Why Ecological Momentary Assessment Surveys Go Incomplete?: When It Happens and How It Impacts Data

2020 ◽  
Author(s):  
Yu-Hsiang Wu ◽  
Jingjing Xu ◽  
Elizabeth Stangl ◽  
Shareka Pentony ◽  
Dhruv Vyas ◽  
...  
Keyword(s):  
Real Time ◽  
Self Report ◽  
Data Logging ◽  
Time Data ◽  
Real Time Data ◽  
Report Data ◽  
Self Report Data ◽  
Ecological Momentary ◽  
The Impact ◽  
Momentary Assessment

Abstract Background Ecological momentary assessment (EMA) often requires respondents to complete surveys in the moment to report real-time experiences. Because EMA may seem disruptive or intrusive, respondents may not complete surveys as directed in certain circumstances. Purpose This article aims to determine the effect of environmental characteristics on the likelihood of instances where respondents do not complete EMA surveys (referred to as survey incompletion), and to estimate the impact of survey incompletion on EMA self-report data. Research Design An observational study. Study Sample Ten adults hearing aid (HA) users. Data Collection and Analysis Experienced, bilateral HA users were recruited and fit with study HAs. The study HAs were equipped with real-time data loggers, an algorithm that logged the data generated by HAs (e.g., overall sound level, environment classification, and feature status including microphone mode and amount of gain reduction). The study HAs were also connected via Bluetooth to a smartphone app, which collected the real-time data logging data as well as presented the participants with EMA surveys about their listening environments and experiences. The participants were sent out to wear the HAs and complete surveys for 1 week. Real-time data logging was triggered when participants completed surveys and when participants ignored or snoozed surveys. Data logging data were used to estimate the effect of environmental characteristics on the likelihood of survey incompletion, and to predict participants' responses to survey questions in the instances of survey incompletion. Results Across the 10 participants, 715 surveys were completed and survey incompletion occurred 228 times. Mixed effects logistic regression models indicated that survey incompletion was more likely to happen in the environments that were less quiet and contained more speech, noise, and machine sounds, and in the environments wherein directional microphones and noise reduction algorithms were enabled. The results of survey response prediction further indicated that the participants could have reported more challenging environments and more listening difficulty in the instances of survey incompletion. However, the difference in the distribution of survey responses between the observed responses and the combined observed and predicted responses was small. Conclusion The present study indicates that EMA survey incompletion occurs systematically. Although survey incompletion could bias EMA self-report data, the impact is likely to be small.

Ecological Momentary Assessment

2014 ◽  
Author(s):  
Saul Shiffman
Keyword(s):  
Substance Use ◽  
Ecological Momentary Assessment ◽  
Self Report ◽  
Use Patterns ◽  
Valid Data ◽  
Report Data ◽  
Self Report Data ◽  
Ecological Momentary ◽  
Ema Data ◽  
Momentary Assessment

Ecological momentary assessment (EMA) is a method for collecting data in real time and in real-world settings in order to avoid retrospective biases, collect ecologically valid data, and study behavioral processes over time. EMA is particularly suited for studying substance use because use is episodic and related to contextual factors like mood, setting, and cues. This chapter addresses the application of EMA to substance use research, describing important elements of EMA design and analysis and illustrating them with examples from substance use research. It discusses and reviews data on methodological issues such as compliance and reactivity and covers considerations in designing EMA studies of substance use. Data on the associations between EMA data on substance use and more traditional self-report data are reviewed. EMA methods reveal substance use patterns not captured by questionnaires or retrospective data and hold promise for substance use research and treatment.

