Abstract
Background
It is important to understand the factors associated with life-space mobility so that mobility disability can be prevented/treated. The purpose of this study was to identify the association between mobility determinants and life space among older adults.
Methods
This study was a cross-sectional analysis of 249 community-dwelling older adults (mean age = 77.4 years, 65.5% female, 88% White), who were recruited for a randomized, controlled, clinical intervention trial. Associations between cognitive, physical, psychosocial, financial, and environmental mobility determinants and the life-space assessment (LSA) at baseline were determined using Spearman’s correlation coefficients and one-way analysis of variance. Multivariate analysis was performed using multivariable linear regression models.
Results
The mean LSA score for the sample was 75.3 (SD = 17.8). Personal factors (age, gender, education, comorbidities), cognitive (Trail Making Test A and B), physical (gait speed, lower extremity power, 6-Minute Walk Test, Figure of 8 Walk Test, tandem stance, energy cost of walking, and Late-Life Function and Disability Function Scale), psychosocial (Modified Gait Efficacy Scale), and financial (neighborhood socioeconomic status) domains of mobility were significantly associated with LSA score. In the final regression model, age (β = −0.43), lower extremity power (β = 0.03), gait efficacy (β = 0.19), and energy cost of walking (β = −57.41) were associated with life space (R2 = 0.238).
Conclusions
Younger age, greater lower extremity power, more confidence in walking, and lower energy cost of walking were associated with greater life space. Clinicians treating individuals with mobility disability should consider personal, physical, and psychosocial factors when assessing barriers to life-space mobility.
Keywords: Aging, Functional independence, Mobility limitation, Physical function, Rehabilitation
Life-space mobility is defined as the area an individual travels extending from the room in which they sleep outward to the community (1). Limited life space and declines in life-space mobility are related to disability, poorer quality of life, fall risk, and mortality in older adults (2–6). Mobility is an important component of life space and is essential for maintaining independence and an active lifestyle among older adults. Loss of mobility is also associated with disability, lower quality of life, institutionalization, and death (3,4,6–8).
There are many interrelated factors that can affect mobility from biological characteristics to societal influences, but much of the existing literature focuses on physical aspects of mobility or only on isolated domains of mobility (9). In order to conceptualize the complexities of mobility determinants, Webber et al. (9) suggested a framework including cognitive, psychosocial, environmental, and financial components to mobility in addition to physical function and gender, cultural, and biographical influences.
Using a conceptual framework to understand the many interrelated factors that are associated with life-space mobility may enable clinicians to address these factors through targeted prevention and/or treatment strategies. Previous studies have used Webber’s mobility framework to identify modifiable factors related to life space and found that gait speed, driving, and social support and age, strength, and other measures of physical function explain some of the variation in life-space mobility among older adults representing the personal, physical, cognitive, and psychosocial domains (10,11). However, to the best of our knowledge, the entirety of Webber’s comprehensive model has not yet been applied to identify mobility determinants associated with life space among older adults. The purpose of this analysis was to utilize Webber’s comprehensive mobility framework to determine the relationship between personal factors, cognitive, psychosocial, physical, environmental, and financial determinants of mobility and life space among community-dwelling older adults. Based on previous findings indicating stronger associations with physical function and the life apace assessment (LSA) than measures of cognitive function and psychological factors (1,12,13), we hypothesized that physical determinants would have the strongest associations with life-space mobility, but that determinants in the other domains would also be significantly associated with life space.
Method
Participants
This cross-sectional analysis utilized baseline data from a randomized, single-blind, 2-arm intervention trial comparing the effects of a standard physical therapy program to a standard plus timing and coordination program on mobility in community-dwelling older adults (14). Older adults were recruited from the Pittsburgh Pepper Center Registry and were included if they were 65 years or older, had a walking speed of 0.6–1.2 m/s, ambulated without an assistive device, and had physician clearance to participate. This gait speed range was selected to recruit participants who were at risk for negative outcomes, but who had sufficient mobility to participate in the intervention (14,15). Participants were excluded if they had a diagnosis of dementia or cognitive impairment defined as a score of less than 79 on the Modified Mini-Mental State Examination (3MS) (14,17). Study recruitment, assessments, and interventions took place at the University of Pittsburgh Physical Therapy Clinical and Translational Research Center. Further details on the recruitment process and the inclusion and exclusion criteria have been described elsewhere (14). The study was approved by the University of Pittsburgh Institutional Review Board, and all participants provided informed consent to participate in the study.
