Momentary predictors of compliance in experience sampling : Integrating context related characteristics and smartphone behaviour
| Autoři | |
|---|---|
| Rok publikování | 2026 |
| Druh | Další prezentace na konferencích |
| Fakulta / Pracoviště MU | |
| Citace | |
| Přiložené soubory | |
| Popis | Background: Intensive longitudinal methods enhance ecological validity by capturing human experience as it unfolds in daily life. However, the same contextual and momentary factors that make these data valuable can also drive patterns of non-compliance. As these methods become increasingly widespread, investigating compliance biases is essential for valid inferences. Most previous studies examined associations of compliance rates with study design and participant characteristics, yet the influence of momentary factors on prompt-level compliance remains understudied. Methods: We analysed data from an experience sampling study of the general population (N = 629, 18-59 years), where participants completed six daily surveys over 21 days. In addition to survey responses, digital trace data on smartphone use and location were collected. A Bayesian multilevel probit regression model examined associations between contextual characteristics and momentary compliance. Results: Preliminary results showed that answering the previous survey increased compliance probability, while longer response latency decreased it. Psychological states reported in the previous survey showed mixed associations with subsequent compliance – survey-related disturbance and momentary stress predicted higher compliance, whereas affect, fatigue, and aggression did not. Compliance was substantially higher when participants were at home compared to other locations and on weekdays compared to weekends. Across the day, compliance was lowest in the morning and highest before sleep. Conclusions: These results demonstrate that predicting prompt-level compliance requires integrating multiple contextual characteristics across temporal, spatial, and psychological domains. Final analyses will incorporate smartphone use data to distinguish active from passive non-compliance, as each may arise from different contextual characteristics. |
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