Intensive Longitudinal Data Collection using Microinteraction Ecological Momentary Assessment (μEMA): Pilot and Preliminary Results (Preprint)

2021 ◽  
Author(s):  
Aditya Ponnada ◽  
Shirlene Wang ◽  
Daniel Chu ◽  
Bridgette Do ◽  
Genevieve Dunton ◽  
...  
Keyword(s):  
Data Collection ◽  
Ecological Momentary Assessment ◽  
Self Report ◽  
Time Study ◽  
Longitudinal Data Collection ◽  
Report Data ◽  
Self Report Data ◽  
Ecological Momentary ◽  
Study Participants ◽  
Momentary Assessment

BACKGROUND Ecological momentary assessment (EMA) uses mobile technology to enable in-situ self-report data collection on behaviors and states. In a typical EMA study, participants are prompted several times a day to answer sets of multiple-choice questions. While the repeated nature of EMA reduces recall bias, it may induce participation burden. There is a need to explore complementary approaches to collecting in-situ self-report data that are less burdensome, yet provide comprehensive information on an individual’s behaviors and states. One new approach, microinteraction ecological momentary assessment (μEMA), restricts EMA items to single, cognitively simple questions answered on a smartwatch with single-tap answers; i.e., EMA is limited to only those answerable with a quick, glanceable microinteraction. However, the viability of using μEMA to capture behaviors and states in a large-scale intensive longitudinal data collection (ILD) study has not yet been demonstrated. OBJECTIVE This paper describes 1) the μEMA protocol currently used in the Temporal Influences on Movement and Exercise (TIME) Study conducted with young adults, 2) the interface of the μEMA app to gather self-report responses on a smartwatch, 3) qualitative feedback from participants following a pilot study of the μEMA app, 4) changes made to the main TIME study μEMA protocol and app based on the pilot feedback, and 5) preliminary μEMA results from a subset of active participants in the TIME Study. METHODS The TIME Study involves data collection on behaviors and states using passive sensors on smartwatches and smartphones along with intensive phone-based EMA, four-day hourly EMA bursts every two weeks among 250 people. Every day, participants also answer a nightly EMA survey. On non-EMA burst days, participants answer μEMA questions on the smartwatch assessing momentary states such as physical activity, sedentary behavior, and affect. At the end of the study, participants take part in a semi-structured interview to describe their experience with EMA and μEMA. A pilot study was used to test and refine the μEMA protocol for the main study. RESULTS Changes made to the μEMA study protocol based on pilot feedback included adjustments to the single-question selection method and watch vibrotactile prompting. We also added sensor-triggered questions for physical activity and sedentary behavior. As of June 2021, 81 participants completed at least six months of data collection in the main study. For 662,397 μEMA questions delivered, the compliance rate was 67.61% (SD = 24.36) and completion rate was 79.03% (SD = 22.19). CONCLUSIONS This study provides opportunities to explore a novel approach for collecting temporally dense intensive longitudinal self-report data in a sustainable manner. Data suggest that μEMA may be valuable for understanding behaviors and states at the individual level, thus possibly supporting future longitudinal interventions that require within-day, temporally dense self-report in the real world. CLINICALTRIAL Not applicable

scholarly journals Ecological Momentary Assessment Using Smartphones in Patients With Depression: Feasibility Study

10.2196/14179 ◽  
2021 ◽  
Vol 5 (2) ◽  
pp. e14179
Author(s):  
Redwan Maatoug ◽  
Nathan Peiffer-Smadja ◽  
Guillaume Delval ◽  
Térence Brochu ◽  
Benjamin Pitrat ◽  
...  
Keyword(s):  
Real Time ◽  
Ecological Momentary Assessment ◽  
Self Esteem ◽  
Smartphone App ◽  
Spearman Correlation ◽  
Time Data ◽  
Real Time Data ◽  
Ecological Momentary ◽  
Momentary Assessment

