Assessing User Engagement of an mHealth Intervention: Development and Implementation of the Growing Healthy App Engagement Index

Background Childhood obesity is an ongoing problem in developed countries that needs targeted prevention in the youngest age groups. Children in socioeconomically disadvantaged families are most at risk. Mobile health (mHealth) interventions offer a potential route to target these families because of its relatively low cost and high reach. The Growing healthy program was developed to provide evidence-based information on infant feeding from birth to 9 months via app or website. Understanding user engagement with these media is vital to developing successful interventions. Engagement is a complex, multifactorial concept that needs to move beyond simple metrics. Objective The aim of our study was to describe the development of an engagement index (EI) to monitor participant interaction with the Growing healthy app. The index included a number of subindices and cut-points to categorize engagement. Methods The Growing program was a feasibility study in which 300 mother-infant dyads were provided with an app which included 3 push notifications that was sent each week. Growing healthy participants completed surveys at 3 time points: baseline (T1) (infant age ≤3 months), infant aged 6 months (T2), and infant aged 9 months (T3). In addition, app usage data were captured from the app. The EI was adapted from the Web Analytics Demystified visitor EI. Our EI included 5 subindices: (1) click depth, (2) loyalty, (3) interaction, (4) recency, and (5) feedback. The overall EI summarized the subindices from date of registration through to 39 weeks (9 months) from the infant’s date of birth. Basic descriptive data analysis was performed on the metrics and components of the EI as well as the final EI score. Group comparisons used t tests, analysis of variance (ANOVA), Mann-Whitney, Kruskal-Wallis, and Spearman correlation tests as appropriate. Consideration of independent variables associated with the EI score were modeled using linear regression models. Results The overall EI mean score was 30.0% (SD 11.5%) with a range of 1.8% - 57.6%. The cut-points used for high engagement were scores greater than 37.1% and for poor engagement were scores less than 21.1%. Significant explanatory variables of the EI score included: parity (P=.005), system type including “app only” users or “both” app and email users (P<.001), recruitment method (P=.02), and baby age at recruitment (P=.005). Conclusions The EI provided a comprehensive understanding of participant behavior with the app over the 9-month period of the Growing healthy program. The use of the EI in this study demonstrates that rich and useful data can be collected and used to inform assessments of the strengths and weaknesses of the app and in turn inform future interventions.

[1]  F. Oberklaid,et al.  Parents, infants and health care: Utilization of health services in the first 12 months of life , 2003, Journal of paediatrics and child health.

[2]  Sheana Salyers Bull,et al.  More than just tracking time: Complex measures of user engagement with an internet-based health promotion intervention , 2016, J. Biomed. Informatics.

[3]  Ann Blandford,et al.  Understanding and Promoting Effective Engagement With Digital Behavior Change Interventions. , 2016, American journal of preventive medicine.

[4]  David W. Hosmer,et al.  Applied Logistic Regression , 1991 .

[5]  M. Petticrew,et al.  Developing and evaluating complex interventions: the new Medical Research Council guidance , 2008, BMJ : British Medical Journal.

[6]  J. Car,et al.  Text Messaging Data Collection for Monitoring an Infant Feeding Intervention Program in Rural China: Feasibility Study , 2013, Journal of medical Internet research.

[7]  Morwenna Kirwan,et al.  Engagement and Nonusage Attrition With a Free Physical Activity Promotion Program: The Case of 10,000 Steps Australia , 2015, Journal of medical Internet research.

[8]  L. Bannon,et al.  Understanding affect in design: an outline conceptual framework , 2004 .

[9]  Lucy Yardley,et al.  Opportunities and Challenges for Smartphone Applications in Supporting Health Behavior Change: Qualitative Study , 2013, Journal of medical Internet research.

[10]  J. Scott,et al.  Theory-Based Design and Development of a Socially Connected, Gamified Mobile App for Men About Breastfeeding (Milk Man) , 2016, JMIR mHealth and uHealth.

[11]  A. Dattilo,et al.  Design of a Digital-Based, Multicomponent Nutrition Guidance System for Prevention of Early Childhood Obesity , 2016, Journal of obesity.

[12]  H. Gage,et al.  Influences on infant feeding decisions of first-time mothers in five European countries , 2012, European Journal of Clinical Nutrition.

[13]  E. Denney-Wilson,et al.  Preventing obesity in infants: the Growing healthy feasibility trial protocol , 2015, BMJ Open.

[14]  K. Hesketh,et al.  The extended Infant Feeding, Activity and Nutrition Trial (InFANT Extend) Program: a cluster-randomized controlled trial of an early intervention to prevent childhood obesity , 2016, BMC Public Health.

[15]  Gary Marchionini,et al.  Synthesis Lectures on Information Concepts, Retrieval, and Services , 2009 .

