Daniel J van Wamelen1, Jirada Sringean2, Dhaval Trivedi3, Camille B Carroll4, Anette E Schrag5, Per Odin6, Angelo Antonini7, Bastiaan R Bloem8, Roongroj Bhidayasiri9, K Ray Chaudhuri3. 1. King's College London, Department of Neurosciences, Institute of Psychiatry, Psychology & Neuroscience, London, United Kingdom; Parkinson's Foundation Centre of Excellence at King's College Hospital, Denmark Hill, London, United Kingdom; Radboud University Medical Centre; Donders Institute for Brain, Cognition and Behaviour; Department of Neurology, Nijmegen, the Netherlands. Electronic address: Daniel.van_Wamelen@kcl.ac.uk. 2. Chulalongkorn Centre of Excellence for Parkinson's Disease & Related Disorders, Department of Medicine, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Thai Red Cross Society, Bangkok, Thailand. 3. King's College London, Department of Neurosciences, Institute of Psychiatry, Psychology & Neuroscience, London, United Kingdom; Parkinson's Foundation Centre of Excellence at King's College Hospital, Denmark Hill, London, United Kingdom. 4. Faculty of Health, University of Plymouth, Plymouth, Devon, United Kingdom. 5. Department of Clinical and Movement Neurosciences, University College London, London, United Kingdom. 6. Division of Neurology, Department of Clinical Sciences, Lund University, Lund, Sweden. 7. Movement Disorders Unit, Department of Neuroscience, University of Padua, Padua, Italy. 8. Radboud University Medical Centre; Donders Institute for Brain, Cognition and Behaviour; Department of Neurology, Nijmegen, the Netherlands. 9. Chulalongkorn Centre of Excellence for Parkinson's Disease & Related Disorders, Department of Medicine, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Thai Red Cross Society, Bangkok, Thailand; The Academy of Science, The Royal Society of Thailand, Bangkok, Thailand.
Abstract
INTRODUCTION: There is an ongoing digital revolution in the field of Parkinson's disease (PD) for the objective measurement of motor aspects, to be used in clinical trials and possibly support therapeutic choices. The focus of remote technologies is now also slowly shifting towards the broad but more "hidden" spectrum of non-motor symptoms (NMS). METHODS: A narrative review of digital health technologies for measuring NMS in people with PD was conducted. These digital technologies were defined as assessment tools for NMS offered remotely in the form of a wearable, downloadable as a mobile app, or any other objective measurement of NMS in PD that did not require a hospital visit and could be performed remotely. Searches were performed using peer-reviewed literature indexed databases (MEDLINE, Embase, PsycINFO, Cochrane Database of Systematic Reviews, Cochrane CENTRAL Register of Controlled Trials), as well as Google and Google Scholar. RESULTS: Eighteen studies deploying digital health technology in PD were identified, for example for the measurement of sleep disorders, cognitive dysfunction and orthostatic hypotension. In addition, we describe promising developments in other conditions that could be translated for use in PD. CONCLUSION: Unlike motor symptoms, non-motor features of PD are difficult to measure directly using remote digital technologies. Nonetheless, it is currently possible to reliably measure several NMS and further digital technology developments are underway to offer further capture of often under-reported and under-recognised NMS.
INTRODUCTION: There is an ongoing digital revolution in the field of Parkinson's disease (PD) for the objective measurement of motor aspects, to be used in clinical trials and possibly support therapeutic choices. The focus of remote technologies is now also slowly shifting towards the broad but more "hidden" spectrum of non-motor symptoms (NMS). METHODS: A narrative review of digital health technologies for measuring NMS in people with PD was conducted. These digital technologies were defined as assessment tools for NMS offered remotely in the form of a wearable, downloadable as a mobile app, or any other objective measurement of NMS in PD that did not require a hospital visit and could be performed remotely. Searches were performed using peer-reviewed literature indexed databases (MEDLINE, Embase, PsycINFO, Cochrane Database of Systematic Reviews, Cochrane CENTRAL Register of Controlled Trials), as well as Google and Google Scholar. RESULTS: Eighteen studies deploying digital health technology in PD were identified, for example for the measurement of sleep disorders, cognitive dysfunction and orthostatic hypotension. In addition, we describe promising developments in other conditions that could be translated for use in PD. CONCLUSION: Unlike motor symptoms, non-motor features of PD are difficult to measure directly using remote digital technologies. Nonetheless, it is currently possible to reliably measure several NMS and further digital technology developments are underway to offer further capture of often under-reported and under-recognised NMS.
Authors: Mubarak A Alanazi; Abdullah K Alhazmi; Osama Alsattam; Kara Gnau; Meghan Brown; Shannon Thiel; Kurt Jackson; Vamsy P Chodavarapu Journal: Sensors (Basel) Date: 2022-07-22 Impact factor: 3.847
Authors: Daniel Johannes van Wamelen; Silvia Rota; Anette Schrag; Alexandra Rizos; Pablo Martinez-Martin; Daniel Weintraub; Kallol Ray Chaudhuri Journal: Mov Disord Clin Pract Date: 2022-08-05