Privacy-by-Design Adaptive Group Assignment for Digital Lifestyle Coaching at Scale

2026-05-19

AI Summary

This study addresses the dual challenges of dynamic user behavior and privacy preservation in large-scale digital health coaching by proposing the PRISM-Coach architecture. PRISM-Coach introduces a novel four-view data isolation mechanism—encompassing identity, operations, learning, and coaching views—combined with vault-based controlled identity recovery, a privacy-constrained contextual bandit algorithm, and a human-AI collaborative coaching assistant. This design enables adaptive peer grouping and personalized support while rigorously preventing leakage of personally identifiable information (PII) and protected health information (PHI). Evaluated over three years with 2,800 users, the system increased daily check-in adherence from 0.35 to 0.74, achieved an average weight loss of 5.2 kg (versus 3.1 kg in the control group), and was perceived as beneficial by 82% of users, with 92% reporting significantly enhanced privacy confidence—demonstrating strong alignment among regulatory compliance, clinical efficacy, and user experience.

Abstract

Digital lifestyle coaching systems must personalize peer support as user behavior and engagement evolve while preventing personally identifiable information (PII) and sensitive health information from leaking into analytics and AI pipelines. This creates a practical tension: personalization requires longitudinal linkability, while privacy engineering requires minimization, separation, and controlled re-identification. We present PRISM-Coach, a stakeholder-centered architecture and adaptive peer-group assignment method for privacy-preserving lifestyle coaching. PRISM-Coach separates each user into four bounded views: Identity, Operational, Learning, and Coaching, each with distinct access controls and risk profiles. Building on this separation, the system uses vault-based controlled identity restoration, a privacy-constrained contextual bandit to assign users to eligible peer groups under coach-capacity and stability constraints, and a human-in-the-loop coaching assistant that generates de-identified summaries and draft messages without sending raw PII or PHI to external AI services. We instantiate PRISM-Coach in a commercially deployed lifestyle coaching platform and evaluate it using three years of telemetry from approximately 2,800 users and an in-app needs assessment survey. At the population level, daily check-in adherence increases from 0.35 to 0.68, and engagement rises to 1.35 baseline. In a matched 19-week comparison window, the AI-enabled workflow achieves adherence of 0.74 versus 0.48 under static grouping and higher average weight loss: 5.2 kg versus 3.1 kg. Survey results show that 82% report positive perceived benefit, and 92% report increased privacy confidence after transparency disclosures. These results position PRISM-Coach as a practical blueprint for privacy-by-design adaptive learning systems in everyday wellness.

Problem

• privacy-by-design
• adaptive group assignment
• digital lifestyle coaching
• personally identifiable information
• peer support

Innovation

• Privacy-by-Design
• adaptive group assignment
• contextual bandit
• vault-based identity restoration
• de-identified AI coaching