Feasibility of Smartphone-Based Testing of Interference in Parkinson’s Disease
Bibliographic record
Abstract
BACKGROUND: Interference refers to learned associations and established behaviors "interfering" with response to new material. It forms a core pillar of executive functions, which are commonly affected in Parkinson's disease (PD). Cognitive interference test (CIT) forms part of a smartphone application designed for ambulatory assessment in PD. OBJECTIVE: The aims of this study were to establish that CIT could effectively demonstrate interference and would perform comparably to the Stroop Color-Word Test Victoria version (VST) despite PD-related motor impairment. METHODS: Ninety-nine patients with PD were recruited. Initial evaluation included CIT, VST, Montreal cognitive assessment (MOCA), and Movement Disorders Society-sponsored revision of the -Unified Parkinson's Disease Rating Scale (MDS-UPDRS-III). A group of patients underwent repeat assessment within 2 weeks. Thirty-four healthy controls were recruited for comparison. RESULTS: Patients' mean age was 66.2 years, disease duration was 8.7 years, on-state MDS-UPDRS-III was 22, and MOCA total score was 27. CIT effectively generated interference, whereby the total time taken to complete the incongruent task was 20% longer compared to that of the baseline task. CIT key test items demonstrated convergent validity to VST (r = 0.478-0.644, p < 0.0001) and satisfactory repeatability (intraclass correlation coefficient 0.46-0.808, p ≤ 0.0002). Performance on key CIT test parameters deteriorated with increasing age (r = 0.225-0.478, p < 0.01) and MDS-UPDRS-III total score (r = 0.354-0.481, p < 0.0001). When compared to controls and patients with less motor impairment, patients MDS-UPDRS-III > 30 took longer to complete CIT and VST and had lower MOCA-attention sub-score, implying that the degree of motor impairment could not be the sole explanation for reduced CIT performance. CONCLUSIONS: We established that despite motor impairment, the novel approach of using smartphone technology to test interference in PD patients is feasible.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.001 | 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".