Development and Validation of Multivariable Prediction Algorithms to Estimate Future Walking Behavior in Adults: Retrospective Cohort Study.

ABSTRACT:

Background

Physical inactivity is associated with numerous health risks, including cancer, cardiovascular disease, type 2 diabetes, increased health care expenditure, and preventable, premature deaths. The majority of Americans fall short of clinical guideline goals (ie, 8000-10,000 steps per day). Behavior prediction algorithms could enable efficacious interventions to promote physical activity by facilitating delivery of nudges at appropriate times.

Objective

The aim of this paper is to develop and validate algorithms that predict walking (ie, >5 min) within the next 3 hours, predicted from the participants' previous 5 weeks' steps-per-minute data.

Methods

We conducted a retrospective, closed cohort, secondary analysis of a 6-week microrandomized trial of the HeartS

SUBMITTER: Park J 

PROVIDER: S-EPMC9919492 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature