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Acceptability of wristband activity trackers among community dwelling older adults
Geriatric Nursing 36 (2015) S21eS25
Contents lists available at ScienceDirect
Geriatric Nursing journal homepage: www.gnjournal.com
Feature Article
Acceptability of wristband activity trackers among community dwelling older adults Tara O’Brien, PhD, RN, CNE a, *, Meredith Troutman-Jordan, PhD, RN b, Donna Hathaway, PhD, RN, FAAN a, Shannon Armstrong, BSN, RN b, Michael Moore, PhD b a b
University of Tennessee Health Science Center’s College of Nursing, 920 Madison Building, Memphis, TN 38163, USA School of Nursing, University of North Carolina at Charlotte, 9201 University City Blvd, Charlotte, NC 28223, USA
a b s t r a c t Keywords: Older adults Physical activity Technology
Wristband activity trackers have become widely used among young adults. However, few studies have explored their use for monitoring and improving health outcomes among older adults. The purpose of this study was to evaluate the feasibility and utility of activity tracker use among older adults for monitoring activity, improving self-efficacy, and health outcomes. A 12-week pilot study was conducted to evaluate the feasibility and utility of mobile wristband activity trackers. The sample (N ¼ 34) was 65% women 73.5 9.4 years of age who had a high school diploma or GED (38%) and reported an income $35,000 (58%). Participants completing the study (95%) experienced a decrease in waist circumference (p > 0.009), however no change in self-efficacy. Participants found activity trackers easy to use which contributed to minimal study withdrawals. It was concluded that activity trackers could be useful for monitoring and promoting physical activity and improving older adults’ health. Ó 2015 Elsevier Inc. All rights reserved.
Introduction
Americans living in the southern region of the United States are less likely to participate in regular physical activity compared to people living in other regions of the US.1 In North Carolina (NC), for example, only 46.4% of older adults meet the current daily physical activity recommendations (30 min of exercise per day).3 This lack of physical activity has been linked to cardiovascular disease among NC residents (CDC, 2013) which is the most costly and preventable cause of morbidity and mortality for older adults in the state.4 Simple low impact physical activity, such as walking, is particularly beneficial for older adults.5 Although the literature indicates that accelerometers (wearable devices that measure physical activity) have been widely accepted and proven useful for increasing activity among young adults6 these findings are not conclusive for older adults. Some studies have found older adults to be more aware of their physical activity levels when wearing a pedometer.7 However, other studies have found barriers with the use of traditional pedometers; including, becoming detached from clothing, loss of the pedometer, improper stride length measurement, and inaccurate recording of information.8 There is some evidence, however, that measurement of physical activity may help increase physical activity among older individuals at least when combined with additional Internet guided activity planning and tracking.9 For example, older adults randomized to treatment
Participation in physical activity is often associated with young rather than older people. According to the Centers for Disease Control,1 80% of older adults living in the United States (US) do not engage in the recommended daily amount of exercise. The lack of physical activity among older adults has been associated with higher rates of cancer, stroke, cardiovascular disease, diabetes, obesity, and Alzheimer’s disease.2 Interventions that enhance selfefficacy for older adults’ ability to participate in regular physical activity are greatly needed to lessen the personal, social, and economic impacts of chronic disease.
Conflict of interest: The authors disclose no conflicts of interest regarding authors’ professional or financial affiliations that may potentially be regarded as having biased the manuscript. Funding sources: The University of North Carolina at Charlotte Faculty Research Grant. Disclosure: The University of North Carolina at Charlotte Faculty Research Grant assisted with buying the Nike Fuel Activity Tracker Bands for the study. * Corresponding author. The University of Tennessee Health Science Center, College of Nursing, 9201 University City Boulevard, Memphis, TN 38163, USA. Tel.: þ1 901 448 1176. E-mail address: [email protected] (T. O’Brien). 0197-4572/$ e see front matter Ó 2015 Elsevier Inc. All rights reserved. http://dx.doi.org/10.1016/j.gerinurse.2015.02.019
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T. O’Brien et al. / Geriatric Nursing 36 (2015) S21eS25
using a combination of an accelerometer and an Internet physical activity planning/tracking mechanism averaged 764 more steps per day than the usual care group which was given only a pedometer.9 There is also some growing evidence that older adults benefit from exposure to websites to deliver computer-tailored physical activity interventions.10 Specifically, this study found that the older adults actually engaged in more total physical activity minutes and more total physical activity sessions than those in younger age groups. These findings suggest that monitoring of activity can be appropriate for older aged adults and may help increase time spent in physical activity and that it may be particularly beneficial to combine this with additional Internet or web-based interventions. Many older adults, particularly those residing in rural areas, may not have access to the Internet. From a practical perspective it may be useful, therefore, to combine an educational program in conjunction with accelerometer activity tracker to strengthen health self-efficacy, physical activity and improve