Teaching Others, Reflecting Self: Does Educating Patients Impact Students’ Own Plaque Control?

Abstract

Introduction

This retrospective exploratory study investigated whether instructing patients in oral hygiene influences dental students’ own hygiene effectiveness.

Methods

Intraoral scans from a full-semester cohort of first-year clinical dental students (n = 30) were analysed. At baseline (T0), students performed mutual intraoral scans (CS 3600, Carestream) of disclosed plaque during scanner training. During the semester, they guided their own patients through an Oral Care Programme (OCP) including 3D plaque visualisation and hands-on oral hygiene training. At follow-up (T1), students repeated mutual disclosed-plaque scans. Plaque coverage (P%) was quantified planimetrically on oral (i.e., lingual and palatal) and vestibular (i.e., buccal and labial) surfaces from T0 and T1 of all teeth. Plaque distribution, areas of loss, gain, and stability were assessed. Unsupervised clustering was performed using P%, gain, loss, stable areas, tooth type, and surface as features.

Results

Mean P% decreased from 14.6 ± 4.4 at T0 to 13.1 ± 3.7 at T1 (p = 0.025), but the magnitude of change was small; P% correlated strongly between time points (r = 0.690, p < 0.001), indicating largely stable individual plaque control profiles. Cluster analysis identified three patterns: (1) overall intermediate P%, predominantly on oral surfaces, (2) high overall P%, especially on vestibular molars and premolars, and (3) consistently low P%. Overall, plaque distribution and the spatiotemporal behaviour of plaque areas remained largely persistent.

Conclusion

Students showed only small reductions in plaque coverage over one semester, while interindividual patterns remained stable. Cluster-specific trajectories highlight heterogeneity in plaque control profiles and may help identify the tooth surfaces and regions where targeted feedback may be most effective. Future curricula should emphasise self-reflection, personalised feedback, and practical skills training.

Introduction

During their studies, dental students acquire knowledge in cariology and other oral diseases, including their prevention, and gain clinical experience in treating patients, particularly in preventive care settings. As dental plaque represents an ecological driver of caries development, plaque coverage and its spatial distribution constitute clinically relevant surrogate outcomes for evaluating the effectiveness of preventive education. Although preventive efforts primarily target patient outcomes, plaque control may be also relevant for dental professionals and dental students themselves, as a determinant of their own oral health and a prerequisite for credible health promotion and preventive competence [1, 2]. To date, oral health-related knowledge, attitudes, and behaviours in dental students have often been assessed using questionnaires; among these, the Hiroshima University Dental Behavioural Inventory (HU-DBI) has been widely used [3]. A recent systematic review [4] reported only moderate HU-DBI scores, and subscale analysis indicated that the increase in knowledge was comparatively stronger whereas attitudes and especially behaviours lagged behind. These findings indicate that, although knowledge generally improves during dental training, changes in attitudes and effective behaviours develop more slowly and remain areas for curricular focus.

In line with these global findings, a cross-sectional multicentre study in Germany [5], including a substantial cohort from Giessen, where the present investigation was performed, reported an overall mean HU-DBI score of 7.67 (out of 12), with knowledge (3.85) higher than attitudes (1.55) and behaviours (2.27). Across the five academic years, only modest increases were observed. Final-year students reached a mean of 7.85 compared with 7.42 in the first year, reflecting only small improvements. Another German study even reported no meaningful increase of HU-DBI at all [6].

How such HU-DBI scores translate into actual oral hygiene effectiveness of dental students has been less well studied. A few cross-sectional investigations suggest that plaque levels decrease from preclinical to final years [7–9], and one longitudinal study reported an improvement in oral hygiene between matriculation and graduation [10]. However, contradictory findings also exist: one study found that final-year students had higher plaque levels than first-year students despite improved HU-DBI scores [11].

Beyond knowledge acquisition, educational research suggests that adopting a teaching role may have formative effects on personal attitudes and practices. A recent meta-analysis [12] demonstrated that both preparing to teach and teaching others are associated with improved learning outcomes, primarily in terms of conceptual understanding and theoretical knowledge. However, these effects have almost exclusively been examined using cognitive or self-reported outcomes, and little is known about whether such role-based learning experiences translate into changes in practical, habitual health-related behaviours. In the context of dental education, this distinction is particularly relevant, as effective oral hygiene requires not only knowledge but also the consistent execution of motor skills in daily routines.

