Abstract
Aim: Life-space mobility (LSM) limitations are a significant concern associated with facility admission, mortality, and quality of life in older adults. Home-visit rehabilitation (HR) users are particularly vulnerable to LSM restrictions, making its maintenance and improvement a priority in this population. This study aimed to assess LSM using the life-space assessment (LSA) and expand existing conceptual models for independent community-dwelling older adults in Japan. Additionally, we analyzed factors influencing LSM in HR users.
Methods: This multicenter cross-sectional study included 105 HR users, comprising 56 men (53.3%) and 49 women (46.7%); mean age 78.5 ± 7.7 years, from urban and rural areas between August 2020 and October 2022. Motor function (grip strength, 30-second chair stand test, CS-30), psychological factors (Self-Efficacy Scale on Going out among community-dwelling Elderly, SEGE), activities of daily living (ADL) ability (functional independence measure, FIM), instrumental ADL (IADL) ability (Frenchay Activities Index, FAI), and environmental factors (home and communication environment, living alone, and day service use frequency), which have been reported in previous studies, were collected for parameters related to LSA. Path analysis examined associations between these factors and LSA.
Results: LSA revealed direct effects on FAI (β = 0.344), FIM-motor score (β = 0.261), living alone (β = -0.196), and day service use frequency (β = 0.184). Indirect effects were observed in CS-30 (β = 0.220), SEGE (β = 0.085), and sex (β = -0.087). The model demonstrated good fit (goodness-of-fit index, GFI, 0.956; adjusted GFI, 0.910; comparative fit index, 1.000; root mean square error of approximation, 0.000).
Conclusion: ADL, IADL, and environmental factors directly affect LSA in home-care older adults using HR, while motor function, psychological factors, and sex have indirect effects. These findings highlight the importance of considering these relationships when designing rehabilitation strategies to support LSM. Future research should examine broader populations, additional variables, and longitudinal data to refine interventions for HR users.
Keywords: environment, home-care services, home-visit rehabilitation, mobility limitation, rehabilitation
Introduction
Life-space mobility (LSM) is defined as the extent of movement within a given time period [1]. Limitations in LSM are associated not only with activities of daily living (ADL) [2,3] and instrumental ADL (IADL) [2,3] disability but also with hospitalization [4], mortality [5], and quality of life decline [6]. Therefore, assessing and improving LSM are crucial for older adults to maintain their independence and live in their familiar environment at home.
The life-space assessment (LSA) is a widely used tool for evaluating LSM [3]. It is considered a valuable indicator in independent community-dwelling and home-care older adults [7]. Factors associated with LSA in community-dwelling older adults include demographic characteristics, such as age [8,9] and sex [8,10]; physical function, such as grip strength, GS [11,12], lower limb function [8], lower limb muscle strength [9], walking status [13], and time up and go test (TUG) [14]; ADL ability [9,14,15]; IADL ability [14,16-18], such as community living and recreational/leisure activities [19] and social participation status [20,21]; and environmental factors, such as number of barriers [22], driving [23], having a roommate [14], and having a car driver [20,24]. Considering these diverse influences, a comprehensive approach integrating multiple factors is necessary to assess LSA effectively.
In Japan, studies have explored structural relationships among parameters affecting LSA [25,26]. One study [25] found that health status, physical function, and environmental factors directly influence LSA in independent community-dwelling older adults, while hobby activities exert an indirect effect through physical function. Another study [26] reported that demographic characteristics, psychological factors, and TUG directly influence LSA, whereas lower limb function has an indirect effect. These studies [25,26] provide a conceptual framework for understanding the pathways through which various factors interact to influence LSA, highlighting the importance of identifying both direct and indirect determinants.
However, to our knowledge, no studies have developed a model to clarify the relationships between LSA parameters in home-visit rehabilitation (HR) users. Previous research [27,28] suggests that HR users exhibit lower LSA and restricted LSM, making them more likely to face challenges in continuing to live at home. It is, therefore, critical to focus on this population.
Previous studies [25,26] have examined independent community-dwelling older adults without assessing ADL or IADL abilities. Given the range of relevant factors [8-24] influencing LSA, a more comprehensive analysis is needed, incorporating ADL abilities, IADL abilities, and environmental factors. Additionally, as these studies were conducted at a single center [25,26], their generalizability is limited. Multicenter studies including participants from diverse geographical areas and residential settings are needed for broader findings.
This study aimed to develop a model for LSA in HR users and examine how parameters previously associated with LSA in independent community-dwelling and home-care older adults affect this group. Therefore, we used an LSA model of independent community-dwelling adults reported in Japan as a reference [25,26]. Since the previous LSA models [25,26] did not consider ADL, IADL, and environmental factors, we created a more comprehensive model of LSA for HR users by adding these factors and examined the differences between this model and the previous LSA models. By clarifying these relationships and their effects, this study aims to provide insights into improving LSA among HR users. The results of this study may help plan programs to improve LSA in HR and identify areas of focus.
Materials and methods
Study design
This multicenter cross-sectional study examined HR users at two home nursing stations in urban and rural Japan. Previous studies [25,26] investigated LSA models based on health status, physical function, environmental factors, hobbies, and demographic information. In addition to these factors [25,26], this study collected data on ADL ability, IADL ability, and psychological factors to explore their association with LSA in HR users. A previous study [25] assessed health status based on the presence of greater than or equal to three medications, regular hospital visits, and hospitalization in the last year. The participants in this study already had medical conditions requiring regular hospital visits. Older adults receiving home care often have multiple comorbidities and use several medications [29]. Therefore, the variables used in the previous study were expected [25] to show less variability among this study’s participants. Accordingly, this study prioritized factors that HR could support, excluding health status, to validate the LSA model. The study parameters were measured by physiotherapists or occupational therapists in charge of the participants, who were trained in the measurement methods before the study.
