Aim: Behavior management approaches in treating children are generally viewed as techniques to enhance communication with patients to promote positive attitudes. Patients, particularly children, consider visiting a dental clinic to be a difficult and painful experience, so a dentist who treats children should have a variety of behavior management techniques. This study aimed to evaluate and compare the efficacy of Tell Show Do (TSD) and a mobile app in reducing dental anxiety among children patients.
Materials And Methods: A clinical trial study with 66 children aged 6-8 years was divided into three groups by the randomization method, which was performed in two steps: the first step used a random group generator template for excel to distribute the 66 children into three groups (each group 22), and the second step by excel function (RAND) to make the sequence for children. The sample was divided into three groups; each group has 22 children: Group I (TSD), Group II mobile app (Little Lovely Dentist), and Group III control. ClI fillings of the primary molars were provided to all participants. Anxiety levels were measured using physiological methods (heart rate and oxygen saturation by pulse oximeter) and psychological methods (Venham picture test) before treatment, during the education stage, and after treatment. The statistical tests were the paired test for intragroup comparison and one-way analysis of variance (ANOVA) for intercomparison.
Results: One-way ANOVA showed a significantly lower pulse rate in the mobile app group after treatment, significantly higher SPO saturation in the mobile app group than in the TSD and control groups, and significantly lower Venham scores in the mobile app group than in the control group.
Conclusion: The use of the Little Lovely Dentist mobile app decreased dental anxiety levels in children's first dental visit more than both the TSD technique and control groups when compared.
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http://dx.doi.org/10.4103/jispcd.JISPCD_112_23 | DOI Listing |
JMIR Aging
January 2025
Institute of Mathematics and Computer Sciences, University of São Paulo, São Carlos SP, Brazil.
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View Article and Find Full Text PDFJ Med Internet Res
January 2025
Department of Internal Medicine, Hospital Clinic, Institut d'Investigacio Biomèdica August Pi i Sunyer, Barcelona, Spain.
Background: Enhancing self-management in health care through digital tools is a promising strategy to empower patients with type 2 diabetes (T2D) to improve self-care.
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Methods: A 12-week, parallel, single-blind randomized controlled trial was conducted with 123 participants (62/123, 50%, female; mean age 58.
JMIR Pediatr Parent
January 2025
Department of Health and Physical Education, Mount Royal University, Calgary, AB, Canada.
Background: Early childhood is a critical period for shaping lifelong health behaviors, making early childhood education and care (ECEC) environments ideal for implementing nutrition and physical activity interventions. eHealth tools are increasingly utilized in ECEC settings due to their accessibility, scalability, and cost-effectiveness, demonstrating promise in enhancing educators' practices. Despite the potential effectiveness of these eHealth approaches, a comprehensive collection of available evidence on eHealth tools designed to assess or support best practices for nutrition or physical activity in ECECs is currently lacking.
View Article and Find Full Text PDFJMIR Form Res
January 2025
Institute of Social Medicine, Occupational Health and Public Health, Leipzig University, Leipzig, Germany.
Background: eHealth interventions constitute a promising approach to disease prevention, particularly because of their ability to facilitate lifestyle changes. Although a rather recent development, eHealth interventions might be able to promote brain health and reduce dementia risk in older adults.
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PLoS One
January 2025
IBM Research, Rio de Janeiro, Brazil.
For optimizing production yield while limiting negative environmental impact, sustainable agriculture benefits from real-time, on-the-spot chemical analysis of soil at low cost. Colorimetric paper sensors are ideal candidates, however, their automated readout and analysis in the field is needed. Using mobile technology for paper sensor readout could, in principle, enable the application of machine-learning models for transforming colorimetric data into threshold-based classes that represent chemical concentration.
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