Digital Biomarkers: Cognitive Health

Updated 4 January 2026

Digital biomarkers of cognitive health are quantifiable measures from wearables, smartphones, and sensors that objectively track cognitive function and decline.
They integrate multimodal data—including speech, gait, and physiological signals—using rigorous signal processing and machine learning for precise assessment.
Their scalable and real-time monitoring capabilities enable early detection and personalized interventions for conditions like MCI and Alzheimer’s disease.

Digital biomarkers of cognitive health are objective, quantifiable behavioral or physiological data elements generated by digital devices that provide proxies for cognitive function, impairment, or decline. These measures are increasingly derived from diverse digital streams—including speech, language, gait, movement, physiological signals, and interaction patterns—collected longitudinally in clinical and naturalistic settings via wearables, smartphones, environmental sensors, point-of-care devices, and digital platforms. Their primary value lies in high-frequency, ecologically valid, and scalable monitoring across populations, enabling detection, stratification, and longitudinal tracking of cognitive disorders such as mild cognitive impairment (MCI), Alzheimer’s disease (AD), and other dementias.

1. Conceptual Foundations and Domains of Digital Cognitive Biomarkers

Digital cognitive biomarkers encompass quantifiable measures from several modalities:

Linguistic and speech markers: Extracted from spontaneous or prompted spoken language, these include lexical diversity, syntactic complexity, semantic content, coherence, phonetic/prosodic features (e.g., MFCCs, pitch, pause structure), and markers of disfluency or semantic drift. Examples include the Open Voice Brain Model (OVBM) suite of 16 speech biomarkers (Soler et al., 2021), language coherence markers (Gkoumas et al., 2023), and Random Forest–based linguistic feature sets (Lima et al., 30 Jan 2025).
Behavioral biomarkers: Quantify cognitive function via task-based metrics—reaction times, error rates, uncertainty responses (e.g., “Don’t know” in questionnaires), social media engagement patterns, or device interaction proficiency (Rutkowski et al., 2019, Lu et al., 15 Dec 2025, Drishti et al., 28 Dec 2025, Andrade et al., 2023).
Physiological sensors: Include heart rate and heart rate variability (HR/HRV) from ECG for autonomic dysfunction, sleep architecture, and voice acoustics (Xavier et al., 2024, Rashid et al., 2023).
Gait and mobility: Quantified via wearable accelerometers or passive sensing, including step timing, stride variability, location entropy, and eigenbehavioral analysis of indoor mobility routines (Ghosal et al., 2021, Botros et al., 2021, Rashid et al., 2023).
Neurophysiological signals: Primarily EEG-derived ERPs (e.g., P300), analyzed with covariance- or geometry-based ML, reflecting synaptic/network integrity (Rutkowski et al., 2018, Rutkowski et al., 2019).

These biomarkers span multiple cognitive domains—memory, attention, executive function, working memory, language processing, social cognition, psychomotor speed—by direct or indirect measurement.

2. Methodological Frameworks and Signal Processing Pipelines

Extraction of digital cognitive biomarkers requires rigorous signal processing and feature engineering adapted to each modality:

Time series and physiological data: HR/HRV biomarkers are derived by detecting R-wave peaks in short-term ECG signals and computing mean NN interval, SDNN, RMSSD, and RMS (Xavier et al., 2024). Wearable streams are summarized via quantile functions or L-moments in scalar-on-quantile regression frameworks (SOQFR) to retain distributional information lost in mere mean/variance summaries, or via entropy-based indices (Ghosal et al., 2021, Rashid et al., 2023).
EEG/ERP biomarkers: Covariance matrices computed from multi-channel ERP epochs are mapped to Riemannian manifolds, with classification using tangent-space SVMs, regularized LDA, or tensor-train deep networks for load discrimination (Rutkowski et al., 2018, Rutkowski et al., 2019).
Speech and language biomarkers: Pipeline includes standardized acoustic feature extraction (STFT, MFCC), ASR transcription, linguistic feature computation (e.g., TTR, Honore’s index, idea density, LIWC/Psycholinguistic category rates), syntactic parsing, coherence scoring using transformer-based architectures fine-tuned for adjacent vs. non-adjacent utterance discrimination, and graph-based aggregation (e.g., OVBM) (Gkoumas et al., 2023, Soler et al., 2021, Lima et al., 30 Jan 2025).
Behavioral and interaction markers: Device-based cognitive assessments quantify touchpad responses, valence/arousal recognition errors, reaction time, and demographic variables (Rutkowski et al., 2019). Social media–derived markers operationalize semantic drift (SBERT-based), coherence, behavioral entropy, engagement decay, and interaction-based features (pause/skip/replay rates) (Drishti et al., 28 Dec 2025).
Ambient context and eigenbehavior: Location occupancy matrices from PIR sensor arrays are decomposed into principal eigenvectors. The mean per-entry reconstruction error for a fixed eigenvector count provides an eigenbehavioral digital biomarker, correlated with cognitive scores (Botros et al., 2021).

3. Statistical Modeling, Machine Learning, and Validation Strategies

Prediction and stratification rely on diverse modeling approaches:

Regression: Linear, ridge-regularized (e.g., for MMSE/MoCA prediction), scalar-on-quantile or L-moment regressions for distributional input (Rutkowski et al., 2019, Ghosal et al., 2021, Wen et al., 2022).
Classification: SVM, random forest, logistic regression, deep feedforward/graph neural networks, and novel architectures (e.g., tensor-train layers, contrastive federated learning) (Lima et al., 30 Jan 2025, Rutkowski et al., 2019, Ouyang et al., 2023).
Cross-validation: Leave-one-subject-out (LOSO), k-fold stratified CV, and external validation on real-world pilot and longitudinal datasets provide realistic estimates of generalization.
Risk modeling: Cox proportional hazards model to quantify questionnaire uncertainty responses and dementia incidence—hazard ratios and dose-response functions (Lu et al., 15 Dec 2025).

