Background: Obesity, hypertension, and type 2 diabetes mellitus (T2DM) are worldwide epidemics that inflict burdens on both public health and health care costs. Self-management plays an important role in the proper management of these 3 chronic diseases, and in this context, mobile health (mHealth) can be a cost-effective self-management tool. Objective: The aim of this pilot study is to evaluate the effects of an integrative mHealth approach for obesity, hypertension, and T2DM on body fat, blood pressure, and blood glucose levels and demonstrate the clinical outcomes. The participants were patients aged 40 to 70 years who were treated for T2DM (hemoglobin A1c [HbA1c] above 6.0%) without insulin or hypertension and obesity, controlled with pharmacotherapy. Methods: This pilot study was performed using a controlled, randomized, 3-month, 2-period crossover design. A total of 37 participants were recruited from 2 university hospitals in South Korea. Integrative mHealth comprised 4 parts: self-measuring home devices for monitoring blood glucose and blood pressure; 2 smartphone apps, where one gathered lifestyle data, giving them feedback with health information, and the other provided drug information and reminders of the medication schedule; unmanned kiosks for official measurement of blood pressure and body composition; and web-based access to participants’ health information. Results: Data from the 32 participants were analyzed. Their mean HbA1c level was 7.5% (SD 0.8, ranging from 6.1% to 9.4%). Approximately 38% (12/32) of the participants had hypertension. BMIs of all participants except 1 were >23 kg/m2. The input rates of food intake and exercise to the smartphone app were very low (24.9% and 5.3%, respectively). On the contrary, the input rate of medicine intake was high (84.0%). Moreover, there was no significant difference in the input rate of taking medicine irrespective of whether the mHealth period was before or after the conventional treatment period (80.3% and 87.3%, respectively; P=.06). Among the 3 input functions of food intake, exercise, and medicine intake in smartphone apps, the input of medicine intake was a more helpful, easier to use, and better-designed function than the others. There were no significant differences in changes in body weight (−0.519 kg vs 0 kg), BMI (−0.133 kg/m2 vs −0.167 kg/m2), body composition (body fat −0.255% vs 0.172%), blood pressure (systolic −0.226 mm Hg vs −2.839 mm Hg), and HbA1c (−0.269% vs –0.009%) between the integrative mHealth and conventional treatment groups. However, in proportion to the elevation in the input rate of taking medicine, body fat mass (P=.04) and HbA1c (P=.03) were lower in the integrative mHealth group. Conclusions: Although smartphone apps can influence body fat and blood glucose levels, they have failed to show clinical improvement. A higher input rate of taking medicine was related to significantly lower body fat mass and HbA1c levels.

So-Jung Mun, Su-Kyung Park, Ji-Eun Heo, Da-Yee Jeung, Won-Gyoun Chung, Ma-I Choi, and Hyun-Sun Jeon. J Dent Hyg Sci 2019;19:245-53. https://doi.org/10.17135/jdhs.2019.19.4.245

Introduction: Lactic acid bacteria (LAB) have emerged as promising adjunctive agents for oral health management due to their antimicrobial and immunomodulatory properties. With the increasing incorporation of probiotics into oral care products, it is critical to evaluate their clinical efficacy. This systematic review and meta-analysis aimed to assess the effectiveness of LAB-based toothpaste in improving oral health outcomes.Methods: Following PRISMA guidelines, five databases (MEDLINE, EMBASE, Scopus, Web of Science, and CENTRAL) were searched through February 2025. Randomized controlled trials (RCTs) evaluating toothpastes containing probiotic, prebiotic, synbiotic, or postbiotic agents were included. Primary outcomes included plaque index (PI), gingival index (GI), bleeding on probing (BOP), probing pocket depth (PPD), and clinical attachment level (CAL). Secondary outcomes assessed oral microbiota changes. Risk of bias was evaluated using the RoB 2 tool.Results: Twelve RCTs were included, with four studies suitable for meta-analysis. Meta-analysis demonstrated significant plaque reduction at 3 months [Mean Difference (MD) = −0.64; 95% confidence interval (CI): −1.14 to −0.15; p = 0.01] and BOP improvement (MD = −1.49; 95% CI: −2.42 to −0.56; p = 0.002). Longer interventions (≥6 months) in periodontitis patients revealed significant PPD reduction (MD = −1.32; 95% CI: −1.81 to −0.84; p < 0.00001) and CAL improvement (MD = −0.79; 95% CI: −1.25 to −0.33; p = 0.0007). Streptococcus mutans levels were significantly reduced across multiple studies.Conclusions: LAB-based toothpaste demonstrates beneficial effects on plaque control and gingival inflammation. However, substantial heterogeneity (I² > 75% for most outcomes) limits effect estimate precision. Lactobacillus paracasei strains showed consistent benefits, while sustained use (≥6 months) appears necessary for periodontal improvements.Systematic Review Registration: https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD420250650340, PROSPERO CRD420250650340.

