Evidence map›Paper›PMID 32579118›Full record

ArticleJMIR mHealth and uHealth2020

A Scalable System for Passively Monitoring Oral Health Behaviors Using Electronic Toothbrushes in the Home Setting: Development and Feasibility Study.

Vivek Shetty, Douglas Morrison, Thomas Belin, Timothy Hnat, Santosh Kumar

Open access · goldAbstract read
In one paragraph

Article in JMIR mHealth and uHealth, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
1.3field-weighted citation impact, top 13% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

12 citing papers in PubMed, 18 citations in OpenAlex.

  1. Trial
  2. The Longitudinal Reciprocal Relationships of Tooth Brushing Behaviors Between Underserved Children and Their Caregivers.Annals of behavioral medicine : a publication of the Society of Behavioral Medicine · 2024
    Trial
  3. Trial
  4. Review
  5. Review
  6. Article
  7. Contextual Bandits with Budgeted Information Reveal.Proceedings of machine learning research · 2024
    Article
  8. The differential predictive utility of two caregiver-targeted self-efficacy measures to promote oral health of underserved children.Health psychology : official journal of the Division of Health Psychology, American Psychological Association · 2023
    Article
  9. Reward Design For An Online Reinforcement Learning Algorithm Supporting Oral Self-Care.Proceedings of the ... Innovative Applications of Artificial Intelligence Conference. Innovative Applications of Artificial Intelligence Conference · 2023
    Article
  10. Article
  11. Observational
  12. Applications of Nanomaterials in Dentistry: A Review.Journal of International Society of Preventive & Community Dentistry
    Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors at 2 institutions in 1 country.

Vivek ShettySection of Oral & Maxillofacial Surgery, Department of Biomedical Engineering, University of California, Los Angeles, Los Angeles, CA, United States.ORCID 0000-0002-3167-3318
Douglas MorrisonSection of Oral & Maxillofacial Surgery, Department of Biomedical Engineering, University of California, Los Angeles, Los Angeles, CA, United States.ORCID 0000-0002-7195-830X
Thomas BelinSection of Oral & Maxillofacial Surgery, Department of Biomedical Engineering, University of California, Los Angeles, Los Angeles, CA, United States.ORCID 0000-0003-4283-2676
Timothy HnatDepartment of Computer Science, The University of Memphis, Memphis, TN, United States.ORCID 0000-0001-8468-8196
Santosh KumarDepartment of Computer Science, The University of Memphis, Memphis, TN, United States.ORCID 0000-0002-9273-0291
University of California, Los Angeles · USUniversity of Memphis · US

Funding

Personalized Digital Behavior Change Interventions to Promote Oral HealthUH3DE028723 · NIDCR · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI VIVEK SHETTY · 2022 to 2026
$3.8M
ROBAS: A Multimodal Sensor System for Remote Assessment of Oral Health BehaviorsR01DE025244 · NIDCR · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI SHETTY, VIVEK · 2015 to 2019
$3.1M
NIDCR NIH HHS R01 DE025244NIDCR NIH HHS UH3 DE028723
6 · The paper itself

Abstract

backgroundDental disease (including dental caries and periodontal disease) is largely preventable and closely linked to inadequate oral health behaviors. Digital health technologies have great potential for unobtrusively monitoring brushing behaviors in home settings and promoting optimal oral self-care routines at scale.

objectiveThe aim of this study is to leverage the ubiquity of electronic toothbrushes and smartphones with the development of a Remote Oral Behaviors Assessment System (ROBAS) and evaluate its feasibility for passively tracking brushing behaviors in real-world settings.

methodsWe developed ROBAS by linking inertial sensors contained within consumer electronic toothbrushes to a scalable software platform comprised of a smartphone app linked to a cloud platform. First, the criterion validity of ROBAS for accurately capturing brushing details was established in a laboratory setting. Next, real-world performance and usability were evaluated in a stratified community sample of 32 participants who used ROBAS daily for 1 month and maintained a diary of their brushing episodes. Semistructured interviews at baseline and exit captured the user experience. We used regression models and Bland-Altman analyses to assess the criterion validity, functionality, accuracy, and consistency of ROBAS.

resultsUsing a stopwatch as the criterion reference, ROBAS showed a mean absolute percent error (MAPE) of 1.8%, an estimated bias of 0.64 seconds that was not statistically distinguishable from zero (95% CI -0.93 to 2.22 seconds, SE 0.79), and a connection failure rate of 6.7% (95% CI 0.8%-22.1%, SE 4.6%). In real-world testing, ROBAS showed close agreement with the daily diary recordings of brushing episodes; estimated average discrepancies between the diary and ROBAS were 0.13 sessions per day (95% CI 0.01-0.26, SE 0.06), 8.0 seconds per brushing session (95% CI 1.4-14.7, SE 3.3), and 30 seconds of brushing per day (95% CI 9.3-50.1, SE 10.0). Retrospective self-reports produced substantially higher estimates of brushing frequency and duration compared to ROBAS measurements. Participants reported ROBAS was easy to use and expressed an interest in receiving ROBAS-delivered feedback on their brushing behaviors. Most participants were bothered by the use of an additional study phone, and some reported connectivity-related issues.

conclusionsROBAS has a high criterion validity for measuring oral health behaviors. It can accurately and reliably monitor brushing patterns in home settings for extended periods. Unobtrusive data collection through ROBAS sets the stage for automated coaching and optimization of oral self-care practices at the individual and population level.

Indexed as

Health BehaviorAdolescentAdultDental CariesElectronicsFeasibility StudiesFemaleHumansMaleMiddle AgedRetrospective StudiesYoung Adultdigital toolshealth behaviorsoral self-carepassive measurementremote monitoring

Identifiers

PMID32579118
PMCPMC7380983
OpenAlexW3015616029

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.