Against this background, the present study extends existing work by examining whether the combination of acquiring preventive knowledge and repeatedly instructing patients in oral hygiene was associated with changes in plaque coverage and, importantly, its spatial and temporal patterns. By quantifying plaque distribution and mapping gain, loss, and stable plaque areas and by identifying distinct plaque control profiles through cluster analysis, we aimed to characterise interindividual variability in oral hygiene effectiveness beyond mean changes alone.

Materials and Methods

Study Design

This retrospective, exploratory study analysed 3D intraoral scans taken at the beginning (T0) and end of the semester (T1) during mutual scanning sessions at the university. Between the two scans, students delivered an Oral Care Programme (OCP) with their own patients. The study was conducted in accordance with the principles of the Declaration of Helsinki and the guidelines of Good Clinical Practice. Ethical approval was obtained from the Ethics Committee of the Faculty of Medicine, Justus Liebig University Giessen (Ref. No. 62/22) prior to the study’s initiation.

Intraoral scans of a complete semester cohort (n = 30) were available. Habitual plaque distribution was assessed at two time points (T0 and T1). No specific inclusion or exclusion criteria were applied. As the dataset consisted of fully anonymised intraoral scans stored under anonymous IDs, no participant demographic information (e.g., age or sex) was available for analysis and therefore cannot be reported.

Oral Care Programme (OCP) and Intraoral Scanning Procedure

The OCP followed a predefined curriculum-based framework. Prior to patient sessions, all students completed a structured seminar covering the theoretical foundations of caries prevention, oral hygiene instruction, fluoride-based preventive measures, remotivation strategies, and patient communication. These topics were further reinforced in a semester-long accompanying lecture series.

Afterwards, all participating dental students took part in a joint training session to familiarise themselves with the use of the intraoral scanner (CS 3600, Carestream Dental, Stuttgart, Germany). This included hands-on instruction focusing on scanning protocols and software operation.

Following a standardised protocol, each student disclosed the habitual plaque of a fellow student using a plaque revelator (Mira-2-Ton^®, Hager & Werken GmbH & Co KG, Duisburg, Germany), applied twice with a saturated foam pellet. After each application, participants rinsed with water for 10 s. Cheek and lip retraction was performed using OptraGate (Ivoclar Vivadent, Schaan, Liechtenstein), and moisture control was ensured with parotid patches (DryTips^®, Microbrush, Algonquin, IL, USA), a saliva ejector, and an air blower. To minimize washout of the disclosing agent, the mandibular arch was scanned first, followed by the maxillary arch. Scan data were stored under anonymous IDs. This session both build technical competence and provided each participant’s baseline oral hygiene status (T0). To remove the disclosing agent, the students performed mutual professional tooth cleaning.

Over the semester, the students conducted the OCP from May to July, with approximately ten sessions per student. Patients were invited for a second visit within this period so that the same student could assess changes in oral hygiene and provide follow-up remotivation. Each session lasted approximately 1.5 h per patient. The students used the 3D intraoral scans to visualise disclosed plaque areas and to provide targeted, hands-on training aimed at improving cleaning in neglected areas. In addition, depending on each patient’s needs, the students conveyed preventive information they had previously learned, including key aspects of caries prevention, oral hygiene, and fluoride-based measures. To structure the consultation, students could use a written script as a guide. The topics covered were documented in the patient record to avoid repeating the same information across visits and to ensure that each session was built on the previous one with an adjusted focus. Patient encounters were chairside supervised by a resident dentist, who ensured that the core components of each session were covered and provided ongoing, formative feedback on patient counselling, motivation, and clinical interaction. Further details on the scope of the OCP can be found in the flow chart in Figure 1.

Fig. 1.

Flow chart.

At the end of the semester, students repeated the mutual intraoral scanning procedure under the same standardised conditions as at T0. This follow-up session aimed to reinforce scanning skills and promote reflection on the scanning process. The resulting scans represented each student’s habitual oral hygiene status at T1. The design of the study is shown in more detail in Figure 1.

