Smartphone software for home monitoring of motor symptoms in Parkinson's disease: the CloudUPDRS smartphone software in Parkinson's (CUSSP) study
Bibliographic record
Abstract
Objective: To determine the validity of smartphone software for objective monitoring of motor symptoms in patients with Parkinson’s disease (PD). \n \nBackground: Although the MDS-Unified Parkinson’s Disease Rating Scale (UPDRS) scale part III remains the most commonly used framework with which to assess motor impairment in Parkinson’s disease in clinical practice and research [1], it remains subjective and is usually performed infrequently due to the clinical effort required to complete it. Markedly confounded by the day-to-day motor fluctuations many patients face, unitary UPDRS scores poorly reflect individual patient symptoms and reduce power in interventional trials. A number of wearable, smartphone and sensor-based solutions have been proposed that allow patients to monitor symptoms either continuously or at high-frequency without the need for clinical input but the validity of these tools remains untested within well-designed prospective clinical trials. In this study, we validate CloudUPDRS smartphone measures [2] against clinical UPDRS assessment. \n \nMethod: The CloudUPDRS Smartphone Software in Parkinson’s (CUSSP) study is a pre-registered pilot multi-site, randomised study. Inclusion criteria were: diagnosis of idiopathic Parkinson’s disease according to Brain Bank criteria, age over 18 years, and Montreal Cognitive Assessment score over 20/30. Sixty patients (females, n = 20) were included so far. Each patient underwent a video-recorded MDS-UPDRS part III clinical examination, and a simultaneous range of UPDRS-style smartphone-based assessments. Objective smartphone measures were used to predict the mean clinical UPDRS rating of 3 neurologists based on video assessment, blinded to the patient’s medication state. \n \nResults: Mean (+/- sd) age was 68 (± 9.5) years. Mean disease duration was 5 (± 4.7) years, with mean Hoehn and Yahr stage of 2. The primary outcome was the predictive accuracy of the smartphone score for the blinded MDS-UPDRS rating score at baseline assessment. Comprehensive analyses are ongoing and will be presented. \n \nConclusion: The current study is ongoing. We suggest that objective smartphone assessments may allow high-frequency at-home assessment of motor symptoms in PD, and that such a granular picture may be empowering to patients and beneficial to their medical teams and clinical researchers alike.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".