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Address correspondence to Daiki Watanabe, RD, PhD, Faculty of Sport Sciences, Waseda University, 2-579-15 Mikajima, Tokorozawa City, Saitama, 359-1192, Japan.
Faculty of Sport Sciences, Waseda University, Tokorozawa City, Saitama, JapanNational Institute of Health and Nutrition, National Institutes of Biomedical Innovation, Health and Nutrition, Shinjuku-ku, Tokyo, JapanInstitute of Interdisciplinary Research, Institute for Active Health, Kyoto University of Advanced Science, Kameoka City, Kyoto, Japan
National Institute of Health and Nutrition, National Institutes of Biomedical Innovation, Health and Nutrition, Shinjuku-ku, Tokyo, JapanInstitute of Interdisciplinary Research, Institute for Active Health, Kyoto University of Advanced Science, Kameoka City, Kyoto, JapanSenior Citizen's Welfare Section, Kameoka City Government, Kameoka City, Kyoto, Japan
National Institute of Health and Nutrition, National Institutes of Biomedical Innovation, Health and Nutrition, Shinjuku-ku, Tokyo, JapanInstitute of Interdisciplinary Research, Institute for Active Health, Kyoto University of Advanced Science, Kameoka City, Kyoto, Japan
National Institute of Health and Nutrition, National Institutes of Biomedical Innovation, Health and Nutrition, Shinjuku-ku, Tokyo, JapanInstitute of Interdisciplinary Research, Institute for Active Health, Kyoto University of Advanced Science, Kameoka City, Kyoto, JapanPhysical Fitness Research Institute, Meiji Yasuda Life Foundation of Health and Welfare, Hachioji City, Tokyo, Japan
Faculty of Sport Sciences, Waseda University, Tokorozawa City, Saitama, JapanNational Institute of Health and Nutrition, National Institutes of Biomedical Innovation, Health and Nutrition, Shinjuku-ku, Tokyo, Japan
Institute of Interdisciplinary Research, Institute for Active Health, Kyoto University of Advanced Science, Kameoka City, Kyoto, JapanDepartment of Nursing, Doshisha Women's College of Liberal Arts, Kyotanabe City, Kyoto, JapanLaboratory of Applied Health Sciences, Kyoto Prefectural University of Medicine, Kamigyo-ku, Kyoto, Japan
Some epidemiological studies of older American adults have reported a relationship between life-space mobility (LSM) and mortality. However, these studies did not show a dose-response relationship and did not include individuals from other countries. Therefore, we evaluated the dose-response relationship between LSM and mortality in older adults.
Design
Prospective cohort study.
Setting and Participants
We used the data of 10,014 older Japanese adults (aged ≥65 years) who provided valid responses to the Life-Space Assessment (LSA) in the Kyoto-Kameoka study in Japan.
Methods
LSM was evaluated using the self-administered LSA consisting of 5 items regarding life-space from person's bedroom to outside town. The LSM score was calculated by multiplying life-space level by frequency score by independence score, yielding a possible range of 0 (constricted life-space) to 120 (broad life-space). These scores were categorized into quartiles (Qs). Mortality data were collected from July 30, 2011 to November 30, 2016. A multivariate Cox proportional hazards model that included baseline covariates were used to evaluate the relationship between LSM score and mortality risk.
Results
A total of 1030 deaths were recorded during the median follow-up period of 5.3 years. We found a negative association between LSM score and overall mortality even after adjusting for confounders [Q1: reference; Q2: hazard ratio (HR) 0.81, 95% CI 0.69-0.95; Q3: HR 0.70, 95% CI 0.59-0.85; Q4: HR 0.68, 95% CI 0.55-0.84, P for trend < .001]. Similar results were observed for the spline model; up to a score of 60 points, LSM showed a strong dose-dependent negative association with mortality, but no significant differences were observed thereafter (L-shaped relationship).
Conclusions and Implications
Our findings demonstrate an L-shaped relationship between LSM and mortality. This study will be useful in establishing target values for expanding the range of mobility among withdrawn older adults with a constricted life-space.
Optimum mobility is defined as an individual's ability to reach a destination safely and reliably and is impeded by gait disturbance or the need for assistance to be mobile.
immobility is an important public health target in countries and communities with rapidly aging populations.
Life-space mobility (LSM) refers to the size of the spatial area that an individual intentionally moves through during daily activities and the frequency of such movement.
The University of Alabama at Birmingham's Life-Space Assessment (LSA) evaluates mobility based on the frequency at which the individual leaves the confines of their bedroom, home, property, neighborhood, or town and evaluates mobility based on the individual's independence in doing so.
the LSA may more accurately reflect actual mobility as it considers both the individual's range of mobility in the community and his or her independence during those movements.
