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

Academic Editor: Thomas Licht Received: 1 April 2025 Revised: 16 May 2025 Accepted: 23 May 2025 Published: 26 May 2025 Citation: Mitsis, A.; Filis, P.; Karanasiou, G.; Georga, E.I.; Mauri, D.; Naka, K.K.; Constantinidou, A.; Keramida, K.; Tsekoura, D.; Mazzocco, K.; et al. 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. Cancers 2025,17, 1780. https://doi.org/10.3390/ cancers17111780 Copyright: © 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/ licenses/by/4.0/). Systematic Review 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 Alexandros Mitsis 1, Panagiotis Filis 2,3 , Georgia Karanasiou 1,4 , Eleni I. Georga 1, Davide Mauri 3, Katerina K. Naka 5, Anastasia Constantinidou 6,7,8, Kalliopi Keramida 9,10, Dorothea Tsekoura 11 , Ketti Mazzocco 12,13 , Alexia Alexandraki 14 , Effrosyni Kampouroglou 11 , Yorgos Goletsis 1,15 , Andri Papakonstantinou 16 , Athos Antoniades 17 , Cameron Brown 17 , Vasileios Bouratzis 5 , Erika Matos 18 , Kostas Marias 19,20 , Manolis Tsiknakis 19 and Dimitrios I. Fotiadis 1,4,* 1Unit of Medical Technology and Intelligent Information Systems, Department of Material Science and Engineering, University of Ioannina, 45110 Ioannina, Greece; [email protected] (A.M.); [email protected] (G.K.); [email protected] (E.I.G.); [email protected] (Y.G.) 2Department of Hygiene and Epidemiology, University of Ioannina School of Medicine, 45110 Ioannina, Greece; [email protected] 3Department of Medical Oncology, University of Ioannina, 45110 Ioannina, Greece; [email protected] 4Department of Biomedical Research, Institute of Molecular Biology and Biotechnology, FORTH, 45110 Ioannina, Greece 5Second Department of Cardiology, University Hospital of Ioannina, Stavros Niarchos Avenue, 45500 Ioannina, Greece; [email protected] (K.K.N.); v[email protected] (V.B.) 6Bank of Cyprus Oncology Centre, Nicosia 2029, Cyprus; [email protected] 7Cyprus Cancer Research Institute, Nicosia 2109, Cyprus 8Medical School, University of Cyprus, Nicosia 2029, Cyprus 9General Anti-Cancer Oncological Hospital, Agios Savvas, 11522 Athens, Greece; [email protected] 10 Department of Cardiology, University Hospital Attikon, National and Kapodistrian University of Athens, 12462 Athens, Greece 11 2nd Department of Surgery, Aretaieio University Hospital, National and Kapodistrian University of Athens, 76 Vas. Sofias Av., 11528 Athens, Greece; [email protected] (D.T.); efrosini.kampour[email protected] (E.K.) 12 European Institute of Oncology IRCCS, 20141 Milan, Italy; [email protected] 13 Department of Oncology and Hemato-Oncology, University of Milan, 20122 Milan, Italy 14 A.G. Leventis Clinical Trials Unit, Bank of Cyprus Oncology Centre, 32 Acropoleos Avenue, Nicosia 2006, Cyprus; [email protected]g.cy 15 Laboratory of Business Economics and Decisions (LABED@UoI), Department of Economics, University of Ioannina, 45110 Ioannina, Greece 16 Department of Oncology-Pathology, Karolinska Institutet and University Hospital, 171 64 Stockholm, Sweden; [email protected] 17 Stremble Ventures Ltd., 59 Christaki Kranou, Limassol 4042, Cyprus; [email protected] (A.A.); [email protected] (C.B.) 18 Department of Medical Oncology, Institute of Oncology Ljubljana, 1000 Ljubljana, Slovenia; [email protected] 19 Department of Electrical and Computer Engineering, Hellenic Mediterranean University, 71410 Heraklion, Greece; [email protected] (K.M.); [email protected] (M.T.) 20 Computational Biomedicine Laboratory, Institute of Computer Science, FORTH, 70013 Heraklion, Greece *Correspondence: [email protected] Simple Summary: Breast cancer is widespread globally and significantly affects patients’ well being. e-Health solutions are rapidly increasing, offering support for patients’ mental health and quality of life. This systematic review includes 27 randomized studies with a total of 2898 patients, which evaluated the effects of e-Health interventions on mental health and quality of life in breast cancer patients. The results show a significant reduction in anxiety and depression and an improvement in quality of life, but no significant effect on reducing distress. Cancers 2025,17, 1780 https://doi.org/10.3390/cancers17111780 Cancers 2025,17, 1780 2 of 19 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 I 2 = 90%) but had no significant effect on distress (SMD = − 0.78; 95% CI: −1.93 to 0.37 ;p= 0.184; and