-
McFarthing, K. et al. Parkinson’s Disease Drug Therapies in the Clinical Trial Pipeline: 2023 Update. J. Parkinsons Dis. 13, 427–439 (2023).
Article CAS PubMed PubMed Central Google Scholar
-
Goetz, C. G. et al. Movement Disorder Society‐sponsored revision of the Unified Parkinson’s Disease Rating Scale (MDS‐UPDRS): scale presentation and clinimetric testing results. Mov. Disord. 23, 2129–2170 (2008).
Article PubMed Google Scholar
-
Evers, L. J. W., Krijthe, J. H., Meinders, M. J., Bloem, B. R. & Heskes, T. M. Measuring Parkinson’s disease over time: The real-world within-subject reliability of the MDS-UPDRS. Mov. Disord. 34, 1480–1487 (2019).
Article PubMed PubMed Central Google Scholar
-
Artusi, C. A. et al. Implementation of Mobile Health Technologies in Clinical Trials of Movement Disorders: Underutilized Potential. Neurotherapeutics 17, 1736–1746 (2020).
Article PubMed PubMed Central Google Scholar
-
Warmerdam, E. et al. Long-term unsupervised mobility assessment in movement disorders. Lancet Neurol. 19, 462–470 (2020).
Article PubMed Google Scholar
-
Zhan, A. et al. Using Smartphones and Machine Learning to Quantify Parkinson Disease Severity: The Mobile Parkinson Disease Score. JAMA Neurol. 75, 876–880, (2018).
Article PubMed PubMed Central Google Scholar
-
Burq, M. et al. Virtual exam for Parkinson’s disease enables frequent and reliable remote measurements of motor function. NPJ Digit. Med. 5, 65 (2022).
Article PubMed PubMed Central Google Scholar
-
Bloem, B. R., Post, E. & Hall, D. A. An Apple a Day to Keep the Parkinson’s Disease Doctor Away? Ann. Neurol. 93, 681–685 (2023).
Article PubMed Google Scholar
-
Lipsmeier, F. et al. Reliability and validity of the Roche PD Mobile Application for remote monitoring of early Parkinson’s disease. Sci. Rep. 12, 12081 (2022).
Article CAS PubMed PubMed Central Google Scholar
-
Mirelman, A. et al. Arm swing as a potential new prodromal marker of Parkinson’s disease. Mov. Disord. 31, 1527–1534 (2016).
Article CAS PubMed PubMed Central Google Scholar
-
Schalkamp, A. K., Peall, K. J., Harrison, N. A. & Sandor, C. Wearable movement-tracking data identify Parkinson’s disease years before clinical diagnosis. Nat. Med., https://doi.org/10.1038/s41591-023-02440-2 (2023).
-
Postuma, R. B. et al. MDS clinical diagnostic criteria for Parkinson’s disease. Mov. Disord. 30, 1591–1601 (2015).
Article PubMed Google Scholar
-
Martinez-Martin, P., Rodriguez-Blazquez, C., Kurtis, M. M., Chaudhuri, K. R. & Group, N. V. The impact of non-motor symptoms on health-related quality of life of patients with Parkinson’s disease. Mov. Disord. 26, 399–406 (2011).
Article PubMed Google Scholar
-
Berg, D. et al. MDS research criteria for prodromal Parkinson’s disease. Mov. Disord. 30, 1600–1611 (2015).
Article PubMed Google Scholar
-
Martinez-Fernandez, R., Schmitt, E., Martinez-Martin, P. & Krack, P. The hidden sister of motor fluctuations in Parkinson’s disease: A review on nonmotor fluctuations. Mov. Disord. 31, 1080–1094 (2016).
Article CAS PubMed Google Scholar
-
FDA. Patient-Focused Drug Development Guidance Series for Enhancing the Incorporation of the Patient’s Voice in Medical Product Development and Regulatory Decision Making (FDA, 2023).
-
FDA. Framework for the Use of Digital Health Technologies in Drug and Biological Product Development (FDA, 2023).
-
van Wamelen, D. J. et al. Digital health technology for non-motor symptoms in people with Parkinson’s disease: Futile or future? Parkinsonism Relat. Disord. 89, 186–194 (2021).
Article PubMed Google Scholar
-
Merola, A. et al. Technology-based assessment of motor and nonmotor phenomena in Parkinson disease. Expert Rev. Neurother. 18, 825–845 (2018).
Article CAS PubMed Google Scholar
-
Sigcha, L. et al. Deep learning and wearable sensors for the diagnosis and monitoring of Parkinson’s disease: A systematic review. Expert Syst. Appl. 229, 120541 (2023).
Article Google Scholar
-
Darweesh, S. K. et al. Trajectories of prediagnostic functioning in Parkinson’s disease. Brain 140, 429–441 (2017).
Article PubMed Google Scholar
-
McGregor, S. et al. The use of accelerometry as a tool to measure disturbed nocturnal sleep in Parkinson’s disease. npj Parkinsons Dis. 4, 1 (2018).
Article PubMed PubMed Central Google Scholar
-
Madrid-Navarro, C. J. et al. Validation of a Device for the Ambulatory Monitoring of Sleep Patterns: A Pilot Study on Parkinson’s Disease. Front Neurol. 10, 356 (2019).
Article PubMed PubMed Central Google Scholar
-
Mirelman, A. et al. Tossing and Turning in Bed: Nocturnal Movements in Parkinson’s Disease. Mov. Disord. 35, 959–968 (2020).
Article PubMed Google Scholar
-
O’Dowd, S. T. et al. Longitudinal assessment of sleep in an incident Parkinson’s disease cohort. Mov. Disord. 31, S117 (2016).
Google Scholar
-
Breen, D. P. et al. Sleep and circadian rhythm regulation in early parkinson disease. JAMA Neurol. 71, 589–595 (2014).
