Comparative assessment of multimodal sensor data quality collected using Android and iOS smartphones in real-world settings
Bibliographic record
Abstract
Abstract Healthcare researchers are increasingly utilizing smartphone sensor data as a scalable and cost-effective approach to studying individualized health-related behaviors in real-world settings. However, to develop reliable and robust digital behavioral signatures that may help predict disease trajectory early and future prognosis, there is a critical need to quantify potential technical variability that may be present in underlying sensor data due to variations in smartphone hardware and software used by large populations. Using sensor data collected in real-world settings from 3000 participants' smartphones for up to 84 days, we compared differences in completeness, correctness, and consistency of the three most common smartphone sensors — accelerometer, gyroscope, and GPS across Android and iOS devices. Our findings show considerable variation in sensor data quality within and across Android and iOS devices. Sensor data from iOS devices showed significantly lower levels of anomalous point density (APD) compared to Android across all sensors (p < 1e-4). iOS devices showed a considerably lower missing data ratio (MDR) for the accelerometer compared to the GPS data (p < 1e-4). Notably, the device type could be predicted with up to 0.98 accuracy 95% CI [0.977, 0.982] using the quality features derived from raw sensor data across devices. With a high degree of heterogeneity in smartphone devices, including iOS and Android, it's crucial to comprehend, measure, and address technical variations in underlying sensor data for creating robust, representative, and clinically actionable digital behavioral signatures linked to health outcomes.
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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 | 0.005 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".