Dataset Information

Statistical Considerations for Analyzing Ecological Momentary Assessment Data.

ABSTRACT:

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.

SUBMITTER: Oleson JJ

PROVIDER: S-EPMC9150728 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature

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Publications

Statistical Considerations for Analyzing Ecological Momentary Assessment Data.

Oleson Jacob J JJ Jones Michelle A MA Jorgensen Erik J EJ Wu Yu-Hsiang YH

Journal of speech, language, and hearing research : JSLHR 20211215 1

<h4>Purpose</h4>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.<h4>Method</h4>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 app ...[more]

PMID: 34910571

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Project description:IntroductionDigital health interventions are an effective way to treat depression, but it is still largely unclear how patients' individual symptoms evolve dynamically during such treatments. Data-driven forecasts of depressive symptoms would allow to greatly improve the personalisation of treatments. In current forecasting approaches, models are often trained on an entire population, resulting in a general model that works overall, but does not translate well to each individual in clinically heterogeneous, real-world populations. Model fairness across patient subgroups is also frequently overlooked. Personalised models tailored to the individual patient may therefore be promising.MethodsWe investigate different personalisation strategies using transfer learning, subgroup models, as well as subject-dependent standardisation on a newly-collected, longitudinal dataset of depression patients undergoing treatment with a digital intervention ( N=65 patients recruited). Both passive mobile sensor data as well as ecological momentary assessments were available for modelling. We evaluated the models' ability to predict symptoms of depression (Patient Health Questionnaire-2; PHQ-2) at the end of each day, and to forecast symptoms of the next day.ResultsIn our experiments, we achieve a best mean-absolute-error (MAE) of 0.801 (25% improvement) for predicting PHQ-2 values at the end of the day with subject-dependent standardisation compared to a non-personalised baseline ( MAE=1.062 ). For one day ahead-forecasting, we can improve the baseline of 1.539 by 12% to a MAE of 1.349 using a transfer learning approach with shared common layers. In addition, personalisation leads to fairer models at group-level.DiscussionOur results suggest that personalisation using subject-dependent standardisation and transfer learning can improve predictions and forecasts, respectively, of depressive symptoms in participants of a digital depression intervention. We discuss technical and clinical limitations of this approach, avenues for future investigations, and how personalised machine learning architectures may be implemented to improve existing digital interventions for depression.

Project description:BackgroundEcological 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. Although 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. A new approach, microinteraction EMA (μEMA), restricts EMA items to single, cognitively simple questions answered on a smartwatch with single-tap assessments using a quick, glanceable microinteraction. However, the viability of using μEMA to capture behaviors and states in a large-scale longitudinal study has not yet been demonstrated.ObjectiveThis paper describes the μEMA protocol currently used in the Temporal Influences on Movement & Exercise (TIME) Study conducted with young adults, the interface of the μEMA app used to gather self-report responses on a smartwatch, qualitative feedback from participants after a pilot study of the μEMA app, changes made to the main TIME Study μEMA protocol and app based on the pilot feedback, and preliminary μEMA results from a subset of active participants in the TIME Study.MethodsThe TIME Study involves data collection on behaviors and states from 246 individuals; measurements include passive sensing from a smartwatch and smartphone and intensive smartphone-based hourly EMA, with 4-day EMA bursts every 2 weeks. 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 describe their experience with EMA and μEMA in a semistructured interview. A pilot study was used to test and refine the μEMA protocol before the main study.ResultsChanges made to the μEMA study protocol based on pilot feedback included adjusting the single-question selection method and smartwatch vibrotactile prompting. We also added sensor-triggered questions for physical activity and sedentary behavior. As of June 2021, a total of 81 participants had completed at least 6 months of data collection in the main study. For 662,397 μEMA questions delivered, the compliance rate was 67.6% (SD 24.4%) and the completion rate was 79% (SD 22.2%).ConclusionsThe TIME 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 data as people go about their lives.

Project description:BACKGROUND:Ecological momentary assessment (EMA) is a method of collecting real-time data based on repeated measures and observations that take place in participant's daily environment. EMA has many advantages over more traditional, retrospective questionnaires. However, EMA faces some challenges to reach its full potential. The aims of this systematic review are to (1) investigate whether and how content validity of the items (i.e. the specific questions that are part of a larger EMA questionnaire) used in EMA studies on physical activity and sedentary behaviour was assessed, and (2) provide an overview of important methodological considerations of EMA in measuring physical activity and sedentary behaviour. METHODS:Thirty papers (twenty unique studies) were systematically reviewed and variables were coded and analysed within the following 4 domains: (1) Content validity, (2) Sampling approach, (3) Data input modalities and (4) Degree of EMA completion. RESULTS:Only about half of the studies reported the specific items (n = 12) and the source of the items (n = 11). None of the studies specifically assessed the content validity of the items used. Only a minority (n = 5) of the studies reported any training, and one tested the comprehensibility of the EMA items. A wide variability was found in the design and methodology of the EMA. A minority of the studies (n = 7) reported a rationale for the used prompt frequency, time selection, and monitoring period. Retrospective assessment periods varied from 'now' to 'in the last 3.5 hours'. In some studies there was a possibility to delay (n = 6) or deactivate (n = 10) the prompt, and some provided reminders after the first prompt (n = 9). CONCLUSIONS:Almost no EMA studies reported the content validation of the items used. We recommend using the COSMIN checklist (COnsensus-based Standards for the selection of health Measurement INstruments) to report on the content validity of EMA items. Furthermore, as often no rationale was provided for several methodological decisions, the following three recommendations are made. First, provide a rationale for choosing the sampling modalities. Second, to ensure assessment 'in the moment', think carefully about the retrospective assessment period, reminders, and deactivation of the prompt. Third, as high completion rates are important for representativeness of the data and generalizability of the findings, report completion rates. TRIAL REGISTRATION:This review is registered in PROSPERO, the International prospective register of systematic reviews (registration number: CRD42017077996).

Dataset Information

Statistical Considerations for Analyzing Ecological Momentary Assessment Data.

Purpose

Method

Results

Conclusion

Supplemental material

Publications

Statistical Considerations for Analyzing Ecological Momentary Assessment Data.

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