Community-Based Activity and Sedentary Patterns Are Associated With Cognitive Performance in Mobility-Limited Older Adults

Over the last few decades, considerable evidence shows that greater levels of aerobic exercise and cardiovascular fitness benefit cognitive performance. However, the degree to which free-living activity in community settings is related to cognitive performance remains unclear, particularly in older adults vulnerable to disability. Also, it is unknown whether the manner in which daily physical activity (PA) and sedentary time are accumulated throughout the day is associated with cognition. Cross-sectional associations between accelerometer-characterized PA and sedentary patterns and cognitive performance were examined in 1,274 mobility-limited older adults. Percent time spent in various bout lengths of PA (≥1, ≥2, and ≥5 min) and sedentary (≥1, ≥30, and ≥60 min) was defined as the number of minutes registered divided by total wear time × 100. Percent time was then tertiled for each bout length. Multiple linear regression models were used to estimate the associations between accelerometer bout variables and separate cognitive domains that included processing speed (Digit Symbol Coding; DSC), immediate and delayed recall (Hopkins Verbal Learning Test; HVLT), information processing and selective attention (Flanker), working memory (n-back), reaction time (switch and non-switch reaction time), and a composite score that averaged results from all cognitive tests. After adjusting for demographics, behavioral factors, and morbid conditions, more time spent in PA was associated with higher DSC for all bout lengths (p < 0.03 for all). Higher PA was associated with higher HVLT and global cognition scores but only for longer bout lengths (p < 0.05 for all). The association was largely driven by participants who spent the lowest amount of time performing activity while awake (p < 0.04). An inverse linear relationship was observed between total sedentary time and DSC (p = 0.02), but not for other measures of cognition. These results suggest that, while higher PA was associated with higher cognitive performance, PA’s association with memory was sensitive to bout duration. The time, but not the manner, spent in sedentary behaviors showed a minor association with executive function. Further research is warranted to characterize longitudinal changes in daily activity and sedentary patterns as potential biophysical markers of cognitive status in older adults

Age Differences in Estimating Physical Activity by Wrist Accelerometry Using Machine Learning

Accelerometer-based fitness trackers and smartwatches are proliferating with incessant attention towards health tracking. Despite their growing popularity, accurately measuring hallmark measures of physical activities has yet to be accomplished in adults of all ages. In this work, we evaluated the performance of four machine learning models: decision tree, random forest, extreme gradient boosting (XGBoost) and least absolute shrinkage and selection operator (LASSO), to estimate the hallmark measures of physical activities in young (20–50 years), middle-aged (50–70 years], and older adults (70–89 years]. Our models were built to recognize physical activity types, recognize physical activity intensities, estimate energy expenditure (EE) and recognize individual physical activities using wrist-worn tri-axial accelerometer data (33 activities per participant) from a large sample of participants (n = 253, 62% women, aged 20–89 years old). Results showed that the machine learning models were quite accurate at recognizing physical activity type and intensity and estimating energy expenditure. However, models performed less optimally when recognizing individual physical activities. F1-Scores derived from XGBoost’s models were high for sedentary (0.955–0.973), locomotion (0.942–0.964) and lifestyle (0.913–0.949) activity types with no apparent difference across age groups. Low (0.919–0.947), light (0.813–0.828) and moderate (0.846–0.875) physical activity intensities were also recognized accurately. The root mean square error range for EE was approximately 1 equivalent of resting EE [0.835–1.009 METs]. Generally, random forest and XGBoost models outperformed other models. In conclusion, machine learning models to label physical activity types, activity intensity and energy expenditure are accurate and there are minimal differences in their performance across young, middle-aged and older adults

Are Machine Learning Models on Wrist Accelerometry Robust against Differences in Physical Performance among Older Adults?

Sufficient physical activity (PA) reduces the risk of a myriad of diseases and preserves physical capabilities in later life. While there have been significant achievements in mapping accelerations to real-life movements using machine learning (ML), errors continue to be common, particularly for wrist-worn devices. It remains unknown whether ML models are robust for estimating age-related loss of physical function. In this study, we evaluated the performance of ML models (XGBoost and LASSO) to estimate the hallmark measures of PA in low physical performance (LPP) and high physical performance (HPP) groups. Our models were built to recognize PA types and intensities, identify each individual activity, and estimate energy expenditure (EE) using wrist-worn accelerometer data (33 activities per participant) from a large sample of participants (n = 247, 57% females, aged 60+ years). Results indicated that the ML models were accurate in recognizing PA by type and intensity while also estimating EE accurately. However, the models built to recognize individual activities were less robust. Across all tasks, XGBoost outperformed LASSO. XGBoost obtained F1-Scores for sedentary (0.932 ± 0.005), locomotion (0.946 ± 0.003), lifestyle (0.927 ± 0.006), and strength flexibility exercise (0.915 ± 0.017) activity type recognition tasks. The F1-Scores for recognizing low, light, and moderate activity intensity were (0.932 ± 0.005), (0.840 ± 0.004), and (0.869 ± 0.005), respectively. The root mean square error for EE estimation was 0.836 ± 0.059 METs. There was no evidence showing that splitting the participants into the LPP and HPP groups improved the models’ performance on estimating the hallmark measures of physical activities. In conclusion, using features derived from wrist-worn accelerometer data, machine learning models can accurately recognize PA types and intensities and estimate EE for older adults with high and low physical function

Are Machine Learning Models on Wrist Accelerometry Robust against Differences in Physical Performance among Older Adults?

