Background: There are currently limited systematic reviews of mobile health interventions for middle-aged and elderly patients with prediabetes from trial studies. This review aimed to gather and analyze information from experimental studies investigating the efficacy of mobile health usability for outcomes among middle-aged and elderly patients with prediabetes. Methods: We conducted a literature search in five databases: Clinicaltrials.gov, the International Clinical Trials Registry Platform (ICTRP), PubMed, ProQuest, and EBSCO, with a date range of January 2007 to July 2022 written in English, following a registered protocol on PROSPERO (CRD42022354351). The quality and possibility of bias were assessed using the Jadad score. The data extraction and analysis were conducted in a methodical manner. Results: A total of 25 studies were included in the qualitative synthesis, with 19 studies using randomized trial designs and 6 studies with non-randomized designs. The study outcomes were the incidence of diabetes mellitus, anthropometric measures, laboratory examinations, measures of physical activity, and dietary behavior. During long-term follow-up, there was no significant difference between mobile health interventions and controls in reducing the incidence of type 2 diabetes. The findings of the studies for weight change, ≥3% and ≥5% weight loss, body mass index, and waist circumference changes were inconsistent. The efficacy of mobile health as an intervention for physical activity and dietary changes was lacking in conclusion. Most studies found that mobile health lacks sufficient evidence to change hbA1c. According to most of these studies, there was no significant difference in blood lipid level reduction. Conclusion: The use of mobile health was not sufficiently proven to be effective for middle-aged and elderly patients with prediabetes.

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The implementation of the Chronic Disease Management Program or PROLANIS has been adopted in Indonesia by National Social Security Implementation on Health Agency (BPJS-K) since 2015. The program focuses on hypertension (HT) and diabetes mellitus (DM). However, since the first time the program was implemented, there was no comprehensive evaluation of it. The aim of this study was to analyze health service quality among HT and DM patients based on five dimensions of quality in 25 community health services (CHSs) in the Sleman district, Yogyakarta, Indonesia. This is a cross-sectional study with a simple random sampling technique that included 230 respondents from 25 CHSs. The instrument was SERVQUAL that consisted of 35 items of a questionnaire. The data were analyzed by a gap analysis, Customer Satisfaction Index (CSI) and Importance Performance Analysis (IPA); meanwhile, a Man–Whitney test was proposed to determine differences in health services quality in the PROLANIS program. Based on the gap analysis, it was found that whole dimensions were below 0-point; the CSI analysis obtained 74.45 for HT and 75.15 for DM; and the IPA analysis found that the distribution of respondents’ answers in the questionnaire were in quadrants 1 and 2. The Man–Whitney analysis was used to get the assurance aspect correlated with health service quality in DM and HT patients (p = 0.001). Health service quality in the PROLANIS program was based on five dimensions of quality was low, unless assurance dimension. The government should improve health services quality in aspects of tangibility, responsiveness, empathy, and reliability to get satisfaction among HT and DM patients in the PROLANIS program.

UNSTRUCTURED Identifying and delivering interventions to patients with prediabetes was one strategy for dealing with the rising prevalence of T2DM. Risk assessment tools help in disease detection by allowing screening of the high risk group. Machine learning was also used to support in the detection and diagnosis of prediabetes. The purpose of this review is to assess the diagnostic test accuracy of various machine learning algorithms for calculating prediabetes risk. This protocol was written in accordance with the Preferred Reporting Items for Systematic Review and Meta-Analysis of Protocols (PRISMA-P) statement. The databases that will be used include PubMed, ProQuest, and EBSCO, with access limited to January 1999 and September 2022 in English only. Two reviewers will identify articles independently by reading the titles, abstracts, and full-text articles. Any disagreement will be resolved through consensus. To assess the quality and potential for bias, the Quality Assessment of Diagnostic Accuracy Studies (QUADAS) tool will be used. Data extraction and content analysis will be carried out in a systematic manner. A forest plot with 95% confidence intervals will be used to visualize quantitative data. The summary receiver operating characteristic curve will describe the diagnostic test outcome. The Review Manager 5.3 (Rev Man 5.3) software package will be used to analyze the data. Discussion: Using the proposed systematic review and meta-analysis, we will determine the diagnostic accuracy of various machine learning algorithms for estimating prediabetes risk. Machine learning classification is a form of artificial intelligence (AI) that allows computers to learn without being specifically programmed. It has been used to develop a scoring method for prediabetes identification and diagnosis. As far as we know, there is no systematic review and meta-analysis regarding machine learning utilization for prediabetes risk estimation. Therefore, we proposed this study to obtain the diagnostic accuracy of machine learning algorithms in estimating prediabetes risk. This protocol has been registered in the Prospective Registry of Systematic Review (PROSPERO) database. The registration number is CRD42021251242.