Abstract
Background
Gait speed is a strong predictor of morbidity and mortality in older adults. Understanding the factors associated with gait speed and the associated adverse outcomes will inform mitigation strategies. We assessed the potentially modifiable and nonmodifiable factors associated with gait speed in a large national cohort of middle and older-aged Canadian adults.
Methods
We examined cross-sectional baseline data from the Canadian Longitudinal Study on Aging (CLSA) Comprehensive cohort. The study sample included 20 201 community-dwelling adults aged 45–85 years. The associations between sociodemographic and anthropometric factors, chronic conditions, and cognitive, clinical, and lifestyle factors and 4-m usual gait speed (m/s) were estimated using hierarchical multivariable linear regression.
Results
The coefficient of determination, R 2, of the final regression model was 19.7%, with 12.9% of gait speed variability explained by sociodemographic and anthropometric factors, and nonmodifiable chronic conditions and 6.8% explained by potentially modifiable chronic conditions, cognitive, clinical, and lifestyle factors. Potentially modifiable factors significantly associated with gait speed include cardiovascular conditions (unstandardized regression coefficient, B = −0.018; p < .001), stroke (B = −0.025; p = .003), hypertension (B = −0.007; p = .026), serum Vitamin D (B = 0.004; p < .001), C-reactive protein (B = −0.005; p = .005), depressive symptoms (B = −0.003; p < .001), physical activity (B = 0.0001; p < .001), grip strength (B = 0.003; p < .001), current smoking (B = −0.026; p < .001), severe obesity (B = −0.086; p < .001), and chronic pain (B = −0.008; p = .018).
Conclusions
The correlates of gait speed in adulthood are multifactorial, with many being potentially modifiable through interventions and education. Our results provide a life-course-perspective framework for future longitudinal assessments risk factors affecting gait speed.
Keywords: CLSA, Gait speed, Mobility, Risk factors, Walking
Gait impairments are prevalent among older adults and can severely affect overall health and particularly physical function (1). Slow gait speed specifically is associated with severe medical outcomes including falls and musculoskeletal injuries, multimorbidity, and mortality (2,3). Recently, it has been shown that even gait speed changes and slowing in midlife are associated with future adverse events and cognitive impairment (4). Given the significant and clinically meaningful benefits of mitigating gait speed declines documented in the current literature (eg, improved community ambulation and longevity), it is clear that the measurement of gait speed and its determinants in clinical settings is key to identify and proactively treat older individuals at risk of facing future declines (5–8).
Maintenance of normal gait speed is complex and reflects the interplay of many clinical and lifestyle factors over adulthood (9,10). While studies have examined a wide range of nonmodifiable and potentially modifiable factors affecting gait speed in older adults (11–13), none have been done in large nationally representative samples including those of middle age, which is important for planning early prevention strategies. To address this gap, we identified correlates of gait speed using population-based data from the Canadian Longitudinal Study on Aging (CLSA). Our objectives were to (i) assess the bivariate associations between gait speed and nonmodifiable and potentially modifiable factors, and (ii) use multivariable regression to explain maximal variability in usual gait speed. Interest was on potentially modifiable factors to help focus intervention and education strategies in at-risk subgroups.
Method
Study Design and Source Population
This was a cross-sectional analysis of baseline data (May 2012 to May 2015) from the CLSA Comprehensive cohort. The CLSA is a Canada-wide longitudinal study of aging in a representative sample of ≈50 000 community-dwelling adults aged 45–85 years at recruitment. About 20 000 people in the Tracking cohort provided information via computer-assisted telephone interviews, and another 30 000 in the Comprehensive cohort underwent face-to-face interviews, performed physical assessments, and provided biological samples in-home or at a data collection site (14).
Sampling methods are detailed in Supplementary Methods 1 in the Supplementary Material and data collection procedures have been described elsewhere (14). Briefly, the Comprehensive cohort baseline assessments evaluated sociodemographic characteristics, health status, lifestyle behaviors, cognitive and physical function, and drawn blood and urine samples. Written informed consent was obtained from CLSA participants and study protocols were approved by the ethical review boards of participating institutions. This study follows the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guidelines.
Gait Speed
Usual gait speed, the primary outcome, was measured using a 4-m walk test with static start (15). Participants stood with their toes behind a marked line and were told to walk straight at their usual pace until they walked a few steps past another line 4 m away. Timing began immediately after the assessor said, “Ready, set, go,” and stopped once the participant completely crossed the 4-m finish line. Participants could use assistive devices such as canes and walkers if needed. Time to complete the walk once was recorded in seconds using a stopwatch and was converted into meters per second (m/s).
Nonmodifiable and Potentially Modifiable Factors
The independent factors selected for this study were grouped into the following categories: sociodemographics, anthropometrics, clinical factors, cognitive factors, lifestyle factors, traditionally nonmodifiable chronic conditions, and potentially modifiable chronic conditions. We adopted the definitions of “modifiable” and “non-modifiable” risk factors used in previous studies that identified risk factors for cardiovascular diseases including myocardial infarction and stroke (16,17). This approach was also used in a previous study of risk factors associated with slowing gait (18). Specifically, we considered factors to be nonmodifiable if they are totally unalterable (eg, age, race) or if their progression is inevitable and strategies currently available for delaying their progression are not well understood or strongly effective (eg, neurodegenerative disease, macular degeneration). Conversely, reflecting usage in the Dictionary of Epidemiology (19), we considered factors to be potentially modifiable if they can be changed, managed, or prevented effectively through various established interventions and behavioral modifications (eg, physical activity, hypertension, and cognitive performance).
Age, sex, province of residence, race, and highest level of education attained were included as sociodemographic covariates. Standing height (meters) was included as a nonmodifiable anthropometric covariate. The clinical factors included were grip strength, BMI (“underweight” [<18.5 kg/m2], “normal weight” [18.5–24.9 kg/m2], “overweight” [25.0–29.9 kg/m2], “obese class I” [30.0–34.9 kg/m2], “obese class II [35.0–39.9 kg/m2], and “obese class III” [≥40.0 kg/m2]), chronic pain evaluated with the question, “Are you usually free of pain or discomfort?,” incontinence evaluated with the question, “Do you ever have trouble getting to the bathroom in time?,” depressive symptoms measured using the Center for Epidemiologic Studies Short Depression scale (CESD-10) (20), and sleep disturbance operationalized using the question, “Over the last month, how often did you wake in the middle of the night or too early in the morning and found it difficult to fall asleep again?” The clinical serum markers included were Vitamin D (mmol/L), high-sensitivity C-reactive protein (hsCRP; mg/L), and high-density lipoprotein (HDL; mmol/L). The values for Vitamin D and hsCRP were transformed using the square-root and natural logarithm transformations, respectively, because of skewness.
Measures of cognitive function were included using the following cognitive domains: memory (Rey Auditory Verbal Learning Test [REY I, REY II]) (21), verbal fluency (Animal Fluency Test [AFT]) (22), and executive function (Mental Alternation Test [MAT]; Stroop Test) (23,24). A continuous Stroop (Interference) variable was created by subtracting participants’ Stroop trial 1 (least difficult) time from their Stroop trial 3 (most difficult) time (25), and extreme outliers ±3 SD from the mean were excluded.
Lifestyle factors included smoking status, alcohol consumption, and physical activity measured using the Physical Activity Scale for the Elderly (PASE) (26). Self-reported traditionally nonmodifiable chronic conditions included neurodegenerative disease (Dementia/Alzheimer’s disease, Parkinson’s disease, and/or Multiple Sclerosis); memory problems; and macular degeneration. Self-reported potentially modifiable chronic conditions included cardiovascular (heart disease, peripheral vascular disease, angina, and/or heart attack); stroke (stroke and/or transient ischemic attack); diabetes; hypertension; cancer; osteoarthritis (knee and/or hip osteoarthritis); sensory impairment (fair/poor self-rated hearing and/or fair/poor self-rated vision); neuropsychiatric condition (anxiety disorder, mood disorder, epilepsy, and/or migraine headaches); and respiratory condition (asthma and/or emphysema/ chronic bronchitis/chronic obstructive pulmonary disease (COPD)/other chronic lung issues).
