Evidence map›Paper›PMID 35544323›Full record

ArticleJMIR formative research2022

Strengthening the Impact of Digital Cognitive Behavioral Interventions Through a Dual Intervention: Proficient Motivational Interviewing-Based Health Coaching Plus In-Application Techniques.

Catherine Serio, Amanda Gabarda, Fatma Uyar-Morency, Valerie Silfee, Justin Ludwig, Eva Szigethy, Susan Butterworth

Open access · goldAbstract read
In one paragraph

Article in JMIR formative research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed, 1 pooled it
1.4field-weighted citation impact, top 21% 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

7 citing papers in PubMed, 1 synthesis or guideline pooled it, 9 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Article
  4. Observational
  5. Article
  6. Article
  7. Article
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

7 authors at 2 institutions in 1 country.

Catherine SerioUPMC Health Plan, Pittsburgh, PA, United States.ORCID https://orcid.org/0000-0002-0758-4032
Amanda GabardaUPMC Health Plan, Pittsburgh, PA, United States.ORCID https://orcid.org/0000-0002-3570-1363
Fatma Uyar-MorencyUPMC Health Plan, Pittsburgh, PA, United States.ORCID https://orcid.org/0000-0002-2677-3322
Valerie Silfee *UPMC Health Plan, Pittsburgh, PA, United States.ORCID https://orcid.org/0000-0001-8505-4726
Justin LudwigUPMC Health Plan, Pittsburgh, PA, United States.ORCID https://orcid.org/0000-0001-6362-8020
Eva SzigethyDepartment of Psychiatry, University of Pittsburgh, Pittsburgh, PA, United States.ORCID https://orcid.org/0000-0001-6783-581X
Susan ButterworthUPMC Health Plan, Pittsburgh, PA, United States.ORCID https://orcid.org/0000-0003-0826-218X
UPMC Health System · USUniversity of Pittsburgh · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe COVID-19 pandemic has accelerated the adoption of digital tools to support individuals struggling with their mental health. The use of a digital intervention plus human coaching ("dual" intervention) is gaining momentum in increasing overall engagement in digital cognitive behavioral interventions (dCBIs). However, there is limited insight into the methodologies and coaching models used by those deploying dual interventions. To achieve a deeper understanding, we need to identify and promote effective engagement that leads to clinical outcomes versus simply monitoring engagement metrics. Motivational interviewing (MI) is a collaborative, goal-oriented communication approach that pays particular attention to the language of change and is an effective engagement approach to help people manage mental health issues. However, this approach has been traditionally used for in-person or telephonic interventions, and less is known about the application of MI to digital interventions.

objectiveWe sought to provide a dual intervention approach and address multiple factors across two levels of engagement to operationalize a dCBI that combined cognitive behavioral therapy-based techniques and MI-based interactions between the digital health coach (DHC) and user.

methodsWe reviewed hundreds of digital exchanges between DHCs and users to identify and improve training and quality assurance activities for digital interventions.

resultsWe tested five hypotheses and found that: (1) users of a dual digital behavioral health intervention had greater engagement levels than users of a noncoached intervention (P<.001); (2) DHCs with a demonstrated competency in applying MI to digital messages had more engaged users, as measured by the DHC-to-user message exchange ratio (P<.001); (3) the DHC-to-user message exchange ratio was correlated with more engagement in app activities (r=0.28, 95% CI 0.23-0.33); (4) DHCs with demonstrated MI proficiency elicited a greater amount of "change talk" from users than did DHCs without MI proficiency (H=25.12, P<.001); and (5) users who were engaged by DHCs with MI proficiency had better clinical outcomes compared to users engaged by DHCs without MI proficiency (P=.02).

conclusionsTo our knowledge, this pilot was the first of its kind to test the application of MI to digital coaching protocols, and it demonstrated the value of MI proficiency in digital health coaching for enhanced engagement and health improvement. Further research is needed to establish coaching models in dCBIs that incorporate MI to promote effective engagement and optimize positive behavioral outcomes.

Indexed as

cognitive behavioral therapyCOVID-19digital healthmental healthmHealthmotivational interviewing

Identifiers

PMID35544323
PMCPMC9133992
OpenAlexW4213259055

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.