I 2 = 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: e-health; breast cancer; anxiety; depression; quality of life 1. Introduction Breast cancer (BC) is the most common type of cancer, commonly diagnosed in women, although it can occur in men [ 1 ]. It poses a significant health burden globally, with over 2 million new cases diagnosed in 2022 [ 2 ]. It is also one of the leading causes of death in women worldwide, despite its downward trend, particularly in developed countries. This highlights the importance of advancing BC management, with a focus on enhancing early detection methods and developing more effective treatment options [3]. Currently, therapeutic options for BC encompass surgical interventions, chemotherapy, endocrine therapy, radiotherapy, targeted therapy, and immunotherapy [ 4 ]. The evolution of novel treatments and therapies has notably improved the survival of BC patients. Approximately 70% of patients experience an increase in life expectancy of more than five years, while 40% experience an increase of more than ten years. Moreover, for 15% of patients, life expectancy extends by over twenty years [ 5 ]. However, it is well recognized that patients’ mental health and QoL are typically impacted by BC treatment and its sequelae, and some of them can be lifelong [ 6 ]. Thus, the transition from the “cancer struggle” to “regular life” for survivors mandates that they first deal with the adverse effects of cancer Cancers 2025,17, 1780 3 of 19 therapy [ 7 ]. Therefore, as survival rates have improved considerably [ 5 ], the mitigation of cancer therapy-related adverse effects is crucial to improving QoL. The management of QoL, in its physical and psychological components, will result in improved treatment efficacy and prognosis [8]. E-health refers to the use and application of digital technologies (e.g., internet, mobile devices, wearables, software tools, etc.) that support healthcare delivery for improved disease monitoring, management, and QoL [ 9 ]. The rapid improvements and increasing accessibility of these technologies have driven the widespread adoption of e-health interventions in cancer care, enabling, among others, patient engagement and communication with healthcare experts throughout the healthcare delivery continuum [ 10 ]. Currently, there are several studies showing that e-health interventions may have a positive effect on cancer patients’ physical, psychological, and social functioning, as well as their self-efficacy, QoL, mental well-being, depression, and anxiety [ 5 , 11 – 13 ]. Nonetheless, the overall impact of e-health interventions on patients’ mental health and QoL is not yet clear, largely due to the variability in study designs, intervention types, and outcome measures used in existing research, and this variability presents a significant challenge in conducting comprehensive reviews to accurately estimate their overall effectiveness. To address this challenge, we performed a systematic review with the aim of quantifying and summarizing the available randomized evidence on the use of supportive interventions delivered via e-health on patients’ mental health and QoL. 2. Materials and Methods 2.1. Search Strategy The PubMed and Scopus databases were systematically scrutinized from inception up to 7 November 2024 for eligible studies. This study was registered in the International Prospective Register of Systematic Reviews. To identify relevant studies for this review, a Boolean string consisting of several relevant keywords was generated. The following string was applied: “((quality of life) AND (mental health AND ((mental OR emotional OR psychological OR social) AND well-being) OR mental disorder OR depression OR anxiety) AND (breast cancer) AND (e-health OR electronic health OR information and communication technolog* OR ICT OR m-health OR mobile health OR digital health OR mobile OR internet OR web OR online OR digital OR remote OR smartphone OR application OR app OR e-coach))”. This string was developed so that the search of each database only identified studies relevant to the topic, thereby ensuring consistency and comprehensiveness in the search process. For a study to be considered for inclusion in our analysis, it had to meet the predefined inclusion criteria listed in Table 1. In addition, the study had to be published in English, and the full text had to be available. The inclusion