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.
Background: Numerous studies have shown the increasing of prediabetes incidence from the time being. Some of the prediabetes screening methods that can be performed at primary health care were American Diabetes Association (ADA) scoring for prediabetes. However, there was no data that describes the validity and applicability of the ADA scoring on prediabetes patients in Indonesia. Objective: To discribe prediabetes screening and to find out the applicability of the ADA scoring method in Yogyakarta primary health care. Method: The diagnostic test by scoring system of the ADA questionnaire was compared with OGTT (oral glucose tolerance test) as the gold standard. The subjects were patients of primary health care in Yogyakarta who fulfill the inclusion and exclusion criteria. Result: The subjects were 279 respondents with 227 female (81.4%) and 52 male patients (18.6%). The mean age of the study subjects was 50.4 years (SD 12.81). The sensitivity and specificity of the scoring method of ADA was 61% and 71%. This could be influenced by the difference in BMI standard as one of the scoring items. Conclusion: Prediabetes prevalence was 11.1% in the study population. The sensitivity and specificity of the scoring method of ADA is 61% and 71%. The scoring method of ADA could not be used in primary health care.
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.
BACKGROUND Individuals in a prediabetic state are much more likely to develop Type 2 diabetes mellitus (T2DM)—4 times more likely than those with normal glucose tolerance. Lifestyle changes such as daily physical activity and healthy diets can decrease the risk of a prediabetic state. Mobile application intervention could be one of the solutions to improve self-management awareness and compliance with prediabetic state intervention. There are only a few studies in systematic reviews of mobile phone application interventions to prevent prediabetes yet. OBJECTIVE As a result, the goal of this study was to collect and summarize evidence from randomized controlled trials (RCTs) investigating the efficacy of mobile phone applications for intervention in prediabetic 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 Clinicaltrials.gov, the International Clinical Trials Registry Platform (ICTRP), PubMed, ProQuest, and EBSCO with date restrictions between January 2007 and December 2022 in the 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 Cochrane tool will be used to assess the risk of bias. If the participants, interventions, comparisons, and outcomes are sufficiently similar, a meta-analysis will be performed. 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 the RevMan software package. RESULTS 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. CONCLUSIONS Technologies (e.g., the internet, e-mail, and mobile phone applications) can provide practical, inexpensive, and scalable alternatives to traditional face-to-face procedures; this could also be applied to prediabetic patients in order to encourage lifestyle modification. CLINICALTRIAL This protocol has been registered in the Prospective Registry of Systematic Review (PROSPERO) database. The registration number is CRD42021243813
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