health outcomes. The overall aim of this pilot study was to evaluate the feasibility and utility of wristband activity tracker use, in combination of weekly health education sessions among adults 60 years old living in the southern United States. Theoretical framework Social Cognitive Theory (SCT) provided the theoretical framework for this pilot study, as behavioral change is required in order to increase physical activity.11 Based on Social Cognitive Theory it is anticipated that individuals become motivated and guide their actions through their beliefs and self-efficacy. The concept of perceived self-efficacy, with regard to physical activity, plays a key role in the older adult’s expectation of setting personal goals to engage in physical activity behaviors that promote daily exercise. In addition to setting goals to promote exercise, individuals need informative feedback on their exercise behaviors to allow them to perfect their skills or routines.12 The objective of combining an educational intervention with wristband activity tracker is to provide information that will encourage goal-setting and to subsequently provide immediate informative feedback about the number of steps taken each day. The goal of this immediate feedback, pertaining to daily steps, is to increase self-efficacy for physical activity.12e14 Material and methods Design This pilot study was set within the Eat Better Move More (EBMM) parent study which was examining the efficacy of nutritional and exercise interventions designed for older adults. The EBBM program was developed by the Older Americans Act (OAA) and the Administration on Aging National, You Can! Campaign.15 This EBMM program included 12 weekly mini-talks focused on skills designed to change nutrition and physical activity behaviors for individuals randomized to either a pedometer or a mobile activity wristband tracker. The purpose of this study was to establish the feasibility and utility of the mobile activity wristband tracker as used during the 12 week study period. Specifically, we evaluated the: a) rate of agreement to use activity trackers, b) ability of participants to physically and cognitively use the activity trackers correctly, and c) the willingness to the activity trackers for a 12-week duration of time. To address utility, we evaluated if the use of the wristband activity trackers influenced, a) physical activity self-efficacy, b)
physical activity, and, c) health outcome measures [blood pressure, heart rate, Time Up and Go test (TUG), waist circumference (WC) and body mass index (BMI)]. A University based Institutional Review Board approval this study. Setting and sample A convenience sample of older adult men and women was recruited over a 2 week period from a rural senior center in North Carolina during the winter of 2013. Flyers were posted at the senior center to specifically recruit participants for the mobile activity wristband tracker group for the EBMM pilot study. Inclusion criteria included: a) age 60 or older, b) ability to travel to the study site, c) participating in the EBBM program, and d) ability to participate in physical activity per the Physical Activity Screening Questionnaire (PASQ).16 Participants were excluded from the EBBM mobile activity wristband tracker study for the following reasons: (a) cognitive memory impairment which was based on the ability to recall at least 1/3 word recall and complete the Clock Drawing on the Mini-cog,17 (b) under age 60, (c) unable to travel to the study site, (d) actively participating in a weight loss program, and (e) planning to move out of the county within the next 12 weeks. Thirty eight participants were approached about participating in the EBBM mobile activity wristband tracker study. Of the 38 participants, two declined the opportunity to be in the study. From the remaining 36 participants, 2 participants were excluded due to age (younger than age 60) and the inability to read. Intervention As part of the EBBM mobile activity wristband tracker study, each participant was given a Nike FuelÒ wristband activity tracker18 which was a slim wristband worn similar to a watch on the non-dominant wrist for 24 h a day. The Nike Fuel wristband measures a person’s steps based on arm movement. Participants wore the Nike Fuel wristband daily for 12-weeks. The participants were given a week of training on how to use the activity tracker to record steps taken each day. During the week of training, information was provided on how to access the daily steps and calories burned from the participant’s wristband digital screen. Because many of the participants did not have access to a computer or a smartphone, they were given a Tips and Tasks sheet, on which they were to document the total steps taken and calories burned at the end of each day. Further they were instructed to bring the weekly Tips and Tasks sheet written record to the senior center each week at which time the research team reviewed the data with the participant for accuracy and completeness prior to recording it into the database. In addition, as part of the EBMM program, all participants were exposed to a 45 min weekly session about strategies to change nutrition and physical activity behaviors. A total of 12 weekly modules were provided to the participants pertaining to the following topics: a) fruits and vegetables b) calcium rich foods, c) fiber rich foods, d) portions sizes, e) tips for stretching, f) proper fitting shoes, g) walking safety, and h) maintaining good posture. In addition, a 30 min group walking session was led by research assistants for the entire group each week. Measures Demographic information was obtained during a baseline faceto-face survey and included: gender, race, marital status, income and education. A health history related to 15 specific conditions was also taken. All other measures were obtained at baseline and at 12 weeks, except for steps taken and calories burned.
