Ecological Momentary Assessment Data Research Articles

Occurrence and characteristics of suicidal ideation in psychiatrically healthy individuals based on ecological momentary assessment.

Decedents with no known mental disorder comprise 5-40% of suicides, suggesting that suicide ideation (SI) and behavior may occur in the psychiatrically healthy with important implications for suicide risk screening. Healthy Volunteers (HV) and patients with Major Depressive Disorder (MDD) provided 7 days of Ecological Momentary Assessment (EMA) data about SI and stressors. Longitudinal mixed effects logistic regression models compared HV and patient SI and stressors. Mixed effects linear regression models compared HVs' and patients' SI score change from the previous epoch's SI score when each stressor occurred. HVs (n = 42) reported less frequent (p < 0.001) and less intense SI (p < 0.003) than patients (n = 80), yet did endorse SI and/or SI-related items in 44% of EMA epochs, endorsing SI items in 25% of epochs with non-zero SI scores. For 7 of 8 stressors, patients reported stressors more often than HVs (all p < 0.001) responding to them with increased SI (0.0001 < p < 0.0472). HVs were relatively resilient to stressors, reporting SI increases only in response to neglect (p < 0.0147). Although SI and SAs are documented among psychiatrically healthy individuals, scientific attention to these observations has been scant. Real-time SI measurement showed that HVs' SI was less pronounced than MDD patients', but was endorsed, nonetheless. Patients were more likely to report stressors than HVs, perhaps due to greater sensitivity to the environment, and reported SI in response to stressors, which was less common in HVs. Both MDD patients and HVs most often manifested passive SI (viz, "decreased wish to live"). However, passive SI (viz, "desire for death"), may predict suicide, even absent SI per se (thinking about killing yourself). This study validates the utility of real-time SI assessment, showing that HVs endorse SI items in 11% of epochs, which implies that suicide risk screening focused on those with mental disorders may be too narrow an approach.

Person-specific dynamics between negative emotions and suicidal thoughts

IntroductionRecent technology has enabled researchers to collect ecological momentary assessments (EMA) to examine within-person correlates of suicidal thoughts. Prior studies examined generalized temporal dynamics of emotions and suicidal thinking over brief periods, but it is not yet known how variable these processes are across people. MethodWe use data EMA data delivered over two weeks with youth/young adults (N = 60) who reported past year self-injurious thoughts/behaviors. We used group iterative multiple model estimation (GIMME) to model group- and person-specific associations of negative emotions (i.e., fear, sadness, shame, guilt, and anger) and suicidal thoughts. Results29 participants (48.33%) reported at least one instance of a suicidal thought and were included in GIMME models. In group level models, we consistently observed autoregressive effects for suicidal thoughts (e.g., earlier thoughts predicting later thoughts), although the magnitude and direction of this link varied from person-to-person. Among emotions, sadness was most frequently associated with contemporaneous suicidal thoughts, but this was evident for less than half of the sample, while other emotional correlates of suicidal thoughts broadly differed across people. No emotion variable was linked to future suicidal thoughts in >14% of the sample, ConclusionsEmotion-based correlates of suicidal thoughts are heterogeneous across people. Better understanding of the individual-level pathways maintaining suicidal thoughts/behaviors may lead to more effective, personalized interventions.

‘I crave not to feel uncomfortable’ – investigating craving for opioids and cannabis among individuals with chronic pain

Background The assessment of drug craving is common in survey studies, including those using real-time data collection methods, such as Ecological Momentary Assessment (EMA). However, few studies investigate how participants with chronic pain interpret the word ‘craving’ and how interpretations impact survey responses. Methods We conducted a mixed-methods study among 12 individuals with chronic pain who were using prescription opioids and cannabis. Participants completed baseline surveys, cognitive interviews, and 14-day smartphone-based EMA data collection. Analyses included deductive and inductive coding of interviews and t-tests of EMA self-reports of opioid and cannabis craving. Results Four participants had negative reactions to the word ‘craving’, including mentions that these questions offended them. The remaining eight participants mentioned no negative connotation. EMA data showed that participants without negative reactions reported a greater range (opioids), higher standard deviation (opioids), and higher maximum (opioids, cannabis) on Likert-type EMA craving items, compared to those with a negative reaction. Conclusions Some individuals with chronic pain may have a negative reaction to the word ‘craving’ related to opioid and cannabis use and this reaction may impact survey responses. Alternative wording of survey items is recommended, for example focused on ‘wanting’ or ‘needing’.

Preoperative Mobile Health Data Improve Predictions of Recovery From Lumbar Spine Surgery.