Article PubMed PubMed Central Google Scholar
-
Carlson, C. et al. Bed-based instrumentation for unobtrusive sleep quality assessment in severely disabled autistic children. Annu Int Conf. IEEE Eng. Med Biol. Soc. 2016, 4909–4912 (2016).
PubMed Google Scholar
-
Li, S. & Chiu, C. A Smart Pillow for Health Sensing System Based on Temperature and Humidity Sensors. Sensors 18, https://doi.org/10.3390/s18113664 (2018).
-
Filardi, M. et al. Objective rest-activity cycle analysis by actigraphy identifies isolated rapid eye movement sleep behavior disorder. Eur. J. Neurol. 27, 1848–1855 (2020).
Article CAS PubMed Google Scholar
-
Kemlink, D., Perinova, P., Dusek, P., Ruzicka, E. & Sonka, K. Actigraphic screening for the rapid eye movement sleep behavior disorder in Czech population. Sleep. Med. 40, e155 (2017).
Article Google Scholar
-
Louter, M., Arends, J. B., Bloem, B. R. & Overeem, S. Actigraphy as a diagnostic aid for REM sleep behavior disorder in Parkinson’s disease. BMC Neurol. 14, 76 (2014).
Article PubMed PubMed Central Google Scholar
-
Moerman, C. et al. Towards a handy screening tool for REM sleep behaviour disorder: RDBAct algorithm from wrist actigraphy data. J. Sleep Res. 29, https://doi.org/10.1111/jsr.13181 (2020).
-
Raschella, F., Scafa, S., Puiatti, A., Martin Moraud, E. & Ratti, P. L. Actigraphy Enables Home Screening of Rapid Eye Movement Behavior Disorder in Parkinson’s Disease. Ann. Neurol., https://doi.org/10.1002/ana.26517 (2022).
-
Stefani, A. et al. Screening for idiopathic REM sleep behavior disorder: Usefulness of actigraphy. Sleep 41, https://doi.org/10.1093/sleep/zsy053 (2018).
-
Naismith, S. L., Rogers, N. L., Mackenzie, J., Hickie, I. B. & Lewis, S. J. The relationship between actigraphically defined sleep disturbance and REM sleep behaviour disorder in Parkinson’s Disease. Clin. Neurol. Neurosurg. 112, 420–423 (2010).
Article PubMed Google Scholar
-
Kemlink, D., Perinova, P., Dusek, P., Ruzicka, E. & Sonka, K. Actigraphic differences in the rapid eye movement sleep behavior disorder patients. J. Sleep. Res. 27, 51 (2018).
Google Scholar
-
Bolitho, S. J. et al. Objective measurement of daytime napping, cognitive dysfunction and subjective sleepiness in Parkinson’s disease. PLoS One 8, e81233 (2013).
Article PubMed PubMed Central Google Scholar
-
Kotschet, K. et al. Daytime sleep in Parkinson’s disease measured by episodes of immobility. Parkinsonism Relat. Disord. 20, 578–583 (2014).
Article CAS PubMed Google Scholar
-
Kundinger, T., Sofra, N. & Riener, A. Assessment of the Potential of Wrist-Worn Wearable Sensors for Driver Drowsiness Detection. Sensors 20, https://doi.org/10.3390/s20041029 (2020).
-
Leng, Y. et al. Excessive daytime sleepiness, objective napping and 11-year risk of Parkinson’s disease in older men. Int J. Epidemiol. 47, 1679–1686 (2018).
Article PubMed PubMed Central Google Scholar
-
Memon, A. et al. Relationship between subjective and objective measures of sleepiness in Parkinson’s disease. In Movement Disorders. Conference: 1st Pan American Parkinson’s Disease and Movement Disorders Congress. Miami, FL United States 32, https://doi.org/10.1002/mds.26972 (2017).
-
Huang, S., Li, J., Zhang, P. & Zhang, W. Detection of mental fatigue state with wearable ECG devices. Int. J. Med. Inform. 119, 39–46 (2018).
Article PubMed Google Scholar
-
Zeng, Z. et al. Nonintrusive Monitoring of Mental Fatigue Status Using Epidermal Electronic Systems and Machine-Learning Algorithms. ACS Sens. 5, 1305–1313 (2020).
Article CAS PubMed Google Scholar
-
Jeklin, A. T. et al. The association between heart rate variability, reaction time, and indicators of workplace fatigue in wildland firefighters. Int. Arch. Occup. Environ. health 94, 823–831 (2021).
Article PubMed Google Scholar
-
Tseng, V. W. S., Valliappan, N., Ramachandran, V., Choudhury, T. & Navalpakkam, V. Digital biomarker of mental fatigue. npj Digital Med. 4, https://doi.org/10.1038/s41746-021-00415-6 (2021).
-
Aqajari, S. A. H. et al. Pain Assessment Tool With Electrodermal Activity for Postoperative Patients: Method Validation Study. JMIR mHealth uHealth 9, e25258 (2021).
Article PubMed PubMed Central Google Scholar
-
Atee, M., Hoti, K., Parsons, R. & Hughes, J. D. Pain Assessment in Dementia: Evaluation of a Point-of-Care Technological Solution. J. Alzheimers Dis. 60, 137–150 (2017).
Article PubMed PubMed Central Google Scholar
-
Jacobson, N. C. & O’Cleirigh, C. Objective digital phenotypes of worry severity, pain severity and pain chronicity in persons living with HIV. Br. J. Psychiatry 218, 165–167 (2021).
Article PubMed Google Scholar
-
Wallen, M. B., Hasson, D., Theorell, T., Canlon, B. & Osika, W. Possibilities and limitations of the Polar RS800 in measuring heart rate variability at rest. Eur. J. Appl Physiol. 112, 1153–1165 (2012).
Article PubMed Google Scholar
-
Gamelin, F. X., Berthoin, S. & Bosquet, L. Validity of the polar S810 heart rate monitor to measure R-R intervals at rest. Med Sci. Sports Exerc 38, 887–893 (2006).
