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Effectiveness of mobile health-based self-management interventions in breast cancer patients: a meta-analysis

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Abstract

Purpose

To determine the effectiveness of mobile health-based self-management interventions on medical/behavioral, role, and emotional management in breast cancer patients.

Methods

The Embase, MEDLINE, SINOMED, the Cochrane Library, Web of Science, China National Knowledge Infrastructure (CNKI), WanFang, and Weipu (VIP) databases were extensively searched from inception to November 30, 2020, to identify eligible clinical trials. Outcomes included medical/behavioral management (self-management behavior, functional exercise compliance, self-efficacy, complications, and symptoms), role management (role functioning), and emotional management (anxiety and depression), social support, and health-related quality of life.

Results

Twenty-four studies were included in this meta-analysis. The results of the meta-analysis indicated that mobile health-based self-management interventions could potentially improve breast cancer patients’ self-management behavior, functional exercise compliance (WMD = 15.80, 95% CI = 10.53 to 21.08, P < 0.001), self-efficacy (SMD = 1.22, 95% CI = 0.57 to 1.87, P < 0.001), and health-related quality of life (SMD = 0.78, 95% CI = 0.44 to 1.12, P < 0.001); reduce the incidence of lymphedema (RR = 0.20, 95% CI = 0.15 to 0.26, P < 0.001); and relieve the level of anxiety (SMD =  − 0.67, 95% CI =  − 0.99 to − 0.35, P < 0.001). However, patients assigned to the mobile health group and the conventional care group did not differ significantly in symptom relief (including pain and fatigue), role functioning, depression, or social support (all P ≥ 0.05).

Conclusion

Mobile health–based self-management interventions can potentially facilitate the self-management and health-related quality of life of breast cancer patients.

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References

  1. The International Agency for Research on Cancer (IARC) (2020) Latest global cancer data: cancer burden rises to 19.3 million new cases and 10.0 million cancer deaths in 2020. https://www.iarc.fr/faq/latest-global-cancer-data-2020-qa/ Accessed 4 Jan 2021

  2. Siegel RL, Miller KD, Jemal A (2020) Cancer statistics, 2020. CA Cancer J Clin 70:7–30. https://doi.org/10.3322/caac.21590

    Article  PubMed  Google Scholar 

  3. De Vrieze T, Nevelsteen I, Thomis S, De Groef A, Tjalma W, Gebruers N, Devoogdt N (2020) What are the economic burden and costs associated with the treatment of breast cancer-related lymphoedema? A systematic review. Support Care Cancer 28:439–449. https://doi.org/10.1007/s00520-019-05101-8

    Article  PubMed  Google Scholar 

  4. Ho PJ, Gernaat S, Hartman M, Verkooijen HM (2018) Health-related quality of life in Asian patients with breast cancer: a systematic review. BMJ Open 8:e20512. https://doi.org/10.1136/bmjopen-2017-020512

    Article  Google Scholar 

  5. Chou YH, Chia-Rong HV, Chen X, Huang TY, Shieh SH (2020) Unmet supportive care needs of survival patients with breast cancer in different cancer stages and treatment phases. Taiwan J Obstet Gynecol 59:231–236. https://doi.org/10.1016/j.tjog.2020.01.010

    Article  PubMed  Google Scholar 

  6. Gálvez-Hernández CL, Boyes A, Ortega-Mondragón A, Romo-González AG, Mohar A, Mesa-Chavez F, Oñate-Ocaña L, Villarreal-Garza C (2021) Unmet needs among breast cancer patients in a developing country and supportive care needs survey validation. Rev Invest Clin 73:245–250. https://doi.org/10.24875/RIC.21000068

    Article  PubMed  Google Scholar 

  7. Lorig KR, Holman H (2003) Self-management education: history, definition, outcomes, and mechanisms. Ann Behav Med 26:1–7. https://doi.org/10.1207/S15324796ABM2601_01

    Article  PubMed  Google Scholar 

  8. Robert WJ (2002) Essential elements of self-management interventions. Center for the Advancement of Health, Washington, DC

