Professorship of Psychology and Digital Mental Health Care, Department of Sports and Health Sciences, Technical University of Munich, Munich, Germany.
Department of Clinical Psychology and Psychotherapy, Institute of Psychology, Friedrich-Alexander -University Erlangen-Nürnberg, Erlangen, Germany.
Health Expect. 2024 Feb;27(1):e13951. doi: 10.1111/hex.13951.
Evidence shows that online interventions could prevent depression. However, to improve the effectiveness of preventive online interventions in individuals with subthreshold depression, it is worthwhile to study factors influencing intervention outcomes. Outcome expectancy has been shown to predict treatment outcomes in psychotherapy for depression. However, little is known about whether this also applies to depression prevention. The aim of this study was to investigate the role of participants' outcome expectancy in an online depression prevention intervention.
A secondary data analysis was conducted using data from two randomised-controlled trials (N = 304). Multilevel modelling was used to explore the effect of outcome expectancy on depressive symptoms and close-to-symptom-free status postintervention (6-7 weeks) and at follow-up (3-6 months). In a subsample (n = 102), Cox regression was applied to assess the effect on depression onset within 12 months. Explorative analyses included baseline characteristics as possible moderators. Outcome expectancy did not predict posttreatment outcomes or the onset of depression.
Small effects were observed at follow-up for depressive symptoms (β = -.39, 95% confidence interval [CI]: [-0.75, -0.03], p = .032, p = .130) and close-to-symptom-free status (relative risk = 1.06, 95% CI: [1.01, 1.11], p = .013, p = 0.064), but statistical significance was not maintained when controlling for multiple testing. Moderator analyses indicated that expectancy could be more influential for females and individuals with higher initial symptom severity.
More thoroughly designed, predictive studies targeting outcome expectancy are necessary to assess the full impact of the construct for effective depression prevention.
This secondary analysis did not involve patients, service users, care-givers, people with lived experience or members of the public. However, the findings incorporate the expectations of participants using the preventive online intervention, and these exploratory findings may inform the future involvement of participants in the design of indicated depression prevention interventions for adults.
Original studies: DRKS00004709, DRKS00005973; secondary analysis: osf.io/9xj6a.
有证据表明，在线干预措施可以预防抑郁症。然而，为了提高针对阈下抑郁个体的预防性在线干预措施的效果，研究影响干预效果的因素是值得的。预期结果在抑郁症的心理治疗中已被证明可以预测治疗效果。然而，对于预防抑郁症，目前还知之甚少。本研究旨在探讨参与者的预期结果在在线预防抑郁症干预中的作用。
使用两项随机对照试验（N=304）的数据进行二次数据分析。使用多层次模型探讨干预后（6-7 周）和随访时（3-6 个月）预期结果对抑郁症状和接近无症状状态的影响。在亚样本（n=102）中，应用 Cox 回归评估 12 个月内抑郁发作的影响。探索性分析包括基线特征作为可能的调节剂。预期结果并未预测治疗后结局或抑郁发作。
随访时观察到抑郁症状（β=-.39，95%置信区间[CI]：[-0.75，-0.03]，p=0.032，p=0.130）和接近无症状状态（相对风险=1.06，95%CI：[1.01，1.11]，p=0.013，p=0.064）的小效应，但在进行多次检验控制后，统计显著性未得到维持。调节分析表明，对于女性和初始症状严重程度较高的个体，预期可能更具影响力。
需要更精心设计、具有预测性的针对预期结果的研究，以评估该构念对有效预防抑郁症的全面影响。
这项二次分析不涉及患者、服务使用者、护理人员、有生活经验的人或公众。然而，这些发现包含了使用预防性在线干预措施的参与者的期望，这些探索性发现可能为未来参与者参与针对成年人的有针对性的预防抑郁症干预措施的设计提供信息。
原始研究：DRKS00004709，DRKS00005973；二次分析：osf.io/9xj6a。