Article PubMed Google Scholar
-
Nunan, D. et al. Levels of agreement for RR intervals and short-term heart rate variability obtained from the Polar S810 and an alternative system. Eur. J. Appl Physiol. 103, 529–537 (2008).
Article PubMed Google Scholar
-
Stone, J. D. et al. Assessing the Accuracy of Popular Commercial Technologies That Measure Resting Heart Rate and Heart Rate Variability. Front. Sports Act. Living 3, 585870 (2021).
Article PubMed PubMed Central Google Scholar
-
Kunkels, Y. K., van Roon, A. M., Wichers, M. & Riese, H. Cross-instrument feasibility, validity, and reproducibility of wireless heart rate monitors: Novel opportunities for extended daily life monitoring. Psychophysiology 58, e13898 (2021).
Article PubMed PubMed Central Google Scholar
-
Sagl, G. et al. Wearables and the Quantified Self: Systematic Benchmarking of Physiological Sensors. Sensors 19, https://doi.org/10.3390/s19204448 (2019).
-
Schuurmans, A. A. T. et al. Validity of the Empatica E4 Wristband to Measure Heart Rate Variability (HRV) Parameters: a Comparison to Electrocardiography (ECG). J. Med. Syst. 44, https://doi.org/10.1007/s10916-020-01648-w (2020).
-
Niwa, F., Kuriyama, N., Nakagawa, M. & Imanishi, J. Circadian rhythm of rest activity and autonomic nervous system activity at different stages in Parkinson’s disease. Auton. Neurosci. 165, 195–200 (2011).
Article PubMed Google Scholar
-
Barbosa, M. P., da Silva, N. T., de Azevedo, F. M., Pastre, C. M. & Vanderlei, L. C. Comparison of Polar(R) RS800G3 heart rate monitor with Polar(R) S810i and electrocardiogram to obtain the series of RR intervals and analysis of heart rate variability at rest. Clin. Physiol. Funct. Imaging 36, 112–117 (2016).
Article PubMed Google Scholar
-
Santarelli, L. et al. Development of a Novel Wearable Ring-Shaped Biosensor. Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. 2018, 3750–3753 (2018).
CAS PubMed Google Scholar
-
Nardelli, M., Vanello, N., Galperti, G., Greco, A. & Scilingo, E. P. Assessing the Quality of Heart Rate Variability Estimated from Wrist and Finger PPG: A Novel Approach Based on Cross-Mapping Method. Sensors 20, https://doi.org/10.3390/s20113156 (2020).
-
Pandian, P. S. et al. Smart Vest: wearable multi-parameter remote physiological monitoring system. Med Eng. Phys. 30, 466–477 (2008).
Article CAS PubMed Google Scholar
-
Sarhaddi, F. et al. A comprehensive accuracy assessment of Samsung smartwatch heart rate and heart rate variability. PLoS One 17, e0268361 (2022).
Article CAS PubMed PubMed Central Google Scholar
-
Kinnunen, H., Rantanen, A., Kentta, T. & Koskimaki, H. Feasible assessment of recovery and cardiovascular health: accuracy of nocturnal HR and HRV assessed via ring PPG in comparison to medical grade ECG. Physiological Meas. 41, 04NT01 (2020).
Article Google Scholar
-
Reali, P., Tacchino, G., Rocco, G., Cerutti, S. & Bianchi, A. M. Heart Rate Variability from Wearables: A Comparative Analysis Among Standard ECG, a Smart Shirt and a Wristband. Stud. Health Technol. Inform. 261, 128–133 (2019).
PubMed Google Scholar
-
Bloem, B. R. et al. The Personalized Parkinson Project: examining disease progression through broad biomarkers in early Parkinson’s disease. BMC Neurol. 19, 160 (2019).
Article CAS PubMed PubMed Central Google Scholar
-
Darweesh, S. K. L. ClinicalTrials.gov. Slow-SPEED-NL: Slowing Parkinson’s Early Through Exercise Dosage-Netherlands (Slow-SPEED-NL), 2024.
-
Park, S. M. et al. A mountable toilet system for personalized health monitoring via the analysis of excreta. Nat. Biomed. Eng. 4, 624–635 (2020).
Article PubMed PubMed Central Google Scholar
-
Molavi, B., Shadgan, B., Macnab, A. J. & Dumont, G. A. Noninvasive Optical Monitoring of Bladder Filling to Capacity Using a Wireless Near Infrared Spectroscopy Device. IEEE Trans. Biomed. Circuits Syst. 8, 325–333 (2014).
Article PubMed Google Scholar
-
El Helou, E. et al. Mobile sonouroflowmetry using voiding sound and volume. Sci. Rep. 11, 11250 (2021).
Article CAS PubMed PubMed Central Google Scholar
-
Schultz, R. E. Smartphone App for In-home Uroflowmetry. Urol. Pr. 9, 524–530 (2022).
Google Scholar
-
Baker, L. B. et al. Skin-interfaced microfluidic system with personalized sweating rate and sweat chloride analytics for sports science applications. Sci. Adv. 6, https://doi.org/10.1126/sciadv.abe3929 (2020).
-
Nyein, H. Y. Y. et al. A wearable patch for continuous analysis of thermoregulatory sweat at rest. Nat. Commun. 12, 1823 (2021).
Article CAS PubMed PubMed Central Google Scholar
-
Gil, B., Anastasova, S. & Yang, G. Z. A Smart Wireless Ear-Worn Device for Cardiovascular and Sweat Parameter Monitoring During Physical Exercise: Design and Performance Results. Sensors 19, https://doi.org/10.3390/s19071616 (2019).
-
Sim, J. K., Yoon, S. & Cho, Y. H. Wearable Sweat Rate Sensors for Human Thermal Comfort Monitoring. Sci. Rep. 8, 1181 (2018).
