A Global, In-Market Evaluation of Toothbrushing Behaviour and Self-assessed Gingival Bleeding with Use of App Data from an Interactive Electric Toothbrush.

PURPOSE: To determine if an interactive electric toothbrush and smartphone application (app) can reduce self-reported gingival bleeding and promote better brushing behaviour based on global, in-market usage data. MATERIALS AND METHODS: Anonymised data were collected worldwide between July 2020 and January 2021 from users of interactive oscillating-rotating electric toothbrushes and app (Oral-B Genius, GeniusX and iO). Self-reported gingival bleeding and brushing behaviour data captured via the app were sent to Google Firebase and Google BigQuery to aid processing and analysis. RESULTS: Data from 16.7 million brushing sessions were analysed. 439,481 new users responded at least once to the app question: 'Do you have gum bleeding?' Of users answering the question over their first two weeks of app use (153,201), the proportion reporting bleeding decreased statistically significantly from week 1 to 2 (28.8% to 17.1%, p < 0.0001). Of users answering the question over each of the first five weeks (43,060) a further statistically significant decrease in those reporting bleeding was seen in each consecutive week, with the week-5 rate being 12.7% (p < 0.0001 vs any previous week). Decreases in duration of excessive pressure (i.e. > 2.5 N - 3.0 N depending on the handle) decreased the proportion of self-reported gingival bleeding (p < 0.0001). Users brushed longer and with less overpressure (p < 0.0001) with vs without live feedback from the app, and showed 94.4% average coverage with live feedback. CONCLUSION: The interactive oscillating-rotating electric toothbrushes and app, particularly with live feedback, promote good brushing behaviour. Self-reported gingival bleeding occurred less frequently the longer the system was used.

Validation of motion tracking as tool for observational toothbrushing studies.

Video observation (VO) is an established tool for observing toothbrushing behaviour, however, it is a subjective method requiring thorough calibration and training, and the toothbrush position is not always clearly visible. As automated tracking of motions may overcome these disadvantages, the study aimed to compare observational data of habitual toothbrushing as well as of post-instruction toothbrushing obtained from motion tracking (MT) to observational data obtained from VO. One-hundred-three subjects (37.4±14.7 years) were included and brushed their teeth with a manual (MB; n = 51) or a powered toothbrush (PB; n = 52) while being simultaneously video-filmed and tracked. Forty-six subjects were then instructed how to brush their teeth systematically and were filmed/tracked for a second time. Videos were analysed with INTERACT (Mangold, Germany); parameters of interest were toothbrush position, brushing time, changes between areas (events) and the Toothbrushing Systematic Index (TSI). Overall, the median proportion (min; max) of identically classified toothbrush positions (both sextant/surface correct) in a brushing session was 87.8% (50.0; 96.9), which was slightly higher for MB compared to PB (90.3 (50.0; 96.9) vs 86.5 (63.7; 96.5) resp.; p = 0.005). The number of events obtained from MT was higher than from VO (p < 0.001) with a moderate to high correlation between them (MB: ρ = 0.52, p < 0.001; PB: ρ = 0.87; p < 0.001). After instruction, both methods revealed a significant increase of the TSI regardless of the toothbrush type (p < 0.001 each). Motion tracking is a suitable tool for observing toothbrushing behaviour, is able to measure improvements after instruction, and can be used with both manual and powered toothbrushes.

Did assessors select attributes by chance alone in your TDS study, and how relevant is it to know?

Dominance rates arising from Temporal Dominance of Sensations (TDS) data are almost always plotted and understood with reference to chance and significance lines, which are based on the assumption of random attribute selection. These lines are auxiliary to the chart, and used to interpret the dominance rates; when the dominance rate for some attribute exceeds the significance line, the conventional interpretation is that this attribute is being noticed significantly. Thus, these auxiliary lines are used to provide a convenient, qualitative interpretation of TDS data. However, the concept of the significance lines has some deficiencies that we explore in this paper. We were interested in what real TDS data look like under a true null hypothesis of random attribute selection. To obtain such data, we sampled from real TDS data (keeping products and assessors' selection patterns intact) to create dominance rates based on random attribute selections. Dominance rates exceed the significance line about 5% of the time (across attributes and time points), as expected. These curves differed from any real TDS curves that we have ever observed previously insofar as that they were unusually flattened. Next, we sampled again from real TDS data (keeping attributes and assessors' selection patterns intact) to create dominance rates based on attribute selections for random (hybrid) products. Derived TDS curves much more closely resemble curves from real TDS studies and show a lot of "significances", even though products are artificially created by the permutations. This is due to properties of the products that are inherent to the product category: e.g., hard will occur early during the evaluation of a chocolate (if at all), but not towards the end. From these simulations, we reach two conclusions. First, we recognize that the hypothesis that assessors select the dominant attribute completely at random is almost always false. Second, the reason that it is false is that, regardless of the product, there is a time-dependent category signature present, as well as competition between attributes, such that significances for one attribute depend on which other attributes are on the list. These properties limit the informative value of the conventional TDS significance lines that are widely used. As an alternative, we explore the possibility of context-dependent significance thresholds which are customized for and aligned with research objectives. These reference lines are recommended for improved interpretation of data arising from TDS studies.

Brush head composition, wear profile, and cleaning efficacy: an assessment of three electric brush heads using in vitro methods.

OBJECTIVE: The objective of this research was to evaluate a current store brand (SB) brush head for composition/physical characteristics, Wear Index (WI), and cleaning efficacy versus the previous SB brush head refill design (SB control) and the Oral-B Precision Clean brush head (positive control, PC). METHODS: This research consisted of three parts: 1) Analytical analysis using Fourier Transform Infrared (FT-IR) spectrometry to evaluate the chemical composition of the current SB brush head bristles relative to the SB control. In addition, physical parameters such as bristle count and diameter were determined. 2) Wear Index (WI) investigation to determine the Wear Index scores of in vitro-aged brush heads at four weeks (one month) and 13 weeks (three months) by a trained investigator. To "age" the brush heads, a robot system was used as a new alternative in vitro method to simulate aging by consumer use. 3) Robot testing to determine the cleaning performance of in vitro-aged brush heads, comparing one month-aged current SB brush heads with the SB control (one and three months-aged) and the PC brush heads (three months-aged) in a standardized fashion. RESULTS: 1) FT-IR analysis revealed that the chemical composition of the current and control SB refill brush heads is identical. In terms of physical parameters, the current SB brush head has 12% more bristles and a slightly oval brush head compared to the round brush head of the SB control. 2) Wear Index analysis showed there was no difference in the one month-aged current SB brush head versus the one month-aged SB control (1.67 vs. 1.50, p = 0.65) or versus the three months-aged PC brush head (1.67 vs. 1.50, p = 0.65). The one month-aged current SB brush head demonstrated statistically significantly less wear than the three months-aged SB control (1.67 vs. 2.67, p = 0.01). 3) Analysis of cleaning efficacy shows that the one month-aged current SB brush head had improved cleaning performance over the one month-aged SB control brush head (p < 0.05), despite no statistically significant difference in wear. Both the one month-aged current and control SB brush heads showed statistically significantly lower cleaning performance compared to the three months-aged PC brush heads (p < 0.01). CONCLUSION: While the current SB brush head showed improved cleaning over the SB control, it demonstrated significantly lower durability and cleaning in comparison to the PC brush head. Dental professionals should be aware of these differences, both in durability and in cleaning performance, when recommending brush heads to their patients.