Mobile Health Apps in Pediatric Obesity Treatment: Process Outcomes From a Feasibility Study of a Multicomponent Intervention

Abstract

Background Multicomponent family interventions underline current best practice in childhood obesity treatment. Mobile health (mHealth) adjuncts that address eating and physical activity behaviors have shown promise in clinical studies. Objective This study aimed to describe process methods for applying an mHealth intervention to reduce the rate of eating and monitor physical activity among children with obesity. Methods The study protocol was designed to incorporate 2 mHealth apps as an adjunct to usual care treatment for obesity. Children and adolescents (aged 9-16 years) with obesity (BMI ≥98th centile) were recruited in person from a weight management service at a tertiary health care center in the Republic of Ireland. Eligible participants and their parents received information leaflets, and informed consent and assent were signed. Participants completed 2 weeks of baseline testing, including behavioral and quality of life questionnaires, anthropometry, rate of eating by Mandolean, and physical activity level using a smart watch and the myBigO smartphone app. Thereafter, participants were randomized to the (1) intervention (usual clinical care+Mandolean training to reduce the rate of eating) or (2) control (usual clinical care) groups. Gender and age group (9.0-12.9 years and 13.0-16.9 years) stratifications were applied. At the end of a 4-week treatment period, participants repeated the 2-week testing period. Process evaluation measures included recruitment, study retention, fidelity parameters, acceptability, and user satisfaction. Results A total of 20 participants were enrolled in the study. A web-based randomization system assigned 8 participants to the intervention group and 12 participants to the control group. Attrition rates were higher among the participants in the intervention group (5/8, 63%) than those in the control group (3/12, 25%). Intervention participants undertook a median of 1.0 training meal using Mandolean (25th centile 0, 75th centile 9.3), which represented 19.2% of planned intervention exposure. Only 50% (9/18) of participants with smart watches logged physical activity data. Significant differences in psychosocial profile were observed at baseline between the groups. The Child Behavior Checklist (CBCL) mean total score was 71.7 (SD 3.1) in the intervention group vs 57.6 (SD 6.6) in the control group, t-test P<.001, and also different among those who completed the planned protocol compared with those who withdrew early (CBCL mean total score 59.0, SD 9.3, vs 67.9, SD 5.6, respectively; t-test P=.04). Conclusions A high early attrition rate was a key barrier to full study implementation. Perceived task burden in combination with behavioral issues may have contributed to attrition. Low exposure to the experimental intervention was explained by poor acceptability of Mandolean as a home-based tool for treatment. Self-monitoring using myBigO and the smartwatch was acceptable among this cohort. Further technical and usability studies are needed to improve adherence in our patient group in the tertiary setting.

Usability and Engagement Testing of mHealth Apps in Paediatric Obesity: A Narrative Review of Current Literature

“…Another study specifically measured "technical usability" by evaluating the relative user efficiency of an app; which compares task completion time between experts and novice end-users, the latter was a group of adolescents living with obesity [24]. Questionnaires were also used to test usability and were successfully tailored for youth and adapted for evaluating smartphone apps including the System Usability Scale (SUS) [32,52] and the standardised software usability measurement inventory (SUMI) [24]. Both of these questionnaires were completed following the task phase of testing and each were combined with open-ended questions for qualitative feedback on the apps [24,32].…”

“…Questionnaires were also used to test usability and were successfully tailored for youth and adapted for evaluating smartphone apps including the System Usability Scale (SUS) [32,52] and the standardised software usability measurement inventory (SUMI) [24]. Both of these questionnaires were completed following the task phase of testing and each were combined with open-ended questions for qualitative feedback on the apps [24,32]. SUMI was used to measure overall user satisfaction with an mHealth app, however, authors recommended further testing of each app feature and measuring satisfaction on completion of each task in future studies [24].…”

“…The National Institute for Health and Clinical Excellence (NICE) [16] guidelines state that from approximately age 12, young people in weight management programmes should be encouraged to monitor their food intake, physical activity (PA) and sedentary behaviour. Common app features include goal setting [22][23][24][25], peer support [25][26][27], and self-monitoring functions for: activity [23,24,28], diet [27,29] and both [25,[30][31][32]. International evidence for paediatric obesity treatment interventions recommends the inclusion of components that target both physical activities (PA) and food/eating behaviours simultaneously [16,33,34].…”

“…As illustrated in Table 2 below, seven studies met the inclusion criteria in terms of reporting usability and/or engagement testing among a paediatric population receiving treatment for obesity. Three studies recruited participants who were receiving obesity treatment in paediatric hospitals [24,32,50]. One study recruited adolescent participants from an evidence-based and medically led weight-loss summer camp including participants from backgrounds of poverty and housing insecurity across 50 states and 23 countries in America [26].…”

Improving Treatment Outcome in Children With Obesity by an Online Self-Control Training: A Randomized Controlled Trial

“…When looking at training adherence, we notice a high drop-out when patients are asked to train at home. Although our outpatient participation is generally better than in previous e-health studies in this population ( 40 , 41 ), this remains a major challenge for successful implementation. Preceding adult studies report better participation ( 15 , 42 , 43 ), which can be explained by the inclusion of normal weight subjects (as excess body weight is a risk factor for lower treatment engagement) ( 15 , 43 , 44 ), the offering of external rewards ( 43 ) and the patient selection (self-presenting participants specifically for the training vs. children seeking general weight management treatment) ( 42 , 43 ).…”