Wearable devices offer the ability to objectively characterize free-living physical activity; however, raw step-count data generated by commercial devices require systematic processing before they can support rigorous inference. We describe a transparent, reproducible standard operating procedure (SOP) for transforming epoch-level step-count data from commercial Garmin devices into participant-level analytic variables and demonstrate its application in the STRRIDE-PD Reunion study: a long-term follow-up of older adults originally enrolled in a supervised exercise intervention trial. This data pipeline standardizes timestamps, reconstructs daily epoch grids, infers wear time from observed step patterns, and applies a prespecified valid-day threshold (≥10 hours inferred wear time) to generate participant-level summaries. Among 67 participants (mean age 71.4 years, 65.7% women), the median valid-day count was 10 days, median average daily steps were 5,794, and participant-level estimates were identical across ≥10-hour and ≥6-hour valid-day thresholds. Wearable-derived step counts were significantly associated with 9 of 16 cardiometabolic and fitness outcomes, including cardiorespiratory fitness, body composition, and lipid profiles. By contrast, self-reported exercise - assessed via a frequency-by-duration composite ranked into deciles - was not significantly associated with any outcome. A regression calibration framework applied to the full sample quantified the attenuation underlying this discrepancy: the naive self-report model systematically underestimated associations relative to both the observed Garmin model and calibration-corrected estimates. These findings demonstrate that measurement approach is a determinant of scientific conclusions in physical activity research, and that reproducible wearable data pipelines are essential infrastructure for aging epidemiology.

BACKGROUND:Wearable activity monitors are widely used to measure daily step counts, a simple and health-relevant metric of physical activity. However, differences in sensor technologies, algorithms, and wear locations across devices can result in substantial variability in step count estimates, creating challenges for comparing and harmonizing data across studies and populations. This study aims to compare free-living daily step counts across commonly used activity monitoring devices in adults using network meta-analysis. METHODS:We systematically searched PubMed, Scopus, and PsycINFO through November 2025 for studies reporting concurrent daily step measurements using at least 2 devices from our target list of 13 manufacturers of research- and consumer-grade accelerometers and pedometers. Network meta-analysis estimated the ratio of means (ROMs) and mean differences in daily steps between devices. RESULTS:Across 59 studies, most consumer devices, pedometers, and the thigh (ActivPAL) and hip-worn research devices (ActiGraph GT, Axivity, and Actical) were within 10% ROM and <700 steps/day. Wear location was the primary source of variation, with wrist-worn and ankle-worn (StepWatch) research-grade accelerometers estimating 15‒30% more daily steps (+1000 to 2200 steps/day) than hip-worn devices, while consumer-grade wrist devices showed smaller differences (3‒15% higher or +200 to 1000 steps/day). CONCLUSION:These findings provide researchers with evidence-based guidance for selecting appropriate step devices, interpreting step count data across studies, and considerations when combining data from multiple device types and wear locations.

Arthritis is a chronic inflammatory disease characterized by joint pain, swelling and limited range of motion. Depression is prevalent among individuals with arthritis and is associated with increased risks of health conditions and mortality. P hysical activity (PA) is a strong predictor of lower mortality and can serve as a protective factor against both depression and arthritis. This study aimed to examine the associations between depression, moderate to vigorous PA (MVPA) and sedentary behavior (SB) with all-cause mortality among individuals with arthritis. Methods: We included 5129 participant aged ≥ 40 years from NHANES 2007-2016. The death records through 2019 were ascertained. Depression was assessed using Patient Health Questionnaire (PHQ9) and categorized into depressed and not depressed groups. MVPA and SB were measured using the Global Physical Activity Questionnaire (GPAQ). Individuals with no leisure time MVPA were classified as inactive and those with ≥ 8 hours of daily sitting were considered sedentary. To investigate joint associations, participants were grouped based on depression status and MVPA or sedentary status. Weighted multivariate Cox regression models adjusted for age (Model 1) and additional covariates (Model 2) were used to examine the associations of depression, MVPA and SB with mortality (Table). Results: Over 8 ± 3 years of follow-up,1247 (24%) participants died. Compared to active group without depression, inactive groups with and without depression had 1.98 (95% CI: 1.56-2.47) and 1.52 (95% CI: 1.25-1.84) times risk of mortality, respectively (Table). Compared to the non-sedentary group without depression, sedentary groups with and without depression had 1.92 (95% CI: 1.62-2.28) and 1.40 (95% CI: 1.16-1.70) times risk of mortality (Table). Conclusion: Depression, MVPA and SB, were independently and jointly associated with all-cause mortality among people with arthritis. Being more active and less sedentary may help lower the risk of mortality in this population.