Dependent Variable
Life-space assessment
At baseline, participants completed a battery of patient-reported outcome measures and clinical performance-based measures. For the purposes of this analysis, the dependent variable of interest was the LSA (12). The LSA was developed to measure self-reported achieved mobility over a 1-month period and is a valid and reliable measure of life space in community-dwelling older adults (1,12). The questionnaire assesses mobility in different life-space areas starting inside the room where the individual sleeps and extending to out of the individual’s town. This measure also considers assistance required and frequency of movement into life-space areas. Scores range from 0 to 120 with higher scores indicating more frequent movement over larger geographic areas with less assistance.
Independent Variables
Personal factors
Demographic variables such as age, gender, race, and level of education were self-reported. Also, living situation, current employment, level of physical activity (categorized by 150 minutes per week), and fall history in the past year were self-reported. Comorbidities were self-reported and measured using the Duke Comorbidity Index taking into consideration cardiac, neurological, musculoskeletal, visual, and pulmonary conditions as well as depression, sleep, pain, diabetes, and cancer (16). These were categorized and summed to create a score ranging from 0 to 8. Body mass index was calculated using measured height and weight.
Cognitive domain
Mental status and executive functioning were measured using the 3MS and the Trail Making Test (TMT) A and B (17,18). Higher scores on the 3MS indicate better general cognitive functioning. The TMT measures components of executive function and is scored as a number of seconds to complete each test. Lower scores on TMT A indicate better speed of processing and lower scores on TMT B indicate better task switching.
Physical domain
Participants completed performance measures and patient-reported outcome measures to assess physical function. Lower extremity power was measured using an electronic pneumatic leg press machine. Power was tested at 40% and 70% of 1-repetition maximum (1RM) for each leg. Right lower extremity power tested at 40% of 1RM was used as the measure of power in the present analysis. The time required to stand up from a chair 5 times was measured in the Short Physical Performance Battery (SPPB), and the score was included as an indicator of functional lower extremity strength (19). The tandem stand balance test included in the SPPB was measured as a categorical variable (able to stand for 10 seconds or not able to stand for 10 seconds) (19).
Usual pace gait speed was measured using an instrumented walkway. Participants completed 6 trials at a self-selected walking speed and then averaged, which is a reliable way to measure gait speed (20). Endurance was measured using the 6-Minute Walk Test (6MWT), which was the distance walked over a 6-minute period including rest breaks (21). The 6MWT demonstrates reliability and validity with other measures of physical function (22). Timing and coordination of walking were measured using the Figure of 8 Walk Test, which was developed to measure motor skills in walking (23,24). The Figure of 8 Walk Test has construct validity with relationships to other measures of gait and physical function. Gait efficiency was measured using the energy cost of walking, which reflects the energy used for bodily functions during walking (25,26). Participants walked on a treadmill at a self-selected pace while oxygen consumption data were collected. The mean rate of oxygen and carbon dioxide consumption was determined over 3 minutes after reaching a steady state. Steady state was determined by identifying the plateau of oxygen consumption (ml/kg/min) during walking, defined as a variation of less than 3 ml/kg/min (27). Gait speed during the steady-state period was held constant. Energy cost was calculated as the mean oxygen consumption during steady state divided by the gait speed (27). The energy cost of walking is reported in ml/kg/min, representing an estimate of energy expenditure per unit of gait speed, where a lower cost is better. Energy cost of walking reflects the physiologic cost of gait and can be compared across individuals and over time, regardless of changes in walking speed (25–28).
