Impact of transition from face-to-face to telehealth on behavioral obesity treatment during the COVID-19 pandemic.

OBJECTIVE: This study evaluated whether the transition of a face-to-face behavioral intervention to videoconferencing-based telehealth delivery during the COVID-19 pandemic resulted in significantly smaller weight losses than those typically observed in gold-standard, face-to-face programs. METHODS: Participants were 160 adults with obesity (mean [SD] age = 49.2 [11.9] years, BMI = 36.1 [4.2] kg/m2 ) enrolled in two cohorts of a 16-week comprehensive weight-management program. Cohort 1 began in person and transitioned to telehealth (Zoom) delivery during week 11 of the intervention because of COVID-19; Cohort 2 was conducted completely remotely. A noninferiority approach (using a clinically relevant noninferiority margin of 2.5%) was used to assess whether the weight losses observed were inferior to the 8% losses from baseline typically produced by gold-standard, face-to-face lifestyle interventions. RESULTS: From baseline to postintervention, participants lost an average of 7.4 [4.9] kg, representing a reduction of 7.2% [4.6%]. This magnitude of weight change was significantly greater than 5.5% (t[159] = 4.7, p < 0.001), and, thus, was within the proposed noninferiority margin. CONCLUSIONS: These findings demonstrate that the results of behavioral weight-management interventions are robust, whether delivered in person or remotely, and that individuals can achieve clinically meaningful benefits from behavioral treatment even during a global pandemic. Pragmatic "lessons learned," including modified trial recruitment techniques, are discussed.

Using Smartwatches to Detect Face Touching.

Frequent spontaneous facial self-touches, predominantly during outbreaks, have the theoretical potential to be a mechanism of contracting and transmitting diseases. Despite the recent advent of vaccines, behavioral approaches remain an integral part of reducing the spread of COVID-19 and other respiratory illnesses. The aim of this study was to utilize the functionality and the spread of smartwatches to develop a smartwatch application to identify motion signatures that are mapped accurately to face touching. Participants (n = 10, five women, aged 20-83) performed 10 physical activities classified into face touching (FT) and non-face touching (NFT) categories in a standardized laboratory setting. We developed a smartwatch application on Samsung Galaxy Watch to collect raw accelerometer data from participants. Data features were extracted from consecutive non-overlapping windows varying from 2 to 16 s. We examined the performance of state-of-the-art machine learning methods on face-touching movement recognition (FT vs. NFT) and individual activity recognition (IAR): logistic regression, support vector machine, decision trees, and random forest. While all machine learning models were accurate in recognizing FT categories, logistic regression achieved the best performance across all metrics (accuracy: 0.93 ± 0.08, recall: 0.89 ± 0.16, precision: 0.93 ± 0.08, F1-score: 0.90 ± 0.11, AUC: 0.95 ± 0.07) at the window size of 5 s. IAR models resulted in lower performance, where the random forest classifier achieved the best performance across all metrics (accuracy: 0.70 ± 0.14, recall: 0.70 ± 0.14, precision: 0.70 ± 0.16, F1-score: 0.67 ± 0.15) at the window size of 9 s. In conclusion, wearable devices, powered by machine learning, are effective in detecting facial touches. This is highly significant during respiratory infection outbreaks as it has the potential to limit face touching as a transmission vector.