Development and Validation of the Reasons to Exergame (RTEX) Scale in Young Adults: Exploratory Factors Analysis (Preprint)
Bibliographic record
Abstract
BACKGROUND Exergaming is associated with positive health benefits; however, little is known about what motivates young people to exergame. OBJECTIVE This study aimed to develop a new Reasons to Exergame (RTEX) scale and describe its psychometric properties (Study 1) including test-retest reliability (Study 2). We also examined the test-retest reliability of self-report exergaming behavior measures (Study 2). METHODS We identified scale items in consultation with experts. In Study 1, we conducted an Exploratory Factor Analysis of RTEX and examined how the factors identified relate to exergaming frequency and intensity in a population-based sample of 272 young adults. In Study 2, we examined the test-retest reliability of RTEX factors and self-report measures of past-week exergaming frequency and intensity among 147 college students. RESULTS We identified four factors in RTEX: exergaming for fitness, exergaming for enjoyment, preferring exergaming over other gaming options, and choosing exergaming over competing interests (eg, sports). Test-retest reliability of RTEX factors (ICC 0.7-0.8) and self-report exergaming frequency (ICC 0.4-0.9) was adequate. Exergaming for fitness and enjoyment were positively associated with the frequency of exergaming with friends and family, and with exergaming intensity. Preferring exergaming over other gaming options and choosing exergaming over competing interests (eg, sports) were not related to exergaming behavior. CONCLUSIONS RTEX is a psychometrically sound scale with four factors that measure reasons to exergame. Replication of these findings is needed in larger, more diverse samples.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
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 | 0.018 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".