Participants in the AI-DPP group were referred to Sweetch Health, Ltd, and received a digital health kit from within 8-12 days post-randomization, which included a Bluetooth scale and Sweetch app registration instructions. The app delivered personalized push notifications for weight management, physical activity (PA), and nutrition, informed by both actively collected data (e.g., weight measurements, meal logging) and passively collected data (e.g., geolocation, accelerometry). PA was tracked via smartphone or wearable devices, meals were logged through a food library or photo-based detection, and weight was recorded either automatically through the scale or manually.

The AI used in the Sweetch intervention consisted of a reinforcement learning algorithm that did not employ large language models. It personalized messaging by continuously learning which prompts, timing, and content elicited greater user engagement. The app also delivered location- and goal-specific nutrition education and included gamification elements and educational resources.

Participants in the Human-DPP group were referred to one of four 12-month accredited lifestyle change programs: Brancati Center and University of Maryland Medical Center (Baltimore, MD), Pottstown Medical Specialists and Montgomery County DPP (Reading, PA). All four participating DPPs had full plus recognition status from the CDC. Due to COVID-19, all sessions transitioned to synchronous distance learning (i.e., group video conferences).

Trained lifestyle coaches led sessions based on the CDC's PreventT2 curriculum, covering healthy eating, food tracking, PA, behavior modification, and long-term weight management, with an initial core phase (16 weekly sessions) and a remaining core maintenance phase (bi-weekly to monthly sessions) to complete the 12-month intervention. Of note, our study participants joined DPP cohorts that were comprised of standard enrollees who were not participants in our trial.

The primary analysis was conducted in all randomized participants. Those who did not attend the 12-month study visit were classified as not achieving the primary composite outcome, providing a conservative estimate of intervention effectiveness. The risk difference was estimated using binomial regression.

Noninferiority was defined a priori as the lower bound of the one-sided 95% CI for the risk difference being greater than or equal to -15%.

Participants in the AI-DPP group were referred to Sweetch Health, Ltd, and received a digital health kit from within 8-12 days post-randomization, which included a Bluetooth scale and Sweetch app registration instructions. The app delivered personalized push notifications for weight management, physical activity (PA), and nutrition, informed by both actively collected data (e.g., weight measurements, meal logging) and passively collected data (e.g., geolocation, accelerometry). PA was tracked via smartphone or wearable devices, meals were logged through a food library or photo-based detection, and weight was recorded either automatically through the scale or manually.

The AI used in the Sweetch intervention consisted of a reinforcement learning algorithm that did not employ large language models. It personalized messaging by continuously learning which prompts, timing, and content elicited greater user engagement. The app also delivered location- and goal-specific nutrition education and included gamification elements and educational resources.

Participants in the Human-DPP group were referred to one of four 12-month accredited lifestyle change programs: Brancati Center and University of Maryland Medical Center (Baltimore, MD), Pottstown Medical Specialists and Montgomery County DPP (Reading, PA). All four participating DPPs had full plus recognition status from the CDC. Due to COVID-19, all sessions transitioned to synchronous distance learning (i.e., group video conferences).

Trained lifestyle coaches led sessions based on the CDC's PreventT2 curriculum, covering healthy eating, food tracking, PA, behavior modification, and long-term weight management, with an initial core phase (16 weekly sessions) and a remaining core maintenance phase (bi-weekly to monthly sessions) to complete the 12-month intervention. Of note, our study participants joined DPP cohorts that were comprised of standard enrollees who were not participants in our trial.

Participants in the AI-DPP group were referred to Sweetch Health, Ltd, and received a digital health kit from within 8-12 days post-randomization, which included a Bluetooth scale and Sweetch app registration instructions. The app delivered personalized push notifications for weight management, physical activity (PA), and nutrition, informed by both actively collected data (e.g., weight measurements, meal logging) and passively collected data (e.g., geolocation, accelerometry). PA was tracked via smartphone or wearable devices, meals were logged through a food library or photo-based detection, and weight was recorded either automatically through the scale or manually.

The AI used in the Sweetch intervention consisted of a reinforcement learning algorithm that did not employ large language models. It personalized messaging by continuously learning which prompts, timing, and content elicited greater user engagement. The app also delivered location- and goal-specific nutrition education and included gamification elements and educational resources.

Participants in the AI-DPP group were referred to Sweetch Health, Ltd, and received a digital health kit from within 8-12 days post-randomization, which included a Bluetooth scale and Sweetch app registration instructions. The app delivered personalized push notifications for weight management, physical activity (PA), and nutrition, informed by both actively collected data (e.g., weight measurements, meal logging) and passively collected data (e.g., geolocation, accelerometry). PA was tracked via smartphone or wearable devices, meals were logged through a food library or photo-based detection, and weight was recorded either automatically through the scale or manually.

The AI used in the Sweetch intervention consisted of a reinforcement learning algorithm that did not employ large language models. It personalized messaging by continuously learning which prompts, timing, and content elicited greater user engagement. The app also delivered location- and goal-specific nutrition education and included gamification elements and educational resources.

Participants in the Human-DPP group were referred to one of four 12-month accredited lifestyle change programs: Brancati Center and University of Maryland Medical Center (Baltimore, MD), Pottstown Medical Specialists and Montgomery County DPP (Reading, PA). All four participating DPPs had full plus recognition status from the CDC. Due to COVID-19, all sessions transitioned to synchronous distance learning (i.e., group video conferences).