    Google Scholar 

  9. Zhou K, Wang W, Zhao W, Li L, Zhang M, Guo P, Zhou C, Li M, An J, Li J, Li X (2020) Benefits of a WeChat-based multimodal nursing program on early rehabilitation in postoperative women with breast cancer: a clinical randomized controlled trial. Int J Nurs Stud 106:103–565. https://doi.org/10.1016/j.ijnurstu.2020.103565

    Article  Google Scholar 

  10. Admiraal JM, van der Velden A, Geerling JI, Burgerhof J, Bouma G, Walenkamp A, de Vries E, Schröder CP, Reyners A (2017) Web-based tailored psychoeducation for breast cancer patients at the onset of the survivorship phase: a multicenter randomized controlled trial. J Pain Symptom Manage 54:466–475. https://doi.org/10.1016/j.jpainsymman.2017.07.009

    Article  PubMed  Google Scholar 

  11. Chee W, Lee Y, Im E, Chee E, Tsai H, Nishigaki M, Yeo SA, Schapira MM, Mao JJ (2017) A culturally tailored Internet cancer support group for Asian American breast cancer survivors: a randomized controlled pilot intervention study. J Telemed Telecare 23:618–626. https://doi.org/10.1177/1357633X16658369

    Article  PubMed  Google Scholar 

  12. Kim HJ, Kim HS (2020) Effects of a web-based expert support self-management program (WEST) for women with breast cancer: a randomized controlled trial. J Telemed Telecare 26:433–442. https://doi.org/10.1177/1357633X19850386

    Article  PubMed  Google Scholar 

  13. Kim HJ, Kim SM, Shin H, Jang JS, Kim YI, Han DH (2018) A mobile game for patients with breast cancer for chemotherapy self-management and quality-of-life improvement: randomized controlled trial. J Med Internet Res 20:e273. https://doi.org/10.2196/jmir.9559

    Article  PubMed  PubMed Central  Google Scholar 

  14. Zhang Y (2019). Construction and empirical study of self-management intervention program based on mobile health application for breast cancer patients. the PLA Naval Medical University

  15. Zhu J, Ebert L, Liu X, Wei D, Chan SW (2018) Mobile breast cancer e-support program for chinese women with breast cancer undergoing chemotherapy (Part 2): Multicenter Randomized Controlled Trial. JMIR MHEALTH AND UHEALTH 6:e104. https://doi.org/10.2196/mhealth.9438

    Article  Google Scholar 

  16. Zhou H, Wang J, Li S (2019) Effect of WeChat supported hospital-family collaborative transitional care on postoperative functional recovery in breast cancer patients. J Nurs Sci 34:63–66

    Google Scholar 

  17. Pope ZC, Zeng N, Zhang R, Lee H Y, Gao Z (2018). Effectiveness of combined smartwatch and social media intervention on breast cancer survivor health outcomes: a 10-week pilot randomized trial. J Clin Med 7:140. https://doi.org/10.3390/jcm7060140

  18. Kapoor A, Nambisan P, Baker E (2020). Mobile applications for breast cancer survivorship and self-management: A systematic review. Health Informatics J 26: 2892–2905. https://doi.org/10.1177/1460458220950853

  19. Liao Y, Wang B, Han L (2017) An application effect study on the self-management of breast cancer patients by the establishment of the “fans” club informatization. Chinese Journal of Practical Nursing 33:241–244

    Google Scholar 

  20. Lu Y (2019). Construction and research of cloud platform of targeted drug therapy based on self-management model for breast cancer patients. Nanjing University of Traditional Chinese Medicine

  21. Wu Q, Kue J, Zhu X, Yin X, Jiang J, Chen J, Yang L, Zeng L, Sun X, Liu X, Duan X, Shi Y (2020) Effects of Nurse-Led Support Via WeChat, a smartphone application, for breast cancer patients after surgery: a quasi-experimental study. Telemedicine and E-Health 26:226–234. https://doi.org/10.1089/tmj.2018.0293

