e.g. mhealth
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Skip search results from other journals and go to results- 18 Journal of Medical Internet Research
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- 2 JMIR mHealth and uHealth
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The main types of variables that have been used to predict dropout have included self-reported baseline data, such as demographics or symptom severity [29], or objective measures of intervention engagement, such as number of loggings and proportion of content completed, among others [28,30].
When exploring the predictors of dropout, analyzing at what point dropout occurs is also of importance. Many studies only report a final figure about the prevalence of dropout during the entire intervention.
JMIR Ment Health 2025;12:e51022
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A recent paper by Bricker et al [6] aimed to identify early markers of dropout that could generalize across platforms and aid the design of rescue interventions to mitigate dropout. Using the data from 2 clinical trials of 4 web- or app-based tobacco cessation platforms, they compared a variety of classification models to predict early dropout. They considered predictors including baseline and demographic variables, as well as daily log-in data from the first 7 days after registration.
J Med Internet Res 2024;26:e54248
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However, attrition—defined as participant dropout before completing an intervention—is prevalent in digital health or e Health [7-9]. In some formal evaluations of app-based health interventions, attrition rates have reached as high as 75%-99% [7,9]. Many factors contribute to this high attrition rate.
J Med Internet Res 2024;26:e58735
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Effective Communication Supported by an App for Pregnant Women: Quantitative Longitudinal Study
However, the HAPA model has been rarely applied to interventions targeting effective communication [8,9], and it is hardly ever used to explain communication behavior in the context of digital interventions or their dropout of pregnant women. Therefore, we review dropout in more detail.
Early dropout from digital interventions is a key problem [39], as the intervention use is discontinued.
JMIR Hum Factors 2024;11:e48218
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Reference 9: Rates of attrition and dropout in app-based interventions for chronic disease: systematic Reference 10: Dropout from an eHealth intervention for adults with type 2 diabetes: a qualitative studydropout
JMIR Mhealth Uhealth 2024;12:e51236
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Attrition or dropout occurs when participants do not complete the randomized controlled trial (RCT) assessments or complete the research protocol.
Digital health interventions typically report rapid and high attrition [13,25]. The overall attrition rate quantifies the level of attrition for the whole sample in a clinical trial, and the differential attrition rate refers to the level of attrition in the intervention group compared with that in the comparison group [26].
J Med Internet Res 2024;26:e48168
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Moreover, mental health clinical studies found that patient preferences (eg, treatment, therapist, and activity preferences) were negatively related to dropout rates [33]. Patients who received the preferred treatment had lower chances of dropping out [34]. It was also found that studies with large samples reported higher dropout rates, and studies offering feedback to participants reported lower dropout rates [1].
J Med Internet Res 2023;25:e43584
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Evaluating a Remote Monitoring Program for Respiratory Diseases: Prospective Observational Study
Retention of patients in the study was evaluated using Cox regression proportional hazard analysis to calculate the time to event, the event being the dropout of a patient. Kaplan-Meier analysis [41] was used to calculate the probabilities of retention at time points from the enrollment dates. Kaplan-Meier plots were grouped based on cohorts, and data types were grouped based on burden and protocol holidays, with group differences assessed using the log rank test.
JMIR Form Res 2023;7:e51507
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Reference 14: Dropout rates in clinical trials of smartphone apps for depressive symptoms: a systematicdropout
J Med Internet Res 2023;25:e44220
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