Self-reported functional impairments were measured using the Late-Life Function and Disability Instrument (LLFDI) functioning component, which includes the upper extremity, basic lower extremity, and advanced lower extremity domains (29). The function component of the LLFDI demonstrates high test–retest reliability and construct validity among community-dwelling older adults (30).
Psychosocial domain
The psychosocial domain was represented using the modified Gait Efficacy Scale (mGES), the Geriatric Depression Scale (GDS), and fear of falling (yes/no) (31,32). The mGES measures confidence in walking and ranges from 0 to 100 with higher scores indicating more confidence in walking ability. The mGES has excellent test–retest reliability and construct validity with other measures of fear and confidence in community-dwelling older adults (31). The GDS was developed as a screening tool for depression in older adults and ranges from 0 to 15 with higher scores indicating more depressive symptoms (32,33). The GDS has adequate internal consistency and construct validity with other measures of depression (33). Fear of falling was measured by asking the participant, “Are you afraid of falling?”.
Environmental domain
The environmental domain was measured using the Active Neighborhood Checklist and auditing participants’ neighborhoods using Google Street View (34). This method is reliable for determining the characteristics of the built environment (35,36). Audits for the participants were performed by one member of the research team. Four participants did not have data available due to having post office boxes as their listed address. The Active Neighborhood Checklist currently does not have an agreed-upon scoring system. Therefore, several items were abstracted from the checklist based on known associations with walkability and summed for a neighborhood walkability score ranging from 0 to 6 with a higher score indicating a more walkable neighborhood environment (37,38). The abstracted items from the Active Neighborhood Checklist were (a) mixed residential land use (vs residential only), (b) living on a street with unmarked lanes or parking lot (vs 2 or more marked lanes), (c) having no intersection or a safe intersection within the audited area (vs an unsafe intersection), (d) having a good or perfect sidewalk (vs fair or not walkable) or if no sidewalk is present, it is safe to walk (vs unsafe), (e) some or a lot of tree shade is present (vs no tree shade present), and (f) having a flat or moderate slope (vs a steep slope). Each of these items was measured on a dichotomous scale and summed for a total score with higher scores indicating greater walkability.
Financial domain
The financial domain was measured by neighborhood socioeconomic status (NSES), which was collected from the 2018 US Census data. The summary NSES z-score was calculated using 6 variables to measure dimensions of neighborhood wealth, education, and occupation (39,40). The variables included median household income, median value of housing units, percentage of households receiving interest, dividend, or net rental income, percentage of adults who completed high school, percentage of adults who completed college, and percentage of persons in managerial or professional specialty occupations (39,40). Higher scores indicate higher NSES.
Statistical Analyses
Personal factors and outcome measures were characterized using descriptive statistics. The bivariate correlations between continuous measures in the cognitive, physical, psychosocial, environmental, and financial domains with LSA were assessed using Spearman’s correlation coefficient (ρ). For categorical variables, the differences in LSA score among subgroups were determined using a one-way analysis of variance. To account for missing data for the lower extremity power measures (14.9% missing) and energy cost of walking (8.0% missing), multivariable linear regression was used to impute the missing data using the following variables: age, gender, gait speed, 6MWT distance, Figure of 8 Walk time, LLFDI overall function scale score, GDS, mGES, LSA total score, and fear of falling. Participants who had missing data demonstrated poorer function than those without missing data, thus the data were likely not missing at random. Those with missing data for lower extremity power did not differ in LSA score from those with nonmissing data; however, participants with missing data for energy cost of walking had lower LSA scores than those with nonmissing data.
Six multivariable linear regression models were analyzed using variables that demonstrated a significant relationship (p < .2) with LSA score in bivariate analyses to determine the relationship between factors included in each domain and LSA total score. Then, a final multivariate linear regression model combining variables from each domain regression model (p < .2) was created using backward elimination (p < .05). We assessed normality and heteroskedasticity with residual plots. We did not introduce random effects in the model; hence, the independent variables (predictors) were considered as fixed effects. Multicollinearity was assessed by using Spearman’s correlation coefficients indicating associations between the independent variables and by the variance inflation factor (VIF). Independent variables with correlation coefficients greater than 0.7 were reviewed, and the variable with the strongest relationship to the LSA was retained in the model. All analyses were conducted using SAS, version 9.4 (SAS Institute, Inc., Cary, NC).
