Digital Wearable Oral Health Navigator for Personalized Preventive Dentistry: Design, Implementation, Evaluation

Thesis Abstract

This study investigates the development, deployment, and evaluation of a Digital Wearable Oral Health Navigator (DWOHN) designed to deliver personalized preventive dentistry guidance through real-time data integration from wearable sensors, mobile app interfaces, and dental health records to improve adherence to preventive regimens and clinical outcomes. The problem addressed is the fragmentation of preventive guidance and the limited personalization of reminders and behavioral support in routine dental care, which undermines sustained oral health improvements. The aim is to design, implement, and evaluate a holistic system that (i) collects multisource data from wearables (salivary flow, glucose proxy, sleep quality, activity) and dental records, (ii) utilizes a modular decision engine to generate individualized preventive plans, prompts, and feedback using evidence-based guidelines, and (iii) assesses usability, engagement, and clinical impact over a 12-month period. Specific objectives include (1) to develop an interoperable architecture integrating wearable sensor data, patient-reported outcomes, and electronic dental records; (2) to implement behavior change support modules grounded in the Health Belief Model and Self-Determination Theory to optimize motivation and adherence; (3) to evaluate the system’s usability, satisfaction, and perceived usefulness among at least 200 adult participants with varying risk profiles; (4) to quantify changes in clinical indicators such as plaque index, gingival index, caries risk scores, and preventive service utilization; (5) to analyze the relationship between engagement metrics and clinical outcomes; and (6) to assess data privacy, ethical considerations, and user trust. A mixed-methods design is employed. The experimental component uses a quasi-experimental, non-randomized control design with 200 participants assigned to either the DWOHN intervention (n=100) or standard care (n=100) across 12 months, with quarterly dental assessments. The population comprises adults aged 18–65 visiting urban dental clinics, with balanced representation across age, gender, socioeconomic status, and baseline caries risk. The sample size was determined through a power calculation to detect a minimum clinically important difference of 15% in plaque index at 12 months (power 0.80, alpha 0.05). Data collection instruments include (i) wearable sensor data streams (salivary indicators via non-invasive proxies, physical activity, sleep quality), (ii) dental clinical indices (plaque index, gingival index, calculus, DMFT components), (iii) validated patient-reported outcome measures (oral health-related quality of life, perceived usefulness, usability via SUS, motivation via BREQ-3), and (iv) system analytics (engagement metrics, feature usage, reminder response rates). Qualitative data are gathered via semi-structured interviews with a purposive subsample (n=40) to explore acceptability and perceived impact, analyzed thematically. Data analysis employs robust statistical and analytical techniques. Descriptive statistics summarize baseline characteristics and engagement. Inferential analyses include mixed-effects linear models to evaluate changes in clinical indices over time, adjusting for covariates; repeated-measures ANOVA to examine temporal trends; logistic regression for adherence of preventive behaviors; and survival analysis for time-to-first preventive service utilization. mediation analysis tests whether engagement mediates the relationship between the DWOHN and clinical outcomes. For the qualitative component, thematic analysis follows Braun and Clarke's approach, with coding conducted by two researchers and triangulated with quantitative findings. The study draws on the Theory of Planned Behavior to interpret intention-behavior gaps and the Technology Acceptance Model to understand adoption drivers. Expected findings include improved oral health outcomes in the intervention group, evidenced by statistically significant reductions in plaque and gingival indices, higher preventive service uptake, and enhanced oral health-related quality of life. It is anticipated that higher engagement with the navigator will mediate improvements, and that user trust and perceived usefulness will predict sustained use. The study contributes to knowledge by integrating wearable-derived physiological proxies with dental health data to deliver personalized preventive guidance, grounded in established behavior change theories and validated within routine dental care settings. It provides a replicable design for digital health interventions in dentistry, clarifies ethical and privacy considerations in wearable data integration, and offers a framework for evaluating digital preventive tools in real-world clinical populations. The main conclusion posits that a well-designed Digital Wearable Oral Health Navigator can enhance adherence to preventive regimens and yield clinically meaningful improvements, with recommendations including scalable deployment strategies, integration with dental informatics ecosystems, and ongoing refinement of personalization algorithms to accommodate diverse patient needs.