Epidemiologic studies of older adults in the United States have shown a negative association between overall mortality risk and LSM as evaluated by the LSA
However, to our knowledge, these associations have only been examined in American community-dwelling older adults and have not been investigated in people residing in other countries or communities. As a 5-year change in LSA-evaluated mobility has been found to differ by race,
it is vital to evaluate the relationship between mortality risk and LSM in other countries. Moreover, no studies have examined a dose-response relationship between mortality risk and LSM. Therefore, in this study, we aimed to evaluate the dose-response relationship between LSM and mortality risk using data from a community-based longitudinal cohort study of older Japanese adults. We hypothesized that there would be a negative association between mortality risk and LSM.
Methods
Study Design and Population
The Kyoto-Kameoka study is a prospective cohort study of older adults aged ≥65 years (range: 65-102 years) residing in Kameoka City, Kyoto Prefecture, Japan. Details of the study are explained elsewhere.
Prevalence of frailty assessed by Fried and Kihon checklist indexes in a prospective cohort study: Design and demographics of the Kyoto-Kameoka longitudinal study.
To survey all residents of Kameoka City who were aged ≥65 years as of July 1, 2011, qualified candidates were selected based on their name, sex, date of birth, and other information obtained from the basic residency register maintained by Kameoka City Hall (Figure 1). Among the candidates selected (n = 19,424), those who required long-term care at level 3 or above (n = 1170), and those who died between July 1 and July 28, 2011 (n = 23), were excluded. The remaining 18,231 candidates participated in the Needs in the Sphere of Daily Life Survey (baseline survey), which includes the LSA that evaluates LSM, on July 29, 2011. Of these participants, 13,294 responded to the survey (response rate: 72.9%).
Fig. 1Participant flow diagram for the analysis of life-space mobility and mortality in the Kyoto-Kameoka study. LSA, life-space assessment.
Of those who participated in the baseline survey (n = 13,294), we excluded participants with an incomplete LSA (n = 2218). We confirmed the validity of the LSM score against independence assessed by the long-term care insurance system in 11,076 older adults. Furthermore, to avoid the possibility of a reverse causal relationship between LSM and mortality, we excluded those who required support level 1 or 2 (n = 555) or long-term care level 1 or 2 (n = 499), and who moved out of the city on an unknown date (n = 8). Ultimately, 10,014 participants were included in the analysis.
This study was approved by the Research Ethics Committee. We obtained informed consent from all participants at the time of their response to the mail survey. Our report adheres to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines.
Average LSM for the past month was evaluated using the following questions on the range of mobility: Have you been to other rooms of your home besides the room where you sleep? (Level 1); Have you been to an area outside your home? (Level 2); Have you been to places in your neighborhood? (Level 3); Have you been to places outside your neighborhood, but within your town? (Level 4); Have you been to places outside your town? (Level 5). Mobility frequency was evaluated for life-space levels 1 through 5 as follows: less than once a week = 1 point, 1 to 3 times a week = 2 points, 4 to 6 times a week = 3 points, daily = 4 points. A uniform value was used to represent independence across the full range of mobility (no equipment or personal assistance = 2 points, required equipment = 1.5 points, required personal assistance = 1 point). Participants who responded that they required assistance with daily activities or hospital visits were defined as “requiring personal assistance,” whereas participants who responded that they used a wheelchair, electric wheelchair, walker, or cane were defined as “requiring equipment.” The LSM score was calculated by multiplying life-space level by frequency score by independence score, yielding a possible range of 0 (constricted life-space) to 120 (broad life-space).
Evaluation of Death Events
Participant survival during the follow-up period was evaluated using the basic resident register maintained by Kameoka City Hall. Kameoka City Hall provided data from July 30, 2011, to November 30, 2016. Data for residents whose registration had been revoked or who had moved out of the country or municipality were censored.
Evaluation of Independence by the Long-Term Care Insurance System
Under the Japanese long-term care insurance system, older adults (aged ≥65 years) or middle-aged adults (aged 40-64 years) with specific illnesses are eligible for financial assistance based on the severity of their physical and cognitive disorders.
To determine eligibility for this system, candidates undergo an in-person assessment of everyday functioning by officials dispatched from the local government using a 74-item questionnaire based on activities of daily living. Based on questionnaire results and a physician's opinion, a candidate's long-term care level is determined by the Long-Term Care Insurance Certification Committee, which consists of academic experts in health care and welfare. Certification is classified into 7 levels beginning with support levels 1 and 2 and advancing to long-term care levels 1 (least disabled) through 5 (most disabled). This information was provided by the officials of Kameoka City Hall.
Statistical Analysis
To confirm the validity of the LSM score, median (interquartile range) LSM scores are shown for those without long-term care certification, those who required support level 1 or 2, and those who required long-term care level 1 or 2. In addition, the Jonckheere-Terpstra trend test and Spearman rank correlation analysis were used to test trends among these values. Using Meng et al's equation,
a correlation coefficient was compared between independence as assessed by the long-term care insurance system and the LSM score with (0-120 point) or without (0-60 point) incorporating subjectively assessed independence.
