Statistical Considerations for Analyzing Ecological Momentary Assessment Data

Purpose: 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. Method: 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. Results: 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. Conclusion: 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. Supplemental Material https://doi.org/10.23641/asha.17155961

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.

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.

Are Retrospective Assessments Means of People’s Experiences? Accounting for Interpersonal and Intrapersonal Variability when Comparing Retrospective Assessment Data to Ecological Momentary Assessment Data

Retrospective Assessment (RA) scores are often found to be higher than the mean of Ecological Momentary Assessment (EMA) scores about a concurrent period. This difference is generally interpreted as bias towards salient experiences in RA. During RA, participants are often asked to summarize their experiences in unspecific terms, which may indeed facilitate bias. At least in this unspecific form, the summary that participants apply to their remembered experiences can take many different forms. In this study, we reanalyzed an existing dataset (N = 92) using a repeated N = 1 approach. We reported on interindividual differences between EMA data and RA score, and assessed for each participant whether it was likely that their RA score was an approximation of the mean of their experiences as captured by their EMA data. We found considerable interpersonal differences in the difference between EMA scores and RA scores, as well as some extreme cases. Furthermore, for a considerable part of the sample (n = 46 for positive affect, n = 60 for negative affect), we did not reject the null hypothesis that their RA score represented the mean of their experiences as captured by their EMA data. We conclude that in its current unspecific form, RA may facilitate bias, although not for everyone. Future studies may determine whether more specific forms of RA reduce bias, while acknowledging interindividual differences.

A Review of Explicit and Implicit Assumptions When Providing Personalized Feedback Based on 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 functioning. Underlying this procedure are some fundamental assumptions about (1) the relation between true moment-to-moment experiences and retrospective evaluations of those experiences, (2) the translation of these experiences and evaluations to different types of data (3) the comparison of these different types of data (4) 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.

Individual Hearing Aid Benefit in Real Life Evaluated Using Ecological Momentary Assessment

Ecological momentary assessment (EMA) was used in 24 adults with mild-to-moderate hearing loss who were seeking first hearing-aid (HA) fitting or HA renewal. At two stages in the aural rehabilitation process, just before HA fitting and after an average 3-month HA adjustment period, the participants used a smartphone-based EMA system for 3 to 4 days. A questionnaire app allowed for the description of the environmental context as well as assessments of various hearing-related dimensions and of well-being. In total, 2,042 surveys were collected. The main objectives of the analysis were threefold: First, describing the “auditory reality” of future and experienced HA users; second, examining the effects of HA fitting for individual participants, as well as for the subgroup of first-time HA-users; and third, reviewing whether the EMA data collected in the unaided condition predicted who ultimately decided for or against permanent HA use. The participants reported hearing-related disabilities across the full range of daily listening tasks, but communication events took the largest share. The effect of the HA intervention was small in experienced HA users. Generally, much larger changes and larger interindividual differences were observed in first-time compared with experienced HA users in all hearing-related dimensions. Changes were not correlated with hearing loss or with the duration of the HA adjustment period. EMA data collected in the unaided condition did not predict the cancelation of HA fitting. The study showed that EMA is feasible in a general population of HA candidates for establishing individual and multidimensional profiles of real-life hearing experiences.

Improving Detection of Cognitive Decline and Impairment Using Everyday Behavioral and Social Markers

Early detection of cognitive decline and mild cognitive impairment (MCI) during the pre-symptomatic phase of Alzheimer’s disease is particularly important for maximizing effectiveness of clinical trials and efficiency of resource allocation. However, it is difficult to distinguish early signs of decline and impairment from normative aging, especially with biomarkers and clinical-based assessments that are expensive and challenging to apply widely. Ambulatory assessments in naturalistic settings provide opportunities to capture everyday markers of cognitive decline and offer cost-effective tools for sensitive, early detection of transitions to MCI in community-dwelling older adults. In this symposium, we present four studies that use ecological momentary assessment (EMA) data from the Einstein Aging Study to showcase how everyday markers of behavioral and social functioning assessed up to six times a day for 14 consecutive days can facilitate early detection of cognitive difficulties. Zhaoyang et al. examine whether older adults with intact cognition versus MCI differ in patterns of daily social interactions. Hyun et al. investigate how the diversity of daily activities is associated with ambulatory cognitive deficits. Cerino et al. compare the sensitivity of everyday markers of stress versus global trait-based stress measures to detect MCI. Roque et al. use completion time from EMA surveys as a reliable and unobtrusive way to measure cognition and distinguish those with and without MCI. Stacey Scott will integrate insights gained from these four papers, and discuss the opportunities and challenges faced when combining ambulatory assessments of everyday markers with traditional methods to better detect cognitive decline and impairment.

Questionnaire-Based Everyday Reaction Time: Reliable, Valid, and Unobtrusive Measure of Cognition

Experience sampling paradigms provide new opportunity for early identification of mild cognitive impairment (MCI). We investigated two research questions: (1) is time to complete a repeatedly administered survey (i.e., questionnaire-based everyday reaction time, q*bert) a reliable and valid measure of cognition? (2) does this measure distinguish MCI status? To answer these questions, we leveraged ecological momentary assessment (EMA) data from the Einstein Aging Study, where older adults (N=240) completed six daily surveys and cognitive assessments on smartphones over 14 days. Q*bert had good between-person reliability after two days (~11 EMAs) and excellent reliability from three to fourteen days. Q*bert moderately correlated with ambulatory cognitive measures of processing speed and memory binding (p’s < .001) and was significantly slower in those with MCI (p < .001). We propose q*bert as a reliable, valid, and unobtrusive measure of cognition when ambulatory cognitive assessments are not feasible.

TO THE PROBLEM OF DIAGNOSTICS OF THE TECHNICAL STATE OF THE DISTRIBUTION NETWORKS LINES ACCORDING TO AIMS EMA DATA

The problem of organization of monitoring and diagnosing of the technical state of a four-wire distribution electric network (DEN) by means of an automated information-measuring system of monitoring and accounting of electricity (AIMS EMA) consumed by subscribers is considered. As parameters characterizing the technical state of the DEN, the values of the active resistances of the lines (wires) in the network are taken. The indicated parameters can change significantly over time depending on the values of flowing currents, climatic factors of the external environment, as well as due to wear out of the wires. A technique of the operational identification of resistances of inter-subscriber DEN sections, which uses the data of the distribution network operating modes is proposed. Its implementation in AIMS EMA allows to monitor changes in values of their active resistances. A procedure of diagnosing lines in DEN is presented, it consists in analyzing the dynamics of active resistances values changes obtained at different points in time, and identifying the increase of the wear level of individual wires against the background of flowing currents and climatic factors. It is believed, that external influences lead to the same relative changes of parameters of all wires for the same interpersonal site. The wear diagnostic criterion is an increase of the active resistance values for individual wires with their unequal change relatively to the baseline values, which are determined during observations of the distribution network condition.