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TD‐03‐03: DEVELOPING AN ELECTRONIC CONSENT FOR ADRC
Author(s) -
Suver Christine,
MacDuffie Woody,
Simon Stockard,
Seroussi Allie,
Chin Erin,
Cobb Nichelle,
Goldstein Felicia C.,
Manzanares Cecelia,
Croes Kenneth,
Dykema Jennifer,
Blazel Hanna,
Gleason Carey E.,
Asthana Sanjay,
Mangravite Lara M.,
Wilbanks John,
Levey Allan I.,
Lah James J.,
Edwards Dorothy Farrar
Publication year - 2019
Publication title -
alzheimer's and dementia
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.713
H-Index - 118
eISSN - 1552-5279
pISSN - 1552-5260
DOI - 10.1016/j.jalz.2019.06.4310
Subject(s) - informed consent , comprehension , plain language , psychology , medical education , reading (process) , nice , computer science , medicine , alternative medicine , linguistics , pathology , philosophy , programming language
3-axis accelerometers. The AMS was significantly associated with certain types of agitated behaviors, see Fig 1. Furthermore the AMS could predict subject’s CMAI values (CMAI total and CMAI physical agitated behaviors). Beyond the mechanistic association between AMS and behavior on the group level, the AMS provided an added value for the prediction of behaviors on an individual level. Therefore we clustered all patients into two groups based on their behavior (Fig 2). The AMS yielded a cross-validated (leave one out) correct classification of 15/17 subjects, compared to 12/17 based on gender and age alone in Linear discriminant analysis. Conclusions: It is possible to get relevant information about the behavior of PwD with accelerometry. We extend previous studies by differentiating various types of agitated behaviors in our analyses and applying long-term measurements in a real-world setting. Our data are relevant to evaluate the effect of interventions on challenging behavior in future studies.