Statistical Analysis
Descriptive statistics were generated using the mean ± standard error (SE) or median [IQR] for continuous variables and frequency (percentage) for categorical variables. A comparison of characteristics of the subjects included in the analysis with the group excluded due to missing data were conducted using 2-tailed t-tests and chi-square tests for continuous and categorical variables, respectively.
Bivariate linear associations between gait speed and each of the independent variables were assessed using the general linear model procedure for complex samples. The associations of 3 clinically relevant interaction terms (chronic pain × osteoarthritis; osteoarthritis × BMI; sensory impairment × executive function [Stroop]) with gait speed were assessed as well using tri-variate analysis (Supplementary Table S4 in the Supplementary Material). Hierarchical multivariable linear regression modeling was performed to examine the correlates of gait speed. The independent variables of interest were grouped a priori into 5 hierarchical model blocks (Supplementary Methods 2 in the Supplementary Material). The rationale for the order the blocks were fitted was to adjust first for fundamental sociodemographic and design-based variables and other traditionally nonmodifiable factors, followed by cognitive factors, potentially modifiable chronic diseases, and finally by potentially modifiable clinical and lifestyle factors. Interactions found to be statistically significant (p < .05) in the tri-variate analyses were added into the final regression model as well to examine their associations with gait speed in the context of all the other correlates.
Regression coefficients, denoted by B, with 95% confidence intervals (CI) and statistical significance were reported for each model. The coefficient of determination, R2, for each model was examined to show the amount of variance in gait speed that was explained by the addition of each block. This was repeated after multiple imputation of missing data to assess the potential for bias due to missing values (Supplementary Methods 3 and Supplementary Table S1 in the Supplementary Material). To further explore if the associations between gait speed and the selected correlates differed by age, the final regression model was also conducted with the study sample divided into 4 age groups: 45–54, 55–64, 65–74, and 75+ years (Supplementary Table S6 in the Supplementary Material).
Analyses were performed using IBM SPSS Statistics for Windows, Version 25.0 and SAS Version 9.4. Because the CLSA is a complex sample survey, sampling weights were used in the analyses to obtain results that are representative of the Canadian population as per CLSA guidelines (27).
Results
Sample Characteristics
Of the 30 097 participants in the Comprehensive cohort baseline, 29 705 (98.7%) had values for gait speed. Overall, 20 201 (67.1%) participants had valid data for gait speed and all other variables of interest. Compared to those with complete data, individuals excluded due to missing data (n = 9896) tended to be older, female, and non-White, with less education, and slower gait speeds (Supplementary Table S2 in the Supplementary Material).
Table 1 shows the characteristics of the analytic sample. The mean age was 58.8 years (SE = 0.08) and 48.6% were female. Most participants were White (95.7%) and had a post-secondary degree or diploma (81%). The most common chronic conditions were hypertension (30.3%), neuropsychiatric condition (29.6%), and osteoarthritis (15.7%). The mean usual gait speed was 1.01 m/s (SE = 0.002).
Table 1.
Baseline Characteristics of the Study Sample
| Variables | Population Estimatea |
|---|---|
| Age, mean ± SE, years | 58.84 ± 0.08 |
| Height, mean ± SE, m | 1.69 ± 0.0009 |
| Weight, mean ± SE, kg | 79.57 ± 0.15 |
| Vitamin D, median [IQR], mmol/L | 82.73 [46.3] |
| hsCRP, median [IQR], mg/L | 1.04 [1.82] |
| HDL, mean ± SE, mmol/L | 1.51 ± 0.004 |
| Gait speed, mean ± SE, m/s | 1.01 ± 0.002 |
| Grip strength, mean ± SE, kg | 35.54 ± 0.11 |
| REY I (Immediate Recall), mean ± SE | 6.11 ± 0.02 |
| REY II (Delayed Recall), mean ± SE | 4.37 ± 0.02 |
| AFT, mean ± SE | 20.70 ± 0.05 |
| MAT, mean ± SE | 27.91 ± 0.08 |
| Stroop (Interference), mean ± SE | 12.79 ± 0.06 |
| Depressive symptoms (CESD-10), mean ± SE | 4.98 ± 0.04 |
| Physical activity (PASE), mean ± SE | 154.38 ± 0.73 |
| Sex, No. (%) | |
| Female | 9971 (48.6) |
| Male | 10 230 (51.4) |
| Province, No. (%) | |
| Alberta | 1871 (11.5) |
| British Columbia | 4237 (29.9) |
| Manitoba | 2286 (8.8) |
| Newfoundland and Labrador | 1446 (2.1) |
| Nova Scotia | 2004 (3.4) |
| Ontario | 4279 (12.9) |
| Quebec | 4078 (31.4) |
| Race, No. (%) | |
| White | 19 479 (95.7) |
| Non-White | 722 (4.3) |
| Highest Education Level, No. (%) | |
| Less than secondary school graduation | 910 (4.0) |
| Secondary school graduation only | 1795 (8.6) |
| Some post-secondary education | 1439 (6.4) |
| Post-secondary degree or diploma | 16 057 (81.0) |
| Trouble getting to bathroom on time, No. (%) | 2382 (10.7) |
| Chronic Pain, No. (%) | 6859 (33.3) |
| BMI, No. (%) | |
| Underweight (<18.5 kg/m2) | 129 (0.6) |
| Normal weight (18.5–24.9 kg/m2) | 6133 (32.6) |
| Overweight (25.0–29.9 kg/m2) | 8291 (40.9) |
| Obese class I (30.0–34.9 kg/m2) | 3846 (17.6) |
| Obese class II (35.0–39.9 kg/m2) | 1214 (5.6) |
| Obese class III (≥40.0 kg/m2) | 588 (2.7) |
| Sleep disturbance, No. (%) | |
| 6–7 times/week | 2272 (10.9) |
| 3–5 times/week | 2458 (12.5) |
| 1–2 times/week | 3263 (16.6) |
| Never or less than once/week | 12 208 (60.0) |
| Smoking status, No. (%) | |
| Current smoker | 1721 (8.8) |
| Former smoker | 8783 (41.0) |
| Never smoker | 9697 (50.2) |
| Alcohol consumption, No. (%) | |
| Regular drinker | 15 725 (79.3) |
| Occasional drinker | 2347 (10.4) |
| Nondrinker | 2129 (10.3) |
| Neurodegenerative disease, No. (%) | 213 (1.0) |
| Memory problem, No. (%) | 257 (1.2) |
| Macular degeneration, No. (%) | 757 (2.8) |
| Cardiovascular condition, No. (%) | 3020 (12.5) |
| Stroke, No. (%) | 735 (2.7) |
| Diabetes, No. (%) | 3311 (14.2) |
| Hypertension, No. (%) | 7121 (30.3) |
| Cancer, No. (%) | 2934 (11.5) |
| Osteoarthritis, No. (%) | 3770 (15.7) |
| Sensory impairment, No. (%) | 3126 (14.6) |
| Neuropsychiatric condition, No. (%) | 5814 (29.6) |
| Respiratory condition, No. (%) | 3253 (15.6) |
Note: n = 20 201. AFT = animal fluency test; BMI = body mass index; CESD-10, Center for Epidemiologic Studies Depression scale; HDL = high-density lipoprotein; hsCRP = high-sensitivity C-reactive protein; IQR = interquartile range; MAT = mental alternation test; PASE = Physical Activity Scale for the Elderly; SE = standard error.
aMeans, standard errors, medians, IQRs, and percentages estimated using (trimmed) inflation weights.
Correlates of Gait Speed: Bivariate Associations
The bivariate relationships between gait speed and its potential correlates are given in Table 2 (additional details in Supplementary Table S3 in the Supplementary Material).
