Methodology for using long-term accelerometry monitoring to describe daily activity patterns in COPD

Ariel Hecht, Shuyi Ma, Janos Porszasz, Richard Casaburi, COPD Clinical Research Network, J J Reilly Jr, G Washko, C Mayo, S Peterson, R K Albert, B Make, M Schwarz, C Welsh, M Gilmartin, C Verano, R Casaburi, J Porszasz, R D Love, D E Niewoehner, C McEvoy, K R Rice, P D Scanlon, C B Bourassa, P Neuenfeldt, G J Criner, W Chatila, N Marchetti, V Kim, G D'Alonzo, S Krachman, F Cordova, K Brennan, N Patel, J Mamary, C Grabianowski, G Jones, W C Bailey, J A D Cooper, M T Dransfield, L B Gerald, P O'Reilly, S Tidwell, S C Lazarus, H A Boushey, P G Woodruff, M Birch, S M Scharf, M Alattar, P Amelung, M Cowan, J Hanson, J Hasday, A Iacono, C Shanholtz, N Todd, A Verceles, T Fitzgerald, F J Martinez, J L Curtis, M K Han, P J Christensen, D White, F Sciurba, R Folger, D Filippino, J E Connett, N R Anthonisen, C Wendt, M Skeans, W Patrek, H Voelker, B B Bender, S F Kelsey, J R Landis, B Phillips, G M Turino, R Veatch, A Waldo, A Wanner, H W Kelly, J Maurer, A J McSweeny, R M Senior, E A Thom, P D Wagner, R L ZuWallack, G Weinmann, T Croxton, A Punturieri, M P Stylianou, Ariel Hecht, Shuyi Ma, Janos Porszasz, Richard Casaburi, COPD Clinical Research Network, J J Reilly Jr, G Washko, C Mayo, S Peterson, R K Albert, B Make, M Schwarz, C Welsh, M Gilmartin, C Verano, R Casaburi, J Porszasz, R D Love, D E Niewoehner, C McEvoy, K R Rice, P D Scanlon, C B Bourassa, P Neuenfeldt, G J Criner, W Chatila, N Marchetti, V Kim, G D'Alonzo, S Krachman, F Cordova, K Brennan, N Patel, J Mamary, C Grabianowski, G Jones, W C Bailey, J A D Cooper, M T Dransfield, L B Gerald, P O'Reilly, S Tidwell, S C Lazarus, H A Boushey, P G Woodruff, M Birch, S M Scharf, M Alattar, P Amelung, M Cowan, J Hanson, J Hasday, A Iacono, C Shanholtz, N Todd, A Verceles, T Fitzgerald, F J Martinez, J L Curtis, M K Han, P J Christensen, D White, F Sciurba, R Folger, D Filippino, J E Connett, N R Anthonisen, C Wendt, M Skeans, W Patrek, H Voelker, B B Bender, S F Kelsey, J R Landis, B Phillips, G M Turino, R Veatch, A Waldo, A Wanner, H W Kelly, J Maurer, A J McSweeny, R M Senior, E A Thom, P D Wagner, R L ZuWallack, G Weinmann, T Croxton, A Punturieri, M P Stylianou

Abstract

We sought to develop procedures for computerized analysis of long-term, high-resolution activity monitoring data that allow accurate assessment of the time course of activity levels suitable for use in chronic obstructive pulmonary disease (COPD) patients. Twenty-two COPD patients utilizing long-term oxygen recruited from 5 sites of the COPD Clinical Research Network wore a triaxial accelerometer (RT3, Stayhealthy, Monrovia, CA) during waking hours over a 14-day period. Computerized algorithms were composed allowing minute-by-minute activity data to be analyzed to determine, for each minute, whether the monitor was being worn. Temporal alignment allowed determination of average time course of activity level, expressed as average vector magnitude units (VMU, the vectorial sum of activity counts in three orthogonal directions) per minute, for each hour of the day. Mid-day activity was quantified as average VMU/minute between 10AM and 4PM for minutes the monitor was worn. Over the 14 day monitoring period, subjects wore the monitor an average of 11.4 +/- 3.0 hours x day(-1). During mid-day hours, subjects wore the monitor 76.3% of the time and generated an average activity level of 112 +/- 55 VMU x min(-1). Increase in precision of activity estimates with longer monitoring periods was demonstrated. This analysis scheme allows a detailed temporal pattern of activity to be defined from triaxial accelerometer recordings and has the potential to facilitate comparisons among subjects and between subject groups. This trial is registered at ClinicalTrials.gov (NCT00325754).

Conflict of interest statement

Conflict of Interest

The authors have no conflicts of interest to report.

Figures

Figure 1
Figure 1
Decision tree by which the time course of vector magnitude unit (VMU) values is analyzed to determine, for a particular minute, whether the activity monitor is worn or not worn.
Figure 2
Figure 2
Selected minute-by-minute vector magnitude unit (VMU) records of activity monitor recording, representing one hour each of high activity (left panel), sedentary activity (center panel), and a non-wearing period during the night (right panel). Note differences in ordinate scales in the three plots. See text for implications of differing analysis strategies for these data.
Figure 3
Figure 3
Examples of averaged daily activity profiles for a subject. Upper plot: vector magnitude units (VMU) per minute for each hour of the day (±SE), averaged over a 14 day period (open circles denote hours when monitor was never worn). Lower plot: minutes per hour the monitor was worn (±SE) for each hour of the day, averaged over a 14 day period.
Figure 4
Figure 4
Average daily activity profiles for all 22 subjects, averaged over the 14 day observation period (mean ± SE). Upper plot: average vector magnitude units (VMU) for each hour of the day. Lower plot: average minutes in which the activity monitor was worn during each hour over the course of the day.
Figure 5
Figure 5
Upper plot: Average vector magnitude unit (VMU) per minute daily activity over the 14 day observation period for all 22 subjects for minutes in which the activity monitor was worn. By the reverse arrangements test, there is no significant trend of activity levels over time (p

Figure 6

Average (±SD) minutes spent per…

Figure 6

Average (±SD) minutes spent per day over a 14 day observation period in…

Figure 6
Average (±SD) minutes spent per day over a 14 day observation period in three vector magnitude unit (VMU) activity ranges in 22 COPD patients.
Figure 6
Figure 6
Average (±SD) minutes spent per day over a 14 day observation period in three vector magnitude unit (VMU) activity ranges in 22 COPD patients.

Source: PubMed

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