Performance metrics include accuracy, sensitivity, specificity, RMSE, median absolute error, and ROC-AUC; features with the highest predictive value are identified by interpretability analyses such as SHAP (Lima et al., 30 Jan 2025).

4. Exemplary Digital Biomarker Suites and Platforms

Major systems anchor their biomarker frameworks in scalable, secure, and multi-modal architectures:

ADMarker integrates multi-modal (depth, mmWave radar, audio) sensors and a three-stage federated learning pipeline for real-time detection of 22 daily living activity biomarkers, with privacy-preserving updates, and achieves up to 93.8% multi-activity accuracy and 88.9% early AD detection (Ouyang et al., 2023).
Health Guardian provides a containerized microservice platform for modular analytic workers (e.g., MMSE prediction from speech), supporting multi-institutional studies, integrated feature stores, and dynamic resource scaling (Wen et al., 2022).
RADAR-base manages remote, continuous data collection from smartphones/wearables; extracts behavioral, physiological, and environmental features; and supports distributional and entropy-based cognitive monitoring at scale (Rashid et al., 2023).
Cogniscope models social media interactions in simulation, formalizing how fusion of linguistic coherence, semantic drift, and engagement entropy can function as synthetic cognitive biomarkers robust to noise and distributional shifts (Drishti et al., 28 Dec 2025).

5. Quantitative Performance, Specific Findings, and Interpretability

Key performance results and interpretability findings reflect biomarker validity, strengths, and weaknesses:

Domain	Marker/Model	Accuracy/AUC	Sensitivity/Specificity	Notes
Speech (OVBM)	16-feature GCN fusion	93.8% accuracy	∼95%	Outperforms prior SOTA (Soler et al., 2021)
Linguistic (RF-NLP)	100-feature RF	ROC-AUC 0.86–0.89	69–80% / 74–83%	Generalizes to home pilots (Lima et al., 30 Jan 2025)
Questionnaire uncertainty	DK-response HR for AD	HR=1.64 [1.26–2.14]	N/A	Dose-dependent risk increase (Lu et al., 15 Dec 2025)
HRV (10-s ECG)	SVM/DA/NB (mean, RMS, SDNN, RMSSD)	80.8%	N/A	All features significant (Xavier et al., 2024)
Gait (SOQFR-L)	AUC (stride time) 0.93	NA	Cross-validated (Ghosal et al., 2021)
Location eigenbehavior	SVM on reconstruction error	AUC 0.93	NA	Contactless, privacy-preserving (Botros et al., 2021)
EEG/ERP (TT-layer DNN)	Two class oddball paradigm	78% accuracy	~78%	Load discrimination proxy (Rutkowski et al., 2019)

Interpretability techniques (e.g., linguistic feature importance, risk stratification by SHAP, analytics of engagement entropy) further link model outputs to pathophysiological or behavioral phenomena such as word-finding difficulty, loss of routine spatial coherence, or autonomic imbalance.

6. Clinical Implications, Limitations, and Future Directions

Digital biomarkers enable objective, scalable, and longitudinal monitoring of cognitive health, but several limitations and research directions are highlighted:

Clinical implications: Digital biomarkers support at-home or point-of-care screening, enable early warning systems, and can facilitate adaptive intervention or triage for neuropsychological testing (Rutkowski et al., 2019, Ouyang et al., 2023, Rashid et al., 2023). Objective measurement (speech, EEG, HR/HRV, behavior) can bridge gaps left by episodic, subjective, or resource-intensive clinical assessments.
Limitations: Generalizability is restricted by cohort specificity, small or homogeneous samples, brief recording windows, missing robust gold-standard diagnostic labels (e.g., only MoCA/MMSE available), and short monitoring periods. Comorbidities, device variability, and cultural differences (e.g., in answering “Don’t know”) confound digital marker interpretation.
Future directions: Expansion to larger and more diverse populations, multimodal fusion of speech, wearable, and ambient sensor data, integration of deeper learning models, privacy-preserving and explainable pipelines, and inclusion of device proficiency measures are under development (Andrade et al., 2023, Ouyang et al., 2023). Longitudinal studies and adaptive systems (real-time feedback, reinforcement learning) for personalized monitoring are prioritized. Ethical, privacy, and fairness safeguards are required as deployment accelerates (Rutkowski et al., 2019, Wen et al., 2022).

7. Standardization and Integration into Practice

Efforts such as the Mobile Device Abilities Test (MDAT) (Andrade et al., 2023) advocate for harmonizing device proficiency metrics across digital biomarker studies to mitigate confounding and enable stratified analyses. Modular, open-source platforms (RADAR-base, Health Guardian) and federated architectures (ADMarker) promote reproducibility, external validation, and scalable deployment. Integration with electronic health records and ePROs is envisioned for continuous, population-level cognitive risk stratification (Lu et al., 15 Dec 2025, Rashid et al., 2023).

In summary, digital biomarkers of cognitive health constitute a multi-modal, quantitatively rigorous, and clinically promising approach to measuring, predicting, and tracking cognitive function and decline. As evidence accumulates for their validity and impact, ongoing method refinement and ecosystem development will be required to realize their translational potential across healthcare and research settings.