Results
Baseline Characteristics
The mean age of the sample was 77.4 (±6.6) years and 65.5% of participants were female (Table 1). The participants were mostly White (88%) and had college (39%) or postgraduate education (44.6%). On average, the comorbidity index for the sample was 2.9, and 29.7% of participants reported experiencing a fall in the past year.
Table 1.
Demographic Characteristics and Outcome Measures for 249 Community-Dwelling Older Adults
| Mean (SD) | N (%) | |
|---|---|---|
| Age, years | 77.4 (6.6) | |
| Gender | ||
| Female | 163 (65.5) | |
| Male | 86 (34.5) | |
| Race | ||
| White | 219 (88) | |
| Other race | 30 (12) | |
| Body mass index | 28.7 (5.8) | |
| Living situation | ||
| Lives alone | 114 (45.8) | |
| Lives with someone | 135 (54.2) | |
| Education | ||
| High school | 41 (16.5) | |
| College | 97 (39) | |
| Postgraduate | 111 (44.6) | |
| Work status | ||
| Work/volunteered in the past week | 79 (31.7) | |
| Did not work in the past week | 170 (68.3) | |
| Comorbidity index (0–8) | 2.9 (1.3) | |
| Physical activity | ||
| Participate in ≥150 minutes per week | 115 (46.2) | |
| Participate in <150 minutes per week | 134 (53.8) | |
| Fall history | ||
| Fall in the past year | 74 (29.7) | |
| No fall in the past year | 175 (70.3) | |
| Life-space assessment (0–120) | 75.3 (17.8) | |
| Cognitive domain | ||
| Modified Mini-Mental State Test (0–100) | 96 (4.2) | |
| Trail Making Test A, seconds | 33.6 (13) | |
| Trail Making Test B, seconds (n = 248) | 84.9 (46.1) | |
| Physical domain | ||
| Right lower extremity 40% 1RM* | 332.9 (139.4) | |
| Gait speed, m/s | 1.07 (0.16) | |
| Repeated chair stands, seconds (n = 242) | 13.5 (3.8) | |
| Tandem balance | ||
| Able to tandem stand 10 s | 142 (57.0) | |
| Unable to tandem stand 10 s | 107 (43.0) | |
| Figure of 8 Walk Test, seconds | 10 (2.1) | |
| Six-Minute Walk Test, minutes | 399.3 (89.8) | |
| Energy cost of walking, ml/kg/min* | 0.24 (0.07) | |
| LLFDI function overall (0–100) | 61.5 (8.5) | |
| LLFDI upper extremity function | 78.5 (11.1) | |
| LLFDI basic lower extremity function | 74.4 (12.9) | |
| LLFDI advanced lower extremity function | 51.8 (13.6) | |
| Psychosocial domain | ||
| Fear of falling | ||
| Fearful | 101 (40.6) | |
| Not fearful | 148 (59.4) | |
| Modified Gait Efficacy Scale (0–100) | 85.1 (13.6) | |
| Geriatric Depression Scale (0–15) | 1.1 (1.3) | |
| Financial domain | ||
| Neighborhood socioeconomic status | −0.011 (1.87) | |
| Environmental domain | ||
| Neighborhood walkability score (0–6) | 4.1 (0.9) |
Notes: LLFDI = Late-Life Function and Disability Instrument; 1RM = 1-repetition maximum. Other race includes Black (n = 22), Asian (n = 1), Native Hawaiian or Pacific Islander (n = 2), multiple (n = 1), and refused (n = 4).
*Imputed missing data for right lower extremity power (n = 212) and energy cost of walking (n = 209).
