The cost-effectiveness of digital health interventions: A systematic review of the literature

Gentili, Andrea; Failla, Giovanna; Melnyk, Andriy; Puleo, Valeria; Tanna, Gian Luca Di; Ricciardi, Walter; Cascini, Fidelia

doi:10.3389/fpubh.2022.787135

Cited by 118 publications

(76 citation statements)

References 47 publications

(238 reference statements)

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“…Digital interventions, including smartphone apps, websites, and SMS text messaging interventions, have proven efficacious for a wide variety of behavioral health outcomes, including mental health, diet, exercise, and smoking cessation [ 1 - 6 ]. An important value of digital interventions for behavioral health is that they are accessible at a low cost, thereby increasing their potential public impact and reducing disparities in behavioral health care [ 7 - 9 ]. An ongoing central challenge to the overall efficacy and broadly scalable implementation of digital interventions is the phenomenon of users dropping out early [ 10 ].…”

Section: Introductionmentioning

confidence: 99%

Can a Single Variable Predict Early Dropout From Digital Health Interventions? Comparison of Predictive Models From Two Large Randomized Trials

Bricker¹,

Miao²,

Mull³

et al. 2023

J Med Internet Res

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Background A single generalizable metric that accurately predicts early dropout from digital health interventions has the potential to readily inform intervention targets and treatment augmentations that could boost retention and intervention outcomes. We recently identified a type of early dropout from digital health interventions for smoking cessation, specifically, users who logged in during the first week of the intervention and had little to no activity thereafter. These users also had a substantially lower smoking cessation rate with our iCanQuit smoking cessation app compared with users who used the app for longer periods. Objective This study aimed to explore whether log-in count data, using standard statistical methods, can precisely predict whether an individual will become an iCanQuit early dropout while validating the approach using other statistical methods and randomized trial data from 3 other digital interventions for smoking cessation (combined randomized N=4529). Methods Standard logistic regression models were used to predict early dropouts for individuals receiving the iCanQuit smoking cessation intervention app, the National Cancer Institute QuitGuide smoking cessation intervention app, the WebQuit.org smoking cessation intervention website, and the Smokefree.gov smoking cessation intervention website. The main predictors were the number of times a participant logged in per day during the first 7 days following randomization. The area under the curve (AUC) assessed the performance of the logistic regression models, which were compared with decision trees, support vector machine, and neural network models. We also examined whether 13 baseline variables that included a variety of demographics (eg, race and ethnicity, gender, and age) and smoking characteristics (eg, use of e-cigarettes and confidence in being smoke free) might improve this prediction. Results The AUC for each logistic regression model using only the first 7 days of log-in count variables was 0.94 (95% CI 0.90-0.97) for iCanQuit, 0.88 (95% CI 0.83-0.93) for QuitGuide, 0.85 (95% CI 0.80-0.88) for WebQuit.org, and 0.60 (95% CI 0.54-0.66) for Smokefree.gov. Replacing logistic regression models with more complex decision trees, support vector machines, or neural network models did not significantly increase the AUC, nor did including additional baseline variables as predictors. The sensitivity and specificity were generally good, and they were excellent for iCanQuit (ie, 0.91 and 0.85, respectively, at the 0.5 classification threshold). Conclusions Logistic regression models using only the first 7 days of log-in count data were generally good at predicting early dropouts. These models performed well when using simple, automated, and readily available log-in count data, whereas including self-reported baseline variables did not improve the prediction. The results will inform the early identification of people at risk of early dropout from digital health interventions with the goal of intervening further by providing them with augmented treatments to increase their retention and, ultimately, their intervention outcomes.

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Section: Introductionmentioning

confidence: 99%

Can a Single Variable Predict Early Dropout From Digital Health Interventions? Comparison of Predictive Models From Two Large Randomized Trials

Bricker¹,

Miao²,

Mull³

et al. 2023

J Med Internet Res

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“…While it is true that the development of the mobile app was expensive initially, after initial deployment this sort of app can be inexpensive to maintain while providing access to large numbers of patients. We 106 and others 107 have shown that initial development and ongoing deployment costs can be substantially offset by benefits such as improved selfmanagement behavior. Lindsay et al, 61 for example, showed that a substantial increase in activation was associated with lower follow-up costs, especially in high risk populations.…”

Section: Discussionmentioning

confidence: 99%

A mobile app for chronic disease self-management for individuals with low health literacy: A multisite randomized controlled clinical trial

Ownby

Waldrop‐Valverde

Davenport

et al. 2023

Preprint

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Objective: The purpose of this study was to evaluate the effects of a mobile app designed to improve chronic disease self-management in older adult patients with low health literacy and who had at least one chronic health condition, and to assess the impact of delivering information at different levels of reading difficulty. Methods: A randomized controlled trial was completed at two sites. Individuals 40 years of age and older screened for low health literacy who had at least one chronic health condition were randomly assigned to a tailored information multimedia app with text at one of three grade levels. Four primary outcomes were assessed: patient activation, chronic disease self-efficacy, health-related quality of life, and medication adherence. Results: All groups showed overall increases in activation, self-efficacy, and health-related quality of life, but no change in medication adherence. No between-group differences were observed. Conclusions: The mobile app was effective in increasing participants' levels of several psychosocial variables, but reading difficulty level was not significantly related to outcomes.

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“…Digital health interventions and self-monitoring offer quick, cost-effective, personalized, accessible medical care and nutritional advice[ 53 ]. Especially when considering the rising number of IBD patients and an insufficient number of specialists, telemedicine could reduce the burden on the healthcare system by providing digital sources.…”

Section: Discussionmentioning

confidence: 99%

Telenutrition for the management of inflammatory bowel disease: Benefits, limits, and future perspectives

Coşkun¹,

Kolay²,

Başaranoğlu³

2023

World J Clin Cases

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Patients with inflammatory bowel disease (IBD) require lifelong and personalized care by a multidisciplinary healthcare team. However, the traditional medical model is not ideal for patients who require continuous close monitoring and whose symptoms may dramatically worsen between regularly scheduled visits. Additionally, close dietary follow-up and monitoring of IBD in a traditional setting are challenging because of the disease complexity, high pressure on outpatient clinics with a small number of IBD specialist dietitians, and rising incidence. Given the significant burden of IBD, there is a need to develop effective dietary management strategies. The coronavirus disease 2019 pandemic caused an unprecedented shift from in-person care to delivering health care via technological remote devices. Traditional nutrition therapy and consultation can be provided by telenutrition through remote electronic communication applications that could greatly benefit patient care. Telenutrition might be useful, safe, and cost-effective compared with standard care. It is likely that virtual care for chronic diseases including IBD will continue in some form into the future. This review article summarizes the evidence about telenutrition applications in the management of IBD patients, and we gave an overview of the acceptance and impact of these interventions on health outcomes.

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The cost-effectiveness of digital health interventions: A systematic review of the literature

Cited by 118 publications

References 47 publications

Can a Single Variable Predict Early Dropout From Digital Health Interventions? Comparison of Predictive Models From Two Large Randomized Trials

Can a Single Variable Predict Early Dropout From Digital Health Interventions? Comparison of Predictive Models From Two Large Randomized Trials

A mobile app for chronic disease self-management for individuals with low health literacy: A multisite randomized controlled clinical trial

Telenutrition for the management of inflammatory bowel disease: Benefits, limits, and future perspectives

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