Ecological Momentary Assessment Data Research Articles

A Data-Driven Clustering Method for Discovering Profiles in the Dynamics of Major Depressive Disorder Using a Smartphone-Based Ecological Momentary Assessment of Mood.

BackgroundAlthough major depressive disorder (MDD) is characterized by a pervasive negative mood, research indicates that the mood of depressed patients is rarely entirely stagnant. It is often dynamic, distinguished by highs and lows, and it is highly responsive to external and internal regulatory processes. Mood dynamics can be defined as a combination of mood variability (the magnitude of the mood changes) and emotional inertia (the speed of mood shifts). The purpose of this study is to explore various distinctive profiles in real-time monitored mood dynamics among MDD patients in routine mental healthcare.MethodsEcological momentary assessment (EMA) data were collected as part of the cross-European E-COMPARED trial, in which approximately half of the patients were randomly assigned to receive the blended Cognitive Behavioral Therapy (bCBT). In this study a subsample of the bCBT group was included (n = 287). As part of bCBT, patients were prompted to rate their current mood (on a 1–10 scale) using a smartphone-based EMA application. During the first week of treatment, the patients were prompted to rate their mood on three separate occasions during the day. Latent profile analyses were subsequently applied to identify distinct profiles based on average mood, mood variability, and emotional inertia across the monitoring period.ResultsOverall, four profiles were identified, which we labeled as: (1) “very negative and least variable mood” (n = 14) (2) “negative and moderate variable mood” (n = 204), (3) “positive and moderate variable mood” (n = 41), and (4) “negative and highest variable mood” (n = 28). The degree of emotional inertia was virtually identical across the profiles.ConclusionsThe real-time monitoring conducted in the present study provides some preliminary indications of different patterns of both average mood and mood variability among MDD patients in treatment in mental health settings. Such varying patterns were not found for emotional inertia.

Time-Varying Mediation of Pharmacological Smoking Cessation Treatments on Smoking Lapse via Craving, Cessation Fatigue, and Negative Mood.

The addictive nature of nicotine makes smoking cessation an extremely challenging process. With prolonged exposure, tobacco smoking transforms from being a positive reinforcer to a negative one, as smoking is used to mitigate aversive withdrawal symptoms. Studying the variations in withdrawal symptoms, especially during their peak in the first week of a quit attempt, could help improve cessation treatment for the future. The time-varying mediation model effectively studies whether altering withdrawal symptoms act as mediators in the pathway between treatment and cessation. This secondary data analysis of a randomized clinical smoking cessation trial of three pharmacotherapy regimens (nicotine patch, varenicline, and nicotine patch + mini-lozenge) analyzes ecological momentary assessment (EMA) data from the first 4 weeks post-target quit day (TQD). We assess whether withdrawal symptoms (eg, negative mood, cessation fatigue, and craving) mediate the pathway between pharmacotherapy and daily smoking status and whether this effect varies over time. We found a statistically significant time-varying mediation effect of varenicline on smoking status through craving, which shows decreasing risk of lapse via reduction in craving. We did not find significant time-varying mediation effects through negative mood and cessation fatigue. This study supports the importance of craving suppression in the smoking cessation process. It also helped identify specific timepoints when withdrawal symptoms increased that would likely benefit from targeted cessation intervention strategies. This study aimed to understand the underlying dynamic mechanisms of the smoking cessation process using a new analytical approach that capitalizes on the intensive longitudinal data collected via EMAs. The findings from this study further elucidate the smoking cessation process and provide insight into behavioral intervention targets and the timing of such interventions through the estimation of time-varying mediation effects.

Using Momentary Assessment and Machine Learning to Identify Barriers to Self-management in Type 1 Diabetes: Observational Study.

