ArticleJournal of oral rehabilitation2026
Towards Optimized Awake Bruxism Metrics: Empirical Definition of Minimal Ecological Momentary Assessment Requirements.
Article in Journal of oral rehabilitation, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundEcological Momentary Assessment (EMA) is increasingly used to assess awake bruxism (AB) in real time. However, current protocols typically require 7 days of monitoring and high daily response rates, potentially limiting compliance.
objectivesTo empirically establish the shortest monitoring duration and daily response threshold that still yield reliable estimates of AB behaviours through smartphone-based EMA.
methodsData from three EMA studies were pooled, yielding 327 participants who completed smartphone-based monitoring. Five core AB behaviours (relaxed jaw muscles, bracing, teeth contact, clenching, and grinding) were analysed. Temporal stability was evaluated using intraclass correlation coefficients (ICC[A,1]) by comparing progressively shorter monitoring windows with the complete 7-day dataset. Compliance-threshold analyses examined the relationship between minimum daily responses, reliability, and participant retention.
resultsThe pooled dataset included over 32 000 EMA reports. Relaxed jaw condition was the most frequently reported behaviour (52.2%), followed by teeth contact (18.6%) and bracing (14.5%). Clenching (6.9%) and grinding (1.4%) were less frequent. Four of five behaviours achieved excellent stability after three monitoring days (ICC ≥ 0.86), whereas grinding reached stability after 2 days (ICC = 0.81). Increasing the minimum daily response threshold improved reliability but substantially reduced sample retention. A threshold of ≥ 12 answered alerts/day provided the best balance between reliability and retention.
conclusionsReliable assessment of the five core awake bruxism behaviours can be achieved using a 3-day EMA protocol with at least 12 answered alerts/day. These findings support a reduction in monitoring burden while maintaining data quality.
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