Participants
HR users registered at the two home nursing stations between August 2020 and October 2022 were eligible. All HR users who did not meet the exclusion criteria were invited to participate. Exclusion criteria included hospitalization, age <65 years, discontinued HR use, nearing the end of HR, or residence in a nursing home. Also excluded were those unable to provide consent, those deemed ineligible by the therapist, and those with missing values. Those deemed ineligible by the therapist were those unable to follow verbal instructions or communicate due to conditions such as dementia, ventilator dependency, or stroke, those with deteriorating conditions, and those unable to participate due to difficulty in establishing rapport and mental instability.
The study was approved by the International University of Health and Welfare Graduate Ethics Review Committee (approval number 19-Io-237). All participants or their primary caregivers were informed of the study’s purpose and content, and written informed consent was obtained. The study adhered to the principles of the Declaration of Helsinki.
All participants received physician-directed HR instructions, including HR times and programs, exercise therapy, and home living support. They were also instructed on ADL and IADL exercises, environmental adjustments (e.g., selecting home support equipment), and caregiving for housemates.
Evaluation of LSA
The primary outcome was LSA [3], assessed based on living space, mobility frequency, and independence over the previous month. The LSA score was calculated by multiplying the five score levels for each life space (1 = home, 2 = out of home, 3 = neighborhood, 4 = town, 5 = out of town) by mobility frequency (1 = less than one day per week, 2 = one to three days per week, 3 = four to six days per week, 4 = daily) and independence (1 = needs help from others, 1.5 = uses assistive devices, 2 = independent). Scores ranged from 0 to 120.
Evaluation of other measurements
Relevant parameters for physical function and environmental factors were collected based on the LSA model [25,26] for independent community-dwelling older adults. Additionally, demographic information, including age, sex, primary illness, HR duration (months), HR intervention frequency (times/week), and number of HR staff, was extracted from medical records.
Beyond the existing conceptual model, additional parameters related to motor function, psychological factors, ADL ability, IADL ability, and environmental factors influencing LSA were collected with reference to previous studies.
Motor function was assessed using GS and the 30-second chair stand test (CS-30). The conceptual LSA model for healthy community-dwelling older adults includes assessments of GS, the TUG test, and the one-leg stand test [25]. While GS can be easily measured in home settings, many participants requiring ADL assistance might be unable to perform TUG or one-leg stand tests, particularly due to high variability in the latter [27]. Therefore, CS-30, which can be evaluated as lower limb function, was used instead. GS was measured twice in a seated resting position using a digital GS meter (GRIP-D-TKK5401; Takei, Niigata, Japan), with the maximum value recorded. CS-30 [30] measured the number of chair stands performed in 30 seconds, recorded twice, with the highest value used.
Psychological factors, including self-efficacy, were assessed using the Self-Efficacy Scale on Going out among community-dwelling Elderly (SEGE) [31]. This scale comprises six items (e.g., I can go out without any particular reason), rated on a 4-point scale (1 = not confident to 4 = confident), with scores ranging from 6 to 24.
ADL ability was assessed using the functional independence measure (FIM) [32], which includes FIM motor (FIM-M) and FIM cognitive (FIM-C) items. Each item is rated on a 7-point scale based on ADL independence. Total scores, FIM-M, and FIM-C were used, with total scores ranging from 18 to 126.
IADL ability was assessed using the Japanese version of the Frenchay Activity Index (FAI) [33], which consists of 15 questions evaluating activity frequency over the past three months on a 4-point scale. Scores ranged from 0 to 45.
Environmental factors were assessed using the Home and Community Environment (HACE) scale [34], which comprises 35 items across six domains. In the domains of home mobility, community mobility, and attitudes, higher scores indicate greater barriers. In the domains of transportation factors, basic mobility devices, and communication devices, higher scores reflect greater facilitating factors. A total score was calculated across all six domains. Additionally, human support and social resource use were assessed by determining whether participants lived alone and recording the frequency of day service use (number of times/week).
Statistical analyses
Collected parameters included age and sex (male: 1, female: 0). Categorical variables were compared using the chi-square test, while quantitative variables were analyzed using the Mann-Whitney U test. Spearman's rank correlation coefficient was used to assess correlations between LSA and collected parameters, as well as multicollinearity among parameters.
A hypothetical model (Figure 1) was then developed to examine the relationships between factors influencing each life space, and a path analysis was conducted to determine interrelationships among these factors. Model fit was evaluated using the goodness-of-fit index (GFI), adjusted GFI (AGFI), comparative fit index (CFI), and root mean square error of approximation (RMSEA). Based on previous studies [35], values of GFI >0.90, AGFI >0.90, CFI >0.90, and RMSEA <0.05 indicated good model fit.
Figure 1. Participant selection flowchart.
The direct, indirect, and total effects of each factor on LSA were further analyzed. Paths and parameters with low standardized path coefficients that lacked statistically significant associations were excluded to derive the final model. Statistical analyses were performed using IBM Statistical Package for the Social Sciences (SPSS) Statistics, version 29.0 (IBM Corp., Armonk, NY) and IBM SPSS Amos, version 29.0 Graphics (IBM Corp., Armonk, NY). Statistical significance was set at 5%.
