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 ﬁtness) 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 diﬀerent needs than younger users; their exercise and activity patterns and preferences have been

reported to be diﬀerent 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 classiﬁcation.

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+

Fuelband

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 diﬀerences as

high as 6000 steps diﬀerent 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 signiﬁcant although the small

sample size explains the lack of statistical signiﬁcance. 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 diﬀerences in step length,

pace of walking and diﬀerent sensitivities and algorithms within each device brand. There is evidence that slower walking

speed aﬀects 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 ﬁndings 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 ﬁnding calls

in to question whether older adults want to use the calorie counts from these devices. The participants were moderately

satisﬁed 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 satisﬁed 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 ﬁtness 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