Psychological flexibility reliably predicts well-being, but the strength, nature and sequence of relationships between components of these constructs are likely to considerably vary between individuals. This study used emerging idionomic methods to examine within-person links between psychological flexibility/inflexibility subprocesses and hedonic well-being using ecological momentary assessment data (n = 167; 76% female college students; Mage = 23.8 years; sampling design: 3 prompts daily for one week; total measurements = 2252). We employed advanced statistical modelling techniques to understand complex time-series data, including modelling of individual behaviour (i-ARIMAX), meta-analyses to pool individual data to see overall heterogeneity (REMA), and multilevel modelling (Multilevel-VAR) to compare the groups which emerged from the data. Results aligned with past literature, demonstrating that psychological flexibility and inflexibility uniquely predicted hedonic well-being, though with substantial heterogeneity. Replicating past findings (Sahdra et al., 2024; see also Catts et al., 2025), we found that for Stoics, values operated independently from affect in within-person networks, while Non-Stoics showed strong value-affect connections. Novel analyses revealed that among Stoics, stress positively connected to acceptance, which then linked to committed action. While loneliness increased sadness for Stoics, they uniquely intensified committed action when sad-an effect only visible through within-person analysis. This contrasted with Non-Stoics, where sadness negatively impacted committed action. This study contributes to growing evidence supporting the advantages of an idionomic approach over purely nomothetic group analyses, particularly in revealing individual, idiosyncratic patterns.

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.

Striving for happiness can sometimes increase happiness but can also backfire and reduce it. To explore this paradox, we used idionomic methods-balancing individual-level analysis with group-level generalization-to examine how striving for happiness influences momentary happiness. Our data included ecological momentary assessment (EMA) surveys (n = 2251) from 167 participants (75.6% female; M age = 23.96, SD = 8.7). Each individual's data were first modelled separately, producing their own estimate and standard error for the association between each striving item and each affect item. These idiographic estimates were then submitted to a multivariate random-effects meta-analysis, which revealed high, non-random heterogeneity. The type of striving-prioritizing positivity (PP) versus experiential attachment (EA) to enjoyment moderated the overall effect. Due to high heterogeneity in the overall effect, we applied group-based multivariate trajectory modelling. This revealed two distinct groups with nonlinear patterns across striving and affect. Multilevel vector autoregressive models showed that EA consistently dampened happiness within-person, despite no between-person association. In contrast, PP was linked to higher happiness in one group, but in the other, it had no direct benefit and indirectly reduced happiness via its connection to EA. The dampening effects of EA held even when accounting for stress, positive events, loneliness, and social connection. Our findings underscore the importance of combining within-person and between-person analyses by replicating known nomothetic effects and highlighting complex subgroup dynamics. This dual approach is a crucial and necessary advancement for modern happiness research.

Higher compassion has been linked with greater subjective sleep quality indicators; however, within-person, longitudinal studies exploring this relationship are limited. We examined how self-compassion and other-compassion relate to subjective sleep quality indicators (sleep hours, sleep quality, and sleep recovery). Specifically, we analyzed the within-person link between compassion and sleep to assess both group-level nomothetic effects and the individual-level heterogeneity of these effects. In total, 154 adult inpatients and outpatients experiencing chronic transdiagnostic disorders engaged in a 1-week study using experience sampling methodology (ESM). The ESM sampled self-compassion, other-compassion, sleep quality, and mood six times daily. Self-compassion and other-compassion were positively correlated with subjective sleep quality indicators and mood in within- and between-person analyses. Sleep recovery was found to be the sleep indicator most strongly linked to compassion. Controlling for mood, within-person multilevel models revealed that higher daily average self-compassion predicted better sleep recovery the next day. Similarly, higher sleep recovery predicted greater self-compassion and other-compassion the next day. Multilevel model-based estimates of the within-person relationships between self-compassion and sleep recovery were found to be heterogeneous across the sample. Most of the sample, however, revealed a positive relationship between self-compassion and sleep recovery. These findings highlight the bidirectional relationship between self-compassion, other-compassion, and sleep recovery, and suggest that compassion-focused interventions may improve sleep recovery in clinical populations. This supports the soothing capacity of self-compassion and provides further evidence for the association of sleep with fundamental psychological processes like mood and compassion. This study was not preregistered.

