Sanders Christopher A, Schueller Stephen M, Parks Acacia C, Howell Ryan T
Department of Psychological Sciences, University of Missouri, Columbia, Columbia, MO, United States.
Department of Psychological Science, University of California Irvine, Irvine, CA, United States.
J Med Internet Res. 2019 Jun 8;21(6):e13253. doi: 10.2196/13253.
A critical issue in understanding the benefits of Web-based interventions is the lack of information on the sustainability of those benefits. Sustainability in studies is often determined using group-level analyses that might obscure our understanding of who actually sustains change. Person-centric methods might provide a deeper knowledge of whether benefits are sustained and who tends to sustain those benefits.
The aim of this study was to conduct a person-centric analysis of longitudinal outcomes, examining well-being in participants over the first 3 months following a Web-based happiness intervention. We predicted we would find distinct trajectories in people's pattern of response over time. We also sought to identify what aspects of the intervention and the individual predicted an individual's well-being trajectory.
Data were gathered from 2 large studies of Web-based happiness interventions: one in which participants were randomly assigned to 1 of 14 possible 1-week activities (N=912) and another wherein participants were randomly assigned to complete 0, 2, 4, or 6 weeks of activities (N=1318). We performed a variation of K-means cluster analysis on trajectories of life satisfaction (LS) and affect balance (AB). After clusters were identified, we used exploratory analyses of variance and logistic regression models to analyze groups and compare predictors of group membership.
Cluster analysis produced similar cluster solutions for each sample. In both cases, participant trajectories in LS and AB fell into 1 of 4 distinct groups. These groups were as follows: those with high and static levels of happiness (n=118, or 42.8%, in Sample 1; n=306, or 52.8%, in Sample 2), those who experienced a lasting improvement (n=74, or 26.8% in Sample 1; n=104, or 18.0%, in Sample 2), those who experienced a temporary improvement but returned to baseline (n=37, or 13.4%, in Sample 1; n=82, or 14.2%, in Sample 2), and those with other trajectories (n=47, or 17.0%, in Sample 1; n=87, or 15.0% in Sample 2). The prevalence of depression symptoms predicted membership in 1 of the latter 3 groups. Higher usage and greater adherence predicted sustained rather than temporary benefits.
We revealed a few common patterns of change among those completing Web-based happiness interventions. A noteworthy finding was that many individuals began quite happy and maintained those levels. We failed to identify evidence that the benefit of any particular activity or group of activities was more sustainable than any others. We did find, however, that the distressed portion of participants was more likely to achieve a lasting benefit if they continued to practice, and adhere to, their assigned Web-based happiness intervention.
在理解基于网络的干预措施的益处时,一个关键问题是缺乏关于这些益处可持续性的信息。研究中的可持续性通常使用组水平分析来确定,这可能会模糊我们对实际维持变化的人的理解。以个人为中心的方法可能会提供更深入的认识,即益处是否得以维持以及哪些人倾向于维持这些益处。
本研究的目的是对纵向结果进行以个人为中心的分析,考察基于网络的幸福干预后前3个月参与者的幸福感。我们预测,随着时间推移,我们会在人们的反应模式中发现不同的轨迹。我们还试图确定干预措施和个体的哪些方面能够预测个体的幸福轨迹。
数据来自两项关于基于网络的幸福干预的大型研究:一项研究中,参与者被随机分配到14种可能的为期1周的活动中的一种(N = 912);另一项研究中,参与者被随机分配完成0、2、4或6周的活动(N = 1318)。我们对生活满意度(LS)和情感平衡(AB)的轨迹进行了K均值聚类分析的变体。确定聚类后,我们使用探索性方差分析和逻辑回归模型来分析组,并比较组成员的预测因素。
聚类分析为每个样本产生了相似的聚类解决方案。在这两种情况下,LS和AB的参与者轨迹都分为4个不同组中的1组。这些组如下:幸福水平高且稳定的人(样本1中n = 118,或42.8%;样本2中n = 306,或52.8%),经历持续改善的人(样本1中n = 74,或26.8%;样本2中n = 104,或18.0%),经历暂时改善但回到基线的人(样本1中n = 37,或13.4%;样本2中n = 82,或14.2%),以及有其他轨迹的人(样本1中n = 47,或17.0%;样本2中n = 87,或15.0%)。抑郁症状的患病率可预测后3组中1组的成员身份。更高的使用频率和更强的依从性可预测持续而非暂时的益处。
我们揭示了完成基于网络的幸福干预的人群中的一些常见变化模式。一个值得注意的发现是,许多人一开始就相当幸福并维持了这些水平。我们没有找到证据表明任何特定活动或一组活动的益处比其他活动更具可持续性。然而,我们确实发现,如果参与者继续实践并坚持他们被分配的基于网络的幸福干预,处于痛苦状态的参与者更有可能获得持久的益处。