The LSM scores were divided into quartiles. For continuous variables, descriptive statistics of mean and SD were calculated, and analysis of variance was performed for between-groups comparisons. Categorical variables are shown as the number of participants and percentage, and Pearson χ2 test was performed for between-group comparisons. Missing data for covariates were supplemented using 5 data sets generated through multivariate imputation by chained equation (MICE) package using the statistical software R.
All missing values were assumed to be missing at random.
The absolute risk of overall mortality for each LSM score quartile is expressed as the number of events per 1000 person-years. We used a multivariate Cox proportional hazards model including baseline covariates to adjust for the effect of confounders on the association between LSM score and overall mortality. Multivariate analysis was performed using the following 2 models. Model 1 adjusted for age (continuous), sex (female or male), and population density (≥1000 or <1000 individuals/km
). The population density of residential neighborhoods was defined by a small administrative unit called Cho using population and area as of October 1, 2010. Model 2 adjusted for body mass index (continuous), living alone (yes or no), socioeconomic status (high or low), educational attainment (<9, 10-12, or ≥13 years), smoking status (never smoker, past smoker, or current smoker), alcohol drinker (yes or no), self-reported general health (good or poor), number of drugs (continuous), and number of chronic diseases (continuous) in addition to the adjustments included in model 1. These variables were selected for adjustment based on previous studies.
Analytic results are expressed as hazard ratios (HRs) and 95% CIs. HRs were calculated using the first quartile as a reference. Furthermore, HR (95% CI) for overall mortality with each 10-point increase in LSM score was estimated by stratifying the sample by the median LSM score for the cohort (<60 points or ≥60 points).
We used multiple logistic regression analysis including the variables from model 2 to estimate the probability of being assigned to each quartile of LSM scores (propensity score). Inverse probability weighting was used to create adjusted Kaplan-Meier survival curves.
We performed a sensitivity analysis using the following 2 methods: (1) we excluded death events recorded in the first 2 follow-up years (205 men, 97 women) to rule out the possibility of a reverse causal relationship; (2) we performed a similar analysis using a complete case data set without missing values.
We also evaluated the curvature of the relationship between LSM score and overall mortality risk using a restricted cubic spline model with 3 knots based on the distribution of LSM scores.
The results are expressed as HRs and 95% CIs, with HRs calculated using an LSM of zero as a reference point.
A P value of <.05 (2-tailed) was considered statistically significant. All analyses were performed using R software 3.4.3 for multiple imputation analyses (R Core Team, Vienna, Austria) and/or Stata MP, version 15.0, for other analyses (StataCorp LP, College Station, TX, USA).
Results
Baseline participant characteristics of the LSM score validation group are shown in Supplementary Table 1. Those certified as needing support or long-term care were older and included a higher percentage of women than those without such certification. LSM score validation is shown in Table 1. Independence assessed by the long-term care insurance system showed a significantly stronger correlation with the LSM score that incorporated independence than the LSM score that did not incorporate independence (−0.373 vs −0.313, Meng Z test: P < .001).
Table 1Validation of Life-Space Mobility Score Against Objective Dependency Level
The objectively evaluated dependency level shows the severity in the order of requiring help at level 1 and level 2 and long-term care at level 1 and level 2.
To compare the ranking of an individual's LSM score with and without the consideration of dependency determined from the life-space assessment, we used the Meng et al equation.25 If the results presented significant differences (P < .05), these relationships were interpreted as not being equivalent.
The LSM scores were calculated using with (0-120 points) and without (0-60 points) independence level for each question [personal assistance (1 point), equipment only (1.5 point), or no equipment or personal assistance (2 points)]. The values are expressed as a number, median (interquartile range), and correlation coefficients. The trend test and correlation analysis were performed by the Jonckheere-Terpstra trend test and Spearman rank correlation analysis, respectively.
∗ The objectively evaluated dependency level shows the severity in the order of requiring help at level 1 and level 2 and long-term care at level 1 and level 2.
† To compare the ranking of an individual's LSM score with and without the consideration of dependency determined from the life-space assessment, we used the Meng et al equation.
Participants' baseline characteristics are shown by the LSM score quartile for the analyzed cohort in Table 2. Participants with higher LSM scores were more educated, more likely to be alcohol drinkers, less likely to use medication, and showed higher physical activity. These participants were also younger, less likely to be female, and had better self-reported health. Those excluded from the study were older and included more women than those included in the study (Supplementary Table 2).
Table 2Baseline Participant Characteristics According to Life-Space Mobility Score Quartiles
Continuous variables are shown in terms of mean with SD and were analyzed using variance analysis. Physical activity was evaluated by validating the International Physical Activity Questionnaire–Short Form.
