Activity monitors may not accurately detect steps in hospitalized older adults who walk slowly. We compared ActiGraph GT3X+ step counts (hip and ankle locations, default and low frequency extension [LFE] analyses) to the StepWatch monitor (ankle) during a hallway walk in 38 geriatric rehabilitation patients (83.2 ± 7.1 years of age, 0.4 ± 0.2 m/s gait speed). Absolute percent error values were low (<3%) and did not differ for the StepWatch and the GT3X+ (ankle, LFE); however, error values were high (19–97%) when the GT3X+ was worn at the hip and/ or analyzed with the default filter. Although these finding suggest the GT3X+ (ankle, LFE) functions as well as the StepWatch in detecting steps during walking in older adults with slow gait speeds, further research is needed to determine whether the GT3X+ is also able to disregard other body movements (e.g., fidgeting) that occur when full day monitoring is utilized.
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Sandra C. Webber and Philip D. St. John
Sandra C. Webber and Michelle M. Porter
This exploratory study examined the feasibility of using Garmin global positioning system (GPS) watches and ActiGraph accelerometers to monitor walking and other aspects of community mobility in older adults. After accuracy at slow walking speeds was initially determined, 20 older adults (74.4 ± 4.2 yr) wore the devices for 1 day. Steps, distances, and speeds (on foot and in vehicle) were determined. GPS data acquisition varied from 43 min to over 12 hr, with 55% of participants having more than 8 hr between initial and final data-collection points. When GPS data were acquired without interruptions, detailed mobility information was obtained regarding the timing, distances covered, and speeds reached during trips away from home. Although GPS and accelerometry technology offer promise for monitoring community mobility patterns, new GPS solutions are required that allow for data collection over an extended period of time between indoor and outdoor environments.
Olayinka Akinrolie, Sandra C. Webber, Nancy M. Salbach, and Ruth Barclay
The aim of this study was to examine the construct and known-groups validity of the total score of five items adapted from the Community Healthy Activities Model Program for Seniors (CHAMPS) questionnaire to measure outdoor walking (CHAMPS-OUTDOORS) in older adults. Data from the baseline assessment of the Getting Older Adult OUTdoors (GO-OUT) trial were used. Construct validity of the CHAMPS-OUTDOORS used objective measures of outdoor walking (accelerometry–GPS), Ambulatory Self-Confidence Questionnaire, RAND-36, 6-min walk test, 10-m walk test, and Mini-Balance Evaluation System Test. For known-groups validity, we compared the CHAMPS-OUTDOORS of those who walked < or ≥1.2 m/s. Sixty-five participants had an average age of 76.5 ± 7.8 years. The CHAMPS-OUTDOORS was moderately correlated with total outdoor walking time (r = .33) and outdoor steps (r = .33) per week measured by accelerometry-GPS, and weakly correlated with Mini-Balance Evaluation System Test score (r = .27). The CHAMPS-OUTDOORS did not distinguish known groups based on crosswalk speed (p = .33). The CHAMPS-OUTDOORS may be used to assess outdoor walking in the absence of accelerometry GPS. Further research examining reliability is needed.
Sandra C. Webber, Sheila M. Magill, Jenessa L. Schafer, and Kaylie C.S. Wilson
The purpose was to compare step count accuracy of an accelerometer (ActiGraph GT3X+), a mechanical pedometer (Yamax SW200), and a piezoelectric pedometer (SC-StepMX). Older adults (n = 13 with walking aids, n = 22 without; M = 81.5 years old, SD = 5.0) walked 100 m wearing the devices. Device-detected steps were compared with manually counted steps. We found no significant differences among monitors for those who walked without aids (p = .063). However, individuals who used walking aids exhibited slower gait speeds (M = 0.83 m/s, SD = 0.2) than non–walking aid users (M = 1.21 m/s, SD = 0.2, p < .001), and for them the SC-StepMX demonstrated a significantly lower percentage of error (Mdn = 1.0, interquartile range [IQR] = 0.5−2.0) than the other devices (Yamax SW200, Mdn = 68.9, IQR = 35.9−89.3; left GT3X+, Mdn = 52.0, IQR = 37.1−58.9; right GT3X+, Mdn = 51.0, IQR = 32.3−66.5; p < .05). These results support using a piezoelectric pedometer for measuring steps in older adults who use walking aids and who walk slowly.