Table 2.
Bivariate Associations Between Gait Speed and Selected Nonmodifiable and Potentially Modifiable Factors
| Variable | B (95% CI) | p a |
|---|---|---|
| Sociodemographic factors | ||
| Age, years | −0.005 (−0.006; −0.005) | <.001 |
| Sex, Male (Ref) | <.001 | |
| Female | −0.015 (−0.021; −0.010) | |
| Race, White (Ref) | <.001 | |
| Non-White | −0.039 (−0.057; −0.022) | |
| Highest education level, post-secondary degree or diploma (Ref) | <.001 | |
| Some post-secondary | −0.034 (−0.045; −0.023) | |
| Secondary only | −0.037 (−0.046; −0.027) | |
| Less than secondary | −0.116 (−0.130; −0.103) | |
| Nonmodifiable anthropometric factors | ||
| Height, m | 0.285 (0.254; 0.315) | <.001 |
| Chronic conditions | ||
| Neurodegenerative disease | −0.092 (−0.128; −0.056) | <.001 |
| Memory problem | −0.085 (−0.113; −0.056) | <.001 |
| Macular degeneration | −0.065 (−0.082; −0.048) | <.001 |
| Cardiovascular condition | −0.076 (−0.085; −0.068) | <.001 |
| Stroke | −0.103 (−0.121; −0.085) | <.001 |
| Diabetes | −0.056 (−0.064; −0.048) | <.001 |
| Hypertension | −0.062 (−0.068; −0.056) | <.001 |
| Cancer | −0.034 (−0.043; −0.026) | <.001 |
| Osteoarthritis | −0.064 (−0.071; −0.056) | <.001 |
| Respiratory condition | −0.015 (−0.022; −0.009) | <.001 |
| Sensory impairment | −0.037 (−0.045; −0.028) | <.001 |
| Neuropsychiatric condition | −0.015 (−0.022; −0.009) | <.001 |
| Cognitive factors | ||
| REY I (Immediate Recall) | 0.014 (0.012; 0.015) | <.001 |
| REY II (Delayed Recall) | 0.010 (0.009; 0.011) | <.001 |
| AFT | 0.006 (0.006; 0.007) | <.001 |
| MAT | 0.004 (0.003; 0.004) | <.001 |
| Stroop (Interference), sec | −0.005 (−0.005; −0.004) | <.001 |
| Clinical factors | ||
| HDL, mmol/L | 0.028 (0.022; 0.034) | <.001 |
| Vitamin D (square root), mmol/L | 0.003 (0.001; 0.004) | .001 |
| hsCRP (natural log), mg/L | −0.030 (−0.033; −0.027) | <.001 |
| Depressive symptoms (CESD-10) | −0.005 (−0.006; −0.005) | <.001 |
| Trouble getting to bathroom on time | −0.085 (−0.095; −0.075) | <.001 |
| Chronic pain | −0.045 (−0.051; −0.038) | <.001 |
| Grip strength, kg | 0.003 (0.003; 0.003) | <.001 |
| Sleep disturbance, Never or less than once/week (Ref) | .011 | |
| 6–7 times/week | −0.011 (−0.021; −0.001) | |
| 3–5 times/week | 0.007 (−0.002; 0.016) | |
| 1–2 times/week | 0.007 (−0.001; 0.015) | |
| BMI (kg/m2), Normal weight (18.5–24.9 kg/m2) (Ref) | <.001 | |
| Underweight (<18.5 kg/m2) | −0.012 (−0.063; 0.040) | |
| Overweight (25.0–29.9 kg/m2) | −0.026 (−0.033; −0.019) | |
| Obese class I (30.0–34.9 kg/m2) | −0.057 (−0.066; −0.049) | |
| Obese class II (35.0–39.9 kg/m2) | −0.102 (−0.114; −0.090) | |
| Obese class III (≥40.0 kg/m2) | −0.147 (−0.163; −0.131) | |
| Lifestyle factors | ||
| Physical activity (PASE) | 0.0004 (0.0003; 0.0004) | <.001 |
| Smoking status, Never (Ref) | ||
| Current smoker | −0.043 (−0.054; −0.032) | <.001 |
| Former smoker | −0.031 (−0.037; −0.024) | |
| Alcohol consumption, Never (Ref) | <.001 | |
| Regular drinker | 0.046 (0.036; 0.057) | |
| Occasional drinker | 0.004 (−0.009; 0.018) |
Note: n = 20 201. AFT = animal fluency test; BMI = body mass index; CESD-10 = Center for Epidemiologic Studies Depression scale; CI = Confidence interval; HDL = high-density lipoprotein; hsCRP = high-sensitivity C-reactive protein; MAT = mental alternation test; PASE = Physical Activity Scale for the Elderly.
aBivariate parameter estimate (B) for each independent factor obtained using complex samples general linear model analysis (with analytic sampling weights) with gait speed (m/s) as the dependent variable..
Age, being female, non-White, and having less education were each significantly negatively associated with gait speed. Height was significantly positively associated with gait speed. Vitamin D (square root transformed) and HDL were positively associated with gait while hsCRP (natural log-transformed) was negatively associated. Depressive symptoms, chronic pain, trouble getting to the bathroom on time, greater BMI, and frequent sleep disturbances were also significantly negatively associated (Table 2).
High performance in immediate and delayed recall, verbal fluency, and attention were significantly associated with faster gait speed. Stroop (interference) scores were negatively associated with gait speed showing that a larger difference between the time taken to complete the first (easiest) and third (hardest) trials of the Stroop test was associated with slower gait speeds.
Greater physical activity and regular alcohol consumption were significantly positively associated with gait speed whereas smoking was significantly negatively associated. All nonmodifiable and potentially modifiable chronic conditions were each significantly negatively associated with gait speed.
Potentially Modifiable and Nonmodifiable Correlates of Gait Speed: Hierarchical Regression
The relationships between gait speed and nonmodifiable and potentially modifiable factors in the final regression model are given in Table 3 (full results are given in Supplementary Table S5 of the Supplementary Material). Inclusion of the first 2 sets of factors alone explained 12.9% of gait speed variability. Cognitive factors, potentially modifiable chronic conditions, and other potentially modifiable clinical and lifestyle factors and interaction terms explained an additional 0.9%, 1.6%, and 4.3% of variability, respectively. The R2 for the final model was 19.7%.
Table 3.