T. O’Brien et al. / Geriatric Nursing 36 (2015) S21eS25
Physical Activity Self-efficacy A five question Physical Activity Self-efficacy questionnaire19 was used to measure the confidence level of the participants’ ability to overcome common barriers for participating in physical activity including: a) being under stress, b) having time for physical activity, c) feeling pressure from family friends not to perform physical activity, d) using weekends, and e) experiencing poor weather conditions. The five questions were in a Likert format with responses ranging from 1 (I know I cannot) to 5 (I know I can). A score of 1e2 was associated with low self-efficacy to overcome barriers to perform physical activity, a score of 3 was neutral, and a score of 4e5 was associated with high self-efficacy to overcome barriers to perform physical activity. The Physical Activity SelfEfficacy questionnaire has been found to be a short, concise, and easy to read instrument that has been previously validated in adolescents and found to have a Cronbach’s alpha of 0.85 and a testretest of R ¼ 0.89.19 Physical activity The participants were asked to record the number of daily steps taken and calories burned as reported by the activity tracker weekly in a logbook. Caloric use was not based on resting metabolic caloric burn, but rather calories burned from steps taken. Health outcomes A resting blood pressure was measured on each participant while in a seated position using a mercury sphygmomanometer by two well-trained registered nurses who were blinded to the study group. The registered nurses also obtained a radial pulse (heart rate) over a 60 s count while the participant remained seated. The same protocol for obtaining blood pressure and heart rate was used at baseline and follow up at 12 weeks. The Timed Up and Go (TUG) test This assessment involves observation of participants while rising from a seated position in an arm chair, walking three feet, turning around, walking back to the chair, and resuming a seated position. The TUG test is measured in seconds. The participants are informed by the research assistance when to stand up from the chair and start walking. If the individual used an assistive device, such as a cane, they were instructed to use the assistive device during the TUG test. However, if an individual used an assistance device, this information was recorded on the data collection form. Prior use found inter- and intra-rater reliability of the TUG to be ICC, 0.99.20,21 Waist circumference (WC) was obtained by measuring the individuals circumference half way between the participants illac crest and the lower anterior ribs with the person standing upright. The WC measurement was obtained during expiration.22 To determine Body Mass Index (BMI), participants were weighed without shoes and in light clothing to the nearest 0.1 pound using a digital scale that was calibrated prior to testing.23 Height was measured without shoes using a self-retracting tape measure and rounded to the nearest 0.1 cm. Height and weight measurements were used to calculate body mass index (BMI; kg/m2) [mass (lb)/height (in)2 703]. BMI is a cost effective method to screen for weight categories in older adults which may lead to health issues.24
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Table 1 Sample descriptives (N ¼ 34). Variable Gender Male Female Race African-American White Asian Native American Did not report Marital status Married Live alone Live with a family member (other than spouse) Other Income $ 35,000 or less More than $ 35,000 Preferred not to answer Did not report Education High school diploma High school diploma Did not report
N (%) 12 (35%) 22 (65%) 10 (30%) 22 (65%) 0 0 2 (5%) 15 14 3 2
(44%) (41%) (9%) (6%)
20 7 6 1
(58%) (21%) (18%) (3%)
13 (38%) 21 (62%)
Feasibility questions were addressed first, by determining what portion of those that were approached about participating in the study signed the consent, and of these, what percent was able to successfully use the activity tracker after one week of training. Subsequently, the number and percent of participants that completed the weekly tips and worksheets. Paired t-tests were used to determine differences for continuous variables. The probability level of <0.05 was considered statically significant in this study. The Complete Case Analysis Method was the process used for missing data. Results The sample (N ¼ 34) included 65% women and 65% White with a mean age of 73.5 9.4 (ranging from age 60e96). The majority of participants (62%) had a high school diploma or GED and over half (58%) had an annual household income of less than $35,000. Fortyfour percent of the participants were married, 41% reported living alone, and the remaining reported living with a family member or someone other than a spouse (see Table 1). The most common three prior health conditions included hypertension (75%), Diabetes (38%) and arthritis (35%). Feasibility findings All 34 of the study participants that completed the training at baseline agreed to use the activity tracker and demonstrated the correct use of the activity tracker. After 6 weeks, 5 of the 34 participants (15%) dropped out of the study because they did not want to wear the activity tracker wristband every day.
Data analysis
Utility findings
Statistical analyses were conducted using Windows version 21.0 of the Statistical Packages for Social Sciences (SPSS, Chicago, IL). Demographic characteristics were described using means and standard deviation for continuous variables (age, income, and education) and frequencies percentages were used to describe categorical variables (gender, race, income, marital status, and education).
There was no change over time in physical activity self-efficacy. Baseline mean scores were 4.17 0.83 and at 12 weeks, mean scores were 3.78 1.09 (t ¼ 1.38, p > 0.18). Mean steps per week are shown in Fig. 1 and the mean daily calories burned each day among this group ranged from 405 to 641. Overall steps increased from week 1 (2526 2286.9) through week 6 (4195 2544.6) with a decrease noted from week 7 through week 11 (M ¼ 3973 2940.7).
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T. O’Brien et al. / Geriatric Nursing 36 (2015) S21eS25
Fig. 1. Weekly average steps and calories burned.
All of the participants were trained using the wristband activity tracker and over the 12 weeks only 5 participants dropped out of the study. The immediate feedback via a digital screen for the average number of calories burned based on their daily steps taken may have been a variable for the high acceptance rate. Upon week 12, the participants were asked an open ended question of what they liked best about the wristband activity tracker. A few of the comments were: “It made me aware of what I ate and my movement for the day.” Another participant said, “I loved having the ability to see the steps that I had taken for the day on my wrist.” Still another participant said, “The wristband activity tracker was very motivating to keep me increasing my steps each day.” Our study findings support those reported by Silveira and colleagues,25 who found strength-balance training information delivered by a selfmanaged interactive app Active Lifestyle, via a tablet or smartphone improved participants acceptance for performing exercises and study completion (91%, N ¼ 44). One significant difference that was observed from baseline to week 12 week was a slower time (p > 0.001) for the TUG test. Because the TUG test was scheduled later in day, it is possible that participants may have become tired after filling out the follow up questionnaires and waiting for their TUG time to be measured. A suggestion for future study would be to measure the TUG time first. Another important finding from our study is that the average number of steps gained from baseline was 1196, and after week 2, the group averaged over 3000 steps per day. This increase in steps from 2526 to 3722 steps remains below the recommended 10,000 steps per day for a healthy adult. However, there is evidence that 3000 steps a day can provide valuable physical health benefits for older adults.26
However, the group gained an average of 1196 steps from week 1 to week 11 (see Fig. 1). Although, there was no significant increase in steps from week 1 (M ¼ 3128 2262) to week 11 (M ¼ 3768 2590), t(1.62) ¼ 22, p ¼ 0.11. Health outcomes As shown in Table 2, there was no difference between baseline and 12 weeks for BMI, systolic blood pressure (BP), diastolic blood pressure (BP), and heart rate. However, TUG increased from baseline to 12 weeks (M ¼ 7.86 1.29 versus M ¼ 9.44 1.66, p < 0.001) and WC decreased (M ¼ 41.8 3.83 versus 40.6 4.21, p < 0.009) (see Table 2). Discussion The aim of this study was to test the feasibility and utility of older adults using a wristband activity tracker in combination with weekly face-to-face educational sessions for improving physical activity and nutrition. The study findings were encouraging, in that our research team found the participants were receptive to the wristband activity tracker and mastered the use of the activity tracker fairly easily. Our study is unique in the fact, that few studies have tested the wristband activity tracker with a digital screen for immediate feedback for steps taken and calories burned with older adults. Moreover, those that have been done generally include well educated young-old individuals. Our sample, however, included mostly women over the age of 70 with a prior diagnosis of hypertension and receiving an annual income of less than $35,000 per year.
