Ecological Momentary Assessment with REDCap: Methods, Feasibility, and User Behaviour in a Parent and Child Study
Bibliographic record
Abstract
Background: Intensive longitudinal data collection, including ecological momentary assessment (EMA), has the potential to reduce recall biases and increase our understanding of dynamic associations between variables. Cost and privacy concerns represent barriers that may limit the use of EMA. Research Electronic Data Capture (REDCap), a freely available online survey application, may allow researchers to overcome these barriers; however, at present, little guidance is available to researchers regarding the setup of EMA in REDCap. Objective: We provide guidance regarding EMA setup and programming in REDCap, along with information on survey completion and user behaviour in a sample of parents and children.Methods: Participants were 66 parents and their children (ages 9-13 years) recruited from an existing longitudinal cohort study to participate in a study on risk and protective factors for children’s mental health. Participants received twice daily survey prompts (morning and evening) by email or text message for 14 days. Results: Completion rates were good (M = 82%) and significantly higher on weekdays than weekends and in dyads with girls than dyads with boys. The number of assessments submitted was significantly higher, and response times significantly faster, among participants who selected text message survey notifications compared to email survey notifications. The use of reminder messages increased survey completion. Conclusions: Our results support the feasibility of using REDCap for EMA studies with parents and children. Offering the option of text message survey notifications and reminders may be important ways to increase completion rates and timeliness of responses. REDCap is a potentially useful tool for researchers wishing to implement EMA in settings in which cost and/or privacy are current barriers.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.046 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".