ReviewJMIR mental health2018
eMental Healthcare Technologies for Anxiety and Depression in Childhood and Adolescence: Systematic Review of Studies Reporting Implementation Outcomes.
Review in JMIR mental health, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers, 5 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
31 citing papers in PubMed, 5 syntheses or guidelines pooled it.
- Barriers to and Facilitators of Implementation of Internet-Delivered Therapist-Guided Therapy in Child and Adolescent Mental Health Services: Systematic Review and Bayesian Meta-Analysis.Journal of medical Internet research · 2025Pooled it
- The effectiveness of e-mental health interventions on stress, anxiety, and depression among healthcare professionals: a systematic review and meta-analysis.Systematic reviews · 2024Pooled it
- Determination of Markers of Successful Implementation of Mental Health Apps for Young People: Systematic Review.Journal of medical Internet research · 2022Pooled it
- Engaging Children and Young People in Digital Mental Health Interventions: Systematic Review of Modes of Delivery, Facilitators, and Barriers.Journal of medical Internet research · 2020Pooled it
- Digital Mental Health Interventions for Depression, Anxiety, and Enhancement of Psychological Well-Being Among College Students: Systematic Review.Journal of medical Internet research · 2019Pooled it
- Use of the Chatbot "Vivibot" to Deliver Positive Psychology Skills and Promote Well-Being Among Young People After Cancer Treatment: Randomized Controlled Feasibility Trial.JMIR mHealth and uHealth · 2019Trial
- Implementation of digital mental health interventions for children and adolescents: A systematic review.Internet interventions · 2026Review
- Clinicians' provision of a self-guided digital mental health intervention to adolescents with depressive symptoms at preventive health visits in California.Preventive medicine reports · 2026Article
- Cancer Patients' Perception, Acceptance, and Utilization of Artificial Intelligence-Based Emotional Distress Assessment Tools: A Scoping Review.Cancer medicine · 2026Article
- Article
- Understanding stakeholder views of the use of digital therapeutic interventions within children and young people's mental health services.Frontiers in psychiatry · 2025Article
- A qualitative interview study of patients' attitudes towards and intention to use digital interventions for depressive disorders on prescription.Frontiers in digital health · 2024Article
- Implementation strategies to scale up self-administered depot medroxyprogesterone acetate subcutaneous injectable contraception: a scoping review.Systematic reviews · 2023Article
- Supporting Adolescent Engagement with Artificial Intelligence-Driven Digital Health Behavior Change Interventions.Journal of medical Internet research · 2023Article
- Engagement of adolescents with ADHD in a narrative-centered game-based behavior change environment to reduce alcohol use.Frontiers in education · 2023Article
- Implementation of eMental health technologies for informal caregivers: A multiple case study.Frontiers in digital health · 2023Article
- Assessment and Prediction of Depression and Anxiety Risk Factors in Schoolchildren: Machine Learning Techniques Performance Analysis.JMIR formative research · 2022Article
- Describing implementation outcomes for a virtual community of practice: The ECHO Ontario Mental Health experience.Health research policy and systems · 2022Article
- Developing a Web-Based App to Assess Mental Health Difficulties in Secondary School Pupils: Qualitative User-Centered Design Study.JMIR formative research · 2022Article
- The Application of e-Mental Health in Response to COVID-19: Scoping Review and Bibliometric Analysis.JMIR mental health · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAnxiety disorders and depression are frequent conditions in childhood and adolescence. eMental healthcare technologies may improve access to services, but their uptake within health systems is limited.
objectiveThe objective of this review was to examine and describe how the implementation of eMental healthcare technologies for anxiety disorders and depression in children and adolescents has been studied.
methodsWe conducted a search of 5 electronic databases and gray literature. Eligible studies were those that assessed an eMental healthcare technology for treating or preventing anxiety or depression, included children or adolescents (<18 years), or their parents or healthcare providers and reported findings on technology implementation. The methodological quality of studies was evaluated using the Mixed Methods Appraisal Tool. Outcomes of interest were based on 8 implementation outcomes: acceptability (satisfaction with a technology), adoption (technology uptake and utilization), appropriateness ("fitness for purpose"), cost (financial impact of technology implementation), feasibility (extent to which a technology was successfully used), fidelity (implementation as intended), penetration ("spread" or "reach" of the technology), and sustainability (maintenance or integration of a technology within a healthcare service). For extracted implementation outcome data, we coded favorable ratings on measurement scales as "positive results" and unfavorable ratings on measurement scales as "negative results." Those studies that reported both positive and negative findings were coded as having "mixed results."
resultsA total of 46 studies met the inclusion criteria, the majority of which were rated as very good to excellent in methodological quality. These studies investigated eMental healthcare technologies for anxiety (n=23), depression (n=18), or both anxiety and depression (n=5). Studies of technologies for anxiety evaluated the following: (1) acceptability (78%) reported high levels of satisfaction, (2) adoption (43%) commonly reported positive results, and (3) feasibility (43%) reported mixed results. Studies of technologies for depression evaluated the following: (1) appropriateness (56%) reported moderate helpfulness and (2) acceptability (50%) described a mix of both positive and negative findings. Studies of technologies designed to aid anxiety and depression commonly reported mixed experiences with acceptability and adoption and positive findings for appropriateness of the technologies for treatment. Across all studies, cost, fidelity, and penetration and sustainability were the least measured implementation outcomes.
conclusionsAcceptability of eMental healthcare technology is high among users and is the most commonly investigated implementation outcome. Perceptions of the appropriateness and adoption of eMental healthcare technology were varied. Implementation research that identifies, evaluates, and reports on costs, sustainability, and fidelity to clinical guidelines is crucial for making high-quality eMental healthcare available to children and adolescents.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.