Rates of Attrition and Dropout in App-Based Interventions for Chronic Disease: Systematic Review and Meta-Analysis

Gideon Meyerowitz-Katz, Sumathy Ravi, Leonard Arnolda, Xiaoqi Feng, Glen Maberly, Thomas Astell-Burt. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 29.09.2020. 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\u3e99%). 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

Evaluating the Diabetes–Cardiology interface: a glimpse into the diabetes management of cardiology inpatients in western Sydney’s ‘diabetes hotspot’ and the establishment of a novel model of care

Abstract Background Approximately two-thirds of individuals presenting to emergency departments in Western Sydney have glucose dysregulation, accelerating their risk of cardiovascular disease (CVD). We evaluated the prevalence and management of type 2 diabetes (T2D) in cardiology inpatients in Western Sydney. A novel model of care between diabetes and cardiology specialist hospital teams (joint specialist case conferencing, JSCC) is described herein and aimed at aligning clinical services and upskilling both teams in the management of the cardiology inpatient with comorbid T2D. Methods Cardiology inpatients at Blacktown-Mount Druitt Hospital were audited during a 1-month period. Results 233 patients were included, mean age 64 ± 16 years, 60% were male, 27% overweight and 35% obese. Known T2D comprised 36% (n = 84), whereas 6% (n = 15) had a new diagnosis of T2D, of which none of the latter were referred for inpatient/outpatient diabetes review. Approximately, 27% (n = 23) and 7% (n = 6) of known diabetes patients suffered hyper- and hypoglycaemia, respectively, and 51% (n = 43) had sub-optimally controlled T2D (i.e. HbA1c > 7.0%); over half (51%, n = 51) had coronary artery disease. Only two patients were treated with an SGLT2 inhibitor and no patients were on glucagon like peptide-1 receptor analogues. The majority were managed with metformin (62%) and therapies with high hypoglycaemic potential (e.g., sulfonylureas (29%)) and in those patients treated with insulin, premixed insulin was used in the majority of cases (47%). Conclusions Undiagnosed T2D is prevalent and neglected in cardiology inpatients. Few patients with comorbid T2D and CVD were managed with therapies of proven cardiac and mortality benefit. Novel models of care may be beneficial in this high-risk group of patients and discussed herein is the establishment of the diabetes-cardiology JSCC service delivery model which has been established at our institution

[In Press] A retrospective case-control cohort analysis of comorbidity and health expenditure in hospitalized adults diagnosed with obesity utilizing ICD-10 diagnostic coding

The cost and comorbidity of obesity in hospitalized inpatients, is less known. A retrospective study of patients presenting to a large district hospital in Western Sydney (April 2016-February 2017) using clinical, pathological as well as diagnostic coding data for obesity as per ICD-10. Of 43 212 consecutive hospital presentations, 390 had an obesity-coded diagnosis (Ob, 0.90%), of which 244 were gender and age matched to a non-obesity coded cohort (NOb). Weight and BMI were higher in the Ob vs NOb group (126 ± 37 vs 82 ± 25 kg; BMI 46 ± 12 vs 29 ± 8 kg/m2 , P < .001) with a medical record documentation rate of 62% for obesity among Ob. The Ob cohort had 2-5 higher rates of cardiopulmonary and metabolic complications (P < .001), greater pharmacologic burden, length of stay (LOS, 225 vs 89 hours, P < .001) and stay in intensive care but no differences in the prevalence of mental disorders. Compared with BMI 35 kg/m2 were 5 more likely to require intensive care (OR 5.08 [1.43-27.3, 95% CI], P = .0047). The initiation of obesity-specific interventions by clinical teams was very low. People with obesity who are admitted to hospital carry significant cost and complications, yet obesity is seldom recognized as a clinical entity or contributor

A retrospective case‐control cohort analysis of comorbidity and health expenditure in hospitalized adults diagnosed with obesity utilizing ICD

The cost and comorbidity of obesity in hospitalized inpatients, is less known. A retrospective study of patients presenting to a large district hospital in Western Sydney (April 2016-February 2017) using clinical, pathological as well as diagnostic coding data for obesity as per ICD-10. Of 43 212 consecutive hospital presentations, 390 had an obesity-coded diagnosis (Ob, 0.90%), of which 244 were gender and age matched to a non-obesity coded cohort (NOb). Weight and BMI were higher in the Ob vs NOb group (126 ± 37 vs 82 ± 25 kg; BMI 46 ± 12 vs 29 ± 8 kg/m2 , P < .001) with a medical record documentation rate of 62% for obesity among Ob. The Ob cohort had 2-5 higher rates of cardiopulmonary and metabolic complications (P < .001), greater pharmacologic burden, length of stay (LOS, 225 vs 89 hours, P < .001) and stay in intensive care but no differences in the prevalence of mental disorders. Compared with BMI 35 kg/m2 were 5 more likely to require intensive care (OR 5.08 [1.43-27.3, 95% CI], P = .0047). The initiation of obesity-specific interventions by clinical teams was very low. People with obesity who are admitted to hospital carry significant cost and complications, yet obesity is seldom recognized as a clinical entity or contributor