Table 2 Descriptive statistics and t-test results for BMI, BP, Heart Rate, and TUG. Outcome
BMI (kg/m2) WC Systolic BP (mm Hg) Diastolic BP (mm Hg) Heart Rate Time Up and Go (TUG) Paired t-test p < 0.05.
Baseline
12 weeks
M
SD
M
SD
31.4 41.8 132.8 77.5 73.8 7.86
4.35 3.83 13.8 10.9 9.29 1.29
30.8 40.6 137.3 73.5 71.2 9.44
5.06 4.21 20.1 9.11 9.50 1.66
n
95% CI for mean
t
p-Value
df
Effect size
29 29 29 29 29 29
0.183, 1.33 0.309, 1.99 12.99, 3.99 1.33, 9.76 1.36, 6.43 2.20, 9.55
1.55 2.82 1.08 1.55 1.33 5.19
0.132 0.009 0.287 0.131 0.193 0.001
28 28 28 28 28 28
0.1 0.3 0.3 0.4 0.3 1.0
T. O’Brien et al. / Geriatric Nursing 36 (2015) S21eS25
Limitations Manufacturers of activity trackers provide little information about their reliability and validity. Because activity tracker use is relatively widespread and growing on an individual level and is part of structured programs, future research needs to consider the reliability and validity for the accuracy of recording steps in older adults. Despite these limitations, the data from this study indicated that wristband activity trackers are an accepted method for recording daily physical activity among older adults. Further research is needed among other chronic disease populations to determine what degree mobile computing technology will foster increased activity and what additional health benefits might be sustained to improve the quality of life for older adults. In addition, future research should be collected to quantify positive and negative aspects of the wristband activity tracker for promoting physical activity among older adults.
References 1. Center for Disease Control. Adult Participation in Aerobic and Musclestrengthening Physical ActivitiesdUnited States, 2011, http://www.cdc.gov/ mmwr/preview/mmwrhtml/mm6217a2.htm?s_cid¼mm6217a2_w; 2013. 2. Maitland J, Sherwood S, Barkhuus L, et al. Increasing the Awareness of Daily Activity Levels With Pervasive Computing, http://ieeexplore.ieee.org/xpl/login. jsp?tp¼&arnumber¼4205158&url¼http%3A%2F%2Fieeexplore.ieee.org%2Fxpls %2Fabs_all.jsp%3Farnumber%3D4205158; 2006. 3. North Carolina State Center for Health Statistics. 2009 BRFSS Survey Results: North Carolina Counties and Regions Fruits and Vegetables Consumption Per Day, http://www.schs.state.nc.us/schs/brfss/2009/nc/nccr/_FV5SRV.html; 2011. 4. Easley C, Petersen R, Holmes M. The health and economic burden of chronic diseases in North Carolina. N C Med J. Jan-Feb 2010;71(1):92e95. 5. Hancock H. The Benefits of Regular Walking for Health Well-being and the Environment, http://www.c3health.org/wp-content/uploads/2009/09/C3-reviewof-physical-activity-and-health-v-1-20110603.pdf; 2011. 6. Yang CC, Hsu YL. A review of accelerometry-based wearable motion detectors for physical activity monitoring. Sensors (Basel). 2010;10(8):7772e7788. 7. Kolt GS, Schofield GM, Kerse N, Garrett N, Ashton T, Patel A. Healthy Steps trial: pedometer-based advice and physical activity for low-active older adults. Ann Fam Med. May-Jun 2012;10(3):206e212.