Outcome Measures
The mean LSA total score for the older adults in the sample was 75.3 (SD = 17.8; range: 32–120; Table 1). The dependent variable (LSA score) was normally distributed. On average, the participants scored 96/100 on the 3MS and took 33.6 seconds to complete the TMT A and 84.9 seconds to complete TMT B. Given the outcome measures in the physical domain, the sample had relatively good function with an average gait speed of 1.07 m/s and an average of 13.5 seconds to complete 5 chair stands. However, only 57.3% of the sample could tandem stand for 10 seconds. Self-reported function on the LLFDI function component was 61.5 (SD = 8.5). Participants reported a mean of 85.1 on the mGES and 1.1 on the GDS. Almost half of the sample (40.6%) reported being fearful of falling. The average NSES summary z-score was −0.011, and the average neighborhood walkability score was 4.1.
Bivariate Associations
Within the personal factors domain, the LSA had significant negative correlations with age (ρ = −0.259, p < .001) and comorbidity index (ρ = −0.127, p = .05; Table 2). The mean LSA total score was higher for male participants (78.7) compared to female participants (73.5) and lower for participants who completed high school education (67.5) versus those who completed college (75.6) or postgraduate education (78.6; p = .03, p = .002).
Table 2.
Bivariate Associations Between Mobility Determinants and Life-Space Assessment in 249 Older Adults
| Mean (SD) LSA Score | ρ | p | |
|---|---|---|---|
| Age (years) | −0.259 | <.001* | |
| Gender | .03* | ||
| Male | 78.7 (15.3) | ||
| Female | 73.5 (18.8) | ||
| Race | .1 | ||
| White | 75.9 (17.4) | ||
| Other race | 70.7 (20.1) | ||
| Body mass index | 0.052 | .4 | |
| Living situation | .7 | ||
| Lives alone | 74.9 (18.5) | ||
| Lives with someone | 75.6 (17.3) | ||
| Education | .002* | ||
| High school | 67.5 (15.2) | ||
| College | 75.6 (19.8) | ||
| Postgraduate | 78.6 (16.3) | ||
| Comorbidity index | −0.127 | .05* | |
| Physical activity | .7 | ||
| Participate in ≥150 minutes per week | 77.3 (15.6) | ||
| Participate in <150 minutes per week | 73.6 (19.3) | ||
| Fall history | .9 | ||
| Fall in the past year | 75.4 (19) | ||
| No fall in the past year | 75.4 (17.3) | ||
| Cognitive domain | |||
| Modified Mini-Mental State Test | 0.063 | .3 | |
| Trail Making Test A | −0.223 | <.001* | |
| Trail Making Test B | −0.174 | .006* | |
| Physical domain | |||
| Right lower extremity 40% 1RM* | 0.343 | <.001* | |
| Gait speed | 0.255 | <.001* | |
| Tandem balance | .004* | ||
| Able to tandem stand 10 s | 78.2 (16.9) | ||
| Unable to tandem stand 10 s | 71.3 (18) | ||
| Repeated chair stands (n = 242) | −0.104 | .1 | |
| Figure of 8 Walk Test | −0.276 | <.001* | |
| Six-Minute Walk Test | 0.332 | <.001* | |
| Energy cost of walking, ml/kg/min* | −0.296 | <.001* | |
| LLFDI function overall | 0.303 | <.001* | |
| LLFDI upper extremity function | 0.175 | .006* | |
| LLFDI basic lower extremity function | 0.230 | <.001* | |
| LLFDI advanced lower extremity function | 0.322 | <.001* | |
| Psychosocial domain | |||
| Modified Gait Efficacy Scale | 0.217 | <.001* | |
| Geriatric Depression Scale | −0.081 | .2 | |
| Fear of falling | .2 | ||
| Fearful | 73.6 (18.3) | ||
| Not fearful | 76.4 (17.4) | ||
| Financial domain | |||
| Neighborhood socioeconomic status | 0.144 | .02* | |
| Environmental domain | |||
| Neighborhood walkability score | 0.102 | .1 |
Notes: LLFDI = Late-Life Function and Disability Instrument; 1RM = 1-repetition maximum. For all categorical variables, a one-way analysis of variance was analyzed to determine differences in life-space assessment scores between groups. For all continuous variables, Spearman’s rho correlation coefficient was analyzed to determine the association between life-space assessment scores.