Continuous variables are shown in terms of mean with SD and were analyzed using variance analysis. Physical activity was evaluated by validating the International Physical Activity Questionnaire–Short Form.
Continuous variables are shown in terms of mean with SD and were analyzed using variance analysis. Physical activity was evaluated by validating the International Physical Activity Questionnaire–Short Form.
Continuous variables are shown in terms of mean with SD and were analyzed using variance analysis. Physical activity was evaluated by validating the International Physical Activity Questionnaire–Short Form.
From the data obtained on the disease status (including the presence of hypertension, stroke, heart disease, diabetes, hyperlipidemia, digestive disease, respiratory disease, urologic diseases, and cancer), the comorbidity scores were summed to obtain a total score ranging from 0 (no comorbidity) to 9 (poor status).
Continuous variables are shown in terms of mean with SD and were analyzed using variance analysis. Physical activity was evaluated by validating the International Physical Activity Questionnaire–Short Form.
55.3 (31.5)
12.2 (10.8)
45.2 (7.8)
69.5 (6.7)
95.2 (10.4)
<.001
HSES, high socioeconomic status; PD, population density; Q, quartiles.
Data for participants with missing values were imputed by multiple imputation: body mass index (n = 531, 5.3%); family structure (n = 725, 7.2%); socioeconomic status (n = 450, 4.5%); education (n = 1149, 11.5%); smoking status (n = 415, 4.1%); alcohol status (n = 355, 3.5%); self-reported health (n = 368, 3.7%); physical activity (n = 185, 1.8%); and medications (n = 761, 7.6%). Q1, Q2, Q3, and Q4 include the life-space mobility score of ≤31.5, 32.0-58.5, 60.0-80.0, and ≥82.0, respectively.
∗ Continuous variables are shown in terms of mean with SD and were analyzed using variance analysis. Physical activity was evaluated by validating the International Physical Activity Questionnaire–Short Form.
† Categorical variables are shown in terms of the number of cases with percentage and were analyzed using Pearson χ2 test.
‡ From the data obtained on the disease status (including the presence of hypertension, stroke, heart disease, diabetes, hyperlipidemia, digestive disease, respiratory disease, urologic diseases, and cancer), the comorbidity scores were summed to obtain a total score ranging from 0 (no comorbidity) to 9 (poor status).
The relationship between LSM score and overall mortality risk is shown in Figure 2 and Table 3. The median follow-up period was 5.3 years (50,311 person-years). A total of 1030 participants (10.3%) died during the follow-up period. We found a significant negative association between LSM score and overall mortality risk even after adjusting for confounders (Q1: reference; Q2: HR 0.81, 95% CI 0.69‒0.95; Q3: HR 0.70, 95% CI 0.59-0.85; Q4: HR 0.68, 95% CI 0.55-0.84, P for trend < .001). The HR (95% CI) for overall mortality when the LSM score was 10 points higher was 0.90 (0.86-0.94) for those with LSM <60 points and 1.00 (0.92-1.07) for those with LSM ≥60 points. Moreover, stratified and sensitivity analyses showed similar results (Supplementary Tables 3 and 4). In the analysis of the dose-response relationship between LSM score and mortality risk using a restricted cubic spline model with an LSM score of zero as a reference point, LSM showed a strong dose-dependent negative association with mortality up to a score of 60 points, but no significant differences were observed thereafter (L-shaped relationship). In other words, when LSM became higher, mortality risk became lower until a score of 60 LSM. This spline analysis model fits the data well compared with the linear regression analysis (Akaike information criterion: 17,752 vs 17,759). In addition, the adjusted model for transportation statuses such as the use of public transportation and cars showed similar results (Supplementary Figure 1).
Fig. 2The association between life-space mobility score and all-cause mortality using a multivariate regression model among older adults. (A) Multivariate adjusted Kaplan-Meier survival curves using inverse probability weighting according to quartile (Qs). (B) Restricted cubic spline model. Solid lines represent hazard ratios, and dashed lines represent 95% CIs. As a reference, we calculated the hazard ratio using 0 points for life-space mobility scores. We estimated that P < .05 when the 95% CI of the hazard ratio did not exceed 1.00, and P ≥ .05 when the 95% CI of the hazard ratio exceeded 1.00. Adjustment factors included the patients' age, sex, population density, body mass index, family structure, economic status, educational attainment, smoking status, alcohol consumption status, self-reported health, physical activity, number of drugs, and the number of chronic diseases.
Table 3Hazard Ratios for Physical Activity and Sedentary Time Status and All-Cause Mortality Calculated Using the Multivariate Cox Proportional Hazards Model
Model 2: In addition to the factors listed in model 1, adjusted for body mass index, family structure, economic status, educational attainment, smoking status, alcohol consumption status, self-reported health status, physical activity, number of drugs, and number of chronic diseases.
