Evidence map›Paper›PMID 34927591›Full record

ArticleJMIR formative research2021

Examining the Impact of Digital Components Across Different Phases of Treatment in a Blended Care Cognitive Behavioral Therapy Intervention for Depression and Anxiety: Pragmatic Retrospective Study.

Monica S Wu, Robert E Wickham, Shih-Yin Chen, Connie Chen, Anita Lungu

Open access · goldAbstract read
In one paragraph

Article in JMIR formative research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed, 2 pooled it
2.3field-weighted citation impact, top 12% of its field
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

13 citing papers in PubMed, 2 syntheses or guidelines pooled it, 19 citations in OpenAlex.

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  12. Real-World Evaluation of a Large-Scale Blended Care-Cognitive Behavioral Therapy Program for Symptoms of Anxiety and Depression.Telemedicine journal and e-health : the official journal of the American Telemedicine Association · 2022
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors at 2 institutions in 1 country.

Monica S Wu *Lyra Health, Burlingame, CA, United States.ORCID https://orcid.org/0000-0003-2984-5368
Robert E Wickham *Department of Psychological Sciences, Northern Arizona University, Flagstaff, AZ, United States.ORCID https://orcid.org/0000-0002-0132-6235
Shih-Yin ChenLyra Health, Burlingame, CA, United States.ORCID https://orcid.org/0000-0003-4617-5538
Connie ChenLyra Health, Burlingame, CA, United States.ORCID https://orcid.org/0000-0002-6051-4316
Anita LunguLyra Health, Burlingame, CA, United States.ORCID https://orcid.org/0000-0003-2084-3315
Wolters Kluwer Health · USPalo Alto University · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundDepression and anxiety incur significant personal and societal costs. Effective psychotherapies exist, such as cognitive behavioral therapy (CBT); however, timely access to quality care is limited by myriad barriers. Blended care therapy models incorporate traditional face-to-face therapy with scalable, digital components of care, expanding the reach of evidence-based care.

objectiveThe aim of this study is to determine the effectiveness of a blended care CBT program (BC-CBT) in real-world settings and examine the unique impacts of the (1) digital components of care (video lessons and digital exercises) and (2) phase of treatment (early versus late) in decreasing symptoms of anxiety and depression.

methodsThis retrospective cohort analysis included 3401 US-based individuals enrolled in a BC-CBT program, who presented with clinical levels of depression and/or anxiety. The treatment program consisted of regular therapy sessions augmented by clinician-assigned digital video lessons and exercises. A growth curve model incorporating time-varying covariates examined the relationship between engagement with BCT components (ie, therapy sessions, digital video lessons, and digital exercises) during the early (weeks 0-7) and late (weeks 8-15) phases of treatment, and weekly symptom reports on depression and anxiety measures.

resultsOn average, a significant decline in depression and anxiety symptoms was observed during the initial weeks of treatment (P<.001), with a continued, though slower, decline over subsequent weeks (P<.001). Each session completed was associated with significant decreases in anxiety (b=-0.72) and depression (b=-0.83) in the early phase, as well as in the late phase (anxiety, b=-0.47; depression, b=-0.27). Significant decreases in anxiety (b=-0.15) and depression (b=-0.12) were observed for time spent on video lessons (measured in 10-minute intervals) in the early phase of treatment. Engaging with exercises was associated with statistically significant increases in anxiety symptoms (b=0.03) during the early phase of treatment. However, sensitivity analyses examining the effects of exercises in isolation revealed significant decreases in anxiety (b=-0.05) in the early phase, suggesting a potential suppression effect in the larger model.

conclusionsUsing a retrospective cohort design, therapy sessions and digital video lessons were uniquely predictive of improvements in depression and anxiety symptoms, and their effects were modulated based on the phase of treatment (early vs late). Future research should investigate whether other treatment variables, such as therapeutic alliance or familiarity with technology, are related to differential effects on various components of care.

Indexed as

anxietyblended carecognitive-behavioral therapydepressiondigitaldigital healthdigital therapymental healthphase

Identifiers

PMID34927591
PMCPMC8726023
OpenAlexW4200229509

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.