Impact of e-Health Interventions on Mental Health and Quality of Life in Breast Cancer Patients: A Systematic Review and Meta-Analysis of Randomized Controlled Trials

Abstract

Background/Objectives: The prevalence of breast cancer (BC) is significant globally. The malignancy itself and the related treatments have a considerable impact on patients' overall well-being. The adoption of e-health solutions for patients is increasing rapidly worldwide, since these innovative tools hold significant potential to positively impact the mental health and quality of life (QoL) of BC patients. However, their overall impact is still being explored, and further understanding and analysis are required. This review paper aims to present, quantify, and summarize the cumulative available randomized evidence on the state of the art of supportive interventions delivered via e-health applications for patients' mental health and QoL before, during, and after BC treatment. Methods: A systematic review was conducted following the PRISMA guidelines in the Scopus and PubMed databases on 7 November 2024 to identify studies that utilized internet-based interventions in BC patients. The inclusion criteria were as follows: adult men and women (aged > 18 years) diagnosed with breast cancer (BC) who received patient-directed e-health interventions, compared to standard care or control interventions. The studies had to focus on outcomes such as quality of life (QoL), anxiety, depression, and distress, and be limited to randomized controlled trials (RCTs). The PRISMA-P guidelines were followed. Risk of bias was assessed using the Cochrane risk-of-bias (RoB) tool for randomized controlled trials. Results: A total of 27 randomized studies, involving 2898 patients, were included in this systematic review. The e-health interventions significantly affected patients' anxiety (SMD = -0.80; 95% CI: -1.33 to -0.27; p < 0.01; and I² = 94%), depression (SMD = -0.74; 95% CI: -1.40 to -0.09; p = 0.026; and I² = 95%) and QoL (SMD = 0.65; 95% CI: 0.27 to 1.04; p < 0.01; and I² = 90%) but had no significant effect on distress (SMD = -0.78; 95% CI: -1.93 to 0.37; p = 0.184; and I² = 95%). Conclusions: This study showed that e-health interventions can improve QoL, reduce anxiety, and decrease depression in adult BC patients. However, no noticeable impact on reducing distress levels was observed. Additionally, given the diversity of interventions, these results should be interpreted with caution. To determine the optimum duration, validate different intervention approaches, and address methodological gaps in previous studies, more extensive clinical studies are needed.

Keywords: anxiety; breast cancer; depression; e-health; quality of life.

Figure 1

PRISMA flow diagram.

Figure 2

Risk-of-bias graph.

Figure 3

Risk-of-bias summary [20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46].

Figure 4

Forest plots—effects of e-health interventions on anxiety, presented by method of intervention (web application: p = 0.083; mobile application: p = 0.019; other: p = 0.142) [20,22,23,24,25,27,30,31,33,34,35,36,37,38,39,40,41,45,46]. SD, standard deviation; SMD, standardized mean difference. The “Other” category includes two studies that used virtual reality-based interventions and one study that used a wearable-based intervention.

Figure 5

Forest plots—effects of e-health interventions on depression, presented by method of intervention (web application: p = 0.008; mobile application: p = 0.155; other: p = 0.195) [20,23,24,25,27,33,34,35,36,37,38,39,40,41,45,46]. SD, standard deviation; SMD, standardized mean difference. The “Other” category includes two studies that used virtual reality-based interventions and one study that used a wearable-based intervention.

Figure 6

Forest plots—effects of e-health interventions on QoL, presented by method of intervention (web application: p = 0.052; mobile application: p = 0.017; other: p = 0.364) [20,21,25,26,28,29,30,31,32,33,35,36,37,39,41,42,44,45,46]. SD, standard deviation; SMD, standardized mean difference. The “Other” category includes two studies that used virtual reality-based interventions and one study that used a wearable-based intervention.

Figure 7

Forest plots—effects of e-health interventions on distress, presented by method of intervention (web application: p = 0.783; mobile application: p = 0.168; other: p = 0.173) [20,24,30,36,37,38,43,44,47]. SD, standard deviation; SMD, standardized mean difference. The “Other” category includes two studies that used virtual reality-based interventions (p = 0.1734).