Linear trend P values were calculated using the likelihood ratio test and a continuous variable of LSM score.
<.001
<.001
<.001
<.001
10-point increment
Total
10,014
0.84 (0.82, 0.86)
0.90 (0.88, 0.92)
0.94 (0.92, 0.96)
<60 points
5260
0.79 (0.75, 0.83)
0.86 (0.82, 0.90)
0.90 (0.86, 0.94)
≥60 points
4754
0.96 (0.88, 1.04)
0.98 (0.90, 1.05)
1.00 (0.92, 1.07)
HR, hazard ratio; LSM, life-space mobility; PY, person-years; Q, quartiles; Ref, reference.
Q1, Q2, Q3, and Q4 include the life-space mobility score of ≤31.5, 32.0-58.5, 60.0-80.0, and ≥82.0, respectively.
∗ Model 1: Adjusted for age, sex, and population density.
† Model 2: In addition to the factors listed in model 1, adjusted for body mass index, family structure, economic status, educational attainment, smoking status, alcohol consumption status, self-reported health status, physical activity, number of drugs, and number of chronic diseases.
‡ Linear trend P values were calculated using the likelihood ratio test and a continuous variable of LSM score.
The relationship between LSM score and overall mortality risk stratified by LSA subdomains is shown in Supplementary Table 5. LSM was associated with a higher mortality risk for those who did not move within the home, outside the home, in the neighborhood, or within town compared to those who did move in these areas. However, there was no significant difference in mortality risk between those who went outside of town and those who did not.
Discussion
This population-based cohort study examined the dose-response relationship between overall mortality risk and LSM score in older adults. We identified an L-shaped relationship, specifying that LSM showed a strong dose-dependent negative association with mortality up to a score of 60 points, but no significant differences were observed thereafter. To our knowledge, this is the first study to verify the dose-response relationship between validated LSM score and mortality risk in older adults.
Validation of the LSA
Participants tend to report socially acceptable responses in self-reported assessments, regardless of their actual behaviors.
Effects of treating depression and low perceived social support on clinical events after myocardial infarction: The Enhancing Recovery in Coronary Heart Disease Patients (ENRICHD) Randomized Trial.
evaluated by subjective questionnaires. LSA validity has also been confirmed with objective indices including motor status as evaluated by the Timed Up-and-Go Test
as evaluated by a wearable sensor. We demonstrated that independence evaluated by the long-term care insurance system was more strongly correlated with the LSA-evaluated LSM score that incorporated independence than the LSM score that did not incorporate independence. This suggests that the LSM score reflected subjective independence and indicates the effectiveness of subjectively evaluated independence.
Main Outcome and Mechanism
Previous studies have shown an association between LSM and overall mortality risk in samples of 599 to 3892 older American adults aged ≥65 years who were followed up for an average of 2.7-8.5 years.
We found similar results using data from 10,014 participants, a sample more than 2.5 times the size of previous studies. Considering some previous studies,
This theory suggests that social relationships provide information and emotional experiences that promote adaptive behavior or neuroendocrine responses in response to acute or chronic stressors.
Lockdowns and physical distancing in response to the COVID-19 pandemic have been associated with increased stress, including mental health problems and loneliness, in older adults,
suggesting that poor social relationships may be associated with the negative effects of stressors on health. The second theory that may explain this is the main effect model,
which suggests that social relationships are directly associated with health-protective effects through unexpected emotional and behavioral changes. Longitudinal studies have reported associations between LSM and HRQoL
Although the above may be potential causes of the negative association between LSM and mortality risk, basic and interventional research is needed to elucidate the underlying mechanisms in detail.
Dose-Response Relationships
In a previous study on mortality risk and LSA-evaluated LSM, 5 groups were analyzed. Older women from the United States with LSM scores between 0 and 60 were associated with a higher risk of all-cause mortality than those with higher scores (81-120 points), but not those with midlevel scores (61-80 points).
Similar to our results regarding mortality risk, their findings showed that changes in LSM caused greater changes in the risk of fall events in those with low LSM scores than in those with high LSM scores, and these previous studies support our findings. Therefore, our results may be able to generalize the target values of the LSM score of ≥60 points in older adults.
Even those with complete independence in LSM need to go to town at least 1 to 3 times per week to reach an LSA-evaluated LSM score of 60. In our analysis of the mobility space subdomains of LSA, we showed that there was a negative association with mortality risk for those who were mobile at levels 1 (home) through 4 (within town) compared to those who were not mobile at these levels. However, there was no association between mortality risk and the presence or absence of LSM outside of town (level 5). Therefore, our results suggested that mobility not only in one's neighborhood but also within one's town at least once a week may be necessary to achieve the maximum effectiveness of LSM on mortality risk among older adults. Our results concerning the dose-response relationship between mortality risk and LSM score should provide useful information for establishing target values for expanding life-space, particularly in withdrawn older adults. Given that the global COVID-19 pandemic is decreasing LSM for many people,
Life-space mobility and active aging as factors underlying quality of life among older people before and during COVID-19 lockdown in Finland—a longitudinal study.
our findings are likely to be of particular use in supporting withdrawn older adults.