Results
Participant selection
Of the 457 home-care patients receiving HR at urban and rural home nursing stations, 56 met the exclusion criteria, and 71 did not consent. And of those deemed ineligible by the therapists, 65 were unable to participate due to conditions such as dementia, ventilator dependence, or stroke that prevented them from following verbal instructions or communicating, nine had deteriorating conditions, and 65 had difficulty establishing rapport and faced mental instability, resulting in 191 participants. After excluding another 86 participants with missing values, 105 were included in the final analysis (Figure 1, Table 1).
Table 1. Participant characteristics.
Measurement variables are expressed as the mean ± standard deviation and the actual value (percentage)
HR: home-visit rehabilitation
| Home-visit rehabilitation users | All participants (n = 105) | |
| Age (years) | 78.5 ± 7.7 | |
| 65-74 years | 69 (65.7) | |
| ≥75 years | 36 (34.3) | |
| Sex | ||
| Male | 56 (53.3) | |
| Female | 49 (46.7) | |
| Facility | ||
| A (urban) | 67 (63.8) | |
| B (rural) | 38 (36.2) | |
| Primary disease | ||
| Cerebrovascular disease | 26 (24.8) | |
| Musculoskeletal disease | 23 (21.9) | |
| Neuromuscular disease | 31 (29.5) | |
| Cardiovascular disease | 4 (3.8) | |
| Respiratory disease | 5 (4.8) | |
| Cancer | 6 (5.7) | |
| Cognitive/psychiatric disease | 1 (1.0) | |
| Others | 9 (8.6) | |
| HR information | ||
| HR duration (months) | 33.6 ± 37.6 | |
| HR intervention frequency (times/week) | 1.4 ± 0.6 | |
| Number of HR staff | 1.4 ± 0.6 | |
HR user demographics and characteristics (105 participants)
Table 2 presents univariate analyses of collected parameters by sex and age. GS was significantly higher in men and in the 65-74 age group, while FAI was significantly higher in women. No other parameters showed significant differences.
Table 2. Comparative analysis results.
This table shows the results of a comparison test by age and sex (male: 1, female: 0)
Significance level: *p < 0.05; **p < 0.001
aMeasurement variables are expressed as medians (interquartile range) and compared using the Mann-Whitney U test
bCategorical variables are expressed as the actual values (percentages) and compared using the chi-square test
CS-30: 30-second chair stand test; SEGE: Self-Efficacy scale on Going out among community-dwelling Elderly; ADL: activities of daily living; FIM: functional independence measure; IADL: instrumental activities of daily living; FAI: Frenchay Activity Index; HACE: home and community environment
| Home-visit rehabilitation users | All participants (n = 105) | 65-74 years (n = 69) | ≥75 years (n = 36) | U-value | Chi-square value | p value | Female (n = 49) | Male (n = 56) | U-value | Chi-square value | p value | |
| Primary outcome | ||||||||||||
| Life-space assessment scoresa (points) | 29 (3-100) | 29 (3-100) | 29 (6-51) | 1,227 | - | 0.919 | 30 (6-73.5) | 29 (3-100) | 1,157 | - | 0.167 | |
| Physical performance | ||||||||||||
| Grip strengtha (kg) | 18.5 (7.5-43.9) | 17.3 (7.5-43.8) | 21.9 (9-43.9) | 1,584.5 | - | 0.021* | 15.5 (7.5-27) | 23.3 (8.5-43.9) | 2,157 | - | <0.001** | |
| CS-30a (times) | 0 (0-21) | 3 (0-21) | 0 (0-16) | 1,123.5 | - | 0.385 | 4 (0-16) | 0 (0-21) | 1,248 | - | 0.387 | |
| SEGEa (points) | 8 (6-23) | 7 (6-23) | 8 (6-18) | 1,330 | - | 0.530 | 8 (6-21) | 7.5 (6-23) | 1,366 | - | 0.968 | |
| ADL ability | ||||||||||||
| FIM-motora (points) | 76 (28-90) | 76 (32-90) | 76 (28-90) | 1,230.5 | - | 0.938 | 77 (32-90) | 75.5 (28-90) | 1,205.5 | - | 0.285 | |
| FIM-cognitivea (points) | 34 (12-35) | 34 (21-35) | 35 (12-35) | 1,428 | - | 0.186 | 34 (23-35) | 34 (12-35) | 1,264.5 | - | 0.467 | |
| FIM-total scorea (points) | 110 (40-125) | 110 (65-124) | 111 (40-125) | 1,309 | - | 0.651 | 110 (65-124) | 108 (40-125) | 1,200 | - | 0.269 | |
| IADL ability | ||||||||||||
| FAI scorea (points) | 6 (0-35) | 6 (0-35) | 7.5 (0-30) | 1,348 | - | 0.472 | 10 (0-35) | 4 (0-25) | 903 | - | 0.002* | |
| Environment factor | ||||||||||||
| Living aloneb (yes) | 23 (21.9) | 16 (23.2) | 7 (19.4) | - | 0.194 | 0.660 | 13 (26.5) | 10 (17.9) | - | 1.149 | 0.284 | |
| HACE facilitatora (points) | 7 (0-15) | 8 (1-15) | 7 (1-15) | 1,280 | - | 0.796 | 8 (1-15) | 7 (1-14) | 1,335 | - | 0.811 | |
| HACE barriera (points) | 4 (0-14) | 4 (0-14) | 4 (0-8) | 1,204 | - | 0.796 | 3 (0-9) | 5 (0-14) | 1,647 | - | 0.075 | |
Spearman's rank correlation coefficient results
Table 3 displays correlations between LSA and collected parameters. LSA was significantly correlated with CS-30 (rs = 0.353, p < 0.001), SEGE (rs = 0.443, p < 0.001), FIM-M (rs = 0.440, p < 0.001), FIM-C (rs = 0.250, p < 0.001), FIM-total score (rs = 0.441, p < 0.001), FAI (rs = 0.508, p < 0.001), and day-care service use frequency (rs = 0.192, p < 0.05). No parameter had a correlation coefficient ≥0.8.