BackgroundFor adolescents living with type 1 diabetes (T1D), completion of multiple daily self-management tasks, such as monitoring blood glucose and administering insulin, can be challenging because of psychosocial and contextual barriers. These barriers are hard to assess accurately and specifically by using traditional retrospective recall. Ecological momentary assessment (EMA) uses mobile technologies to assess the contexts, subjective experiences, and psychosocial processes that surround self-management decision-making in daily life. However, the rich data generated via EMA have not been frequently examined in T1D or integrated with machine learning analytic approaches.ObjectiveThe goal of this study is to develop a machine learning algorithm to predict the risk of missed self-management in young adults with T1D. To achieve this goal, we train and compare a number of machine learning models through a learned filtering architecture to explore the extent to which EMA data were associated with the completion of two self-management behaviors: mealtime self-monitoring of blood glucose (SMBG) and insulin administration.MethodsWe analyzed data from a randomized controlled pilot study using machine learning–based filtering architecture to investigate whether novel information related to contextual, psychosocial, and time-related factors (ie, time of day) relate to self-management. We combined EMA-collected contextual and insulin variables via the MyDay mobile app with Bluetooth blood glucose data to construct machine learning classifiers that predicted the 2 self-management behaviors of interest.ResultsWith 1231 day-level SMBG frequency counts for 45 participants, demographic variables and time-related variables were able to predict whether daily SMBG was below the clinical threshold of 4 times a day. Using the 1869 data points derived from app-based EMA data of 31 participants, our learned filtering architecture method was able to infer nonadherence events with high accuracy and precision. Although the recall score is low, there is high confidence that the nonadherence events identified by the model are truly nonadherent.ConclusionsCombining EMA data with machine learning methods showed promise in the relationship with risk for nonadherence. The next steps include collecting larger data sets that would more effectively power a classifier that can be deployed to infer individual behavior. Improvements in individual self-management insights, behavioral risk predictions, enhanced clinical decision-making, and just-in-time patient support in diabetes could result from this type of approach.

Ecological momentary assessment of outcomes in allergic rhinitis and chronic rhinosinusitis: A review

Allergic rhinitis (AR) and chronic rhinosinusitis (CRS) rely on patient-reported symptoms and quality-of-life measures, which are subject to bias. Ecological momentary assessment (EMA) captures data in real time through repeated short surveys, thus reducing errors/biases. EMA's use in sinonasal conditions has not been well described, and the goal of this study was to examine the literature on EMA and AR/CRS. METHODS: A literature review was performed using the following search terms: AR, CRS, and EMA. Inclusion criteria were the use of EMA reporting of sinonasal symptoms at more than one time point. Systematic reviews and non-full text articles were excluded. Population demographics, sinonasal disease, type of EMA platform used, type and severity of symptoms reported, medication use and symptom correlation with location/pollen/pollution were collected. Eight studies met the inclusion criteria, and all focused on AR. All studies were conducted outside the United States in both children and adults. Seven studies used a smartphone application for reporting symptoms, and one used WeChat surveys. EMA data collection varied, with repetitive survey intervals determined either by patients (n=6) or research team (n=2). All studies reported sinonasal severity scores, while six reported additional symptoms (e.g., ocular, pulmonary, sleep, general health). Five collected self-reported allergy medication use. Seven studies correlated symptoms with location, pollen, or pollution. Few studies in AR and no studies in CRS assessed the use of EMA. EMA may provide a better understanding of the real-time relationship of environmental triggers with symptoms, in turn guiding treatment decisions.

Feasibility and Acceptability of Home-Based Exercise Snacking and Tai Chi Snacking Delivered Remotely to Self-Isolating Older Adults During COVID-19.

The purpose of this study was to examine the feasibility and acceptability of remotely delivered, home-based exercise programs on physical function and well-being in self-isolating older adults during the COVID-19 pandemic. In a four-arm randomized controlled trial, 63 participants (aged 65years and older) were allocated to one of three home-based daily (2 × 10-min) exercise interventions (exercise snacking, tai chi snacking, and combination) or control (UK National Health Service Web pages). Functional assessments were conducted via video call at baseline and 4-week follow-up. A web-based survey assessed the acceptability of each exercise program and secondary psychological/well-being outcomes. Ecological momentary assessment data, collected in Weeks 1 and 4, explored feeling states as antecedents and consequences of exercise. All intervention groups saw increased physical function at follow-up and displayed good adherence with exercise snacking considered the most acceptable program. Multilevel models revealed reciprocal associations between feelings of energy and exercise engagement. Further studies are needed with larger, more diverse demographic samples.