Breathing interventions are a commonly used and effective tool for improving well-being. However, assessments of breathing are seldom included within these interventions, which limits understanding of how specific aspects of breathing contribute to specific changes in well-being. Existing assessments of breathing are not well suited for use in well-being interventions because they typically only capture negative aspects of breathing, such as dysfunction. The present study aimed to address this gap by developing and evaluating the first positive, self-report breathing measure (the Perceived Breath Mastery Scale [PBM-S]). Expert panel review and exploratory factor analysis led to a 3-factor, 23-item scale that showed good structural validity. Criterion, discriminant, incremental, and known-group validity were also established. The PBM-S proved to be a better predictor of flourishing than well-established measures, such as mindfulness and self-efficacy. We also used a machine learning algorithm to construct a 9-item version of the PBM-S and a 12-item version that included dysfunctional breathing as a fourth factor. We recommend using the PBM-S instruments in breathing interventions to deepen insights into the psychological processes they affect and to customize, assess, and enhance interventions for targeted well-being outcomes.

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.

To examine the relationship between valued action and mood, this study analyzed Ecological Momentary Assessment data from a transdiagnostic in-and out-patient sample (EMA; N = 134; 62 female, 72 male; 62 inpatient, 72 outpatient; Mage = 36.6 years, SD = 11.6). Individual time series models were constructed to capture each participant's unique relationship between valued action and mood. The models were then meta-analyzed, revealing substantial variability, with two subgroups; Stoics (n = 64) and Non-Stoics (n = 70). The Stoics subgroup showed null or negative links between valued action and mood, replicating past findings from a nonclinical sample. The Non-Stoic group engaged significantly more in valued actions characterized by enjoyment and relaxation. Subsequent multilevel VAR networks were created to examine differences between Stoics and Non-Stoics. Within-person analyses indicated that, unlike Non-Stoics, Stoics showed no significant association between valued action and mood in contemporaneous networks. Temporal networks revealed that, for Non-Stoics, mood positively influenced future engagement in valued action. These findings challenge assumptions of a universally positive relationship between valued action and mood, suggesting divergent paths to wellbeing based on individual differences in mood-action dynamics.

The traditional approach to assessing mindfulness has largely focused on between-person variability, often overlooking the unique, idiographic patterns that emerge within individuals over time. Idionomic assessment emphasizes the importance of modeling processes and their dynamic interactions in particular people (or couples, families, and so one) before generating nomothetic extensions. By critically examining the assumptions underlying conventional psychometric methods, particularly the ergodic theorem, we highlight the limitations of applying group-level findings to particular people. We propose an idionomic approach that integrates intensive longitudinal data to capture the nuanced and individualized nature of mindfulness and its impact on well-being. This method allows for a more precise understanding of how mindfulness practices influence personal outcomes, acknowledging the significant heterogeneity in these effects. Through a series of empirical examples, we demonstrate the practical applications of idionomic assessment in potentially identifying personalized intervention strategies that are more effective than traditional “one size fits all” approaches. The data we provide show that mindfulness processes are highly individualized, and their effects on psychological outcomes varies significantly between persons. This has profound implications for the field of applied psychology, suggesting a shift towards more personalized, process-based, and contextually sensitive assessments and intervention strategies. Ultimately, the idionomic approach offers a promising avenue for enhancing the treatment utility of mindfulness assessments, paving the way for more tailored and effective therapeutic practices.

Trichotillomania, characterized by repetitive hair-pulling, leads to significant distress and impairment. Heterogeneity in symptom profiles challenges the effectiveness of treatment protocols for trichotillomania. Recent research endorses personalized treatment, emphasizing the assessment of biopsychosocial processes to tailor interventions more closely to the individual. This shift to a process-based, person-centered framework necessitates analytic methods capable of probing beyond nomothetic patterns to unveil nuanced individual-level processes. This study utilized Group Iterative Multiple Model Estimation (GIMME) to examine group-level and individual-level network dynamics as an initial step towards a process-based treatment framework for trichotillomania. Ecological momentary assessment data from 54 affected individuals were analyzed to identify shared patterns applicable at the group-level and individual-level for individualized treatment. Analysis revealed a nomothetic process dynamically related to cognitive fixation on the urge to pull. At the individual level, notable variability in network structures emerged. While centrality measures consistently identified the urge to pull as a pivotal process within GIMME individual-level networks, the influence of other processes differed considerably between individuals. Results indicate that despite some shared components, the heterogeneity within individual networks calls for customized treatment approaches, and the assessment of psychological process dynamics at the individual level. These insights support incorporating idionomic methods into the developmental stages of personalized interventions.