Hierarchical Regression Analysis of Nonmodifiable and Potentially Modifiable Correlates of Gait Speed (Final Model)
| Final Model R2 = 19.7% | Variables | B (95% CI) | Standardized Coefficient (β) | p a |
|---|---|---|---|---|
| Constant | 0.799 (0.710; 0.888) | - | <.001 | |
| Block 1 (Sociodemographic factors) | Age, years | −0.003 (−0.003; −0.002) | −0.148 | <.001 |
| Sex, Female | 0.051 (0.041; 0.061) | 0.133 | <.001 | |
| Province, ON (Ref) | ||||
| AB | −0.034 (−0.046; −0.022) | −0.051 | <.001 | |
| BC | 0.025 (0.017; 0.033) | 0.053 | <.001 | |
| MB | −0.018 (−0.028; −0.009) | −0.030 | <.001 | |
| NL | −0.051 (−0.062; −0.040) | −0.069 | <.001 | |
| NS | 0.085 (0.074; 0.097) | 0.134 | <.001 | |
| QC | 0.020 (0.012; 0.029) | 0.043 | <.001 | |
| Race, White (Ref) | ||||
| Non-White | −0.026 (−0.042; −0.009) | −0.026 | .002 | |
| Education, post-secondary degree or diploma (Ref) | ||||
| Some post-secondary education | −0.005 (−0.015; 0.006) | −0.006 | .386 | |
| Secondary education only | −0.006 (−0.015; 0.002) | −0.009 | .154 | |
| Less than secondary school graduation | −0.029 (−0.042; −0.016) | −0.029 | <.001 | |
| Block 2 (Traditionally nonmodifiable anthropometric factors and chronic conditions) | Height, m | 0.105 (0.059; 0.151) | 0.056 | <.001 |
| Neurodegenerative disease | −0.050 (−0.083; −0.017) | −0.026 | .003 | |
| Memory problem | −0.031 (−0.057; −0.006) | −0.018 | .017 | |
| Macular degeneration | 0.002 (−0.013; 0.018) | 0.002 | .753 | |
| Block 3 (Cognitive factors) | REY I (Immediate recall) | 0.003 (0.001; 0.005) | 0.031 | .004 |
| REY II (Delayed recall) | −0.002 (−0.004; −0.0001) | −0.022 | .036 | |
| AFT | 0.002 (0.001; 0.002) | 0.048 | <.001 | |
| MAT | 0.0008 (0.0005; 0.001) | 0.038 | <.001 | |
| Stroop (Interference), sec | −0.0002 (−0.0006; 0.0002) | −0.007 | .415 | |
| Block 4 (Potentially modifiable chronic conditions) | Cardiovascular condition | −0.018 (−0.025; −0.010) | −0.030 | <.001 |
| Stroke | −0.025 (−0.042; −0.009) | −0.022 | .003 | |
| Diabetes | −0.004 (−0.011; 0.004) | −0.007 | .308 | |
| Hypertension | −0.007 (−0.013; −0.0008) | −0.017 | .026 | |
| Cancer | 0.002 (−0.005; 0.010) | 0.004 | .531 | |
| Osteoarthritis | 0.009 (−0.008; 0.026) | 0.017 | .316 | |
| Sensory impairment | −0.006 (−0.013; 0.002) | −0.010 | .158 | |
| Neuropsychiatric condition | −0.0006 (−0.007; 0.006) | −0.002 | .842 | |
| Respiratory condition | −0.00001 (−0.007; 0.007) | −0.00003 | .996 | |
| Block 5 (Clinical and lifestyle factors) |
HDL, mmol/L Vitamin D (square root), mmol/L hsCRP (natural log), mg/L Depressive symptoms (CESD-10) Trouble getting to bathroom on time Chronic pain BMI, normal weight (18.5–24.9 kg/m2) (Ref) Underweight (<18.5 kg/m2) Overweight (25.0–29.9 kg/m2) Obese class I (30.0–34.9 kg/m2) Obese class II (35.0–39.9 kg/m2) Obese class III (≥40.0 kg/m2) Grip strength, kg Sleep disturbance, Never or less than once/week (Ref) 6–7 times/week 3–5 times/week 1–2 times/week Physical activity (PASE) Smoking status, Never (Ref) Current smoker Former smoker Alcohol consumption, Never Drinker (Ref) Regular drinker Occasional drinker Chronic pain × Osteoarthritis Osteoarthritis × BMI (Underweight) |
0.006 (−0.001; 0.013) 0.004 (0.002; 0.005) −0.005 (−0.008; −0.001) −0.003 (−0.003; −0.002) −0.030 (−0.039; −0.021) −0.008 (−0.015; −0.001) 0.030 (−0.018; 0.078) −0.013 (−0.020; −0.005) −0.026 (−0.036; −0.017) −0.058 (−0.072; −0.043) −0.086 (−0.107; −0.066) 0.003 (0.002; 0.003) 0.007 (−0.003; 0.016) 0.007 (−0.001; 0.016) 0.004 (−0.003; 0.012) 0.0001 (0.00005; 0.0001) −0.026 (−0.037; −0.016) −0.009 (−0.015; −0.003) 0.015 (0.006; 0.025) 0.009 (−0.003; 0.021) −0.016 (−0.030; −0.002) −0.109 (−0.241; 0.024) |
0.014 0.035 −0.024 −0.065 −0.047 −0.020 0.012 −0.033 −0.053 −0.072 −0.077 0.166 0.011 0.013 0.009 0.037 −0.039 −0.023 0.032 0.012 −0.024 −0.011 |
.112 <.001 .005 <.001 <.001 .018 .220 .001 <.001 <.001 <.001 <.001 .167 .087 .235 <.001 <.001 .002 .002 .144 .024 .109 |
| Osteoarthritis × BMI (Overweight) | −0.008 (−0.027; 0.011) | −0.010 | .396 | |
| Osteoarthritis × BMI (Obese class I) | −0.021 (−0.042; −0.0003) | −0.021 | .047 | |
| Osteoarthritis × BMI (Obese class II) | −0.030 (−0.055; −0.004) | −0.020 | .023 | |
| Osteoarthritis × BMI (Obese class III) | −0.046 (−0.080; −0.013) | −0.025 | .007 |
Notes: n = 20 201. AFT = animal fluency test; BMI = body mass index; CESD-10 = Center for Epidemiologic Studies Depression scale; HDL = high-density lipoprotein; hsCRP = high-sensitivity C-reactive protein; MAT = mental alternation test; PASE = Physical Activity Scale for the Elderly.
aAnalytic sampling weights applied in hierarchical regression analysis.
Several individual factors were significantly associated with gait speed in the final regression model. Those with less than secondary school graduation had significantly slower gait speed compared to those with a post-secondary degree or diploma (unstandardized coefficient, B = −0.029, 95% CI = −0.042; −0.016). Being a current smoker was also associated with slower gait speed in comparison to those who never smoked (B = −0.026, 95% CI = −0.037; −0.016). Contrarily, physical activity (PASE score) was positively associated with gait speed (B = 0.0001, 95% CI = 0.00005; 0.0001).
The potentially modifiable chronic conditions that were significantly associated with slower gait speed were any cardiovascular condition (B = −0.018, 95% CI = −.025; −0.010), stroke (B = −0.025, 95% CI = −0.042; −0.009), and hypertension (B = −0.007, 95% CI = −0.013; −0.0008). In terms of serum factors, Vitamin D concentration was positively associated with gait speed (B = 0.004, 95% CI = 0.002; 0.005), and hsCRP concentration was negatively associated (B = −0.005, 95% CI = −0.008; −0.001).
Depressive symptoms (CESD-10 score) were negatively associated with gait speed (B = −0.003, 95% CI = −0.003; −0.002), while grip strength was positively associated (B = 0.003, 95% CI = 0.002; 0.003). Compared to those with normal weight, slower gait speeds were found for those who were overweight (B = −0.013, 95% CI = −0.020; −0.005) or obese regardless of obesity severity (class I: B = −0.026, 95% CI = −0.036; −0.017, class II: B = −0.058, 95% CI = −0.072; −0.043, and class III: B = −0.086, 95% CI = −0.107; −0.066). Reporting chronic pain (B = −0.008, 95% CI = −0.015; −0.001) or trouble getting to the bathroom on time (B = −0.030, 95% CI = −0.039; −0.021) were also both associated with slower gait speed.
Cognitive tests for memory, executive function, and verbal fluency were significantly associated with gait speed as well. Better performance on immediate recall (REY I) was positively associated with gait speed (B = 0.003, 95% CI = 0.001; 0.005) along with performance on tests of verbal fluency (AFT: B = 0.002, 95% CI = 0.001; 0.002) and executive function (MAT: B = 0.0008, 95% CI = 0.0005; 0.001).
Analysis of Interactions
The associations of 3 clinically relevant interaction terms (chronic pain × osteoarthritis; osteoarthritis × BMI; sensory impairment × executive function [Stroop]) with gait speed were also assessed. Both the chronic pain × osteoarthritis and osteoarthritis × BMI interaction terms were significantly associated with gait speed in the tri-variate analyses (Supplementary Table S4 and Supplementary Figures 1 and 2 in the Supplementary Material). Upon inclusion of these terms in the final regression model with all selected correlates (Table 3), they were found to remain significantly associated with gait speed, indicating that gait speed was additionally slower with concurrent chronic pain and osteoarthritis (B = −0.016, 95% CI = −0.030; −0.002), and concurrent osteoarthritis and obesity (obese class I: B = −0.021, 95% CI = −0.042; −0.0003, obese class II: B = −0.030, 95% CI = −0.055; −0.004, and obese class III: B = −0.046, 95% CI = −0.080; −0.013).
