Validation of a Smartphone-Based Approach to In Situ Cognitive Fatigue Assessment
Bibliographic record
Abstract
BACKGROUND: Acquired Brain Injuries (ABIs) can result in multiple detrimental cognitive effects, such as reduced memory capability, concentration, and planning. These effects can lead to cognitive fatigue, which can exacerbate the symptoms of ABIs and hinder management and recovery. Assessing cognitive fatigue is difficult due to the largely subjective nature of the condition and existing assessment approaches. Traditional methods of assessment use self-assessment questionnaires delivered in a medical setting, but recent work has attempted to employ more objective cognitive tests as a way of evaluating cognitive fatigue. However, these tests are still predominantly delivered within a medical environment, limiting their utility and efficacy. OBJECTIVE: The aim of this research was to investigate how cognitive fatigue can be accurately assessed in situ, during the quotidian activities of life. It was hypothesized that this assessment could be achieved through the use of mobile assistive technology to assess working memory, sustained attention, information processing speed, reaction time, and cognitive throughput. METHODS: The study used a bespoke smartphone app to track daily cognitive performance, in order to assess potential levels of cognitive fatigue. Twenty-one participants with no prior reported brain injuries took place in a two-week study, resulting in 81 individual testing instances being collected. The smartphone app delivered three cognitive tests on a daily basis: (1) Spatial Span to measure visuospatial working memory; (2) Psychomotor Vigilance Task (PVT) to measure sustained attention, information processing speed, and reaction time; and (3) a Mental Arithmetic Test to measure cognitive throughput. A smartphone-optimized version of the Mental Fatigue Scale (MFS) self-assessment questionnaire was used as a baseline to assess the validity of the three cognitive tests, as the questionnaire has already been validated in multiple peer-reviewed studies. RESULTS: The most highly correlated results were from the PVT, which showed a positive correlation with those from the prevalidated MFS, measuring 0.342 (P<.008). Scores from the cognitive tests were entered into a regression model and showed that only reaction time in the PVT was a significant predictor of fatigue (P=.016, F=2.682, 95% CI 9.0-84.2). Higher scores on the MFS were related to increases in reaction time during our mobile variant of the PVT. CONCLUSIONS: The results show that the PVT mobile cognitive test developed for this study could be used as a valid and reliable method for measuring cognitive fatigue in situ. This test would remove the subjectivity associated with established self-assessment approaches and the need for assessments to be performed in a medical setting. Based on our findings, future work could explore delivering a small set of tests with increased duration to further improve measurement reliability. Moreover, as the smartphone assessment tool can be used as part of everyday life, additional sources of data relating to physiological, psychological, and environmental context could be included within the analysis to improve the nature and precision of the assessment process.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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, 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".