    Article  PubMed  Google Scholar 

  22. Lee MK, Yun YH, Park HA, Lee ES, Jung KH, Noh DY (2014) A Web-based self-management exercise and diet intervention for breast cancer survivors: pilot randomized controlled trial. Int J Nurs Stud 51:1557–1567. https://doi.org/10.1016/j.ijnurstu.2014.04.012

    Article  PubMed  Google Scholar 

  23. Huang J, Han Y, Wei J, Liu X, Du Y, Yang L, Li Y, Yao W, Wang R (2020) The effectiveness of the Internet-based self-management program for cancer-related fatigue patients: a systematic review and meta-analysis. Clin Rehabil 34:287–298. https://doi.org/10.1177/0269215519889394

    Article  PubMed  Google Scholar 

  24. Xu A, Wang Y, Wu X (2019) Effectiveness of e-health based self-management to improve cancer-related fatigue, self-efficacy and quality of life in cancer patients: Systematic review and meta-analysis. J Adv Nurs 75:3434–3447. https://doi.org/10.1111/jan.14197

    Article  PubMed  Google Scholar 

  25. Kim AR, Park HA (2015) Web-based self-management support interventions for cancer survivors: a systematic review and meta-analyses. Stud Health Technol Inform 216:142–147

    PubMed  Google Scholar 

  26. Liberati A, Altman DG, Tetzlaff J, Mulrow C, Gøtzsche PC, Ioannidis JP, Clarke M, Devereaux PJ, Kleijnen J, Moher D (2009) The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate health care interventions: explanation and elaboration. Ann Intern Med 151:W65–W94. https://doi.org/10.7326/0003-4819-151-4-200908180-00136

    Article  PubMed  Google Scholar 

  27. Versel N (2020). Mobile takes its place on health IT scene. https://www.himss.org/mhealth. Accessed 21 Oct 2020

  28. Moher D, Liberati A, Tetzlaff J, Altman DG (2009) Preferred reporting items for systematic reviews and meta-analyses: the PRISMA Statement. J Clin Epidemiol 62:1006–1012. https://doi.org/10.1016/j.jclinepi.2009.06.005

    Article  PubMed  Google Scholar 

  29. Higgins JP, Altman DG, Gøtzsche PC, Jüni P, Moher D, Oxman AD, Savovic J, Schulz KF, Weeks L, Sterne JA (2011) The Cochrane Collaboration’s tool for assessing risk of bias in randomised trials. BMJ 343:d5928. https://doi.org/10.1136/bmj.d5928

    Article  PubMed  PubMed Central  Google Scholar 

  30. Sterne JA, Hernán MA, Reeves BC, Savović J, Berkman ND, Viswanathan M, Henry D, Altman DG, Ansari MT, Boutron I, Carpenter JR, Chan AW, Churchill R, Deeks JJ, Hróbjartsson A, Kirkham J, Jüni P, Loke YK, Pigott TD, Ramsay CR, Regidor D, Rothstein HR, Sandhu L, Santaguida PL, Schünemann HJ, Shea B, Shrier I, Tugwell P, Turner L, Valentine JC, Waddington H, Waters E, Wells GA, Whiting PF, Higgins JP (2016) ROBINS-I: a tool for assessing risk of bias in non-randomised studies of interventions. BMJ 355:i4919. https://doi.org/10.1136/bmj.i4919

    Article  PubMed  PubMed Central  Google Scholar 

  31. Hong Z, Zhen W (2011) Calculation of standardized mean difference effect size in Meta analysis. Chinese Journal of Tissue Engineering Research 15:737–740

    Google Scholar 

  32. Noble D, Lagisz M, O’Dea RE, Nakagawa S (2017) Nonindependence and sensitivity analyses in ecological and evolutionary meta-analyses. Mol Ecol 26:2410–2425. https://doi.org/10.1111/mec.14031

