An Internet-Based Ecological Momentary Assessment Study Relying on Participants' Own Mobile Phones: Insights from a Study with Young Adult Smokers
Bibliographic record
Abstract
BACKGROUND: In this paper we describe a novel Internet-based cell phone-optimized assessment technique (ICAT) to conduct an ecological momentary assessment (EMA) study. Participants could access the assessment instrument via the web browsers of their mobile phones. METHODS: We report results from 92 young adult smokers (18-25 years old) who completed the baseline assessment and the first of 4 waves (3 days/wave) of EMA. Random prompts were issued via text messages sent to the participants. The participants were also instructed to self-initiate reports of smoking situations. RESULTS: Compliance with the study protocols was low. In total, the participants completed 885 assessments during the 3 days of monitoring. Only 50.2% of random prompts were responded to, and 52.4% of those were completed within the first 10 min after issuing. Furthermore, reports of smoking situations were rarely self-initiated. In a multivariate regression analysis, age (positively) and female gender (negatively) predicted the number of completed assessments. CONCLUSIONS: This study adds to the limited experiences made with ICAT in substance use research. Similar to the few prior ICAT studies, compliance was low compared to traditional EMA studies. While using ICAT is technically feasible, specific improvements should be implemented to tap ICAT's full potential in future studies.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".