Characteristics of Adopters of an Online Social Networking Physical Activity Mobile Phone App: Cluster Analysis

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; https://www.anzctr.org .au/Trial/Registration/TrialReview.aspx?id=371463 International Registered Report Identifier (IRRID) RR2-10.1186/s12889-017-4882-7

[1]  C. Vandelanotte,et al.  More real-world trials are needed to establish if web-based physical activity interventions are effective , 2018, British Journal of Sports Medicine.

[2]  Tim Olds,et al.  “Active Team” a social and gamified app-based physical activity intervention: randomised controlled trial study protocol , 2017, BMC Public Health.

[3]  Camille Roth,et al.  Natural Scales in Geographical Patterns , 2017, Scientific Reports.

[4]  Vineet Chopra,et al.  Internet-Delivered Health Interventions That Work: Systematic Review of Meta-Analyses and Evaluation of Website Availability , 2017, Journal of medical Internet research.

[5]  Xin Sun,et al.  Mobile App-Based Interventions to Support Diabetes Self-Management: A Systematic Review of Randomized Controlled Trials to Identify Functions Associated with Glycemic Efficacy , 2017, JMIR mHealth and uHealth.

[6]  Margaret L. Kern,et al.  The PERMA-Profiler: A brief multidimensional measure of flourishing , 2016 .

[7]  C. Vandelanotte,et al.  Past, Present, and Future of eHealth and mHealth Research to Improve Physical Activity and Dietary Behaviors. , 2016, Journal of nutrition education and behavior.

[8]  V. Dickson-Swift,et al.  Community participation for rural health: a review of challenges , 2015, Health expectations : an international journal of public participation in health care and health policy.

[9]  Sharyn Rundle-Thiele,et al.  Social Marketing Physical Activity Interventions Among Adults 60 Years and Older , 2015 .

[10]  Sharyn Rundle-Thiele,et al.  Using two-step cluster analysis to identify homogeneous physical activity groups , 2015 .

[11]  L. Lechner,et al.  Profiling physical activity motivation based on self-determination theory: a cluster analysis approach , 2015, BMC psychology.

[12]  R. Davey,et al.  Validating two self-report physical activity measures in middle-aged adults completing a group exercise or home-based physical activity program. , 2014, Journal of science and medicine in sport.

[13]  Kerry A. Sherman,et al.  The Clustering of Health Behaviours in Older Australians and its Association with Physical and Psychological Status, and Sociodemographic Indicators , 2014, Annals of behavioral medicine : a publication of the Society of Behavioral Medicine.

[14]  C. Vandelanotte,et al.  Are Health Behavior Change Interventions That Use Online Social Networks Effective? A Systematic Review , 2014, Journal of medical Internet research.

[15]  C. Vandelanotte,et al.  Meta-analysis of internet-delivered interventions to increase physical activity levels , 2012, International Journal of Behavioral Nutrition and Physical Activity.

[16]  Pedro C Hallal,et al.  Worldwide prevalence of physical inactivity and its association with human development index in 76 countries. , 2011, Preventive medicine.

[17]  P. Lovibond,et al.  Percentile Norms and Accompanying Interval Estimates from an Australian General Adult Population Sample for Self‐Report Mood Scales (BAI, BDI, CRSD, CES‐D, DASS, DASS‐21, STAI‐X, STAI‐Y, SRDS, and SRAS) , 2011 .

[18]  Hae-Sang Park,et al.  A simple and fast algorithm for K-medoids clustering , 2009, Expert Syst. Appl..

[19]  Ronald C. Plotnikoff,et al.  Physical Activity and Social Cognitive Theory : A Test in a Population Sample of Adults with Type 1 or Type 2 Diabetes , 2008 .

[20]  A. Gloster,et al.  Psychometric properties of the Depression Anxiety and Stress Scale-21 in older primary care patients. , 2008, Journal of affective disorders.

[21]  F. Penedo,et al.  Exercise and well-being: a review of mental and physical health benefits associated with physical activity , 2005, Current opinion in psychiatry.

[22]  Richard A. Winett,et al.  Social cognitive determinants of physical activity in young adults: A prospective structural equation analysis , 2002, Annals of behavioral medicine : a publication of the Society of Behavioral Medicine.

[23]  R. Plotnikoff,et al.  Validation of the Decisional Balance Scales in the Exercise Domain From the Transtheoretical Model: A Longitudinal Test , 2001 .

[24]  J. Ware,et al.  A 12-Item Short-Form Health Survey: construction of scales and preliminary tests of reliability and validity. , 1996, Medical care.

[25]  Daniel J Buysse,et al.  The Pittsburgh sleep quality index: A new instrument for psychiatric practice and research , 1989, Psychiatry Research.

[26]  William M. Rand,et al.  Objective Criteria for the Evaluation of Clustering Methods , 1971 .

[27]  M. Sarstedt,et al.  A Concise Guide to Market Research , 2019, Springer Texts in Business and Economics.

[28]  R. Rhodes,et al.  Disentangling motivation, intention, and planning in the physical activity domain , 2006 .

[29]  Grant Schofield,et al.  10,000 Steps Rockhampton : evaluation of a whole community approach to improving population levels of physical activity , 2006 .