Ecological Momentary Assessment Data Research Articles

Effects of social drinking context on subjective effects, affect, and next-day appraisals in the natural environment.

Drinking commonly occurs in social settings and may bolster social reinforcement. Laboratory studies suggest that subjective effects and mood are mechanisms through which the social context influences alcohol consumption. Ecological momentary assessment (EMA) may be useful for extending these findings to the natural environment. This pre-registered secondary analysis of EMA data investigated the influence of the social environment on: (1) stimulating and sedating subjective effects of alcohol, (2) contentedness and negative affect, and (3) next-day evaluations of the drinking occasion. Nontreatment seeking adults reporting past-month heavy drinking (N = 131; Mage = 28.09; 42% female) completed 7 days of EMA (in the morning, at random, and following drinking prompts), which included questions on their social context (drinking in the presence of known others or alone), contemporaneous stimulating and sedating effects, contentedness and negative affect, alcohol consumption, and next-day evaluations of a prior day's drinking event (how satisfying/pleasant was drinking). We used multi-level models in SAS 9.4 M7 software to examine relations among the variables. Contemporaneous subjective effects (stimulating or sedating), negative affect, and contentedness did not significantly depend on the social context. For next-day evaluations of pleasure/satisfaction from drinking, context effects were dependent on consumption totals. As the total number of standard drinks consumed increased, recollections of pleasure/satisfaction were higher when drinking had occurred with others, relative to alone. At lower consumption totals, next-day evaluations did not appear to depend on social context. When reported contemporaneously, subjective effects and affect do not appear dependent upon the presence of known others. However, heavier drinking events, relative to lighter drinking events, are appraised more favorably the following day when occurring within social contexts.

A screen-time-based mixture model for identifying and monitoring careless and insufficient effort responding in ecological momentary assessment data.

Ecological momentary assessment (EMA) involves repeated real-time sampling of respondents' current behaviors and experiences. The intensive repeated assessment imposes an increased burden on respondents, rendering EMAs vulnerable to respondent noncompliance and/or careless and insufficient effort responding (C/IER). We developed a mixture modeling approach that equips researchers with a tool for (a) gauging the degree of C/IER contamination of their EMA data and (b) studying the trajectory of C/IER across the study. For separating attentive from C/IER behavior, the approach leverages collateral information from screen times, which are routinely recorded in electronically administered EMAs, and translates theoretical considerations on respondents' behavior into component models for attentive and careless screen times as well as for the functional form of C/IER trajectories. We show how a sensible choice of component models (a) allows disentangling short screen times due to C/IER from familiarity effects due to repeated exposure to the same measures, (b) aids in gaining a fine-grained understanding of C/IER trajectories by distinguishing within-day from between-day effects, and (c) allows investigating interindividual differences in attentiveness. The approach shows good parameter recovery when attentive and C/IER screen time distributions exhibit sufficient separation and yields valid conclusions even in scenarios of uncontaminated data. The approach is illustrated on EMA data from the German Socio-Economic Panel innovation sample. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Feasibility of and reactivity to ecological momentary assessment (EMA) during electronic cigarette use initiation in adults who smoke daily

Background and objectivesThis pilot study tested the feasibility of tracking e-cigarette initiation over three months using a burst-of-measurement approach and examined reactivity to the ecological momentary assessment (EMA) data capture technique in this context. MethodsAdults who smoked daily and were interested in trying e-cigarette use received an e-cigarette starter kit and were randomized to complete EMA reports (n = 30) vs. not (n = 29). Participants (Mage=44.1 years [SD=12.6], 61.0 % male, 54.3 % White, 38.9 % Black/African American) smoked 13.0 cigarettes per day on average (SD=8.4) and reported mild-to-moderate nicotine dependence (MFTND=4.0 [SD=2.3]). Biochemical assessment and surveys were conducted at enrollment, e-cigarette initiation (1 week later), and 1-week, 1-month, and 3-month post e-cigarette initiation. ResultsA subset (17 %) of participants did not achieve the minimum 75 % EMA adherence at Week 1. The remaining participants showed excellent adherence with the EMA protocol, with a consistently high response rate to audibly prompted mini-surveys during screening (93 %) and follow-up weeks (93 %, 93 %, and 92 %, respectively). No consistent trend emerged in comparing the two randomized groups, either on variables relevant to the public health impact of e-cigarette initiation (i.e., cigarettes/day, exposure to smoke, exposure to nicotine, motivation to quit smoking), or in e-cigarette use (i.e., bouts/day, puffs/bout), with average Cohen's d values across timepoints ranging from d = 0.02 for exposure to smoke to d = 0.06 for cigarettes per day. ConclusionsConducting EMA with adults who smoke daily and are initiating e-cigarette use appears feasible, with some caveats. Reactivity to EMA during e-cigarette initiation appears to be small or non-significant.

