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. 2023 Sep 12:9:e49775.
doi: 10.2196/49775.

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  1 , Jesper Alvarsson-Hjort  2   3 , Gergö Hadlaczky  1   2 , Danuta Wasserman  1 , Manuel Ottaviano  4 , Sergio Gonzalez-Martinez  4 , Sara Carletto  5 , Enzo Pasquale Scilingo  6 , Gaetano Valenza  6 , Vladimir Carli  1
Affiliations

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 et al. JMIR Cancer. .

Abstract

Background: eHealth 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.

Objective: This 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.

Methods: Data 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.

Results: The 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 (βuse at 12 weeks is predicted by PU at 12 weeks=.384), whereas system use at 2 weeks moderately predicted system use at 12 weeks (βuse at 12 weeks is predicted by use at 2 weeks=.239). Notably, there were uncertain associations between baseline variables (education, sex, and mental health symptoms) and system use at 2 weeks, indicating a need for better predictors for early system use.

Conclusions: This 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.

Keywords: NEVERMIND system; Technology Acceptance Model; cancer; digital health; eHealth system; mental health; perceived usefulness; structural equation modeling; usability.

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Conflict of interest statement

Conflicts of Interest: None declared.

Figures

Figure 1
Figure 1
The Technology Acceptance Model (adopted and modified from Davis [15]). PEOU: perceived ease of use; PU: perceived usefulness.
Figure 2
Figure 2
Model of the study. PEOU: perceived ease of use; PU: perceived usefulness.
Figure 3
Figure 3
Timeline of data collection. BDI-II: Beck Depression Inventory–II; DASS-21: Depression, Anxiety, and Stress Scale–21; PEOU: perceived ease of use; PU: perceived usefulness.
Figure 4
Figure 4
The Bayesian structural regression model results showing standardized regression coefficients (β) for all paths. *The highest posterior density credibility intervals are in the same direction (positive or negative relation), thus not crossing 0. BDI-II: Beck Depression Inventory–II; DASS-21: Depression, Anxiety, and Stress Scale–21; PEOU: perceived ease of use; PU: perceived usefulness.
Figure 5
Figure 5
Prior and posterior distributions for the 4 associations with less uncertainty. The prior distributions are shown in blue, and the posterior distributions are shown in red. A and B show the direct predictors of use at 12 weeks. C and D show the indirect predictors of use at 12 weeks preceding the distribution in A. PEOU: perceived ease of use; PU: perceived usefulness.
See this image and copyright information in PMC

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