Robotic Outcome Assessment in a Non-Human Primate Model of Middle Cerebral Artery Stroke

Stroke is a prevalent disorder with immense socioeconomic impact. A variety of chronic neurological deficits result from stroke. In particular, sensorimotor deficits are a significant barrier to achieving post-stroke independence. Unfortunately, the majority of pre-clinical studies that show improved outcomes in animal stroke models have failed in clinical trials. Pre-clinical studies using non-human primate (NHP) stroke models prior to initiating human trials are a potential step to improving translation from animal studies to clinical trials. Robotic assessment tools represent a quantitative, reliable, and reproducible means to assess reaching behaviour following stroke in both humans and NHPs. We investigated the use of robotic technology to assess sensorimotor impairments in NHPs following middle cerebral artery occlusion (MCAO). Two cynomolgus macaques underwent transient MCAO for 90 minutes. Approximately 1.5 years following the procedure these NHPs and two non-stroke control monkeys were trained in a reaching task with both arms in the KINARM exoskeleton. This robot permits elbow and shoulder movements in the horizontal plane. The task required NHPs to make reaching movements from a centrally positioned start target to 1 of 8 peripheral targets uniformly distributed around the first target. We analyzed four movement parameters: reaction time, movement time (MT), initial direction error (IDE), and number of speed maxima to characterize sensorimotor deficiencies. We hypothesized reduced performance in these attributes during a neurobehavioural task with the paretic limb of NHPs following MCAO compared to controls. Reaching movements in the non-affected limbs of control and experimental NHPs showed bell-shaped velocity profiles. In contrast, the reaching movements with the affected limbs were highly variable. We found distinctive patterns in MT, IDE, and number of speed peaks between control and experimental monkeys and between limbs of NHPs with MCAO. NHPs with MCAO demonstrated more speed peaks, longer MTs, and greater IDE in their paretic limb compared to controls. These initial results qualitatively match human stroke subjects’ performance, suggesting that robotic neurobehavioural assessment in NHPs with stroke is feasible and could have translational relevance in subsequent human studies. Further studies will be necessary to replicate and expand on these preliminary findings.

Imputing accelerometer non-wear time when assessing children's moderate-to-vigorous physical activity

BACKGROUND: Moderate-to-vigorous physical activity (MVPA) is an important determinant of children’s physical health, and is commonly measured using accelerometers. A major limitation of accelerometers is non-wear time, which is the time the participant did not wear their device. Given that non-wear time is traditionally discarded from the dataset prior to estimating MVPA, final estimates of MVPA may be biased. Therefore, alternate approaches should be explored. OBJECTIVES: The objectives of this thesis were to 1) develop and describe an imputation approach that uses the socio-demographic, time, health, and behavioural data from participants to replace non-wear time accelerometer data, 2) determine the extent to which imputation of non-wear time data influences estimates of MVPA, and 3) determine if imputation of non-wear time data influences the associations between MVPA, body mass index (BMI), and systolic blood pressure (SBP). METHODS: Seven days of accelerometer data were collected using Actical accelerometers from 332 children aged 10-13. Three methods for handling missing accelerometer data were compared: 1) the “non-imputed” method wherein non-wear time was deleted from the dataset, 2) imputation dataset I, wherein the imputation of MVPA during non-wear time was based upon socio-demographic factors of the participant (e.g., age), health information (e.g., BMI), and time characteristics of the non-wear period (e.g., season), and 3) imputation dataset II wherein the imputation of MVPA was based upon the same variables as imputation dataset I, plus organized sport information. Associations between MVPA and health outcomes in each method were assessed using linear regression. RESULTS: Non-wear time accounted for 7.5% of epochs during waking hours. The average minutes/day of MVPA was 56.8 (95% CI: 54.2, 59.5) in the non-imputed dataset, 58.4 (95% CI: 55.8, 61.0) in imputed dataset I, and 59.0 (95% CI: 56.3, 61.5) in imputed dataset II. Estimates between datasets were not significantly different. The strength of the relationship between MVPA with BMI and SBP were comparable between all three datasets. CONCLUSION: These findings suggest that studies that achieve high accelerometer compliance with unsystematic patterns of missing data can use the traditional approach of deleting non-wear time from the dataset to obtain MVPA measures without substantial bias.