In keeping with these behavioral abnormalities, Col6a1–/– mice displayed alterations in dopaminergic signaling, primarily in the prefrontal cortex. Here, a thorough behavioral characterization of COL6-null (Col6a1–/–) mice unexpectedly revealed that COL6 deficiency leads to a significant impairment in sensorimotor gating and memory/attention functions. Although COL6 genetic variants were recently linked to brain pathologies, the impact of COL6 deficiency in brain function is still largely unknown. Mutations of genes coding for collagen VI (COL6) cause muscle diseases, including Ullrich congenital muscular dystrophy and Bethlem myopathy. Patients therefore underwent the following tests: (1) Mini-Mental State Examination (MMSE) (Folstein et al., 1975) to assess general cognitive functioning (normal≥24/30) (Creavin et al., 2016) (2) phonemic fluency, Stroop Color and Word test (SCWT) (Scarpina and Tagini, 2017), digit cancellation test (Hatta et al., 2012), Trail Making Test A and B (Giovagnoli et al., 1996), and modified Wisconsin Card Sorting Test (WCST) (Cianchetti et al., 2005) to assess executive function and attention (3) generating as many words as possible belonging to the same semantic category (Novelli et al., 1986) to assess an internal lexicon with semantic criteria (4) Rey-Osterrieth complex figure (ROCF) test (Caffarra et al., 2002) to assess visuospatial abilities (5) the digit span test (Choi et al., 2014) and the Corsi Block tapping test (Busch et al., 2005) to assess short-term memory (6) the story recall test (de Renzi et al., 1977) and the Rey Auditory Learning Test (Carlesimo et al., 2002) to assess longterm memory (7) Raven Progressive Matrices (RPM) (Raven, 2000) to assess abstract reasoning and (8) Facial Expression Matching (FEM) and the Neutral Face Memory task (FaMe-N) (de Gelder et al., 2015) to assess social cognition. Thus, deficits in executive tasks were expected to contrast with otherwise spared cognitive functions. By monitoring multiple data streams simultaneously in ecological settings, this technology could uniquely contribute to the evolution of mobility measurement and risk factors for mobility loss. Additional work with a larger and more diverse sample is necessary to confirm associations between smartwatch-measured features and traditional measures. ROAMM was usable, acceptable, and effective at measuring mobility and risk factors for mobility decline in our pilot sample. Some smartwatch features were correlated with their respective traditional measurements (e.g., certain GPS-derived life-space mobility features (r=0.50-0.51, p<0.05) and ecologically-measured pain (r=0.72, p=0.01)), but others were not (e.g., ecologically-measured fatigue). Participants were satisfied with ROAMM's function (87.1%) and ranked the usability as "above average." Most were highly engaged (average adjusted compliance = 70.7%) and the majority reported being "likely" to enroll in a two-year study (77.4%). We describe the usability and feasibility of ROAMM, summarize prompt data using descriptive metrics, and compare prompt data with traditional survey-based questionnaires or other established measures. We aim to describe findings from a pilot study of our Real-time Online Assessment and Mobility Monitor (ROAMM) smartwatch application, which uniquely captures multiple streams of data in real-time in ecological settings.ĭata come from a sample of 31 participants (Mage=74.7, 51.6% female) who used ROAMM for approximately two weeks. However, traditional approaches to mobility assessment are limited in their ability to capture daily fluctuations that align with sporadic health events. Early detection of mobility decline is critical to prevent subsequent reductions in quality of life, disability, and mortality.
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