Evidence map›Paper›PMID 37698900›Full record

ArticleJMIR cancer2023

Predictors of the Use of a Mental Health-Focused eHealth System in Patients With Breast and Prostate Cancer: Bayesian Structural Equation Modeling Analysis of a Prospective Study.

Nuhamin Gebrewold Petros, Jesper Alvarsson-Hjort, Gergö Hadlaczky, Danuta Wasserman, Manuel Ottaviano, Sergio Gonzalez-Martinez, Sara Carletto, Enzo Pasquale Scilingo, Gaetano Valenza, Vladimir Carli

Open access · goldAbstract read
In one paragraph

Article in JMIR cancer, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.4field-weighted citation impact, top 36% 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

1 citing paper in PubMed, 2 citations in OpenAlex.

  1. 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

10 authors at 5 institutions in 3 countries.

Nuhamin Gebrewold PetrosNational Centre for Suicide Research and Prevention of Mental Ill-Health, Department of Learning, Informatics, Management and Ethics, Karolinska Institute, Stockholm, Sweden.ORCID https://orcid.org/0000-0002-9738-2222
Jesper Alvarsson-HjortNational Centre for Suicide Research and Prevention of Mental Ill-Health, Centre for Health Economics, Informatics, and Health Services Research, Stockholm Health Care Services, Stockholm, Sweden.ORCID https://orcid.org/0000-0003-2059-0514
Gergö HadlaczkyNational Centre for Suicide Research and Prevention of Mental Ill-Health, Department of Learning, Informatics, Management and Ethics, Karolinska Institute, Stockholm, Sweden.ORCID https://orcid.org/0000-0002-0556-6244
Danuta WassermanNational Centre for Suicide Research and Prevention of Mental Ill-Health, Department of Learning, Informatics, Management and Ethics, Karolinska Institute, Stockholm, Sweden.ORCID https://orcid.org/0000-0002-8436-3989
Manuel OttavianoLife Supporting Technologies, Universidad Politécnica de Madrid, Madrid, Spain.ORCID https://orcid.org/0000-0003-0002-4988
Sergio Gonzalez-MartinezLife Supporting Technologies, Universidad Politécnica de Madrid, Madrid, Spain.ORCID https://orcid.org/0000-0003-1256-6389
Sara CarlettoDepartment of Clinical and Biological Sciences, University of Turin, Turin, Italy.ORCID https://orcid.org/0000-0002-4951-7479
Enzo Pasquale ScilingoResearch Center "E.Piaggio", School of Engineering, University of Pisa, Pisa, Italy.ORCID https://orcid.org/0000-0003-2588-4917
Gaetano ValenzaResearch Center "E.Piaggio", School of Engineering, University of Pisa, Pisa, Italy.ORCID https://orcid.org/0000-0001-6574-1879
Vladimir CarliNational Centre for Suicide Research and Prevention of Mental Ill-Health, Department of Learning, Informatics, Management and Ethics, Karolinska Institute, Stockholm, Sweden.ORCID https://orcid.org/0000-0001-6922-0675
Karolinska Institutet · SEUniversidad Politécnica de Madrid · ESUniversity of Pisa · ITSödertörn University · SEUniversity of Turin · IT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundeHealth systems have been increasingly used to manage depressive symptoms in patients with somatic illnesses. However, understanding the factors that drive their use, particularly among patients with breast and prostate cancer, remains a critical area of research.

objectiveThis study aimed to determine the factors influencing use of the NEVERMIND eHealth system among patients with breast and prostate cancer over 12 weeks, with a focus on the Technology Acceptance Model.

methodsData from the NEVERMIND trial, which included 129 patients with breast and prostate cancer, were retrieved. At baseline, participants completed questionnaires detailing demographic data and measuring depressive and stress symptoms using the Beck Depression Inventory-II and the Depression, Anxiety, and Stress Scale-21, respectively. Over a 12-week period, patients engaged with the NEVERMIND system, with follow-up questionnaires administered at 4 weeks and after 12 weeks assessing the system's perceived ease of use and usefulness. Use log data were collected at the 2- and 12-week marks. The relationships among sex, education, baseline depressive and stress symptoms, perceived ease of use, perceived usefulness (PU), and system use at various stages were examined using Bayesian structural equation modeling in a path analysis, a technique that differs from traditional frequentist methods.

resultsThe path analysis was conducted among 100 patients with breast and prostate cancer, with 66% (n=66) being female and 81% (n=81) having a college education. Patients reported good mental health scores, with low levels of depression and stress at baseline. System use was approximately 6 days in the initial 2 weeks and 45 days over the 12-week study period. The results revealed that PU was the strongest predictor of system use at 12 weeks (β

conclusionsThis study underscores the importance of PU and early engagement in patient engagement with eHealth systems such as NEVERMIND. This suggests that, in general eHealth implementations, caregivers should educate patients about the benefits and functionalities of such systems, thus enhancing their understanding of potential health impacts. Concentrating resources on promoting early engagement is also essential given its influence on sustained use. Further research is necessary to clarify the remaining uncertainties, enabling us to refine our strategies and maximize the benefits of eHealth systems in health care settings.

Indexed as

cancerdigital healtheHealth systemmental healthNEVERMIND systemperceived usefulnessstructural equation modelingTechnology Acceptance Modelusability

Identifiers

PMID37698900
PMCPMC10523218
OpenAlexW4386026291

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.