4.7 Article

Prompt-level predictors of compliance in an ecological momentary assessment study of young adults' mental health

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JOURNAL OF AFFECTIVE DISORDERS
卷 322, 期 -, 页码 125-131

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ELSEVIER
DOI: 10.1016/j.jad.2022.11.014

关键词

Ecological momentary assessment; Compliance; Momentary predictors; Dynamic structural equation modelling; Non-response

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This study used data from a general population young adult EMA study and fitted dynamic structural equation models to explore potential predictors of missing the next prompt. The results suggest that higher levels of stress, overall negative affect, and the specific negative affective state of 'upset' at a given prompt predict a greater likelihood of missing the next prompt.
Background: Ecological momentary assessment (EMA) has become a popular method of gathering information about participants as they go about their daily lives. However, participant non-compliance, especially nonrandom compliance, in EMA is a concern. Better knowledge of the moment-to-moment factors that predict prompt non-response can inform the design of strategies to mitigate it.Method: We used data from a general population young adult (n = 260) EMA study, 'decades-to-minutes' (D2M) and fitted dynamic structural equation models (DSEMs) to explore a range of candidate momentary predictors of missing the next prompt.Results: We found that higher levels of stress, overall negative affect, and the specific negative affective state of 'upset' at a given prompt predicted a greater likelihood of missing the next prompt. However, no other specific affective states, alcohol use, experiencing social provocations nor aggressive behaviour predicted missing the next prompt. Limitations: The primary limitation of the present study was a lack of information on predictors concurrent with missed prompts.Conclusions: Findings point to the potential value of gathering information on momentary negative affect (especially feeling upset) and stress to help inform strategies that intervene to prevent application disengagement at optimal moments and to feed into strategies to mitigate bias due to non-random non-response in EMA studies.

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