Article PubMed PubMed Central Google Scholar
-
Ummel, J. D. et al. Kick Ring LL: A Multi-Sensor Ring Capturing Respiration, Electrocardiogram, Oxygen Saturation, and Skin Temperature(1). Annu Int Conf. IEEE Eng. Med Biol. Soc. 2020, 4394–4397 (2020).
PubMed Google Scholar
-
Madrid-Navarro, C. J. et al. Multidimensional Circadian Monitoring by Wearable Biosensors in Parkinson’s Disease. Front Neurol. 9, 157 (2018).
Article PubMed PubMed Central Google Scholar
-
Kim, H. et al. Smart Patch for Skin Temperature: Preliminary Study to Evaluate Psychometrics and Feasibility. Sensors 21, https://doi.org/10.3390/s21051855 (2021).
-
van Marken Lichtenbelt, W. D. et al. Evaluation of wireless determination of skin temperature using iButtons. Physiol. Behav. 88, 489–497 (2006).
Article PubMed Google Scholar
-
Ibrahim, B. & Jafari, R. Cuffless Blood Pressure Monitoring from an Array of Wrist Bio-Impedance Sensors Using Subject-Specific Regression Models: Proof of Concept. IEEE Trans. Biomed. Circuits Syst. 13, 1723–1735 (2019).
Article PubMed PubMed Central Google Scholar
-
Narasimhan, R., Parlikar, T., Verghesel, G. & McConnell, M. V. Finger-Wearable Blood Pressure Monitor. Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. 2018, 3792–3795 (2018).
PubMed Google Scholar
-
Nachman, D. et al. Comparing blood pressure measurements between a photoplethysmography-based and a standard cuff-based manometry device. Sci. Rep. 10, 16116 (2020).
Article CAS PubMed PubMed Central Google Scholar
-
Sola, J. et al. Validation of the optical Aktiia bracelet in different body positions for the persistent monitoring of blood pressure. Sci. Rep. 11, 20644 (2021).
Article CAS PubMed PubMed Central Google Scholar
-
Gaurav, A., Maheedhar, M., Tiwari, V. N. & Narayanan, R. Cuff-less PPG based continuous blood pressure monitoring: a smartphone based approach. Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. 2016, 607–610 (2016).
PubMed Google Scholar
-
Rachim, V. P. & Chung, W. Y. Multimodal Wrist Biosensor for Wearable Cuff-less Blood Pressure Monitoring System. Sci. Rep. 9, 7947 (2019).
Article PubMed PubMed Central Google Scholar
-
Mark, E. B. et al. Ambulatory assessment of colonic motility using the electromagnetic capsule tracking system. Neurogastroenterol. Motil. 31, e13451 (2019).
Article PubMed Google Scholar
-
Axelrod, L., Axelrod, S., Navalgund, A. & Triadafilopoulos, G. Pilot Validation of a New Wireless Patch System as an Ambulatory, Noninvasive Tool That Measures Gut Myoelectrical Signals: Physiologic and Disease Correlations. Digestive Dis. Sci. 66, 3505–3515 (2021).
Article Google Scholar
-
Su, A., Gandhy, R., Barlow, C. & Triadafilopoulos, G. Utility of high-resolution anorectal manometry and wireless motility capsule in the evaluation of patients with Parkinson’s disease and chronic constipation. BMJ Open Gastroenterol. 3, e000118 (2016).
Article PubMed PubMed Central Google Scholar
-
Maqbool, S., Parkman, H. P. & Friedenberg, F. K. Wireless capsule motility: comparison of the SmartPill GI monitoring system with scintigraphy for measuring whole gut transit. Dig. Dis. Sci. 54, 2167–2174 (2009).
Article PubMed Google Scholar
-
Pehlivan, M. et al. An electronic device measuring the frequency of spontaneous swallowing: digital phagometer. Dysphagia 11, 259–264 (1996).
Article CAS PubMed Google Scholar
-
Hadley, A. J., Krival, K. R., Ridgel, A. L., Hahn, E. C. & Tyler, D. J. Neural network pattern recognition of lingual-palatal pressure for automated detection of swallow. Dysphagia 30, 176–187 (2015).
Article PubMed Google Scholar
-
O’Brien, M. K. et al. Advanced Machine Learning Tools to Monitor Biomarkers of Dysphagia: A Wearable Sensor Proof-of-Concept Study. Digital Biomark. 5, 167–175 (2021).
Article Google Scholar
-
Jayatilake, D. et al. Smartphone-Based Real-time Assessment of Swallowing Ability from the Swallowing Sound. IEEE J. Transl. Eng. Health Med. 3, https://doi.org/10.1109/JTEHM.2015.2500562 (2015).
-
Kantarcigil, C. et al. Validation of a Novel Wearable Electromyography Patch for Monitoring Submental Muscle Activity During Swallowing: A Randomized Crossover Trial. J. Speech Lang. Hearing Res. 63, 3293–3310 (2020).
Article Google Scholar
-
Li, P. et al. Circadian disturbances in Alzheimer’s disease progression: a prospective observational cohort study of community-based older adults. Lancet Healthy Longev. 1, e96–e105 (2020).
Article PubMed PubMed Central Google Scholar
-
Targa, A. D. S. et al. The circadian rest-activity pattern predicts cognitive decline among mild-moderate Alzheimer’s disease patients. Alzheimer’s Res. Therapy 13, https://doi.org/10.1186/s13195-021-00903-7 (2021).
-
Tranah, G. J. et al. Circadian activity rhythms and risk of incident dementia and mild cognitive impairment in older women. Ann. Neurol. 70, 722–732 (2011).
Article PubMed PubMed Central Google Scholar
-
Rogers-Soeder, T. S. et al. Rest-Activity Rhythms and Cognitive Decline in Older Men: The Osteoporotic Fractures in Men Sleep Study. J. Am. Geriatrics Soc. 66, 2136–2143 (2018).