Age-Stratified Analysis
To examine gait speed correlate associations by age, the final regression model was conducted with participants divided into 4 age groups (Supplementary Table S6 in the Supplementary Material). The proportion of gait speed variability explained was greatest for participants aged 75+ years and nearly double that for those aged 45–54 years (R2 = 24.4% vs 12.6%). Trends in parameter estimates by age group were seen for the included correlates. For example, among the 45–54-year age group, potentially modifiable correlates including grip strength (B = 0.002, 95% CI = 0.001; 0.003), depressive symptoms (B = −0.002, 95% CI = −0.004; −0.001), current smoking (B = −0.029, 95% CI = −0.046; −0.011), and performance on tests of executive function (MAT: B = 0.0008, 95% CI = 0.0002; 0.001) and verbal fluency (AFT: B = 0.002, 95% CI = 0.0007; 0.003) were each statistically significantly associated with gait speed, and remained significantly associated with gait speed across the older age groups. Those with obese BMI also had significantly slower gait speed than those with normal weight among all 4 age groups. Particularly, compared to those with normal weight, participants with the most severe obesity (BMI ≥ 40.0 kg/m2) had the slowest gait speed, especially among the 65–74 year group (B = −0.094, 95% CI = −0.139; −0.048) and the 75+ year group (B = −0.143, 95% CI = −0.200; −0.085).
Correlates that were not significantly associated with gait speed in the 45–54-year age group but showed significant negative associations in the older age groups include having a cardiovascular condition, having trouble getting to the bathroom on time, physical activity, and serum vitamin D. Additionally, correlates that appeared to be statistically significantly associated with gait speed predominantly among older participants include high sensitivity C-reactive protein (65–74 group: B = −0.007, 95% CI = −0.013; −0.0007 and 75+ group: B = −0.008, 95% CI = −.015; −.001), diabetes (75+ group: B = −0.020, 95% CI = −0.035; −0.004), osteoarthritis (75+ group: B = −0.035, 95% CI = −0.063; −0.007), and hypertension (75+ group: B = −0.018, 95% CI = −0.031; −0.003).
Sensitivity Analysis
To account for potential selection bias as a result of listwise exclusion of missing cases, the models were re-run with missing cases imputed (Supplementary Table S7 in the Supplementary Material). Overall, the imputed parameter estimates were of similar magnitude and direction. The only notable change was that 2 additional potentially modifiable chronic conditions that were not initially statistically significant (diabetes and sensory impairment) became so in the imputed sample.
Discussion
This study showed the associations of nonmodifiable and potentially modifiable risk factors with gait speed in a large representative sample of community-dwelling middle and older aged adults and estimated the amount of variation in gait speed that could be explained by these factors. To our knowledge, this is one of the first such studies showing these associations, particularly in middle age adults, and provides a framework for future longitudinal assessments of gait speed risk factors across the adult life span.
Our study demonstrated that in community-dwelling adults, nearly 13% of variation in gait speed was explained solely by sociodemographic and anthropometric factors, and traditionally nonmodifiable chronic conditions. Other potentially modifiable factors (ie, cognitive measures, clinical factors, potentially modifiable chronic conditions, and lifestyle behaviors) also significantly explained about 7% of gait speed variability.
It is well understood that average gait speed is strongly influenced by multiple nonmodifiable biological factors including age and height (28,29). Research has also shown that gait disturbances are a hallmark of progressive neurodegenerative diseases that involve the degeneration of neural structures directly involved in motor control along with brain regions associated with cognitive functions that are essential for maintaining normal gait (30–32).
The variability in gait speed explained by potentially modifiable factors further provides evidence that gait speed is a complex motor function not solely determined by age and other unalterable characteristics. Rather, it can be thought of as the product of a multifactorial set of etiological factors acting through a network of pathophysiological pathways (33). Following this paradigm, isolated factors may insufficiently explain variations in gait speed; however, when considered as part of an interactive system, their effects may be clearer and more substantial. Ultimately, applying this multidimensional approach is crucial to gain a more complete understanding of the correlates of gait speed, and to further mitigate abnormal declines in gait speed that indicate underlying morbidity not associated with natural biological processes (34).
Several potentially modifiable chronic conditions were significantly associated with slower gait speed in the final regression model including osteoarthritis (interacting with chronic pain and BMI), stroke, and any cardiovascular condition. Previous studies have found that older adults with osteoarthritis tend to walk slower and are more likely to experience gait speed declines as a result of the mobility-impairing symptoms (35,36). However, improvement of functionality and quality of life is possible with intervention. Significant negative associations between history of stroke and gait speed in older adults have been previously reported (11,37,38), although findings are not consistent (12,18). The significant result we found for having any cardiovascular disease (ie, heart disease, myocardial infarction, angina, and peripheral vascular disease) has a clinical plausibility although conflicts with several nonsignificant findings in the literature (12,18,39), which may suggest that the directionality of this relationship could be reversed, with slower gait speed predicting adverse cardiovascular events instead (40). Regardless, prevention of strokes and cardiovascular conditions through clinical intervention, lifestyle modification, and education may partially mitigate detrimental mobility impairments and subsequent morbidity in at-risk individuals (41,42).
Having below a secondary school education was also significantly negatively associated with gait speed. Analyses of socioeconomic disparities within communities have demonstrated that individuals with fewer years of education are more likely to become disabled, engage in negative health behaviors, and face greater barriers to accessing essential health care services (43,44); all of which can negatively influence gait speed performance in adulthood. Formal education attained during younger years is not directly modifiable later in life. However, middle- and older-aged adults who achieved fewer years of education as a result of socioeconomic barriers may benefit from participating in cognitive training and public health promotion interventions in ways that can directly or indirectly influence maintenance of optimal gait speed. Thus, while only 4% of CLSA participants had not completed high school, the significant negative association of low education level with gait speed in this cohort not only highlights the importance of improving graduation rates in socioeconomically disadvantaged groups but also the potential benefit of targeting adults with lower educational attainment for interventions to mitigate gait speed impairments.
The negative association we found for smoking is consistent with previous studies and supports arguments for the detrimental effects of smoking on physiological systems underlying mobility (12,45). The positive association of alcohol consumption on gait speed in our sample is also in line with other investigations (45,46); however, a plausible explanation for this finding is that individuals who can tolerate alcohol may simply be healthier and more mobile than those who do not drink. It is also well understood that engaging in physical activity across the adult life span promotes cardiovascular health and improves muscle strength and our finding contributes to the growing body of literature demonstrating the benefits of active lifestyles on walking speed in older age (47).
We additionally found a significant positive association for serum vitamin D and a significant negative association for C-reactive protein. This supports previous findings that lower vitamin D levels and higher C-reactive protein levels are linked to slower gait speed in older adults (48–51), and ultimately highlights the importance of considering the roles of these and other biomarkers in the multifactorial causality of gait impairments.
Grip strength, depressive symptoms, chronic pain, overweight and obese BMI, and trouble getting to the bathroom on time were also significantly associated with gait speed. Weaker grip strength has been linked to mobility declines (52). Although many older adults experience natural age-related muscle loss, this can be mitigated through various lifestyle changes including regular physical activity (53). Next, while we found an inverse cross-sectional association between number of depressive symptoms and gait speed, longitudinal investigations have suggested potentially bidirectional and reversed relationships (54). Models of the biological pathways shared by depression and gait impairments have been proposed (55), and comorbid issues may further partially explain their relationship.