A new statistical model for longitudinal ecological momentary assessment data on dual use of electronic and combustible cigarettes

ABSTRACT Background: Existing studies of dual use of electronic and combustible cigarettes either collected longitudinal data with long gaps in between waves or conducted ecological momentary assessment (EMA) over a short period of time. In recent years, the measurement burst design that embeds an EMA protocol in each wave assessment of a traditional longitudinal study has become more popular and yet conventional generalized linear mixed models (GLMM) have important limitations for handling data from this design. Objectives:This study proposed a new statistical method to analyze data from the measurement burst design. Methods:This new statistical method was designed to model the short-term (within-wave) as well as long-term (between-wave) changes and was validated by a simulation study. Secondary analysis was conducted to analyze data from 205 dual users (52% male) and 146 exclusive smokers (50% male) who participated in a recent study using the measurement burst design. Results:The simulation study shows that the proposed method can handle the gap between waves well and is also robust to nonlinear changes across waves. Although no short-term change in smoking was found, dual users reported a long-term reduction in cigarette use that was more rapid compared to exclusive smokers (). Vaping more was associated with smoking less (). Conclusion:The proposed method is highly applicable as it can be easily implemented by substance use researchers and the results can be straightforwardly interpreted. The results suggest that e-cigarette use may play a role in promoting a long-term reduction in smoking among dual users.

Naltrexone moderates the association of alcohol use and affect among adolescent drinkers in daily life.

Naltrexone is an efficacious medication for the treatment of alcohol use disorder in adults. As an opioid receptor antagonist, naltrexone blocks activation of the endogenous opioid system, which is involved in the affectively reinforcing properties of substance use. Few studies, however, have examined the moderating effect of naltrexone on the association between affect and alcohol use. Additionally, most existing research on naltrexone has been with adults in the human laboratory. We conducted a secondary analysis of ecological momentary assessment data from a randomized, double-blinded, placebo-controlled cross-over study that compared naltrexone (50mg/daily) and placebo in 26 adolescents (15 to 19years old) who exhibited problematic drinking patterns. Multilevel models tested whether naltrexone moderated associations of alcohol use with both positive and negative affect (PA, NA). Results indicated that, during naltrexone treatment, greater estimated blood alcohol concentration (eBAC) levels were associated with greater NA further into drinking episodes. In turn, greater NA after the first drink of an episode was associated with reduced subsequent eBAC values during naltrexone treatment. Low PA was also associated with lower subsequent eBAC levels in the naltrexone condition after the first drink. These findings support the idea that naltrexone can disrupt the association between affect and alcohol use, effects that emerge later in drinking episodes. Greater attention to the effects of naltrexone on affect and reinforcement may help to tailor psychotherapy to maximize the benefits of naltrexone. However, in the present study, as most drink reports were in the first 2h of the drinking episode and participants reported affect only at the first three end-drink reports of a drinking episode (limiting the number of drinks reported), we had reduced power to detect effects in the continuation phase. Thus, replication of the findings is needed using a design that assesses the impact of naltrexone across the entire episode.

Adapting Research to a Global Pandemic: A Fully Remote Actigraphy and Ecological Momentary Assessment Study.

Mandated social distancing practices and quarantines in response to COVID-19 have resulted in challenges for research on healthcare workers, such as hospital nurses. It remains unknown whether nursing studies utilizing complex methodology like sleep actigraphy and ecological momentary assessment (EMA) can be conducted remotely without compromising data quality. We aimed to (a) disseminate our remote study protocol for sleep actigraphy and EMA data from hospital nurses during COVID-19, (b) assess feasibility and acceptability of this approach for studies on hospital nurses, and (c) examine the reliability and ecological validity of sleep characteristics measured across 14 days. Using an online platform, we provided 86 outpatient nurses from a cancer hospital with detailed video/text instructions regarding the study and facilitated virtual study onboarding meetings. Feasibility was assessed by comparing adherence rates to a similar in-person study of nurses from the same hospital; acceptability was evaluated through content analysis of qualitative study feedback. Multilevel modeling was conducted to assess changes in sleep characteristics as a function of study day and daily stressful experiences. Adherence to EMA (91.8%) and actigraphy (97.9%) was high. EMA adherence was higher than the in-person study of inpatient day-shift nurses from the same hospital. Content analyses revealed primarily positive feedback, with 51.2% reporting "easy, clear, simple onboarding" and 16.3% reporting the website was "helpful." Six participants provided only negative feedback. Sleep characteristics did not change as a function of study day except for self-reported quality, which increased slightly during Week 1 and regressed toward baseline after that. A higher incidence of stressor days or higher stressor severity followed nights with shorter-than-usual time in bed or poorer-than-usual sleep quality, supporting the ecological validity for these methods of assessing sleep in nurses. Findings suggest that a fully remote study protocol for EMA and actigraphy studies in nursing yields robust feasibility, acceptability, reliability, and validity. Given the busy schedules of nurses, the convenience of this approach may be preferable to traditional in-person data collection. Lessons learned from COVID-19 may apply to improving nursing research postpandemic.

