Background: Women with breast cancer and improved survival face some specific quality of life (QOL) issues. Electronic health (eHealth) is a useful tool aiming to enhance health services. However, evidence remains controversial about the effect of eHealth on QOL in women with breast cancer. Another unstudied factor is the effect on specific QOL functional domains. Therefore, we undertook a meta-analysis about whether eHealth could improve the overall and specific functional domains of QOL in women with breast cancer.
Methods: PubMed, Cochrane Library, EMBASE, and Web of Science were searched to identify appropriate randomized clinical trials from database inception to March 23, 2022. The standard mean difference (SMD) served as the effect size and the DerSimonian-Laird random effects model was constructed for meta-analysis. Subgroup analyses were conducted according to different participant, intervention, and assessment scale characteristics.
Results: We initially identified 1954 articles excluding duplicates and ultimately included 13 of them involving 1448 patients. The meta-analysis revealed that the eHealth group had significantly higher QOL (SMD 0.27, 95% confidence interval [95% CI] 0.13-0.40, p < 0.0001) than the usual care group. Additionally, although not statistically significant, eHealth tended to improve the physical (SMD 2.91, 95% CI -1.18 to 6.99, p = 0.16), cognitive (0.20 [-0.04, 0.43], p = 0.10), social (0.24 [-0.00, 0.49], p = 0.05), role (0.11 [0.10, 0.32], p = 0.32), and emotional (0.18 [0.08, 0.44], p = 0.18) domains of QOL. Overall, consistent benefits were observed in both the subgroup and pooled estimates.
Conclusions: eHealth is superior to usual care in women with breast cancer for improved QOL. Implications for clinical practice should be discussed based on subgroup analysis results. Further confirmation is needed for the effect of different eHealth patterns on specific domains of QOL, which would help improve specific health issues of the target population.
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|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10358200 | PMC |
| http://dx.doi.org/10.1002/cam4.6094 | DOI Listing |
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