Evaluating Machine Learning-Based Automated Personalized Daily Step Goals Delivered Through a Mobile Phone App: Randomized Controlled Trial

Mo Zhou, Yoshimi Fukuoka, Yonatan Mintz, Ken Goldberg, Philip Kaminsky, Elena Flowers, Anil Aswani, Mo Zhou, Yoshimi Fukuoka, Yonatan Mintz, Ken Goldberg, Philip Kaminsky, Elena Flowers, Anil Aswani

Abstract

Background: Growing evidence shows that fixed, nonpersonalized daily step goals can discourage individuals, resulting in unchanged or even reduced physical activity.

Objective: The aim of this randomized controlled trial (RCT) was to evaluate the efficacy of an automated mobile phone-based personalized and adaptive goal-setting intervention using machine learning as compared with an active control with steady daily step goals of 10,000.

Methods: In this 10-week RCT, 64 participants were recruited via email announcements and were required to attend an initial in-person session. The participants were randomized into either the intervention or active control group with a one-to-one ratio after a run-in period for data collection. A study-developed mobile phone app (which delivers daily step goals using push notifications and allows real-time physical activity monitoring) was installed on each participant's mobile phone, and participants were asked to keep their phone in a pocket throughout the entire day. Through the app, the intervention group received fully automated adaptively personalized daily step goals, and the control group received constant step goals of 10,000 steps per day. Daily step count was objectively measured by the study-developed mobile phone app.

Results: The mean (SD) age of participants was 41.1 (11.3) years, and 83% (53/64) of participants were female. The baseline demographics between the 2 groups were similar (P>.05). Participants in the intervention group (n=34) had a decrease in mean (SD) daily step count of 390 (490) steps between run-in and 10 weeks, compared with a decrease of 1350 (420) steps among control participants (n=30; P=.03). The net difference in daily steps between the groups was 960 steps (95% CI 90-1830 steps). Both groups had a decrease in daily step count between run-in and 10 weeks because interventions were also provided during run-in and no natural baseline was collected.

Conclusions: The results showed the short-term efficacy of this intervention, which should be formally evaluated in a full-scale RCT with a longer follow-up period.

Trial registration: ClinicalTrials.gov: NCT02886871; https://ichgcp.net/clinical-trials-registry/NCT02886871 (Archived by WebCite at http://www.webcitation.org/6wM1Be1Ng).

Keywords: cell phone; clinical trial; fitness tracker; physical activity.

Conflict of interest statement

Conflicts of Interest: None declared.

©Mo Zhou, Yoshimi Fukuoka, Yonatan Mintz, Ken Goldberg, Philip Kaminsky, Elena Flowers, Anil Aswani. Originally published in JMIR Mhealth and Uhealth (http://mhealth.jmir.org), 25.01.2018.

Figures

Figure 1
Figure 1
The CalFit app interface. (a) The landing page; (b) The homepage showing the steps done today and today’s goal; (c) The "History" tab showing the performance of the past week. The black bar is the goal, and the bars are green for achieved goals and red for unachieved goals; (d) The "Contact Us" tab where participants can easily send messages to the study team.
Figure 2
Figure 2
Screening, randomization, and assessments of study participants.
Figure 3
Figure 3
Weekly average and moving average steps for the 2 groups over the course of the study for intention-to-treat analysis after run-in adjustment. Left panel: mean weekly steps for intention-to-treat; Right panel: weekly moving average for intention-to-treat.
Figure 4
Figure 4
Weekly average step goals and average fraction of goals achieved for the 2 groups for intention-to-treat analysis. Left panel: weekly average step goals for intention-to-treat; Right panel: weekly average fraction of achieved goals for intention-to-treat.
Figure 5
Figure 5
Weekly average and moving average steps for the 2 groups over the course of the study for per-protocol analysis after run-in adjustment. Left panel: mean weekly steps for per-protocol; Right panel: weekly moving average for per-protocol.
Figure 6
Figure 6
Weekly average step goals and average fraction of goals achieved for the 2 groups for per-protocol analysis. Left panel: weekly average step goals for per-protocol; Right panel: weekly average fraction of achieved goals for per-protocol.

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