*Imputed missing data for right lower extremity power (n = 212) and energy cost of walking (n = 209).
*p < .05.
In the cognitive domain, the TMT A (ρ = −0.223, p < .001) and B (ρ = −0.174, p = .006) were significantly associated with total LSA score. In the physical domain, there were significant positive associations between LSA total score and gait speed (ρ = 0.255), right lower extremity power (ρ = 0.343), 6MWT distance (ρ = 0.332), LLFDI overall function (ρ = 0.303), upper extremity function (ρ = 0.175), basic lower extremity function (ρ = 0.230), and advanced lower extremity function (ρ = 0.322) subscale scores. There were significant negative associations between the LSA total score and time to complete the Figure of 8 Walk Test (ρ = −0.276) and energy cost of walking (ρ = −0.296). Also, participants who could tandem stand for at least 10 seconds had significantly higher LSA scores (78.2) than participants who could not (71.7; p = .004). In the psychosocial domain, the mGES demonstrated a positive association with the LSA total score (ρ = 0.217, p < .001). Finally, in the financial domain, the NSES summary z-score had a significant positive relationship with the total LSA score (ρ = 0.144, p = .02). There was no significant association between the neighborhood walkability score and life space.
Multivariate Linear Regression Models
The domain-specific variables that were entered into the final model encompassed all mobility domains: personal (age, gender, race, education, and comorbidity index), cognitive (TMT B), physical (right lower extremity power, Figure of 8 Walk Test, tandem balance, energy cost of walking, and LLFDI upper extremity function score), psychosocial (mGES score), financial (NSES summary score), and environmental (neighborhood walkability score). After backward elimination, the significant variables (p < .05) that were retained in the model were age, mGES score, energy cost of walking, and right lower extremity power (Table 3). The assumptions of normality and heteroskedasticity were met for all regression models after inspection of the residual plots. When evaluating the correlations between independent variables, we found only one association that was greater than 0.70 (gait speed and 6MWT distance). Therefore, gait speed was not included in any multivariate linear regression model because 6MWT had a stronger association to LSA score. All other correlation coefficients between independent variables were ≤0.63. The VIF for all independent variables in all regression models was less than 2, meeting the assumption of no multicollinearity (41).
Table 3.
Multivariate Linear Regression Model Predicting Life Space Among 249 Older Adults
| Full Model* R2 = 0.273 |
Parsimonious Model† R2 = 0.238 |
|||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| Standardized B | Unstandardized B | SE | p | VIF | Standardized B | Unstandardized B | SE | p | VIF | |
| Personal factors | ||||||||||
| Age | −0.17 | −0.45 | 0.18 | .01* | 1.39 | −0.16 | −0.43 | 0.16 | .006* | 1.06 |
| Female gender | 0.07 | 2.71 | 2.85 | .3 | 1.85 | |||||
| White race | 0.07 | 3.64 | 3.37 | .3 | 1.19 | |||||
| High school education‡ | −0.09 | −4.63 | 3.28 | .2 | 1.51 | |||||
| College education‡ | −0.03 | −1.42 | 2.30 | .5 | 1.27 | |||||
| Comorbidity index | 0.86 | −0.83 | 0.86 | .3 | 1.23 | |||||
| Cognitive domain | ||||||||||
| Trail Making Test B | −0.02 | −0.01 | 0.03 | .8 | 1.38 | |||||
| Physical domain | ||||||||||
| Figure of 8 Walk Test | −0.02 | −0.23 | 0.59 | .7 | 1.60 | |||||
| Right lower extremity power§ | 0.20 | 0.02 | 0.01 | .008* | 1.71 | 0.25 | 0.03 | 0.01 | <.001* | 1.07 |
| Energy cost of walking§ | −0.20 | −50.65 | 14.93 | .001* | 1.15 | −0.24 | −57.41 | 14.0 | <.001* | 1.08 |
| LLFDI UE function | 0.04 | 0.06 | 0.10 | .6 | 1.32 | |||||
| Unable to tandem stand | −0.06 | −2.28 | 2.24 | .3 | 1.24 | |||||
| Psychosocial domain | ||||||||||
| Gait efficacy | 0.07 | 0.09 | 0.09 | .4 | 1.57 | 0.14 | 0.19 | 0.08 | .01* | 1.08 |
| Financial domain | ||||||||||
| Neighborhood SES | 0.04 | 0.39 | 0.64 | .5 | 1.45 | |||||
| Environmental domain | ||||||||||
| Neighborhood walkability score | 0.06 | 1.24 | 1.19 | .3 | 1.21 |
Note: LLFDI = Late-Life Function and Disability Instrument; UE = upper extremity; SES = socioeconomic status; VIF = variance inflation factor.