criteria were determined using the PICOS framework [14]. Table 1. Inclusion criteria for studies. Parameter Inclusion Criteria Population Adult men or women (aged > 18 years old) diagnosed with BC Intervention Patient-directed e-health intervention Comparator Studies in which patients received standard care or control intervention Outcomes QoL, anxiety, depression, distress Study Design Randomized controlled trials Cancers 2025,17, 1780 4 of 19 2.2. Data Extraction The papers retrieved were subsequently handled with an automated tool (Zotero 6.0.36, Corporation for Digital Scholarship, Vienna, VA, USA), which was used to remove duplicate entries. The remaining articles were then independently screened for title and abstract by each of the two reviewers who participated in the study selection process. Potentially eligible articles were then assessed in full text by the same reviewers independently once again. Any disagreements were resolved through discussion or, if necessary, with the involvement of a third reviewer. The data from the remaining studies were extracted by two independent reviewers according to the PRISMA guidelines. A predefined data extraction sheet was used to collect information from each study. The focus was on evaluating changes in anxiety, depression, quality of life, and distress before and after the intervention involving e-health applications. Studies that did not report outcomes for any of these categories were excluded from the current analysis. Data extraction was performed independently by two authors, and the accuracy of the extracted data was verified by a third author. For each study, we extracted information regarding the authors, publication year, ID, sample size, cancer and therapy information, intervention type, duration of the intervention, study design, outcomes of interest, and a summary of the results. 2.3. Risk of Bias and Quality Assessment Two independent reviewers assessed the quality of the included studies using the Cochrane risk-of-bias tool, a commonly used method for assessing the risk of bias in various study designs, including randomized controlled trials (RCTs) [ 15 ]. The quality of each study was evaluated in the following domains: adequate sequence generation, allocation concealment, adequate blinding of patients and personnel, adequate blinding of outcome assessors, incomplete outcome data assessment, and selective reporting bias [16]. 2.4. Statistical Analysis We performed a meta-analysis using RStudio software (version 4.3.1; R Core Team, 2023) with the ‘meta’ and ‘metafor’ packages. Standardized mean differences (SMDs) and 95% confidence intervals were used for the outcomes, measured as the mean and standard deviation. A random-effects model was employed to account for potential differences in samples and interventions across the included studies. A p-value of <0.05 was considered statistically significant. Statistical heterogeneity was assessed using the I 2 statistic [ 17 ]. Values of 0–25%, 25–50%, and 50–100% were considered to indicate low, moderate, and substantial heterogeneity, respectively [ 18 ]. Moreover, subgroup analyses were performed based on the mode of e-health intervention (Web, mobile applications, other), as well as the duration of the intervention (less than 12 weeks and 12 weeks or more). Small study effects, indicative of publication bias, were assessed by visual inspection of funnel plots and Egger’s test [ 19 ]. This assessment was performed only for analyses that included 10 or more studies, and a p-value < 0.1 was considered indicative of small study effects. 3. Results A total of 1488 records were identified following the search of Scopus and PubMed. After title and abstract screening, 109 publications were identified as potentially eligible (Figure 1). Following detailed screening, our systematic review retained a total of 27 studies focusing on e-health interventions targeting mental health and QoL in BC patients. Cancers 2025,17, 1780 5 of 19 3.1. Characteristics of Included Studies All the studies included in the final analysis focused on adult patients and cancer survivors aged 18 years or older. All the studies included were RCTs specifically targeting patients diagnosed with BC. The sample size ranged from 35 to 363 patients. Five studies had a sample size of less than 50 patients, twelve between 50 and 100 patients, while the remaining studies