    Article  PubMed  Google Scholar 

  33. Sterne JA, Sutton AJ, Ioannidis JP, Terrin N, Jones DR, Lau J, Carpenter J, Rücker G, Harbord RM, Schmid CH, Tetzlaff J, Deeks JJ, Peters J, Macaskill P, Schwarzer G, Duval S, Altman DG, Moher D, Higgins JP (2011) Recommendations for examining and interpreting funnel plot asymmetry in meta-analyses of randomised controlled trials. BMJ 343:d4002. https://doi.org/10.1136/bmj.d4002

    Article  PubMed  Google Scholar 

  34. Begg CB, Mazumdar M (1994) Operating characteristics of a rank correlation test for publication bias. Biometrics 50:1088–1101

    Article  CAS  PubMed  Google Scholar 

  35. Egger M, Davey SG, Schneider M, Minder C (1997) Bias in meta-analysis detected by a simple, graphical test. BMJ 315:629–634. https://doi.org/10.1136/bmj.315.7109.629

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Kong H, Zhou L, Hong L, Zhang Y, Ji F, Sun L (2018) The effect of continuous nursing based on WeChat platform on quality of life and psychological quality of postoperative breast cancer patients. Journal of Nursing and Rehabilitation 17:19–23

    Google Scholar 

  37. Qin Q (2018) Application of WeChat platform in postoperative extended care of breast cancer. Modern Diagnosis and Treatment 29:2319–2321

    Google Scholar 

  38. Zha G (2020) Effects of WeChat continuous nursing on psychological stress, self-care ability and quality of life of breast cancer patients undergoing chemotherapy after radical mastectomy. Practical Journal of Cardiac Cerebral Pneumal and Vascular Disease 28:148–150

    Google Scholar 

  39. Zhou L, Kong H (2019) Effects of WeChat platform continuing nursing intervention on mental resilience and stigma of postoperative breast cancer patients. Chinese Journal of General Practice 17:1773–1776

    Google Scholar 

  40. Shi S (2018) Application and effect analysis of WeChat platform in postoperative nursing of breast cancer patients. Hospital Management Forum 35:44–46

    Google Scholar 

  41. Wei S, Yang X (2019) Effects of WeChat-based continuous care on quality of life and functional exercise compliance of breast cancer patients. Health Medicine Research and Practice 16:66–68

    Google Scholar 

  42. Omidi Z, Kheirkhah M, Abolghasemi J, Haghighat S (2020) Effect of lymphedema self-management group-based education compared with social network-based education on quality of life and fear of cancer recurrence in women with breast cancer: a randomized controlled clinical trial. Qual Life Res 29:1789–1800. https://doi.org/10.1007/s11136-020-02455-z

    Article  PubMed  PubMed Central  Google Scholar 

  43. Dong X, Yi X, Gao D, Gao Z, Huang S, Chao M, Chen W, Ding M (2019) The effects of the combined exercise intervention based on internet and social media software (CEIBISMS) on quality of life, muscle strength and cardiorespiratory capacity in Chinese postoperative breast cancer patients:a randomized controlled trial. Health Qual Life Outcomes 17:109. https://doi.org/10.1186/s12955-019-1183-0

    Article  PubMed  PubMed Central  Google Scholar 

  44. van den Berg SW, Gielissen MF, Custers JA, van der Graaf WT, Ottevanger PB, Prins JB (2015) BREATH: web-based self-management for psychological adjustment after primary breast cancer—results of a multicenter randomized controlled trial. J Clin Oncol 33:2763–2771. https://doi.org/10.1200/JCO.2013.54.9386

    Article  PubMed  Google Scholar 

  45. Zhang L, Fan A, Yan J, He Y, Zhang H, Zhang H, Zhong Q, Liu F, Luo Q, Zhang L, Tang H, Xin M (2016) Combining manual lymph drainage with physical exercise after modified radical mastectomy effectively prevents upper limb lymphedema. Lymphat Res Biol 14:104–108. https://doi.org/10.1089/lrb.2015.0036

    Article  PubMed  Google Scholar 

  46. Li Y, Li L, Liu A, Zhang X (2019). Application of case management mode based on mobile phone APP in functional exercise of patients after breast cancer operation. Chinese Journal of Modern Nursing 12: 1518–1522

