The Aim2Be mHealth Intervention for Children With Overweight or Obesity and Their Parents: Person-Centered Analyses to Uncover Digital Phenotypes

BACKGROUND: Despite the growing number of mobile health (mHealth) interventions targeting childhood obesity, few studies have characterized user typologies derived from individuals’ patterns of interactions with specific app features (digital phenotypes). OBJECTIVE: This study aims to identify digital phenotypes among 214 parent-child dyads who used the Aim2Be mHealth app as part of a randomized controlled trial conducted between 2019 and 2020, and explores whether participants’ characteristics and health outcomes differed across phenotypes. METHODS: Latent class analysis was used to identify distinct parent and child phenotypes based on their use of the app’s behavioral, gamified, and social features over 3 months. Multinomial logistic regression models were used to assess whether the phenotypes differed by demographic characteristics. Covariate-adjusted mixed-effect models evaluated changes in BMI z scores (zBMI), diet, physical activity, and screen time across phenotypes. RESULTS: Among parents, 5 digital phenotypes were identified: socially engaged (35/214, 16.3%), independently engaged (18/214, 8.4%) (socially and independently engaged parents are those who used mainly the social or the behavioral features of the app, respectively), fully engaged (26/214, 12.1%), partially engaged (32/214, 15%), and unengaged (103/214, 48.1%) users. Married parents were more likely to be fully engaged than independently engaged (P=.02) or unengaged (P=.01) users. Socially engaged parents were older than fully engaged (P=.02) and unengaged (P=.01) parents. The latent class analysis revealed 4 phenotypes among children: fully engaged (32/214, 15%), partially engaged (61/214, 28.5%), dabblers (42/214, 19.6%), and unengaged (79/214, 36.9%) users. Fully engaged children were younger than dabblers (P=.04) and unengaged (P=.003) children. Dabblers lived in higher-income households than fully and partially engaged children (P=.03 and P=.047, respectively). Fully engaged children were more likely to have fully engaged (P<.001) and partially engaged (P<.001) parents than unengaged children. Compared with unengaged children, fully and partially engaged children had decreased total sugar (P=.006 and P=.004, respectively) and energy intake (P=.03 and P=.04, respectively) after 3 months of app use. Partially engaged children also had decreased sugary beverage intake compared with unengaged children (P=.03). Similarly, children with fully engaged parents had decreased zBMI, whereas children with unengaged parents had increased zBMI over time (P=.005). Finally, children with independently engaged parents had decreased caloric intake, whereas children with unengaged parents had increased caloric intake over time (P=.02). CONCLUSIONS: Full parent-child engagement is critical for the success of mHealth interventions. Further research is needed to understand program design elements that can affect participants’ engagement in supporting behavior change. TRIAL REGISTRATION: ClinicalTrials.gov NCT03651284; https://clinicaltrials.gov/ct2/show/NCT03651284 INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.1186/s13063-020-4080-2