测量电子健康干预中抑郁症状对参与、依从性和体重减轻的影响。

PLOS digital health Pub Date : 2025-03-25 eCollection Date: 2025-03-01 DOI:10.1371/journal.pdig.0000766
Lex Hurley, Nisha G O'Shea, Julianne Power, Christopher Sciamanna, Deborah F Tate
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引用次数: 0

摘要

背景:已知数字行为改变干预(eHealth, mHealth)能够促进一些参与者的临床显著体重减轻。然而,随着时间的推移,这些项目的参与度和依从性可能会下降,这可能会影响它们的有效性。本分析考察了抑郁症状对电子健康干预的参与、依从性和6个月体重变化的负面影响程度。方法:采用结构方程模型来检验基线抑郁症状对体重改变结果的影响,分别通过潜在构念投入和依从性介导。这些结构在该数据集中高度相关,需要两个单独的模型进行测试。用户粘性是通过6个月的网站登录次数、用户创建的目标、访问各种网页以及在线讨论板上的帖子来衡量的。坚持通过6个月的总运动周数、记录体重的天数和完全饮食跟踪的天数来表示。结果:抑郁症状与体重变化无直接关联(p's≥0.6),但与敬业度和依从性两个构式均呈负相关(p's < 0.001),而这两个构式又与体重变化呈负相关(p's < 0.001)。确定抑郁症状与体重变化有间接正相关,这完全通过这些变量介导,意味着体重减轻或可能体重增加较少(p < 0.001)。讨论:本分析表明,在电子健康干预中,抑郁症状对减肥结果有显著的不良影响,完全通过测量参与者的参与和依从性来调节。进一步的研究需要在纵向模型中测试这些结构,以更好地理解它们之间的动态相互关系,并考虑在未来的数字干预中解决抑郁症的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Measuring the influence of depressive symptoms on engagement, adherence, and weight loss in an eHealth intervention.

Background: Digital behavior change interventions (eHealth, mHealth) are known to be capable of promoting clinically significant weight loss among some participants. However, these programs can struggle with declining engagement and adherence over time, which can hamper their effectiveness. This analysis examines the extent that depression symptoms may negatively influence engagement, adherence, and 6 month weight change in an eHealth intervention.

Methods: Structural equation modeling is applied to test the effects of baseline depression symptoms on weight change outcomes, mediated through latent constructs of engagement and adherence, respectively. These constructs were highly correlated within this dataset and necessitated two separate models to be tested. Engagement was indicated by 6 month sums of website logins, user-created goals, visiting various webpages, and posts on the online discussion boards. Adherence was indicated by 6 month sums of weeks exercise goals met, days weight logged, and days of complete dietary tracking.

Results: Depression symptoms showed no direct association with weight change (p's ≥ 0.6), but were negatively associated with both constructs of engagement and adherence (p's < 0.001), which in turn were negatively associated with weight change in both models (p's < 0.001). It was determined depression symptoms had a positive indirect association with weight change fully mediated through these variables, meaning less weight loss or possible weight gain (p < 0.001).

Discussion: This analysis shows that depression symptoms had a significant, undesirable effect on weight loss outcomes within this eHealth intervention, fully mediated through measured participant engagement and adherence. Further research is needed to test these constructs within a longitudinal model to better understand their dynamic interrelationships, and consider means to address depression in future digital interventions.

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