Background Ecological momentary assessment (EMA) is a promising tool in the management of psychiatric disorders and particularly depression. It allows for a real-time evaluation of symptoms and an earlier detection of relapse or treatment efficacy. The generalization of the smartphone in the modern world offers a new, large-scale support for EMA. Objective The main objective of this study was twofold: (1) to assess patients’ compliance with an EMA smartphone app defined by the number of EMAs completed, and (2) to estimate the external validity of the EMA using a correlation between self-esteem/guilt/mood variables and Hamilton Depression Rating Scale (HDRS) score. Methods Eleven patients at the Pitié-Salpêtrière Hospital, Paris, France, were monitored for 28 days by means of a smartphone app. Every patient enrolled in the study had two types of assessment: (1) three outpatient consultations with a psychiatrist at three different time points (days 1, 15, and 28), and (2) real-time data collection using an EMA smartphone app with a single, fixed notification per day at 3 pm for 28 days. The results of the real-time data collected were reviewed during the three outpatient consultations by a psychiatrist using a dashboard that aggregated all of the patients’ data into a user-friendly format. Results Of the 11 patients in the study, 6 patients attended the 3 outpatient consultations with the psychiatrist and completed the HDRS at each consultation. We found a positive correlation between the HDRS score and the variables of self-esteem, guilt, and mood (Spearman correlation coefficient 0.57). Seven patients completed the daily EMAs for 28 days or longer, with an average response rate to the EMAs of 62.5% (175/280). Furthermore, we observed a positive correlation between the number of responses to EMAs and the duration of follow-up (Spearman correlation coefficient 0.63). Conclusions This preliminary study with a prolonged follow-up demonstrates significant patient compliance with the smartphone app. In addition, the self-assessments performed by patients seemed faithful to the standardized measurements performed by the psychiatrist. The results also suggest that for some patients it is more convenient to use the smartphone app than to attend outpatient consultations.

Ecological Momentary Assessment and Technological Advances in Clinical Care

2020 ◽  
pp. 275-288
Author(s):  
Isabelle Morris ◽  
Saul Shiffman ◽  
Ellen Beckjord ◽  
Stuart G. Ferguson
Keyword(s):  
Data Collection ◽  
Real Time ◽  
Ecological Momentary Assessment ◽  
Clinical Care ◽  
Time Data ◽  
Data Collection Methods ◽  
Real Time Data ◽  
Ecological Momentary ◽  
Momentary Assessment ◽  
Collection Methods

Ecological momentary assessment (EMA) methods provide a means by which researchers may attain highly detailed, ecologically valid, and contextually rich data on everyday experience and behavior. EMA methods are now widely used by researchers, particularly those studying health behaviors. A key reason for the popularity of EMA methods is that they allow researchers to examine both between- and within-individual differences in treatment efficacy and to explore the temporal sequences related to events of interest. Until relatively recently, EMA methods have predominately been employed as assessment and research tools. However, in recent years clinicians and researchers have begun to explore the value of real-time data collection methods as the foundation for providing tailored interventions that can respond to a patient’s behaviors, moods, social context, and geographical location. This chapter discusses how EMA data can potentially be utilized to improve the delivery of health interventions. The authors conclude that while there are clearly potential benefits of utilizing real-time data collection methods for treatment delivery, considerable work remains to ensure that EMA-based interventions are appropriate, theoretically derived, and ethical in their effects on privacy and confidentiality.

scholarly journals A Smartphone Ecological Momentary Assessment/Intervention “App” for Collecting Real-Time Data and Promoting Self-Awareness

PLoS ONE ◽  
2013 ◽  
Vol 8 (8) ◽  
pp. e71325 ◽  
Author(s):  
Jason D. Runyan ◽  
Timothy A. Steenbergh ◽  
Charles Bainbridge ◽  
Douglas A. Daugherty ◽  
Lorne Oke ◽  
...  
Keyword(s):  
Real Time ◽  
Ecological Momentary Assessment ◽  
Self Awareness ◽  
Time Data ◽  
Real Time Data ◽  
Ecological Momentary ◽  
Momentary Assessment

scholarly journals Trait-like nocturnal sleep behavior identified by combining wearable, phone-use, and self-report data