Trained lifestyle coaches led sessions based on the CDC's PreventT2 curriculum, covering healthy eating, food tracking, PA, behavior modification, and long-term weight management, with an initial core phase (16 weekly sessions) and a remaining core maintenance phase (bi-weekly to monthly sessions) to complete the 12-month intervention. Of note, our study participants joined DPP cohorts that were comprised of standard enrollees who were not participants in our trial.

Participants in the AI-DPP group were referred to Sweetch Health, Ltd, and received a digital health kit from within 8-12 days post-randomization, which included a Bluetooth scale and Sweetch app registration instructions. The app delivered personalized push notifications for weight management, physical activity (PA), and nutrition, informed by both actively collected data (e.g., weight measurements, meal logging) and passively collected data (e.g., geolocation, accelerometry). PA was tracked via smartphone or wearable devices, meals were logged through a food library or photo-based detection, and weight was recorded either automatically through the scale or manually.

The AI used in the Sweetch intervention consisted of a reinforcement learning algorithm that did not employ large language models. It personalized messaging by continuously learning which prompts, timing, and content elicited greater user engagement. The app also delivered location- and goal-specific nutrition education and included gamification elements and educational resources.

Participants in the AI-DPP group were referred to Sweetch Health, Ltd, and received a digital health kit from within 8-12 days post-randomization, which included a Bluetooth scale and Sweetch app registration instructions. The app delivered personalized push notifications for weight management, physical activity (PA), and nutrition, informed by both actively collected data (e.g., weight measurements, meal logging) and passively collected data (e.g., geolocation, accelerometry). PA was tracked via smartphone or wearable devices, meals were logged through a food library or photo-based detection, and weight was recorded either automatically through the scale or manually.

The AI used in the Sweetch intervention consisted of a reinforcement learning algorithm that did not employ large language models. It personalized messaging by continuously learning which prompts, timing, and content elicited greater user engagement. The app also delivered location- and goal-specific nutrition education and included gamification elements and educational resources.

Participants in the Human-DPP group were referred to one of four 12-month accredited lifestyle change programs: Brancati Center and University of Maryland Medical Center (Baltimore, MD), Pottstown Medical Specialists and Montgomery County DPP (Reading, PA). All four participating DPPs had full plus recognition status from the CDC. Due to COVID-19, all sessions transitioned to synchronous distance learning (i.e., group video conferences).

Trained lifestyle coaches led sessions based on the CDC's PreventT2 curriculum, covering healthy eating, food tracking, PA, behavior modification, and long-term weight management, with an initial core phase (16 weekly sessions) and a remaining core maintenance phase (bi-weekly to monthly sessions) to complete the 12-month intervention. Of note, our study participants joined DPP cohorts that were comprised of standard enrollees who were not participants in our trial.

Participants in the AI-DPP group were referred to Sweetch Health, Ltd, and received a digital health kit from within 8-12 days post-randomization, which included a Bluetooth scale and Sweetch app registration instructions. The app delivered personalized push notifications for weight management, physical activity (PA), and nutrition, informed by both actively collected data (e.g., weight measurements, meal logging) and passively collected data (e.g., geolocation, accelerometry). PA was tracked via smartphone or wearable devices, meals were logged through a food library or photo-based detection, and weight was recorded either automatically through the scale or manually.

The AI used in the Sweetch intervention consisted of a reinforcement learning algorithm that did not employ large language models. It personalized messaging by continuously learning which prompts, timing, and content elicited greater user engagement. The app also delivered location- and goal-specific nutrition education and included gamification elements and educational resources.

Participants in the Human-DPP group were referred to one of four 12-month accredited lifestyle change programs: Brancati Center and University of Maryland Medical Center (Baltimore, MD), Pottstown Medical Specialists and Montgomery County DPP (Reading, PA). All four participating DPPs had full plus recognition status from the CDC. Due to COVID-19, all sessions transitioned to synchronous distance learning (i.e., group video conferences).

Trained lifestyle coaches led sessions based on the CDC's PreventT2 curriculum, covering healthy eating, food tracking, PA, behavior modification, and long-term weight management, with an initial core phase (16 weekly sessions) and a remaining core maintenance phase (bi-weekly to monthly sessions) to complete the 12-month intervention. Of note, our study participants joined DPP cohorts that were comprised of standard enrollees who were not participants in our trial.

Participants in the AI-DPP group were referred to Sweetch Health, Ltd, and received a digital health kit from within 8-12 days post-randomization, which included a Bluetooth scale and Sweetch app registration instructions. The app delivered personalized push notifications for weight management, physical activity (PA), and nutrition, informed by both actively collected data (e.g., weight measurements, meal logging) and passively collected data (e.g., geolocation, accelerometry). PA was tracked via smartphone or wearable devices, meals were logged through a food library or photo-based detection, and weight was recorded either automatically through the scale or manually.

The AI used in the Sweetch intervention consisted of a reinforcement learning algorithm that did not employ large language models. It personalized messaging by continuously learning which prompts, timing, and content elicited greater user engagement. The app also delivered location- and goal-specific nutrition education and included gamification elements and educational resources.