行为改变理论和行为改变技术在癌症康复干预中的应用:二次分析。

IF 4.3 3区 材料科学 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC
M Lauren Voss, Rachelle Brick, Lynne S Padgett, Stephen Wechsler, Yash Joshi, Genevieve Ammendolia Tomé, Sasha Arbid, Grace Campbell, Kristin L Campbell, Dima El Hassanieh, Caroline Klein, Adrienne Lam, Kathleen D Lyons, Aisha Sabir, Alix G Sleight, Jennifer M Jones
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引用次数: 0

摘要

背景:将行为理论和技术纳入癌症康复干预的证据有限。目的:本二次分析旨在描述行为改变理论和行为改变技术(BCTs)在之前进行的两篇癌症康复干预系统综述中的使用情况:对两篇系统综述中的随机对照试验(RCT)进行二次分析,这两篇综述研究了癌症康复干预对功能和残疾的影响:人群:成年癌症幸存者:人群:成年癌症幸存者:数据提取包括:行为改变理论的使用、功能结果数据以及使用行为改变技术分类标准(BCTTv1)的行为改变技术。根据其对功能的影响,干预措施被分为 "非常有效"、"相当有效 "或 "无效"。为了评估已编码 BCT 的相对有效性,我们计算了 BCT 承诺比率(包括 BCT 的有希望干预与无希望干预的比率):在 180 项符合条件的 RCT 中,有 25 项(14%)报告使用了行为改变理论。在 93 项 BCT 中,54 项(58%)至少在一项干预中使用(范围为 0-29)。与没有使用理论的干预相比,报告使用理论的干预使用了更多的 BCT(中位数=7)(中位数=3.5;U=2827.00,P=0.001)。非常、相当和无前景干预组之间的 BCT 数量没有差异(H(2)=0.24, P=0.85)。20项BCT被认为是有前景的(前景比大于2),其中目标设定、分级任务和社会支持(未指定)的前景比最高:虽然使用的 BCT 种类繁多,但它们很少基于理论上提出的路径,而且报告的 BCT 数量与干预效果无关:临床康复影响:临床医生应考虑根据相关的行为改变理论制定新的干预措施。有意识地纳入目标设定、分级任务和社会支持等BCTs可能会提高干预效果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Behavior change theory and behavior change technique use in cancer rehabilitation interventions: a secondary analysis.

Background: There is limited evidence depicting ways that behavioral theory and techniques have been incorporated into cancer rehabilitation interventions. Examining their use within cancer rehabilitation interventions may provide insight into the active ingredients that can maximize patient engagement and intervention effectiveness.

Aim: This secondary analysis aimed to describe the use of behavior change theory and behavior change techniques (BCTs) in two previously conducted systematic reviews of cancer rehabilitation interventions.

Design: Secondary analysis of randomized controlled trials (RCTs) drawn from two systematic reviews examining the effect of cancer rehabilitation interventions on function and disability.

Setting: In-person and remotely delivered rehabilitation interventions.

Population: Adult cancer survivors.

Methods: Data extraction included: behavior change theory use, functional outcome data, and BCTs using the Behavior Change Technique Taxonomy (BCTTv1). Based on their effects on function, interventions were categorized as "very", "quite" or "non-promising". To assess the relative effectiveness of coded BCTs, a BCT promise ratio was calculated (the ratio of promising to non-promising interventions that included the BCT).

Results: Of 180 eligible RCTs, 25 (14%) reported using a behavior change theory. Fifty-four (58%) of the 93 BCTs were used in least one intervention (range 0-29). Interventions reporting theory use utilized more BCTs (median=7) compared to those with no theory (median=3.5; U=2827.00, P=0.001). The number of BCTs did not differ between the very, quite, and non-promising intervention groups (H(2)=0.24, P=0.85). 20 BCTs were considered promising (promise ratio >2) with goal setting, graded tasks, and social support (unspecified) having the highest promise ratios.

Conclusions: While there was a wide range of BCTs utilized, they were rarely based on theoretically-proposed pathways and the number of BCTs reported was not related to intervention effectiveness.

Clinical rehabilitation impact: Clinicians should consider basing new interventions upon a relevant behavior change theory. Intentionally incorporating the BCTs of goal setting, graded tasks, and social support may improve intervention efficacy.

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CiteScore
7.20
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