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Stijn A. A. Massar ◽  
Xin Yu Chua ◽  
Chun Siong Soon ◽  
Alyssa S. C. Ng ◽  
Ju Lynn Ong ◽  
...  
Keyword(s):  
Self Report ◽  
Sleep Behavior ◽  
Wake Time ◽  
Time Estimates ◽  
Time In Bed ◽  
Sleep Measurement ◽  
Report Data ◽  
Self Report Data ◽  
Ecological Momentary ◽  
Momentary Assessment

AbstractUsing polysomnography over multiple weeks to characterize an individual’s habitual sleep behavior while accurate, is difficult to upscale. As an alternative, we integrated sleep measurements from a consumer sleep-tracker, smartphone-based ecological momentary assessment, and user-phone interactions in 198 participants for 2 months. User retention averaged >80% for all three modalities. Agreement in bed and wake time estimates across modalities was high (rho = 0.81–0.92) and were adrift of one another for an average of 4 min, providing redundant sleep measurement. On the ~23% of nights where discrepancies between modalities exceeded 1 h, k-means clustering revealed three patterns, each consistently expressed within a given individual. The three corresponding groups that emerged differed systematically in age, sleep timing, time in bed, and peri-sleep phone usage. Hence, contrary to being problematic, discrepant data across measurement modalities facilitated the identification of stable interindividual differences in sleep behavior, underscoring its utility to characterizing population sleep and peri-sleep behavior.

scholarly journals Financing entrepreneurship in times of crisis: Exploring the impact of COVID-19 on the market for entrepreneurial finance in the United Kingdom

2020 ◽  
Vol 38 (5) ◽  
pp. 380-390 ◽  
Author(s):  
Ross Brown ◽  
Augusto Rocha ◽  
Marc Cowling
Keyword(s):  
United Kingdom ◽  
Real Time ◽  
Entrepreneurial Finance ◽  
Time Data ◽  
Face To Face ◽  
Policy Interventions ◽  
The United Kingdom ◽  
Real Time Data ◽  
Start Ups ◽  
The Impact

This commentary explores the manner in which the current COVID-19 crisis is affecting key sources of entrepreneurial finance in the United Kingdom. We posit that the unique relational nature of entrepreneurial finance may make it highly susceptible to such a shock owing to the need for face-to-face interaction between investors and entrepreneurs. The article explores this conjecture by scrutinising a real-time data source of equity investments. Our findings suggest that the volume of new equity transactions in the United Kingdom has declined markedly since the outbreak of the COVID-19 pandemic. It appears that seed finance is the main type of entrepreneurial finance most acutely affected by the crisis, which typically goes to the most nascent entrepreneurial start-ups facing the greatest obstacles obtaining finance. Policy makers can utilise these real-time data sources to help inform their strategic policy interventions to assist the firms most affected by crisis events.

Using self-report and skin conductance measures to evaluate theme park experiences

2020 ◽  
pp. 135676672096973
Author(s):  
Shanshi Li
Keyword(s):  
Skin Conductance ◽  
Self Report ◽  
Future Research ◽  
Theme Park ◽  
Retrospective Evaluation ◽  
Report Data ◽  
Self Report Data ◽  
Affective Intensity ◽  
The Impact ◽  
Practical Implications

This study examines the impact of the key affective moments of a theme park experience on visitors’ post-trip evaluations measured immediately after their visits. One hundred and twenty-three participants visited a theme park while their real-time skin conductance and self-report data were collected. Results indicate that visitors’ pleasure levels (i.e. average, beginning, peak, and end) consistently correlate with satisfaction, which in turn, positively influences behavioural intention. In particular, visitors’ satisfaction levels are better aligned with the affective intensity at the end moment and the average emotion intensity of a theme park experience. Arousal, however, was not found to be a significant indicator of post-trip evaluation. The study extends literature on key moments and retrospective evaluation by illustrating how visitors rely on affective moments of a theme park experience to construct overall evaluations. The study concludes with practical implications and scope for future research.

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