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电子健康干预措施对减轻员工心理健康问题的有效性:一项系统评价与荟萃分析

Effectiveness of eHealth interventions for reducing mental health conditions in employees: A systematic review and meta-analysis.

作者信息

Stratton Elizabeth, Lampit Amit, Choi Isabella, Calvo Rafael A, Harvey Samuel B, Glozier Nicholas

机构信息

Brain and Mind Centre, Sydney Medical School, University of Sydney, Sydney, Australia.

School of Psychiatry, University of Sydney, Sydney, Australia.

出版信息

PLoS One. 2017 Dec 21;12(12):e0189904. doi: 10.1371/journal.pone.0189904. eCollection 2017.

Abstract

BACKGROUND

Many organisations promote eHealth applications as a feasible, low-cost method of addressing mental ill-health and stress amongst their employees. However, there are good reasons why the efficacy identified in clinical or other samples may not generalize to employees, and many Apps are being developed specifically for this group. The aim of this paper is to conduct the first comprehensive systematic review and meta-analysis evaluating the evidence for the effectiveness and examine the relative efficacy of different types of eHealth interventions for employees.

METHODS

Systematic searches were conducted for relevant articles published from 1975 until November 17, 2016, of trials of eHealth mental health interventions (App or web-based) focused on the mental health of employees. The quality and bias of all identified studies was assessed. We extracted means and standard deviations from published reports, comparing the difference in effect sizes (Hedge's g) in standardized mental health outcomes. We meta-analysed these using a random effects model, stratified by length of follow up, intervention type, and whether the intervention was universal (unselected) or targeted to selected groups e.g. "stressed".

RESULTS

23 controlled trials of eHealth interventions were identified which overall suggested a small positive effect at both post intervention (g = 0.24, 95% CI 0.13 to 0.35) and follow up (g = 0.23, 95% CI 0.03 to 0.42). There were differential short term effects seen between the intervention types whereby Mindfulness based interventions (g = 0.60, 95% CI 0.34 to 0.85, n = 6) showed larger effects than the Cognitive Behaviour Therapy (CBT) based (g = 0.15, 95% CI 0.02 to 0.29, n = 11) and Stress Management based (g = 0.17, 95%CI -0.01 to 0.34, n = 6) interventions. The Stress Management interventions however differed by whether delivered to universal or targeted groups with a moderately large effect size at both post-intervention (g = 0.64, 95% CI 0.54 to 0.85) and follow-up (g = 0.69, 95% CI 0.06 to 1.33) in targeted groups, but no effect in unselected groups.

INTERPRETATION

There is reasonable evidence that eHealth interventions delivered to employees may reduce mental health and stress symptoms post intervention and still have a benefit, although reduced at follow-up. Despite the enthusiasm in the corporate world for such approaches, employers and other organisations should be aware not all such interventions are equal, many lack evidence, and achieving the best outcomes depends upon providing the right type of intervention to the correct population.

摘要

背景

许多组织推广电子健康应用程序,将其作为一种可行的低成本方法,来解决员工的心理健康问题和压力。然而,临床或其他样本中所确定的疗效可能无法推广至员工群体,这是有充分理由的,并且许多应用程序是专门为该群体开发的。本文的目的是进行首次全面系统综述和荟萃分析,评估电子健康干预措施有效性的证据,并考察不同类型的电子健康干预措施对员工的相对疗效。

方法

对1975年至2016年11月17日发表的相关文章进行系统检索,这些文章是关于以员工心理健康为重点的电子健康心理健康干预措施(应用程序或基于网络)的试验。评估所有已识别研究的质量和偏倚。我们从已发表的报告中提取均值和标准差,比较标准化心理健康结果中的效应量差异(赫奇斯g值)。我们使用随机效应模型对这些数据进行荟萃分析,按随访时间长度、干预类型以及干预是针对全体员工(无特定选择)还是针对特定群体(如“压力大的员工”)进行分层。

结果

共识别出23项电子健康干预措施的对照试验,总体表明在干预后(g = 0.24,95%置信区间0.13至0.35)和随访时(g = 0.23,95%置信区间0.03至0.42)均有小的积极效果。不同干预类型之间在短期效果上存在差异,基于正念的干预措施(g = 0.60,95%置信区间0.34至0.85;n = 6)显示出比基于认知行为疗法(CBT)的干预措施(g = 0.15,95%置信区间0.02至0.29;n = 11)和基于压力管理的干预措施(g = 0.17,95%置信区间-0.01至0.34;n = 6)更大的效果。然而,基于压力管理的干预措施在针对全体员工或特定群体时效果有所不同,在特定群体的干预后(g = 0.64,95%置信区间0.54至0.85)和随访时(g = 0.69,95%置信区间0.06至1.33)有中等较大的效果,但在无特定选择的群体中无效果。

解读

有合理证据表明,针对员工实施的电子健康干预措施可能会在干预后减轻心理健康和压力症状,并且在随访时仍有益处,尽管益处有所减少。尽管企业界对这类方法充满热情,但雇主和其他组织应意识到并非所有此类干预措施都是等同的,许多缺乏证据,要取得最佳效果取决于为正确的人群提供正确类型的干预措施。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b29c/5739441/c0f86032afd5/pone.0189904.g001.jpg

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