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. Conclusions: The use of mobile health was not sufficiently proven to be effective for middle-aged and elderly patients with prediabetes.

As an endocrine disruptor chemical, pesticide exposure may affect the regulation of growth hormones such as insulin-like growth factor-1 (IGF-1). A few current studies to date have noted that long-term pesticide exposure disrupted IGF-1, a potential risk of stunting in children. This study aims to evaluate studies to date of the effect of pesticide exposure on stunting incidence. This systematic review and meta-analysis adhered to the PRISMA (Pre-ferred Reporting Items for Systematic Review and Meta-Analysis) guidelines. The PubMed and EBSCO databases were searched for relevant articles without publication restrictions. This review aimed to include reviews, randomized controlled trials (RCT), and cohort, case-control, and cross-sectional studies, which provide actual exposure types of pesticides with stunting measurement by height-age z score. A screening, extraction, and synthesis were conducted, leading to a consensus for reaching mutual agreement. The analysis was performed using Microsoft Excel 2017 for the screening and extraction, Revman version 5.4 software for the meta-analysis, and OpenMEE software for the meta-regression. Of the 13 studies subjected to the qualitative analysis, 6 were eligible for inclusion in the meta-analysis: 2 reviews, 2 RCTs, 2 cohorts, 2 case-control, and 5 cross-sectional studies. Exposure to organophosphate and pyrethroid pesticides was not associated with stunting (P=0.78; odds ratio [OR], 1.11; 95% confidence interval [CI], 0.65–1.88). Heterogeneity existed for 79% of the meta-analysis (P≤0.000; z=-5.37; 95% CI, -0.034 to -0.016), and the meta-regression identified age as the causative covariate. Pesticide exposure, regardless of type, is not associated with stunting in children.

The partner of this community service program is the Banguntapan II Health Center which is located in Kapanewon Banguntapan, Bantul, Regency. The problems are Health workers don’t understand the manufacture and management of medical records according to applicable regulations; Limited public understanding of medical emergencies; The limited number of puskesmas staff in relation to excellent service. Solutions of theses problems are providing counseling about the legal aspects of medical records and medical malpractice, medical emergencies from the medical aspect, as well as knowledge service at the puskesmas. The method used to carry out this series of activities is counseling related to legal aspects of medical records and medical malpractice, medical emergencies from the medical aspect, as well as knowledge of excellent service to health workers of Banguntapan II Health Center, Community Health, Center HR, and Community Leaders. The results show that before counseling was held regarding the manufacture and management of medical records and malpractice according to applicable regulations, regarding medical emergencies, and excellent service, there were still some service participants who did not understand the counseling material. After the counseling was held, all service participants understood the counseling material. Thus, the provision of counseling shows that the service participants have understood the material that has been given during the counseling, so that the participants' understanding is significantmethodology, results, and conclusions but do not need to make these words explicit.

Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet #Preprint #PeerReviewMe: Warning: This is a unreviewed preprint. Readers are warned that the document has not been peer-reviewed by expert/patient reviewers or an academic editor, may contain misleading claims, and is likely to undergo changes before final publication, if accepted, or may have been rejected/withdrawn. Readers with interest and expertise are encouraged to sign up as peer-reviewer, if the paper is within an open peer-review period. Please cite this preprint only for review purposes or for grant applications and CVs (if you are the author). Background: 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. Objective: 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. Methods: 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. Results: Using the proposed systematic review and meta-analysis, we will determine the diagnostic accuracy of various machine learning algorithms for estimating prediabetes risk. Conclusions: 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. Clinical Trial: This protocol has been registered in the Prospective Registry of Systematic Review (PROSPERO) database. The registration number is CRD42021251242.

Background: Individuals with prediabetic state are much more likely to develop Type-2 Diabetes Mellitus (TD2M) 4 times greater than those with normal glucose tolerance. Lifestyle changes such as daily physical activity and healthy diets can decrease the risk of prediabetic state . Mobile applications intervention could be one of the solutions to improve self-management awareness and compliance of prediabetic state intervention. There are no studies in systematic reviews of mobile phone applications intervention to prevent prediabetic state yet. Therefore, the objective of this study was to collect and summarize the evidence from randomized controlled trials (RCTs) exploring the effectiveness of mobile phone applications for intervention in prediabetic state patients.Methods: This protocol was prepared in accordance with the preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) statement. The database that will be used includes PubMed, ProQuest and EBSCO with date restriction between January 2007 and July 2019 in English language only. Identification of articles will be done independently by three reviewers through the title of the articles, reviewing the abstract, and then the full-text-article. Any disagreement will be resolved by consensus. The quality assessment and possible risk of bias will be evaluated using forms adapted from the Jadad score. Extraction and content analysis will be performed systematically. Quantitative data will be presented graphically via forest plot with 95% confidence intervals. Where possible we will explore the heterogeneity and continue to conduct meta-analysis using RevMan software package. Discussion: Changes in lifestyle, such as daily physical activity and a nutritious diet, can help to reduce the risk of prediabetes. Mobile phone applications, including health-related applications, is demonstrated to have a lot of promise in terms of providing tailored medical recommendations. Conclusion: The proposed systematic review and meta-analyses will allow us to obtain the evidence exploring the effectiveness of mobile phone applications for intervention in prediabetic state patients.Systematic review registration: This protocol has been registered in the Prospective Registry of Systematic Review (PROSPERO) database (CRD42021243813).

BACKGROUND 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. OBJECTIVE The purpose of this review is to assess the diagnostic test accuracy of various machine learning algorithms for calculating prediabetes risk. METHODS 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. RESULTS Using the proposed systematic review and meta-analysis, we will determine the diagnostic accuracy of various machine learning algorithms for estimating prediabetes risk. CONCLUSIONS 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. CLINICALTRIAL This protocol has been registered in the Prospective Registry of Systematic Review (PROSPERO) database. The registration number is CRD42021251242.