Effects of a novel mobile health intervention compared to a multi-component behaviour changing program on body mass index, physical capacities and stress parameters in adolescents with obesity: a randomized controlled trial

Abstract

Background Less than 2% of overweight children and adolescents in Switzerland can participate in multi-component behaviour changing interventions (BCI), due to costs and lack of time. Stress often hinders positive health outcomes in youth with obesity. Digital health interventions, with fewer on-site visits, promise health care access in remote regions; however, evidence for their effectiveness is scarce. Methods This randomized controlled not blinded trial (1:1) was conducted in a childhood obesity center in Switzerland. Forty-one youth aged 10–18 years with body mass index (BMI) > P.90 with risk factors or co-morbidities or BMI > P.97 were recruited. During 5.5 months, the PathMate2 group (PM) received daily conversational agent counselling via mobile app, combined with standardized counselling (4 on-site visits). Controls (CON) participated in a BCI (7 on-site visits). We compared the outcomes of both groups after 5.5 (T1) and 12 (T2) months. Primary outcome was reduction in BMI-SDS (BMI standard deviation score: BMI adjusted for age and sex). Secondary outcomes were changes in body fat and muscle mass (bioelectrical impedance analysis), waist-to-height ratio, physical capacities (modified Dordel-Koch-Test), blood pressure and pulse. Additionally, we hypothesized that less stressed children would lose more weight. Thus, children performed biofeedback relaxation exercises while stress parameters (plasma cortisol, stress questionnaires) were evaluated. Results At intervention start median BMI-SDS of all patients (18 PM, 13 CON) was 2.61 (obesity > + 2SD). BMI-SDS decreased significantly in CON at T1, but not at T2, and did not decrease in PM during the study. Muscle mass, strength and agility improved significantly in both groups at T2; only PM reduced significantly their body fat at T1 and T2. Average daily PM app usage rate was 71.5%. Cortisol serum levels decreased significantly after biofeedback but with no association between stress parameters and BMI-SDS. No side effects were observed. Conclusions Equally to BCI, PathMate2 intervention resulted in significant and lasting improvements of physical capacities and body composition, but not in sustained BMI-SDS decrease. This youth-appealing mobile health intervention provides an interesting approach for youth with obesity who have limited access to health care. Biofeedback reduces acute stress and could be an innovative adjunct to usual care.

The Use of Artificial Intelligence–Based Conversational Agents (Chatbots) for Weight Loss: Scoping Review and Practical Recommendations

“…A summary and detailed description of the study characteristics are shown in Table 1 and Multimedia Appendix 3 [ 25 , 26 , 29 - 47 ] and Multimedia Appendix 4 [ 25 , 26 , 29 - 37 , 39 - 47 ]. The chatbot programs included Wakamola [ 30 - 32 ], WaznApp [ 33 ], WeightMentor [ 25 ], SWITCHes [ 34 ], MobileCoach [ 35 ], PathMate2 [ 36 ], and Lark Weight Loss Health Coach AI [ 37 ].…”

“…Core functions of the chatbot were to provide personalized weight loss recommendations (20/23, 87%) [ 25 , 26 , 29 - 34 , 37 - 39 , 41 - 46 ] and motivational messages (18/23, 78%) [ 25 , 26 , 29 - 33 , 37 - 39 , 41 - 43 , 45 , 46 ] ( Multimedia Appendix 5 [ 25 , 26 , 29 - 37 , 39 - 47 ]). Only 26% (6/23) studies [ 30 - 32 , 36 , 40 , 43 ] mentioned the use of gamification to enhance user engagement, and 26% (6/23) studies [ 37 , 39 , 40 , 42 , 43 , 45 ] mentioned the use of sentiment analysis to provide emotional support through emotionally appropriate messages ( Multimedia Appendix 5 ). These functions were generally achieved by (1) collecting various user-centric data through chatbot-based self-reports or device-detected metrics, (2) integrating collected parameters using machine learning techniques (including NLP to convert chatbot-collected information for prediction modeling) to predict and generate weight-related recommendations, (3) profiling users according to needs and preferences, and (4) providing personalized chatbot-delivered recommendations.…”

“…The parameters collected included sociodemographic profiles (eg, age, race, ethnicity, education, work status, and income), food consumption, physical activity (eg, intensity, duration, frequency, and type), stress level, sleep (ie, duration), and clinical profiles (eg, presence of specific chronic diseases, medication use, smoking status, heart rate, and blood pressure; Multimedia Appendix 6 [ 25 , 26 , 29 - 37 , 39 - 47 ]). Majority of the parameters were collected through chatbots, except in 3 studies that estimated food consumption using smart refrigerator appliances [ 30 ] and nutritional information provided by retailers (scanning bar codes) [ 38 , 42 ]; 5 that estimated physical exercise type, intensity, and frequency wearable or smartphone sensors [ 33 , 37 , 38 , 42 , 43 ]; 2 that estimated stress levels using plasma cortisol and skin conductance response [ 36 ] and phone detection [ 37 ]; and 3 that measured heart rate and blood pressure [ 36 , 38 , 42 ]. Others that did not mention the use of wearables may have relied on information from in-built sensors of the phone.…”

“…Only 4 studies evaluated the effectiveness of a chatbot-delivered program on diet [ 26 , 37 ], physical activity [ 44 ], and weight loss [ 36 ] ( Multimedia Appendix 3 ). Although 3 of these studies showed greater effectiveness in chatbot-delivered weight loss programs on the measured outcomes, 1 study reported that a higher proportion of adolescents in the control group lost weight as compared with those who interacted with the PathMate 2 chatbot (92% vs 61%).…”

“…Although 3 of these studies showed greater effectiveness in chatbot-delivered weight loss programs on the measured outcomes, 1 study reported that a higher proportion of adolescents in the control group lost weight as compared with those who interacted with the PathMate 2 chatbot (92% vs 61%). Those in the control group underwent 7 in-person counseling sessions with a health care professional, whereas those in the intervention group interacted with the PathMate 2 chatbot daily with 4 in-person counseling sessions (61%) [ 36 ]. Other studies (19/23, 83%) either used chatbots mainly to collect data on diet, physical activity, sitting time, and sleep [ 31 , 32 ] or were still in the developmental stage.…”