Chronic pain interferes with physical function and participation in everyday activities. Although definitions of pain vary, studies generally report significant associations with slower gait speed in adults, reinforcing the importance of including pain management in efforts to mitigate mobility decline (11,12,56–58). Greater BMI—specifically obesity—has also been linked to mobility disability and slower gait speed in adults (59,60). As the incidence of obesity continues to rise in Canada, this potentially treatable condition should remain a focus for public health interventions to promote health and better mobility in all adults regardless of age. Research has suggested that incontinence can impose severe burdens on well-being as well. Evidence is available for the possible correlations between urinary incontinence specifically with gait speed (61). While studies have also suggested that incontinence and gait speed directly influence one another (ie, slow walking hinders one’s ability to travel to the bathroom in a timely fashion and fear of incontinence contributes to mobility limitations as affected individuals feel less comfortable engaging in regular daily activities) (62), it is possible that incontinence is instead indirectly associated with gait speed; that is, incontinence may be a risk marker that is useful in predicting the incidence of slowing gait in affected individuals.
We found significant positive associations for most of the cognitive tests (ie, REY, AFT, and MAT) and gait speed performance. This finding is aligned with emerging evidence that multiple cognitive domains play important roles in the motor control of initiation, maintenance, and adaptability of gait (63), and that impairments in cognition and structural brain changes may be associated with the development of slower gait speed (64,65). Given this connection, implementing strategies to bolster cognitive performance throughout the adult life span may impart benefits on mobility well into older age, and prevent future decline (66).
Finally, our age stratified analysis of gait speed correlates offered further insights into the variations in factor associations across stages of middle and older adulthood. Our results provide granularity regarding specific potentially modifiable clinical, cognitive, and lifestyle factors that may have important relationships with gait speed in older versus younger years. For instance, our results demonstrate that the role of factors such as obesity, muscle strength, smoking, depressive symptoms, and physical activity in maintaining gait speed in middle ages may have important clinical and policy implications for mitigating gait speed and mobility declines in later years. These findings highlight the necessity of studying individuals prospectively from middle ages to older years to better understand how the correlates of gait speed differ over the adult life span so that gait speed impairments can be mitigated proactively in earlier stages of aging.
Limitations
Some limitations need to be outlined. Due to the cross-sectional design, we were not able assess the directionality of the associations between the selected correlates and gait speed or analyze gait speed changes prospectively. The statistically significant results observed were moderate and the clinically significance is uncertain. Most participants in the CLSA cohort were also community dwelling, White, highly educated, and relatively healthy—all of which may limit the generalizability of the results to other adult populations. Finally, we included a nonexhaustive set of correlates in this analysis and were unable to include other social determinants of health (eg, access to medical care and social support) that play major roles in healthy aging.
Conclusion
In this cross-sectional analysis of over 20 000 middle- and older-aged Canadians, we found associations of nonmodifiable and potentially modifiable factors with gait speed in middle-aged and older adults. We showed that while the variation in gait speed was largely explained by nonmodifiable factors, multiple factors that were significantly associated with gait speed are potentially modifiable or manageable including chronic pain, depressive symptoms, smoking, cognitive performance, obesity, muscle weakness, and cardiovascular conditions. We provide a conceptual framework for future longitudinal analyses of gait speed and its complex determinants and preliminary evidence to inform potential intervention strategies in at-risk adult populations. Overall, our results support the adoption of a life-course approach to understand and potentially treat the slowing gait and mobility decline seen in aging.
Supplementary Material
Acknowledgments
E.F., M.S., and M.M-O. conceptualized the design and methodology of the study. E.F. conducted the statistical analyses and drafted the manuscript. E.F., M.S., Y-H.C., and M.M-O. interpreted the analysis results. M.M-O obtained funding. All authors provided critical revisions and approved the final manuscript draft.
This research was made possible using the data/biospecimens collected by the Canadian Longitudinal Study on Aging (CLSA). Data are available from the CLSA (www.clsa-elcv.ca) for researchers who meet the criteria for access to de-identified CLSA data. Funding for the Canadian Longitudinal Study on Aging (CLSA) is provided by the Government of Canada through the Canadian Institutes of Health Research (CIHR) under grant reference: LSA 94473 and the Canada Foundation for Innovation. This research has been conducted using the CLSA Baseline Comprehensive version 4.0, under Application Number 180609. The CLSA is led by Drs. Parminder Raina, Christina Wolfson, and Susan Kirkland. The opinions expressed in this manuscript are the author’s own and do not reflect the views of the Canadian Longitudinal Study on Aging.
MM-O’s program in Gait and Brain Health is supported by grants from the Canadian Institutes of Health Research (CIHR; MOP 211220, PJT 153100), the Ontario Ministry of Research and Innovation (ER11– 08–101), the Ontario Neurodegenerative Diseases Research Initiative (OBI 34739), the Canadian Consortium on Neurodegeneration in Aging (FRN CNA 137794) and the Department of Medicine Program of Experimental Medicine Research Award (POEM 768915), University of Western Ontario. He is the first recipient of the Schulich Clinician–Scientist Award.
Funding
This work was supported by a project grant from the Canadian Institutes of Health Research (CIHR; PJT 153100, PI: M.M-O.). E.F. is also a recipient of a 2019–2020 Ontario Graduate Scholarship.
Conflict of Interest
None declared.
References
- 1.Verghese J, LeValley A, Hall CB, Katz MJ, Ambrose AF, Lipton RB. Epidemiology of gait disorders in community-residing older adults. J Am Geriatr Soc. 2006;54:255–261. doi: 10.1111/j.1532-5415.2005.00580.x [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Abellan van Kan G, Rolland Y, Andrieu S, et al. Gait speed at usual pace as a predictor of adverse outcomes in community-dwelling older people an International Academy on Nutrition and Aging (IANA) Task Force. J Nutr Health Aging. 2009;13:881–889. doi: 10.1007/s12603-009-0246-z [DOI] [PubMed] [Google Scholar]
- 3.Studenski S, Perera S, Patel K, et al. Gait speed and survival in older adults. JAMA. 2011;305:50–58. doi: 10.1001/jama.2010.1923 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Rasmussen LJH, Caspi A, Ambler A, et al. Association of neurocognitive and physical function with gait speed in midlife. JAMA Netw Open. 2019;2:e1913123. doi: 10.1001/jamanetworkopen.2019.13123 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Montero-Odasso M, Schapira M, Soriano ER, et al. Gait velocity as a single predictor of adverse events in healthy seniors aged 75 years and older. J Gerontol A Biol Sci Med Sci. 2005;60:1304–1309. doi: 10.1093/gerona/60.10.1304 [DOI] [PubMed] [Google Scholar]
- 6.Cummings SR, Studenski S, Ferrucci L. A diagnosis of dismobility–giving mobility clinical visibility: a Mobility Working Group recommendation. JAMA. 2014;311:2061–2062. doi: 10.1001/jama.2014.3033 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Perera S, Mody SH, Woodman RC, Studenski SA. Meaningful change and responsiveness in common physical performance measures in older adults. J Am Geriatr Soc. 2006;54:743–749. doi: 10.1111/j.1532-5415.2006.00701.x [DOI] [PubMed] [Google Scholar]
- 8.Schmid A, Duncan PW, Studenski S, et al. Improvements in speed-based gait classifications are meaningful. Stroke. 2007;38:2096–2100. doi: 10.1161/STROKEAHA.106.475921 [DOI] [PubMed] [Google Scholar]
- 9.Montero-Odasso M, Verghese J, Beauchet O, et al. Gait and cognition: a complementary approach to understanding brain function and the risk of falling. JAGS. 2012;60(11):2127–2136. doi: 10.1111/j.1532-5415.2012.04209.x [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Montero-Odasso M, Sarquis-Adamson Y, Song HY, et al. Polypharmacy, gait performance, and falls in community-dwelling older adults. JAGS. 2019;67(6):1182–1188. doi: 10.1111/jgs.15774 [DOI] [PubMed] [Google Scholar]
- 11.Quiben MU, Hazuda HP. Factors contributing to 50-ft walking speed and observed ethnic differences in older community-dwelling Mexican Americans and European Americans. Phys Ther. 2015;95:871–883. doi: 10.2522/ptj.20140152 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Forrest KY, Zmuda JM, Cauley JA. Correlates of decline in lower extremity performance in older women: a 10-year follow-up study. J Gerontol A Biol Sci Med Sci. 2006;61:1194–1200. doi: 10.1093/gerona/61.11.1194 [DOI] [PubMed] [Google Scholar]
- 13.Granic A, Mossop H, Engstrom G, et al. Factors associated with physical performance measures in a multiethnic cohort of older adults. Gerontol Geriatr Med. 2018;4:2333721418778623. doi: 10.1177/2333721418778623 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Raina PS, Wolfson C, Kirkland SA, et al. The Canadian longitudinal study on aging (CLSA). Can J Aging Rev Can Vieil. 2009;28(3):221–229. doi: 10.1017/S0714980809990055 [DOI] [PubMed] [Google Scholar]
- 15.Moss L. SOP: timed (4-metre) walk test. https://www.clsa-elcv.ca/doc/530. Published online 2014. Accessed February 13, 2020.