Article Google Scholar
-
Dodge, H. H., Mattek, N. C., Austin, D., Hayes, T. L. & Kaye, J. A. In-home walking speeds and variability trajectories associated with mild cognitive impairment. Neurology 78, 1946–1952 (2012).
Article CAS PubMed PubMed Central Google Scholar
-
Leng, Y., Redline, S., Stone, K. L., Ancoli-Israel, S. & Yaffe, K. Objective napping, cognitive decline, and risk of cognitive impairment in older men. Alzheimers Dement. 15, 1039–1047 (2019).
Article PubMed Google Scholar
-
Khan, T. & Jacobs, P. G. Prediction of Mild Cognitive Impairment Using Movement Complexity. IEEE J. Biomed. Health Inform. 25, 227–236 (2021).
Article PubMed PubMed Central Google Scholar
-
Gunn, D. G., Naismith, S. L., Bolitho, S. J. & Lewis, S. J. Actigraphically-defined sleep disturbance in Parkinson’s disease is associated with differential aspects of cognitive functioning. J. Clin. Neurosci. 21, 1112–1115 (2014).
Article PubMed Google Scholar
-
Akl, A. et al. Clustering Home Activity Distributions for Automatic Detection of Mild Cognitive Impairment in Older Adults. J. Ambient Intell. Smart Environ. 8, 437–451 (2016).
Article PubMed PubMed Central Google Scholar
-
Dawadi, P. N., Cook, D. J. & Schmitter-Edgecombe, M. Automated Cognitive Health Assessment From Smart Home-Based Behavior Data. IEEE J. Biomed. Health Inf. 20, 1188–1194 (2016).
Article Google Scholar
-
Alberdi, A. et al. Smart Home-Based Prediction of Multidomain Symptoms Related to Alzheimer’s Disease. IEEE J. Biomed. Health Inf. 22, 1720–1731 (2018).
Article Google Scholar
-
Akl, A., Taati, B. & Mihailidis, A. Autonomous unobtrusive detection of mild cognitive impairment in older adults. IEEE Trans. Biomed. Eng. 62, 1383–1394 (2015).
Article PubMed PubMed Central Google Scholar
-
Sacco, G. et al. Detection of activities of daily living impairment in Alzheimer’s disease and mild cognitive impairment using information and communication technology. Clin. Interventions Aging 7, 539–549, (2012).
Article Google Scholar
-
Lussier, M. et al. Smart Home Technology: A New Approach for Performance Measurements of Activities of Daily Living and Prediction of Mild Cognitive Impairment in Older Adults. J. Alzheimers Dis. 68, 85–96 (2019).
Article PubMed Google Scholar
-
Jeon, S. Y. et al. Circadian rest-activity rhythm and longitudinal brain changes underlying late-life cognitive decline. Psychiatry Clin. Neurosci. 77, 205–212 (2023).
Article PubMed PubMed Central Google Scholar
-
Antonsdottir, I. M. et al. 24 h Rest/Activity Rhythms in Older Adults with Memory Impairment: Associations with Cognitive Performance and Depressive Symptomatology. Adv. Biol., e2300138, https://doi.org/10.1002/adbi.202300138 (2023).
-
Hooghiemstra, A. M., Eggermont, L. H., Scheltens, P., van der Flier, W. M. & Scherder, E. J. The rest-activity rhythm and physical activity in early-onset dementia. Alzheimer Dis. Assoc. Disord. 29, 45–49 (2015).
Article PubMed Google Scholar
-
Sevil, M. et al. Discrimination of simultaneous psychological and physical stressors using wristband biosignals. Comput. Methods Programs Biomed. 199, https://doi.org/10.1016/j.cmpb.2020.105898 (2021).
-
Jacobson, N. C., Lekkas, D., Huang, R. & Thomas, N. Deep learning paired with wearable passive sensing data predicts deterioration in anxiety disorder symptoms across 17–18 years. J. Affect. Disord. 282, 104–111 (2021).
Article PubMed Google Scholar
-
Di Matteo, D. et al. Smartphone-Detected Ambient Speech and Self-Reported Measures of Anxiety and Depression: Exploratory Observational Study. JMIR Form. Res 5, e22723 (2021).
Article PubMed PubMed Central Google Scholar
-
Opoku Asare, K. et al. Predicting Depression From Smartphone Behavioral Markers Using Machine Learning Methods, Hyperparameter Optimization, and Feature Importance Analysis: Exploratory Study. JMIR mHealth uHealth 9, e26540 (2021).
Article PubMed PubMed Central Google Scholar
-
Rykov, Y., Thach, T. Q., Bojic, I., Christopoulos, G. & Car, J. Digital Biomarkers for Depression Screening With Wearable Devices: Cross-sectional Study With Machine Learning Modeling. JMIR Mhealth Uhealth 9, e24872 (2021).
Article PubMed PubMed Central Google Scholar
-
Narziev, N. et al. STDD: Short-Term Depression Detection with Passive Sensing. Sensors 20, https://doi.org/10.3390/s20051396 (2020).
-
Saeb, S., Lattie, E. G., Kording, K. P. & Mohr, D. C. Mobile Phone Detection of Semantic Location and Its Relationship to Depression and Anxiety. JMIR Mhealth Uhealth 5, e112 (2017).
Article PubMed PubMed Central Google Scholar
-
Choi, J., Lee, S., Kim, S., Kim, D. & Kim, H. Depressed Mood Prediction of Elderly People with a Wearable Band. Sensors 22, https://doi.org/10.3390/s22114174 (2022).
-
Saeb, S., Lattie, E. G., Schueller, S. M., Kording, K. P. & Mohr, D. C. The relationship between mobile phone location sensor data and depressive symptom severity. PeerJ 4, e2537 (2016).