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8. Cyarto EV, Myers A, Tudor-Locke C. Pedometer accuracy in nursing home and community-dwelling older adults. Med Sci Sports Exerc. Feb 2004;36(2): 205e209. 9. Reid RD, Morrin LI, Beaton LJ, et al. Randomized trial of an internet-based computer-tailored expert system for physical activity in patients with heart disease. Eur J Prev Cardiol. Dec 2012;19(6):1357e1364. 10. Ammann R, Vandelanotte C, de Vries H, Mummery WK. Can a website-delivered computer-tailored physical activity intervention be acceptable, usable, and effective for older people? Health Educ Behav. Apr 2013;40(2):160e170. 11. Bandura A. Self-efficacy: The Exercise of Control. New York: W.H. Freeman; 1997. 12. Bandura A, Locke EA. Negative self-efficacy and goal effects revisited. J Appl Psychol. Feb 2003;88(1):87e99. 13. George JB. Nursing Theories: The Base for Professional Nursing Practice. Upper Saddle River, NJ: Pearson Education; 2011. 14. Franklin VL, Greene A, Waller A, Greene SA, Pagliari C. Patients’ engagement with “Sweet Talk” e a text messaging support system for young people with diabetes. J Med Internet Res. 2008;10(2):e20. 15. Wellman NS, Kamp B, Kirk-Sanchez NJ, Johnson PM. Eat better & move more: a community-based program designed to improve diets and increase physical activity among older Americans. Am J Public Health. Apr 2007;97(4):710e717. 16. Wellman N, Weddle D, Sanchez N, Rosenzweig L. Eat Better & Move More: A Guidebook for Community Programs, http://nutritionandaging.fiu.edu/you_can/ 07.2YouCanGuidebook.pdf; 2004. 17. Borson S, Scanlan J, Brush M, Vitaliano P, Dokmak A. The mini-cog: a cognitive ‘vital signs’ measure for dementia screening in multi-lingual elderly. Int J Geriatr Psychiatry. Nov 2000;15(11):1021e1027. 18. Nike Incorporation. Nike Fuel Band, http://store.nike.com/us/en_us/pd/ fuelband-se/pid-924485/pgid-924484; 2014. 19. Trost SG, Sallis JF, Pate RR, Freedson PS, Taylor WC, Dowda M. Evaluating a model of parental influence on youth physical activity. Am J Prev Med. Nov 2003;25(4):277e282. 20. Podsiadlo D, Richardson S. The timed “Up & Go”: a test of basic functional mobility for frail elderly persons. J Am Geriatr Soc. Feb 1991;39(2):142e148. 21. Barry E, Galvin R, Keogh C, Horgan F, Fahey T. Is the Timed Up and Go test a useful predictor of risk of falls in community dwelling older adults: a systematic review and meta- analysis. BMC Geriatr. 2014;14:14. 22. Cetin DC, Nasr G. Obesity in the elderly: more complicated than you think. Cleve Clin J Med. Jan 2014;81(1):51e61. 23. Withings. Wi-Fi Body Scale, http://www.withings.com/eu/smart-body-analyzer. html; 2011. 24. Center for Disease Control. About BMI for Adults. http://www.cdc.gov/ healthyweight/assessing/bmi/adult_bmi/index.html. 25. Silveira P, van de Langenberg R, van Het Reve E, Daniel F, Casati F, de Bruin ED. Tablet-based strength-balance training to motivate and improve adherence to exercise in independently living older people: a phase II preclinical exploratory trial. J Med Internet Res. 2013;15(8):e159. 26. Tudor-Locke C, Craig CL, Aoyagi Y, et al. How many steps/day are enough? For older adults and special populations. Int J Behav Nutr Phys Act. 2011;8:80.
Contents lists available at ScienceDirect
Geriatric Nursing journal homepage: www.gnjournal.com
Feature Article
Acceptability of wristband activity trackers among community dwelling older adults Tara O’Brien, PhD, RN, CNE a, *, Meredith Troutman-Jordan, PhD, RN b, Donna Hathaway, PhD, RN, FAAN a, Shannon Armstrong, BSN, RN b, Michael Moore, PhD b a b
University of Tennessee Health Science Center’s College of Nursing, 920 Madison Building, Memphis, TN 38163, USA School of Nursing, University of North Carolina at Charlotte, 9201 University City Blvd, Charlotte, NC 28223, USA
a b s t r a c t Keywords: Older adults Physical activity Technology
Wristband activity trackers have become widely used among young adults. However, few studies have explored their use for monitoring and improving health outcomes among older adults. The purpose of this study was to evaluate the feasibility and utility of activity tracker use among older adults for monitoring activity, improving self-efficacy, and health outcomes. A 12-week pilot study was conducted to evaluate the feasibility and utility of mobile wristband activity trackers. The sample (N ¼ 34) was 65% women 73.5 9.4 years of age who had a high school diploma or GED (38%) and reported an income $35,000 (58%). Participants completing the study (95%) experienced a decrease in waist circumference (p > 0.009), however no change in self-efficacy. Participants found activity trackers easy to use which contributed to minimal study withdrawals. It was concluded that activity trackers could be useful for monitoring and promoting physical activity and improving older adults’ health. Ó 2015 Elsevier Inc. All rights reserved.
Introduction
Americans living in the southern region of the United States are less likely to participate in regular physical activity compared to people living in other regions of the US.1 In North Carolina (NC), for example, only 46.4% of older adults meet the current daily physical activity recommendations (30 min of exercise per day).3 This lack of physical activity has been linked to cardiovascular disease among NC residents (CDC, 2013) which is the most costly and preventable cause of morbidity and mortality for older adults in the state.4 Simple low impact physical activity, such as walking, is particularly beneficial for older adults.5 Although the literature indicates that accelerometers (wearable devices that measure physical activity) have been widely accepted and proven useful for increasing activity among young adults6 these findings are not conclusive for older adults. Some studies have found older adults to be more aware of their physical activity levels when wearing a pedometer.7 However, other studies have found barriers with the use of traditional pedometers; including, becoming detached from clothing, loss of the pedometer, improper stride length measurement, and inaccurate recording of information.8 There is some evidence, however, that measurement of physical activity may help increase physical activity among older individuals at least when combined with additional Internet guided activity planning and tracking.9 For example, older adults randomized to treatment
Participation in physical activity is often associated with young rather than older people. According to the Centers for Disease Control,1 80% of older adults living in the United States (US) do not engage in the recommended daily amount of exercise. The lack of physical activity among older adults has been associated with higher rates of cancer, stroke, cardiovascular disease, diabetes, obesity, and Alzheimer’s disease.2 Interventions that enhance selfefficacy for older adults’ ability to participate in regular physical activity are greatly needed to lessen the personal, social, and economic impacts of chronic disease.