- 16.O’Donnell MJ, Xavier D, Liu L, et al. ; INTERSTROKE investigators . Risk factors for ischaemic and intracerebral haemorrhagic stroke in 22 countries (the INTERSTROKE study): a case-control study. Lancet. 2010;376:112–123. doi: 10.1016/S0140-6736(10)60834-3 [DOI] [PubMed] [Google Scholar]
- 17.Yusuf S, Hawken S, Ounpuu S, et al. ; INTERHEART Study Investigators . Effect of potentially modifiable risk factors associated with myocardial infarction in 52 countries (the INTERHEART study): case-control study. Lancet. 2004;364:937–952. doi: 10.1016/S0140-6736(04)17018-9 [DOI] [PubMed] [Google Scholar]
- 18.Verghese J, Wang C, Allali G, Holtzer R, Ayers E. Modifiable risk factors for new-onset slow gait in older adults. J Am Med Dir Assoc. 2016;17(5):421–425. doi: 10.1016/j.jamda.2016.01.017 [DOI] [PubMed] [Google Scholar]
- 19.Porta M.A Dictionary of Epidemiology. 6th ed. Oxford University Press; 2014. [Google Scholar]
- 20.Andresen EM, Malmgren JA, Carter WB, Patrick DL. Screening for depression in well older adults: evaluation of a short form of the CES-D (Center for Epidemiologic Studies Depression Scale). Am J Prev Med. 1994;10:77–84. doi: 10.1016/S0749-3797(18)30622-6 [DOI] [PubMed] [Google Scholar]
- 21.Rey A. L’examen clinique en psychologie. https://psycnet.apa.org/record/1959-03776-000. Published online 1958. Accessed June 2, 2020.
- 22.Crossley M, D’Arcy C, Rawson NS. . Letter and category fluency in community-dwelling Canadian seniors: a comparison of normal participants to those with dementia of the Alzheimer or vascular type. J Clin Exp Neuropsychol. 1997;19:52–62. doi: 10.1080/01688639708403836 [DOI] [PubMed] [Google Scholar]
- 23.Teng E. The mental alternations test (MAT). Clin Neuropsychol. 1995;9:287. doi: 10.1080/13854049508400492 [DOI] [Google Scholar]
- 24.Troyer AK, Leach L, Strauss E. Aging and response inhibition: normative data for the Victoria Stroop Test. Neuropsychol Dev Cogn B Aging Neuropsychol Cogn. 2006;13:20–35. doi: 10.1080/138255890968187 [DOI] [PubMed] [Google Scholar]
- 25.Demnitz N, Hogan DB, Dawes H, et al. Cognition and mobility show a global association in middle- and late-adulthood: analyses from the Canadian Longitudinal Study on Aging. Gait Posture. 2018;64:238–243. doi: 10.1016/j.gaitpost.2018.06.116 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Washburn RA, Smith KW, Jette AM, Janney CA. The Physical Activity Scale for the Elderly (PASE): development and evaluation. J Clin Epidemiol. 1993;46:153–162. doi: 10.1016/0895-4356(93)90053-4 [DOI] [PubMed] [Google Scholar]
- 27.Canadian Longitudinal Study on Aging. CLSA technical document: sampling and computation of response rates and sample weights for the tracking (telephone interview) participants and comprehensive participants.https://www.clsa-elcv.ca/doc/1041. Published online July 11, 2017. Accessed January 30, 2020. [Google Scholar]
- 28.Boros K, Freemont T. Physiology of ageing of the musculoskeletal system. Best Pract Res Clin Rheumatol. 2017;31:203–217. doi: 10.1016/j.berh.2017.09.003 [DOI] [PubMed] [Google Scholar]
- 29.Elbaz A, Artaud F, Dugravot A, Tzourio C, Singh-Manoux A. The gait speed advantage of taller stature is lost with age. Sci Rep. 2018;8:1485. doi: 10.1038/s41598-018-19882-1 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Montero-Odasso M, Pieruccini-Faria F, Bartha R, et al. ; ONDRI Investigators . Motor phenotype in neurodegenerative disorders: gait and balance platform study design protocol for the Ontario Neurodegenerative Research Initiative (ONDRI). J Alzheimers Dis. 2017;59:707–721. doi: 10.3233/JAD-170149 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.Muir SW, Speechley M, Wells J, Borrie M, Gopaul K, Montero-Odasso M. Gait assessment in mild cognitive impairment and Alzheimer’s disease: the effect of dual-task challenges across the cognitive spectrum. Gait Posture. 2012;35:96–100. doi: 10.1016/j.gaitpost.2011.08.014 [DOI] [PubMed] [Google Scholar]
- 32.Morris R, Lord S, Bunce J, Burn D, Rochester L. Gait and cognition: mapping the global and discrete relationships in ageing and neurodegenerative disease. Neurosci Biobehav Rev. 2016;64:326–345. doi: 10.1016/j.neubiorev.2016.02.012 [DOI] [PubMed] [Google Scholar]
- 33.Inouye SK, Studenski S, Tinetti ME, Kuchel GA. Geriatric syndromes: clinical, research, and policy implications of a core geriatric concept. J Am Geriatr Soc. 2007;55:780–791. doi: 10.1111/j.1532-5415.2007.01156.x [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34.Cruz-Jentoft AJ, Michel J-P. Sarcopenia: a useful paradigm for physical frailty. Eur Geriatr Med. 2013;4:102–105. doi: 10.1016/j.eurger.2013.02.009 [DOI] [Google Scholar]
- 35.Vennu V, Misra H. Evaluation of gait speed over time in adults with arthritis: data from the osteoarthritis initiative. Indian J Rheumatol. 2018;13:154. doi: 10.4103/injr.injr_34_18 [DOI] [Google Scholar]
- 36.White DK, Niu J, Zhang Y. Is symptomatic knee osteoarthritis a risk factor for a trajectory of fast decline in gait speed? Results from a longitudinal cohort study. Arthritis Care Res (Hoboken). 2013;65:187–194. doi: 10.1002/acr.21816 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 37.Auyeung TW, Kwok T, Lee J, Leung PC, Leung J, Woo J. Functional decline in cognitive impairment–the relationship between physical and cognitive function. Neuroepidemiology. 2008;31:167–173. doi: 10.1159/000154929 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 38.Kawai H, Ihara K, Kera T, et al. Association between statin use and physical function among community-dwelling older Japanese adults. Geriatr Gerontol Int. 2018;18:623–630. doi: 10.1111/ggi.13228 [DOI] [PubMed] [Google Scholar]
- 39.Schrack JA, Simonsick EM, Chaves PH, Ferrucci L. The role of energetic cost in the age-related slowing of gait speed. J Am Geriatr Soc. 2012;60:1811–1816. doi: 10.1111/j.1532-5415.2012.04153.x [DOI] [PMC free article] [PubMed] [Google Scholar]
- 40.Veronese N, Stubbs B, Volpato S, et al. Association between gait speed with mortality, cardiovascular disease and cancer: a systematic review and meta-analysis of prospective cohort studies. J Am Med Dir Assoc. 2018;19:981–988.e7. doi: 10.1016/j.jamda.2018.06.007 [DOI] [PubMed] [Google Scholar]
- 41.Straus SE, Majumdar SR, McAlister FA. New evidence for stroke prevention: scientific review. JAMA. 2002;288:1388–1395. doi: 10.1001/jama.288.11.1388 [DOI] [PubMed] [Google Scholar]