Table 3. Results of the correlation analyses between life-space assessment scores and other measurements.
Significance level: *p < 0.05; **p < 0.001
Spearman’s rank correlation coefficient was used for the correlation analysis
CS-30: 30-second chair stand test; SEGE: Self-Efficacy scale on Going out among community-dwelling Elderly; FIM: functional independence measure; FAI: Frenchay Activity Index; HACE: home and community environment
| Home-visit rehabilitation users: all participants (n = 105) | Spearman’s rank correlation coefficient | p value |
| Age | -0.065 | 0.513 |
| Sex | -0.135 | 0.168 |
| Grip strength (kg) | 0.089 | 0.366 |
| CS-30 (times) | 0.353 | <0.001** |
| SEGE (points) | 0.443 | <0.001** |
| FIM-motor (points) | 0.440 | <0.001** |
| FIM-cognitive (points) | 0.250 | <0.001** |
| FIM-total score (points) | 0.441 | <0.001** |
| FAI score (points) | 0.508 | <0.001** |
| HACE facilitators (points) | -0.112 | 0.257 |
| HACE barriers (points) | 0.148 | 0.133 |
| Home mobility scores (points) | 0.065 | 0.508 |
| Community mobility scores (points) | 0.154 | 0.117 |
| Transportation factors scores (points) | 0.145 | 0.140 |
| Attitude scores (points) | -0.069 | 0.487 |
| Basic mobility devices scores (points) | -0.145 | 0.139 |
| Communication devices scores (points) | 0.057 | 0.560 |
| Living alone | -0.011 | 0.091 |
| Day-care service use frequency (times/week) | 0.192 | 0.050* |
Structural equation modeling results
Examination of Hypothetical Models
Using a model of LSA in independent community-dwelling older people as reference [25,26], motor function (GS), environmental factors (living alone, HACE facilitator/barrier factors, and frequency of day service use), and psychological factors (SEGE) were assumed to influence LSA directly. In contrast, lower limb function (CS-30) was assumed to have an indirect effect. Additionally, ADL ability [9,14,15] (FIM-M and FIM-C) and IADL ability [14,19-21] (FAI), previously associated with LSA, were included as direct factors. The psychological factor (SEGE) self-efficacy is also an important factor [36] related to behavioral choices [37] and activities [9] and has been reported as one of the determinants in LSA. In particular, studies with wheelchair users have reported that self-efficacy is related to LSA via movement ability [38]. Based on these findings, psychological factors (SEGE) were hypothesized to influence LSA indirectly via ADL ability (FIM-M) and IADL ability (FAI).
Based on the above hypotheses, a model was developed and examined. The hypothetical model demonstrated poor fit (GFI = 0.860, AGFI = 0.772, CFI = 0.752, RMSEA = 0.097) (Figure 2).
Figure 2. Hypothetical model of the factors that affect life-space assessment in users of home-visit rehabilitation.
Solid lines indicate a p value of <0.05. The dotted lines indicate that the path coefficients were not significant
Model fit: goodness-of-fit index, 0.860; adjusted goodness-of-fit index, 0.772; comparative fit index, 0.752; root mean square error of approximation, 0.097
FIM-M: functional independence measure motor; FAI: Frenchay Activity Index; LSA: life-space assessment; CS-30: 30-second chair stand test; FIM-C: functional independence measure cognitive; SEGE: Self-Efficacy scale on Going out among community-dwelling Elderly; HACE: home and community environment
Consideration in the Final Model
The model was refined by removing nonsignificant paths. In the final model, LSA was directly affected by FAI (standardized β = 0.344), FIM-M (standardized β = 0.261), living alone (standardized β = -0.196), and day service use frequency (standardized β = 0.184). The CS-30 (standardized β = -0.196) was directly affected. CS-30 (standardized β = 0.220), SEGE (standardized β = 0.160), and sex (standardized β = -0.087) indirectly affected LSA. The final model demonstrated excellent fit (GFI = 0.956, AGFI = 0.910, CFI = 1.000, RMSEA = 0.000) (Figure 3, Table 4).
Table 4. Correlation of each variable with life-space assessment scores in the final model. The results of the path analysis in the final model are displayed.
