Association of Shoulder Dysfunction with Mobility Limitation in Older Adults of the BLSA

Mobility limitation affects one-third of older adults; yet, the impact of shoulder dysfunction which effects roughly 20%, is inadequately documented. As arm swing is a fundamental component of ambulation, we investigated the cross-sectional association between shoulder range of motion (ROM) and walking endurance using time to walk 400m as quickly as possible and lower extremity performance using the expanded Short Physical Performance Battery (e-SPPB). Data are from 614 men (50.5%) and women aged ≥ 60 years (mean 71.8 ±8 years) in the Baltimore Longitudinal Study of Aging (BLSA) who performed bilateral shoulder elevation and/or bilateral shoulder external rotation (ER) during nurse-administered physical examination. We examined odds of poor 400m-walk and e-SPPB performance defined as the worst quartile associated with abnormal shoulder elevation (≤9%) relative to bilateral normal shoulder elevation and abnormal shoulder external rotation (≤5%) relative to bilateral normal shoulder external rotation (ER) in separate analyses. Analyses were adjusted for age, sex, weight and height. Adjusted odds (95% confidence interval) of poor 400m-walk performance associated with abnormal shoulder elevation (N=254) were 4.7 (1.1-19.5;p=0.035) and with abnormal shoulder ER (n=401) were 4.8 (1.4-16.7;p=0.010). Adjusted odds of poor e-SPPB performance associated with abnormal shoulder elevation (N=462) were 3.5 (1.6-7.7;p=0.002) and with abnormal shoulder ER (n=457) were 2.9 (1.1-7.4;p=0.030). Results suggest abnormal shoulder ROM is associated with poorer walking endurance capacity and lower-extremity functional performance in the relatively healthy older adults. Future research is warranted to develop novel screening paradigms that mitigate mobility limitation and functional decline in older adults with shoulder dysfunction.

Machine Learning Prediction Models for Mobility Limitation Over Time in Older Adults: The Health ABC Study

Mobility limitation in older adults is common and associated with poor health outcomes and loss of independence. Identification of at-risk individuals remains challenging because of time-consuming clinical assessments and limitations of statistical models for dynamic outcomes over time. Therefore, we aimed to develop machine learning models for predicting mobility limitation in older adults using repeated measures and variable selection. We used nine years of follow-up data from the Health, Aging, and Body Composition study to model mobility limitation, defined as self-report of any difficulty walking ¼ mile or up a flight of stairs, assessed annually. We considered 46 predictors for modeling, including demographic, lifestyle, chronic condition and physical function variables. We developed three models with Binary Mixed Model Forest, using: 1) all 46 predictors, 2) an automated variable selection algorithm, and 3) the top five most important predictors. Area under the receiver operating curve ranged from 0.78 to 0.84 for the models for two validation datasets (with and without previous annual visit data for participants). Across the three models, the most important predictors of mobility limitation were ease of getting up from chair, gait speed, self-reported health status, body mass index and depression. Longitudinal, machine learning models predicting mobility limitation had good performance for identifying at-risk older adults based on current and previous annual visit data. Future studies should evaluate the utility and efficiency of the prediction models as a tool in a clinical setting for identifying at-risk older adults who may benefit from interventions aimed to prevent mobility limitation.

Predicting Future Mobility Limitation in Older Adults: A Machine Learning Analysis of Health ABC Study Data

Background Mobility limitation in older adults is common and associated with poor health outcomes and loss of independence. Identification of at-risk individuals remains challenging because of time-consuming clinical assessments and limitations of statistical models for dynamic outcomes over time. Therefore, we aimed to develop machine learning models for predicting future mobility limitation in older adults using repeated measures data. Methods We used annual assessments over nine years of follow-up from the Health, Aging, and Body Composition study to model mobility limitation, defined as self-report of any difficulty walking a quarter mile or climbing 10 steps. We considered 46 predictors, including demographics, lifestyle, chronic conditions and physical function. With a split sample approach, we developed mixed models (generalized linear and Binary Mixed Model forest) using: 1) all 46 predictors, 2) a variable selection algorithm, and 3) the top five most important predictors. Age was included in all models. Performance was evaluated using area under the receiver operating curve (AUC) in two internal validation datasets. Results AUC ranged from 0.80-0.84 for the models. The most important predictors of mobility limitation were ease of getting up from a chair, gait speed, self-reported health status, body mass index and depression. Conclusions Machine learning models using repeated measures had good performance for identifying older adults at-risk of developing mobility limitation. Future studies should evaluate the utility and efficiency of the prediction models as a tool in clinical settings for identifying at-risk older adults who may benefit from interventions aimed to prevent or delay mobility limitation.

