Individual Study / Zhou et al.; JMIR Mhealth Uhealth. 2018

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

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

Initiatives -
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. Note: All published information has been collected from the article referenced in the Marker Paper box below. Therefore, there may be variations with more advanced versions of the study.
Start Year
2016
Funding
. AA and YM were supported in part by funding from the Philippine-California Advanced Research Institutes (PCARI). MZ and KG were supported in part by funding from the UC Center for Information Technology Research in the Interest of Society and the PCARI grant IIID-2015-07. YF’s effort for this project was in part supported by a grant (K24NR015812) from the National Institute of Nursing Research and a grant (R01HL104147) from the National Heart, Lung, and Blood Institute. EF’s effort for this project was supported by a grant from the National Center for Advancing Translational Sciences of the National Institutes of Health (KL2TR000143).

Design

Study design
Clinical trial cohort

Marker Paper

Zhou M, Fukuoka Y, Mintz Y, et al. Evaluating Machine Learning-Based Automated Personalized Daily Step Goals Delivered Through a Mobile Phone App: Randomized Controlled Trial. JMIR Mhealth Uhealth. 2018;6(1):e28. Published 2018 Jan 25. doi:10.2196/mhealth.9117

PUBMED 29371177

Recruitment

Sources of Recruitment
  • Individuals

Number of participants

Number of participants
64
Number of participants with biosamples

Access

Availability of data and biosamples

Data
Biosamples
Other

Timeline

Population

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.
Selection Criteria
Minimum age
18
Maximum age
65
Newborns
Twins
Countries
  • United States of America
Territory
California, Berkeley
Ethnic Origin
Health Status

Recruitment

Sources of recruitment
  • Specific population

Number of participants

Number of participants
64
Number of participants with biosamples
Data Collection Event
The overall goal of this study is to test personalized mobile phone-based physical activity interventions among staff members at the University of California, Berkeley.
Start Date
2016-08
End Date
2016-11
Data sources
  • Mobile data collection
    • Mobile phone
    • Smartphone
    • Smartphone apps