CS-30: 30-second chair stand test; SEGE: Self-Efficacy scale on Going out among community-dwelling Elderly; FIM: functional independence measure; FAI: Frenchay Activity Index; HACE: home and community environment
| Home-visit rehabilitation users: all participants (n = 105) | Standardized direct effect | Standardized indirect effect | Standardized total effect |
| CS-30 (times) | - | 0.220 | 0.220 |
| SEGE (points) | - | 0.160 | 0.160 |
| FIM-motor (points) | 0.261 | 0.124 | 0.385 |
| FAI score (points) | 0.344 | - | 0.344 |
| HACE facilitators (points) | -0.160 | - | -0.160 |
| Living alone (yes) | -0.196 | 0.118 | -0.077 |
| Sex (male: 1) | - | -0.087 | -0.087 |
| Day-care service use frequency (times/week) | 0.184 | - | 0.184 |
Figure 3. Final model of the factors that affect life-space assessment in users of home-visit rehabilitation.
Solid lines indicate a p value of <0.05. The dotted lines indicate that the path coefficients were not significant
Model fit: goodness-of-fit index, 0.956; adjusted goodness-of-fit index, 0.910; comparative fit index, 1.000; root mean square error of approximation, 0.000
FIM-M: functional independence measure motor; FAI: Frenchay Activity Index; LSA: life-space assessment; CS-30: 30-second chair stand test; SEGE: Self-Efficacy scale on Going out among community-dwelling Elderly; HACE: home and community environment
Discussion
This study found that LSA in HR users was directly influenced by FAI, FIM-M, living alone, and frequency of day service use. Additionally, CS-30, sex, and SEGE indirectly influenced LSA through IADL, ADL, and environmental factors. These findings suggest that interventions focusing on ADL, IADL, and environmental adjustments may enhance LSA in HR users.
To our knowledge, this is the first comprehensive analysis of factors affecting LSA in HR users, incorporating urban and rural settings and multiple parameters identified in previous studies. A prior study using structural equation modeling [25] reported that environmental factors directly influence LSA, aligning with our findings. A well-developed residential environment [39,40] and convenient transportation [41,42] have been linked to increased physical activity in independently living older adults. Similarly, household composition [20,43] and day service use [44] have been associated with LSA in homebound older adults. Our results reinforce these associations and highlight the direct effects of living alone and day service use on LSA.
However, the expected influence of HACE facilitator factors on LSA was not observed. Previous findings [28] may explain this, indicating lower LSA among urban HR users than their rural counterparts. Urban HR users may function within smaller living spaces with fewer facilitating factors, weakening the relationship between HACE facilitator factors and LSA in this study. Additionally, supportive aids for daily living and communication devices included in HACE facilitator factors may have had a minimal impact on LSA, as HR already supports them well.
Regarding physical function, our findings differ from those in independent community-dwelling older adults [25]. While prior studies [25] reported a direct association between motor function and LSA, our results underscore the mediating roles of ADL and IADL. This suggests that interventions prioritizing ADL and IADL, rather than focusing solely on physical function, may be more effective in improving LSA in HR users. Engagement in hobbies has also been reported to influence LSA indirectly via motor function [25]. Hobbies are included in the IADL index FAI [33], which mediated the relationship between motor function and LSA in this study. These findings underscore the importance of promoting IADL alongside HR interventions targeting physical function to enhance LSA.
The structural differences identified between HR users and independent community-dwelling older adults suggest that distinct models are needed to improve LSA in these populations. Unlike independent community-dwelling older adults, HR users may benefit more from support focused on ADL, IADL, and environmental factors.
Additionally, this study found that sex influenced LSA via IADL competence. As sex has been linked to LSA in previous research [8,10], our findings support these reports. The indirect influence of sex on LSA may be explained by the relationship between sex and IADL frequency in Japan, where domestic tasks are performed more frequently by women [45]. The univariate analysis in this study also showed significantly higher FAI scores in women. Since IADL was sex-differentiated among HR users, it can be inferred that sex indirectly influenced LSA via IADL ability.
Another key finding was the indirect influence of SEGE on LSA. As described in Bandura's theory [37], self-efficacy reflects confidence in one's ability to perform activities, shaping perceptions, behaviors, and motivation for outdoor activities. The indirect effect of SEGE on LSA suggests that HR users need confidence in their ability to engage in activities to maintain LSM. Self-efficacy may promote outdoor activities and outings that expand LSM in daily life. The effects of SEGE on FIM-M and FAI further suggest that improving self-efficacy, alongside enhancing ADL and IADL abilities, is essential for increasing LSA. Previous studies [46] have shown that real movement tasks improve LSA and self-efficacy, highlighting the need for HR programs incorporating self-efficacy support.
Collectively, the results of this study suggest that HR programs may improve LSA in HR users by enhancing physical function, ADL and IADL abilities, and self-efficacy through environmental modifications and practice of actual ADL and IADL tasks.
Limitations
This study has four main limitations. First, although it was multicenter, the sample size (n = 105) was relatively small, and participants were drawn from a limited geographical area. Future studies should increase the sample size and include more diverse geographical regions to enhance generalizability.
Second, the parameters used were selected based on previous studies, but cognitive function measures, such as the mini-mental state examination, were not included despite their reported association with LSA. Future studies should incorporate these measures to strengthen the model.
Third, motor function parameters reported to be associated with LSA were not collected. Most previous studies used walking speed, TUG, and one-leg standing tests in highly active community-dwelling older adults [11-14,25,26]. Although important, these measures are challenging to implement in limited home environments and may not be feasible for home-care older adults requiring ADL assistance. The absence of these parameters may partly explain why LSA and physical function were not directly affected in this study. Future research should consider participants’ activity levels and include these motor function parameters, such as knee extension strength and straight leg raising repetition count [47].