  47. Uhm KE, Yoo JS, Chung SH, Lee JD, Lee I, Kim JI, Lee SK, Nam SJ, Park YH, Lee JY, Hwang JH (2017) Effects of exercise intervention in breast cancer patients: is mobile health (mHealth) with pedometer more effective than conventional program using brochure? Breast Cancer Res Treat 161:443–452. https://doi.org/10.1007/s10549-016-4065-8

    Article  PubMed  Google Scholar 

  48. Glanz K, Bishop DB (2010) The role of behavioral science theory in development and implementation of public health interventions. Annu Rev Public Health 31:399–418. https://doi.org/10.1146/annurev.publhealth.012809.103604

    Article  PubMed  Google Scholar 

  49. Kim SH, Kim K, Mayer DK (2017) Self-Management Intervention for Adult Cancer Survivors After Treatment: A Systematic Review and Meta-Analysis. Oncol Nurs Forum 44:719–728. https://doi.org/10.1188/17.ONF.719-728

    Article  PubMed  Google Scholar 

  50. De Groef A, Van Kampen M, Dieltjens E, Christiaens MR, Neven P, Geraerts I, Devoogdt N (2015) Effectiveness of postoperative physical therapy for upper-limb impairments after breast cancer treatment: a systematic review. Arch Phys Med Rehabil 96:1140–1153. https://doi.org/10.1016/j.apmr.2015.01.006

    Article  PubMed  Google Scholar 

  51. Sai Z, Fan-Jie M (2013) Upper limb exercise and breast cancer related lymphedema. Int J Oncol 40:36–39. https://doi.org/10.3760/cma.j.issn.1673-422X.2013.01.011

    Article  Google Scholar 

  52. Chen YY, Guan BS, Li ZK, Li XY (2018) Effect of telehealth intervention on breast cancer patients’ quality of life and psychological outcomes: a meta-analysis. J Telemed Telecare 24:157–167. https://doi.org/10.1177/1357633X16686777

    Article  PubMed  Google Scholar 

  53. Paterson C, Robertson A, Nabi G (2015) Exploring prostate cancer survivors’ self-management behaviours and examining the mechanism effect that links coping and social support to health-related quality of life, anxiety and depression: a prospective longitudinal study. Eur J Oncol Nurs 19:120–128. https://doi.org/10.1016/j.ejon.2014.10.008

    Article  PubMed  Google Scholar 

  54. Maughan KL, Lutterbie MA, Ham PS (2010) Treatment of breast cancer. Am Fam Physician 81:1339–1346

    PubMed  Google Scholar 

  55. Gottrup H, Andersen J, Arendt-Nielsen L, Jensen TS (2000) Psychophysical examination in patients with post-mastectomy pain. Pain 87:275–284. https://doi.org/10.1016/S0304-3959(00)00291-8

    Article  PubMed  Google Scholar 

  56. Pieretti S, Di Giannuario A, Di Giovannandrea R, Marzoli F, Piccaro G, Minosi P, Aloisi AM (2016) Gender differences in pain and its relief. Ann Ist Super Sanita 52:184–189. https://doi.org/10.4415/ANN_16_02_09

    Article  PubMed  Google Scholar 

  57. Singer S, Kuhnt S, Zwerenz R, Eckert K, Hofmeister D, Dietz A, Giesinger J, Hauss J, Papsdorf K, Briest S, Brown A (2011) Age- and sex-standardised prevalence rates of fatigue in a large hospital-based sample of cancer patients. Br J Cancer 105:445–451. https://doi.org/10.1038/bjc.2011.251

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Gamper EM, Petersen MA, Aaronson N, Costantini A, Giesinger JM, Holzner B, Kemmler G, Oberguggenberger A, Singer S, Young T, Groenvold M (2016) Development of an item bank for the EORTC Role Functioning Computer Adaptive Test (EORTC RF-CAT). Health Qual Life Outcomes 14:72. https://doi.org/10.1186/s12955-016-0475-x

