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ObjectivesMicrointeraction-based Ecological Momentary Assessment (micro-EMA) is a smartwatch-based tool that delivers single-question surveys, enabling respondents to quickly report their real-time experiences. The objectives of the two studies presented here were to evaluate micro-EMA's psychometric characteristics and feasibility across three response formats (2-point, 5-point, and 10-point scales) for adults with hearing loss. DesignIn the first study, thirty-two participants completed a dual-task experiment aimed at assessing the construct validity, responsiveness, intrusiveness, and test-retest reliability of micro-EMA across the three response formats. Participants listened to sentences at five signal-to-noise ratios (SNRs) ranging from −3 to 9 dB relative to the SNR for 50% speech understanding, answered the question “Hearing well?” on smartwatches, and repeated the sentences. In the second study, twenty-one participants wore smartwatches over 6 days. Every 15 min, participants were prompted to answer the question “Hearing well?” using one of the three response formats for 2 days. Participants provided feedback on their experience with micro-EMA. ResultsIn the dual-task experiment, participants reported improved hearing performance in micro-EMA as SNRs and speech recognition scores increased across all three response formats, supporting the tool's construct validity. Statistical models indicated that the 5-point and 10-point scales yielded larger relative changes between SNRs, suggesting higher responsiveness, compared to the 2-point scale. Participants completed surveys significantly faster with the 2-point scale, indicating lower intrusiveness, compared to the 5-point and 10-point scales. Correlation analysis revealed that over two visits 1 week apart, the 2-point scale had the poorest test-retest reliability, while the 5-point scale had the highest. In the field trial, participants completed 79.6% of the prompted surveys, with each participant averaging 42.9 surveys per day. Although participants experienced interruptions due to frequent prompts, annoyance and distraction levels were low. Most participants preferred the 5-point scale. ConclusionsThe dual-task experiment suggested that micro-EMA using the 5-point scale demonstrated superior psychometric characteristics compared to the 2-point and 10-point scales at the tested SNRs. The field trial further supported its feasibility for evaluating hearing performance in adults with hearing loss. Additional research is needed to explore the potential applications of micro-EMA in audiology research.
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Investigating Receptivity and Affect Using Machine Learning: Ecological Momentary Assessment and Wearable Sensing Study
BackgroundAs 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. ObjectiveThis 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. MethodsWe 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. ResultsOur 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. ConclusionsOur 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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- PAR ID:
- 10498550
- Publisher / Repository:
- JMIR publications
- Date Published:
- Journal Name:
- JMIR mHealth and uHealth
- Volume:
- 12
- ISSN:
- 2291-5222
- Page Range / eLocation ID:
- e46347
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript
- Journal Article:
- https://doi.org/10.2196/46347