You Don't Need an App-Conducting Mobile Smoking Research Using a Qualtrics-Based Approach.

With the increasing availability of smartphones, many tobacco researchers are exploring smartphone-delivered mobile smoking interventions as a disseminable means of treatment. Most effort has been focused on the development of smartphone applications (apps) to conduct mobile smoking research to implement and validate these interventions. However, developing project-specific smartphone apps that work across multiple mobile platforms (e.g., iOS and Android) can be costly and time-consuming. Here, using a hypothetical study, we present an alternate approach to demonstrate how mobile smoking cessation and outcome evaluation can be conducted without the need of a dedicated app. Our approach uses the Qualtrics platform, a popular online survey host that is used under license by many academic institutions. This platform allows researchers to conduct device-agnostic screening, consenting, and administration of questionnaires through Qualtrics's native survey engine. Researchers can also collect ecological momentary assessment data using text messaging prompts with the incorporation of Amazon Web Services' Pinpoint. Besides these assessment capabilities, Qualtrics has the potential for delivering personalized behavioral interventions through the use of JavaScript code. By customizing the question's web elements in Qualtrics (e.g., using texts, images, videos, and buttons), researchers can integrate interactive web-based interventions and complicated behavioral and cognitive tasks into the survey. In conclusion, this Qualtrics-based methodology represents a novel and cost-effective approach for conducting mobile smoking cessation and assessment research.

Stress during pregnancy: An ecological momentary assessment of stressors among Black and White women with implications for maternal health.

Background:Stress can lead to adverse physiological and psychological outcomes. Therefore, understanding stress during pregnancy provides insight into racial disparities in maternal health, particularly Black maternal health.Objectives:This study aimed to describe (1) daily exposure to self-reported stress levels during pregnancy, and (2) sources of stress among participants that identified as Black or White using data collected via ecological momentary assessment.Methods:We leveraged survey data from the Postpartum Mothers Mobile Study, a prospective longitudinal study using ecological momentary assessment data collection methods to describe patterns of stress during pregnancy. This article is descriptive and documents patterns of self-reported stress levels and sources of stress. Frequencies and percentages of stress responses were computed to describe these patterns.Results:The sample (n = 296) was 27% Black (n = 78) and 63% White (n = 184). Results were based on at least one measurement of that stress level during pregnancy. A similar number of Black and White participants reported no stress during pregnancy. White (85%–95%) and Black (60%–70%) participants reported low to moderate levels of stress. Black participants (38%) and White participants (35%) reported experiencing high stress. Black and White participants reported similar sources of stress: stress from a partner, too many things to do, a baby or other children, and financial concerns. White participants reported work as a top stressor, and Black participants reported financial issues as a top source of stress.Conclusion:This study provides insight into daily exposure to stress that has implications for maternal health. We described patterns of self-reported stress and sources of stress among Black and White participants. The daily exposures to stress reported by this sample exist within a context of root causes of structural inequities in education, health care, income, wealth, and housing that must be addressed to achieve maternal health equity.

Assessing Sleep Quality Using Mobile EMAs: Opportunities, Practical Consideration, and Challenges

Sleep is one of the most important factors in maintaining both physical and mental health. There are many causes of sleep problems, it is generally necessary to maintain a healthy lifestyle to avoid them. In the medical field, information related to sleep problems including lifestyle information is obtained through interviews, but this approach is limited because it is dependent on the patient’s memory. Thus, there are many studies adopting ecological momentary assessments (EMAs) to collect patient’s lifestyles. Some of them also use smart devices to collect data effectively. However, these studies focused on specific factors such as smoking, exercising so that they have limits to reflect complex narrative of lifestyle patterns. Therefore, we proposed indicators consist of EMAs data for assessing everyday sleep quality and these indicators contain the complex lifestyle contexts in a quantitative manner. First, we collected real-life data using a smartphone through a 4-week data collection experiment. Second, we develop a method of generating daily indexes reflecting geospatial and social habits, social condition, activity level, and emotional condition using self-report data. Third, we evaluated daily indexes whether could use to supplement indicators comprising features using EMAs from conventional sleep questionnaires. The goal of analysis consists of five metrics of sleep quality that explain perceived sleep quality. The result of analysis indicates that features using both daily indexes and sleep questionnaires lead to better prediction of sleep quality. Additionally, it also shows the potential to generate indicators identifying complex human behaviors with the help of mobile devices and EMAs. Further research on user-friendly data acquisition methods and more diverse lifestyle information should be useful to support behavior decisions for better sleep in well-being services and in specialized medical fields.