Despite the global nature of psychological issues, an overwhelming majority of research originates from a small segment of the world’s population living in high-income countries (HICs). This disparity risks distorting our understanding of psychological phenomena by underrepresenting the cultural and contextual diversity of human experience. Research from lower- and middle-income countries (LMIC) is also less frequently cited, both because it is seemingly viewed as a ‘special case’ and because it is less well known due to language differences and biases in indexing algorithms. Acknowledging and actively addressing this imbalance is crucial for a more inclusive, diverse, and effective science of evidence-based intervention. In this State of the Science review, we used a machine learning method to identify key topics in LMIC research on Acceptance and Commitment Therapy (ACT), choosing ACT due to the significant body of work from LMICs. We also examined one indication of study quality (study size), and overall citations. Research in LMICs was often non-indexed, leading to lower citations, but study size could not explain a lack of indexing. Many objectively identified topics in ACT research became invisible when LMIC research was ignored. Specific countries exhibited potentially important differences in the topics. We conclude that strong and affirmative actions are needed by scientific associations and others to ensure that research from LMICs is conducted, known, indexed, and used by CBT researchers and others interested in evidence-based intervention science.

This article critiques the "protocol-for-syndrome" model in mental health research, highlighting two primary concerns: the complexity of protocols that include change processes irrelevant to many individuals, and the inadequacy of Diagnostic and Statistical Manual of Mental Disorders syndromes to capture the nuances of individual well-being and suffering. Advocating a shift to a process-based therapy (PBT) approach, the article proposes a coherent integration of diverse change processes and interventions to enrich therapy practices. It introduces a slightly revised extended evolutionary metamodel (EEMM) as a comprehensive framework that provides a consistent language for discussing change processes, focusing on the key drivers of variation, selection, and retention, and categorizing these into dimensions (such as cognition, emotion, self, motivation) and levels (from biology/physiology to psychology and social relationships/culture). The article details the application of EEMM in classifying therapeutic processes, validated through both human and artificial intelligence (AI) ratings. Furthermore, we developed an AI tool built on Distilled Bidirectional Encoder Representations from Transformers (distilBERT) models for categorizing therapeutic content, proving effective and accessible for community engagement and ongoing enhancement. The article also explores network theory and new analytics as tools for therapists to customize therapy to individual client needs. In summary, PBT supports therapeutic diversity while establishing common ground among different methods and approaches. This enhances communication, cooperation, and comparison, fostering the development of tailored and effective therapy strategies. It also opens the door to the potential unification of psychotherapy.

A consistent association has been observed between internet addiction and symptoms of social anxiety. However, there is a lack of empirical research that delves into potential explanations for this relationship and its directionality, making it difficult to translate findings into development of interventions for social anxiety that account for technology-related behaviors. The present study aimed to evaluate the longitudinal dynamics between internet addiction, symptoms of social anxiety, avoidance of social interactions, and using the internet to cope with loneliness. By means of an ecological momentary assessment study, we evaluated a sample of 122 young adults from Chile using intensive self-report measurements five times a day, for a period of 10 days. Using mixed-effects models, we examined the directionality between internet addiction and symptoms of social anxiety, together with an explanation of their relationship. Results indicate that internet addiction antecedes symptoms of social anxiety; however, the reverse relationship was not observed. Furthermore, instances where individuals avoided social interactions or used the internet to cope with loneliness were predictive of later increases in levels of internet addiction, suggesting a vicious cycle. Significant heterogeneity was observed in these effects, highlighting the need for a more personalized approach when including technology-related behaviors in social anxiety interventions. Theoretical and clinical implications are discussed.

