研究背景：癌因性疲憊為長期困擾癌症患者的症狀，逾半數癌症患者苦其所擾。網路化媒介與傳統的非藥物介入措施結合，能夠改善癌因性疲憊，然現有的網路化的癌因性疲憊非藥物介入結果並不一致，介入方案的設計與網路元素也不盡相同，並未有統合分析確立其效益和調節因子。又網絡化統合分析能夠綜合直接和間接比較，並為眾多的介入方案列出優劣順序，可作為未來發展臨床實證指引使用。
研究目的：檢驗網路化的癌因性疲憊非藥物介入措施對癌因性疲憊之比較效益，並確立當中有效介入措施的調節因子。
研究方法：遵循PRISMA進行文獻搜尋及篩選，在五個英語電子資料庫搜索隨機控制試驗研究文章，並以考科藍風險評估工具評讀。網絡統合分析以網絡森林圖檢驗現有各式網路化介入措施間的比較效益，而累積排行曲線下面積(SUCRA)可針對緩解癌因性疲憊之效益，排序每項介入措施之優劣。進行調節因子分析時，考量不同介入措施研究數量及樣本數，足夠樣本者進行次群組分析，其他則以質性整合。
研究結果：本網絡統合分析納入31篇研究，可分為5類網路化介入措施及控制組，無發表偏差及網絡迴圈不一致。綜合直接與間接比較，可見緩解癌因性疲憊的網路化介入措施效益優劣為心理社會衛教、認知行為治療、多模組治療、身心療法、控制組及效果最末之運動組。因僅有網路化認知行為治療符合次群組分析之研究數量及樣本數，故以其進行分析可見年齡、治療階段、單次介入時長、頻率、網路式量身打造和網路社會化為癌因性疲憊之調節因子。
討論：不同介入措施對於緩解癌因性疲憊之機制皆不同，心理社會衛教只需透過「資訊傳遞」便能達到效果，然其他須管理受試者「行為」的介入方案尚缺乏監測，如運動方案若未正確執行動作，則無法緩解癌因性疲憊，也因此效益排序最差。調節因子中，較年輕者對於網路已具備相對的熟稔度，且認知或行為能力亦與年齡正相關。探討介入方案設計，單次時長長易令人感到厭倦而降低參與，且若以開放式設計供患者自主瀏覽操作，無法監測或確認其使用頻次和操作正確與否。另網路式量身打造為有效提升認知行為療法之因子，但若結合網路社群元素則須慎選，避免參與者在團體中感受到無貢獻、不同質等壓力，反而無法達到效果。
結論：現有網路化介入措施以單純資訊傳遞較其他行為管理方案有效，應強化監測介入行為以檢核效果。以認知療法為例，可見非開放式短時長、量身打造、非社群化為調節因子。未來應對不同非藥物網路化介入措施設計進行修正，善用網路化有效元素並避免負面效應，以建立新時代的癌症照護有效介入方案。

