Comparison of the Wrist-Worn Fitbit Charge 2 and the Waist-Worn Actigraph GTX3 for Measuring Steps Taken in Occupational Settings

Addressing the Chronic Pain-Early Cognitive Decline Comorbidity Among Older Adults: Protocol for the Active Brains Remote Efficacy Trial

BACKGROUND

Chronic pain and early cognitive decline, which are costly to treat and highly prevalent among older adults, commonly co-occur, exacerbate one another over time, and can accelerate the development and progression of Alzheimer disease and related dementias. We developed the first mind-body activity program (Active Brains [AB]) tailored to the needs of older adults with chronic pain and early cognitive decline. Results from our previous study strongly supported the feasibility of conducting AB remotely and provided evidence for improvements in outcomes.

OBJECTIVE

We are conducting a single-blinded, National Institutes of Health stage-2, randomized clinical trial to establish the efficacy of AB versus a time-matched and dose-matched education control (Health Enhancement Program [HEP]) in improving self-reported and objective outcomes of physical, cognitive, and emotional functions in 260 participants. The methodology described in this paper was informed by the lessons learned from the first year of the trial.

METHODS

Participants are identified and recruited through multidisciplinary clinician-referred individuals (eg, pain psychologists and geriatricians), the Rally Research platform, social media, and community partnerships. Interested participants complete eligibility screening and electronic informed consent. Baseline assessments include self-report, performance-based measures (eg, 6-min walk test) and objective measures (eg, Repeatable Battery for the Assessment of Neuropsychological Status). Participants are mailed a wrist-worn ActiGraph device (ActiGraph LLC) to passively monitor objective function (eg, steps) during the week between the baseline assessment and the beginning of the programs, which they continue to wear throughout the programs. After baseline assessments, participants are randomized to either AB or HEP and complete 8 weekly, remote, group sessions with a Massachusetts General Hospital psychologist. The AB group receives a Fitbit (Fitbit Inc) to help reinforce increased activity. Assessments are repeated after the intervention and at the 6-month follow-up. Coprimary outcomes include multimodal physical function (self-report, performance based, and objective). Secondary outcomes are cognitive function (self-report and objective), emotional function, and pain.

RESULTS

We began recruitment in July 2022 and recruited 37 participants across 4 cohorts. Of them, all (n=37, 100%) have completed the baseline assessment, 26 (70%) have completed the posttest assessment, and 9 (24%) are actively enrolled in the intervention (total dropout: n=2, 5%). In the three cohorts (26/37, 70%) that have completed the AB or HEP, 26 (100%) participants completed all 8 group sessions (including minimal makeups), and watch adherence (1937/2072, 93.48%, average across ActiGraph and Fitbit devices) has been excellent. The fourth cohort is ongoing (9/37, 24%), and we plan to complete enrollment by March 2026.

CONCLUSIONS

We aim to establish the efficacy of the AB program over a time-matched and dose-matched control in a live video-based trial and test the mechanisms through theoretically driven mediators and moderators. Findings will inform the development of a future multisite effectiveness-implementation trial.

TRIAL REGISTRATION

ClinicalTrials.gov NCT05373745; https://classic.clinicaltrials.gov/ct2/show/NCT05373745.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/47319.

A Physical Activity Intervention in Older African Americans: The PAACE Pilot Randomized Controlled Trial

INTRODUCTION

Older African Americans have low levels of physical activity, which increases the risk of aging-related health conditions. This study aimed to determine the effectiveness of, and satisfaction with, a community-engaged physical activity intervention targeting older African Americans.

METHODS

Fifty-six older African Americans were randomized to a successful aging group (SAG) or a physical activity group (PAG) for 12 wk. The PAG consisted of two weekly group exercise sessions and two to three home-based exercise sessions per week. The SAG consisted of weekly group educational sessions related to various aspects of healthy aging. Physical activity was measured by ActiGraph accelerometers and the Community Healthy Activities Model Program for Seniors Activities Questionnaire in both groups, and a Fitbit was continuously worn by PAG participants.

RESULTS

Group session attendance was 93% and 86% in the PAG and SAG, respectively. The increase in ActiGraph-derived steps per day (1085.3 ± 265.6 vs 34.7 ± 274.3; P = 0.008) and daily minutes of moderate-to-vigorous physical activity (MVPA; 6.2 ± 1.6 vs 0.3 ± 1.7; d = 0.68; P = 0.01), and self-reported Community Healthy Activities Model Program for Seniors Activities Questionnaire MVPA bouts (3.5 ± 0.77 vs 0.33 ± 0.79; P < 0.001) were significantly greater within the PAG than the SAG. ActiGraph-derived minutes of daily MVPA and steps as well as Fitbit-derived steps per day were significantly greater on days when PAG participants engaged in group sessions compared with days when they self-reported home-based exercise or no exercise ( P < 0.016). Participants in both study arms reported high levels of satisfaction (>4- on 5-point Likert scale).

CONCLUSIONS

The intervention increased physical activity, was well attended, and resulted in high satisfaction. Future studies should assess long-term sustainability in this population.

Predicting changes in glycemic control among adults with prediabetes from activity patterns collected by wearable devices

The use of wearables is increasing and data from these devices could improve the prediction of changes in glycemic control. We conducted a randomized trial with adults with prediabetes who were given either a waist-worn or wrist-worn wearable to track activity patterns. We collected baseline information on demographics, medical history, and laboratory testing. We tested three models that predicted changes in hemoglobin A1c that were continuous, improved glycemic control by 5% or worsened glycemic control by 5%. Consistently in all three models, prediction improved when (a) machine learning was used vs. traditional regression, with ensemble methods performing the best; (b) baseline information with wearable data was used vs. baseline information alone; and (c) wrist-worn wearables were used vs. waist-worn wearables. These findings indicate that models can accurately identify changes in glycemic control among prediabetic adults, and this could be used to better allocate resources and target interventions to prevent progression to diabetes.