This study evaluated idionomic methods for identifying within-person links between therapeutically relevant processes and outcomes, using an ecological momentary assessment dataset of valued action and hedonic well-being (participants (n) = 425; 71.76% female; age = M(SD) = 22.20 (6.85); sampling design: 3-4 prompts per day; total measurements (n) = 6,456). We compared the idionomic approach, integrating idiographic and nomothetic insights, with traditional multilevel modeling (MLM). Our methods included idiographic autoregressive integrative moving average models with an exogenous variable (i-ARIMAX), multivariate random-effects meta-analysis (RE-MA), deep Gaussian mixture modeling (DGMM), and multilevel vector autoregression modeling (Multilevel-VAR). The results showed that i-ARIMAX outperformed MLM in capturing within-person heterogeneity in the links between valued action and affect variables. Increases in values-based living were positively related to hedonic well-being but this effect showed a high degree of heterogeneity. A sub-group was identified, which we labeled the ‘Stoics,’ whose daily engagement in valued actions did not produce higher hedonic well-being (e.g., lower sadness or higher joy). Multilevel-VAR further revealed that for Stoics, stressful situations were linked to valued action, but not hedonic well-being. For Non-Stoics, valued action was less likely in stressful situations, but when valued action did occur it was associated with more joy and less sadness. The study offers initial evidence suggesting the superiority of an idionomic approach over a purely nomothetic one in capturing diverse pathways to clinically relevant outcomes. Idionomic methods may be useful or even necessary in personalizing psychological interventions, and thus may need to be considered by researchers and practitioners alike.

Considerable research has now documented the beneficial effects of mindfulness-based practices on psychological functioning. Less is known about the long-term durability of such changes following formal meditation training. In a sample of adults (N = 67, 52% female, ages 22-70 years), we examined changes in 16 measures of psychological adaptive functioning across a 3-month residential meditation intervention and across a subsequent 7-year period. We observed general training-related improvements followed by multi-year returns toward pre-training levels. However, beneficial changes in two personality attributes (agreeableness and neuroticism) were of moderate effect size (d = 0.51 and d = 0.45, respectively) and were retained across the 7-year follow-up. We further found that individual variation in changes was represented by three latent attributes: (Changes related to) Mindful Well-being, Resilient Extraversion, and Self-Compassionate Openness. These results suggest that intensive meditation training is associated with improved adaptive functioning and enduring changes in aspects of personality.

Our study examines the relationship between self-compassion, other-compassion, and romantic attraction in couples, and questions the psychological homogeneity assumption—the idea that psychological responses are uniform across individuals and couples. We analyzed data from 161 participants in 84 couples, with an average age 32 (SD = 12.02), using smartphones for event sampling six times daily over a week to measure self-compassion, other-compassion, and attraction. Through within-person and network analysis, we discovered significant variability in how self and other-compassion influence attraction, identifying two distinct couple types: "synergistic," where compassion significantly affects attraction, and "independent," where it does not. Further analysis revealed that, when other-compassion is accounted for, males with high self-compassion were less attracted to their female partners. The significant diversity in how individuals and couples experience compassion and attraction challenges the assumption that conclusions drawn from group averages can be universally applied to individual couples. Clinically this means that efforts to enhance compassion in couples therapy should be tailored to the couple's unique dynamics. Indeed, for some men, emphasizing self-compassion without considering other-compassion could even be detrimental to the relationship. Our findings highlight the need for nuanced case formulation and personalized treatment planning in couples therapy, underscoring the complexity of relationship dynamics and the importance of rejecting "one size fits all" assumptions.

Individuals’ subjective well-being (SWB) is an important marker of development and social progress. As psychological health issues often begin during adolescence, understanding the factors that enhance SWB among adolescents is critical to devising preventive interventions. However, little is known about how institutional contexts contribute to adolescent SWB. Using Programme for International Student Assessment (PISA) 2015 and 2018 data from 78 countries ( N = 941,475), we find that gender gaps in adolescents’ SWB (life satisfaction, positive and negative affect) are larger in more gender-equal countries. Results paradoxically indicated that gender equality enhances boys’ but not girls’ SWB, suggesting that greater gender equality may facilitate social comparisons across genders. This may lead to an increased awareness of discrimination against females and consequently lower girls’ SWB, diluting the overall benefits of gender equality. These findings underscore the need for researchers and policy-makers to better understand macro-level factors, beyond objective gender equality, that support girls’ SWB.