Background: Over half of the patients with cancer suffered from cancer-related fatigue, the most disturbing complication along with cancer disease progression. Combinations of Internet-based medium and original nonpharmacological intervention programs were effective in alleviating CRF. However, the designs and results were not consistent. A network meta-analysis should be computed to verify the effectiveness and moderators of Internet-based nonpharmacological interventions in patients with cancer. Network meta-analysis could be used to rank the interventions by the relative efficacy for future clinical practice guidelines.
Objectives: To evaluate the comparative effects and moderators of Internet-based nonpharmacological interventions on cancer-related fatigue in patients with cancer.
Methods: The search and screening followed the PRISMA guideline. A literature search was performed in five electronic databases to identify relevant randomized controlled trials. The network forest plot was performed to examine the comparative effectiveness in the included studies. The surface under the cumulative ranking curve was performed to examine the effectiveness rank for each type of intervention. The moderators were identified by either subgroup analysis or qualitative integration based on the specific interventions’ study numbers and sample sizes.
Results: This network meta-analysis included 31 studies that were categorized into 5 groups of interventions and 1 control group. There were no publication bias and network loop inconsistence. The effectiveness of Internet-based nonpharmacological interventions ranking in order were psychosocial education, cognitive-behavior therapy, multiple model therapy, control groups, and physical activities. Only numbers of cognitive-behavior therapy group were sufficient to perform subgroup analysis, and showed age, treatment status, single session time, frequency, Internet-based social interaction, Internet-based individual tailoring were the moderators of alleviating cancer-related fatigue in Internet-based cognitive-behavior therapy.
Discussion: Compared to the interventions that rely on behavioral change or management to release cancer-related fatigue, psychosocial education was relatively simple and focus on receiving information that reduces the risk of uncertainty about the validity of the intervention. The result and reason illustrated the most significant difference between face-to-face and Internet-based interventions. In the case of Internet-based cognitive-behavior therapy, the age which was associated with cognition and Internet familiarity play a crucial role. Patients who still undergoing the curative treatment would suffer the most severe cancer-related fatigue, leading to the insignificant effect of interventions. Considering the protocol design, our quantitative examination echoed the previous qualitative research that the shorter single session time was better for patients to reach effectiveness. Additionally, engaging in therapeutic intervention without fixed schedules presented the feature of the Internet, while losing monitors to achieve the effect. The innovative Internet-based elements were the moderators especially Internet-based individual tailoring met the gap of expectation from patients when participating in Internet-based interventions. Contrary, Internet-based social interaction might reduce the effect when patients were in a group of heterogeneous people and might feel unable to fit in that leading to stress.
Conclusion: In the future, researchers should re-design the traditional face-to-face protocol into digital modules to not only achieve effectiveness but also enhance it by making good use of the Internet elements and avoid the weakness of Internet-related confounding factors on cancer-related fatigue.

Abrahams, H., Gielissen, M., Schmits, I., Verhagen, C., Rovers, M., & Knoop, H. (2016). Risk factors, prevalence, and course of severe fatigue after breast cancer treatment: a meta-analysis involving 12327 breast cancer survivors. Annals of Oncology, 27(6), 965-974. https://doi.org/10.1093/annonc/mdw099

Andersson, G., & Titov, N. (2014). Advantages and limitations of Internet‐based interventions for common mental disorders. World Psychiatry, 13(1), 4-11. https://doi.org/10.1002/wps.20083

Basche, M., Barón, A. E., Eckhardt, S. G., Balducci, L., Persky, M., Levin, A., Jackson, N., Zeng, C., Vranas, P., & Steiner, J. F. (2008). Barriers to enrollment of elderly adults in early-phase cancer clinical trials. Journal of Oncology Practice, 4(4), 162-168.
Bower, J. E. (2014). Cancer-related fatigue—mechanisms, risk factors, and treatments. Nature reviews Clinical oncology, 11(10), 597-609. https://doi.org/10.1038/nrclinonc.2014.127

Bower, J.E., Ganz, P.A., Irwin, M.R., Cole, S.W., Garet, D., & Petersen, L. (2021). Do all patients with cancer experience fatigue? A longitudinal study of fatigue trajectories in women with breast cancer. Cancer, 127(8), 1334–1344. https://doi.org/10.1002/cncr.33327

Bradbury, K., Steele, M., Corbett, T., Geraghty, A. W. A., Krusche, A., Heber, E., Easton, S., Cheetham-Blake, T., Slodkowska-Barabasz, J., Muller, A. M., Smith, K., Wilde, L. J., Payne, L., Singh, K., Bacon, R., Burford, T., Summers, K., Turner, L., Richardson, A., Watson, E., Foster, C., Little, P., & Yardley, L. (2019). Developing a digital intervention for cancer survivors: an evidence-, theory- and person-based approach. Npj Digital Medicine, 2, 85. https://doi.org/10.1038/s41746-019-0163-4

Egger, M., Zellweger-Zähner, T., Schneider, M., Junker, C., Lengeler, C., & Antes, G. (1997). Language bias in randomised controlled trials published in English and German. The Lancet, 350(9074), 326-329. https://doi.org/10.1016/S0140-6736(97)02419-7

