Actigraphy Detects Greater Intra-Individual Variability During Gait in Non-Manifesting <i>LRRK2</i> Mutation Carriers
Bibliographic record
Abstract
BACKGROUND: With recent advances in the search for disease-modifying therapies for Parkinson's disease (PD) the importance of identifying prodromal markers becomes greater. Non-manifesting LRRK2 mutation carriers (NMC) are at risk for developing PD, and provide a population in which to identify possible markers. OBJECTIVE: The aim of this study was to test the hypothesis that NMC have differences in daily activity, fragmentation of sleep, arm swing asymmetry, and movement variability during walking, detectable by actigraphy, as compared to matched control subjects. METHODS: Eleven NMC, fourteen PD patients (4 LRRK2-PD, 10 idiopathic PD (iPD)), and twenty-nine controls wore wristbands containing an accelerometer for seven days, and performed a daily walking task. Outcome measures included daily activity, fragmentation of activity, fragmentation of sleep, arm swing asymmetry during walking, and intra-individual variability. RESULTS: Compared to healthy controls, both NMC and LRRK2/iPD showed higher intra-individual variability in activity during walking compared to healthy controls. Individuals with LRRK2-PD/iPD, but not NMC, tend to have lower activity levels, more arm swing asymmetry and less increase of arm swing with transition from slow to faster walking speed compared to healthy controls. CONCLUSION: Higher intra-individual variability of gait-associated movements might be a useful biomarker of prodromal PD. These results encourage replication in a larger sample and longitudinal analysis is warranted.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
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".