Nocturnal hypoglycemia is associated with next day cognitive performance in adults with type 1 diabetes: Pilot data from the GluCog study

Objective: Individuals with type 1 diabetes (T1D) have increased risk for cognitive dysfunction and high rates of sleep disturbance. Despite associations between glycemia and cognitive performance using cross-sectional and experimental methods few studies have evaluated this relationship in a naturalistic setting, or the impact of nocturnal versus daytime hypoglycemia. Ecological Momentary Assessment (EMA) may provide insight into the dynamic associations between cognition, affective, and physiological states. The current study couples EMA data with continuous glucose monitoring (CGM) to examine the within-person impact of nocturnal glycemia on next day cognitive performance in adults with T1D. Due to high rates of sleep disturbance and emotional distress in people with T1D, the potential impacts of sleep characteristics and negative affect were also evaluated. Methods: This pilot study utilized EMA in 18 adults with T1D to examine the impact of glycemic excursions, measured using CGM, on cognitive performance, measured via mobile cognitive assessment using the TestMyBrain platform. Multilevel modeling was used to test the within-person effects of nocturnal hypoglycemia and hyperglycemia on next day cognition. Results: Results indicated that increases in nocturnal hypoglycemia were associated with slower next day processing speed. This association was not significantly attenuated by negative affect, sleepiness, or sleep quality. Conclusions: These results, while preliminary due to small sample size, showcase the power of intensive longitudinal designs using ambulatory cognitive assessment to uncover novel determinants of cognitive fluctuation in real world settings, an approach that may be utilized in other populations. Findings suggest reducing nocturnal hypoglycemia may improve cognition in adults with T1D.

The impact of measurement on clinical trials: Comparison of preliminary outcomes of a brief mobile intervention for autistic adults using multiple measurement approaches.

Initial studies of the emotional safety plan (ESP), a new, brief telehealth and mobile intervention to support autistic adults to cope with periods of distress, have reported feasibility and acceptability (Bal et al., 2023, Autism, 1-13). Herein we report the preliminary clinical outcomes of thirty-six autistic adults who developed a personalized ESP, with a specific interest in comparing "outcomes" demonstrated by different instruments and assessment frequencies in order to inform outcome measurement in future clinical trials. Comparison of pre-intervention baseline to post-monitoring outcome (pre-post) anxiety symptoms (Generalized Anxiety Disorder-7 [GAD-7]) and depressive symptoms (Patient Health Questionnaire-9 [PHQ-9]) revealed medium effect sizes for reduction in symptoms, though, low effect sizes were observed for pre-post Adult Self-report Anxiety and Depressive Problems scales and the Emotion Dysregulation Inventory Reactivity and Dysphoria scales. Weekly assessments showed an initial decrease in GAD-7 anxiety symptoms but no effect on weekly PHQ-9 depressive ratings. Ecological momentary assessment (EMA) data suggested that, when participants reported feeling sad or agitated and used the ESP, reduced negative feelings and increased positive states were reported in subsequent EMA. Perhaps not surprisingly, preliminary outcomes of these feasibility trials differed depending on measure chosen. Implications for the design of clinical trials are discussed.

Introducing FRED: Software for Generating Feedback Reports for Ecological Momentary Assessment Data.

