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Type: Journal article
Title: Characteristics of adopters of an online social networking physical activity mobile phone app: cluster analysis
Author: Sanders, I.
Short, C.E.
Bogomolova, S.
Stanford, T.
Plotnikoff, R.
Vandelanotte, C.
Olds, T.
Edney, S.
Ryan, J.
Curtis, R.G.
Maher, C.
Citation: JMIR mHealth and uHealth, 2019; 7(6):e12484-1-e12484-11
Publisher: JMIR Publications
Issue Date: 2019
ISSN: 2291-5222
Statement of
Chang Gao, Jacqueline Miller, Philippa F. Middleton, Yi-Chao Huang, Andrew J.McPhee, Robert A.Gibson
Abstract: BACKGROUND:To date, many online health behavior programs developed by researchers have not been translated at scale. To inform translational efforts, health researchers must work with marketing experts to design cost-effective marketing campaigns. It is important to understand the characteristics of end users of a given health promotion program and identify key market segments. OBJECTIVE:This study aimed to describe the characteristics of the adopters of Active Team, a gamified online social networking physical activity app, and identify potential market segments to inform future research translation efforts. METHODS:Participants (N=545) were Australian adults aged 18 to 65 years who responded to general advertisements to join a randomized controlled trial (RCT) evaluating the Active Team app. At baseline they provided demographic (age, sex, education, marital status, body mass index, location of residence, and country of birth), behavioral (sleep, assessed by the Pittsburgh Quality Sleep Index) and physical activity (assessed by the Active Australia Survey), psychographic information (health and well-being, assessed by the PERMA [Positive Emotion, Engagement, Relationships, Meaning, Achievement] Profile; depression, anxiety and stress, assessed by the Depression, Anxiety, and Stress Scale [DASS-21]; and quality of life, assessed by the 12-Item Short Form Health Survey [SF-12]). Descriptive analyses and a k-medoids cluster analysis were performed using the software R 3.3.0 (The R Foundation) to identify key characteristics of the sample. RESULTS:Cluster analyses revealed four clusters: (1) younger inactive women with poor well-being (218/545), characterized by a higher score on the DASS-21, low mental component summary score on the SF-12, and relatively young age; (2) older, active women (153/545), characterized by a lower score on DASS-21, a higher overall score on the SF-12, and relatively older age; (3) young, active but stressed men (58/545) with a higher score on DASS-21 and higher activity levels; and (4) older, low active and obese men (30/545), characterized by a high body mass index and lower activity levels. CONCLUSIONS:Understanding the characteristics of population segments attracted to a health promotion program will guide the development of cost-effective research translation campaigns. TRIAL REGISTRATION:Australian New Zealand Clinical Trial Registry ACTRN12617000113358; .au/Trial/Registration/TrialReview.aspx?id=371463. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID):RR2-10.1186/s12889-017-4882-7.
Keywords: k-medoid cluster analysis
mobile phone app
online social networking
physical activity
social marketing
Rights: © 2019 Elsevier Ltd. All rights reserved.
DOI: 10.2196/12484
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