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Home Healthcare, Hospice, and Information Technology Innovations Conference

Innovations in Home Healthcare, Hospice, and Information Technology

A forum towards achieving evidence-based diffusion and implementation of innovations

Fri, Nov 3, 2017
Friday, Nov 3, 2017, Washington - D.C.
H3IT: Home Healthcare, Hospice, and Information Technology Conference Washington D.C., 2014
Use and Satisfaction With Wearable Activity
Trackers Among Community Dwelling Older People
Elizabeth Madigan
, Chia Hua Lin
, Mehran Mehregany
he use of wearable devices for activity tracking (including exercise and tness) has increased greatly yet there
is relatively little research on the use of wearable devices among older people. This is important as older users
may have dierent needs than younger users; their exercise and activity patterns and preferences have been
reported to be dierent as well. The purpose of this study was to compare three existing wearable devices on
measures of use (step and calorie counts) and satisfaction. This study is a secondary analysis of a larger study that tested
an investigator developed device and algorithms for human activity classication.
Methods: Thirteen community dwelling older people from a continuing care retirement community were recruited to wear
JawBone Up
, Nike+ Fuelband
TM and Fitbit Flex
TM for one day from the time they awoke until they went to bed.
Participants wore all three devices simultaneously as part of the testing of the investigator developed device. The devices
recorded the step and calorie counts as programmed and released by the manufacturers. Falls risk assessment was done using
the Missouri Alliance for Home Care (MAHC) falls risk assessment. Following the use of the devices, the System Usability
Scale (SUS) was completed by the participants to determine satisfaction with the devices.
Results: The 13 participants ranged in age from 72-92 years; 6 were female. The falls risk assessment scores ranged from
2 to 7, with a mean = 3.84 and SD = 1.2. Based on the recommended MAHC cut score of 4 or higher for being at risk
of falls, 11 of 13 (84.6%) participants were considered at risk for falls. The recorded number of steps and calories varied
widely between the three devices. Mean step counts by device were: JawBone Up
TM = 3894 (SD = 3089.8), Nike+
TM = 2273 (SD = 1705.6) and Fitbit Flex
TM = 4998 (SD = 3153). Mean calorie counts by device were:
JawBone Up
TM = 2107 (SD = 152.8), Nike+ Fuelband
TM = 316 (SD = 136.5) and Fitbit Flex
TM = 2511 (SD =
221.2). Although the bivariate correlations between the step counts were all above 0.92, there were individual dierences as
high as 6000 steps dierent at the individual participant level. There were low correlations between the calorie counts, with
correlations as low as 0.39 between JawBone Up and FitBit. Correlations between falls risk and step counts ranged from
-0.31 to -0.41 and between falls risk and calorie count from 0.09 to -.30; none were statistically signicant although the small
sample size explains the lack of statistical signicance. The SUS scores ranged from 37.5 to 82.5 on a scale from 1 to 100,
with higher scores indicating more satisfaction; the mean was 66.3 and SD was 11.9.
Discussion:The MAHC falls risk assessment has two sets of cut scores: 4 vs 6 (Calys, Gagnon and Jerrigan). Use of the
higher cut score would result in only one of the participants b eing considered at risk. The wide range in step counts as
measured by the device brands within each participant was surprising and may be accounted for by dierences in step length,
pace of walking and dierent sensitivities and algorithms within each device brand. There is evidence that slower walking
speed aects the accuracy of step count from a tri-axial accelerometer (Cleland et al 2011) and that testing in non-laboratory
environments decreases the accuracy of the devices, although there was wide variation in how much the accuracy fell (Feito
2012). To determine which of the commercial devices is most accurate requires further study to measure actual step count
and step count as recorded by the devices. Performing this research with older adults with varying step lengths and walking
pace would validate the ndings from other research. Calorie counts were derived from the devices based on the proprietary
algorithms. The calorie counts had even lower correlations and wider ranges within the same participants. This nding calls
in to question whether older adults want to use the calorie counts from these devices. The participants were moderately
satised with the devices using the SUS, perhaps from wearing all three devices simultaneously for testing the investigator-
1developed device.
Conclusion: Findings from the present study suggest that older adults are moderately satised with the wearable devices.
The measure of step count indicated wide variations within some participants although the correlations between the devices
were high. Further research is needed to validate the step counts and calorie tracking in community-dwelling older adults
before recommendations can be made for use of these devices by home health clinicians. While tness devices are important
technological supports for measuring physical activity, it is not clear whether these devices will meet the needs of community-
dwelling older adults.
Frances Payne Bolton School of Nursing
Case Case Western Reserve University
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