micro-Stress EMA

King, Zachary; Moskowitz, Judith T.; Egilmez, Begum; Zhang, Shibo; Zhang, Lida; Bass, Michael; Li, Jinghua; Ghaffari, Roozbeh; Wakschlag, Laurie; Alshurafa, Nabil

doi:10.1145/3351249

Cited by 50 publications

(4 citation statements)

References 53 publications

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“…Thus, there is preliminary evidence that μEMA may enable gathering high-frequency self-report with manageable burden, a complementary approach to EMA. Recently, μEMA has been used to gather data on stress [14], hyperarousal [15], and perceived comfort [16], and it has also been used with small pervasive displays [17].…”

Section: Introductionmentioning

confidence: 99%

Measuring Criterion Validity of Microinteraction Ecological Momentary Assessment (Micro-EMA): Exploratory Pilot Study With Physical Activity Measurement

Ponnada¹,

Thapa-Chhetry²,

Manjourides³

et al. 2021

JMIR Mhealth Uhealth

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Background Ecological 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. Objective In 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. Methods A 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. Results Participants 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). Conclusions For 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.

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Section: Introductionmentioning

confidence: 99%

Measuring Criterion Validity of Microinteraction Ecological Momentary Assessment (Micro-EMA): Exploratory Pilot Study With Physical Activity Measurement

Ponnada¹,

Thapa-Chhetry²,

Manjourides³

et al. 2021

JMIR Mhealth Uhealth

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“…Currently, many researchers have reduced interruptability by altering message-related attributes, often involving strategies such as reducing the complexity or frequency of an EMA or increasing the incentives for a response [ 4 , 5 ]. Reducing the size of the instrument relieves some of the burden associated with answering an EMA [ 6 ].…”

Section: Introductionmentioning

confidence: 99%

Investigating Receptivity and Affect Using Machine Learning: Ecological Momentary Assessment and Wearable Sensing Study

King,

Yu,

Vaessen

et al. 2024

JMIR Mhealth Uhealth

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Background As mobile health (mHealth) studies become increasingly productive owing to the advancements in wearable and mobile sensor technology, our ability to monitor and model human behavior will be constrained by participant receptivity. Many health constructs are dependent on subjective responses, and without such responses, researchers are left with little to no ground truth to accompany our ever-growing biobehavioral data. This issue can significantly impact the quality of a study, particularly for populations known to exhibit lower compliance rates. To address this challenge, researchers have proposed innovative approaches that use machine learning (ML) and sensor data to modify the timing and delivery of surveys. However, an overarching concern is the potential introduction of biases or unintended influences on participants’ responses when implementing new survey delivery methods. Objective This study aims to demonstrate the potential impact of an ML-based ecological momentary assessment (EMA) delivery system (using receptivity as the predictor variable) on the participants’ reported emotional state. We examine the factors that affect participants’ receptivity to EMAs in a 10-day wearable and EMA–based emotional state–sensing mHealth study. We study the physiological relationships indicative of receptivity and affect while also analyzing the interaction between the 2 constructs. Methods We collected data from 45 healthy participants wearing 2 devices measuring electrodermal activity, accelerometer, electrocardiography, and skin temperature while answering 10 EMAs daily, containing questions about perceived mood. Owing to the nature of our constructs, we can only obtain ground truth measures for both affect and receptivity during responses. Therefore, we used unsupervised and supervised ML methods to infer affect when a participant did not respond. Our unsupervised method used k-means clustering to determine the relationship between physiology and receptivity and then inferred the emotional state during nonresponses. For the supervised learning method, we primarily used random forest and neural networks to predict the affect of unlabeled data points as well as receptivity. Results Our findings showed that using a receptivity model to trigger EMAs decreased the reported negative affect by >3 points or 0.29 SDs in our self-reported affect measure, scored between 13 and 91. The findings also showed a bimodal distribution of our predicted affect during nonresponses. This indicates that this system initiates EMAs more commonly during states of higher positive emotions. Conclusions Our results showed a clear relationship between affect and receptivity. This relationship can affect the efficacy of an mHealth study, particularly those that use an ML algorithm to trigger EMAs. Therefore, we propose that future work should focus on a smart trigger that promotes EMA receptivity without influencing affect during sampled time points.

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“…To this end, we develop a prediction system based on the history data of microinstruction ecological momentary assessment (micro-EMA) to forecast an individual’s perceived stress for the next 7 days. micro-EMA repeatedly prompts users to answer a small number of questions to capture an individual’s mental health states in close proximity to the time that symptoms and behaviors happen, so it can mitigate recall bias and track users’ stress with fine-grained resolution and less burden ( King et al, 2019 ). To realize the prediction system, we first identify an optimal set of micro-EMA questions that yields strong correlations with the Perceived Stress Scale (PSS) ( Cohen, Kamarck, Mermelstein, et al, 1994 ).…”

Section: Introductionmentioning

confidence: 99%

Stress prediction using micro-EMA and machine learning during COVID-19 social isolation

Zheng

Zhong

et al. 2022

Smart Health

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Accurately predicting users’ perceived stress is beneficial to aid early intervention and prevent both mental illness and physical disease during the COVID-19 pandemic. However, the existing perceived stress predicting system needs to collect a large amount of previous data for training but has a limited prediction range (i.e., next 1–2 days). Therefore, we propose a perceived stress prediction system based on the history data of micro-EMA for identifying risks 7 days earlier. Specifically, we first select and deliver an optimal set of micro-EMA questions to users every Monday, Wednesday, and Friday for reducing the burden. Then, we extract time-series features from the past micro-EMA responses and apply an Elastic net regularization model to discard redundant features. After that, selected features are fed to an ensemble prediction model for forecasting fine-grained perceived stress in the next 7 days. Experiment results show that our proposed prediction system can achieve around 4.26 (10.65% of the scale) mean absolute error for predicting the next 7 day’s PSS scores, and higher than 81% accuracy for predicting the next 7 day’s stress labels.

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micro-Stress EMA

Cited by 50 publications

References 53 publications

Measuring Criterion Validity of Microinteraction Ecological Momentary Assessment (Micro-EMA): Exploratory Pilot Study With Physical Activity Measurement

Measuring Criterion Validity of Microinteraction Ecological Momentary Assessment (Micro-EMA): Exploratory Pilot Study With Physical Activity Measurement

Investigating Receptivity and Affect Using Machine Learning: Ecological Momentary Assessment and Wearable Sensing Study

Stress prediction using micro-EMA and machine learning during COVID-19 social isolation

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