Ecological Momentary Assessment (EMA) is a data collection approach utilizing smartphone applications or wearable devices to gather insights into daily life. EMA has advantages over traditional surveys, such as increasing ecological validity. However, especially prolonged data collection can burden participants by disrupting their everyday activities. Consequently, EMA studies can have comparably high rates of missing data and face problems of compliance. Giving participants access to their data via accessible feedback reports, as seen in citizen science initiatives, may increase participant motivation. Existing frameworks to generate such reports focus on single individuals in clinical settings and do not scale well to large datasets. Here, we introduce FRED (Feedback Reports on EMA Data) to tackle the challenge of providing personalized reports to many participants. FRED is an interactive online tool in which participants can explore their own personalized data reports. We showcase FRED using data from theWARN-D study, where 867 participants were queried for 85 consecutive days with four daily and one weekly survey, resulting in up to 352 observations per participant. FRED includes descriptive statistics, time-series visualizations, and network analyses on selected EMA variables. Participants can access the reports online as part of a Shiny app, developed via the R programming language. We make the code and infrastructure of FRED available in the hope that it will be useful for both research and clinical settings, given that it can be flexibly adapted to the needs of other projects with the goal of generating personalized data reports.

A Tutorial on Analyzing Ecological Momentary Assessment Data in Psychological Research With Bayesian (Generalized) Mixed-Effects Models

In this tutorial, we introduce the reader to analyzing ecological momentary assessment (EMA) data as applied in psychological sciences with the use of Bayesian (generalized) linear mixed-effects models. We discuss practical advantages of the Bayesian approach over frequentist methods and conceptual differences. We demonstrate how Bayesian statistics can help EMA researchers to (a) incorporate prior knowledge and beliefs in analyses, (b) fit models with a large variety of outcome distributions that reflect likely data-generating processes, (c) quantify the uncertainty of effect-size estimates, and (d) quantify the evidence for or against an informative hypothesis. We present a workflow for Bayesian analyses and provide illustrative examples based on EMA data, which we analyze using (generalized) linear mixed-effects models to test whether daily self-control demands predict three different alcohol outcomes. All examples are reproducible, and data and code are available at https://osf.io/rh2sw/ . Having worked through this tutorial, readers should be able to adopt a Bayesian workflow to their own analysis of EMA data.

RESP-FIT: A Technology-Enhanced Combined Inspiratory and Expiratory Muscle Strength Training Intervention for Adults With COPD.

Chronic obstructive pulmonary disease (COPD) is a progressive respiratory disease associated with respiratory muscle weakness and activity-limiting symptoms such as dyspnea. Respiratory muscle strength training (RMST) is an empirically validated therapy to increase respiratory muscle strength. The theoretically-informed, technology-enhanced RESP-FIT intervention for COPD is a 6-week combined inspiratory and expiratory muscle strength training program with symptom measurement in real time via ecological momentary assessment (EMA). In addition to hypothesis generating purposes, the purpose of this randomized control pilot study was to explore whether observed effects (on symptoms, patient-reported outcomes, and respiratory muscle strength) support carrying out a future large-scale trial of RESP-FIT. Thirty adults with COPD were randomized to intervention (n=15) or control, with intervention group undergoing 6 weeks of mHealth-enhanced RMST. Daily symptom data were collected in real time over the 6-week intervention period using EMA. Compared to the control group, participants in the intervention group reported decreased dyspnea and anxiety, increased happiness, and improved respiratory muscle strength (PIMax). However, reports of fatigue and sleep disturbance increased in the intervention group compared to the control group. Results support the hypothesis that the 6-week RESP-FIT program will improve respiratory muscle strength, emotional state (anxiety and happiness), and breathlessness in COPD but may contribute to fatigue, at least in the short-term. Future work is needed to determine efficacy of RESP-FIT, determine mechanisms of action on dyspnea and fatigue, and conduct within-subject comparisons of EMA data to explore individual or environmental fluctuations in COPD symptoms.

DAILY STRESS AND SAME-DAY SUBJECTIVE MEMORY: AGE DIFFERENCES AND PHYSICAL ACTIVITY AS A BUFFER

Abstract Growing research indicates that daily stress is associated with poor same-day memory performance, but it is unclear whether this relationship varies by age and whether moderate-to-vigorous physical activity (MPVA) might offset the within-person link between stress and memory in daily life. Ecological momentary assessment data were collected from adults aged 25-88 across British Columbia, Canada. For 14 days, participants (N = 249) wore a tri-axial physical activity monitor, reported stressor occurrence and perceived stressfulness of the events in mobile surveys 4 times per day, and rated their memory at the end of each day. Multilevel modeling was run to evaluate daily perceived stressfulness as a predictor of subjective memory, with same-day MVPA engagement (activity counts >570 counts/60s) and age (years) entered as moderators. At the within-person level, on days when stressors were perceived as more stressful than usual, subjective memory was rated as relatively worse (b = -0.037, CI[-0.061, -0.017], p < .001). MVPA did not buffer against the within-person associations of daily stress with subjective memory, nor did age (p > .05). Future work could examine the mechanisms that might explain the link between stress and memory, for example cortisol, as well as the associations of different intensity and forms of physical activity on stress across age groups.

