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Objectives <p>This study investigated whether habitual toothbrushing performance is stable within individuals across repeated observations, whether brushing performance changes after different experimental conditions and whether chewing frequency is associated with rhythmic brushing variables.</p> Materials and methods <p>In this randomized crossover study, 65 healthy young adults (24.6 ± 2.7 years) completed two study visits. At each visit, habitual manual toothbrushing was video-recorded before and after the assigned experimental condition, which consisted of brushing either after gum chewing or landscape-video viewing. Brushing performance was analysed with regard to active brushing time, surface-specific time allocation, switching behaviour, and brushing stroke rate. Sequence similarity was quantified using dynamic time warping (DTW), and brushing phenotypes were identified by k-means clustering of baseline spatiotemporal features.</p> Results <p>Time spent on oral, vestibular, and occlusal surfaces did not differ significantly after chewing (all <i>p</i> ≥ 0.300) or landscape-video viewing (all <i>p</i> ≥ 0.340). Baseline within-subject DTW distances were markedly lower than between-subject distances (0.38 ± 0.16 vs. 0.69 ± 0.12), corresponding to a 93.4% probability that a randomly selected within-subject distance was smaller than a randomly selected between-subject distance. Three brushing phenotypes (clusters) were identified: (I) long-dwell/systematic (<i>n</i> = 13); (II) local-switching (<i>n</i> = 23); and (III) global-hopping (<i>n</i> = 29). All core spatiotemporal features differed significantly across clusters (all <i>p</i> &lt; 0.001). Brushing stroke rate showed very high cross-session consistency (Spearman’s ρ = 0.90–0.95) and a weak positive association with chewing frequency (ρ = 0.28–0.34), whereas switching rate showed no such association.</p> Conclusions <p>Core features of toothbrushing performance appear highly stable within individuals and are not meaningfully altered by brief contextual manipulations. Toothbrushing performance therefore reflects habitual execution, but in distinct subject-specific phenotypes.</p> Clinical relevance <p>Stable but heterogeneous brushing routines may help explain why oral hygiene often remains suboptimal despite toothbrushing instructions and hands-on-trainings.</p>

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Subject-specific stability of toothbrushing performance: insights from dynamic time warping and phenotype analysis

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月16日

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  • Katja Jung,
  • Carolina Ganss,
  • Elisa-Maria Ressel

摘要

Objectives

This study investigated whether habitual toothbrushing performance is stable within individuals across repeated observations, whether brushing performance changes after different experimental conditions and whether chewing frequency is associated with rhythmic brushing variables.

Materials and methods

In this randomized crossover study, 65 healthy young adults (24.6 ± 2.7 years) completed two study visits. At each visit, habitual manual toothbrushing was video-recorded before and after the assigned experimental condition, which consisted of brushing either after gum chewing or landscape-video viewing. Brushing performance was analysed with regard to active brushing time, surface-specific time allocation, switching behaviour, and brushing stroke rate. Sequence similarity was quantified using dynamic time warping (DTW), and brushing phenotypes were identified by k-means clustering of baseline spatiotemporal features.

Results

Time spent on oral, vestibular, and occlusal surfaces did not differ significantly after chewing (all p ≥ 0.300) or landscape-video viewing (all p ≥ 0.340). Baseline within-subject DTW distances were markedly lower than between-subject distances (0.38 ± 0.16 vs. 0.69 ± 0.12), corresponding to a 93.4% probability that a randomly selected within-subject distance was smaller than a randomly selected between-subject distance. Three brushing phenotypes (clusters) were identified: (I) long-dwell/systematic (n = 13); (II) local-switching (n = 23); and (III) global-hopping (n = 29). All core spatiotemporal features differed significantly across clusters (all p < 0.001). Brushing stroke rate showed very high cross-session consistency (Spearman’s ρ = 0.90–0.95) and a weak positive association with chewing frequency (ρ = 0.28–0.34), whereas switching rate showed no such association.

Conclusions

Core features of toothbrushing performance appear highly stable within individuals and are not meaningfully altered by brief contextual manipulations. Toothbrushing performance therefore reflects habitual execution, but in distinct subject-specific phenotypes.

Clinical relevance

Stable but heterogeneous brushing routines may help explain why oral hygiene often remains suboptimal despite toothbrushing instructions and hands-on-trainings.