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Record W4405444939 · doi:10.2196/46860

Participant Adherence and Contact Behavior in a Guided Internet Intervention for Depressive Symptoms: Exploratory Study

2024· article· en· W4405444939 on OpenAlexvenueno aff
Oliver Thomas Bur, Thomas Berger

Bibliographic record

VenueJMIR Formative Research · 2024
Typearticle
Languageen
FieldPsychology
TopicDigital Mental Health Interventions
Canadian institutionsnot available
FundersUniversity of Bern
KeywordsRandomized controlled trialIntervention (counseling)Clinical psychologyMedicineDepressive symptomsExploratory researchPhysical therapyPsychologyPsychiatryAnxietyInternal medicine

Abstract

fetched live from OpenAlex

Background: The number of studies on internet-based guided self-help has rapidly increased during the last 2 decades. Guided self-help comprises 2 components: a self-help program that patients work through and usually weekly guidance from therapists who support patients using the self-management program. Little is known about participants' behavior patterns while interacting with therapists and their use of self-help programs in relation to intervention outcomes. Objective: This exploratory study aimed to investigate whether the number of messages sent to the therapist (ie, contact behavior) is an indicator of the outcome, that is, a reduction in depressive symptoms. Furthermore, we investigated whether adherence was associated with outcome. Most importantly, we investigated whether different combinations of adherence and contact behavior were associated with outcome. Methods: Drawing on a completer sample (n=113) from a randomized full factorial trial, participants were categorized into 4 groups. The groups were based on median splits of 2 variables, that is, the number of messages sent to therapists (low: groups 1 and 2; high: groups 3 and 4) and adherence (low: groups 1 and 3; high: groups 2 and 4). The 4 groups were compared in terms of change in depressive symptoms (measured with the Patient Health Questionnaire-9) from pre- to posttreatment and pretreatment to follow-up, respectively. Results: On average, participants sent 4.5 (SD 3.7) messages to their therapist and completed 18.2 (SD 5.2) pages of the program in 6.39 (SD 5.39) hours. Overall, analyses revealed no main effect for participants' messages (H1=0.18, P=.67) but a significant main effect for adherence on changes in depressive symptoms from pre- to posttreatment (H1=5.10, P=.02). The combined consideration of adherence and messages sent to the therapist revealed group differences from pre- to posttreatment (H3=8.26, P=.04). Group 3 showed a significantly smaller improvement in symptoms compared with group 4 (Z=-2.84, P=.002). Furthermore, there were group differences from pretreatment to follow-up (H3=8.90, P=.03). Again, group 3 showed a significantly smaller improvement in symptoms compared with group 4 (Z=-2.62, P=.004) and group 2 (Z=-2.47, P=.007). All other group comparisons did not yield significant differences. Conclusions: This exploratory study suggests that participants characterized by low adherence and frequent messaging do not improve their symptoms as much as other participants. These participants might require more personalized support beyond the scope of guided internet interventions. The paper underscores the importance of considering individual differences in contact behavior when tailoring interventions. The results should be interpreted with caution and further investigated in future studies.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.014
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.294
GPT teacher head0.567
Teacher spread0.273 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations1
Published2024
Admission routes1
Has abstractyes

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