Understanding Long-Term Trajectories in Web-Based Happiness Interventions: Secondary Analysis From Two Web-Based...

Area of Science:

• Psychology
• Digital Health
• Behavioral Science

Background:

• Understanding the long-term sustainability of benefits from Web-based interventions is crucial.
• Group-level analyses may obscure individual differences in sustained change.
• Person-centric methods offer deeper insights into who sustains benefits from digital health interventions.

Purpose of the Study:

• To conduct a person-centric analysis of longitudinal well-being outcomes after a Web-based happiness intervention.
• To identify distinct individual response trajectories over the first 3 months post-intervention.
• To determine predictors of well-being trajectories, including intervention aspects and individual characteristics.

Main Methods:

• Utilized data from two large Web-based happiness intervention studies (N=912 and N=1318).
• Applied K-means cluster analysis to longitudinal trajectories of life satisfaction (LS) and affect balance (AB).
• Employed analyses of variance and logistic regression to identify predictors of cluster membership.

Main Results:

• Identified four distinct trajectory groups for LS and AB in both samples.
• Found that 42.8%-52.8% maintained high happiness levels; 18.0%-26.8% experienced lasting improvement.
• Prevalence of depression symptoms predicted non-static trajectories; higher usage and adherence predicted sustained benefits.

Conclusions:

• Common patterns of change were observed, with many participants maintaining high happiness.
• No specific intervention activity demonstrated superior sustainability.
• Distressed participants achieving lasting benefits were more likely to adhere to the intervention.