Objectives: This study aimed to identify the core competencies required of to inform the development of a social dental hygiene curriculum. Methods: This study analyzed the job roles and essential competencies of 17 nonclinical dental hygienists from four sectors (community health, public institutions, policy advocacy, and entrepreneurship), using qualitative interviews. Results: Nonclinical dental hygienists performed tasks involving health program planning, policy analysis, interdisciplinary collaboration, health content development, and administrative management. Essential competencies include planning, problem-solving, data analysis, communication, and leadership skills. The participants emphasized the need for dental hygiene education to shift from clinical techniques to a competency-based curriculum that reflects expanded professional roles. Conclusions: Practical skills, such as project management, policy interpretation, interdisciplinary teamwork, and content creation, must be integrated into the social dental hygiene curriculum. This educational transition would enable dental hygienists to respond effectivelyto evolving societal needs and fulfill their expanded professional roles.

Eun-Mi Choi, Su-Kyung Park. J Dent Hyg Sci 2025;25:99-116. https://doi.org/10.17135/jdhs.2025.25.2.99

BACKGROUND Obesity, hypertension, and type 2 diabetes mellitus (T2DM) are worldwide epidemics that inflict burdens on both public health and health care costs. Self-management plays an important role in the proper management of these 3 chronic diseases, and in this context, mobile health (mHealth) can be a cost-effective self-management tool. OBJECTIVE The aim of this pilot study is to evaluate the effects of an integrative mHealth approach for obesity, hypertension, and T2DM on body fat, blood pressure, and blood glucose levels and demonstrate the clinical outcomes. The participants were patients aged 40 to 70 years who were treated for T2DM (hemoglobin A1c [HbA1c] above 6.0%) without insulin or hypertension and obesity, controlled with pharmacotherapy. METHODS This pilot study was performed using a controlled, randomized, 3-month, 2-period crossover design. A total of 37 participants were recruited from 2 university hospitals in South Korea. Integrative mHealth comprised 4 parts: self-measuring home devices for monitoring blood glucose and blood pressure; 2 smartphone apps, where one gathered lifestyle data, giving them feedback with health information, and the other provided drug information and reminders of the medication schedule; unmanned kiosks for official measurement of blood pressure and body composition; and web-based access to participants’ health information. RESULTS Data from the 32 participants were analyzed. Their mean HbA1c level was 7.5% (SD 0.8, ranging from 6.1% to 9.4%). Approximately 38% (12/32) of the participants had hypertension. BMIs of all participants except 1 were >23 kg/m2. The input rates of food intake and exercise to the smartphone app were very low (24.9% and 5.3%, respectively). On the contrary, the input rate of medicine intake was high (84.0%). Moreover, there was no significant difference in the input rate of taking medicine irrespective of whether the mHealth period was before or after the conventional treatment period (80.3% and 87.3%, respectively; P=.06). Among the 3 input functions of food intake, exercise, and medicine intake in smartphone apps, the input of medicine intake was a more helpful, easier to use, and better-designed function than the others. There were no significant differences in changes in body weight (−0.519 kg vs 0 kg), BMI (−0.133 kg/m2 vs −0.167 kg/m2), body composition (body fat −0.255% vs 0.172%), blood pressure (systolic −0.226 mm Hg vs −2.839 mm Hg), and HbA1c (−0.269% vs –0.009%) between the integrative mHealth and conventional treatment groups. However, in proportion to the elevation in the input rate of taking medicine, body fat mass (P=.04) and HbA1c (P=.03) were lower in the integrative mHealth group. CONCLUSIONS Although smartphone apps can influence body fat and blood glucose levels, they have failed to show clinical improvement. A higher input rate of taking medicine was related to significantly lower body fat mass and HbA1c levels.