How the Wakamola chatbot studied a university community’s lifestyle during the COVID-19 confinement

Abstract

This work aimed to study the effect of confinement on weight and lifestyle using the Wakamola chatbot to collect data from 739 adults divided into two groups (341 case-control, 398 confinement). Nutrition score (0–100 scale) improved for men (medians 81.77–82.29, p < 0.05), with no difference for women (medians 82.29 in both cases). Both genders reduced the consumption of sweetmeats and sugared drinks ( p < 0.01); men increased their consumption of vegetables, salad, and legumes ( p < 0.01). Both genders reduced their physical activity score (men 100–40.14, p < 0.01, women 80.42–36.12, p < 0.01). Women sat less hours/week, men’s medians 28.81–28.27, women’s medians 35.97–23.33, p = 0.03. Both genders slept longer (hours/day), men 7–7.5, women 7–8 ( p < 0.01) (medians). Their overall health score was significantly reduced (men 85.06–74.05, p < 0.01, women 84.47–72.42, p < 0.01), with no significant weight difference in either gender. Wakamola helped to contact participants and confirm changes in their lifestyle during confinement.

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 ].…”

“…Most (8/23, 35%) of the studies focused on promoting a healthy diet and exercise, whereas only 4% (1/23) studies focused on a healthy diet, exercise, and stress management ( Figure 2 and Multimedia Appendix 4 ). In all, 11 out of the 14 (79%) planned or trialed experimental studies [ 26 , 29 - 33 , 35 - 37 , 40 , 43 - 45 ] reported program durations that ranged from 1 hour to 12 months [ 30 - 32 ]. Only 1 study mentioned the intention of comparing algorithms to yield accurate behavioral predictions [ 43 ].…”

“…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.…”

“…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. Future studies should consider adopting an experimental design that evaluates the use of chatbots on objective weight-related outcomes such as weight loss, diet (eg, food choices, calorie intake, and consumption frequency), and physical activity (eg, energy expenditure, activity type, and activity frequency) using inferential statistics that suggest repeatability.…”

“…Some techniques used were the deployment of humanoid robots [ 43 ] and embodied chatbots [ 26 ] that were capable of displaying visual social cues such as eye contact and hand gestures. Other techniques used in text-based chatbots were the delivery of colloquial, personally and culturally appropriate conversational tones and content [ 26 , 30 ]; emojis to emulate human emotional expressions [ 30 - 32 ]; positively framed words [ 32 ]; citations of credible information sources [ 26 , 33 ]; and validation (eg, acknowledgments and compliments) of not only behaviors but also thoughts and feelings [ 41 ]. Participants of the included studies were also found to have appreciated the personification of the chatbot (eg, funny, animated, empathetic, or playful) [ 25 , 29 , 31 , 32 , 37 ] and the provision of real-time, fast, and reliable recommendations [ 26 , 29 , 38 ].…”