Exploring Test-Retest Reliability and Longitudinal Stability of Digital Biomarkers for Parkinson Disease in the m-Power Data Set: Cohort Study

Journals

1. Goni M, Eickhoff S, Far M, Patil K, Dukart J. Smartphone-Based Digital Biomarkers for Parkinson’s Disease in a Remotely-Administered Setting. IEEE Access 2022;10:28361 View
2. Ozbolt A, Moro-Velazquez L, Lina I, Butala A, Dehak N. Things to Consider When Automatically Detecting Parkinson’s Disease Using the Phonation of Sustained Vowels: Analysis of Methodological Issues. Applied Sciences 2022;12(3):991 View
3. Amato F, Saggio G, Cesarini V, Olmo G, Costantini G. Machine learning- and statistical-based voice analysis of Parkinson’s disease patients: A survey. Expert Systems with Applications 2023;219:119651 View
4. Templeton J, Poellabauer C, Schneider S. Classification of Parkinson’s disease and its stages using machine learning. Scientific Reports 2022;12(1) View
5. F. Alenezi D, Shi H, Li J. A Ranking-based Weakly Supervised Learning model for telemonitoring of Parkinson’s disease. IISE Transactions on Healthcare Systems Engineering 2022;12(4):322 View
6. Bennett C, Ross M, Baek E, Kim D, Leow A. Smartphone accelerometer data as a proxy for clinical data in modeling of bipolar disorder symptom trajectory. npj Digital Medicine 2022;5(1) View
7. Graves J, Elantkowski M, Zhang Y, Dondelinger F, Lipsmeier F, Bernasconi C, Montalban X, Midaglia L, Lindemann M. Assessment of Upper Extremity Function in Multiple Sclerosis: Feasibility of a Digital Pinching Test. JMIR Formative Research 2023;7:e46521 View
8. Broeder S, Roussos G, De Vleeschhauwer J, D’Cruz N, de Xivry J, Nieuwboer A. A smartphone-based tapping task as a marker of medication response in Parkinson’s disease: a proof of concept study. Journal of Neural Transmission 2023;130(7):937 View
9. Xue Z, Lu H, Zhang T, Guo X, Gao L. Remote Parkinson's disease severity prediction based on causal game feature selection. Expert Systems with Applications 2024;241:122690 View
10. Milner T, Brown M, Jones C, Leung A, Brémault-Phillips S. Multidimensional digital biomarker phenotypes for mild cognitive impairment: considerations for early identification, diagnosis and monitoring. Frontiers in Digital Health 2024;6 View
11. Macias Alonso A, Hirt J, Woelfle T, Janiaud P, Hemkens L. Definitions of digital biomarkers: a systematic mapping of the biomedical literature. BMJ Health & Care Informatics 2024;31(1):e100914 View
12. Lavine J, Scotina A, Haney S, Bakker J, Izmailova E, Omberg L. Impacts on study design when implementing digital measures in Parkinson's disease-modifying therapy trials. Frontiers in Digital Health 2024;6 View
13. Polvorinos-Fernández C, Sigcha L, Borzì L, Olmo G, Asensio C, López J, de Arcas G, Pavón I. Evaluating Motor Symptoms in Parkinson’s Disease Through Wearable Sensors: A Systematic Review of Digital Biomarkers. Applied Sciences 2024;14(22):10189 View
14. Templeton J, Poellabauer C, Schneider S, Rahimi M, Braimoh T, Tadamarry F, Margolesky J, Burke S, Al Masry Z. Modernizing the Staging of Parkinson Disease Using Digital Health Technology. Journal of Medical Internet Research 2025;27:e63105 View
15. Qi W, Shen S, dong C, Zhao M, Zang S, Zhu X, Li J, Wang B, Shi Y, Dong Y, Shen H, Kang J, Lu X, Jiang G, Du J, Shu E, Zhou Q, Wang J, Cao S. Digital Biomarkers for Parkinson's Disease: A Bibliometric Analysis and a Scoping Review of Deep Learning for Freezing of Gait (Preprint). Journal of Medical Internet Research 2025 View