Sufficient physical activity (PA) reduces the risk of a myriad of diseases and preserves physical capabilities in later life. While there have been significant achievements in mapping accelerations to real-life movements using machine learning (ML), errors continue to be common, particularly for wrist-worn devices. It remains unknown whether ML models are robust for estimating age-related loss of physical function. In this study, we evaluated the performance of ML models (XGBoost and LASSO) to estimate the hallmark measures of PA in low physical performance (LPP) and high physical performance (HPP) groups. Our models were built to recognize PA types and intensities, identify each individual activity, and estimate energy expenditure (EE) using wrist-worn accelerometer data (33 activities per participant) from a large sample of participants (n = 247, 57% females, aged 60+ years). Results indicated that the ML models were accurate in recognizing PA by type and intensity while also estimating EE accurately. However, the models built to recognize individual activities were less robust. Across all tasks, XGBoost outperformed LASSO. XGBoost obtained F1-Scores for sedentary (0.932 ± 0.005), locomotion (0.946 ± 0.003), lifestyle (0.927 ± 0.006), and strength flexibility exercise (0.915 ± 0.017) activity type recognition tasks. The F1-Scores for recognizing low, light, and moderate activity intensity were (0.932 ± 0.005), (0.840 ± 0.004), and (0.869 ± 0.005), respectively. The root mean square error for EE estimation was 0.836 ± 0.059 METs. There was no evidence showing that splitting the participants into the LPP and HPP groups improved the models’ performance on estimating the hallmark measures of physical activities. In conclusion, using features derived from wrist-worn accelerometer data, machine learning models can accurately recognize PA types and intensities and estimate EE for older adults with high and low physical function

Serum Concentrations of Polyfluoroalkyl Compounds in Faroese Whale Meat Consumers

To learn the extent of human exposure to polyfluoroalkyl compounds (PFCs) in a remote fishing population, we measured, in Faroese children and pregnant women, the serum concentrations of nine PFCs, including perfluorooctane sulfonate (PFOS), perfluorooctanoate (PFOA), and perfluorononanoate (PFNA), by using on-line solid-phase extraction coupled to isotope dilution high-performance liquid chromatography-tandem mass spectrometry. The serum samples analyzed had been collected between 1993 and 2005 from 103 children 7 years of age, 79 of these children at 14 years of age, and from 12 pregnant women and their children 5 years later. PFOS was detected in all samples analyzed, and both PFOA and PFNA in all but one of the samples. The concentrations found are comparable to those reported elsewhere. Correlations between paired concentrations were poor. However, PFOS and PFNA concentrations correlated well with the frequency of pilot whale dinners and with concentrations of mercury and polychlorinated biphenyls. One whale meal every two weeks increased the PFOS concentration in 14-year olds by about 25% and PFNA by 50%. The high frequency of detection of most PFCs suggests widespread exposure in the Faroe Islands already by the early 1990s, with whale meat being an important source. Synopsis: Pilot whale meat may have been an important source of dietary exposure to PFOS and PFNA among Faroe Islands residents since the 1990

Walking energetics and white matter hyperintensities in mid‐to‐late adulthood

Abstract INTRODUCTION White matter hyperintensities (WMHs) increase with age and contribute to cognitive and motor function decline. Energy costs for mobility worsen with age, as the energetic cost of walking increases and energetic capacity declines. We examined the cross‐sectional associations of multiple measures of walking energetics with WMHs in mid‐ to late‐aged adults. METHODS A total of 601 cognitively unimpaired adults (mean age 66.9 ± 15.3 years, 54% women) underwent brain magnetic resonance imaging scans and completed standardized slow‐ and peak‐paced walking assessments with metabolic measurement (V̇O2). T1‐weighted scans and fluid‐attenuated inversion recovery images were used to quantify WMHs. Separate multivariable linear regression models examined associations adjusted for covariates. RESULTS Lower slow‐paced V̇O2 (B = 0.07; P = 0.030), higher peak‐paced V̇O2 (B = –0.10; P = 0.007), and lower cost‐to‐capacity ratio (B = .12; P < 0.0001) were all associated with lower WMH volumes. DISCUSSION The cost‐to‐capacity ratio, which describes the percentage of capacity required for ambulation, was the walking energetic measure most strongly associated with WMHs

Metabolic costs of daily activity in older adults (Chores XL) study: Design and methods

Background: For over 20 years, normative data has guided the prescription of physical activity. This data has since been applied to research and used to plan interventions. While this data seemingly provides accurate estimates of the metabolic cost of daily activities in young adults, the accuracy of use among older adults is less clear. As such, a thorough evaluation of the metabolic cost of daily activities in community dwelling adults across the lifespan is needed. Methods: The Metabolic Costs of Daily Activity in Older Adults Study is a cross-sectional study designed to compare the metabolic cost of daily activities in 250 community dwelling adults across the lifespan. Participants (20 + years) performed 38 common daily activities while expiratory gases were measured using a portable indirect calorimeter (Cosmed K4b2). The metabolic cost was examined as a metabolic equivalent value (O2 uptake relative to 3.5 mL min−1 kg−1), a function of work rate – metabolic economy, and a relative value of resting and peak oxygen uptake. Results: The primary objective is to determine age-related differences in the metabolic cost of common lifestyle and exercise activities. Secondary objectives include (a) investigating the effect of functional impairment on the metabolic cost of daily activities, (b) evaluating the validity of perception-based measurement of exertion across the lifespan, and (c) validating activity sensors for estimating the type and intensity of physical activity. Conclusion: Results of this study are expected to improve the effectiveness by which physical activity and nutrition is recommended for adults across the lifespan