Finally, as this is a cross-sectional study, causality cannot be established. Longitudinal studies are needed to clarify influencing factors. The results of this study were obtained in a limited area and cannot be generalized as a system for expanding the LSA of HR users. Although this study has comprehensively assessed HR users based on previous studies and examined factors contributing to the expansion of the LSA, ideally, the study area should be expanded, and HR users should be followed longitudinally to reexamine the findings. Addressing these limitations will allow for a more comprehensive analysis and the development of a more detailed model of LSA in HR users.
Conclusions
This study found that ADL, IADL, and environmental factors directly influence LSA in HR users, while motor function and psychological factors exert indirect effects. These findings highlight the need for comprehensive HR addressing movement capacity, activity capacity, and environmental factors. Future research should examine a broader population, additional parameters, and longitudinal data to clarify how best to support individuals in HR.
Acknowledgments
This work was supported by the Japan Society for the Promotion of Science KAKENHI (grant number 22K21121). We would like to express our deepest gratitude to the users and staff at the Nishinasuno Marronnier Visiting Nurse Station and Rehabilitation Progress Center Incorporated for their cooperation in conducting this study. We would like to thank Editage (www.editage.jp) for the English language editing. Data supporting the results of this study are available from the corresponding author upon reasonable request.
Funding Statement
This work was supported by Japan Society for the Promotion of Science KAKENHI (grant number 22K21121).
Disclosures
Human subjects: Consent for treatment and open access publication was obtained or waived by all participants in this study. The International University of Health and Welfare Graduate Ethics Review Committee issued approval 19-Io-237. The study was approved by the International University of Health and Welfare Graduate Ethics Review Committee (approval number 19-Io-237). All participants or their primary caregivers were informed of the study’s purpose and content, and written informed consent was obtained. The study adhered to the principles of the Declaration of Helsinki.
Animal subjects: All authors have confirmed that this study did not involve animal subjects or tissue.
Conflicts of interest: In compliance with the ICMJE uniform disclosure form, all authors declare the following:
Payment/services info: This work was supported by Japan Society for the Promotion of Science KAKENHI (grant number 22K21121).
Financial relationships: All authors have declared that they have no financial relationships at present or within the previous three years with any organizations that might have an interest in the submitted work.
Other relationships: All authors have declared that there are no other relationships or activities that could appear to have influenced the submitted work.
Author Contributions
Concept and design: Yuta Sugita
Acquisition, analysis, or interpretation of data: Yuta Sugita, Eisuke Kogure, Tsuyoshi Hara, Takeshi Ohnuma
Drafting of the manuscript: Yuta Sugita, Eisuke Kogure, Tsuyoshi Hara, Takeshi Ohnuma
Critical review of the manuscript for important intellectual content: Yuta Sugita, Eisuke Kogure, Tsuyoshi Hara, Takeshi Ohnuma
Supervision: Tsuyoshi Hara
References
- 1.The life-space diary: a measure of mobility in old people at home. May D, Nayak US, Isaacs B. Int Rehabil Med. 1985;7:182–186. doi: 10.3109/03790798509165993. [DOI] [PubMed] [Google Scholar]
- 2.How often and how far do frail elderly people need to go outdoors to maintain functional capacity? Shimada H, Ishizaki T, Kato M, Morimoto A, Tamate A, Uchiyama Y, Yasumura S. Arch Gerontol Geriatr. 2010;50:140–146. doi: 10.1016/j.archger.2009.02.015. [DOI] [PubMed] [Google Scholar]
- 3.Measuring life-space mobility in community-dwelling older adults. Baker PS, Bodner EV, Allman RM. J Am Geriatr Soc. 2003;51:1610–1614. doi: 10.1046/j.1532-5415.2003.51512.x. [DOI] [PubMed] [Google Scholar]
- 4.Impact of emergency department visits and hospitalization on mobility among community-dwelling older adults. Brown CJ, Kennedy RE, Lo AX, Williams CP, Sawyer P. Am J Med. 2016;129:1124–1115. doi: 10.1016/j.amjmed.2016.05.016. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Life-space mobility and mortality in older women: prospective results from the study of osteoporotic fractures. Mackey DC, Lui LY, Cawthon PM, Ensrud K, Yaffe K, Cummings SR. J Am Geriatr Soc. 2016;64:2226–2234. doi: 10.1111/jgs.14474. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Changes in life-space mobility and quality of life among community-dwelling older people: a 2-year follow-up study. Rantakokko M, Portegijs E, Viljanen A, Iwarsson S, Kauppinen M, Rantanen T. Qual Life Res. 2016;25:1189–1197. doi: 10.1007/s11136-015-1137-x. [DOI] [PubMed] [Google Scholar]