Daily Evening Electronic Media Use and Sleep in Later Life

Sleep complaints and disorders are one of the most common disturbances to health and well-being in later life. Evening electronic media use has been shown to influence the subsequent quantity and quality of sleep, but most research focused on younger age groups who are more likely to use new media (e.g., social media) to replace or complement traditional mass media such as television. To investigate how different types of evening media use is related to sleep in later life, we used ecological momentary assessment data from the Daily Experiences and Well-being Study (N = 231; Mage = 73.61) to examine how evening computer use and television viewing affect subsequent sleep hours and perceived sleep quality. Across all evening assessments, 43% of the evenings were spent using computers, and 80% of the evenings were spent watching television. Findings from a series of within-between random effects models indicated that evening computer use and television viewing had independent associations with sleep quantity and quality. That is, older adults reported fewer hours of sleep, more difficulty falling asleep, and worse overall sleep quality on nights following the evening computer use. In contrast, evening television viewing was associated with feeling less tired the next day morning. The results highlight the continued presence of television viewing in older adults’ daily lives and their distinction from general computer use. The social context in which older adults watch television in the evening may potentially explain how different electronic media use influences sleep in later life.

Developing Multimodal IoMT Monitoring System of Geriatric Depression: A Feasibility Study

The Internet of Medical Things (IoMT) is a promising tool to monitor depression and relevant symptoms. However, the multimodal IoMT monitoring system has been rarely developed considering the characteristics of older adults, particularly living in the community. Therefore, it is necessary to know how to develop multimodal IoMT monitoring systems tailored for older adults and evaluate the feasibility for research and practice. We developed a multimodal IoMT monitoring system that included a smartphone for facial and verbal expressions, smartwatch for activity and heart rates, and ecological momentary assessment (EMA) application. A convenience sample of 21 older Korean adults aged over 65 years was recruited from a community center, and 19 participants completed it. The data were collected in four weeks using self-report questionnaires, IoMT devices, and semi-structured interviews between July and December 2020 and were analyzed in mixed methods. Based on the Geriatric Depression Scale-Short Form scores, eight participants were classified in the depressive group (38.1%) and 13 in the non-depressive group (61.9%). A total of 1,505 (70.72%) EMA data were collected, and 1,277 (60.00%) were analyzed. Furthermore, 1,421 (66.78%) facial expression data were collected and labeled, including anger, happiness, neutral, sadness, surprise, and exception. Voice dialogues were transformed into 5,264 scripts. The depressive group showed lower user acceptance relative to the non-depressive group. However, both groups experienced positive emotions, had regular life patterns, and increased their self-interest. Thus, our multimodal IoMT monitoring system is a feasible and useful measure for acquiring mental health information in older adults’ depression.

Statistical Considerations for Analyzing Ecological Momentary Assessment Data.

The analysis of Ecological Momentary Assessment (EMA) data can be difficult to conceptualize due to the complexity of how the data are collected. The goal of this tutorial is to provide an overview of statistical considerations for analyzing observational data arising from EMA studies. EMA data are collected in a variety of ways, complicating the statistical analysis. We focus on fundamental statistical characteristics of the data and general purpose statistical approaches to analyzing EMA data. We implement those statistical approaches using a recent study involving EMA. The linear or generalized linear mixed-model statistical approach can adequately capture the challenges resulting from EMA collected data if properly set up. Additionally, while sample size depends on both the number of participants and the number of survey responses per participant, having more participants is more important than the number of responses per participant. Using modern statistical methods when analyzing EMA data and adequately considering all of the statistical assumptions being used can lead to interesting and important findings when using EMA. https://doi.org/10.23641/asha.17155961.

Understanding contexts of smoking and vaping among dual users: analysis of ecological momentary assessment data.

