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Evaluating Machine Learning-Based Automated Personalized Daily Step Goals Delivered Through a Mobile Phone App:
Mo Zhou1, Yoshimi Fukuoka2,3, Yonatan Mintz1
1Department of Industrial Engineering and Operations Research, University of California, Berkeley, CA, United States.
JMIR Mhealth and Uhealth
|January 27, 2018
Summary
Personalized, adaptive daily step goals delivered via mobile app show short-term efficacy in a randomized controlled trial (RCT). This automated intervention may offer a more effective approach to physical activity compared to fixed goals.
Area of Science:
- Digital Health
- Behavioral Science
- Machine Learning Applications
Background:
- Fixed daily step goals can demotivate individuals and lead to reduced physical activity.
- Personalized interventions are increasingly recognized as crucial for promoting sustained engagement.
Purpose of the Study:
- To evaluate the efficacy of an automated, mobile phone-based personalized and adaptive goal-setting intervention.
- To compare this intervention against an active control group with fixed 10,000 daily step goals.
Main Methods:
- A 10-week randomized controlled trial (RCT) involving 64 participants.
- Participants used a mobile app for step monitoring and goal delivery; intervention group received adaptive goals, control group received fixed goals.
- Daily step count was objectively measured via the mobile app.
Main Results:
- The intervention group showed a smaller decrease in daily steps compared to the control group over 10 weeks.
- A net difference of 960 steps was observed between the groups.
- Baseline demographics were similar between the intervention and control groups.
Conclusions:
- The automated personalized and adaptive step goal intervention demonstrated short-term efficacy.
- Further evaluation in a larger randomized controlled trial with longer follow-up is warranted.
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