Sheila M. Korpan, Jenessa L. Schafer, Kaylie C.S. Wilson, and Sandra C. Webber
Accelerometer step count accuracy may be affected by gait speed, device positioning, and analysis algorithm selection. We assessed ActiGraph GT3X+ step count accuracy related to device placement and analysis algorithm in older adults with walking aids (n = 13) and without walking aids (n = 22). Participants (81.5 ± 5.0 years of age) completed a timed 100-m walk wearing five GT3X+ monitors (hips, ankles, lumbar spine). Individuals with walking aids had slower gait speeds (0.8 ± 0.20 m/s versus 1.2 ± 0.20 m/s without walking aids, p < .001). Intraclass correlation coefficient values for observed versus monitored steps were highest when ankle placement and the low frequency extension (LFE) algorithm were used (left ankle ICC = .989, right ankle ICC = .998). Using the GT3X+ ankle placement and analyzing data with the LFE algorithm resulted in the most accurate step counts in older adults.
Sandra C. Webber, Francine Hahn, Lisa M. Lix, Brenda J. Tittlemier, Nancy M. Salbach, and Ruth Barclay
Objective: To determine the optimal threshold, based on cadence and lifestyle counts per minute, to detect outdoor walking in mobility-limited older adults. Methods: Older adults (N = 25, median age: 77.0 years, interquartile range: 10.5) wore activity monitors during 80 outdoor walks. Walking bouts were identified manually (reference standard) and compared with identification using cadence thresholds (≥30, ≥35, ≥40, ≥45, and ≥50 steps/min) and >760 counts per minute using low frequency extension analysis. Results: Median walking bout duration was 10.5 min (interquartile range 4.8) and median outdoor walking speed was 0.70 m/s (interquartile range 0.20). Cadence thresholds of ≥30, ≥35, and ≥40 steps/min demonstrated high sensitivity (1.0, 95% confidence intervals [0.95, 1.0]) to detect walking bouts; estimates for specificity and positive predictive value were highest for ≥40 steps/min. Conclusion: A cadence threshold of ≥40 steps/min is recommended for detecting sustained outdoor walking in this population.
Sandra C. Webber, Scott Anderson, Logan Biccum, Sava Jin, Shahd Khawashki, and Brenda J. Tittlemier
The purpose of this study was to measure heart rate, activity intensity, and steps in recreational singles and doubles pickleball players. We collected data in 22 singles and 31 doubles players (62.1 ± 9.7 years of age) using Garmin Fenix 5 watches (Garmin International, Inc.) and ActiGraph GT3X+ (ActiGraph LLC) accelerometers. Mean heart rates during singles and doubles were 111.6 ± 13.5 and 111.5 ± 16.2 beats/min (70.3% and 71.2% of predicted maximum heart rate), respectively. Over 70% of singles and doubles playing time was categorized in moderate to vigorous heart rate zones whereas 80.5% of singles time and 50.4% of doubles time were moderate based on Freedson accelerometer cut-points. Steps per hour were higher in singles versus doubles (3,322 ± 493 vs. 2,791 ± 359), t(51) = 4.540, p < .001. Singles and doubles pickleball are moderate- to vigorous-intensity activities that can contribute substantially toward older adults meeting physical activity guidelines.
Nancy E. Mayo, Kedar Mate, Olayinka Akinrolie, Hong Chan, Nancy M. Salbach, Sandra C. Webber, and Ruth Barclay
This study aimed to inform a measurement approach for older persons who wish to engage in active living such as participating in a walking program. The Patient Generated Index, an individualized measurement approach, and directed and summative content analyses were carried out. A sample size of 204 participants (mean age 75 years; 62% women) was recruited; it generated 934 text threads mapped to 460 unique categories within 45 domains with similarities and differences for women and men. The Capability, Opportunity, Motivation, and Behaviors Model best linked the domains. The results suggest that older persons identify the need to overcome impaired capacity, low motivation, and barriers to engagement to live actively. These are all areas that active living programs could address. How to measure the outcomes of these programs remains elusive.