- 42.Winkleby MA, Jatulis DE, Frank E, Fortmann SP. Socioeconomic status and health: how education, income, and occupation contribute to risk factors for cardiovascular disease. Am J Public Health. 1992;82:816. doi: 10.2105/AJPH.82.6.816 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 43.Sulander T, Martelin T, Sainio P, Rahkonen O, Nissinen A, Uutela A. Trends and educational disparities in functional capacity among people aged 65-84 years. Int J Epidemiol. 2006;35:1255–1261. doi: 10.1093/ije/dyl183 [DOI] [PubMed] [Google Scholar]
- 44.Pampel FC, Krueger PM, Denney JT. Socioeconomic disparities in health behaviors. Annu Rev Sociol. 2010;36:349–370. doi: 10.1146/annurev.soc.012809.102529 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 45.Verlinden VJ, Maksimovic A, Mirza SS, et al. The associations of alcohol, coffee and tobacco consumption with gait in a community-dwelling population. Eur J Clin Nutr. 2016;70:116–122. doi: 10.1038/ejcn.2015.120 [DOI] [PubMed] [Google Scholar]
- 46.Ortolá R, García-Esquinas E, León-Muñoz LM, et al. Patterns of alcohol consumption and risk of frailty in community-dwelling older adults. J Gerontol Ser Biomed Sci Med Sci. 2016;71:251–258. doi: 10.1093/gerona/glv125 [DOI] [PubMed] [Google Scholar]
- 47.Chang M, Saczynski JS, Snaedal J, et al. Midlife physical activity preserves lower extremity function in older adults: age gene/environment susceptibility-Reykjavik study. J Am Geriatr Soc. 2013;61:237–242. doi: 10.1111/jgs.12077 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 48.Toffanello ED, Perissinotto E, Sergi G, et al. Vitamin D and physical performance in elderly subjects: the Pro.V.A study. PLoS One. 2012;7:e34950. doi: 10.1371/journal.pone.0034950 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 49.Kositsawat J, Barry LC, Kuchel GA. C-reactive protein, vitamin D deficiency, and slow gait speed. J Am Geriatr Soc. 2013;61:1574–1579. doi: 10.1111/jgs.12403 [DOI] [PubMed] [Google Scholar]
- 50.Mendes J, Santos A, Borges N, et al. Vitamin D status and functional parameters: a cross-sectional study in an older population. PLoS One. 2018;13:e0201840. doi: 10.1371/journal.pone.0201840 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 51.Annweiler C, Henni S, Walrand S, Montero-Odasso M, Duque G, Duval GT. Vitamin D and walking speed in older adults: systematic review and meta-analysis. Maturitas. 2017;106:8–25. doi: 10.1016/j.maturitas.2017.07.012 [DOI] [PubMed] [Google Scholar]
- 52.Rantanen T, Guralnik JM, Foley D, et al. Midlife hand grip strength as a predictor of old age disability. JAMA. 1999;281:558–560. doi: 10.1001/jama.281.6.558 [DOI] [PubMed] [Google Scholar]
- 53.Dodds R, Kuh D, Aihie Sayer A, Cooper R. Physical activity levels across adult life and grip strength in early old age: updating findings from a British birth cohort. Age Ageing. 2013;42:794–798. doi: 10.1093/ageing/aft124 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 54.Demakakos P, Cooper R, Hamer M, de Oliveira C, Hardy R, Breeze E. The bidirectional association between depressive symptoms and gait speed: evidence from the English Longitudinal Study of Ageing (ELSA). PLoS One. 2013;8:e68632. doi: 10.1371/journal.pone.0068632 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 55.Brown PJ, Rutherford BR, Yaffe K, et al. The depressed frail phenotype: the clinical manifestation of increased biological aging. Am J Geriatr Psychiatry. 2016;24:1084–1094. doi: 10.1016/j.jagp.2016.06.005 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 56.Eggermont LH, Bean JF, Guralnik JM, Leveille SG. Comparing pain severity versus pain location in the MOBILIZE Boston study: chronic pain and lower extremity function. J Gerontol A Biol Sci Med Sci. 2009;64:763–770. doi: 10.1093/gerona/glp016 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 57.Sawa R, Doi T, Misu S, et al. The severity and number of musculoskeletal pain associated with gait in community-dwelling elderly individuals. Gait Posture. 2017;54:242–247. doi: 10.1016/j.gaitpost.2017.03.013 [DOI] [PubMed] [Google Scholar]
- 58.Cruz-Almeida Y, Rosso A, Marcum Z, et al. ; Health ABC Study . Associations of musculoskeletal pain with mobility in older adults: potential cerebral mechanisms. J Gerontol A Biol Sci Med Sci. 2017;72:1270–1276. doi: 10.1093/gerona/glx084 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 59.Vincent HK, Vincent KR, Lamb KM. Obesity and mobility disability in the older adult. Obes Rev. 2010;11:568–579. doi: 10.1111/j.1467-789X.2009.00703.x [DOI] [PubMed] [Google Scholar]
- 60.Beavers KM, Beavers DP, Houston DK, et al. Associations between body composition and gait-speed decline: results from the Health, Aging, and Body Composition study. Am J Clin Nutr. 2013;97:552–560. doi: 10.3945/ajcn.112.047860 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 61.Ruggero CR, Bilton TL, Teixeira LF, et al. Gait speed correlates in a multiracial population of community-dwelling older adults living in Brazil: a cross-sectional population-based study. BMC Public Health. 2013;13:182. doi: 10.1186/1471-2458-13-182 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 62.Fritel X, Lachal L, Cassou B, Fauconnier A, Dargent-Molina P. Mobility impairment is associated with urge but not stress urinary incontinence in community-dwelling older women: results from the Ossébo study. BJOG Int J Obstet Gynaecol. 2013;120:1566–1574. doi: 10.1111/1471-0528.12316 [DOI] [PubMed] [Google Scholar]
- 63.Hausdorff JM, Yogev G, Springer S, Simon ES, Giladi N. Walking is more like catching than tapping: gait in the elderly as a complex cognitive task. Exp Brain Res. 2005;164:541–548. doi: 10.1007/s00221-005-2280-3 [DOI] [PubMed] [Google Scholar]
- 64.Montero-Odasso MM, Barnes B, Speechley M, et al. Disentangling cognitive-frailty: results from the gait and brain study. J Gerontol A Biol Sci Med Sci. 2016;71:1476–1482. doi: 10.1093/gerona/glw044 [DOI] [PubMed] [Google Scholar]
- 65.Annweiler C, Beauchet O, Bartha R, Montero-Odasso M. Slow gait in MCI is associated with ventricular enlargement: results from the Gait and Brain Study. Journal of Neural Transmission. 2013;120:1083–1092. doi: 10.1007/s00702-012-0926-4 [DOI] [PubMed] [Google Scholar]
- 66.Montero-Odasso M, Hachinski V. Preludes to brain failure: executive dysfunction and gait disturbances. Neurological Sciences. 2014;35:601–604. doi: 10.1007/s10072-013-1613-4 [DOI] [PubMed] [Google Scholar]
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