Gegechkori, N., Haines, L., & Lin, J. J. (2017). Long-term and latent side effects of specific cancer types. Medical Clinics, 101(6), 1053-1073. https://doi.org/10.1016/j.mcna.2017.06.003

Gorlick, A., Bantum, E. O. C., & Owen, J. E. (2014). Internet‐based interventions for cancer‐related distress: exploring the experiences of those whose needs are not met. Psycho‐Oncology, 23(4), 452-458. https://doi.org/10.1002/pon.3443

Harris, L. N., Cleary, E. H., & Stanton, A. L. (2015). Project connect online: User and visitor experiences of an Internet‐based intervention for women with breast cancer. Psycho‐Oncology, 24(9), 1145-1151.

Higgins, J. P., Jackson, D., Barrett, J. K., Lu, G., Ades, A. E., & White, I. R. (2012). Consistency and inconsistency in network meta-analysis: concepts and models for multi-arm studies. Research Synthesis Methods, 3(2), 98-110. https://doi.org/10.1002/jrsm.1044

Jang, A., Brown, C., Lamoury, G., Morgia, M., Boyle, F., Marr, I., Clarke, S., Back, M., & Oh, B. (2020). The effects of acupuncture on cancer-related fatigue: updated systematic review and meta-analysis. Integrative cancer therapies, 19, 1534735420949679. https://doi.org/10.1177/1534735420949679

Kalter, J., Verdonck‐de Leeuw, I. M., Sweegers, M. G., Aaronson, N. K., Jacobsen, P. B., Newton, R. U., ... & Buffart, L. M. (2018). Effects and moderators of psychosocial interventions on quality of life, and emotional and social function in patients with cancer: An individual patient data meta‐analysis of 22 RCTs. Psycho‐oncology, 27(4), 1150-1161. https://doi.org/10.1002/pon.4648

Kessels, E., Husson, O., & Van der Feltz-Cornelis, C. M. (2018). The effect of exercise on cancer-related fatigue in cancer survivors: a systematic review and meta-analysis. Neuropsychiatric Disease and Treatment, 14, 479.

Kim, H. J., Kim, S. M., Shin, H., Jang, J. S., Kim, Y. I., & Han, D. H. (2018). A mobile game for patients with breast cancer for chemotherapy self-management and quality-of-life improvement: Randomized controlled trial. Journal of Medical Internet Research, 20(10), e273.

Liu, C., Qin, M., Zheng, X., Chen, R., & Zhu, J. (2021). A meta-analysis: intervention effect of mind-body exercise on relieving cancer-related fatigue in breast cancer patients. Evidence-Based Complementary and Alternative Medicine, 2021. https://doi.org/10.1155/2021/9980940

Ma, Y., He, B., Jiang, M., Yang, Y., Wang, C., Huang, C., & Han, L. (2020). Prevalence and risk factors of cancer-related fatigue: A systematic review and meta-analysis. International Journal of Nursing Studies, 111, 103707. https://doi.org/10.1016/j.ijnurstu.2020.103707

Martins, J., Costa, J., Sarmento, H., Marques, A., Farias, C., Onofre, M., & Valeiro, M. G. (2021). Adolescents’ Perspectives on the Barriers and Facilitators of Physical Activity: An Updated Systematic Review of Qualitative Studies. International Journal of Environmental Research and Public Health, 18(9), 4954. https://doi.org/10.3390/ijerph18094954

Mock, V., Atkinson, A., Barsevick, A., Cella, D., Cimprich, B., Cleeland, C., ... & Stahl, C. (2000). NCCN Practice Guidelines for Cancer-Related Fatigue. Oncology (Williston Park, NY), 14(11A), 151-161.

Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., ... & Moher, D. (2021). The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. Systematic reviews, 10(1), 1-11. https://doi.org/10.1186/s13643-021-01626-4

Pang, C. p., Gi, Y. B., & Hus, T. P. (2019). Correlation among symptom clusters, inflammatory cytokine and quality of life among cancer patients based on cluster analysis. Chinese Nursing Research, 33(24), 4205-4210.