Strengths and Limitations
One strength of this research is that we were able to confirm the validity of LSA-evaluated LSM scores in a large-scale cohort study of community-dwelling older adults. This is evidence of a more accurate estimation of the dose-response relationship between mortality risk and LSM score. The LSA is the most commonly used method for evaluating LSM around the globe,
and using the LSA to investigate the association between LSM and mortality risk makes our results more accurate and generalizable.
However, this study has some methodologic limitations. First, self-reported LSM scores may introduce systematic reporting bias. In addition, the independence measure included on the LSA was evaluated using a single index, rather than an assessment for each life-space level. Although this is different from the original LSA method, our results are likely accurate because we validated the LSM score against independence assessed by the long-term care insurance system. Second, although this study was a complete survey of older adults aged ≥65 years residing in Kameoka City, baseline participant characteristics differed between participants who were included and those who were excluded from the present study, which may reflect selection bias. Third, the follow-up period of this study was relatively short. Furthermore, because of the unavailability of data on mortality causes, we could not examine the relationship between the LSM score and the cause of death. Lastly, although the present study adjusted for confounders, there is still a possibility of residual confounding bias in the association between LSM score and overall mortality risk. The LSM score is associated with risk factors for adverse events other than mortality risk and is related to the same social factors and lifestyle habits as other adverse events.
These limitations may limit the generalization of our results. Careful interpretation of our results is necessary because we could not elucidate the causal relationships between the LSM score and mortality. For example, to determine whether going outside the city in the passenger seat of a car could reduce the risk of death for high-risk older adults, an intervention study would be necessary. As such, there is a need to reevaluate our results through prospective studies that are better designed and have longer follow-up periods.
Conclusion and Implications
Our results showed an L-shaped relationship between LSM score and mortality risk. These results suggest that even a slight expansion of the mobility range of older adults with a constricted life-space can reduce mortality risk. These findings may encourage many withdrawn older adults whose life-spaces are restricted for various reasons and provide useful information for establishing target values to aim for when increasing life-space.
Acknowledgments
We would like to express our appreciation to all participants of this study and to all individuals involved in the data collection. We acknowledge the several administrative staff of Kameoka city and Kyoto prefecture who contributed. We would like to thank the Kyoto-Kameoka Study Group who contributed their resources to the development of this study. We also thank Editage (www.editage.jp) for English-language editing.
Supplementary Data
Supplementary Table 1Characteristics of Participants With and Without Certification of Requiring Help and Long-Term Care
From the data obtained on disease status (including the presence of hypertension, stroke, heart disease, diabetes, hyperlipidemia, digestive disease, respiratory disease, urological diseases, and cancer), the comorbidity scores were summed to obtain a total score ranging from 0 (no comorbidity) to 9 (poor status).
0.94 (0.98)
1.31 (1.06)
1.23 (1.04)
1.26 (1.33)
1.13 (1.13)
<.001
HSES, high socioeconomic status; PD, population density.
Data for participants with missing values were imputed by multiple imputation: body mass index (n = 684, 6.2%); family structure (n = 786, 7.1%); socioeconomic status (n = 502, 4.5%); education (n = 1346, 12.2%); smoking status (n = 448, 4.0%); alcohol status (n = 385, 3.5%); self-reported health (n = 410, 3.7%); physical activity (n = 204, 1.8%); and medications (n = 811, 7.3%).
∗ Continuous variables are shown in terms of mean with SD and were analyzed using variance analysis.
† Categorical variables are shown in terms of the number of cases with percentage and were analyzed using Pearson χ2 test.
‡ From the data obtained on disease status (including the presence of hypertension, stroke, heart disease, diabetes, hyperlipidemia, digestive disease, respiratory disease, urological diseases, and cancer), the comorbidity scores were summed to obtain a total score ranging from 0 (no comorbidity) to 9 (poor status).
From the data obtained on disease status (including the presence of hypertension, stroke, heart disease, diabetes, hyperlipidemia, digestive disease, respiratory disease, urologic diseases, and cancer), the comorbidity scores were summed to obtain a total score ranging from 0 (no comorbidity) to 9 (poor status).
0.94 (0.98)
1.00 (1.05)
.004
HSES, high socioeconomic status; PD, population density.