Plaque Quantification

The intraoral scans at T0 and T1 were aligned using MeshLab software (version 2020.09) [13]. For quantitative plaque assessment, standardized screenshots of oral (i.e., lingual and palatal) and vestibular (i.e., buccal and labial) surfaces of all teeth were captured and further processed using GIMP (version 2.10.30). The tooth surfaces of interest were cropped, resulting in 3,314 images, and placed on a black background. Plaque quantification was performed using custom software developed in the Julia programming language, utilising the “ImageMagick” and “Images” packages. The JPG images were converted into RGB format, and the red, green, and blue values of each pixel were extracted and transformed into a three-component vector system for separate storage. Two filters were applied to calculate plaque coverage (P%): the first excluded all black background pixels (RGB = 0,0,0), while the second applied a threshold to distinguish plaque-covered areas from plaque-free tooth surfaces. Batch processing enabled the automatic calculation and export of P% values for each image. The approach has been previously described in detail [14, 15].

To assess the reliability of plaque quantification, the intraoral scans at T0 and T1 of a randomly chosen student were reanalysed including all processing steps (alignment, screenshot capture, segmentation of tooth surfaces, and automated planimetric analysis). This resulted in 112 individual P% values ranging from 0.45% to 26.2%. The intraclass correlation coefficient (model: two-way mixed, type: absolute agreement; 95% CI) was 0.999 [0.998; 0.999] for T0 and 0.994 [0.989; 0.996] for T1 (both p < 0.001).

Spatiotemporal Plaque Patterns

Spatiotemporal plaque pattern analyses were conducted using Python 3.10 in a Spyder IDE 5.4.5 environment utilizing OpenCV (v4.11.0.86), NumPy (v2.2.5), Matplotlib (v3.10.1), pandas (v2.2.3), and seaborn (v0.12.2). A binary classification mask was applied to the image outputs generated after the plaque quantification step. This mask was constructed via colour thresholding to isolate plaque-positive regions. Blue pixels (blue >100, red <80, green <80) were classified as clean tooth areas and black pixels (all channels <50) as background. All remaining pixels were classified as plaque-positive. For each T0-T1 image pair, three logical masks were computed per comparison, as illustrated in Figure 2. Plaque gain: pixels newly classified as plaque at T1 but absent at T0; plaque loss: pixels classified as plaque at T0 but absent at T1; and stable plaque areas: pixels consistently classified as plaque across both time points, indicating plaque persistence zones. Areas of plaque gain, plaque loss, and stable plaque were calculated as a percentage of pixels relative to the total number of plaque pixels.

Fig. 2.

Spatiotemporal changes in P% illustrated on the vestibular surface of a second upper molar. a P% area at baseline (green). b P% at the end of the semester (green). c Overlay of both time points: the yellow area indicates the area of stable plaque, plaque gain is indicated in red, and plaque loss is indicated in green.

Statistical Analysis

Statistical analyses were performed using IBM SPSS Statistics (v27; IBM Deutschland GmbH, Böblingen, Germany) and Python 3.10 (scikit-learn v1.6.1, SciPy v1.15.2). Normality of plaque coverage (P%) was confirmed (Kolmogorov-Smirnov test), permitting parametric analyses. P% is reported as mean ± SD. Changes from T0 to T1 were tested with paired t tests and differences between surfaces with independent t tests and associations between T0 and T1 with Pearson correlations. A significance level of 0.05 was applied, with Bonferroni correction for multiple testing.

For cluster analysis, feature vectors included P% at T0 and T1, as well as plaque gain, loss, and stable plaque areas, calculated separately by tooth type (molars, premolars, anterior teeth) and surface (oral, vestibular). Features were z-standardised, and k-means clustering (k = 3, 10 initialisations, fixed seed) was applied. Results were visualised using principal component analysis and cluster-wise heatmaps. Intercluster differences in regional values were tested with Kruskal-Wallis tests.

A post hoc power analysis indicated that, with an SD of paired differences of 3.28 percentage points, the minimal detectable mean difference at 80% power (α = 0.05) was 1.68 percentage points. Thus, even small changes in P% could be reliably detected, and a 5-percentage-point change would have been captured with essentially 100% power.