Papanastasiou, A., Seliniotaki, T., Rizos, E., Kampoli, K., Ntavatzikos, A., Arkadopoulos, N., ... & Koumarianou, A. (2019). Role of stress, age and adjuvant therapy in the cognitive function of patients with breast cancer. Oncology Letters, 18(1), 507-517. https://doi.org/10.3892/ol.2019.10361

Sandler, C. X., Goldstein, D., Horsfield, S., Bennett, B. K., Friedlander, M., Bastick, P. A., ... & Lloyd, A. R. (2017). Randomized evaluation of cognitive-behavioral therapy and graded exercise therapy for post-cancer fatigue. Journal of pain and symptom management, 54(1), 74-84. https://doi.org/10.1016/j.jpainsymman.2017.03.015

Schuster, R., Laireiter, A. R., Berger, T., Moritz, S., Meyer, B., Hohagen, F., & Klein, J. P. (2020). Immediate and long-term effectiveness of adding an Internet intervention for depression to routine outpatient psychotherapy: subgroup analysis of the evident trial. Journal of Affective Disorders, 274, 643-651. https://doi.org/10.1016/j.jad.2020.05.122

Sterne, J. A., & Egger, M. (2001). Funnel plots for detecting bias in meta-analysis: guidelines on choice of axis. Journal of Clinical Epidemiology, 54(10), 1046-1055. https://doi.org/10.1016/s0895-4356(01)00377-8

Sterne, J. A. C., & Harbord, R. M. (2004). Funnel Plots in Meta-analysis. The Stata Journal, 4(2), 127-141. https://doi.org/10.1177/1536867x0400400204

Sterne, J. A. C., Savović, J., Page, M. J., Elbers, R. G., Blencowe, N. S., Boutron, I., Cates, C. J., Cheng, H. Y., Corbett, M. S., Eldridge, S. M., Emberson, J. R., Hernán, M. A., Hopewell, S., Hróbjartsson, A., Junqueira, D. R., Jüni, P., Kirkham, J. J., Lasserson, T., Li, T., McAleenan, A., Reeves, B. C., Shepperd, S., Shrier, I., Stewart, L. A., Tilling, K., White, I. R., Whiting, P. F., & Higgins, J. P. T. (2019). RoB 2: a revised tool for assessing risk of bias in randomised trials. Bmj, 366, l4898. https://doi.org/10.1136/bmj.l4898

Tsai, H. Y., Wang, C. J., Mizuno, M., Muta, R., Fetzer, S. J., & Lin, M. F. (2022). Predictors of cancer‐related fatigue in women with breast cancer undergoing 21 days of a cyclic chemotherapy. Worldviews on Evidence‐Based Nursing, 19(3), 211-218. https://doi.org/10.1111/wvn.12573

Wang, X., Zhao, K., & Street, N. (2017). Analyzing and predicting user participations in online health communities: a social support perspective. Journal of medical Internet research, 19(4), e6834.

Watt, J., Tricco, A. C., Straus, S., Veroniki, A. A., Naglie, G., & Drucker, A. M. (2019). Research techniques made simple: network meta-analysis. Journal of Investigative Dermatology, 139(1), 4-12. e11. https://doi.org/10.1016/j.jid.2018.10.028

Wei, J., Zeng, C., & Lei, G. H. (2016). Heterogeneity, consistency and model fit should be assessed in Bayesian network meta-analysis. Annals of Rheumatic Diseases, 75(1), e5. https://doi.org/10.1136/annrheumdis-2015-208658

Wu, C., Zheng, Y., Duan, Y., Lai, X., Cui, S., Xu, N., Tang, C., & Lu, L. (2019). Nonpharmacological interventions for cancer‐related fatigue: a systematic review and Bayesian network meta‐analysis. Worldviews on Evidence‐Based Nursing, 16(2), 102-110. https://doi.org/10.1111/wvn.12352