To understand dual users' cigarette and e-cigarette use patterns, including the contexts in which they vape versus smoke and to understand how environmental and internal contexts and smoking patterns differ between dual users and exclusive smokers. Longitudinal observational trial. Research center in Wisconsin, USA. Adult dual users (n = 162) and adults who exclusively smoked (n = 143), with no plans to quit smoking or vaping in the next 30 days. Participants carried smartphones for 2 weeks at baseline to record each use event for the two products and report on the context of their product use. The percentage of mornings where participants vaped first versus smoked were used to compute e-cigarette dependence. Hierarchical linear regression models with random slopes and intercepts examined the within- and between-subject effects of context on the likelihood of vaping (versus smoking); significant fixed effects were tested for moderation by e-cigarette dependence. Dual users reported significantly more puffs/cigarette [mean = 13.1, standard deviation (SD) = 10.2] than puffs/vape event (mean = 11.7, SD = 11.5; P = 0.01). E-cigarette dependence moderated the influence of social cues (t-ratio = 2.4, P = 0.02) and smoking restrictions (t-ratio = 3.1, P = 0.003) on the likelihood of vaping versus smoking [odds ratio (OR) = 2.30, P = 0.02]. Context was more related to which product was used in those of low versus higher e-cigarette dependence. Reports of strong cravings to smoke and positive expectancies for cigarettes were associated with a reduced likelihood of vaping, whereas strong cravings to vape and positive vaping expectancies were related to increased likelihood of vaping. Among dual users of cigarettes and e-cigarettes with no motivation to quit, vaping appears to be related to internal cues and more highly linked with social contexts and smoking restrictions (i.e. under stronger external stimulus control) among those with low to moderate e-cigarette dependence compared with high e-cigarette dependence. These findings illustrate the importance of contextual factors in tobacco product use among dual users with the influence of context being reduced at high levels of e-cigarette dependence.

A Review of Explicit and Implicit Assumptions When Providing Personalized Feedback Based on Self-Report EMA Data.

Ecological Momentary Assessment (EMA) in which participants report on their moment-to-moment experiences in their natural environment, is a hot topic. An emerging field in clinical psychology based on either EMA, or what we term Ecological Retrospective Assessment (ERA) as it requires retrospectivity, is the field of personalized feedback. In this field, EMA/ERA-data-driven summaries are presented to participants with the goal of promoting their insight in their experiences. Underlying this procedure are some fundamental assumptions about (i) the relation between true moment-to-moment experiences and retrospective evaluations of those experiences, (ii) the translation of these experiences and evaluations to different types of data, (iii) the comparison of these different types of data, and (iv) the impact of a summary of moment-to-moment experiences on retrospective evaluations of those experiences. We argue that these assumptions deserve further exploration, in order to create a strong evidence-based foundation for the personalized feedback procedure.

Lonely in a crowd: investigating the association between overcrowding and loneliness using smartphone technologies

Loneliness is a major public health concern with links to social and environmental factors. Previous studies have typically investigated loneliness as a stable emotional state using retrospective cross-sectional designs. Yet people experience different levels of loneliness throughout the day depending on their surrounding environment. In the present study, we investigated the associations between loneliness and social and environmental factors (i.e. overcrowding, population density, social inclusivity and contact with nature) in real-time. Ecological momentary assessment data was collected from participants using the Urban Mind smartphone application. Data from 756 participants who completed 16,602 assessments between April 2018 and March 2020 were used in order to investigate associations between momentary feeling of loneliness, the social environment (i.e. overcrowding, social inclusivity, population density) and the built environment (i.e. contact with nature) using multilevel modelling. Increased overcrowding and population density were associated with higher levels of loneliness; in contrast, social inclusivity and contact with nature were associated with lower levels of loneliness. These associations remained significant after adjusting for age, gender, ethnicity, education and occupation. The positive association between social inclusivity and lower levels of loneliness was more pronounced when participants were in contact with nature, indicating an interaction between the social and built environment on loneliness. The feeling of loneliness changes in relation to both social and environmental factors. Our findings have potential implications for public health strategies and interventions aimed at reducing the burden of loneliness on society. Specific measures, which would increase social inclusion and contact with nature while reducing overcrowding, should be implemented, especially in densely populated cities.