Data for participants with missing values were imputed by multiple imputation [n = (n in included participants) and (n in excluded participants)]: body mass index (n = 531 and 507); family structure (n = 725 and 394); socioeconomic status (n = 450 and 280); education (n = 1149 and 745); smoking status (n = 415 and 287); alcohol status (n = 355 and 251); self-reported health (n = 368 and 218); physical activity (n = 185 and 369); and medications (n = 761 and 379).
∗ Continuous variables are shown in terms of mean with SD and were analyzed using variance analysis.
† Categorical variables are shown in terms of the number of cases with percentage and were analyzed using Pearson χ2 test.
‡ From the data obtained on disease status (including the presence of hypertension, stroke, heart disease, diabetes, hyperlipidemia, digestive disease, respiratory disease, urologic diseases, and cancer), the comorbidity scores were summed to obtain a total score ranging from 0 (no comorbidity) to 9 (poor status).
Model 2: In addition to the factors listed in model 1, adjusted for body mass index, family structure, economic status, educational attainment, smoking status, alcohol consumption status, self-reported health status, physical activity, number of drugs, and number of chronic diseases.
Model 2: In addition to the factors listed in model 1, adjusted for body mass index, family structure, economic status, educational attainment, smoking status, alcohol consumption status, self-reported health status, physical activity, number of drugs, and number of chronic diseases.
1.00 (Ref)
0.76 (0.63-0.92)
0.69 (0.55-0.86)
0.76 (0.59-0.96)
<.001
HR, hazard ratio; LSM, life-space mobility; PY, person-years; Q, quartiles; Ref, reference.
Q1, Q2, Q3, and Q4 include the life-space mobility score of ≤31.5, 32.0-58.5, 60.0-80.0, and ≥82.0 in participants with complete case, respectively; life-space mobility score of ≤31.5, 32.0-58.5, 60.0-80.0, and ≥82.0 in participants with only ≥2 years' events, respectively.
∗ Linear trend P values were calculated using the likelihood ratio test and a continuous variable of LSM score.
† Model 1: Adjusted for age, sex, and population density.
‡ Model 2: In addition to the factors listed in model 1, adjusted for body mass index, family structure, economic status, educational attainment, smoking status, alcohol consumption status, self-reported health status, physical activity, number of drugs, and number of chronic diseases.
Model 2: In addition to the factors listed in model 1, adjusted for body mass index, family structure, economic status, educational attainment, smoking status, alcohol consumption status, self-reported health status, physical activity, number of drugs, and number of chronic diseases.
Model 2: In addition to the factors listed in model 1, adjusted for body mass index, family structure, economic status, educational attainment, smoking status, alcohol consumption status, self-reported health status, physical activity, number of drugs, and number of chronic diseases.
Model 2: In addition to the factors listed in model 1, adjusted for body mass index, family structure, economic status, educational attainment, smoking status, alcohol consumption status, self-reported health status, physical activity, number of drugs, and number of chronic diseases.
Model 2: In addition to the factors listed in model 1, adjusted for body mass index, family structure, economic status, educational attainment, smoking status, alcohol consumption status, self-reported health status, physical activity, number of drugs, and number of chronic diseases.
Model 2: In addition to the factors listed in model 1, adjusted for body mass index, family structure, economic status, educational attainment, smoking status, alcohol consumption status, self-reported health status, physical activity, number of drugs, and number of chronic diseases.
Model 2: In addition to the factors listed in model 1, adjusted for body mass index, family structure, economic status, educational attainment, smoking status, alcohol consumption status, self-reported health status, physical activity, number of drugs, and number of chronic diseases.
1.00 (Ref)
0.82 (0.70-0.98)
0.68 (0.55-0.83)
0.68 (0.55-0.85)
<.001
HR, hazard ratio; LSM, life-space mobility; PY, person-years; Q, quartiles; Ref, reference.
Q1, Q2, Q3, and Q4 include the life-space mobility score of ≤35.0, 36.0-61.5, 62.0-82.5, and ≥84.0, respectively, in men; ≤27.0, 28.0-55.5, 56.0-76.0, and ≥78.0, respectively, in women; ≤43.5, 44.0-64.0, 66.0-84.0, and ≥86.0, respectively, in participants <75 years old; ≤16.5, 17.0-40.5, 42.0-66.0, and ≥68.0, respectively, in participants ≥75 years old; ≤30.0, 31.5-54.0, 56.0-76.0, and ≥78.0, respectively, in participants living alone; and ≤31.5, 32.0-58.5, 60.0-80.0, and ≥82.0, respectively, in participants with other family structures.
∗ Linear trend P values were calculated using the likelihood ratio test and a continuous variable of LSM score.
† Model 1: Adjusted for age, sex, and population density.
‡ Model 2: In addition to the factors listed in model 1, adjusted for body mass index, family structure, economic status, educational attainment, smoking status, alcohol consumption status, self-reported health status, physical activity, number of drugs, and number of chronic diseases.
