Digital health technologies and digital biomarkers in REM sleep behavior disorder: need for order out of chaos
Ambra Stefani, Matteo Cesari
Abstract
Digital health technologies' development and constantly increasing use over the past decade have radically changed healthcare [1], including the sleep clinical and research field.The wide use of digital health technologies as personal self-monitoring, and their integration into clinical healthcare and in research studies, allow collection of a large data amount in different settings, including in a real-world environment (i.e.real-world data) [2], and over long periods of time.Digital health developments allow, for example, sleep assessment in the home environment, and the possibility of gathering these data for several days or weeks.Nonetheless, there are also drawbacks related to validation and assessment of performance of these technologies [3,4].Isolated rapid eye movement (REM) sleep behavior disorder (RBD) is a prodromal alpha-synucleinopathy.Its diagnosis, its characterization in terms of risk of progression, of phenoconversion-i.e. from isolated RBD into a clinical manifest alpha-synucleinopathy-(including type of phenoconversion, i.e. dementia with Lewy bodies, Parkinson's disease, or multiple system atrophy), as well as screening for the disorder in large cohorts, are of utmost importance [5,6].Digital health technologies have advanced this field of research in the past several years, opening up new possibilities in terms of digital biomarkers.However, the large availability but limited evaluation of performance of these new technologies may lead to problems in terms of their application and data interpretation [3,4,7,8].Gnarra et al. [9] ., in this issue of SLEEP, provide a narrative review of digital health technologies employed in RBD, i.e. wearable and nearable devices, and automatic algorithms applied to polysomnography.Merits of this work are the exclusive inclusion of studies conducted in polysomnography-confirmed isolated and secondary RBD, and the overview of several digital health technologies identifying digital biomarkers with different applications in RBD.The review divides digital health technologies into four sections: actigraphy, gait analysis systems, computerized algorithms, and novel technologies.Despite being useful for providing an overview of digital health technologies investigated in RBD, this structure does not consider for which purpose each digital