- 7.Life-space mobility in the elderly: current perspectives. Johnson J, Rodriguez MA, Al Snih S. Clin Interv Aging. 2020;15:1665–1674. doi: 10.2147/CIA.S196944. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Life-space mobility in Mexican Americans aged 75 and older. Al Snih S, Peek KM, Sawyer P, Markides KS, Allman RM, Ottenbacher KJ. J Am Geriatr Soc. 2012;60:532–537. doi: 10.1111/j.1532-5415.2011.03822.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.The association of mobility determinants and life space among older adults. Dunlap PM, Rosso AL, Zhu X, Klatt BN, Brach JS. J Gerontol A Biol Sci Med Sci. 2022;77:2320–2328. doi: 10.1093/gerona/glab268. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Gender and racial disparities in life-space constriction among older adults. Choi M, O'Connor ML, Mingo CA, Mezuk B. Gerontologist. 2016;56:1153–1160. doi: 10.1093/geront/gnv061. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.The association of activity assessed by life-space assessment with physical function and instrumental activities of daily living for elderly people. [Article in Japanese] Abe T, Hashidate H, Shimada H, Ohnuma T, Suzuki T. Rigakuryoho Kagaku. 2009;24:721–726. [Google Scholar]
- 12.Short-term change and its predictors in life-space: a small multicentre study among non-active older outpatients in rehabilitation facilities. [Article in Japanese] Morikawa S, Tamari K, Taniguchi C, Tokumaru K. https://doi.org/10.15063/rigaku.Y098 Rigakuryouhougaku. 2015;42:494–502. [Google Scholar]
- 13.Determination of the minimal important change in the life-space assessment. Kennedy RE, Almutairi M, Williams CP, Sawyer P, Allman RM, Brown CJ. J Am Geriatr Soc. 2019;67:565–569. doi: 10.1111/jgs.15707. [DOI] [PubMed] [Google Scholar]
- 14.Validation of the German Life-Space Assessment (LSA-D): cross-sectional validation study in urban and rural community-dwelling older adults. Mümken SA, Gellert P, Stollwerck M, O'Sullivan JL, Kiselev J. BMJ Open. 2021;11:0. doi: 10.1136/bmjopen-2021-049926. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Longitudinal changes in life-space mobility and the factors influencing it among chronic community-dwelling post-stroke patients. Tsunoda S, Shimizu S, Suzuki Y, et al. Disabil Rehabil. 2022;44:7872–7876. doi: 10.1080/09638288.2021.2001054. [DOI] [PubMed] [Google Scholar]
- 16.Life-space mobility and social support in elderly adults with orthopaedic disorders. Suzuki T, Kitaike T, Ikezaki S. Int J Nurs Pract. 2014;20 Suppl 1:32–38. doi: 10.1111/ijn.12248. [DOI] [PubMed] [Google Scholar]
- 17.Life-space mobility among home-living older adults with care needs and clinical depression-a cross-sectional analysis. Lech S, Mümken S, Kessler EM, Gellert P. Int J Geriatr Psychiatry. 2023;38:0. doi: 10.1002/gps.5875. [DOI] [PubMed] [Google Scholar]
- 18.Factors associated with life-space in older adults with amnestic mild cognitive impairment. Uemura K, Shimada H, Makizako H, Yoshida D, Doi T, Yamada M, Suzuki T. Geriatr Gerontol Int. 2013;13:161–166. doi: 10.1111/j.1447-0594.2012.00878.x. [DOI] [PubMed] [Google Scholar]
- 19.Changes in social participation and life-space mobility in newly enrolled home-based rehabilitation users over 6 months. Kamioka Y, Miura Y, Matsuda T, et al. J Phys Ther Sci. 2020;32:375–384. doi: 10.1589/jpts.32.375. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Life-space mobility and Parkinson's disease. a multiple-methods study. Ryder-Burbidge C, Wieler M, Nykiforuk CI, Jones CA. Mov Disord Clin Pract. 2022;9:351–361. doi: 10.1002/mdc3.13406. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Mobility, disability, and social engagement in older adults. Rosso AL, Taylor JA, Tabb LP, Michael YL. J Aging Health. 2013;25:617–637. doi: 10.1177/0898264313482489. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Associations between environmental characteristics and life-space mobility in community-dwelling older people. Rantakokko M, Iwarsson S, Portegijs E, Viljanen A, Rantanen T. J Aging Health. 2015;27:606–621. doi: 10.1177/0898264314555328. [DOI] [PubMed] [Google Scholar]
- 23.Impact of driving cessation on trajectories of life-space scores among community-dwelling older adults. Huisingh C, Levitan EB, Sawyer P, Kennedy R, Brown CJ, McGwin G. J Appl Gerontol. 2017;36:1433–1452. doi: 10.1177/0733464816630637. [DOI] [PubMed] [Google Scholar]
- 24.Short physical performance battery score and driving a car are independent factors associated with life-space activities in older adults with cardiovascular disease. Hashimoto K, Hirashiki A, Kawamura K, et al. Geriatr Gerontol Int. 2021;21:900–906. doi: 10.1111/ggi.14254. [DOI] [PubMed] [Google Scholar]
- 25.The correlates of life-space mobility in older adults using structural equation modelling. [Article in Japanese] Shimada H, Makizako H, Suzukawa M, Furuna T, Suzuki T. Rigakuryouhougaku. 2009;36:370–376. [Google Scholar]
- 26.A path analysis of the interdependent relationships between life space assessment scores and relevant factors in an elderly Japanese community. Matsuda K, Hamachi N, Yamaguchi T, et al. J Phys Ther Sci. 2019;31:326–331. doi: 10.1589/jpts.31.326. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Clinical usefulness of indoor life-space assessment in community-dwelling older adults certified as needing support or care. Ohnuma T, Hashidate H, Yoshimatsu T, Abe T. Nihon Ronen Igakkai Zasshi. 2014;51:151–160. doi: 10.3143/geriatrics.51.151. [DOI] [PubMed] [Google Scholar]
