Dataset Information

Adolescent dropout from brief digital mental health interventions within and beyond randomized trials.

ABSTRACT:

Objective

Many adolescents struggle to access appropriate mental health care due to structural or psychological barriers. Although traditional barriers to participation (e.g., location, cost) are hypothetically reduced or removed in internet interventions, low retention reduces the likelihood that adolescents will receive the intervention dosage intended to produce beneficial effects. It is therefore key to determine what factors are associated with dropout in digital mental health interventions with adolescents both within and beyond the context of research studies.

Methods

We compare completion rates from two projects evaluating self-guided, online single-session mental health interventions (SSIs) for adolescents. One was a randomized controlled trial (RCT) in which participants were paid for participation. The other was a program evaluation project in which participants were not paid for participation. We additionally compare SSI completion rates across various demographic groups and across baseline hopelessness levels.

Results

There was a statistically significant difference in SSI completion status between the RCT (84.75% full-completers) and the program evaluation (36.86% full-completers), X ² (2, N = 2436) = 583.5, p < 0.05. There were no significant differences in the baseline hopelessness scores across completion statuses in either study. There were no significant differences in full-completion rates across demographic groups in either project.

Conclusion

Adolescents may be more likely to complete a brief digital mental health intervention in a paid, research-based context than in an unpaid, naturalistic context. Additionally, it is possible that the brevity of SSIs reduces demographic disparities related to retention by minimizing the time required to complete an intervention.

SUBMITTER: Cohen KA

PROVIDER: S-EPMC8897204 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Adolescent dropout from brief digital mental health interventions within and beyond randomized trials.

Cohen Katherine A KA Schleider Jessica L JL

Internet interventions 20220117

<h4>Objective</h4>Many adolescents struggle to access appropriate mental health care due to structural or psychological barriers. Although traditional barriers to participation (e.g., location, cost) are hypothetically reduced or removed in internet interventions, low retention reduces the likelihood that adolescents will receive the intervention dosage intended to produce beneficial effects. It is therefore key to determine what factors are associated with dropout in digital mental health inter ...[more]

PMID: 35257001

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Project description:BackgroundA 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.ObjectiveThis 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).MethodsStandard 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.ResultsThe 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).ConclusionsLogistic 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.

Project description:ObjectiveTo test the utility of brief social contact-based video interventions of a Black adolescent girl to reduce stigmatized attitudes and increase help-seeking intentions around adolescent depression.MethodsWe conducted a randomized controlled trial (RCT) with 14- to18-year-old healthy volunteers drawn from the general US population. We enrolled participants through a crowdsourcing platform (n = 1,093) and randomly assigned participants to one of three video conditions (117 s each): depressed (DEP); depressed, adjusted to aspects unique to being a Black adolescent girl (including experienced or internalized racism; ADJ); and control (CONT). The primary outcome was the Depression Stigma Scale (DSS); secondary outcomes were the General Health-Seeking Questionnaire (GHSQ), and thermometers for Black and white race perception "warmth".ResultsFollowing the intervention, the DSS changed from baseline across the three conditions (p < .001). ADJ outperformed both DEP (p = .031) and CONT (p < .001). A race-by-intervention interaction (p < .001) revealed different response profiles between Black (ADJ = DEP = CONT; p = .726) and non-Black participants (ADJ > DEP > CONT; p < .001). DEP and ADJ both resulted in higher treatment-seeking intentions for both the emotional problems and the suicidal thought subscales of the GHSQ. We found a race-by-intervention interaction (p = .01) for the Black thermometer, which revealed a significant 2° increase in warmth among white (p < .001), but not Black, viewers (p = .06).ConclusionsOn a short-term basis, brief social contact-based videos proved effective among adolescents in reducing depression-related stigma, increasing help-seeking intentions, and providing an "empathic foothold" in the lives of racially stigmatized groups. Even as the enduring effects of these interventions remain to be determined, the deployment on social media of short videos opens new opportunities to reach a large number of at-risk youth."

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Project description:BackgroundDigital care has become an essential component of health care. Interventions for patients with cancer need to be effective and safe, and digital health interventions must adhere to the same requirements.ObjectiveThe purpose of this study was to identify currently available digital health interventions developed and evaluated in randomized controlled trials (RCTs) targeting adult patients with cancer.MethodsA scoping review using the JBI methodology was conducted. The participants were adult patients with cancer, and the concept was digital health interventions. The context was open, and sources were limited to RCT effectiveness studies. The PubMed, CINAHL, Embase, Cochrane Library, Research Information Sharing Service, and KoreaMed databases were searched. Data were extracted and analyzed to achieve summarized results about the participants, types, functions, and outcomes of digital health interventions.ResultsA total of 231 studies were reviewed. Digital health interventions were used mostly at home (187/231, 81%), and the web-based intervention was the most frequently used intervention modality (116/231, 50.2%). Interventions consisting of multiple functional components were most frequently identified (69/231, 29.9%), followed by those with the self-manage function (67/231, 29%). Web-based interventions targeting symptoms with the self-manage and multiple functions and web-based interventions to treat cognitive function and fear of cancer recurrence consistently achieved positive outcomes. More studies supported the positive effects of web-based interventions to inform decision-making and knowledge. The effectiveness of digital health interventions targeting anxiety, depression, distress, fatigue, health-related quality of life or quality of life, pain, physical activity, and sleep was subject to their type and function. A relatively small number of digital health interventions specifically targeted older adults (6/231, 2.6%) or patients with advanced or metastatic cancer (22/231, 9.5%).ConclusionsThis scoping review summarized digital health interventions developed and evaluated in RCTs involving adult patients with cancer. Systematic reviews of the identified digital interventions are strongly recommended to integrate digital health interventions into clinical practice. The identified gaps in digital health interventions for cancer care need to be reflected in future digital health research.

Project description:BackgroundProviding the growing number of children and young people seeking mental health support with timely access to care poses a significant challenge. Increased use of digital technology in the delivery of children and young people's mental health services has been proposed as a means of increasing access to treatment.MethodsWe conducted three interrelated studies to provide multi-perspective insights into the use of digital therapeutic interventions within children and young people's mental health services in the UK. Study 1 used semi-structured interviews and an online survey to collect the views of digital therapeutic interventions of families who self-identified as facing additional barriers to accessing mental health support (n=13). Study 2 involved eight focus groups with children and young people's clinicians, service managers, commissioners, and policy leads (n=28), exploring participants' views and experiences of implementing and sustaining digital therapeutic interventions. Study 3 was a consensus exercise which aimed to identify actions needed to bridge the gap between the development and use of digital therapeutic interventions for children and young people's mental health through focus groups with parents/carers and professionals (n=17), and three Delphi-survey rounds.ResultsOur findings revealed considerable enthusiasm for the increased use of digital mental health interventions for children and young people across stakeholder groups, but also identified key barriers to their implementation. Actions perceived to facilitate more effective implementation included: a) co-producing interventions, commissioning decisions and implementation plans with children and parents/carers, b) enhancing national guidance and local leadership, c) integration of digital offers within existing clinical pathways, and d) efforts to ensure accessibility and inclusivity.DiscussionDigital therapeutic interventions offer a promising solution to the challenge of improving access to mental health support for children and young people. Strengthened guidance and leadership, sustained funding and further evidence-generation are urgently needed to enable this promise to be realised.

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Dataset Information

Adolescent dropout from brief digital mental health interventions within and beyond randomized trials.

Objective

Methods

Results

Conclusion

Publications

Adolescent dropout from brief digital mental health interventions within and beyond randomized trials.

Similar Datasets

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