What cut-point in gait speed best discriminates community dwelling older adults with mobility complaints from those without? A pooled analysis from the Sarcopenia Definitions and Outcomes Consortium

Background Cut-points to define slow walking speed have largely been derived from expert opinion. Methods Study participants (13,589 men and 5,043 women aged ≥65years) had walking speed (m/s) measured over 4-6 meters (mean ± SD: 1.20 ± 0.27 m/s in men and 0.94 ± 0.24 m/s in women.) Mobility limitation was defined as self-reported any difficulty with walking ~1/4 mile (prevalence: 12.6% men, 26.4% women). Sex-stratified classification and regression tree (CART) models with 10-fold cross-validation identified walking speed cut-points that optimally discriminated those who reported mobility limitation from those who did not. Results Among 5,043 women, CART analysis identified two cut-points, classifying 4,144 (82.2%) with walking speed ≥0.75 m/s, which we labeled as “fast”; 478 (9.5%) as “intermediate” (walking speed ≥0.62 m/s but <0.75 m/s); and 421 (8.3%) as “slow” (walking speed <0.62 m/s). Among 13,589 men, CART analysis identified three cut-points, classifying 10,001 (73.6%) with walking speed ≥1.00 m/s (“very fast”); 2,901 (21.3%) as “fast” (walking speed ≥0.74 m/s but <1.00 m/s); 497 (3.7%) as “intermediate” (walking speed ≥0.57 m/s but <0.74 m/s); and 190 (1.4%) as “slow” (walking speed <0.57 m/s). Prevalence of self-reported mobility limitation was lowest in the “fast” or “very fast” (11% for men and 19% for women) and highest in the “slow” (60.5% in men and 71.0% in women). Rounding the two slower cut-points to 0.60 m/s and 0.75 m/s reclassified very few participants. Conclusions Cut-points in walking speed of ~0.60 m/s and 0.75 m/s discriminate those with self-reported mobility limitation from those without.

Body Weight, BMI, Percent Fat and Associations with Mortality and Incident Mobility Limitation in Older Men

How different measures of adiposity are similarly or differentially related to mobility limitation and mortality is not clear. In total, 5849 community-dwelling men aged ≥65 years (mean age: 72 years) were followed mortality over 10 years and self-reported mobility limitations (any difficulty walking 2–3 blocks or with climbing 10 steps) at six contacts over 14 years. Baseline measures of adiposity included weight, BMI and percent fat by DXA. Appendicular lean mass (ALM, by DXA) was analyzed as ALM/ht2. Proportional hazards models estimated the risk of mortality, and repeated measures generalized estimating equations estimated the likelihood of mobility limitation. Over 10 years, 27.9% of men died; over 14 years, 48.0% of men reported at least one mobility limitation. We observed U-shaped relationships between weight, BMI, percent fat and ALM/ht2 with mortality. There was a clear log-linear relationship between weight, BMI and percent fat with incident mobility limitation, with higher values associated with a greater likelihood of mobility limitation. In contrast, there was a U-shaped relationship between ALM/ht2 and incident mobility limitation. These observational data suggest that no single measure of adiposity or body composition reflects both the lowest risk of mortality and the lowest likelihood for developing mobility limitation in older men.

Mass Tracking in Cellular Networks for the COVID-19 Pandemic Monitoring

The year 2020 was marked by the emergence of the COVID-19 pandemic. After months of uncontrolled spread worldwide, a clear conclusion is that controlling the mobility of the general population can slow down the propagation of the pandemic. Tracking the location of the population enables better use of mobility limitation policies and the prediction of potential hotspots, as well as improved alert services to individuals that may have been exposed to the virus. With mobility in their core functionality and a high degree of penetration of mobile devices within the general population, cellular networks are an invaluable asset for this purpose. This paper shows an overview of the possibilities offered by cellular networks for the massive tacking of the population at different levels. The major privacy concerns are also reviewed and a specific use case is shown, correlating mobility and number of cases in the province of Málaga (Spain).