MyTrack+: Human-centered design of an mHealth app to support long-term weight loss maintenance.

A growing body of research has focused on the utility of adaptive intervention models for promoting long-term weight loss maintenance; however, evaluation of these interventions often requires customized smartphone applications. Building such an app from scratch can be resource-intensive. To support a novel clinical trial of an adaptive intervention for weight loss maintenance, we developed a companion app, MyTrack+, to pair with a main commercial app, FatSecret (FS), leveraging a user-centered design process for rapid prototyping and reducing software engineering efforts. MyTrack+ seamlessly integrates data from FS and the BodyTrace smart scale, enabling participants to log and self-monitor their health data, while also incorporating customized questionnaires and timestamps to enhance data collection for the trial. We iteratively refined the app by first developing initial mockups and incorporating feedback from a usability study with 17 university students. We further improved the app based on an in-the-wild pilot study with 33 participants in the target population, emphasizing acceptance, simplicity, customization options, and dual app usage. Our work highlights the potential of using an iterative human-centered design process to build a companion app that complements a commercial app for rapid prototyping, reducing costs, and enabling efficient research progress.

Predicting high-risk periods for weight regain following initial weight loss.

OBJECTIVE: The aim of this study was to develop a predictive algorithm of "high-risk" periods for weight regain after weight loss. METHODS: Longitudinal mixed-effects models and random forest regression were used to select predictors and develop an algorithm to predict weight regain on a week-to-week basis, using weekly questionnaire and self-monitoring data (including daily e-scale data) collected over 40 weeks from 46 adults who lost ≥5% of baseline weight during an initial 12-week intervention (Study 1). The algorithm was evaluated in 22 adults who completed the same Study 1 intervention but lost <5% of baseline weight and in 30 adults recruited for a separate 30-week study (Study 2). RESULTS: The final algorithm retained the frequency of self-monitoring caloric intake and weight plus self-report ratings of hunger and the importance of weight-management goals compared with competing life demands. In the initial training data set, the algorithm predicted weight regain the following week with a sensitivity of 75.6% and a specificity of 45.8%; performance was similar (sensitivity: 81%-82%, specificity: 30%-33%) in testing data sets. CONCLUSIONS: Weight regain can be predicted on a proximal, week-to-week level. Future work should investigate the clinical utility of adaptive interventions for weight-loss maintenance and develop more sophisticated predictive models of weight regain.

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.

Score of Toxic Epidermal Necrosis Predicts the Outcomes of Pediatric Epidermal Necrolysis.

BACKGROUND/OBJECTIVES: Epidermal necrolysis (Stevens-Johnson syndrome and toxic epidermal necrolysis) includes immune-mediated, life-threatening inflammatory blistering disorders that can affect children. The Score of Toxic Epidermal Necrosis (SCORTEN) tool has accurately predicted the outcome of these disorders in adults but has not been tested in children. METHODS: We performed a retrospective chart review to compare the accuracy of the adult SCORTEN tool with that of two modifications tailored to children in predicting disease outcome. RESULTS: The longer the patient's median length of hospital stay was, the higher the adult and two proposed pediatric SCORTENs were. In addition, all patients who died had SCORTENs greater than 4. CONCLUSION: The pediatric-modified tools were not superior to the adult SCORTEN, which accurately predicted outcome.