-
Schutz, N. et al. A Sensor-Driven Visit Detection System in Older Adults Homes: Towards Digital Late-Life Depression Marker Extraction. IEEE J. Biomed. Health Informatics., https://doi.org/10.1109/JBHI.2021.3114595 (2021).
-
Huang, M. et al. Association of Depressive Symptoms with Sleep Disturbance: A Co-twin Control Study. Ann. Behav. Med. 15, https://doi.org/10.1093/abm/kaab040 (2021).
-
Zhang, Y. et al. Relationship Between Major Depression Symptom Severity and Sleep Collected Using a Wristband Wearable Device: Multicenter Longitudinal Observational Study. JMIR mHealth uHealth 9, e24604 (2021).
Article PubMed PubMed Central Google Scholar
-
Mulryan, P., Affonso, S. & Sullivan, A. Sleep and depression in Parkinson’s disease: Investigating the relationship between sleep and depression using a combination of subjective and objective sleep assessment methods. Ann. Neurol. 88, S196 (2020).
Google Scholar
-
Coutts, L. V., Plans, D., Brown, A. W. & Collomosse, J. Deep learning with wearable based heart rate variability for prediction of mental and general health. J. Biomed Inf 112, https://doi.org/10.1016/j.jbi.2020.103610 (2020).
-
Dogrucu, A. et al. Moodable: On feasibility of instantaneous depression assessment using machine learning on voice samples with retrospectively harvested smartphone and social media data. Smart Health 17, https://doi.org/10.1016/j.smhl.2020.100118 (2020).
-
Palmius, N. et al. Detecting bipolar depression from geographic location data. IEEE Trans. Biomed. Eng. 64, 1761–1771 (2017).
Article CAS PubMed Google Scholar
-
Braund, T. A. et al. Smartphone Sensor Data for Identifying and Monitoring Symptoms of Mood Disorders: A Longitudinal Observational Study. JMIR Ment. Health 9, e35549 (2022).
Article PubMed PubMed Central Google Scholar
-
Lee, D. et al. Changes in the Circadian Rhythm of High-Frequency Heart Rate Variability Associated With Depression. J. Korean Med Sci. 38, e142 (2023).
Article PubMed PubMed Central Google Scholar
-
Siddi, S. et al. The usability of daytime and night-time heart rate dynamics as digital biomarkers of depression severity. Psychol. Med 53, 3249–3260 (2023).
Article CAS PubMed Google Scholar
-
Hayashi, M., Takeshima, M., Hosoya, T. & Kume, Y. 24-Hour Rest-Activity Rhythm in Middle-Aged and Older Persons with Depression. Int. J. Environ. Res. Public Health 20, https://doi.org/10.3390/ijerph20075275 (2023).
-
de Feijter, M., Kocevska, D., Ikram, M. A. & Luik, A. I. The bidirectional association of 24-h activity rhythms and sleep with depressive symptoms in middle-aged and elderly persons. Psychol. Med. 53, 1418–1425 (2023).
Article PubMed Google Scholar
-
Whitehead, D. L., Davies, A. D., Playfer, J. R. & Turnbull, C. J. Circadian rest-activity rhythm is altered in Parkinson’s disease patients with hallucinations. Mov. Disord. 23, 1137–1145 (2008).
Article PubMed Google Scholar
-
Godkin, F. E. et al. Feasibility of a continuous, multi-sensor remote health monitoring approach in persons living with neurodegenerative disease. J. Neurol., 1–14, https://doi.org/10.1007/s00415-021-10831-z (2021).
-
Keogh, A., Dorn, J. F., Walsh, L., Calvo, F. & Caulfield, B. Comparing the Usability and Acceptability of Wearable Sensors Among Older Irish Adults in a Real-World Context: Observational Study. JMIR mHealth uHealth 8, e15704 (2020).
Article PubMed PubMed Central Google Scholar
-
Areia, C. et al. Wearability Testing of Ambulatory Vital Sign Monitoring Devices: Prospective Observational Cohort Study. JMIR mHealth uHealth 8, e20214 (2020).
Article PubMed PubMed Central Google Scholar
-
Saif, N. et al. Feasibility of Using a Wearable Biosensor Device in Patients at Risk for Alzheimer’s Disease Dementia. J. Prev. Alzheimers Dis. 7, 104–111 (2020).
CAS PubMed PubMed Central Google Scholar
-
Silva de Lima, A. L. et al. Feasibility of large-scale deployment of multiple wearable sensors in Parkinson’s disease. PLoS One 12, e0189161 (2017).
Article PubMed PubMed Central Google Scholar
-
Meinders, M. J., Donnelly, A. C., Sheehan, M. & Bloem, B. R. Including People with Parkinson’s Disease in Clinical Study Design and Execution: A Call to Action. J. Parkinsons Dis. 12, 1359–1363 (2022).
Article PubMed PubMed Central Google Scholar
-
Schneider, R. et al. Feasibility of using a smartphone application for the objective evaluation of Parkinson’s disease. Mov. Disord. 32, 973 (2017).
Google Scholar
-
Seelye, A. et al. Feasibility of In-Home Sensor Monitoring to Detect Mild Cognitive Impairment in Aging Military Veterans: Prospective Observational Study. JMIR Form. Res. 4, e16371 (2020).
Article PubMed PubMed Central Google Scholar
-
Betthauser, L. M. et al. Mobile app for mental health monitoring and clinical outreach in veterans: Mixed methods feasibility and acceptability study. J. Med. Internet Res. 22, https://doi.org/10.2196/15506 (2020).
-
Heinzel, S. et al. Update of the MDS research criteria for prodromal Parkinson’s disease. Mov. Disord. 34, 1464–1470 (2019).
Article PubMed Google Scholar
-
Otaiku, A. I. Association of sleep abnormalities in older adults with risk of developing Parkinson’s disease. Sleep 45, https://doi.org/10.1093/sleep/zsac206 (2022).