*n = 245.
† n = 249.
‡Reference group is postgraduate education.
§Imputed missing data for right lower extremity power (n = 212) and energy cost of walking (n = 209).
*p < .05.
Discussion
In this analysis of mobility determinants associated with life space among community-dwelling older adults, we found that personal factors, cognitive, physical, psychosocial, and financial determinants of mobility were associated with life space in bivariate models. In the final parsimonious model, younger age, greater lower extremity power, more confidence in walking, and lower energy cost of walking were significantly associated with greater life-space mobility. These variables in the parsimonious model represent personal factors as well as psychosocial and physical determinants of mobility.
With bivariate associations, we found that age, gender, education, and comorbidities were significantly associated with life space, representing personal factors, or gender, culture, and personal life history in Webber’s model of mobility. In the cognitive domain, only the executive function measures TMT A and B were significantly associated with the LSA, which concurs with a recent review that identified moderate relationships between measures of cognitive function and life space (42). All of the physical function measures included to measure the physical domain of mobility were significantly associated with life space, except for repeated chair stands. Previous works have demonstrated the association between measures of physical function and life-space mobility (1,10–12,43). The mGES score was significantly associated with the LSA, representing the psychosocial domain. In a multisite study, fear of falling measured with the Falls Efficacy Scale was significantly associated with life space even after controlling for other physical and demographic variables (44). Finally, the NSES variable, which represents a summary of neighborhood income, education, and employment, was significantly associated with life-space mobility. This is in agreement with a previous study examining the associations between LSA and level of education and other indicators of SES (45). While we did not find a significant relationship between neighborhood walkability and life space, others have found that the presence of environmental barriers is associated with a greater odds of having restricted life space (46).
The results of our final linear regression model suggest that approximately 24% of the variance in LSA score can be explained by age, lower extremity power, gait efficacy, and gait efficiency. This is interesting given our comprehensive measurement of all proposed mobility domains and demonstrates the complexity of the construct of life-space mobility. However, we recognize that we could not include all potential factors related to life space, such as driving status and social support, which other investigators have found to be related to life space (11,47). Furthermore, as a recent study by Seinsche et al. points out, it may be prudent to consider more than one mobility framework, such as Kaufmann’s motility framework that takes into consideration motivations, skills, and access to services (48,49). In the study, they found that social skills and executive function were most strongly associated with LSA (48). It is also interesting that age remained in the regression model even after controlling for many other health-related factors linked to mobility. Giannouli et al. (10) also found age to be significantly related to maximum action range (a GPS indicator of life space) after controlling for other variables related to physical function. However, it is unlikely that age itself is related to life space and is likely a proxy for other potentially modifiable variables that we were not able to measure in this study. For example, others have found associations between driving ability and gait quality measures and life space, which we did not measure here (11,50).