People’s decision-making abilities often fail to improve or may even erode when they rely on AI for decision-support, even when the AI provides informative explanations. We argue this is partly because people intuitively seek contrastive explanations, which clarify the difference between the AI’s decision and their own reasoning, while most AI systems offer “unilateral” explanations that justify the AI’s decision but do not account for users’ knowledge and thinking. To address potential human knowledge gaps, we introduce a framework for generating human-centered contrastive explanations which explain the difference between AI’s choice and a predicted, likely human choice about the same task. Results from a large-scale experiment (N = 628) demonstrate that contrastive explanations significantly enhance users’ independent decision-making skills compared to unilateral explanations, without sacrificing decision accuracy. As concerns about deskilling in AI-supported tasks grow, our research demonstrates that integrating human reasoning into AI design can promote human skill development.

Introduction: Identifying and monitoring frailty can inform optimal care for older adults. This study aimed to detect frailty using wearable device-measured movement behaviors (MBs). Hypothesis: We hypothesized that random forest models were able to detect frailty using MBs. Methods: This cross-sectional study included 44 older adults living in the community (79.6±9.3 years old; 84% females). The Fried Frailty Phenotype (FFP) was defined as having 3 or more of unintentional weight loss, exhaustion, low physical activity, slowness, and weakness. Participants wore a thigh-worn ActivPAL for 10 consecutive days. MBs were quantified as: 1) overall activity (activity score, daily steps and number of sit-to-stand), 2) time in postures (standing, stepping, sitting and lying), 3) time in bed, 4) time in sitting bouts over 30 min and 60 min, 5) stepping counts and time in <1, 1-5, 5-10 and 10-20 min bouts, 6) stepping counts and time in cadences >75 and >100, and 7) peak stepping counts in 10 seconds, 2, 6, and 10 min. Random forest models were developed to classify FFP and its 5 individual components, with age, sex, BMI, and MBs as predictor variables. Results: Eleven (25%) participants were frail. The average ActivPAL wear time was 9.14±1.49 days. The model achieved an AUC [95% CI] of 0.85 [0.71-0.99] for FFP. The 5 most important predictors were time in standing, stepping time in < 1 min bouts, time in stepping, stepping counts in 10-20 min bouts, and stepping counts in 1-5 min bouts (Table). The models for individual FFP components also achieved AUC [95% CI] of 0.90 [0.77-1.00] for unintentional weight loss, 0.89 [0.78-1.00] for exhaustion, 0.88 [0.76-0.99] for slowness, 0.81 [0.63-0.99] for low physical activity, and 0.94 [0.87-1.00] for weakness (Table). Conclusions: A thigh-worn wearable device can detect FPP with high accuracy. Once validated in an independent sample, our algorithm can be useful for frailty assessment and monitoring.

This review summarizes the evidence on the number and intensity of steps associated with health benefits. For older adults, 6000-8000 daily steps is associated with substantial cardiovascular disease (CVD) and mortality benefits and taking more than 8000 daily steps appears to be associated with additional benefit. For younger adults, taking 8000-10,000 daily steps is associated with substantial mortality benefit.