-
Bugalho, P. et al. Polysomnographic predictors of sleep, motor and cognitive dysfunction progression in Parkinson’s disease: a longitudinal study. Sleep. Med. 77, 205–208 (2021).
Article PubMed Google Scholar
-
Dijkstra, F., de Volder, I., Viaene, M., Cras, P. & Crosiers, D. Polysomnographic Predictors of Sleep, Motor, and Cognitive Dysfunction Progression in Parkinson’s Disease. Curr. Neurol. Neurosci. Rep. 22, 657–674 (2022).
Article PubMed Google Scholar
-
Bugalho, P. et al. Heart rate variability in Parkinson disease and idiopathic REM sleep behavior disorder. Clin. Auton. Res 28, 557–564 (2018).
Article PubMed Google Scholar
-
Yang, J. H. et al. Association of heart rate variability with REM sleep without atonia in idiopathic REM sleep behavior disorder. J. Clin. Sleep. Med. 17, 461–469 (2021).
Article PubMed PubMed Central Google Scholar
-
Suzuki, M. et al. Wearable sensor device-based detection of decreased heart rate variability in Parkinson’s disease. J. Neural Transm. 129, 1299–1306 (2022).
Article PubMed Google Scholar
-
Haapaniemi, T. H. et al. Ambulatory ECG and analysis of heart rate variability in Parkinson’s disease. J. Neurol. Neurosurg. Psychiatry 70, 305–310 (2001).
Article CAS PubMed PubMed Central Google Scholar
-
Speelberg, D. H. B. et al. Prodromal Cognitive Deficits and the Risk of Subsequent Parkinson’s Disease. Brain Sci. 12, https://doi.org/10.3390/brainsci12020199 (2022).
-
Roheger, M., Kalbe, E. & Liepelt-Scarfone, I. Progression of Cognitive Decline in Parkinson’s Disease. J. Parkinsons Dis. 8, 183–193 (2018).
Article PubMed PubMed Central Google Scholar
-
Frazzitta, G., Ferrazzoli, D., Folini, A., Palamara, G. & Maestri, R. Severe Constipation in Parkinson’s Disease and in Parkinsonisms: Prevalence and Affecting Factors. Front Neurol. 10, 621 (2019).
Article PubMed PubMed Central Google Scholar
-
Yoo, S. W. et al. Delayed orthostatic hypotension in Parkinson’s disease. NPJ Parkinsons Dis. 7, 37 (2021).
Article CAS PubMed PubMed Central Google Scholar
-
Fedor, S. et al. Wearable Technology in Clinical Practice for Depressive Disorder. N. Engl. J. Med. 389, 2457–2466 (2023).
Article PubMed Google Scholar
-
Cheng, P. G. et al. Psychologist in a Pocket: Lexicon Development and Content Validation of a Mobile-Based App for Depression Screening. JMIR Mhealth Uhealth 4, e88 (2016).
Article PubMed PubMed Central Google Scholar
-
Ozkanca, Y. et al. Depression Screening from Voice Samples of Patients Affected by Parkinson’s Disease. Digital Biomark. 3, 72–82 (2019).
Article Google Scholar
-
Robin, J., Xu, M., Kaufman, L. D. & Simpson, W. Using Digital Speech Assessments to Detect Early Signs of Cognitive Impairment. Front. Digit Health 3, 749758 (2021).
Article PubMed PubMed Central Google Scholar
-
Konig, A. et al. Objective measurement of gait parameters in healthy and cognitively impaired elderly using the dual-task paradigm. Aging Clin. Exp. Res. 29, 1181–1189 (2017).
Article PubMed PubMed Central Google Scholar
-
Pagano, G. et al. Trial of Prasinezumab in Early-Stage Parkinson’s Disease. N. Engl. J. Med. 387, 421–432 (2022).
Article CAS PubMed Google Scholar
-
Torrado, J. C. et al. Digital phenotyping by wearable-driven artificial intelligence in older adults and people with Parkinson’s disease: Protocol of the mixed method, cyclic ActiveAgeing study. PLoS One 17, e0275747 (2022).
Article CAS PubMed PubMed Central Google Scholar
-
Dorsey, E. R. et al. Deep Phenotyping of Parkinson’s Disease. J. Parkinsons Dis. 10, 855–873 (2020).
Article PubMed PubMed Central Google Scholar
-
Sung, H. Y. et al. The prevalence and patterns of pharyngoesophageal dysmotility in patients with early stage Parkinson’s disease. Mov. Disord. 25, 2361–2368 (2010).
Article PubMed Google Scholar
-
Barnes, J. & David, A. S. Visual hallucinations in Parkinson’s disease: a review and phenomenological survey. J. Neurol. Neurosurg. Psychiatry 70, 727–733 (2001).
Article CAS PubMed PubMed Central Google Scholar
-
Winge, K., Skau, A. M., Stimpel, H., Nielsen, K. K. & Werdelin, L. Prevalence of bladder dysfunction in Parkinsons disease. Neurourol. Urodyn. 25, 116–122 (2006).
Article PubMed Google Scholar
-
Mueller, C. A. et al. A self-administered odor identification test procedure using the “Sniffin’ Sticks. Chem. Senses 31, 595–598 (2006).
Article PubMed Google Scholar
-
Elhanbly, S. M., Abdel-Gawad, M. M., Elkholy, A. A. & State, A. F. Nocturnal penile erections: A retrospective study of the role of RigiScan in predicting the response to sildenafil in erectile dysfunction patients. J. Adv. Res. 14, 93–96 (2018).
Article CAS PubMed PubMed Central Google Scholar
-
Taore, A., Lobo, G., Turnbull, P. R. & Dakin, S. C. Diagnosis of colour vision deficits using eye movements. Sci. Rep. 12, 7734 (2022).
