Steven C. Hayes

The Process-Based Assessment Tool (PBAT) was developed within the framework of process-based therapy, an alternative to symptom-focused diagnostic models, to monitor biopsychosocial change processes and support personalized therapeutic interventions. This cross-sectional study validated the Spanish PBAT by replicating the original U.S. study protocol in 602 Spanish adults. We also aimed to compare predictive process-outcome networks across Spain, the U.S., Sweden, and Poland using link community network analysis. Structural validity was assessed through inter-item correlations, and criterion validity through associations between PBAT items and clinical outcomes and need satisfaction/frustration. The Boruta algorithm identified the most predictive items for each outcome. Results confirmed the structural and criterion validity of the Spanish PBAT. Link community network analysis revealed country-specific differences in which processes most strongly predicted distress: difficulties finding appropriate emotional outlets were most central in Spain, perceived inability to change ineffective behavior was relatively prominent in the U.S., and cognitive interference and attentional difficulties were most prominent in Sweden, while Poland showed a more distributed pattern across these three processes. Future research could extend this approach to individual longitudinal data, potentially enabling clinicians to identify and target core psychological processes based on a person's unique profile.

The replication crisis in the social and behavioral sciences is widely attributed to methodological flaws like low power and publication bias. However, these accounts assume replication failure is merely sampling error around a stable, universal truth. Human biopsychosocial processes are nearly universally non-ergodic: they vary significantly across individuals and over time. When such heterogeneity meets standard convenience sampling, it produces estimand drift, creating a mathematically guaranteed floor on replicability that persists even in perfectly designed, fully powered studies. Because cross-person ensemble averages fail to describe particular people, irreducible replication failure rates emerge. To resolve this structural crisis, an idionomic approach is needed, which requires measuring processes longitudinally within the unit of application (e.g., individuals, couples, families), and modeling at that level before seeking general laws that apply across them. By quantifying the idionomic distribution of within-person effects, the field can distinguish methodological failures from the mathematical consequences of human complexity.

Idiographic approaches offer a promising path to personalized psychological interventions, though guidance on selecting optimal methods remains limited. Researchers have applied various time series, network, and tree-based methods to identify individual-level process-outcome relationships, yet their comparative effectiveness is under-evaluated. We examined six advanced methods: two ARIMAX-based time series models (bivariate and multivariate i-ARIMAX), two network approaches (GIMME, indSEM), and two tree-based algorithms (Boruta and our novel Time Series Boruta, or tsBoruta). Simulations tested these methods across diverse longitudinal data conditions, varying in linear/nonlinear effects, autoregressive patterns, trends, interactions, predictor counts, and sample sizes. We evaluated performance using sensitivity, specificity, precision, and F1 scores. For linear effects, i-ARIMAX, iBoruta, and tsBoruta achieved the highest F1 scores and specificity. Tree-based approaches performed best at detecting nonlinear effects and interactions. Methods that failed to account for time-series dependencies showed significant performance drops as these factors increased. In an empirical application, i-ARIMAX and tsBoruta replicated the group-level loneliness–depressed mood link while revealing substantial individual-level heterogeneity and distinct predictor patterns, informing potential personalized targets. Combining i-ARIMAX and tsBoruta offers a practical workflow that balances sensitivity and specificity, captures both linear and nonlinear processes, and supports data-driven, idiographic psychological treatment planning.

Personalized psychological interventions aim to tailor therapy to an individual’s unique needs, preferences, and functioning. Although recent meta-analyses have demonstrated the effectiveness of personalization, less is known about how personalization is actually operationalized in practice. This systematic review examined the methods and measures used to personalize psychological interventions. We analyzed twenty-nine studies identified through two recent meta-analyses and an updated systematic search. We classified study methods, decision strategies, and the content of 51 unique measures using the Collect-Share-Act model. Most personalization relied on static, pre-treatment data and used limited measurement domains, predominantly focused on affective and cognitive domains, neglecting other areas like self and motivation. Therapist input and patient engagement in the personalization process were infrequent. These findings suggest that current personalization practices are narrow in scope and underutilize feedback-informed and process-based frameworks. Broadening measurement domains and strengthening patient–therapist input may enhance the flexibility and impact of personalized interventions.

Terapia de Aceitação e Compromisso (ACT) é um tratamento empiricamente sus­tentado e uma abordagem baseada em processos que endereça tanto o sofrimento psicológico quanto a promoção da prosperidade humana. Este artigo apresenta um guia rápido sobre a ACT, destinado especialmente a estudantes e profissionais que estão lendo sobre o tema pela primeira vez. O leitor encontrará os primeiros passos para entender o que é a ACT, o modelo de flexibilidade psicológica, o estado atual das evidências e algumas palavras sobre o futuro dessa abordagem.

This study analyzed the efficacy of acceptance and commitment therapy (ACT) in six individuals with chronic low back pain plus depressive symptoms using an idiographic approach within a randomized controlled trial. Daily ecological momentary assessments (EMA) and full assessments at baseline, posttreatment, and follow-up were collected. Outcomes included pain interference, pain intensity, and depressed mood, and the process variable was psychological inflexibility. Analyses involved visual inspection, non-overlap of all pairs, Tau, Tau-U, and idiographic network analysis. Moderate improvements were observed in pain interference (5/6), depressed mood (5/6), and psychological inflexibility (3/6), with limited change in pain intensity (1/6). Most participants (4/6) reported an overall relevant improvement. Idiographic networks showed considerable variability across participants, with psychological inflexibility and depressed mood playing a central role. Findings suggest ACT may help reduce pain interference and depressed mood, highlighting the need for personalized approaches and the continued use of single-case methods combined with EMA.