Assessing Dynamic Cognitive Function in the Daily Lives of Youths With and Without Type 1 Diabetes: Usability Study
Bibliographic record
Abstract
Background: Studies have shown a relationship between worse glycemic control and lower cognitive scores in youths with type 1 diabetes (T1D). However, most studies assess long-term glucose control (eg, years-decades) and cognition at a single time point. Understanding this relationship at a higher temporal resolution (eg, minutes-hours) and in naturalistic settings has potential clinical implications. Newer technology (eg, continuous glucose monitoring [CGM] and ecological momentary assessment) provides a unique opportunity to explore the glucose dynamics that influence dynamic cognition; that is, cognitive functions that fluctuate short-term and are influenced by environmental factors. Objective: Before we can assess this relationship, we need to determine the feasibility of measuring cognition in youths in daily life and determine the plausibility of obtaining glucose variation with CGM to be integrated with real-time cognition measures. This study's purpose was to assess the acceptability of measuring dynamic cognition using a smartphone app and adherence to cognitive testing in daily life in youths with and without T1D. Further, we assessed CGM-derived glucose measures at temporally related timeframes to cognitive testing in naturalistic settings. Methods: Data were obtained from 3 studies including one in-laboratory study and 2 remote studies. For all studies, youths were asked to complete cognitive tests on the Ambulatory Research in Cognition (ARC) smartphone app that measured processing speed, associative memory, and working memory. For the in-laboratory study, youths completed testing 4 times during 1 session. For the remote studies, youths were asked to complete cognitive tests 5 times per day for either 10 or 14 consecutive days in daily life. Youths were asked to rate their impressions of the app. Youths with T1D wore a CGM. Results: 74 youths (n=53 control; n=21 T1D) aged 4-16 years participated. Youths generally reported liking or understanding the ARC app tasks in a laboratory and remote setting. Youths had high testing adherence in daily life (2350/3080 to 721/900, 76.3%-80.2%) and none dropped out. The percentage of measurements within each glycemic range taken immediately before the app's cognitive testing was 3% (28/942) low glucose, 51% (484/942) euglycemia, 23% (221/942) high glucose, and 22% (210/942) very high glucose. In the 2-hour window before each cognitive task, mean glucose was 182.5 (SD 76.2) mg/dL, SD in glucose was 27.1 mg/dL (SD 18.7), and the mean maximum difference between the highest and lowest glucose was 85.5 (SD 53.7) mg/dL. Conclusions: The results suggest that using the ARC smartphone app to assess dynamic cognitive functions in youths with and without T1D is feasible. Further, we showed CGM-derived glycemic variability at temporally associated timeframes of dynamic cognitive assessments. The next steps include using ecological momentary assessment in a fully powered study to determine the relationship between short-term glycemic control and cognition in youths with T1D.
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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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".