4.6 Article

Companion: A Pilot Randomized Clinical Trial to Test an Integrated Two-Way Communication and Near-Real-Time Sensing System for Detecting and Modifying Daily Inactivity among Adults >60 Years-Design and Protocol

Journal

SENSORS
Volume 23, Issue 4, Pages -

Publisher

MDPI
DOI: 10.3390/s23042221

Keywords

sedentary behavior; physical inactivity; move more and sit less; older adults; near-real-time interventions; sensor-based physical activity measurement; mHealth interventions; pilot randomized controlled trials; study protocols

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Supervised personal training is most effective for improving exercise health effects in older adults. However, low frequency of trainer contact limits behavior change outside sessions. Implementing strategies to extend trainer contact and provide meaningful two-way communication can motivate older adults to move more and sit less, leading to improved health outcomes.
Supervised personal training is most effective in improving the health effects of exercise in older adults. Yet, low frequency (60 min, 1-3 sessions/week) of trainer contact limits influence on behavior change outside sessions. Strategies to extend the effect of trainer contact outside of supervision and that integrate meaningful and intelligent two-way communication to provide complex and interactive problem solving may motivate older adults to move more and sit less and sustain positive behaviors to further improve health. This paper describes the experimental protocol of a 16-week pilot RCT (N = 46) that tests the impact of supplementing supervised exercise (i.e., control) with a technology-based behavior-aware text-based virtual Companion that integrates a human-in-the-loop approach with wirelessly transmitted sensor-based activity measurement to deliver behavior change strategies using socially engaging, contextually salient, and tailored text message conversations in near-real-time. Primary outcomes are total-daily and patterns of habitual physical behaviors after 16 and 24 weeks. Exploratory analyses aim to understand Companion's longitudinal behavior effects, its user engagement and relationship to behavior, and changes in cardiometabolic and cognitive outcomes. Our findings may allow the development of a more scalable hybrid AI Companion to impact the ever-growing public health epidemic of sedentariness contributing to poor health outcomes, reduced quality of life, and early death.

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