To determine whether unhealthy lifestyle behaviors were associated with similar increases in the risk of incident T2D among individuals with low, intermediate, and high genetic risk, we performed a genetic risk score (GRS) by lifestyle interaction analysis within 460,133 individuals from the UK Biobank. Multi-ancestry GRS were calculated by summing the effects of 1,286 T2D-associated variants (number of risk alleles multiplied by the reported effect size); low, intermediate, and high GRS were defined by tertiles of GRS. We used baseline self-reported data on smoking, BMI, physical activity, and diet to categorize participants as having an ideal, intermediate, or poor level of lifestyle factors. Cox proportional hazards regression models were used to generate adjusted hazards ratios (HR) and associated 95% confidence intervals (95% CI). During follow-up (median 8.9 years), 21,569 (4.7%) participants developed T2D. GRS (P<2e-16) and lifestyle classification (P<2e-16) were independently associated with increased risk for T2D. Compared with “ideal” lifestyle, “poor” lifestyle was associated with substantially increased risk in all genetic risk strata, with HR ranging from 7.5 to 29.5 (Figure 1). Overall, high genetic risk and poor lifestyle were the strongest risk factors for incident T2D. Individuals at all levels of genetic risk greatly mitigate their risk through their behavioral lifestyle. Disclosure C.N. Spracklen: None. C. Zhao: None. E. Bertone-Johnson: None. N. Cai: None. L. Huang: None. M. Janiczek: None. C. Lee: None. C. Ma: None. A. Paluch: None. S. sturgeon: None. N. VanKim: None. Funding American Diabetes Association (11-22-JDFPM-06)

Background:Type 2 diabetes (T2D) results from a complex interplay between genetic predisposition and lifestyle factors. Both genetic susceptibility and unhealthy lifestyle are known to be associated with elevated T2D risk. However, their combined effects on T2D risk are not well studied. We aimed to determine whether unhealthy modifiable health behaviors were associated with similar increases in the risk of incident T2D among individuals with different levels of genetic risk. Methods:We performed a genetic risk score (GRS) by lifestyle interaction analysis within 332,251 non-diabetic individuals at baseline from the UK Biobank. Multi-ancestry GRS were calculated by summing the effects of 783 T2D-associated variants and ranked into tertiles. We used baseline self-reported data on smoking, BMI, physical activity level, and diet quality to categorize participants as having a healthy, intermediate, or unhealthy lifestyle. Cox proportional hazards regression models were used to generate adjusted hazards ratios (HR) of T2D risk and associated 95% confidence intervals (CI). Results:During follow-up (median 13.6 years), 13,128 (4.0%) participants developed T2D. GRS (P < 0.001) and lifestyle classification (P < 0.001) were independently associated with increased risk for T2D. Compared with healthy lifestyle, unhealthy lifestyle was associated with increased T2D risk in all genetic risk strata, with adjusted HR ranging from 7.11 (low genetic risk) to 16.33 (high genetic risk). Conclusions:High genetic risk and unhealthy lifestyle were the most significant contributors to the development of T2D. Individuals at all levels of genetic risk can greatly mitigate their risk for T2D through lifestyle modifications.

Human studies examining the cellular mechanisms behind sarcopenia, or age-related loss of skeletal muscle mass and function, have produced inconsistent results. A systematic review and meta-analysis were performed to determine the aging effects on protein expression, size, and distribution of fibers with various myosin heavy chain (MyHC) isoforms. Study eligibility included MyHC comparisons between young (18-49 yr) and older (≥60 yr) adults, with 27 studies identified. Relative protein expression was higher with age for the slow-contracting MyHC I fibers, with correspondingly lower fast-contracting MyHC II and IIA values. Fiber sizes were similar with age for MyHC I, but smaller for MyHC II and IIA. Fiber distributions were similar with age. When separated by sex, the few studies that examined females showed atrophy of MyHC II and IIA fibers with age, but no change in MyHC protein expression. Additional analyses by measurement technique, physical activity, and muscle biopsies provided important insights. In summary, age-related atrophy in fast-contracting fibers lead to more of the slow-contracting, lower force-producing isoform in older male muscles, which helps explain their age-related loss in whole muscle force, velocity, and power. Exercise or pharmacological interventions that shift MyHC expression toward faster isoforms and/or increase fast-contracting fiber size should decrease the prevalence of sarcopenia. Our findings also indicate that future studies need to include or focus solely on females, measure MyHC IIA and IIX isoforms separately, examine fiber type distribution, sample additional muscles to the vastus lateralis (VL), and incorporate an objective measurement of physical activity.

