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Background: Parkinson's disease (PD) is the fastest-growing neurological condition with over 10 million cases worldwide. While age and sex are known predictors of incident PD, there is a need to identify other predictors. This study compares the prediction performance of accelerometry-derived physical activity (PA) measures and traditional risk factors for incident PD in the UK Biobank. Methods: The study population consisted of 92,352 UK Biobank participants without PD at baseline (43.8% male, median age 63 years with interquartile range 43–69). 245 participants were diagnosed with PD by April 1, 2021 (586,604 person-years of follow-up). The incident PD prediction performances of 10 traditional predictors and 8 objective PA measures were compared using single- and multi-variable Cox models. Prediction performance was assessed using a novel, stable statistic: the repeated cross-validated concordance (rcvC). Sensitivity analyses were conducted where PD cases diagnosed within the first six months, one year, and two years were deleted. Results: Single-predictor Cox regression models indicated that all PA measures were statistically significant (p-values < 0.0001). The highest-performing individual predictors were total acceleration (TA) (rcvC = 0.813) among PA measures, and age (rcvC = 0.757) among traditional predictors. The two-step forward-selection process produced a model containing age, sex, and TA (rcvC = 0.851). Adding TA to the model increased the rcvC by 9.8% (p-value < 0.0001). Results were largely unchanged in sensitivity analyses. Conclusions: Objective PA summaries have better single-predictor model performance than known risk factors and increase the prediction performance substantially when added to models with age and sex. [ABSTRACT FROM AUTHOR]

Objectives: Reaching the moderate-to-vigorous physical activity (MVPA) recommendations of 150 min/wk is difficult for older adults, particularly among those living with frailty and its associated risk of dementia. We examined the dose-response relationship between MVPA and dementia risk among at-risk persons living with and without frailty enrolled in the UK Biobank study., Design: Survival analysis within a prospective cohort study., Settings and Participants: Participants at risk for all-cause dementia who wore an Axivity AX3 triaxial wrist-worn accelerometer between February 2013 and December 2015., Methods: MVPA was estimated from wrist-worn accelerometry in a subpopulation of the UK Biobank study. A modified version of the physical frailty phenotype was used to define frailty. Associations between MVPA dose (including interactions with frailty) and first-time incident dementia were analyzed using Cox regression models. MVPA was treated continuously and categorically across 5 levels to estimate the dose-response curve. Models were adjusted for demographics, frailty status, and comorbidities., Results: This study included 89,667 adults (median age, 63 years; 56% women), with 735 participants developing dementia over an average of 4.4 years. Average weekly MVPA was 126 minutes. Each 30 minutes higher MVPA was associated with a 4% reduction in the risk of all-cause dementia (hazard ratio, 0.96; 95% CI, 0.93-0.99). The hazard ratios for engaging in 0-34.9, 35-69.9, 70-139.9, and ≥140 MVPA minutes per week were 0.59, 0.40, 0.37, and 0.31, respectively (P < .05 for all) compared with 0 MVPA minutes per week. All associations were similar across frailty status (interaction P for all models > .21)., Conclusions and Implications: Our results suggest engaging in any additional amount of MVPA reduces dementia risk, with the highest benefit appearing among individuals with no MVPA. These associations are not substantially modified by frailty status., Competing Interests: Disclosure J.A.S. is a consultant for Edwards Lifescience and serves on the board of BellSant, Inc. C.M.C. is consulting with Bayer and Johnson and Johnson on methods development for wearable devices in clinical trials. The details of the contracts are disclosed through the Johns Hopkins University eDisclose system and have no direct or apparent relationship with this manuscript. The other authors declare no conflicts of interest., (Copyright © 2024 Post-Acute and Long-Term Care Medical Association. Published by Elsevier Inc. All rights reserved.)

Background: Alzheimer's disease (AD) affects over 6 million people and is the seventh-leading cause of death in the United States. This study compares wrist-worn accelerometry-derived PA measures against traditional risk factors for incident AD in the UK Biobank., Methods: Of 42 157 UK Biobank participants 65 years and older who had accelerometry data and no prior AD diagnosis, 157 developed AD by April 1, 2021 (264 988 person-years or on average 6.2 years of follow-up). Twelve traditional predictors and 8 accelerometer-based physical activity (PA) measures were used in single- and multivariate Cox models. Their predictive performances for future AD diagnosis were compared across models using the repeated cross-validated concordance (rcvC). To account for potential reverse causality, sensitivity analyses were conducted by removing dropouts and cases within the first 6 months, 1 year, and 2 years., Results: The best-performing individual predictors of incident AD were age (p < .0001, rcvC = 0.658) and moderate-to-vigorous PA (MVPA, p = .0001, rcvC = 0.622). Forward selection produced a model that included age, diabetes, and MVPA (rcvC = 0.681). Adding MVPA to the model containing age and diabetes improved its rcvC from 0.665 to 0.681 (p = .0030), more than all other potential risk factors considered., Conclusions: Objective PA summaries are the best single predictors among modifiable risk factors with a predictive performance close to that of age. Adding PA summaries to traditional risk factors for AD substantially increases the predictive performance of these models. Increasing MVPA by 14.5 minutes per day reduces the hazard substantially, equivalent to 2 years younger., (© The Author(s) 2024. Published by Oxford University Press on behalf of the Gerontological Society of America. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.)

Introduction: Objectively measured physical activity (PA) data were collected in the accelerometry substudy of the UK Biobank. UK Biobank also contains information about multiple sclerosis (MS) diagnosis at the time of and after PA collection. This study aimed to 1) quantify the difference in PA between prevalent MS cases and matched healthy controls, and 2) evaluate the predictive performance of objective PA measures for incident MS cases., Methods: The first analysis compared eight accelerometer-derived PA summaries between MS patients ( N = 316) and matched controls (30 controls for each MS case). The second analysis focused on predicting time to MS diagnosis among participants who were not diagnosed with MS. A total of 19 predictors including eight measures of objective PA were compared using Cox proportional hazards models (number of events = 47; 585,900 person-years of follow-up)., Results: In the prevalent MS study, the difference between MS cases and matched controls was statistically significant for all PA summaries ( P < 0.001). In the incident MS study, the most predictive variable of progression to MS in univariate Cox regression models was lower age ( C = 0.604), and the most predictive PA variable was lower relative amplitude (RA, C = 0.594). A two-stage forward selection using Cox regression resulted in a model with concordance C = 0.693 and four predictors: age ( P = 0.015), stroke ( P = 0.009), Townsend deprivation index ( P = 0.874), and RA ( P = 0.004). A model including age, stroke, and RA had a concordance of C = 0.691., Conclusions: Objective PA summaries were significantly different and consistent with lower activity among study participants who had MS at the time of the accelerometry study. Among individuals who did not have MS, younger age, stroke history, and lower RA were significantly associated with a higher risk of a future MS diagnosis., (Copyright © 2023 by the American College of Sports Medicine.)