Results

Overall Plaque Coverage (P%)

P% ranged from 6.5 to 25.4 at T0 and from 5.2 to 19.4 at T1. Mean P% decreased slightly from 14.6 ± 4.4 to 13.1 ± 3.7 (p = 0.025), with a strong correlation between time points (r = 0.690, p < 0.001). A significant reduction was observed only in the highest tertile (T0: 19.4 ± 2.9 vs. T1: 14.9 ± 3.1, p < 0.001), while middle and lower tertiles showed no change.

Distribution of Plaque Coverage (P%) across the Dentition

Plaque distribution varied across the dentition, showing similar patterns at T0 and T1 (Fig. 3). On vestibular surfaces of the maxilla, the highest P% was observed on the second molars, with a gradual decline towards the anterior teeth. A comparable, though less pronounced, gradient was found in the mandible. In contrast, on oral surfaces, P% remained relatively high and exhibited a more uniform distribution.

Fig. 3.

Distribution of P% across the dentition (mean ± SD) at T0 and T1.

Gain, Loss, and Stable Plaque Areas

With regard to changes in P% from T0 to T1, distinct patterns emerged across different tooth regions and surfaces. The highest stable plaque values were found on mandibular oral surfaces, most notably in premolars (16.8 ± 10.8) and molars (16.0 ± 9.8), whereas maxillary vestibular molars also showed comparatively high stable plaque values (13.5 ± 11.9). Overall, plaque gain and plaque loss areas were relatively balanced with similar magnitude overall. The most pronounced loss was observed on maxillary vestibular molars (9.3 ± 9.5) and mandibular oral premolars (9.7 ± 8.8).

Cluster Analysis

Three distinct clusters were identified (Fig. 4). PC1 was mainly driven by P% and stable plaque areas on vestibular molars and premolars, while PC2 reflected P% and stable plaque areas on oral surfaces, particularly anterior teeth. Significant intercluster differences in plaque levels and dynamics (gain, loss, stability) were found for nearly all tooth-surface combinations, with the highest Kruskal-H values for anterior teeth (all p < 0.001); only molars and premolars showed some nonsignificant results. Heatmaps (Fig. 5, 6) illustrate cluster profiles: cluster 1 (n = 12) showed intermediate P%, mainly on oral surfaces, with balanced gain and loss but persistent oral plaque. Cluster 2 (n = 4) had the highest baseline P%, especially on molars, and despite some reduction, retained the greatest plaque amounts alongside large stable areas. Cluster 3 (n = 14) consistently showed the lowest P%, with minimal gain, moderate loss, and few stable areas.

Fig. 4.

Principal component analysis representing three clusters of students based on plaque-related variables.

Fig. 5.

Heatmaps showing P% (mean ± SD) across tooth types and surfaces at baseline (T0) and end of semester (T1) for each cluster.

Fig. 6.

Heatmaps of plaque gain and loss as well as stable plaque areas (P%; mean ± SD) across clusters, tooth types, and surfaces.

Discussion

This retrospective study evaluates an indirect, role-based educational intervention, in which students repeatedly instructed patients in oral hygiene, and examines whether this experience translated into changes in students’ own oral hygiene effectiveness. Central to this approach was the use of 3D intraoral scans of disclosed plaque, providing direct, high-resolution visual feedback and allowing plaque patterns to be explored dynamically on screen. Combined with hands-on training, this extended conventional oral hygiene instruction beyond static or purely verbal explanation. Students first saw their own plaque in three dimensions during peer-to-peer training sessions aimed at familiarising them with both the intraoral scanner and the software. They then used the intraoral scans in the OCP to instruct their patients with hands-on training. This dual exposure, confronting personal shortcomings while repeatedly addressing those of others, was expected to foster self-awareness and motivate behavioural change in terms of effective plaque control.