Article CAS PubMed PubMed Central Google Scholar
-
Ozgur, O. K., Emborgo, T. S., Vieyra, M. B., Huselid, R. F. & Banik, R. Validity and Acceptance of Color Vision Testing on Smartphones. J. Neuroophthalmol. 38, 13–16 (2018).
Article PubMed Google Scholar
-
He, R. et al. Olfactory Dysfunction Predicts Disease Progression in Parkinson’s Disease: A Longitudinal Study. Front Neurosci. 14, 569777 (2020).
Article PubMed PubMed Central Google Scholar
-
Iravani, B., Arshamian, A., Schaefer, M., Svenningsson, P. & Lundstrom, J. N. A non-invasive olfactory bulb measure dissociates Parkinson’s patients from healthy controls and discloses disease duration. NPJ Parkinsons Dis. 7, 75 (2021).
Article CAS PubMed PubMed Central Google Scholar
-
Taylor, K. I., Staunton, H., Lipsmeier, F., Nobbs, D. & Lindemann, M. Outcome measures based on digital health technology sensor data: data- and patient-centric approaches. NPJ Digit Med. 3, 97 (2020).
Article PubMed PubMed Central Google Scholar
-
Manta, C., Patrick-Lake, B. & Goldsack, J. C. Digital Measures That Matter to Patients: A Framework to Guide the Selection and Development of Digital Measures of Health. Digit Biomark. 4, 69–77 (2020).
Article PubMed PubMed Central Google Scholar
-
Espay, A. J. et al. A roadmap for implementation of patient-centered digital outcome measures in Parkinson’s disease obtained using mobile health technologies. Mov. Disord. 34, 657–663 (2019).
Article PubMed PubMed Central Google Scholar
-
Stephenson, D., Badawy, R., Mathur, S., Tome, M. & Rochester, L. Digital Progression Biomarkers as Novel Endpoints in Clinical Trials: A Multistakeholder Perspective. J. Parkinsons Dis. 11, S103–S109 (2021).
Article PubMed PubMed Central Google Scholar
-
Evers, L. J. W., Peeters, J. M., Bloem, B. R. & Meinders, M. J. Need for personalized monitoring of Parkinson’s disease: the perspectives of patients and specialized healthcare providers. Front. Neurol. 14, 1150634 (2023).
Article PubMed PubMed Central Google Scholar
-
Martinez-Martin, N., Insel, T. R., Dagum, P., Greely, H. T. & Cho, M. K. Data mining for health: staking out the ethical territory of digital phenotyping. NPJ Digit Med. 1, https://doi.org/10.1038/s41746-018-0075-8 (2018).
-
Berger, A. M. et al. Methodological Challenges When Using Actigraphy in Research. J. Pain. Symptom Manag. 36, 191–199 (2008).
Article Google Scholar
-
Maetzler, W., Klucken, J. & Horne, M. A clinical view on the development of technology-based tools in managing Parkinson’s disease. Mov. Disord. 31, 1263–1271 (2016).
Article PubMed Google Scholar
-
Roussos, G. et al. Identifying and characterising sources of variability in digital outcome measures in Parkinson’s disease. NPJ Digit Med. 5, 93 (2022).
Article PubMed PubMed Central Google Scholar
-
Djeldjli, D., Reguig, F. B. & Maaoui, C. A robust photoplethysmographic imaging for contactless heart and respiratory rates measurement using a simple webcam. Int. J. Med. Eng. Inform. 13, 224–236 (2021).
Google Scholar
-
Grossman, P., Spoerle, M. & Wilhelm, F. H. Reliability of respiratory tidal volume estimation by means of ambulatory inductive plethysmography. Biomed. Sci. Instrum. 42, 193–198 (2006).
PubMed Google Scholar
-
Sano, E. et al. Developing a novel mobile wearable tool of measuring real-time respiratory pattern and thoracic motion non-invasively. Respirology 24, 104 (2019).
Article Google Scholar
-
Sneddon, G. et al. Cardiorespiratory physiology remotely monitored via wearable wristband photoplethysmography: Feasibility and initial benchmarking. Thorax 73, A197 (2018).
Google Scholar
-
Yilmaz, G. et al. A Wearable Stethoscope for Long-Term Ambulatory Respiratory Health Monitoring. Sensors 20, https://doi.org/10.3390/s20185124 (2020).
-
Sheikh, M., Qassem, M. & Kyriacou, P. A. Wearable, Environmental, and Smartphone-Based Passive Sensing for Mental Health Monitoring. Front. Digit Health 3, 662811 (2021).
Article PubMed PubMed Central Google Scholar
-
Gonzalez-Robles, C. et al. Outcome Measures for Disease-Modifying Trials in Parkinson’s Disease: Consensus Paper by the EJS ACT-PD Multi-Arm Multi-Stage Trial Initiative. J. Parkinsons Dis. 13, 1011–1033 (2023).
Article PubMed PubMed Central Google Scholar
-
Chaudhuri, K. R. et al. The metric properties of a novel non-motor symptoms scale for Parkinson’s disease: Results from an international pilot study. Mov. Disord. 22, 1901–1911 (2007).
Article PubMed Google Scholar
-
EU. Horizon 2020 Work Programme 2014-2015 (EU, 2014)
-
Elhanbly, S. & Elkholy, A. Nocturnal penile erections: the role of RigiScan in the diagnosis of vascular erectile dysfunction. J. Sex. Med. 9, 3219–3226 (2012).
Article PubMed Google Scholar
-
Ortiz-Tudela, E., Martinez-Nicolas, A., Campos, M., Rol, M. A. & Madrid, J. A. A new integrated variable based on thermometry, actimetry and body position (TAP) to evaluate circadian system status in humans. PLoS Comput. Biol. 6, e1000996 (2010).
Article PubMed PubMed Central Google Scholar
-
Mankins, J. C. Technology readiness levels. White Pap. 6, 1995 (1995).
Google Scholar
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