had a sample size of more than 100 patients. In total, (i) patients in 10 studies had a mean age between 50 and 60 years, patients in 1 study had a mean age of over 60 years, and patients in the remaining studies had a mean age of 50 years or younger; (ii) the publications were from 2018 onward: nine studies were published in 2024, two studies in 2023, three studies in 2022, two studies in 2021, four studies in 2020, three studies in 2019, and four studies in 2018; and (iii) the majority (13/27) were conducted in Asia, followed by seven, four, and three in Europe, America, and Australia, respectively. The included studies had different intervention durations, ranging from 3 to 24 weeks, and different e-health tools were used. Notably, the majority of studies (16/27) used mHealth apps [20–37], while the remaining studies used web applications [38–46]. Figure 1. PRISMA flow diagram. The main characteristics and findings of the studies included are summarized in Tables 2–4. Cancers 2025,17, 1780 6 of 19 Table 2. Studies on e-health interventions in cancer patients. Authors PMID/DOI Number of Patients Stage/Status Therapy Experimental Intervention Comparison Duration of Intervention (Weeks) Study Outcomes (Compared to Control Group) Akkol-Solakoglu, et al. [46]36635249 Total = 72 (I = 49, C = 23), Mean age: 47.8 0, I, II, III, IV Chemotherapy, Radiotherapy, Hormonal therapy, Surgery Web-based cognitive behavioral therapy Usual care 8 No significant effect on anxiety, depression, fear of recurrence, and QoL. Atema et al. [38] 30763176 Total = 169 (I = 85, C = 85), Mean age: 47.4 I, II, III, IV Surgery, Chemotherapy, Radiation therapy, Immunotherapy, Endocrine therapy, Oophorectomy Internet-based cognitive behavioral therapy Waiting list 24 Improvements in hot flushes, sleep quality, and menopausal symptoms. Chen et al. [20] 38889503 Total = 94 (I = 47, C = 47), Mean age: 49.3 I, II Chemotherapy Phone-based support program Usual care 7 Higher self-care efficacy, better QoL, less symptom distress, reduced anxiety and depression. Dong et al. [21] 31242926 Total = 60 (I = 30, C = 30), Mean age: 49.7 I, II, III Chemotherapy Internet and social media software (CEIBISMS) Traditional rehab care 12 Improvements in vitality, mental health, and health transition. Ghanbari et al. [22] 34003138 Total = 82 (I = 41, C = 41), Mean age: 46.4 Nonmetastatic Not reported mHealth psychoeducational intervention Waiting list 5 Lower anxiety and higher self-esteem. Graham et al. [39] 38752788 Total = 79 (I = 40, C = 39), Mean age: 59.4 I, II, III Surgery, Chemotherapy, Radiation therapy, Hormone therapy Remotely delivered one-to-one therapy Usual care 24 Improvements in medication adherence, QoL, distress, and flexibility. Handa et al. [23] 32201165 Total = 102 (I = 52, C = 50), Mean age: 49.9 ER+, ER-, PR+, PR-, HER2+, HER2Chemotherapy Smartphone app during chemotherapy Usual care 12 No significant anxiety/depression change; possible enhanced care via info sharing. Heinrich et al. [24] 39439014 Total = 70 (I = 32, C = 38), Mean age: 57.6 Primary breast cancer Surgery, Chemotherapy, Radiation therapy mHealth cognitive behavioral therapy Usual care 12 Improved anxiety, HRQoL, and illness perception. Holtdirk et al. [40] 33961667 Total = 363 (I = 181, C = 182), Mean age: 49.9 Not reported Surgery, Chemotherapy, Radiation treatment Website with CBT Usual care 12 Improved QoL and diet; no change in exercise. Jacobs et al. [41] 35924869 Total = 100 (I = 50, C = 50), Mean age: 56.1 0, I, II, III Surgery, Chemotherapy, Radiation therapy, Endocrine therapy Telehealth for symptom management Medication monitoring 12 Less distress, better self-management, coping, mood, and QoL. Kim et al. [35] 30578205 Total = 76 (I = 36, C = 40), Mean age: 51.0 IV Chemotherapy (taxanes, anthracyclines, capecitabine, platinum compounds) mHealth game to reduce chemotherapy side effects Conventional education group 3 Better drug adherence, fewer chemotherapy adverse effects, better QoL, no significant difference in depression or anxiety. Cancers 2025,17, 1780 7 of 19 Table 3. Studies on e-health interventions in cancer patients. Authors PMID/DOI Number of Patients Stage/Status Therapy Experimental Intervention Comparison Duration of Intervention (Weeks) Study Outcomes (Compared to Control Group) Korkmaz et al. [42] 31119709 Total = 48 (I = 24, C = 24), Mean age: 47.7 II, III Surgery Web-based