Difference In Average Activity Research Articles

A Comparison of Activity Monitor Data from Devices Worn on the Wrist and the Waist in People with Parkinson's Disease.

It is unclear if it is appropriate for people with Parkinson's disease (PD) to wear activity monitors on the wrist because of the potential influence of impairments on the data. The objective of this study was to determine (1) whether activity monitor data collected from devices worn at the wrist and waist are comparable and (2) the contribution of PD impairments to any differences in step and activity counts at the wrist and waist. A total of 46 community-dwelling people with PD wore an accelerometer at the wrist and waist simultaneously for 1 week. Motor impairments (rigidity, bradykinesia, tremor, dyskinesia) were assessed using the Movement Disorders Society Unified Parkinson's Disease Rating Scale part III (motor examination) and part IV (motor complications). Higher values were recorded by the wrist monitor for steps/day (wrist, 9236 [standard deviation (SD) 3812]; waist, 5324 [SD 2800]; difference 3912; P < 0.001) and activity counts/day (wrist, 872,590 [SD 349,148]; waist, 186,491 [SD 101,989]; difference 686,099; P < 0.001). However, the wrist and waist values were strongly correlated (steps, r = 0.89; counts, r = 0.74; P ≤ 0.001). Increased tremor and dyskinesia explained 19% of the variation in the difference in average steps/day, and these variables plus reduced bradykinesia explained 24% of the variation in the difference in average activity counts/day. Wrist monitors are likely to overestimate activity, particularly in people with tremor and dyskinesia. Nonetheless, activity monitors can be worn on the wrist if the aim is to monitor change rather than accurately record activity.

0390 MEASURING ACTIVITY BY ACTIGRAPHY - ARE WE USING THE RIGHT METHODS?

Actigraphy is commonly used in research to provide an objective measure of sleep. Many attempts to evaluate activity using this measure have been undertaken. These tend to involve day/night activity averages which disregards the variability of the hour-by-hour data and variability. To our knowledge, there have been no attempts to aggregate minute-by-minute activity data into hour-by-hour bins which may provide for a better description of any activity changes across the diurnal day. The present analysis focuses on this data aggregation approach. 15 patients (Mean Age=42.9 ± 10.4; f=12) with chronic pain (non-malignant neck or back pain) who received 8-session CBT-I were evaluated by 7-day actigraphy at baseline and post-treatment (only 10 subjects completed actigraphy at both time-points). Daytime activity averages were computed for baseline and post-tx. Daytime activity averages were evaluated using paired sample t-test. Hour-by-hour activity averages were computed for an average 24-hr period using activity count from minute-by-minute epochs over the 7-days. Mixed models were utilized to evaluate the difference between baseline to post-tx (phase) over the 24-hr period (time). Model included main effect for phase and time along with a phase*time interaction. Weekly activity averages showed no difference in average activity throughout the day between baseline and post-tx (Baseline Activity=376, Post-tx Activity=408.6, p=0.26). However, mixed models assessing the daily activity hour-by-hour revealed a significant main effect for time-of-day (p<0.001) and for phase (p=0.001). While the phase*time-of-day was not significant (p=0.25), descriptive data suggest marked improvement post CBT-I especially in the morning (6-11AM). Utilizing daily activity averages may result in a failure to resolve findings that are evident when assessing activity on an hour-by-hour basis. Further, arraying data in this manner may allow for an appreciation of how activity patterns across the diurnal phase of the 24-hr day. Supported by: NINR NR5R21NR009080-02.

Electroencephalographic Differences between Resting and Mental Multiplication

The average frequency of 30 sec. of EEG tracing for two states, resting and mental multiplication (thinking), was determined for 20 Ss by counting each visually detectable change in pen deflection regardless of amplitude. (1) Thinking gave higher average activity scores than resting. (2) The difference in average activity between thinking and resting was greatest in the two frontal and left temporal areas and it was significantly different from the differences in the other areas. (3) In the resting state there was a difference between left and right frontal and temporal areas (which increased while thinking) in contrast with a left-right symmetry of the other areas tested. These findings were interpreted as representing characteristic average activity for the areas and condiions tested and were offered as evidence for the differential utilization of brain areas in the given tasks.