Analyzing Mobility Indicators Using Machine Learning to Detect Mild Cognitive Impairment: A Systematic Scoping Review
Bibliographic record
Abstract
The global aging population is rapidly increasing, and the prevalence of age-related cognitive conditions, such as mild cognitive impairment (MCI), is becoming more common. This condition, which represents intermediate stages between normal aging and dementia, underscores the importance of early detection and timely intervention to address the growing demand for health services. Traditional cognitive assessments have limitations, such as the consistency of results, prompting the need for innovative technology-based solutions.This study aimed to examine how technology-based mobility data collection methods and machine learning algorithms are used to detect MCI in adults.A systematic scoping review was conducted to identify papers that analyzed mobility-related data using machine learning algorithms, focusing on adults aged 18 or older with MCI. Seven databases were searched: MEDLINE, EMBASE, IEEE Xplore, PsycINFO, Scopus, Web of Science, and ACM Digital Library, yielding 2,901 papers.Twenty-four papers met the inclusion criteria, highlighting 116 mobility indicators used to classify or indicate MCI. Wearable devices were the most common data collection method, with mobile applications being the least utilized. The most frequently reported mobility indicator for walking was walking speed. For driving, indicators included the number of hard braking events, the number of night trips, and speed. Logistic regression, random forest, and neural networks were the most used machine learning algorithms. Overall, the mean accuracy, sensitivity, and specificity of all the algorithms were 86.1% (standard deviation [SD] = 6.7%), 84% (SD = 6.5%), and 72.8% (SD = 12%), respectively. The mean area under the curve and the harmonic mean of precision and recall scores (F1) were 0.77 (SD = 0.08) and 0.83 (SD = 0.16), respectively.This review highlights the use of technology-based methods, particularly wearable devices, in assessing mobility and applying machine learning algorithms to detect MCI. However, a notable gap in research on mobile app-based mobility monitoring suggests a promising direction for future studies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.018 | 0.096 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.009 |
| Bibliometrics | 0.021 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".