerebral palsy. Acta Paediatr. 2003;92(10):1197–1204. 14632338 10.1111/j.1651-2227.2003.tb02484.x [PubMed] [Google Scholar]
- 51.Persad CC, Jones JL, Ashton-Miller JA, Alexander NB, Giordani B. Executive function and gait in older adults with cognitive impairment. J Gerontol A Biol Sci Med Sci. 2008;63(12):1350–1355. doi: 10.1093/gerona/63.12.1350. 19126848 10.1093/gerona/63.12.1350 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 52.Lemke MR, Wendorff T, Mieth B, Buhl K, Linnemann M. Spatiotemporal gait patterns during over ground locomotion in major depression compared with healthy controls. J Psychiatr Res. 2000;34(4–5):277–283. doi: 10.1016/s0022-3956(00)00017-0. 11104839 10.1016/S0022-3956(00)00017-0 [DOI] [PubMed] [Google Scholar]
- 53.Fredman L, Hawkes WG, Black S, Bertrand RM, Magaziner J. Elderly patients with hip fracture with positive affect have better functional recovery over 2 years. J Am Geriatr Soc. 2006;54(7):1074–1081. doi: 10.1111/j.1532-5415.2006.00786.x. 16866678 10.1111/j.1532-5415.2006.00786.x [DOI] [PubMed] [Google Scholar]
- 54.Langlois JA, Keyl PM, Guralnik JM, Foley DJ, Marottoli RA, Wallace RB. Characteristics of older pedestrians who have difficulty crossing the street. Am J Public Health. 1997;87(3):393–397. doi: 10.2105/ajph.87.3.393. 9096539 1381010 10.2105/AJPH.87.3.393 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 55.Cesari M, Kritchevsky S B, Newman A B, Simonsick E M, Harris T B, Penninx B W, Brach J S, Tylavsky F A, Satterfield S, Bauer D C, Rubin S M, Visser M, Pahor M, Health, Aging and Body Composition Study 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(2):251–259. doi: 10.1111/j.1532-5415.2008.02126.x. 19207142 2695653 10.1111/j.1532-5415.2008.02126.x [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Supplementary material, approximately 86.7 KB.