Physical Activity Levels And Marital Status In The Health, Aging, And Body Composition Study

The purpose of this study was to compare fatigue, strength, body composition, muscle thickness, and muscle quality between Hodgkin’s lymphoma survivors (HLS) and apparently healthy subjects matched by age, gender, and physical activity levels (CON). Twelve HLS (32.16 ± 8.06) and 36 CON (32.42 ± 7.64) were enrolled in the study. Fatigue was assessed using the 20-item Multidimensional Fatigue Inventory, muscle strength using an isokinetic dynamometer, body composition using dual-energy X-ray absorptiometry, and thickness and muscle quality using B-mode ultrasound. Differences between HLS and CON were analyzed using independent samples t tests. No significant differences were observed between groups for any demographic characteristics: age ( P = .922), weight ( P = .943), height ( P = .511), body mass index ( P = .796), fat mass ( P = .688), fat-free mass ( P = .520), and percent body fat ( P = .446). No significant differences were observed for strength (peak torque; P = .552), relative peak torque ( P = .200), muscle thickness ( P > .05) and muscle quality ( P > .05). However, self-perceived fatigue was significantly higher in HLS than in CON ( P = .009). It appears that when HLS are matched by age and physical activity levels to CON, no significant difference in body composition, muscle thickness, muscle quality, or strength is observed. Self-perceived fatigue, as predicted, is higher in HLS, which may have implications and should be considered when prescribing exercise training to this cancer population.

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All-cause Dementia Prediction by Machine Learning: The Health, Aging, and Body Composition Study

Innovation in Aging ◽

10.1093/geroni/igaa057.1575 ◽

2020 ◽

Vol 4 (Supplement_1) ◽

pp. 487-487

Author(s):

Chenkai Wu ◽

Xurui Jin

Keyword(s):

Machine Learning ◽

Body Composition ◽

Risk Prediction ◽

Risk Score ◽

Body Composition Study ◽

C Statistic ◽

Dementia Risk ◽

Health Aging ◽

Composition Study ◽

Traditional Approaches

Abstract There are several shortcomings of the currently available risk prediction models for dementia. We developed a risk prediction model for dementia using machine-learning approach and compared its performance with traditional approaches. Data were from the Health, Aging, and Body Composition Study, comprising 3,075 older adults (at least 70 years). Dementia was defined as (1) use of a prescribed dementia medication, (2) adjudicated dementia diagnosis, or (3) a race-stratified cognitive decline>1.5 SDs from the baseline mean. We selected 275 predictors collected from questionnaires, imaging data, performance testing, and biospecimen. We used random survival forest (RSF) to build the full model and rank the importance of predictors. Subsequently, we built parsimonious models with top-20 predictors using RSF and Cox regression. A dementia risk score was developed using top-ranked variables. We used the C-statistic for performance evaluation. Over a median of 11.4 years of follow-up, 659 dementias (21.4%) occurred. The RSF model (both including all and top-20 variables) showed a higher C-statistic than the regression model. Digit symbol score, physical performance battery, finger tapping score, weight change since age 50, serum adiponectin, and APOE genotype were the top-6 variables. We created a dementia risk score (0-10) using the top-6 variables. A 1-unit increase in the risk score was associated with an 8% higher risk of dementia. The risk score demonstrated good discrimination (C-statistic=0.75). Machine learning methods offered improvement over traditional approaches in predicting dementia. The risk prediction score derived from a parsimonious model had good prediction performance.

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Comparison of Self-Reported Physical Activity Levels to Cardiorespiratory Fitness and Body Composition Measures in Firefighters

Medicine & Science in Sports & Exercise ◽

10.1249/01.mss.0000386166.59419.67 ◽

2010 ◽

Vol 42 ◽

pp. 747

Author(s):

Caleb Beraud ◽

Kelly Brooks ◽

Luke Thomas

Keyword(s):

Physical Activity ◽

Body Composition ◽

Cardiorespiratory Fitness ◽

Activity Levels ◽

Physical Activity Levels

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