Neurosurgeons and hospitals devote tremendous resources to improving recovery from lumbar spine surgery. Current efforts to predict surgical recovery rely on one-time patient report and health record information. However, longitudinal mobile health (mHealth) assessments integrating symptom dynamics from ecological momentary assessment (EMA) and wearable biometric data may capture important influences on recovery. Our objective was to evaluate whether a preoperative mHealth assessment integrating EMA with Fitbit monitoring improved predictions of spine surgery recovery. Patients age 21-85 years undergoing lumbar surgery for degenerative disease between 2021 and 2023 were recruited. For up to 3 weeks preoperatively, participants completed EMAs up to 5 times daily asking about momentary pain, disability, depression, and catastrophizing. At the same time, they were passively monitored using Fitbit trackers. Study outcomes were good/excellent recovery on the Quality of Recovery-15 (QOR-15) and a clinically important change in Patient-Reported Outcomes Measurement Information System Pain Interference 1 month postoperatively. After feature engineering, several machine learning prediction models were tested. Prediction performance was measured using the c-statistic. A total of 133 participants were included, with a median (IQR) age of 62 (53, 68) years, and 56% were female. The median (IQR) number of preoperative EMAs completed was 78 (61, 95), and the median (IQR) number of days with usable Fitbit data was 17 (12, 21). 63 patients (48%) achieved a clinically meaningful improvement in Patient-Reported Outcomes Measurement Information System pain interference. Compared with traditional evaluations alone, mHealth evaluations led to a 34% improvement in predictions for pain interference (c = 0.82 vs c = 0.61). 49 patients (40%) had a good or excellent recovery based on the QOR-15. Including preoperative mHealth data led to a 30% improvement in predictions of QOR-15 (c = 0.70 vs c = 0.54). Multimodal mHealth evaluations improve predictions of lumbar surgery outcomes. These methods may be useful for informing patient selection and perioperative recovery strategies.

Development and Initial Validation of a Momentary Cannabis Craving Scale Within a Homogeneous Sample of U.S. Emerging Adults.

Given the popularity and ease of single-item craving assessments, we developed a multi-item measure and compared it to common single-item assessments in an ecological momentary assessment (EMA) context. Two weeks of EMA data were collected from 48 emerging adults (56.25% female, 85.42% White) who frequently used cannabis. Eight craving items were administered, and multilevel factor analyses were used to identify the best fitting model. The resulting scale's factors represented purposefulness/general desire and emotionality/negative affect craving. Convergent validity was examined using measures of craving, cannabis use disorder symptoms, frequency of use, cannabis cue reactivity, cannabis use, negative affect, and impulsivity. The scale factors were associated with cue-reactivity craving, negative affect, impulsivity, and subfactors of existing craving measures. For researchers interested in using a single item to capture craving, one item performed particularly well. However, the new scale may provide a more nuanced assessment of mechanisms underlying craving.

Daily vitality fluctuations in older adults with depressive symptoms: A multilevel location-scale model

BackgroundExamining the daily experiences of older adults with depression facilitates the development and application of personalized effective treatments for them. In previous clinical research on depression, traditional mean-based approaches have mainly been employed. However, the within-person residual variance as a random effect provides greater insight into the heterogeneity of daily experiences among geriatric samples. ObjectiveThis study aimed to examine the relationship between depression and daily vitality in older adults. Specifically, it focused on the mean and residual variance of daily vitality measured by the Ecological Momentary Assessment (EMA). MethodsData from 64 older adults aged 65 years or more, who participated in community welfare centers or retirees’ associations, were used. Daily vitality was examined using EMA surveys for seven consecutive days (four random surveys per day). The data were analyzed using a location-scale model. ResultsThe intraclass correlation computed from the empty model for the EMA data was 0.488, indicating significant variances in daily vitality across time between individuals. Older adults with higher levels of depressive symptoms showed low mean levels of daily vitality and a large log-residual variance of daily vitality. ConclusionsThe findings from the current study suggest that individuals experiencing depression not only exhibit low vitality in their daily lives but also struggle to maintain stable levels of vitality in their lives. These insights could contribute to the facilitation and advancement of personalized interventions tailored for older adults.

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.

Dynamics of Perceived Social Isolation, Secondary Conditions, and Daily Activity Patterns Among Individuals With Stroke: A Network Analysis of Ecological Momentary Assessment Data

ObjectiveTo assess contemporaneous and temporal dynamics of perceived social isolation (PSI), secondary conditions, and daily activity patterns in individuals post-stroke. DesignLongitudinal observational study using ecological momentary assessment (EMA) as a real-time assessment of an individual's lived experiences. We conducted dynamic network analyses to examine longitudinal associations among EMA variables. SettingsHome and Community. Participants202 individuals with mild-to-moderate chronic stroke (median age=60 years; 45% women; 44% black; 90% ischemic stroke; median NIHSS score=2; N=202). InterventionNot applicable. Main Outcome MeasuresEMA questions measured PSI, secondary conditions (pain, tiredness, stress, anxiety, worthlessness, difficulty concentrating, and cheerfulness), and daily activity patterns (being at home, being alone, and participating in productive activities). ResultsThe median EMA response rate was 84%. The contemporaneous model showed that PSI was associated with being home, alone, and all symptoms except pain. The temporal model revealed a pathway indicating that feelings of worthlessness predicted PSI (regression coefficient=0.06, P=.019), followed by stress (regression coefficient=0.06, P=.024), and then by being not at home (regression coefficient=-0.04, P=.013). ConclusionImplementing dynamic network analyses on EMA data can uncover dynamic connections among PSI, secondary conditions, and daily activity patterns after stroke. This study found a significant temporal association between PSI and negative emotions. Feeling isolated was followed by feeling stressed, which was followed by a tendency to be out of home, indicating adaptive behaviors in individuals with stroke. These findings highlight the importance of engaging in out-of-home or outdoor activities to mitigate PSI and negative emotions.

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.

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 &amp;gt;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 &amp;lt; .001). MVPA did not buffer against the within-person associations of daily stress with subjective memory, nor did age (p &amp;gt; .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 &amp; 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.