SEDENTARY BEHAVIOR DIFFERENTIALLY INFLUENCES COGNITIVE HEALTH IN OLDER ADULTS AT DIFFERENT TIMESCALES

Abstract The negative health effects of sedentary behavior have received increased attention over the past decade. Whether sedentary behavior negatively influences cognitive health at any timescale remains poorly understood. Our study sought to determine whether sedentary behavior negatively affects cognitive health throughout the adult lifespan at multiple timescales (acute within-day, day-to-day, cross-sectionally). We examined ambulatory assessment data generated by an adult lifespan sample (N=161; age range:19-88 years; 84% F) where cognition was repeatedly assessed via ultra-brief cognitive assessments of processing speed and working memory in an ecological momentary assessment (EMA) framework (6 assessments/day for 14 days). Activity was measured continuously via thigh-worn ActivPAL monitors. ActivPAL and EMA data were combined and modeled in a multi-level (3-level; moments within days within persons) isotemporal substitution modeling framework. Increased sedentary behavior was associated with acute decreases in processing speed for older participants (65+ years of age) and substitution modeling indicated that exchanging bouts of walking or standing for sedentary time would result in short-term improvements. In contrast, a consistent result across younger and older adults indicated that days with generally more sedentary time (as well as lying and standing) were associated with overall better processing speed and working memory capacity. No cross-sectional associations were observed. A lack of cross-sectional associations between sedentary behavior and cognitive performance was consistent with previous studies. Our current results extend previous findings by showing that sedentary behavior might influence cognitive performance differently at different timescales in younger and older adults.

REAL-TIME HEALTH OUTCOMES AMONG OLDER ADULTS INITIATING MEDICAL CANNABIS FOR CHRONIC PAIN

Abstract Background: Rigorous data are needed on the short- and long-term effects of medical cannabis (MC) on older adults with chronic pain. In response, this prospective study combines technology-based ecological momentary assessments (EMA) and in-person visits over 12 months to obtain subjective and objective data on MC’s effects on older adults. Method: The study recruits and follows older adults (50 years or older, 50% male) with chronic pain for one year. Some will have initiated MC (MC group) and others will not use MC (comparison group). Target enrollment will be 328 (1:1 ratio for MC and comparison group) participants. Collection of subjective and objective data occurs at in-person visits (baseline & 12 months) and via smartphone- and sensor-based measurement bursts at 1, 3, 6, and 9 months. The EMA data capture detailed MC use patterns and subjective short-term outcomes (e.g., pain intensity rating). These data will be integrated with objective data collected using a wearable sensor-based Fitbit (e.g., physical activity and sleep). Results: Multilevel modeling will examine changes in real-time symptoms measured by EMA and objective, sensor-based behavioral outcomes. Results will include changes in momentary pain intensity rated on 0-100 visual analog, real-time anxiety and depressive symptoms, daily sleep duration and quality, and level of physical activity. Conclusions: Our results will provide evidence on whether older adults who initiate MC will have greater reductions in real-time pain intensity and more improvements in physical and emotional functioning compared to those in the comparison group.

FUNSAT: A FUNCTIONAL SKILLS TRAINING PROGRAM

Abstract Performance of everyday activities is often challenging for older adults, especially for aging adults with a cognitive impairment (CI), which threatens independence and quality of life. We developed a novel computer-based functional skills assessment and training (FUNSAT) program, which includes simulations of everyday technology-based tasks (e.g., online banking, prescription refill, online shopping) important to everyday living. This presentation will present preliminary findings from our study that is evaluating the FUNSAT program with aging adults with and without a cognitive impairment. The sample includes 153 adults (83 with a CI, 130 females and 23 males) ranging in age from 60-92 yrs. Initially, participants completed the FUNSAT assessment and then engaged in the FUNSAT training program for up to 24 hours over 12 weeks at home after an initial in-person training. The non-cognitively impaired participants completed the FUNSAT training alone. The participants with a cognitive impairment were randomized into one of two conditions; FUNSAT training alone, or Cognitive Training (3 weeks) followed by FUNSAT training. We also evaluated transfer of training using ecological momentary assessment (EMA). Our preliminary findings indicate that participants irrespective of cognitive status are demonstrating significant gains in performance following training on the training tasks. Further, they are reporting an increase in confidence with respect to performing the tasks in daily living and the EMA data suggest transfer of training to other technology-based tasks. We will also discuss adherence to the home training protocol, which has important implications for implementation of the program beyond the research setting.