    Article  PubMed  PubMed Central  Google Scholar 

  59. Li Q, Lin Y, Qiu Y, Gao B, Xu Y (2014) The assessment of health-related quality of life and related factors in Chinese elderly patients undergoing chemotherapy for advanced cancer: a cross-sectional study. Eur J Oncol Nurs 18:425–435. https://doi.org/10.1016/j.ejon.2014.03.005

    Article  CAS  PubMed  Google Scholar 

  60. Goodman SH, Sewell DR, Cooley EL, Leavitt N (1993) Assessing levels of adaptive functioning: the Role Functioning Scale. Community Ment Health J 29:119–131. https://doi.org/10.1007/BF00756338

    Article  CAS  PubMed  Google Scholar 

  61. Lu Q, Man J, You J, Leroy AS (2015) The link between ambivalence over emotional expression and depressive symptoms among Chinese breast cancer survivors. J Psychosom Res 79:153–158. https://doi.org/10.1016/j.jpsychores.2015.01.007

    Article  PubMed  PubMed Central  Google Scholar 

  62. Kim E, Han JY, Moon TJ, Shaw B, Shah DV, Mctavish FM, Gustafson DH (2012) The process and effect of supportive message expression and reception in online breast cancer support groups. Psychooncology 21:531–540. https://doi.org/10.1002/pon.1942

    Article  PubMed  Google Scholar 

  63. Pingree S, Hawkins R, Baker T, Dubenske L, Roberts LJ, Gustafson DH (2010) The value of theory for enhancing and understanding e-health interventions. Am J Prev Med 38:103–109. https://doi.org/10.1016/j.amepre.2009.09.035

    Article  PubMed  PubMed Central  Google Scholar 

  64. Ventura F, Ohlén J, Koinberg I (2013) An integrative review of supportive e-health programs in cancer care. Eur J Oncol Nurs 17:498–507. https://doi.org/10.1016/j.ejon.2012.10.007

    Article  PubMed  Google Scholar 

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Funding

This work was supported by China’s National Natural Science Foundation (no. 71974217).

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Contributions

All authors contributed substantially to conception and design, or acquisition of data, or analysis and interpretation of data. All authors contributed in drafting the article or revising it critically for important intellectual content. All authors approved the version to be published.

Conceptualization: Jun Yan

Acquisition of data: Xia Luo and Yuzhen Chen

Analysis and/or interpretation of data: Xia Luo and Jing Chen

Drafting the manuscript: Xia Luo and Yuzhen Chen

Revising the manuscript critically for important intellectual content: Jun Yan and Yue Zhang, and Jing Chen

Data validation: Jun Yan, Mingfang Li, Chenxia Xiong, and Jing Chen

Supervision: Jun Yan

Corresponding author

Correspondence to Jun Yan.

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Appendix

Appendix

Fig. 5
figure 5

Risk of bias summary of RCTs

Fig. 6
figure 6

Forest plot of functional exercise compliance with the m-health group versus control group

Fig. 7
figure 7

Forest plot of self-efficacy with the m-health group versus control group

Fig. 8
figure 8

Forest plot of lymphedema with the m-health group versus control group

Fig. 9
figure 9

Forest plot of pain with the m-health group versus control group

Fig. 10
figure 10

Forest plot of fatigue with the m-health group versus control group

Fig. 11
figure 11

Forest plot of role functioning with the m-health group versus control group

Fig. 12
figure 12

Forest plot of anxiety with the m-health group versus control group

Fig. 13
figure 13

Forest plot of depression with the m-health group versus control group

Fig. 14
figure 14

Forest plot of social support with the m-health group versus control group

Fig. 15
figure 15

Forest plot of HRQOL with the m-health group versus control group

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Luo, X., Chen, Y., Chen, J. et al. Effectiveness of mobile health-based self-management interventions in breast cancer patients: a meta-analysis. Support Care Cancer 30, 2853–2876 (2022). https://doi.org/10.1007/s00520-021-06568-0

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