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Record W2742442474 · doi:10.2196/jmir.7573

How do eHealth Programs for Adolescents With Depression Work? A Realist Review of Persuasive System Design Components in Internet-Based Psychological Therapies

2017· review· en· W2742442474 on OpenAlexafffund
Lori Wozney, Anna Huguet, Kathryn Bennett, Ashley D Radomski, Lisa Hartling, Michele P. Dyson, Patrick J. McGrath, Amanda S. Newton

Bibliographic record

VenueJournal of Medical Internet Research · 2017
Typereview
Languageen
FieldPsychology
TopicDigital Mental Health Interventions
Canadian institutionsUniversity of AlbertaMcMaster UniversityIzaak Walton Killam Health Centre
FundersCanadian Institutes of Health Research
KeywordseHealthThe InternetCognitive behavioral therapyPsychologyClinical psychologyCritical appraisalInterpersonal psychotherapyIntervention (counseling)PsychotherapistMedicinePsychiatryCognitionRandomized controlled trialComputer scienceWorld Wide WebAlternative medicineHealth care

Abstract

fetched live from OpenAlex

BACKGROUND: Major depressive disorders are common among adolescents and can impact all aspects of their daily life. Traditional therapies, cognitive behavioral therapy (CBT), and interpersonal psychotherapy (IPT) have been delivered face-to-face. However, Internet-based (online) delivery of these therapies is emerging as an option for adolescents. Internet-based CBT and IPT involve therapeutic content, interaction between the user and the system, and different technological features embedded into the online program (eg, multimedia). Studies of Internet-based CBT and IPT for adolescent depression differ on all three aspects, and variable, positive therapy effects have been reported. A better understanding of the treatment conditions that influence therapy outcomes is important to designing and evaluating these novel therapies. OBJECTIVE: Our aim was to examine the technological and program delivery features of Internet-based CBT and IPT for adolescent depression and to document their potential relation to treatment outcomes and program use. METHODS: We performed a realist synthesis. We started with an extensive search of published and gray literature. We included intervention studies that evaluated Internet-based CBT or IPT for adolescent depression. We included mixed-methods and qualitative studies, theoretical papers, and policy/implementation documents if they included a focus on how Internet-based psychological therapy is proposed to work for adolescents with depression/depressive symptoms. We used the Mixed-Methods Appraisal Tool to assess the methodological quality of studies. We used the Persuasive System Design (PSD) model as a framework for data extraction and analysis to examine how Internet-based CBT and IPT, as technology-based systems, influence the attitudes and behaviors of system users. PSD components described for the therapies were linked to reported outcomes using a cross-case comparison method and thematic synthesis. RESULTS: We identified 19 Internet-based CBT programs in 59 documents. Of those, 71% (42/59) were of moderate to high quality. The PSD features surface credibility (competent "look and feel"), dialogue support (online program + in-person support), liking and similarity (esthetics and content appeal to adolescent users), the reduction and tunneling of therapeutic content (reducing online content into simple tasks, guiding users), and use of self-monitoring were present in therapies that resulted in improved therapy engagement, satisfaction, and adherence, as well as symptom and functional impairments. CONCLUSIONS: When incorporated into Internet-based CBT for adolescent depression, PSD features may improve adolescent adherence, satisfaction, and depression-related outcomes. Testing of these features using hypothesis-driven dismantling approaches is recommended to advance our understanding of how these features contribute to therapy effectiveness.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.015
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.801
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0150.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0030.000
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.513
GPT teacher head0.582
Teacher spread0.069 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designSystematic review
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations107
Published2017
Admission routes2
Has abstractyes

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