Using Ecological Momentary Assessments to Study How Daily Fluctuations in Psychological States Impact Stress, Well-Being, and Health.

Despite great interest in how dynamic fluctuations in psychological states such as mood, social safety, energy, present-focused attention, and burnout impact stress, well-being, and health, most studies examining these constructs use retrospective assessments with relatively long time-lags. Here, we discuss how ecological momentary assessments (EMAs) address methodological issues associated with retrospective reports to help reveal dynamic associations between psychological states at small timescales that are often missed in stress and health research. In addition to helping researchers characterize daily and within-day fluctuations and temporal dynamics between different health-relevant processes, EMAs can elucidate mechanisms through which interventions reduce stress and enhance well-being. EMAs can also be used to identify changes that precede critical health events, which can in turn be used to deliver ecological momentary interventions, or just-in-time interventions, to help prevent such events from occurring. To enable this work, we provide examples of scales and single-item questions used in EMA studies, recommend study designs and statistical approaches that capitalize on EMA data, and discuss limitations of EMA methods. In doing so, we aim to demonstrate how, when used carefully, EMA methods are well poised to greatly advance our understanding of how intrapersonal dynamics affect stress levels, well-being, and human health.

Bedtime regularity predicts positive affect among veterans with posttraumatic stress disorder: an ecological momentary assessment study

BackgroundRegularizing bedtime and out-of-bed times is a core component of behavioral treatments for sleep disturbances common among patients with posttraumatic stress disorder (PTSD). Although improvements in subjective sleep complaints often accompany improvements in PTSD symptoms, the underlying mechanism for this relationship remains unclear. Given that night-to-night sleep variability is a predictor of physical and mental well-being, the present study sought to evaluate the effects of bedtime and out-of-bed time variability on daytime affect and explore the optimal window lengths of over which variability is calculated.MethodsFor about 30 days, male U.S. military veterans with PTSD (N = 64) in a residential treatment program provided ecological momentary assessment data on their affect and slept on beds equipped with mattress actigraphy. We computed bedtime and out-of-bed time variability indices with varying windows of days. We then constructed multilevel models to account for the nested structure of our data and evaluate the impact of bedtime and out-of-bed time variability on daytime affect.ResultsMore regular bedtime across 6–9 days was associated with greater subsequent positive affect. No similar effects were observed between out-of-bed time variability and affect.ConclusionsMultiple facets of sleep have been shown to differently predict daily affect, and bedtime regularity might represent one of such indices associated with positive, but not negative, affect. A better understanding of such differential effects of facets of sleep on affect will help further elucidate the complex and intertwined relationship between sleep and psychopathology.Trial registrationThe trial retrospectively was registered on the Defense Technical Information Center website: Award # W81XWH-15–2-0005.

Evaluating multivariate time-series clustering using simulated ecological momentary assessment data

During an Ecological Momentary Assessment (EMA) study, through repeated digital questionnaires, we have the opportunity to collect multiple multivariate time-series (MTS) data for all participants. Although, it is common that individual data is analyzed per participant, the richness of such dataset poses the question of whether meaningful groups of individuals could be uncovered to better understand the underlying processes on an individual and a group level. Such grouping could be obtained by clustering. Therefore, this paper examines the performance of various clustering approaches for grouping individuals based on the similarity of their raw time-series data patterns. Clustering is an unsupervised task, where the true underlying groups are not usually available, making the result difficult to evaluate. Therefore, in the current paper, simulated irregular time-series data, resembling EMA, are used to validate the performance of several methods under different clustering-related choices, such as the distance metric. Data are generated with a varying number of clusters, total number of individuals and time-points as well as number of variables and proportions of noisy variables, while their time-series represent well-shaped patterns, typically observed in emotional behavior. After applying clustering to all simulated datasets, clustering performance was first assessed by comparing the true and predicted labels, while the impact of the different datasets’ parameters was also examined. Because ground truth labels are not always available, or do not even exist, in real-world scenarios, clustering evaluation through distance-based and distance-free measures was further investigated. Overall, all clustering methods (e.g. k-means, Hierarchical clustering, Fuzzy k-medoids) proved reliable in different configurations, revealing the true number of clusters. Moreover, kernel-based methods appeared more efficient when highly noisy variables are involved, becoming more promising for real-world data. As a second part, an illustration of two specific simulated scenarios (datasets) is provided, showing, in more detail, all different analysis steps before drawing a conclusion about the choice of the optimal number of clusters.

