Journal of medical Internet research · 2025
Effects of Mobile-Based AI-Enhanced Health Counseling on Protection Motivation, Self-Care Behaviors, and Periodontal Treatment Outcomes in Patients With Periodontitis: Randomized Controlled Trial.
СтатьяРандомизированное исследованиеНазвание и аннотация: Перевод пока не подготовлен
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Hu You-Jie, Lin Pei-Chen, Lin Pei-Chao, Huang Chiung-Lin, Chen Chih-Chang, Matsuo Koichiro, Lee Chien-Hung, Huang Hsiao-Ling
Аннотация
BACKGROUND: Patients' protection motivation (PM) and self-care behaviors are essential for sustaining oral hygiene adherence and long-term periodontal health.
OBJECTIVE: This study aimed to evaluate the effects of artificial intelligence (AI)-assisted dental monitoring on PM, behavioral change, and periodontal treatment outcomes.
METHODS: Patients with periodontitis were randomly assigned to an AI group (n=32), an AI-assisted health counseling (AIHC) group (n=33), or a control group (n=33). All participants received nonsurgical periodontal treatment and oral hygiene instruction. The AI and AIHC groups additionally received AI-based monitoring and, for the AIHC group, personalized counseling for 3 months. PM (threat and coping appraisal), self-care behavior, plaque control record, probing pocket depth, and clinical attachment level were assessed at baseline and follow-ups. Generalized estimating equations were used to analyze the follow-up effects over time, with an intention-to-treat analysis for group comparisons.
RESULTS: Significant time effects were observed for threat appraisal and coping appraisal at 6 months (adjusted mean ratio 1.07 and 1.06, respectively), with no significant group-by-time interaction. At 6 months, both AI and AIHC groups showed greater improvements in self-care behavior (AI: adjusted regression coefficient [aβ] 0.32; AIHC: aβ 0.29) and plaque control record (AI: aβ -13.28; AIHC: aβ -23.22) compared to the control group. At 3 months, the AI and AIHC groups also demonstrated greater reductions in probing pocket depth (aβ -0.43 and -0.55, respectively) and clinical attachment level (aβ -0.44 and -0.51, respectively).
CONCLUSIONS: AI-assisted monitoring improved self-care behaviors, plaque control, and periodontal clinical outcomes. Although PM constructs (threat appraisal and coping appraisal) improved similarly across all groups, the greater behavioral and clinical improvements observed in the AI-assisted groups highlight the potential of AI monitoring as an effective tool to enhance periodontal care. Incorporating AI technologies into routine practice may empower patients to take a more active role in managing their oral health and support more effective, patient-centered periodontal therapy.
OBJECTIVE: This study aimed to evaluate the effects of artificial intelligence (AI)-assisted dental monitoring on PM, behavioral change, and periodontal treatment outcomes.
METHODS: Patients with periodontitis were randomly assigned to an AI group (n=32), an AI-assisted health counseling (AIHC) group (n=33), or a control group (n=33). All participants received nonsurgical periodontal treatment and oral hygiene instruction. The AI and AIHC groups additionally received AI-based monitoring and, for the AIHC group, personalized counseling for 3 months. PM (threat and coping appraisal), self-care behavior, plaque control record, probing pocket depth, and clinical attachment level were assessed at baseline and follow-ups. Generalized estimating equations were used to analyze the follow-up effects over time, with an intention-to-treat analysis for group comparisons.
RESULTS: Significant time effects were observed for threat appraisal and coping appraisal at 6 months (adjusted mean ratio 1.07 and 1.06, respectively), with no significant group-by-time interaction. At 6 months, both AI and AIHC groups showed greater improvements in self-care behavior (AI: adjusted regression coefficient [aβ] 0.32; AIHC: aβ 0.29) and plaque control record (AI: aβ -13.28; AIHC: aβ -23.22) compared to the control group. At 3 months, the AI and AIHC groups also demonstrated greater reductions in probing pocket depth (aβ -0.43 and -0.55, respectively) and clinical attachment level (aβ -0.44 and -0.51, respectively).
CONCLUSIONS: AI-assisted monitoring improved self-care behaviors, plaque control, and periodontal clinical outcomes. Although PM constructs (threat appraisal and coping appraisal) improved similarly across all groups, the greater behavioral and clinical improvements observed in the AI-assisted groups highlight the potential of AI monitoring as an effective tool to enhance periodontal care. Incorporating AI technologies into routine practice may empower patients to take a more active role in managing their oral health and support more effective, patient-centered periodontal therapy.