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An application of a mixed-effects location scale model for analysis of Ecological Momentary Assessment (EMA) data.

ABSTRACT: For longitudinal data, mixed models include random subject effects to indicate how subjects influence their responses over repeated assessments. The error variance and the variance of the random effects are usually considered to be homogeneous. These variance terms characterize the within-subjects (i.e., error variance) and between-subjects (i.e., random-effects variance) variation in the data. In studies using ecological momentary assessment (EMA), up to 30 or 40 observations are often obtained for each subject, and interest frequently centers around changes in the variances, both within and between subjects. In this article, we focus on an adolescent smoking study using EMA where interest is on characterizing changes in mood variation. We describe how covariates can influence the mood variances, and also extend the standard mixed model by adding a subject-level random effect to the within-subject variance specification. This permits subjects to have influence on the mean, or location, and variability, or (square of the) scale, of their mood responses. Additionally, we allow the location and scale random effects to be correlated. These mixed-effects location scale models have useful applications in many research areas where interest centers on the joint modeling of the mean and variance structure.

SUBMITTER: Hedeker D

PROVIDER: S-EPMC2424261 | biostudies-literature | 2008 Jun

REPOSITORIES: biostudies-literature

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An application of a mixed-effects location scale model for analysis of Ecological Momentary Assessment (EMA) data.

Hedeker Donald D Mermelstein Robin J RJ Demirtas Hakan H

Biometrics 20071026 2

For longitudinal data, mixed models include random subject effects to indicate how subjects influence their responses over repeated assessments. The error variance and the variance of the random effects are usually considered to be homogeneous. These variance terms characterize the within-subjects (i.e., error variance) and between-subjects (i.e., random-effects variance) variation in the data. In studies using ecological momentary assessment (EMA), up to 30 or 40 observations are often obtained ...[more]

PMID: 17970819

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Project description:Ecological momentary assessment studies usually produce intensively measured longitudinal data with large numbers of observations per unit, and research interest is often centered around understanding the changes in variation of people's thoughts, emotions and behaviors. Hedeker et al developed a 2-level mixed effects location scale model that allows observed covariates as well as unobserved variables to influence both the mean and the within-subjects variance, for a 2-level data structure where observations are nested within subjects. In some ecological momentary assessment studies, subjects are measured at multiple waves, and within each wave, subjects are measured over time. Li and Hedeker extended the original 2-level model to a 3-level data structure where observations are nested within days and days are then nested within subjects, by including a random location and scale intercept at the intermediate wave level. However, the 3-level random intercept model assumes constant response change rate for both the mean and variance. To account for changes in variance across waves, as well as clustering attributable to waves, we propose a more comprehensive location scale model that allows subject heterogeneity at baseline as well as across different waves, for a 3-level data structure where observations are nested within waves and waves are then further nested within subjects. The model parameters are estimated using Markov chain Monte Carlo methods. We provide details on the Bayesian estimation approach and demonstrate how the Stan statistical software can be used to sample from the desired distributions and achieve consistent estimates. The proposed model is validated via a series of simulation studies. Data from an adolescent smoking study are analyzed to demonstrate this approach. The analyses clearly favor the proposed model and show significant subject heterogeneity at baseline as well as change over time, for both mood mean and variance. The proposed 3-level location scale model can be widely applied to areas of research where the interest lies in the consistency in addition to the mean level of the responses.

Project description:Digital interventions often suffer from low usage, which may reflect insufficient attention to user experience. Moreover, the existing evaluation methods have limited applicability in the remote study of user experience of complex interventions that have expansive content and that are used over an extensive period of time. To alleviate these challenges, we describe here a novel qualitative Ecological Momentary Assessment (EMA) method: the CORTO method (Contextual, One-item, Repeated, Timely, Open-ended). We used it to gather digital intervention user experience data from Finnish adults (n = 184) who lived with interview-confirmed major depressive disorder (MDD) and took part in a randomized controlled trial (RCT) that studied the efficacy of a novel 12-week game-based digital intervention for depression. A second dataset on user experience was gathered with retrospective interviews (n = 22). We inductively coded the CORTO method and retrospective interview data, which led to four user experience categories: (1) contextual use, (2) interaction-elicited emotional experience, (3) usability, and (4) technical issues. Then, we used the created user experience categories and Template Analysis to analyze both datasets together, and reported the results qualitatively. Finally, we compared the two datasets with each other. We found that the data generated with the CORTO method offered more insights into usability and technical categories than the interview data that particularly illustrated the contextual use. The emotional valence of the interview data was more positive compared with the CORTO data. Both the CORTO and interview data detected 55 % of the micro-level categories; 20 % of micro-level categories were only detected by the CORTO data and 25 % only by the interview data. We found that the during-intervention user experience measurement with the CORTO method can provide intervention-specific insights, and thereby further the iterative user-centered intervention development. Overall, these findings highlight the impact of evaluation methods on the categories and qualities of insights acquired in intervention research.

Project description:BackgroundEcological momentary assessment (EMA) is an in situ method of gathering self-report on behaviors using mobile devices. In typical phone-based EMAs, participants are prompted repeatedly with multiple-choice questions, often causing participation burden. Alternatively, microinteraction EMA (micro-EMA or μEMA) is a type of EMA where all the self-report prompts are single-question surveys that can be answered using a 1-tap glanceable microinteraction conveniently on a smartwatch. Prior work suggests that μEMA may permit a substantially higher prompting rate than EMA, yielding higher response rates and lower participation burden. This is achieved by ensuring μEMA prompt questions are quick and cognitively simple to answer. However, the validity of participant responses from μEMA self-report has not yet been formally assessed.ObjectiveIn this pilot study, we explored the criterion validity of μEMA self-report on a smartwatch, using physical activity (PA) assessment as an example behavior of interest.MethodsA total of 17 participants answered 72 μEMA prompts each day for 1 week using a custom-built μEMA smartwatch app. At each prompt, they self-reported whether they were doing sedentary, light/standing, moderate/walking, or vigorous activities by tapping on the smartwatch screen. Responses were compared with a research-grade activity monitor worn on the dominant ankle simultaneously (and continuously) measuring PA.ResultsParticipants had an 87.01% (5226/6006) μEMA completion rate and a 74.00% (5226/7062) compliance rate taking an average of only 5.4 (SD 1.5) seconds to answer a prompt. When comparing μEMA responses with the activity monitor, we observed significantly higher (P<.001) momentary PA levels on the activity monitor when participants self-reported engaging in moderate+vigorous activities compared with sedentary or light/standing activities. The same comparison did not yield any significant differences in momentary PA levels as recorded by the activity monitor when the μEMA responses were randomly generated (ie, simulating careless taps on the smartwatch).ConclusionsFor PA measurement, high-frequency μEMA self-report could be used to capture information that appears consistent with that of a research-grade continuous sensor for sedentary, light, and moderate+vigorous activity, suggesting criterion validity. The preliminary results show that participants were not carelessly answering μEMA prompts by randomly tapping on the smartwatch but were reporting their true behavior at that moment. However, more research is needed to examine the criterion validity of μEMA when measuring vigorous activities.

Dataset Information

An application of a mixed-effects location scale model for analysis of Ecological Momentary Assessment (EMA) data.

Publications

An application of a mixed-effects location scale model for analysis of Ecological Momentary Assessment (EMA) data.

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