Conflict of interest: The authors disclose no conflicts of interest regarding authors’ professional or financial affiliations that may potentially be regarded as having biased the manuscript. Funding sources: The University of North Carolina at Charlotte Faculty Research Grant. Disclosure: The University of North Carolina at Charlotte Faculty Research Grant assisted with buying the Nike Fuel Activity Tracker Bands for the study. * Corresponding author. The University of Tennessee Health Science Center, College of Nursing, 9201 University City Boulevard, Memphis, TN 38163, USA. Tel.: þ1 901 448 1176. E-mail address: [email protected] (T. O’Brien). 0197-4572/$ e see front matter Ó 2015 Elsevier Inc. All rights reserved. http://dx.doi.org/10.1016/j.gerinurse.2015.02.019
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using a combination of an accelerometer and an Internet physical activity planning/tracking mechanism averaged 764 more steps per day than the usual care group which was given only a pedometer.9 There is also some growing evidence that older adults benefit from exposure to websites to deliver computer-tailored physical activity interventions.10 Specifically, this study found that the older adults actually engaged in more total physical activity minutes and more total physical activity sessions than those in younger age groups. These findings suggest that monitoring of activity can be appropriate for older aged adults and may help increase time spent in physical activity and that it may be particularly beneficial to combine this with additional Internet or web-based interventions. Many older adults, particularly those residing in rural areas, may not have access to the Internet. From a practical perspective it may be useful, therefore, to combine an educational program in conjunction with accelerometer activity tracker to strengthen health self-efficacy, physical activity and improve health outcomes. The overall aim of this pilot study was to evaluate the feasibility and utility of wristband activity tracker use, in combination of weekly health education sessions among adults 60 years old living in the southern United States. Theoretical framework Social Cognitive Theory (SCT) provided the theoretical framework for this pilot study, as behavioral change is required in order to increase physical activity.11 Based on Social Cognitive Theory it is anticipated that individuals become motivated and guide their actions through their beliefs and self-efficacy. The concept of perceived self-efficacy, with regard to physical activity, plays a key role in the older adult’s expectation of setting personal goals to engage in physical activity behaviors that promote daily exercise. In addition to setting goals to promote exercise, individuals need informative feedback on their exercise behaviors to allow them to perfect their skills or routines.12 The objective of combining an educational intervention with wristband activity tracker is to provide information that will encourage goal-setting and to subsequently provide immediate informative feedback about the number of steps taken each day. The goal of this immediate feedback, pertaining to daily steps, is to increase self-efficacy for physical activity.12e14 Material and methods Design This pilot study was set within the Eat Better Move More (EBMM) parent study which was examining the efficacy of nutritional and exercise interventions designed for older adults. The EBBM program was developed by the Older Americans Act (OAA) and the Administration on Aging National, You Can! Campaign.15 This EBMM program included 12 weekly mini-talks focused on skills designed to change nutrition and physical activity behaviors for individuals randomized to either a pedometer or a mobile activity wristband tracker. The purpose of this study was to establish the feasibility and utility of the mobile activity wristband tracker as used during the 12 week study period. Specifically, we evaluated the: a) rate of agreement to use activity trackers, b) ability of participants to physically and cognitively use the activity trackers correctly, and c) the willingness to the activity trackers for a 12-week duration of time. To address utility, we evaluated if the use of the wristband activity trackers influenced, a) physical activity self-efficacy, b)
physical activity, and, c) health outcome measures [blood pressure, heart rate, Time Up and Go test (TUG), waist circumference (WC) and body mass index (BMI)]. A University based Institutional Review Board approval this study. Setting and sample A convenience sample of older adult men and women was recruited over a 2 week period from a rural senior center in North Carolina during the winter of 2013. Flyers were posted at the senior center to specifically recruit participants for the mobile activity wristband tracker group for the EBMM pilot study. Inclusion criteria included: a) age 60 or older, b) ability to travel to the study site, c) participating in the EBBM program, and d) ability to participate in physical activity per the Physical Activity Screening Questionnaire (PASQ).16 Participants were excluded from the EBBM mobile activity wristband tracker study for the following reasons: (a) cognitive memory impairment which was based on the ability to recall at least 1/3 word recall and complete the Clock Drawing on the Mini-cog,17 (b) under age 60, (c) unable to travel to the study site, (d) actively participating in a weight loss program, and (e) planning to move out of the county within the next 12 weeks. Thirty eight participants were approached about participating in the EBBM mobile activity wristband tracker study. Of the 38 participants, two declined the opportunity to be in the study. From the remaining 36 participants, 2 participants were excluded due to age (younger than age 60) and the inability to read. Intervention As part of the EBBM mobile activity wristband tracker study, each participant was given a Nike FuelÒ wristband activity tracker18 which was a slim wristband worn similar to a watch on the non-dominant wrist for 24 h a day. The Nike Fuel wristband measures a person’s steps based on arm movement. Participants wore the Nike Fuel wristband daily for 12-weeks. The participants were given a week of training on how to use the activity tracker to record steps taken each day. During the week of training, information was provided on how to access the daily steps and calories burned from the participant’s wristband digital screen. Because many of the participants did not have access to a computer or a smartphone, they were given a Tips and Tasks sheet, on which they were to document the total steps taken and calories burned at the end of each day. Further they were instructed to bring the weekly Tips and Tasks sheet written record to the senior center each week at which time the research team reviewed the data with the participant for accuracy and completeness prior to recording it into the database. In addition, as part of the EBMM program, all participants were exposed to a 45 min weekly session about strategies to change nutrition and physical activity behaviors. A total of 12 weekly modules were provided to the participants pertaining to the following topics: a) fruits and vegetables b) calcium rich foods, c) fiber rich foods, d) portions sizes, e) tips for stretching, f) proper fitting shoes, g) walking safety, and h) maintaining good posture. In addition, a 30 min group walking session was led by research assistants for the entire group each week. Measures Demographic information was obtained during a baseline faceto-face survey and included: gender, race, marital status, income and education. A health history related to 15 specific conditions was also taken. All other measures were obtained at baseline and at 12 weeks, except for steps taken and calories burned.
