A Mobile App Delivering a Gamified Battery of Cognitive Tests Designed for Repeated Play (OU Brainwave): App Design and Cohort Study
Bibliographic record
Abstract
Background: Mobile phone and tablet apps are an increasingly common platform for collecting data. A key challenge for researchers has been participant “buy-in” and attrition for designs requiring repeated testing. Objective: The objective of this study was to develop and assess the utility of 1-2 minute versions of both classic and novel cognitive tasks using a user-focused and user-driven mobile phone and tablet app designed to encourage repeated play. Methods: A large sample of app users (N=13,979 at first data collection) participated in multiple, self-paced sessions of classic working memory (N-back), spatial cognition (mental rotation), sustained attentional focus (persistent vigilance task), and split attention (multiple object tracking) tasks, along with the implementation of a comparatively novel action-learning task. The “OU Brainwave” app was designed to measure time-of-day variation in cognitive performance and did not offer any training program or promise any cognitive enhancement. To record participants’ chronotype, a full Morningness-Eveningness questionnaire was also included, which measures whether a person's circadian rhythm produces peak alertness in the morning, in the evening, or in between. Data were collected during an 18-month period. While the app prompted re-engagement at set intervals, participants were free to complete each task as many times as they wished. Results: We found a significant relationship between morningness and age (r=.298, n=12,755, P4 times each—our minimum engagement level for analysis. Repeated plays of these games allowed us to replicate commonly reported gender effects in gamified spatial cognition (F1,4216=154.861, P<.001, η2ρ=.035), split attention (F1,4185=11.047, P=.001, η2ρ=.003), and sustained attentional focus (F1,4238=15.993, P<.001, η2ρ=.004) tasks. We also report evidence of a small gender effect in an action-learning task (F1,3988=90.59, P<.001, η2ρ=.022). Finally, we found a strong negative effect of self-reported age on performance, when controlling for number of plays, in sustained attentional focus (n=1596, F6,1595=30.23, P<.001, η2=.102), working memory (n=1627, F6,1626=19.78, P<.001, η2=.068), spatial cognition (n=1640, F6,1639=23.74, P<.001, η2=.080), and split attention tasks (n=1616, F6,1615=2.48, P=.02, η2=.009). Conclusions: Using extremely short testing periods and permitting participants to decide their level of engagement—both in terms of which gamified task they played and how many sessions they completed—we were able to collect a substantial and valid dataset. We suggest that the success of OU Brainwave should inform future research oriented apps—particularly in issues of balancing participant engagement with data fidelity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".