Digital phenotyping by wearable-driven artificial intelligence in older adults and people with Parkinson’s disease: Protocol of the mixed method, cyclic ActiveAgeing study
Torrado JC, Husebo BS, Allore HG, Erdal A, Fæø SE, Reithe H, Førsund E, Tzoulis C, Patrascu M. 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 2022, 17: e0275747. PMID: 36240173, PMCID: PMC9565381, DOI: 10.1371/journal.pone.0275747.Peer-Reviewed Original ResearchMeSH KeywordsAgedArtificial IntelligenceHumansParkinson DiseaseQuality of LifeREM Sleep Behavior DisorderWearable Electronic DevicesConceptsREM Sleep Behavior Disorder Screening QuestionnaireUnified Parkinson's Disease Rating ScaleDisease Rating ScaleGeriatric Depression ScaleApathy Evaluation ScaleParkinson's diseaseOlder adultsGeriatric Anxiety InventoryParkinson's Disease Rating ScaleClinical assessment scalesMontreal Cognitive AssessmentNew outcome measureQuality of lifeGlobal public healthHealthy older adultsClinical trialsOutcome measuresDepression ScaleLifestyle changesScreening QuestionnaireWaiting listFunctional limitationsSymptom evolutionNeurological disordersAssessment Scale