Dynamic contextual influences on social motivation and behavior in schizophrenia: a case-control network analysis

Contextual influences on social behavior and affective dynamics are not well understood in schizophrenia. We examined the role of social context on emotions, and the motivation to interact in the future, using dynamic network analysis of ecological momentary assessment (EMA) data. Participants included 105 outpatients with schizophrenia or schizoaffective disorder (SZ) and 76 healthy comparators (HC) who completed 7 days, 7 times a day of EMA. Dynamic networks were constructed using EMA data to visualize causal interactions between emotional states, motivation, and context (e.g., location, social interactions). Models were extended to include the type and frequency of interactions and the motivation to interact in the near future. Results indicated SZ networks were generally similar to HC but that contextual influences on emotion and social motivation were more evident in SZ. Further, feedback loops in HC were likely adaptive (e.g., positive emotions leading to social motivation), but most were likely maladaptive in SZ (e.g., sadness leading to reduced happiness leading to increased sadness). Overall, these findings indicate that network analyses may be useful in specifying emotion regulation problems in SZ and that instability related to contextual influences may be a central aspect of aberrant regulation.

Predicting college student prescription stimulant misuse: An analysis from ecological momentary assessment.

Prescription stimulant misuse (PSM) is common in young adult college students, at over 10% in the past year, and it is associated with other substance use and risk behaviors. Research focused on the real-time drivers of PSM is absent, impeding prevention and intervention. This research aimed to fill that gap by examining the relationships between affect, global stress, or academic stress and PSM via ecological momentary assessment (EMA); we also investigated baseline predictors of PSM frequency during the 21-day EMA period. Forty-one full-time college students (mean age: 20.5, 66% female) who endorsed current PSM (≥ 6 past-year episodes) participated. Participants were asked to complete EMA questions in response to 3 daily investigator-initiated prompts and after every PSM episode. Assessments were selected based on affect regulation (e.g., positive affect [PA], negative affect [NA]) and drug instrumentalization (e.g., academic stress and/or demands) theories of substance use. Mixed-effects linear models examined EMA data, and negative binomial regression analyses examined baseline predictors of PSM episode frequency. PA was higher on PSM days and increased post-PSM, whereas NA was unrelated to PSM. Although global and academic stress were largely unrelated to PSM, when the motive endorsed for PSM was "to study," pre-PSM ratings of academic demand and stress were significantly higher. Finally, a history of recreational motives (e.g., to get high) or higher levels of attention-deficit/hyperactivity disorder symptoms predicted a greater number of PSM episodes over the EMA period. The results offered mixed support for both affect regulation and instrumentalization as applied to PSM. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Exploring Parental Responses to Pre-schoolers' "Everyday" Pain Experiences Through Electronic Diary and Ecological Momentary Assessment Methodologies.

Objective: Parental influence during children’s “everyday” pain events is under-explored, compared to clinical or experimental pains. We trialed two digital reporting methods for parents to record the real-world context surrounding their child’s everyday pain events within the family home.Methods: Parents (N = 21) completed a structured e-diary for 14 days, reporting on one pain event experienced by their child (aged 2.5–6 years) each day, and describing child pain responses, parental supervision, parental estimates of pain severity and intensity, and parental catastrophizing, distress, and behavioral responses. During the same 2-week period, a subsample of parent-child pairs (N = 9) completed digital ecological momentary assessments (EMA), immediately after any chosen pain event. Children reported their current pain while parents estimated the child’s pain and indicated their own distress.Results: “Everyday” pain events frequently featured minor injuries to the child’s head, hands or knees, and child responses included crying and non-verbal comments (e.g., “Ouch!”). Pain events occurred less frequently when parents had been supervising their child, and supervising parents reported lower levels of worry and anxiety than non-supervising parents. Child sex was significantly associated with parental estimates of pain intensity, with parents of girls giving higher estimates than parents of boys. Child age was significantly associated with both the number of pain events and with parental estimates of pain intensity and child distress: the youngest children (2–3 years) experienced the fewest pain events but received higher pain and distress estimates from parents than older children. Hierarchal Linear Modeling revealed that parental estimates of pain severity were significant positive predictors of parental distress and catastrophizing in response to a specific pain event. Furthermore, higher levels of parental catastrophic thinking in response to a specific pain event resulted in increased distress, solicitousness, and coping-promoting behaviors in parents. The EMA data revealed that children reported significantly higher pain intensity than their parents.Conclusion: The electronic pain diary provided a key insight into the nature of “everyday” pain experiences around the family home. Digital daily reporting of how the family copes with “everyday” events represents a viable means to explore a child’s everyday pains without disrupting their home environment.