移动医疗干预对乳腺癌患者患者报告结果的影响证据图谱:系统回顾

IF 3.3 Q2 ONCOLOGY JCO Clinical Cancer Informatics Pub Date : 2024-05-01 DOI:10.1200/CCI.24.00014
Santiago Frid, Clara Amat-Fernández, María Ángeles Fuentes-Expósito, Montserrat Muñoz-Mateu, Antonis Valachis, Antoni Sisó-Almirall, Immaculada Grau-Corral
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引用次数: 0

摘要

目的:全面综述有关针对乳腺癌(BC)患者的移动医疗干预措施的现有证据:2023年7月30日,我们在PubMed、PsycINFO和Google Scholar上搜索了使用以下纳入标准的文章:对癌症患者的移动医疗干预进行评估,至少有30名BC患者参与,随机对照试验或前瞻性前后研究,健康决定因素(患者报告结果[PROs]和生活质量[QoL])为主要结果,干预持续至少8周,2015年1月后发表。对远程医疗进行评估或仅在台式设备上使用基于网络的软件的文章将被排除在外。纳入研究的质量采用 Cochrane 协作偏倚风险工具和非随机研究方法指数进行分析:我们共纳入了 30 项研究(20 项侧重于 BC),涵盖 5,691 名癌症患者(中位数为 113,IQR 为 135.5)。其中 3,606 人患有 BC(中位数为 99,IQR 为 75)。所有研究都包含多种干预措施,包括体育锻炼、量身定制的疾病自我管理信息和症状追踪器。干预措施在自我效能(3/3)、质量生活(10/14)和体育锻炼(5/7)方面取得了较好的效果。生活方式计划(3/3)、专家咨询(4/4)和定制信息(10/11)的效果最好。大多数研究(17/30)都没有向公众提供移动医疗工具。结论:移动医疗干预在不同结果上产生了不同的结果。确定临床方案(如接受化疗以外的系统治疗的患者)缺乏证据,有助于完善该领域即将开展的研究工作的战略规划。
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Mapping the Evidence on the Impact of mHealth Interventions on Patient-Reported Outcomes in Patients With Breast Cancer: A Systematic Review.

Purpose: To comprehensively synthesize the existing evidence concerning mHealth interventions for patients with breast cancer (BC).

Design: On July 30, 2023, we searched PubMed, PsycINFO, and Google Scholar for articles using the following inclusion criteria: evaluation of mHealth interventions in patients with cancer, at least 30 participants with BC, randomized control trials or prospective pre-post studies, determinants of health (patient-reported outcomes [PROs] and quality of life [QoL]) as primary outcomes, interventions lasting at least 8 weeks, publication after January 2015. Publications were excluded if they evaluated telehealth or used web-based software for desktop devices only. The quality of the included studies was analyzed with the Cochrane Collaboration Risk of Bias Tool and the Methodological Index for Non-Randomized Studies.

Results: We included 30 studies (20 focused on BC), encompassing 5,691 patients with cancer (median 113, IQR, 135.5). Among these, 3,606 had BC (median 99, IQR, 75). All studies contained multiple interventions, including physical activity, tailored information for self-management of the disease, and symptom tracker. Interventions showed better results on self-efficacy (3/3), QoL (10/14), and physical activity (5/7). Lifestyle programs (3/3), expert consulting (4/4), and tailored information (10/11) yielded the best results. Apps with interactive support had a higher rate of positive findings, while interventions targeted to survivors showed worse results. mHealth tools were not available to the public in most of the studies (17/30).

Conclusion: mHealth interventions yielded heterogeneous results on different outcomes. Identifying lack of evidence on clinical scenarios (eg, patients undergoing systemic therapy other than chemotherapy) could aid in refining strategic planning for forthcoming research endeavors within this field.

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