education program on anxiety and QoL Routine education 4 Lower levels of anxiety and improvements in QoL. Lally et al. [43] 31414245 Total = 100 (I = 57, C = 43), Mean age: 54.2 0, I, II Surgery, Chemotherapy, Radiation therapy Tailored self-management psychoeducational program Usual care 12 No significant outcomes. Li et al. [25] 39363984 Total = 44 (I = 23, C = 21), Mean age: 47.9 I, II, III Chemotherapy Wearable device-based aerobic exercise for physical and mental health Waiting list 12 Improvements in physical fitness, mental health, sleep quality, QoL, and fewer adverse effects. Okuyama et al. [26] 38796818 Total = 125 (I = 61, C = 64), Mean age: 63.5 I, II, III Chemotherapy, Radiotherapy, endocrine therapy, Combination therapy Electronic patient-reported outcome app Usual care 12 No improvements in BC patients’ QoL. Philips et al. [27] 39014267 Total = 49 (I = 25, C = 24), Mean age: 54.8 IV Chemotherapy, Radiation therapy, Immunotherapy, Targeted therapy, Hormone therapy Physical activity promotion via mHealth intervention Healthy lifestyle control 12 Improvements in activity, QoL, some PROs, social cognitive theory constructs, and functional performance. Peng et al. [28] 36347151 Total = 60 (I = 30, C = 30), Mean age: 41.8 I, II, III, IV mastectomy, conservative therapy, mastectomy + breast construction Online mindfulness-based intervention on fear of cancer recurrence and quality of life Usual care 6 Lower level of fear of cancer recurrence (FCR) and an improvement in quality of life Rigg et al. [44] 39438337 Total = 35 (I = 17, C = 18), Mean age: 57.4 IV Surgery, Chemotherapy, Radiotherapy, Hormonal therapy, Other treatment Web-based self-guided psychosocial program Usual care 6 Small improvements in fear of progression and global QoL, alongside some deteriorations in distress and mental QoL. Rosen et al. [29] 10.1002/pon.4764 Total = 112 (I = 57, C = 55), Mean age: 52.2 Not reported Not reported mHealth mindfulness training Waiting list 8 Improvements in QoL. Sarac et al. [30] 39257013 Total = 82 (I = 42, C = 40), Mean age: 49.0 Not reported Adjuvant, Neoadjuvant, Surgery (BCS + SLNB, Mastectomy + SLNB, MRM) Informative mobile app use on anxiety, distress, and QoL Usual care 4 Lower anxiety and distress levels, but no difference in overall QoL. Singleton et al. [31] 35460441 Total = 156 (I = 78, C = 78), Mean age: 55.1 Not reported Surgery, Radiotherapy, Chemotherapy, Endocrine therapy, Targeted therapy Supporting women’s health outcomes through text messages. Usual care 24 No significant differences between groups for self-efficacy, adjusted mean difference, QoL, mental health, physical activity, or BMI. Cancers 2025,17, 1780 8 of 19 Table 4. Studies on e-health interventions in cancer patients. Authors PMID/DOI Number of Patients Stage/Status Therapy Experimental Intervention Comparison Duration of Intervention (Weeks) Study Outcomes (Compared to Control Group) White et al. [45] 30137657 Total = 337 (I = 202, C = 177), Mean age: 43.7 I, II Surgery, Chemotherapy, Radiotherapy, Targeted therapy, Hormonal therapy Information-based breast cancer-specific website Usual care 24 Mean level of QoL scores did not differ between groups. Zhang et al. [36] 38418478 Total = 36 (I = 19, C = 17), Mean age: 47.2 IV Not reported Virtual reality intervention for managing cancer and living meaningfully. Waiting list 12 CALM therapy led to reductions in depression, distress, and attachment avoidance, as well as improvements in quality of life. Zhang et al. [37] 35712124 Total = 90 (I = 45, C = 45), Mean age: 51.6 I, II, III, IV Surgery, Chemotherapy Virtual reality intervention for psychological distress and symptom management. Usual care 12 VR-CALM improves well-being in survivors. Zhou et al. [32] 32272281 Total = 111 (I = 56, C = 55), Mean age: 49.9 I, II, III Surgery, Chemotherapy, Radiotherapy, Endocrine therapy WeChat-based nursing program for postoperative BC rehabilitation Usual care 24 Significant improvement in HRQoL. Zhou et al. [34] 31342310 Total = 132 (I = 66, C = 66), Mean age: 44.5 I, II, III Surgery, Chemotherapy, Radiotherapy, Endocrine therapy Mobile-based training on resilience, depression, and anxiety management Usual care 12 Improvements were observed in psychological resilience, anxiety, and depression scores. Zhu et al. [33] 29712622 Total = 114 (I = 57, C = 57), Mean age: 47.2 I, II, III, IV Surgery, Chemotherapy Mobile breast