- 28.Factors related to regional differences among home-visit rehabilitation users. [Article in Japanese] Sugita Y, Hara T, Ohnuma T, Kogure E, Urano T. Nihon Ronen Igakkai Zasshi. 2022;59:49–57. doi: 10.3143/geriatrics.59.49. [DOI] [PubMed] [Google Scholar]
- 29.Polypharmacy and potentially inappropriate medications in older adults who use long-term care services: a cross-sectional study. Hagiwara S, Komiyama J, Iwagami M, Hamada S, Komuro M, Kobayashi H, Tamiya N. BMC Geriatr. 2024;24:696. doi: 10.1186/s12877-024-05296-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.A 30-s chair-stand test as a measure of lower body strength in community-residing older adults. Jones CJ, Rikli RE, Beam WC. Res Q Exerc Sport. 1999;70:113–119. doi: 10.1080/02701367.1999.10608028. [DOI] [PubMed] [Google Scholar]
- 31.Development of a self-efficacy scale for going out among community-dwelling elderly. [Article in Japanese] Yamazaki S, Imuta H, Hashimoto M, Nomura S, Yasumura S. Nihon Koshu Eisei Zasshi. 2010;57:439–447. [PubMed] [Google Scholar]
- 32.Performance profiles of the functional independence measure. Granger CV, Hamilton BB, Linacre JM, Heinemann AW, Wright BD. Am J Phys Med Rehabil. 1993;72:84–89. doi: 10.1097/00002060-199304000-00005. [DOI] [PubMed] [Google Scholar]
- 33.The Japanese version of the Frenchay Activities Index self-assessment form and its clinical application and standard values. [Article in Japanese] Shiratsuchi M, Saeki S, Hachisuka K. Sogo Rehabil. 1999;10:469–474. [Google Scholar]
- 34.Validity and reliability of the Japanese version of home and community environment (HACE). [Article in Japanese] Kato G, Tamiya N, Kashiwagi M, Akasaka K. Sogo Rehabil. 2010;38:475–483. [Google Scholar]
- 35.Structural equation modelling: guidelines for determining model fit. Hooper D, Coughlan J, Mullen MR. Electron J Bus Res Methods. 2008;6:53–60. [Google Scholar]
- 36.Self-efficacy: implications for physical activity, function, and functional limitations in older adults. McAuley E, Szabo A, Gothe N, Olson EA. Am J Lifestyle Med. 2011;5 doi: 10.1177/1559827610392704. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 37.Self-efficacy mechanism in human agency. Bandura A. Am Psychol. 1982;37:122–147. [Google Scholar]
- 38.Influences of wheelchair-related efficacy on life-space mobility in adults who use a wheelchair and live in the community. Sakakibara BM, Miller WC, Eng JJ, Backman CL, Routhier F. Phys Ther. 2014;94:1604–1613. doi: 10.2522/ptj.20140113. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 39.Neighborhood amenities and mobility in older adults. Rosso AL, Grubesic TH, Auchincloss AH, Tabb LP, Michael YL. Am J Epidemiol. 2013;178:761–769. doi: 10.1093/aje/kwt032. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 40.Built environmental correlates of older adults' total physical activity and walking: a systematic review and meta-analysis. Barnett DW, Barnett A, Nathan A, Van Cauwenberg J, Cerin E. Int J Behav Nutr Phys Act. 2017;14:103. doi: 10.1186/s12966-017-0558-z. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 41.Associations between motorized transport access, out-of-home activities, and life-space mobility in older adults in Japan. Tran Y, Hashimoto N, Ando T, Sato T, Konishi N, Takeda Y, Akamatsu M. BMC Public Health. 2022;22:676. doi: 10.1186/s12889-022-13033-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 42.The association between transportation and life-space mobility in community-dwelling older people with or without walking difficulties. Viljanen A, Mikkola TM, Rantakokko M, Portegijs E, Rantanen T. J Aging Health. 2016;28:1038–1054. doi: 10.1177/0898264315618919. [DOI] [PubMed] [Google Scholar]
- 43.Assessing timewise changes over 15 months in life-space mobility among community-dwelling elderly persons. Hayashi C, Tanaka H, Ogata S. BMC Geriatr. 2020;20:502. doi: 10.1186/s12877-020-01882-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 44.Factors related to life-space in community-dwelling patients after stroke. [Article in Japanese] Naito T, Matsuda N, Suzuki H, et al. Rigakuryouhougaku. 2017;44:323–331. [Google Scholar]
- 45.Gender-related differences in scores of the Barthel Index and Frenchay Activities Index in randomly sampled elderly persons living at home in Japan. Hachisuka K, Saeki S, Tsutsui Y, Chisaka H, Ogata H, Iwata N, Negayama S. J Clin Epidemiol. 1999;52:1089–1094. doi: 10.1016/s0895-4356(99)00085-2. [DOI] [PubMed] [Google Scholar]
- 46.Effects of an intervention to improve life-space mobility and self-efficacy in patients following total knee arthroplasty. Hiyama Y, Kamitani T, Mori K. J Knee Surg. 2019;32:966–971. doi: 10.1055/s-0038-1672199. [DOI] [PubMed] [Google Scholar]
- 47.Examination of the relationship between straight leg raising repetition count and both knee extension strength and walking independence in patients with collagen disease. Yamauchi S, Morishita S, Uchiyama Y, Kodama N, Domen K. Prog Rehabil Med. 2018;3:20180007. doi: 10.2490/prm.20180007. [DOI] [PMC free article] [PubMed] [Google Scholar]