Dusti R. Jones

Variability in daily emotional experiences may be important for both mental and physical health and may be influenced by individual differences such as age, gender, race, and ethnicity. Research on individual differences in affect variability is limited, and what is available does not encompass diverse, multilayered identities. Therefore, the present study aims to provide more inclusive evidence of the role of individual differences in affect variability. We assessed momentary positive and negative affect in a diverse community sample of adults. A sample of 300 adults (age 18-80; 50% women; 19% Hispanic, 15% Black) completed 2 days of ecological momentary assessment sampling randomly within 45-min blocks during waking hours. Using individual standard deviation for positive affect (PA) and negative affect (NA) variability, independent samples t tests were conducted to assess individual differences in PA and NA variability. General linear models were used to assess interaction effects between individual differences in PA and NA variability. Correlation analyses demonstrate that older age was correlated with lower PA and NA variability. Independent sample t tests demonstrate that being female was associated with greater PA variability, and identifying as White was associated with lower PA and NA variability. Two-way and three-way interactions between age, gender, and race/ethnicity showed no significant association with PA or NA variability. Although the observed effects were not uniform across all demographics, together they highlight the relevance of individual differences in affect dynamics and underscore the value of incorporating them into research on affect variability. (PsycInfo Database Record (c) 2026 APA, all rights reserved).

Ecological momentary assessments (EMAs) effectively measure dynamic changes in constructs such as affect. Accurate assessment of affect variability may depend on study design characteristics. While EMAs delivered at random intervals are typically considered superior for capturing variability compared to EMAs delivered at set timepoints, there have been few experimental comparisons of these assessment methodologies. The present study used data from a nationwide factorial experiment to identify best practices in EMA methods. Participants ( N = 205) were randomized to groups across five EMA study design factors, including prompting schedules (random or fixed). Daily EMAs assessed affect and health behaviors for 28 days. Multilevel regressions indicated significantly lower affect variability for the fixed prompt group in 8/16 models: compared to the random prompt group, there was between a 0.18 and 0.36 standard deviation decrease in variability in the fixed prompt group. A significant Prompt × Time interaction indicated that variability in the fixed group decreased over time relative to the random group in 6/16 models. However, prompt schedule did not reliably moderate associations between daily affect means/variability and next-day health behaviors. Thus, differences in affect variability between prompt schedules may exist for some affect items but may not be sufficiently large to influence associations with health behaviors. Trial Registration : ClinicalTrials.gov number NCT05194228; https://clinicaltrials.gov/study/NCT05194228

Digital cognitive interventions (DCIs) have emerged as scalable approaches for treating cognitive dysfunction across psychiatric, neurological, and aging populations. Despite growing evidence of efficacy, little is known about which intervention components drive therapeutic effects or through which neurocognitive mechanisms they operate. As a result, null findings are often difficult to interpret, making it unclear whether interventions failed to engage their intended targets, or whether the targets themselves are not causally related to meaningful outcomes. This limits intervention refinement, comparative evaluation, and precision personalization. Here, we argue that DCI research should shift from broad efficacy testing toward mechanistic trials designed to identify active ingredients—the intervention components responsible for engaging prespecified neurocognitive targets and producing clinically meaningful benefits. We propose adapting dismantling design methodology from psychotherapy research in order to integrate Research Domain Criteria constructs, mechanistic neuroscience, and high-resolution digital behavioral data to identify factors driving cognitive and functional outcomes. This approach aligns with the National Institute of Mental Health experimental therapeutics framework by explicitly linking target specification and target engagement with downstream clinical and functional outcomes. Mechanistic dismantling trials can determine whether specific DCI features, including adaptive difficulty, reward schedules, feedback contingencies, task variability, cognitive targets, and human support, are necessary, sufficient, or synergistic for engaging neural circuitry and producing durable and clinically meaningful transfer. Beyond optimizing intervention design, such studies may transform null or negative trials into mechanistically interpretable findings, while clarifying disease mechanisms and supporting the development of personalized, optimized, and usable DCIs.