The National Heart, Lung, and Blood Institute convened a virtual workshop in September 2022 to discuss “Optimal Instruments for Measurement of Diet, Physical Activity, and Sleep.” This report summarizes the proceedings, identifying current research gaps and future directions for measuring different lifestyle behaviors in adult population‐based studies. Key discussions centered on integrating report‐based methods, like questionnaires, with device‐based assessments, including wearables and physiological measures such as biomarkers and omics to enhance self‐reported metrics and better understand the underlying biologic mechanisms of chronic diseases. Emphasis was placed on the need for data harmonization, including the adoption of standard terminology, reproducible metrics, and accessible raw data, to enhance the analysis through artificial intelligence and machine learning techniques. The workshop highlighted the importance of standardizing procedures for integrated behavioral phenotypes using time‐series data. These efforts aim to refine data accuracy and comparability across studies and populations, thereby advancing our understanding of lifestyle behaviors and their impact on chronic disease outcomes over the life course.

Introduction: Frailty is a significant health challenge for the aging population. Older adults with cardiovascular diseases (CVD) tend to be physically inactive and have a high risk of frailty. Little is known about the relationship between diurnal movement patterns and frailty and whether this association differs in older adults with and without CVD. Hypothesis: Less morning, afternoon, and evening movements but more night movements are associated with frailty, especially among those with CVD. Methods: This cross-sectional study included 10,082 participants ≥ 65 years (66.8±1.5 years; 49% female) from UK Biobank. CVD status was identified using ICD-9&10 diagnosis and self-reported CVD. Participants wore Axivity AX3 on the dominant wrist for 7 days, 24 hours/day. Mean accelerations during night (12-6 am), morning (6 am-12 pm), afternoon (12-6 pm) and evening (6 pm-12 am) were calculated. Frailty was determined using Modified Fried Frailty Phenotype. Multivariate logistic regression models were used to examine associations between total movement and diurnal movement patterns with frailty, stratified by CVD status. Results: The diurnal movement patterns showed lower movements during night, increasing during morning, peaking at noon, and decreasing in afternoon and evening, with frail CVD adults having the lowest accelerations during all daytime hours (6 am-midnight). In both CVD and non-CVD older adults, each 5 mg higher overall mean acceleration was associated with 11-18% lower odds of frailty ( Table ). Each 5 mg higher mean acceleration during morning, afternoon and evening was associated with 5-10% lower odds of frailty ( Table ). Every 5 mg higher mean acceleration during night was associated with 14% higher odds of frailty only in non-CVD older adults ( Table ). Conclusions: More activity during any daytime period was associated with lower odds of frailty among older adults in both with and without CVD. Greater night activity was associated with higher odds of frailty, primarily observed in older adults without CVD.

As AI assistance is increasingly infused into decision-making processes, we may seek to optimize human-centric objectives beyond decision accuracy, such as skill improvement or task enjoyment of individuals interacting with these systems. With this aspiration in mind, we propose offline reinforcement learning (RL) as a general approach for modeling human-AI decision-making to optimize such human-centric objectives. Our approach seeks to optimize different objectives by adaptively providing decision support to humans – the right type of assistance, to the right person, at the right time. We instantiate our approach with two objectives: human-AI accuracy on the decision-making task and human learning about the task, and learn policies that optimize these two objectives from previous human-AI interaction data. We compare the optimized policies against various baselines in AI-assisted decision-making. Across two experiments (N = 316 and N = 964), our results consistently demonstrate that people interacting with policies optimized for accuracy achieve significantly better accuracy – and even human-AI complementarity – compared to those interacting with any other type of AI support. Our results further indicate that human learning is more difficult to optimize than accuracy, with participants who interacted with learning-optimized policies showing significant learning improvement only at times. Our research (1) demonstrates offline RL to be a promising approach to model dynamics of human-AI decision-making, leading to policies that may optimize various human-centric objectives and provide novel insights about the AI-assisted decision-making space, and (2) emphasizes the importance of considering human-centric objectives beyond decision accuracy in AI-assisted decision-making, while also opening up the novel research challenge of optimizing such objectives.

