Background Chronic disease represents a large and growing burden to the health care system worldwide. One method of managing this burden is the use of app-based interventions; however attrition, defined as lack of patient use of the intervention, is an issue for these interventions. While many apps have been developed, there is some evidence that they have significant issues with sustained use, with up to 98% of people only using the app for a short time before dropping out and/or dropping use down to the point where the app is no longer effective at helping to manage disease. Objective Our objectives are to systematically appraise and perform a meta-analysis on dropout rates in apps for chronic disease and to qualitatively synthesize possible reasons for these dropout rates that could be addressed in future interventions. Methods MEDLINE (Medical Literature Analysis and Retrieval System Online), PubMed, Cochrane CENTRAL (Central Register of Controlled Trials), and Embase were searched from 2003 to the present to look at mobile health (mHealth) and attrition or dropout. Studies, either randomized controlled trials (RCTs) or observational trials, looking at chronic disease with measures of dropout were included. Meta-analysis of attrition rates was conducted in Stata, version 15.1 (StataCorp LLC). Included studies were also qualitatively synthesized to examine reasons for dropout and avenues for future research. Results Of 833 studies identified in the literature search, 17 were included in the review and meta-analysis. Out of 17 studies, 9 (53%) were RCTs and 8 (47%) were observational trials, with both types covering a range of chronic diseases. The pooled dropout rate was 43% (95% CI 29-57), with observational studies having a higher dropout rate (49%, 95% CI 27-70) than RCTs in more controlled scenarios, which only had a 40% dropout rate (95% CI 16-63). The studies were extremely varied, which is represented statistically in the high degree of heterogeneity (I2>99%). Qualitative synthesis revealed a range of reasons relating to attrition from app-based interventions, including social, demographic, and behavioral factors that could be addressed. Conclusions Dropout rates in mHealth interventions are high, but possible areas to minimize attrition exist. Reducing dropout rates will make these apps more effective for disease management in the long term. Trial Registration International Prospective Register of Systematic Reviews (PROSPERO) CRD42019128737; https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42019128737

ObjectiveWe assessed the efficacy of routine glycated hemoglobin (HbA1c) testing to detect undiagnosed diabetes and prediabetes in an urban Australian public hospital emergency department (ED) located in an area of high diabetes prevalence.MethodsOver 6 weeks, all patients undergoing blood sampling in the ED had their random blood glucose measured. If ≥5.5 mmol/L (99 mg/dL), HbA1c was measured on the same sample. HbA1c levels ≥6.5% (48 mmol/mol) and 5.7–6.4% (39–46 mmol/mol) were diagnostic of diabetes and prediabetes, respectively. Hospital records were reviewed to identify patients with previously diagnosed diabetes.ResultsAmong 4580 presentations, 2652 had blood sampled of which 1267 samples had HbA1c measured. Of these, 487 (38.4%) had diabetes (either HbA1c≥6.5% or a prior diagnosis), and a further 347 (27.4%) had prediabetes. Among those with diabetes, 32.2% were previously undiagnosed.ConclusionsRoutine HbA1c testing in the ED identifies a large number of people with undiagnosed diabetes and prediabetes, and provides an opportunity to improve their care.

Purpose Type 2 diabetes mellitus has become a major concern of Australian healthcare providers. From rates of barely more than 1 percent in the mid-90s, diabetes is now the leading cause of morbidity in the country. To combat the growing diabetes epidemic, Western Sydney Local Health District created the Western Sydney Diabetes (WSD) initiative. One of the key components of the WSD initiative since 2014 has been joint specialist case conferencing (JSCC). The purpose of this paper is to evaluate the JSCC service including both individual- and practice-based changes. Design/methodology/approach The authors evaluated the JSCC program by conducting an analysis of patient-level data in addition to a discrete practice-level study. The study aim was to examine both the effect on individual patients and the practice, as well as acceptability of the program for both doctors and their patients. The evaluation included data collection and analysis of primary patient outcomes, as well as a survey of GPs and patients. Patient data on primary outcomes were obtained by accessing and downloading them through GP practice management software by GP practice staff. Findings The authors found significant improvements at both the patient levels, with reductions in BMI, HbA1c and blood pressure sustained at three years, and at the practice level with improvements in markers of patient management. The authors also found high acceptability of the program from both patients and GPs. Originality/value This paper provides good evidence for the use of a JSCC program to improve diabetes management in primary care through capacity building with GPs.

BACKGROUND Chronic disease represents a large and growing burden to the healthcare system worldwide. One method of managing this burden is app-based interventions, however attrition, defined as lack of patient use of the intervention, is an issue for these interventions. While many apps have been developed, there is some evidence that they have significant issues with sustained use, with up to 98% of people only using the application for a short time before dropping out and/or dropping use down to the point where the app is no longer effective at helping to manage disease. OBJECTIVE To systematically review and meta-analyze the rate and causes of dropout in mHealth interventions for diabetes and other chronic health issues. METHODS Medline, Pubmed, Cochrane CENTRAL, and Embase were searched from 2003 to the present, looking at mHealth and attrition or dropout. Studies – either randomized or observational - looking at chronic disease with measures of dropout were included. Meta-analysis of attrition rates was conducted in Stata 15.1. Included studies were also qualitatively synthesized to examine reasons for dropouts and avenues for future research. RESULTS Of 833 studies identified in the literature search, 17 were included in the review and meta-analysis. Meta-analysis results are presented below. Chronic disease management apps had an overall dropout rate of 47%. Apps specifically targeting metabolic disease had a lower rate of 32%. The studies were extremely varied, which is represented statistically in the high degree of heterogeneity (I2>99%). Qualitative synthesis revealed a range of reasons relating to attrition from apps, including social, demographic, and behavioural factors that could be addressed. CONCLUSIONS Dropout rates in mHealth interventions are high, but possible areas to minimize attrition exist. Reducing dropout rates will make these apps more effective for disease management in the long term. CLINICALTRIAL https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=128737