BACKGROUND Digital cognitive interventions (DCIs) are increasingly used to address cognitive deficits across psychiatric, neurological, and aging populations. However, the field lacks consensus about which intervention components are causally responsible for cognitive and clinical benefits. This limits interpretation of negative trials, impedes optimization, and constrains personalization. Mechanistic dismantling trials can clarify whether specific DCI features (e.g., adaptive difficulty, reward schedules, feedback contingencies, task variability, specific cognitive target, and human support) are necessary, sufficient, or synergistic for engaging neural circuitry and producing durable generalization. Further, dismantling designs hold potential to more precisely probe underlying disease mechanisms and identify specific neurocognitive processes responsible for clinical impairment in the target population. OBJECTIVE We argue that DCI research should shift from broad efficacy testing to mechanistic studies designed to identify “active ingredients” e.g. the minimal components required to engage a prespecified target and improve clinically meaningful outcomes. We propose adapting dismantling designs from psychotherapy research and integrating them with mechanistic neuroscience, cognitive theory, and high-resolution digital behavioral data. This approach aligns with the National Institute of Mental Health’s Experimental Therapeutics framework by explicitly linking: (1) target specification, (2) target engagement, and (3) downstream clinical and functional benefit. METHODS No Methods RESULTS No Results CONCLUSIONS Mechanistic dismantling trials can clarify whether specific DCI features (e.g., adaptive difficulty, reward schedules, feedback contingencies, task variability, specific cognitive target, and human support) are necessary, sufficient, or synergistic for engaging neural circuitry and producing durable generalization. Further, dismantling designs hold potential to more precisely probe underlying disease mechanisms and identify specific neurocognitive processes responsible for clinical impairment in the target population.

Loneliness is linked to a wide range of negative outcomes, including worse emotional well-being. Although prior research has demonstrated the relationship between loneliness and typical levels of negative and positive emotions, the degree to which loneliness is associated with emotional instability, a potential indicator of unhealthy patterns of emotional experiences, is unknown. We evaluated whether individual differences in loneliness were related to instability across days in positive emotions (PE) and negative emotions (NE) in daily life. A diverse community sample of 252 adults (age 25-65) completed a baseline assessment of loneliness followed by 14 days of ecological momentary assessments, during which participants reported PE and NE five times each day. Loneliness was significantly associated with greater instability in both PE and NE, after adjusting for demographic characteristics, objective social isolation, and person-mean emotion levels. Notably, the association with PE instability remained significant even after controlling for depressive symptoms, whereas the association with NE instability was attenuated and no longer significant. These findings suggest that loneliness is characterised not only by differences in average emotional states but also by greater emotional variability, particularly in PE, which may be a key feature linking loneliness to broader health and well-being outcomes.

Background: Affect states are posited to play a pivotal role in addiction-related processes, including tobacco lapse (i.e., smoking during a quit attempt), and distinct affective states (e.g., joy vs. happiness) may differentially influence lapse likelihood. However, few studies have examined the influence of distinct affective states on tobacco lapse. Purpose: This study examines the influence of 23 distinct affect states on tobacco lapse among a sample of tobacco users attempting to quit. Methods: Participants were 220 adults who identified as African American (50% female, ages 18–74). Ecological momentary assessment was used to assess affect and lapse in real-time. Between and within-person associations testing links between distinct affect states and lapse were examined with multilevel modeling for binary outcomes. Results: After adjusting for previous time’s lapse and for all other positive or negative affect items, results suggested that at the between-person level, joy was associated with lower odds of lapse, and at the within-person level, attentiveness was associated with lower odds of lapse. Results also suggested that at the between-person level, guilt and nervous were associated with higher odds of lapse, and at the within-person level, shame was associated with higher odds of lapse. Conclusions: The present study uses real-time, real-world data to demonstrate the role of distinct positive and negative affects on momentary tobacco lapse. This work helps elucidate specific affective experiences that facilitate or hinder the ability to abstain from tobacco use during a quit attempt.