A Remote Oral Self-Care Behaviors Assessment System in Vulnerable Populations: Usability and Feasibility Study
Bibliographic record
Abstract
BACKGROUND: Preventative self-care can reduce dental disease that disproportionately burdens vulnerable populations. Personalized digital oral self-care behavioral interventions offer a promising solution. However, the success of these digital interventions depends on toothbrushing data collection e-platforms attuned to the needs and preferences of vulnerable communities. OBJECTIVE: The aim of this study is to assess the usability and feasibility of the Remote Oral Behaviors Assessment System (ROBAS), which has been adapted to address the unique requirements of socioeconomically disadvantaged minority individuals. METHODS: A cohort of 53 community-clinic participants, including 31 (58%) Latino and 22 (42%) Black individuals with no prior experience using electric toothbrushes, were recruited to use ROBAS, with planned assessments at baseline, 2 months, and 4 months. Beyond evaluating ROBAS's technical performance, extensive feedback was gathered to gauge users' experiences, viewpoints, and overall contentment. The System Usability Scale (SUS) served as a primary metric for assessing user satisfaction and acceptability. RESULTS: ROBAS exhibited largely reliable and consistent data-gathering capabilities. SUS scores (mean 75.6, SD 14.5) reflected participant contentment within a range of values for other commonly used digital devices and technologies. Among participants who answered questions about willingness to pay for ROBAS, 97% (30/31) indicated that they were willing to pay for ROBAS either as a one-time payment or as a subscription-based service. Additionally, 87.5% of participants expressed that they would endorse it to acquaintances. Most participants expressed no reservations about privacy; among those who expressed privacy concerns (n=20, 50%), the concerns included exposure of information (n=18, 45%), monitoring of brushing habits (n=12, 30%), and collection of information (n=14, 35%), although these concerns did not significantly correlate with specific participant traits. In qualitative terms, users valued ROBAS's ability to monitor brushing habits but called for refinements, especially in Wi-Fi and application connectivity. Recommendations for system improvements encompassed enhanced app functionality, individualized coaching, more comprehensive brushing data, and the addition of flossing activity tracking. CONCLUSIONS: The research highlights ROBAS's promise as a digital platform for unobtrusively tracking daily oral self-care activities in marginalized communities. The system proved to be both feasible, as evidenced by its stable and accurate data capture of brushing behaviors, and user-friendly, as reflected by strong SUS scores and positive user feedback. Influential factors for its uptake included ease of learning and operation, and the feedback provided.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".