Gait efficiency measured by energy cost of walking and lower extremity power accounted for much of the variance in life space in the final regression model. This supports the body of literature recognizing the importance of energy cost of walking to maintaining independence and its potential contributions to declines in gait speed and mobility disability (27,51,52). Fortunately, there is evidence that physical therapy interventions with a focus on timing and coordination of gait can reduce the energy cost of walking and perhaps, subsequently, improve life-space mobility (53,54). The fact that energy cost of walking remained in the model even after controlling for gait speed, walking endurance, and other gait measures supports the hypothesis that it may be an early marker of mobility decline (51). The participants in this study may have had enough energy reserve to complete clinical performance tests but may exhibit restrictions in life-space mobility in their real-world environment.
Limitations
There were limitations to this analysis, including the fact that this sample of community-dwelling older adults had a relatively high level of function and were generally well educated and cognitively intact, therefore limiting generalizability to the population of older adults. For the variables lower extremity power and energy cost of walking, data were likely not missing at random as individuals with missing data had a lower function, which may bias the results of the study to higher functioning older adults. However, there was a small percentage of missing data (14.9% and 8%), LSA scores did not differ among those with missing versus nonmissing data for lower extremity power, and we attempted to address this issue with imputation of missing data.
For some variables, there was a limited range of values, and some variables were measured categorically rather than continuously. Also, some of the mobility domains were not measured in a comprehensive way, which may have influenced the representation of some of the domains in the final model. The NSES was used as a proxy for individual SES, and the neighborhood walkability score has not been validated. In addition, some of the independent variables may have been related as they all measure some aspect of mobility. However, in our assessment of the associations between the independent variables, we found that there were no associations stronger than ρ = 0.63 included in our regression models and the VIFs were within an acceptable range. Finally, this was a cross-sectional analysis, and therefore, we cannot draw conclusions regarding cause and effect from this study. However, this study did provide a very comprehensive measurement of the physical domain and, to the best of our knowledge, was the first study to measure all the mobility domains identified by Webber et al. (9).
Conclusions
In this sample of cognitively intact, community-dwelling older adults, younger age, greater lower extremity power, more confidence in walking, and lower energy cost of walking were significantly associated with greater life-space mobility. The findings highlight the importance of considering personal and psychosocial factors in addition to physical determinants of life-space mobility when treating older adults at risk for mobility disability. Future work should be conducted to identify mobility determinants that are associated with improvements in life space over time so that targeted treatments can be developed to promote increased life-space mobility. Also, research using objective measures, such as GPS mobility indicators, can be used to quantify life space and to identify mobility determinants associated with actually achieved community mobility.
Contributor Information
Pamela M Dunlap, Department of Physical Therapy, University of Pittsburgh, Pennsylvania, USA.
Andrea L Rosso, Department of Epidemiology, University of Pittsburgh, Pennsylvania, USA.
Xiaonan Zhu, Department of Epidemiology, University of Pittsburgh, Pennsylvania, USA.
Brooke N Klatt, Department of Physical Therapy, University of Pittsburgh, Pennsylvania, USA.
Jennifer S Brach, Department of Physical Therapy, University of Pittsburgh, Pennsylvania, USA.
Funding
This work was supported by the National Institutes of Health (R01 AG045252 and K24 AG057728 to J.S.B., R01 AG057671-01A1 to A.L.R., and P30 AG024827 to the Pittsburgh Claude D. Pepper Older Americans Independence Center).
Conflict of Interest
None declared.
Aspects of this work were presented by the first author at the American Physical Therapy Association Combined Sections Meeting 2021 (February 1–28, 2021, virtual meeting).
Impact Statement: We certify that this work is novel. This study provides a comprehensive evaluation of mobility determinants and their association with life space and finds that personal, physical, and psychosocial factors are related to life-space mobility in older adults.
Author Contributions
Study concept and design: P.M.D., A.L.R., and J.S.B. Acquisition of data: J.S.B. Analysis and interpretation of data: P.M.D., A.L.R., X.Z., B.N.K., and J.S.B. Drafting of the manuscript: P.M.D., A.L.R., and J.S.B. Critical revision of the manuscript for important intellectual content: P.M.D., A.L.R., X.Z., B.N.K., and J.S.B.
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