Identification of careless responding in ecological momentary assessment research: From posthoc analyses to real-time data monitoring.

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.

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Ecological Momentary Assessment and Technological Advances in Clinical Care

The Oxford Handbook of Digital Technologies and Mental Health ◽

10.1093/oxfordhb/9780190218058.013.24 ◽

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.

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

PLoS ONE ◽

10.1371/journal.pone.0071325 ◽

2013 ◽

Vol 8 (8) ◽

pp. e71325 ◽

Cited By ~ 83

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

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

Journal of the American Academy of Audiology ◽

10.1055/s-0040-1719135 ◽

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.

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Ecological Momentary Assessment Using a Mobile Phone

Technological Advancements in Biomedicine for Healthcare Applications - Advances in Bioinformatics and Biomedical Engineering ◽

10.4018/978-1-4666-2196-1.ch039 ◽

2012 ◽

pp. 398-407

Author(s):

David Habsara Hareva ◽

Hiroki Okada ◽

Hisao Oka

Keyword(s):

Mobile Phone ◽

Real Time ◽

Ecological Momentary Assessment ◽

Health Conditions ◽

Time Data ◽

Physical Disorders ◽

Ecological Momentary ◽

Ema Data ◽

Momentary Assessment ◽

Supportive Intervention

The mobile phone has become a popular tool for providing information and capturing responses from different groups of people because of its technological features and portability. EMA (Ecological Momentary Assessment) is commonly used by health researchers to contemporaneously capture information regarding human experience. The authors proposed the use of a mobile EMA system as a supportive intervention to collect real-time patient data and to give back real-time advice. In this study, a mobile EMA system has been utilized by patients with a variety of conditions, including mood disorders, behavior disorders, and physical disorders. The real-time data collection included one or more pieces of information at each moment to improve understanding the causal mechanisms of disease. The effectiveness of real-time advice has been examined by comparing a mobile EMA system with and without this function. Patient compliance was high on average, at approximately 89%, and was higher, at approximately 93%, when advice was given. In several cases, the supportive intervention was shown to help patients improve their health conditions. However, the results were dependent on the patients’ motivation, environment, and relationship with their doctor. The EMA data regarding advice given showed that symptoms tended to improve in most cases.

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Compliance with Ecological Momentary Assessment Protocols in Substance Users: a Meta-Analysis

10.31234/osf.io/k4agy ◽

2018 ◽

Author(s):

Andrew Jones ◽

Danielle Remmerswaal ◽

Ilse Verveer ◽

eric robinson ◽

Ingmar H.A. Franken ◽

...

Keyword(s):

Substance Use ◽

Ecological Momentary Assessment ◽

Meta Analysis ◽

Compliance Rate ◽

Time Data ◽

Substance Users ◽

Assessment Period ◽

Real Time Data ◽

Ecological Momentary ◽

Momentary Assessment

Introduction: Ecological Momentary Assessment (EMA) methods allow for real-time data collection in naturalistic environments, and are particularly informative for the examination of substance use which is both time and context dependent. Whilst there are considerable benefits to EMA, poor compliance to assessment protocols has been identified as a limitation, particularly in substance users. Little research has analysed factors which might influence compliance. Methods: The aim of this meta-analysis was to systematically review and meta-analyse potential variables that may influence compliance to EMA protocols in substance users; such as, prompt frequency, total length of assessment period, substance use population and device used to administer EMA prompts. We pre-registered our design, hypotheses and analysis strategy. Results: Following systematic searches of relevant databases we identified k = 128 reported compliance rates in the literature. The pooled compliance rate across all studies was 78.68% (95% CI 76.53%, 80.69%). There was no evidence that any proposed moderators were associated with compliance rates; prompt frequency (Q(3) = 0.98, p = .805) length of assessment period (Q(2) = 1.42, p = .493), substance use population (Q(1) = 1.830, p = .176) or device administration (Q(3) = 4.715, p =.194). Conclusions: The overall compliance rate was similar to that of other fields and recommended rates of compliance (80%), although compliance was not associated with any procedural variables. Furthermore, we identified various limitations in reporting of compliance data and improvement is needed to further elucidate factors which might influence compliance. These findings suggest intensive real-time data collection techniques can be administered in substance using populations, despite previous concerns.

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