Evidence map›Paper›PMID 37725423›Full record

ArticleJMIR aging2023

Web-Based Cognitive Behavioral Therapy for Depression Among Homebound Older Adults: Development and Usability Study.

Xiaoling Xiang, Jay Kayser, Samson Ash, Chuxuan Zheng, Yihang Sun, Addie Weaver, Ruth Dunkle, James A Blackburn, Alex Halavanau, Jia Xue and 1 more

Abstract read
In one paragraph

Article in JMIR aging, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed, 1 pooled it
–field-weighted citation impact
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

8 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Trial
  3. Article
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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

11 authors.

Xiaoling XiangSchool of Social Work, University of Michigan-Ann Arbor, Ann Arbor, MI, United States.ORCID https://orcid.org/0000-0002-4926-4707
Jay KayserSchool of Social Work, University of Michigan-Ann Arbor, Ann Arbor, MI, United States.ORCID https://orcid.org/0000-0002-5313-792X
Samson AshSchool of Social Work, University of Michigan-Ann Arbor, Ann Arbor, MI, United States.ORCID https://orcid.org/0009-0009-7430-7084
Chuxuan ZhengDepartment of Psychology, University of Michigan-Ann Arbor, Ann Arbor, MI, United States.ORCID https://orcid.org/0009-0006-8887-8927
Yihang SunSchool of Social Work, Columbia University, New York City, NY, United States.ORCID https://orcid.org/0000-0002-3088-9504
Addie WeaverSchool of Social Work, University of Michigan-Ann Arbor, Ann Arbor, MI, United States.ORCID https://orcid.org/0000-0002-6865-3740
Ruth DunkleSchool of Social Work, University of Michigan-Ann Arbor, Ann Arbor, MI, United States.ORCID https://orcid.org/0000-0002-1121-9695
James A BlackburnSchool of Social Work, University of Michigan-Ann Arbor, Ann Arbor, MI, United States.ORCID https://orcid.org/0009-0001-5082-0213
Alex HalavanauSLAC National Accelerator Laboratory, Stanford University, Menlo Park, CA, United States.ORCID https://orcid.org/0000-0002-5879-9578
Jia XueFactor-Inwentash Faculty of Social Work, University of Toronto, Toronto, ON, Canada.ORCID https://orcid.org/0000-0002-1668-2531
Joseph A HimleSchool of Social Work, University of Michigan-Ann Arbor, Ann Arbor, MI, United States.ORCID https://orcid.org/0000-0002-3161-7850

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundHomebound older adults are a high-risk group for depression. However, many of them face barriers to accessing evidence-supported mental health treatments. Digital mental health interventions can potentially improve treatment access, but few web-based interventions are explicitly tailored for depression in older adults.

objectiveThis paper describes the development process of Empower@Home, a web-delivered intervention for depression in homebound older adults that is based on cognitive behavioral therapy, and reports on the outcomes of usability studies.

methodsEmpower@Home was developed in collaboration with community agencies, stakeholders, and older adults, guided by user-centered design principles. User needs were assessed through secondary data analysis, demographic and health profiles from administrative data, and interviews and surveys of community partners. A comparative usability evaluation was conducted with 10 older adults to assess the usability of Empower@Home compared to 2 similar programs. Field testing was conducted with 4 end users to detect additional usability issues.

resultsFeedback and recommendations from community partners heavily influenced the content and design of Empower@Home. The intervention consists of 9 sessions, including psychoeducation and an introduction to cognitive behavioral therapy skills and tools through short video clips, in-session exercises, an animated storyline, and weekly out-of-session home practice. A printed workbook accompanies the web-based lessons. In comparative usability testing (N=10), Empower@Home received a System Usability Scale score of 78 (SD 7.4), which was significantly higher than the 2 comparator programs (t

conclusionsCollaboration with community stakeholders and careful consideration of potential implementation issues during the design process can result in more usable, engaging, and effective digital mental health interventions.

Indexed as

agingCBTcognitive behavioral therapycommunitycommunity-engaged researchdepressiondesigndevelopmentdigital healthdigital mental healthgeriatricgeriatric depressiongeriatricsinternetinternet-based cognitive behavioral therapymental healthold ageolder adultolder adultspsychotherapyusabilityuser centered designweb-basedweb-based health

Identifiers

PMID37725423
PMCPMC10548322

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.