BACKGROUND
Anxiety and depression are prevalent mental health disorders, but traditional treatments often have limited effectiveness. Process-Based Therapy (PBT), which targets the underlying mechanisms driving these conditions, offers a promising alternative. The purpose of this retrospective study was to examine how processes of change relate to changes in anxiety and depressive symptoms in patients with elevated anxiety and/or depression symptoms who underwent PBT at Lightfully Behavioral Health, a behavioral health treatment clinic.
METHODS
This retrospective study analyzed data from adults (≥ 18 years) with elevated anxiety (n = 396) and depression (n = 434) who received treatment at Lightfully Behavioral Health, a PBT-based behavioral health treatment clinic. Mental health symptoms and PBT processes (i.e., psychological inflexibility, emotion dysregulation, thwarted belongingness, self-compassion, and personal values alignment) were measured at admission and discharge. Multiple logistic regression models, including stepwise models, tested associations between changes in processes and achievement of remission at discharge (yes vs. no) among those with elevated anxiety and depression. Bayesian structural equation models assessed whether changes in processes mediated the relationship between treatment dose and achieving anxiety and depression remission.
RESULTS
Improvements in all PBT processes were related to achieving anxiety and depression remission (p < 0.001). However, only improvements in self-compassion (OR 2.92, p < 0.001) and psychological inflexibility (OR 1.05, p = 0.007) were associated with greater likelihood of achieving anxiety symptom remission, and self-compassion (OR 2.33, p < 0.001), alignment with personal values (OR 1.06, p = 0.006), psychological inflexibility (OR 1.08, p < 0.001), with greater likelihood of achieving depression symptom remission. Self-compassion was supported as a mediator in the relationship between treatment dose and anxiety remission, while both self-compassion and psychological inflexibility were supported as mediators for depression remission, as their 95% credible intervals excluded zero.
CONCLUSION
Self-compassion and psychological inflexibility may be important for the remission of anxiety and depression symptoms among patients receiving PBT. These findings suggest that changes in self-compassion and psychological inflexibility are associated with remission and may represent important targets for intervention. While this study contributes to the limited evidence on PBT, further research is needed to better understand the long-term effects of these therapeutic processes.
Lara M Baez, K. McAlister, Chris Pleman et al.· BMC Psychology· 0 citations
Abstract Background AI-powered digital mental health interventions (DMHIs) are a promising approach to address barriers to traditional mental health care. However, real-world evidence of their immediate and sustained benefits remains limited. Objective The purpose of this real-world, retrospective study is to explore patterns of perceived mood and stress change associated with the use of Yuna, an AI-powered DMHI. We aimed to (1) describe user demographics and session characteristics, (2) quantify the magnitude of mood and stress change within sessions and over time with continued Yuna use, and (3) identify session-level factors associated with changes in mood and stress. Methods Adult Yuna users (aged ≥18 y) who initiated at least one session with the Yuna app were included in this study. Users self-reported mood and stress on Visual Analog Scales (VASs; range 0‐1) before and after sessions. Linear mixed effects models were used to explore the immediate, within-session improvements in mood and stress, and the sustained, between-session changes in symptoms of mood and stress. Linear mixed effects models were also used to examine session-level predictors of within-session improvements, including baseline symptom severity, session duration, total number of unique therapeutic approaches used, safety guardrail activation, and gender. Results A total of 5549 real-world users were included (2901/5549, 52.3% female; mean sessions 3.44, SD 9.83). Users demonstrated significant, immediate within-session improvements in both mood (d=0.55) and stress (d=0.56), with sensitivity analyses yielding consistent results. Between-session analyses revealed gradual improvements in baseline mood (d=−0.009) and stress (d=−0.011). Baseline symptom severity was the strongest predictor of immediate, within-session change (mood: β=.118, SE 0.003; P<.001; stress: β=.131, SE 0.004; P<.001), followed by session duration (mood: β=.027, SE 0.003; P=.003; stress: β=.029, SE 0.003; P<.001). A greater number of unique therapeutic approaches used was associated with smaller improvements in both outcomes (mood: β=−0.007, SE 0.003; P=.043; stress: β=-0.011, SE 0.003; P=.002). Conclusions Use of Yuna, an AI-powered DMHI, was associated with perceived within-session improvements in mood and stress, with preliminary evidence of gradual improvements in mood and stress across repeated sessions. However, the absence of a control group and potential selection bias preclude causal conclusions. These findings offer promising, real-world evidence for AI-powered DMHIs as accessible, on-demand support tools. Prospective, controlled designs are needed to establish causal effects and evaluate the sustainability of observed improvements.
K. McAlister, Courtney C. Jewell, Chad Stecher et al.· JMIR mHealth and uHealth· 1 citation