T. O’Brien et al. / Geriatric Nursing 36 (2015) S21eS25
Physical Activity Self-efficacy A five question Physical Activity Self-efficacy questionnaire19 was used to measure the confidence level of the participants’ ability to overcome common barriers for participating in physical activity including: a) being under stress, b) having time for physical activity, c) feeling pressure from family friends not to perform physical activity, d) using weekends, and e) experiencing poor weather conditions. The five questions were in a Likert format with responses ranging from 1 (I know I cannot) to 5 (I know I can). A score of 1e2 was associated with low self-efficacy to overcome barriers to perform physical activity, a score of 3 was neutral, and a score of 4e5 was associated with high self-efficacy to overcome barriers to perform physical activity. The Physical Activity SelfEfficacy questionnaire has been found to be a short, concise, and easy to read instrument that has been previously validated in adolescents and found to have a Cronbach’s alpha of 0.85 and a testretest of R ¼ 0.89.19 Physical activity The participants were asked to record the number of daily steps taken and calories burned as reported by the activity tracker weekly in a logbook. Caloric use was not based on resting metabolic caloric burn, but rather calories burned from steps taken. Health outcomes A resting blood pressure was measured on each participant while in a seated position using a mercury sphygmomanometer by two well-trained registered nurses who were blinded to the study group. The registered nurses also obtained a radial pulse (heart rate) over a 60 s count while the participant remained seated. The same protocol for obtaining blood pressure and heart rate was used at baseline and follow up at 12 weeks. The Timed Up and Go (TUG) test This assessment involves observation of participants while rising from a seated position in an arm chair, walking three feet, turning around, walking back to the chair, and resuming a seated position. The TUG test is measured in seconds. The participants are informed by the research assistance when to stand up from the chair and start walking. If the individual used an assistive device, such as a cane, they were instructed to use the assistive device during the TUG test. However, if an individual used an assistance device, this information was recorded on the data collection form. Prior use found inter- and intra-rater reliability of the TUG to be ICC, 0.99.20,21 Waist circumference (WC) was obtained by measuring the individuals circumference half way between the participants illac crest and the lower anterior ribs with the person standing upright. The WC measurement was obtained during expiration.22 To determine Body Mass Index (BMI), participants were weighed without shoes and in light clothing to the nearest 0.1 pound using a digital scale that was calibrated prior to testing.23 Height was measured without shoes using a self-retracting tape measure and rounded to the nearest 0.1 cm. Height and weight measurements were used to calculate body mass index (BMI; kg/m2) [mass (lb)/height (in)2 703]. BMI is a cost effective method to screen for weight categories in older adults which may lead to health issues.24
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Table 1 Sample descriptives (N ¼ 34). Variable Gender Male Female Race African-American White Asian Native American Did not report Marital status Married Live alone Live with a family member (other than spouse) Other Income $ 35,000 or less More than $ 35,000 Preferred not to answer Did not report Education High school diploma High school diploma Did not report
N (%) 12 (35%) 22 (65%) 10 (30%) 22 (65%) 0 0 2 (5%) 15 14 3 2
(44%) (41%) (9%) (6%)
20 7 6 1
(58%) (21%) (18%) (3%)
13 (38%) 21 (62%)
Feasibility questions were addressed first, by determining what portion of those that were approached about participating in the study signed the consent, and of these, what percent was able to successfully use the activity tracker after one week of training. Subsequently, the number and percent of participants that completed the weekly tips and worksheets. Paired t-tests were used to determine differences for continuous variables. The probability level of <0.05 was considered statically significant in this study. The Complete Case Analysis Method was the process used for missing data. Results The sample (N ¼ 34) included 65% women and 65% White with a mean age of 73.5 9.4 (ranging from age 60e96). The majority of participants (62%) had a high school diploma or GED and over half (58%) had an annual household income of less than $35,000. Fortyfour percent of the participants were married, 41% reported living alone, and the remaining reported living with a family member or someone other than a spouse (see Table 1). The most common three prior health conditions included hypertension (75%), Diabetes (38%) and arthritis (35%). Feasibility findings All 34 of the study participants that completed the training at baseline agreed to use the activity tracker and demonstrated the correct use of the activity tracker. After 6 weeks, 5 of the 34 participants (15%) dropped out of the study because they did not want to wear the activity tracker wristband every day.