Background/Purpose: Frailty is a significant health challenge for the aging population, increasing vulnerability to adverse health outcomes such as chronic disease, falls, disability, and mortality. Early detection and monitoring of frailty are critical for effectively managing frailty. Daily steps, as a measure of physical activity, hold valuable information about health status and may serve as an indicator of frailty. This study aimed to examine the association between daily steps and frailty in older adults. Method: PubMed, SPORTDiscus, and Web of Science databases were searched for published studies up to June 2023. The search terms were ("daily step" or "steps per day" or "step count" or "number of steps" or "step volume") AND ("frailty" or "frail" or "pre-frailty" or "pre-frail" or "prefrailty" or "prefrail"). The inclusion criteria were peer-reviewed articles in English involving older adults aged ≥ 65 years, device-measured daily steps, and reported frailty status. Pooled estimates of mean differences with 95% confidence intervals (CI) between frailty groups (non-frail, prefrail, frail) were obtained using random-effects models. The prefrail and frail groups were combined in the primary analysis. Results: Thirteen articles comprising 3,383 participants (71.21 ± 6.69 years, 75.58% female, 42.45% prefrail and frail) were included in the analysis. Daily steps were significantly lower in the prefrail and frail combined group compared to the non-frail group (MD = 2,008, 95% CI: 1,003, 3,013, I 2 = 86%). This association was consistent across subgroups stratified by health conditions, regions, wearables placement, and frailty measurement. Further analyses within the three frailty groups revealed that the prefrail group accumulated 995 steps/day (95% CI: 407, 1,582, I 2 = 73%) less than the non-frail group, but 947 steps/day (95% CI: 567, 1,327, I 2 = 32%) more than the frail group. Conclusion/Discussion: Current evidence suggested that older adults with frailty tend to have lower daily steps compared to non-frail older adults. Daily steps may be an indicator of frailty status in older adults.

Although it is clear that the bioenergetic basis of skeletal muscle fatigue (transient decrease in peak torque or power in response to contraction) involves intramyocellular acidosis (decreased pH) and accumulation of inorganic phosphate (Pi) in response to the increased energy demand of contractions, the effects of old age on the build-up of these metabolites has not been evaluated systematically. The purpose of this study was to compare pH and [Pi] in young (18-45 yr) and older (55+ yr) human skeletal muscle in vivo at the end of standardized contraction protocols. Full study details were prospectively registered on PROSPERO (CRD42022348972). PubMed, Web of Science, and SPORTDiscus databases were systematically searched and returned 12 articles that fit the inclusion criteria for the meta-analysis. Participant characteristics, contraction mode (isometric, dynamic), and final pH and [Pi] were extracted. A random-effects model was used to calculate the mean difference (MD) and 95% confidence interval (CI) for pH and [Pi] across age groups. A subgroup analysis for contraction mode was also performed. Young muscle acidified more than older muscle (MD = -0.12 pH; 95%CI = -0.18,-0.06; p<0.01). There was no overall difference by age in final [Pi] (MD = 2.14 mM; 95%CI = -0.29,4.57; p = 0.08), although sensitivity analysis revealed that removing one study resulted in greater [Pi] in young than older muscle (MD = 3.24 mM; 95%CI = 1.72,4.76; p<0.01). Contraction mode moderated these effects (p = 0.02) such that young muscle acidified (MD = -0.19 pH; 95%CI = -0.27,-0.11; p<0.01) and accumulated Pi (MD = 4.69 mM; 95%CI = 2.79,6.59; p<0.01) more than older muscle during isometric, but not dynamic, contractions. The smaller energetic perturbation in older muscle indicated by these analyses is consistent with its relatively greater use of oxidative energy production. During dynamic contractions, elimination of this greater reliance on oxidative energy production and consequently lower metabolite accumulations in older muscle may be important for understanding task-specific, age-related differences in fatigue.

