Characteristics of adopters of an online social networking physical activity mobile phone app: Cluster analysis

Ilea Sanders, Camille E. Short, Svetlana Bogomolova, Tyman Stanford, Ronald Plotnikoff, Corneel Vandelanotte, Tim Olds, Sarah Edney, Jillian Ryan, Rachel G. Curtis, Carol Maher

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)

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.

Original languageEnglish
Article numbere12484
Number of pages11
JournalJMIR mHealth and uHealth
Volume7
Issue number6
DOIs
Publication statusPublished - Jun 2019
Externally publishedYes

Keywords

  • K-medoid cluster analysis
  • Mobile phone app
  • Online social networking
  • Physical activity
  • Social marketing

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