cancer e-support program Usual care 12 E-support + care improved self-efficacy, symptom interference, and QoL but not social support, symptom severity, anxiety, or depression. Cancers 2025,17, 1780 9 of 19 3.2. Risk of Bias Within Studies We used the Cochrane risk-of-bias tool to assess the quality of the included studies. Of the 27 included studies, only three RCTs met all the requirements to be considered as having a low risk of bias. Of the 27 included studies, 24 (89%) had an appropriate sequence generation process, while only 13 used allocation concealment. Most of the studies (81.5%) did not blind their patients or professionals, or it was unclear whether blinding was used, resulting in a high or unclear risk of bias. Thirteen studies (48%) implemented blinding of outcome assessors. Twenty studies (74%) were judged as posing a low risk of incomplete outcome data. Altogether, 21 (78%) studies were considered as having a low risk of reporting bias. Figures 2and 3show the results of the quality assessment of the included studies using the Cochrane Collaboration’s tool. Figure 2. Risk-of-bias graph. Figure 3. Risk-of-bias summary [20–46]. Cancers 2025,17, 1780 16 of 19 Therefore, to define the optimal type and regimen of e-health intervention, high-quality RCTs are needed. 5. Conclusions E-health support has been shown to significantly improve QoL and reduce anxiety and depression in BC patients. These results imply that e-health interventions have been at least to some extent successful and beneficial. Considering the decades-long dominance of traditional treatments and supportive care management, these e-health intervention outcomes are more than encouraging for the future of medical care. Additionally, the reasons for the moderate effectiveness of some e-health interventions need to be further analyzed, including potential biases, implementation issues, and methodological weaknesses that may impact the results. Supplementary Materials: The following supporting information can be downloaded at https: //www.mdpi.com/article/10.3390/cancers17111780/s1, Figure S1: Forest plots showing the effects of e-Health interventions on anxiety, presented by intervention duration (less than 12 weeks: p= 0.011; 12 weeks or more: p= 0.033); Figure S2: Forest plots showing the effects of e-Health interventions on depression, presented by intervention duration (less than 12 weeks: p= 0.278; 12 weeks or more: p= 0.065 ); Figure S3: Forest plots showing the effects of e-Health interventions on quality of life (QoL), presented by intervention duration (less than 12 weeks: p= 0.022; 12 weeks or more: p= 0.012 ); Figure S4 : Forest plots showing the effects of e-Health interventions on distress, presented by intervention duration (less than 12 weeks: p= 0.52; 12 weeks or more: p= 0.011); Figure S5: Funnel plot of publication bias on anxiety. The result of Egger’s test (p= 0.003) indicates the presence of small study effects, as suggested by the asymmetry in the plot; Figure S6: funnel plot of publication bias on depression. The result of Egger’s test (p= 0.037) indicates the presence of small study effects, as suggested by the asymmetry in the plot; Figure S7: Funnel plot of publication bias on QoL. The result of Egger’s test (p= 0.032) indicates the presence of small study effects, as suggested by the asymmetry in the plot. Author Contributions: Conceptualization: A.M., P.F., E.I.G., G.K. and K.M. (Kostas Marias); Methodology: A.M., P.F. and E.I.G.; Validation: A.M. and E.I.G.; Formal Analysis: A.M. and P.F.; Investigation: A.M. and E.I.G.; Data Curation: A.M., P.F. and E.I.G.; Writing—Original Draft: A.M. and P.F.; Visualization: A.M.; Supervision: K.K.N. and D.I.F.; Writing—Review and Editing: P.F., G.K., E.I.G., D.M., K.K.N., A.C., K.K., D.T., K.M. (Ketti Mazzocco), A.A. (Alexia Alexandraki), E.K., Y.G., A.P., A.A. (Athos Antoniades), C.B., V.B., E.M., K.M. (Kostas Marias), M.T. and D.I.F. All authors have read and agreed to the published version of the manuscript. Funding: This research received no external funding. Data Availability Statement: The data presented in this study are available in this article and Supplementary Materials. Conflicts of Interest: Authors Athos Antoniades and Cameron Brown were employed by the company Stremble Ventures Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. 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