Multiple imputation of missing data in multilevel ecological momentary assessments: an example using smoking cessation study data.

Advances in digital technology have greatly increased the ease of collecting intensive longitudinal data (ILD) such as ecological momentary assessments (EMAs) in studies of behavior changes. Such data are typically multilevel (e.g., with repeated measures nested within individuals), and are inevitably characterized by some degrees of missingness. Previous studies have validated the utility of multiple imputation as a way to handle missing observations in ILD when the imputation model is properly specified to reflect time dependencies. In this study, we illustrate the importance of proper accommodation of multilevel ILD structures in performing multiple imputations, and compare the performance of a multilevel multiple imputation (multilevel MI) approach relative to other approaches that do not account for such structures in a Monte Carlo simulation study. Empirical EMA data from a tobacco cessation study are used to demonstrate the utility of the multilevel MI approach, and the implications of separating participant- and study-initiated EMAs in evaluating individuals' affective dynamics and urge.

Is everything really okay?: Using ecological momentary assessment to evaluate daily co-fluctuations in anxiety and reassurance seeking

ObjectiveReassurance seeking, a behavior prominent in anxiety disorders and depression, is associated with poorer quality of interpersonal relationships and acts as a mechanism of stress generation. However, little research has elucidated momentary associations between state anxiety and reassurance seeking behaviors. MethodIn a sample of 104 university-affiliated young adults, we sought to replicate cross-sectional associations of reassurance seeking with trait anxiety (Aim 1) and intolerance of uncertainty (Aim 2). We then used ecological momentary assessment (EMA) to evaluate concurrent fluctuations in daily anxiety and reassurance seeking across 14 consecutive days (Aim 3). Hierarchical multi-level models for intensive longitudinal data were used to evaluate the relationship between state anxiety and daily reassurance seeking. ResultsIn baseline analyses, trait anxiety and intolerance of uncertainty were significantly associated with greater trait reassurance seeking, controlling for depression. Analyses of the EMA data showed that daily reassurance seeking behaviors fluctuated concurrently with daily anxiety during the 14 days, while controlling for trait anxiety and depression. ConclusionGiven evidence of concurrent fluctuations between state anxiety and reassurance seeking, the behavior should be considered as a potential target in treatment for anxiety disorders.

Neurophysiological responses to emotional faces predict dynamic fluctuations in affect in adolescents.

The ability to accurately identify and interpret others' emotions is critical for social and emotional functioning during adolescence. Indeed, previous research has identified that laboratory-based indices of facial emotion recognition and engagement with emotional faces predict adolescent mood states. Whether socioemotional information processing relates to real-world affective dynamics using an ecologically sensitive approach, however, has rarely been assessed. In the present study, adolescents (N = 62; ages 13-18) completed a Facial Recognition Task, including happy, angry, and sad stimuli, while EEG data were acquired. Participants also provided ecological momentary assessment (EMA) data probing their current level of happiness, anger, and sadness for 1-week, resulting in indices of emotion (mean-level, inertia, instability). Analyses focused on relations between (1) accuracy for and (2) prolonged engagement with (LPP) emotional faces and EMA-reported emotions. Greater prolonged engagement with happy faces was related to less resistance to changes in happiness (i.e., less happiness inertia), whereas greater prolonged engagement with angry faces associated with more resistance to changes in anger (i.e., greater anger inertia). Results suggest that socioemotional processes captured by laboratory measures have real-world implications for adolescent affective states and highlight potentially actionable targets for novel treatment approaches (e.g., just-in-time interventions). Future studies should continue to assess relations among socioemotional informational processes and dynamic fluctuations in adolescent affective states.