Supplementary Table 5Hazard Ratios for Life-Space Assessment Subdomains and All-Cause Mortality Calculated Using the Multivariate Cox Proportional Hazards Model
Model 2: In addition to the factors listed in model 1, adjusted for body mass index, family structure, economic status, educational attainment, smoking status, alcohol consumption status, self-reported health status, physical activity, number of drugs, and number of chronic diseases.
Rate (95% CI)
HR (95% CI)
HR (95% CI)
HR (95% CI)
Within home
Case
8398
755
42,528
17.8 (16.5, 19.1)
1.00 (Ref)
1.00 (Ref)
1.00 (Ref)
Noncase
1616
275
7783
35.3 (31.4, 39.8)
2.00 (1.74, 2.30)
1.56 (1.36, 1.79)
1.37 (1.19, 1.58)
Outside home
Case
7458
629
37,824
16.6 (15.4, 18.0)
1.00 (Ref)
1.00 (Ref)
1.00 (Ref)
Noncase
2556
401
12,487
32.1 (29.1, 35.4)
1.94 (1.71, 2.20)
1.57 (1.38, 1.79)
1.35 (1.18, 1.54)
Neighborhood
Case
8354
716
34,963
16.9 (15.7, 18.2)
1.00 (Ref)
1.00 (Ref)
1.00 (Ref)
Noncase
1660
314
16,020
39.5 (35.4, 44.2)
2.36 (2.06, 2.69)
1.69 (1.48, 1.94)
1.41 (1.23, 1.62)
Within town
Case
8477
712
40,694
16.6 (15.4, 17.8)
1.00 (Ref)
1.00 (Ref)
1.00 (Ref)
Noncase
1537
318
10,290
43.6 (39.1, 48.7)
2.66 (2.33, 3.03)
1.76 (1.53, 2.03)
1.50 (1.30, 1.73)
Outside town
Case
5796
453
29,470
15.4 (14.0, 16.9)
1.00 (Ref)
1.00 (Ref)
1.00 (Ref)
Noncase
4218
577
20,841
27.7 (25.5, 30.0)
1.81 (1.60, 2.04)
1.24 (1.09, 1.41)
1.07 (0.94, 1.23)
HR, hazard ratio; PY, person-years; Ref, reference.
Average LSM for the past month was evaluated using the following questions on range of mobility: Have you been to other rooms of your home besides the room where you sleep? (level 1: within home); Have you been to an area outside your home? (level 2: outside home); Have you been to places in your neighborhood? (level 3: neighborhood); Have you been to places outside your neighborhood, but within your town? (level 4: within town); Have you been to places outside your town? (level 5: outside town). Participants who responded “no” to these questions were defined as “noncase” for each life-space level.
∗ Model 1: Adjusted for age, sex, and population density.
† Model 2: In addition to the factors listed in model 1, adjusted for body mass index, family structure, economic status, educational attainment, smoking status, alcohol consumption status, self-reported health status, physical activity, number of drugs, and number of chronic diseases.
Supplementary Fig. 1Association between life-space mobility score and all-cause mortality using a multivariate regression model adjusted of transportation status among older adults. (A) Multivariate adjusted Kaplan-Meier survival curves using inverse probability weighting according to quartile (Qs). (B) Restricted cubic spline model. Solid lines represent hazard ratios, and dashed lines represent 95% CIs. The hazard ratio based on 0 point for life-space mobility score as reference was calculated. We estimated that P < .05 when the 95% CI of the hazard ratio did not exceed 1.00, and P ≥ .05 when the 95% CI of the hazard ratio exceeded 1.00. The adjustment factors are age, sex, population density, body mass index, family structure, economic status, educational attainment, smoking status, alcohol consumption status, self-reported health, physical activity, number of drugs, number of chronic diseases, use public transportation, get in car, and drive car myself.
Prevalence of frailty assessed by Fried and Kihon checklist indexes in a prospective cohort study: Design and demographics of the Kyoto-Kameoka longitudinal study.
Effects of treating depression and low perceived social support on clinical events after myocardial infarction: The Enhancing Recovery in Coronary Heart Disease Patients (ENRICHD) Randomized Trial.
Life-space mobility and active aging as factors underlying quality of life among older people before and during COVID-19 lockdown in Finland—a longitudinal study.
The Kyoto-Kameoka study was conducted with JSPS KAKENHI and was supported by a research grant provided to Misaka Kimura (24240091), Yosuke Yamada (15H05363), and Daiki Watanabe (21K17699); a grant and administrative support by the Kyoto Prefecture Community-based Integrated older adults Care Systems Promotion Organization since 2011; and by Kameoka City under the program of the Long-term Care Insurance and Planning Division of the Health and Welfare Bureau for the older adults, Ministry of Health, Labour and Welfare and the World Health Organization (WHO) Collaborating Centre on Community Safety Promotion.
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