[16]  Elaine Toms,et al.  What is user engagement? A conceptual framework for defining user engagement with technology , 2008, J. Assoc. Inf. Sci. Technol..

[17]  Robert West,et al.  The Behaviour Change Wheel: A Guide To Designing Interventions , 2014 .

[18]  Timothy W. Bickmore,et al.  MAINTAINING ENGAGEMENT IN LONG-TERM INTERVENTIONS WITH RELATIONAL AGENTS , 2010, Appl. Artif. Intell..

[19]  J. Brug,et al.  A conceptual framework for understanding and improving adolescents' exposure to Internet-delivered interventions. , 2009, Health promotion international.

[20]  Eric N. Wiebe,et al.  Measuring engagement in video game-based environments: Investigation of the User Engagement Scale , 2014, Comput. Hum. Behav..

[21]  L. Wen Obesity in young children: what can we do about? , 2014 .

[22]  Lucy Yardley,et al.  A Visualization Tool to Analyse Usage of Web-Based Interventions: The Example of Positive Online Weight Reduction (POWeR) , 2015, JMIR human factors.

[23]  C. K. F. Wen,et al.  Prevention and treatment of pediatric obesity using mobile and wireless technologies: a systematic review , 2015, Pediatric obesity.

[24]  P. Morgan,et al.  Targeted Health Behavior Interventions Promoting Physical Activity: A Conceptual Model , 2016, Exercise and sport sciences reviews.

[25]  S. Michie,et al.  Using the Internet to Promote Health Behavior Change: A Systematic Review and Meta-analysis of the Impact of Theoretical Basis, Use of Behavior Change Techniques, and Mode of Delivery on Efficacy , 2010, Journal of medical Internet research.

[26]  Mu Li,et al.  Can Mobile Phone Apps Influence People’s Health Behavior Change? An Evidence Review , 2016, Journal of medical Internet research.

[27]  Amanda Burls,et al.  Exploring the Usability of a Mobile App for Adolescent Obesity Management , 2014, JMIR mHealth and uHealth.

[28]  Clare E Collins,et al.  Dropout, Nonusage Attrition, and Pretreatment Predictors of Nonusage Attrition in a Commercial Web-Based Weight Loss Program , 2010, Journal of medical Internet research.

[29]  Ann Blandford,et al.  Conceptualising engagement with digital behaviour change interventions: a systematic review using principles from critical interpretive synthesis , 2016, Translational behavioral medicine.

[30]  Brian G. Danaher,et al.  From black box to toolbox: Outlining device functionality, engagement activities, and the pervasive information architecture of mHealth interventions , 2015, Internet interventions.

[31]  Elizabeth Denney-Wilson,et al.  Infant Feeding Websites and Apps: A Systematic Assessment of Quality and Content , 2015, Interactive journal of medical research.

[32]  J. Powell,et al.  Empirical studies assessing the quality of health information for consumers on the world wide web: a systematic review. , 2002, JAMA.

[33]  E. Denney-Wilson,et al.  A Comparison of Recruitment Methods for an mHealth Intervention Targeting Mothers: Lessons from the Growing Healthy Program , 2016, Journal of medical Internet research.

[34]  Mick P Couper,et al.  Engagement and Retention: Measuring Breadth and Depth of Participant Use of an Online Intervention , 2010, Journal of medical Internet research.

[35]  Corneel Vandelanotte,et al.  Prospective Associations Between Intervention Components and Website Engagement in a Publicly Available Physical Activity Website: The Case of 10,000 Steps Australia , 2012, Journal of medical Internet research.

[36]  Mildred A. Horodynski,et al.  Tools for teen moms to reduce infant obesity: a randomized clinical trial , 2015, BMC Public Health.

[37]  Elizabeth Denney-Wilson,et al.  Effectiveness of a mHealth Lifestyle Program With Telephone Support (TXT2BFiT) to Prevent Unhealthy Weight Gain in Young Adults: Randomized Controlled Trial , 2015, JMIR mHealth and uHealth.

[38]  K. Hesketh,et al.  The Early Prevention of Obesity in CHildren (EPOCH) Collaboration - an Individual Patient Data Prospective Meta-Analysis , 2010, BMC public health.

[39]  J. Gazmararian,et al.  Successful Enrollment in Text4Baby More Likely With Higher Health Literacy , 2012, Journal of health communication.

[40]  Ross Shegog,et al.  The intersection of youth, technology, and new media with sexual health: moving the research agenda forward. , 2012, The Journal of adolescent health : official publication of the Society for Adolescent Medicine.

[41]  S. Sloan,et al.  Sources of feeding advice in the first year of life: who do parents value? , 2009, Community practitioner : the journal of the Community Practitioners' & Health Visitors' Association.