Temporal associations linking alcohol and cannabis use to cigarette smoking in young adults engaged in a tobacco cessation and relapse monitoring study

Young adulthood remains a developmental period in which cigarette smoking initiation and progression to dependence and regular use is common. Moreover, co-use of alcohol and/or cannabis with tobacco is common in this age group and may have detrimental effects on tobacco use rates and cessation outcomes. Although young adults are interested in quitting smoking, achieving abstinence remains difficult, even with evidence-based treatment strategies. Understanding proximal associations between other substance use (e.g., alcohol and cannabis) and smoking may have important treatment implications. This exploratory analysis investigated the role of alcohol and/or cannabis use in contributing to smoking events on the same day or next day among young adults engaged in a smoking cessation and relapse monitoring study. We used ecological momentary assessment (EMA) data from 43 young adults (ages 18–25; 932 observations) who smoked cigarettes daily and agreed to participate in a 5-week study that included a 2-day smoking quit attempt and provision of tobacco treatment in the form of nicotine replacement therapy, brief cessation counseling, and financial incentives for abstinence (incentives were provided only during the 2-day quit attempt). We tested multilevel time-series models of daily associations between alcohol use, cannabis use, and smoking. Consistent with hypotheses, days on which participants were more likely to drink alcohol predicted increased likelihood of smoking the next day (OR = 2.27, p =.003). This effect was significant after controlling for both the one-day lagged effect of smoking (i.e., autoregression) and the concurrent (i.e., same day) effects of drinking and cannabis use. Although there was a positive concurrent effect of cannabis use on smoking (OR = 12.86, p =.003), the one-day lagged effect of cannabis use and the concurrent effect of drinking was not significant, contrary to hypotheses. Results indicate that alcohol use presents a potential threat to successful smoking cessation that extends to the following day. This suggests a risk-window in which treatment could be supplemented with just-in-time interventions and extending the focus on co-use to include this lagged impact on cessation outcomes.

Investigating the day-level associations between affective variability and physical activity using ecological momentary assessment

BackgroundUnderstanding affect as a determinant of physical activity has gained increased attention in health behavior research. Fluctuations in affect intensity from moment-to-moment (i.e., affective variability) may interfere with cognitive and regulatory processes, making it difficult to engage in goal-directed behaviors such as physical activity. Preliminary evidence indicates that those with greater trait-level affective variability engage in lower levels of habitual physical activity. However, the extent to which daily fluctuations in affect variability are associated with same-day physical activity levels is unknown. This study used ecological momentary assessment (EMA) to investigate day-level associations between affective variability (i.e., within-subject variance) and physical activity. MethodsYoung adults (N = 231, M = 23.58 ± 3.02 years) provided three months of smartphone-based EMA and smartwatch-based activity data. Every two weeks, participants completed a 4-day EMA measurement burst (M = 5.17 ± 1.28 bursts per participant). Bursts consisted of hourly randomly-prompted EMA surveys assessing momentary positive-activated (happy, energetic), positive-deactivated (relaxed), negative-activated (tense, stressed), and negative-deactivated (sad, fatigued) affect. Participants continuously wore a smartwatch to measure physical activity across the three months. Mixed-effects location scale modeling examined the day-level associations of affective variability (i.e., positive-activated, positive-deactivated, negative-activated, and negative-deactivated) and physical activity, controlling for covariates such as mean levels of affect, between-subject effects of physical activity, time of day, day of week, day in study, and smartwatch wear time. ResultsThere were 41,546 completed EMA surveys (M = 182.22 ± 69.82 per participant) included in the analyses. Above and beyond mean levels of affect, greater day-level variability in positive-activated affect was associated with greater physical activity on that same day compared to other days (τ = 0.01, p < .001), whereas greater day-level variability in negative-deactivated affect was associated with less physical activity on that same day compared to other days (τ = −0.01, p < .001). Day-level variability in positive-deactivated affect or negative-activated affect were not associated with day-level physical activity (ps > .05) ConclusionsIndividuals were less active on days with greater variability in feeling sad and fatigued but more active on days with greater variability in feeling happy and energetic. Understanding the dynamic relationships of affective variability with day-level physical activity can strengthen physical activity interventions by considering how these processes differ within individuals and unfold within the context of daily life. Future research should examine causal pathways between affective variability and physical activity across the day.