Physical activity (PA) and sedentary behavior (SB) volumes and patterns may be useful in identifying individuals who are at elevated cardiometabolic risk. Identifying individuals at risk will facilitate early interventions for prevention of cardiovascular diseases. The objective of this project is to identify important free-living physical activity patterns to classify cardiometabolic markers including insulin, plasma glucose, and triglyceride. Methods: We used cross-sectional NHANES 2003-2004 accelerometer, blood exam and demographic data. We included adults age ≥18 years who wore a hip-worn ActiGraph 7164 during waking hours for at least 4 days of 10 hours/day of wear time. We included PA and SB predictors including minutes per day (min/d) in sedentary time (0-99 counts per minute, cpm), light intensity PA (100-759 cpm), Lifestyle PA (760-2019 cpm), moderate to vigorous PA (MVPA, ≥2019 cpm), number of breaks in SB, and number of 30-min and 60-min SB, 10-min MVPA bouts etc. The cut points for cardiovascular disease risk were defined based on literature as: fasting serum insulin ≤ 5 μU/mL, plasma glucose ≤ 100 mg/dL and triglyceride < 150 mg/dL. Three random forest (RF) classification models were developed for each cardiometabolic marker using age, body-mass-index, general health, and PA variables as predictors. Models were trained and tested using a 60% training - 40% testing split. We tuned the hyperparameters (final selection: number of trees = 500 and variables = 3) to find the model with the highest testing accuracy based on the out-of-bag error. The importance of each predictor was measured and ranked by the mean decrease in GINI index. Results: The three datasets include insulin (n = 2810, age=34 ±23, 50% females), glucose (n = 1852, age = 49±20, 51% females) and triglyceride (n = 3180, age=36 ±24, 50% females). For insulin model, the PA variables with largest mean decrease in GINI index were SB time, light intensity PA, MVPA, and SB bouts of 30-min. Similarly, time spent in Lifestyle PA, light intensity PA, number of SB breaks and SB bouts of 30-min were the most important variables for abnormal fasting glucose prediction. The number of sedentary breaks, light intensity PA, SB time and SB bouts of 30-min were the most important PA predictors for risk of high triglyceride. Both BMI and age were important demographic variables for prediction of increased risk in each model. The prediction accuracy and precision are as following: Insulin(73%; 76%), Plasma Glucose(68%; 58%), and Triglyceride(64%; 46%). Conclusion: Cardiometabolic markers using free-living PA has the potential to provide insights for classifying the risk for cardiovascular disease based on given physiological marker cut-off points. The time spent lower intensity PA patterns and the number of sedentary breaks, and 30-minutes bouts are important predictors for identifying individuals who may have increased cardiometabolic risk.

Wearable biosensors (wearables) enable continual, noninvasive physiologic and behavioral monitoring at home for those with pediatric or congenital heart disease. Wearables allow patients to access their personal data and monitor their health. Despite substantial technologic advances in recent years, issues with hardware design, data analysis, and integration into the clinical workflow prevent wearables from reaching their potential in high-risk congenital heart disease populations. This science advisory reviews the use of wearables in patients with congenital heart disease, how to improve these technologies for clinicians and patients, and ethical and regulatory considerations. Challenges related to the use of wearables are common to every clinical setting, but specific topics for consideration in congenital heart disease are highlighted.

Physical inactivity is a growing societal concern with significant impact on public health. Identifying barriers to engaging in physical activity (PA) is a critical step to recognize populations who disproportionately experience these barriers. Understanding barriers to PA holds significant importance within patient-facing healthcare professions like nursing. While determinants of PA have been widely studied, connecting individual and social factors to barriers to PA remains an understudied area among nurses. The objectives of this study are to categorize and model factors related to barriers to PA using the National Institute on Minority Health and Health Disparities (NIMHD) Research Framework. The study population includes nursing students at the study institution (N = 163). Methods include a scoring system to quantify the barriers to PA, and regularized regression models that predict this score. Key findings identify intrinsic motivation, social and emotional support, education, and the use of health technologies for tracking and decision-making purposes as significant predictors. Results can help identify future nursing workforce populations at risk of experiencing barriers to PA. Encouraging the development and employment of health-informatics solutions for monitoring, data sharing, and communication is critical to prevent barriers to PA before they become a powerful hindrance to engaging in PA.