However, the present findings indicate that, despite intensive engagement with preventive content and repeated practical implementation on patients, no relevant reduction in plaque levels was achieved. At the beginning of the semester, oral hygiene levels varied considerably, with overall P% ranging from 6 to over 25. These findings align with those of a previous study [14], in which the participating student participants were of similar age but from non-dental academic backgrounds. This is particularly surprising, as one would expect that dental students who are aware that their oral hygiene levels will be visibly displayed on intraoral scans for their peers and supervisors would achieve better results. The average P% reduction at the end of the semester was a mere 1.4. However, relatively low overall plaque values mask that plaque was unevenly distributed across the dentition, with consistently higher accumulation in typical areas such as the vestibular surfaces of upper molars and the oral surfaces of lower teeth. This pattern was stable over time and mirrors plaque distribution patterns reported in a non-dental student population [14] and in the literature [16, 17]. In addition, spatial and temporal analyses revealed both gain and loss of plaque, which may represent true changes or simple redistribution, resulting in little overall difference in plaque amounts. Most striking, however, was the persistence of identical plaque-positive areas across both time points, particularly in these high-accumulation regions, despite professional tooth cleaning at semester start.

From the perspective of caries control, this observed persistence of plaque patterns is noteworthy. Although the study population consisted of dental students with low overall caries risk, stable plaque accumulation at specific tooth surfaces represents a risk constellation for the initiation of early caries lesions. Importantly, even when current disease experience is limited, persistent plaque areas may indicate enduring self-care routines that, under changing conditions over time (e.g., altered diet, reduced professional monitoring, or life stage-related constraints), could contribute to unfavourable caries risk trajectories.

To better capture interindividual variability underlying these patterns, the analysis was extended to a cluster-based approach, which revealed three distinct plaque control profiles. Cluster 1 showed high plaque levels and large stable areas on oral surfaces but relatively clean vestibular sites, suggesting technical rather than motivational deficits and potential benefit from hands-on training. Cluster 2 exhibited the highest overall plaque amounts, including anterior teeth, with extensive stable areas and also the greatest loss, indicating partial but ineffective adaptation and a need for more intensive support. Cluster 3 demonstrated generally good oral hygiene with only localized weaknesses (oral molars/premolars, vestibular molars) and the fewest stable plaque areas, suggesting that small additional efforts could yield excellent results. This indicates that individualised oral hygiene guidance is also necessary for dental students. Importantly, the objective would not be to achieve complete plaque-free status. As there is no clear cutoff between “healthy” and “unhealthy” plaque amounts, the observed overall plaque level may represent a realistic benchmark under routine self-care conditions. Rather than focusing on small changes in overall mean plaque values, a more meaningful preventive approach would be to identify and reduce persistent plaque hotspots, as reflected by the cluster patterns. For example, cluster 1 might benefit from targeted instruction on how to systematically reach and clean oral surfaces, whereas cluster 2 could indicate the need for more focused guidance on toothbrush positioning and movement along the vestibular surfaces of molars and premolars. Individualised refinement of brushing techniques, supported by structured, site-specific feedback, might therefore help optimise plaque control and support long-term oral health.

With respect to health behaviour change, the present results align with broader evidence indicating that knowledge acquisition and even repeated instructional practice do not necessarily translate into sustained changes in habitual self-care. The plaque distribution patterns suggest that students may exhibit brushing patterns similar to those consistently documented in video observation studies [18–21], characterised by a marked preference for anterior vestibular surfaces and the neglect of oral surfaces. Taken together, these patterns point to the deeply ingrained nature of toothbrushing patterns. Furthermore, there is evidence that oral hygiene behaviours have features of automaticity [22]. Thus, oral hygiene routines, embedded in daily life in a highly automatized way, may therefore be resistant to change without explicit self-focused feedback, structured reflection, and targeted skills training. From this perspective, professional practice experiences may support identity development and preventive competence, but additional educational strategies appear necessary to translate these experiences into improvements in students’ own oral hygiene effectiveness.