Data analysis
Utility findings
Statistical analyses were conducted using Windows version 21.0 of the Statistical Packages for Social Sciences (SPSS, Chicago, IL). Demographic characteristics were described using means and standard deviation for continuous variables (age, income, and education) and frequencies percentages were used to describe categorical variables (gender, race, income, marital status, and education).
There was no change over time in physical activity self-efficacy. Baseline mean scores were 4.17 0.83 and at 12 weeks, mean scores were 3.78 1.09 (t ¼ 1.38, p > 0.18). Mean steps per week are shown in Fig. 1 and the mean daily calories burned each day among this group ranged from 405 to 641. Overall steps increased from week 1 (2526 2286.9) through week 6 (4195 2544.6) with a decrease noted from week 7 through week 11 (M ¼ 3973 2940.7).
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T. O’Brien et al. / Geriatric Nursing 36 (2015) S21eS25
Fig. 1. Weekly average steps and calories burned.
All of the participants were trained using the wristband activity tracker and over the 12 weeks only 5 participants dropped out of the study. The immediate feedback via a digital screen for the average number of calories burned based on their daily steps taken may have been a variable for the high acceptance rate. Upon week 12, the participants were asked an open ended question of what they liked best about the wristband activity tracker. A few of the comments were: “It made me aware of what I ate and my movement for the day.” Another participant said, “I loved having the ability to see the steps that I had taken for the day on my wrist.” Still another participant said, “The wristband activity tracker was very motivating to keep me increasing my steps each day.” Our study findings support those reported by Silveira and colleagues,25 who found strength-balance training information delivered by a selfmanaged interactive app Active Lifestyle, via a tablet or smartphone improved participants acceptance for performing exercises and study completion (91%, N ¼ 44). One significant difference that was observed from baseline to week 12 week was a slower time (p > 0.001) for the TUG test. Because the TUG test was scheduled later in day, it is possible that participants may have become tired after filling out the follow up questionnaires and waiting for their TUG time to be measured. A suggestion for future study would be to measure the TUG time first. Another important finding from our study is that the average number of steps gained from baseline was 1196, and after week 2, the group averaged over 3000 steps per day. This increase in steps from 2526 to 3722 steps remains below the recommended 10,000 steps per day for a healthy adult. However, there is evidence that 3000 steps a day can provide valuable physical health benefits for older adults.26
However, the group gained an average of 1196 steps from week 1 to week 11 (see Fig. 1). Although, there was no significant increase in steps from week 1 (M ¼ 3128 2262) to week 11 (M ¼ 3768 2590), t(1.62) ¼ 22, p ¼ 0.11. Health outcomes As shown in Table 2, there was no difference between baseline and 12 weeks for BMI, systolic blood pressure (BP), diastolic blood pressure (BP), and heart rate. However, TUG increased from baseline to 12 weeks (M ¼ 7.86 1.29 versus M ¼ 9.44 1.66, p < 0.001) and WC decreased (M ¼ 41.8 3.83 versus 40.6 4.21, p < 0.009) (see Table 2). Discussion The aim of this study was to test the feasibility and utility of older adults using a wristband activity tracker in combination with weekly face-to-face educational sessions for improving physical activity and nutrition. The study findings were encouraging, in that our research team found the participants were receptive to the wristband activity tracker and mastered the use of the activity tracker fairly easily. Our study is unique in the fact, that few studies have tested the wristband activity tracker with a digital screen for immediate feedback for steps taken and calories burned with older adults. Moreover, those that have been done generally include well educated young-old individuals. Our sample, however, included mostly women over the age of 70 with a prior diagnosis of hypertension and receiving an annual income of less than $35,000 per year.
Table 2 Descriptive statistics and t-test results for BMI, BP, Heart Rate, and TUG. Outcome
BMI (kg/m2) WC Systolic BP (mm Hg) Diastolic BP (mm Hg) Heart Rate Time Up and Go (TUG) Paired t-test p < 0.05.
Baseline
12 weeks
M
SD
M
SD
31.4 41.8 132.8 77.5 73.8 7.86
4.35 3.83 13.8 10.9 9.29 1.29
30.8 40.6 137.3 73.5 71.2 9.44
5.06 4.21 20.1 9.11 9.50 1.66
n
95% CI for mean
t
p-Value
df
Effect size
29 29 29 29 29 29
0.183, 1.33 0.309, 1.99 12.99, 3.99 1.33, 9.76 1.36, 6.43 2.20, 9.55
1.55 2.82 1.08 1.55 1.33 5.19
0.132 0.009 0.287 0.131 0.193 0.001
28 28 28 28 28 28
0.1 0.3 0.3 0.4 0.3 1.0
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Limitations Manufacturers of activity trackers provide little information about their reliability and validity. Because activity tracker use is relatively widespread and growing on an individual level and is part of structured programs, future research needs to consider the reliability and validity for the accuracy of recording steps in older adults. Despite these limitations, the data from this study indicated that wristband activity trackers are an accepted method for recording daily physical activity among older adults. Further research is needed among other chronic disease populations to determine what degree mobile computing technology will foster increased activity and what additional health benefits might be sustained to improve the quality of life for older adults. In addition, future research should be collected to quantify positive and negative aspects of the wristband activity tracker for promoting physical activity among older adults.
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