The present observations are consistent with survey findings showing that, although knowledge about maintaining oral health increases significantly during dental training, corresponding changes in attitudes and behaviour are much smaller [4, 5]. However, there are also other factors that may have influenced our results. At the beginning of clinical training, students are confronted with multiple new challenges, including treating real patients, operating intraoral scanning systems, and establishing clinical routines. These demands can be considerable sources of stress, which has been shown to negatively affect personal oral hygiene and contribute to increased plaque accumulation [23]. Another factor may be students’ subjective perception of risk. None of the participants had acute treatment needs or a significant history of restorative work. As a result, they may have assumed their oral hygiene was already adequate and did not perceive a strong need for improvement during the initial phase of clinical training, an example of the so-called “false sense of security.” Similar discrepancies between self-perceived and actual oral health have been reported among dental students in different countries, where favourable self-assessments coexisted with measurable levels of plaque and gingivitis [24, 25]. This pattern suggests that low restorative experience or absence of symptoms may reduce perceived vulnerability, thereby diminishing motivation for further improvement.

From a professional education perspective, repeated engagement in health promotion activities, such as oral hygiene instruction, is considered a key mechanism for consolidating meaningful learning and shaping professional identity. Acting as a health educator requires students to translate abstract knowledge into practice, communicate preventive messages, and model desirable behaviours. This process has been described as central to professional identity formation in health professions education, potentially influencing attitudes toward self-care and responsibility for prevention [26]. However, our findings suggest that assuming this professional role alone may be insufficient to modify entrenched personal routines, as reflected by the persistence of individual plaque control patterns. These considerations highlight the need to integrate behavioural psychology and metacognitive elements more systematically into dental education. Emphasis should be placed on fostering self-monitoring and reflective practice regarding one’s own oral hygiene. To support sustainable behavioural change, personalised feedback and structured training in effective brushing techniques should be implemented on a regular basis, not only for patients but also for students themselves.

One limitation of this study is the relatively small sample size, which, on the other hand, enabled an in-depth analysis of plaque distribution patterns and their changes. In addition, no information was available on the actual toothbrushing performance of the students, including the type of toothbrushes or interdental cleaning methods used. This investigation should also be regarded as exploratory. The absence of a control group reflects the specific context and retrospective design, which focused on identifying patterns of plaque distribution and plaque pattern clusters rather than testing predefined interventions. Future research should broaden the scope by including students at different stages of their studies and combining plaque assessment with complementary methods such as questionnaires or video observations. A key strength lies in the detailed plaque assessment, the longitudinal design, and, in particular, the retrospective use of intraoral scans that had originally been obtained for purposes other than oral hygiene assessment.

In summary, this exploratory study demonstrates that dental students, through actively delivering an OCP that combined patient instruction in oral hygiene with structured teaching in caries prevention and supported by innovative 3D visualisation tools, did not achieve meaningful improvements in their own plaque control. Despite their advanced training, students showed persistent plaque distribution patterns, with considerable variability across clusters. These findings highlight the limits of knowledge-based and patient-centred instruction for changing personal oral hygiene efficacy. Future curricula should therefore place greater emphasis on behavioural and metacognitive components, fostering self-reflection, personalised feedback, and practical skills training to support lasting improvements in both students and their future patients.

Declaration of Generative AI

The authors declare that they used ChatGPT (OpenAI) to assist with the drafting of Python scripts used in the analyses. Scripts were run in a local environment, all content was critically reviewed, verified, and is the sole responsibility of the authors.

Statement of Ethics

This study was performed in accordance with the Declaration of Helsinki. This human study was approved by Ethics Committee of the Medical Faculty, Justus Liebig University Giessen, Giessen, Germany (Approval No. 62/22). The Ethics Committee of the Medical Faculty, Justus Liebig University Giessen, Giessen, Germany waived the requirement for written informed consent, as this retrospective study was based solely on anonymized data and did not involve any intervention or additional data collection. Since no prospective enrolment or interventional procedures were performed, registration in a clinical trials registry was not required.

Conflict of Interest Statement

All authors declare that they have no conflict of interest.

Funding Sources

This research received no external funding.

Author Contributions

Conceptualization, methodology, and writing – original draft preparation: Carolina Ganss and Katja Jung. Investigation, data collection, and writing – review and editing: Lukas Dübgen. Data curation and statistics: Carolina Ganss.

Funding Statement

This research received no external funding.

Data Availability Statement

All data generated or analysed during this study are included in this article. Further enquiries can be directed to the corresponding author.