COMPARISON OF DAILY STEPS AND ACTIVE MINUTES USING A FITBIT
DEVICE AS PART OF AN ONLINE COMMUNITY VERSUS TRACKING ALONE
KAREN KAWOLICS
Bachelor of Arts in Telecommunications
Kent State University
December 1988
Masters of Science in Dietetics and Nutrition
Kent State University
December 2010
Submitted in partial fulfillment of requirements for the degree
MASTER OF EDUCATION
at
CLEVELAND STATE UNIVERSITY
December 2017
We hereby approve this Master’s thesis for Karen Kawolics
Candidate for the Master of Education Degree in Exercise Science
For the Department of Health and Human Performance And CLEVELAND STATE UNIVERSITY’S College of Graduate studies by
Thesis Chairperson, Dr. Kathleen Little
Department & Date
Committee Member, Dr. Emily Kullman
Department & Date
Committee Member, Dr. Sheila Patterson
Department & Date
Associate Dean of Student Services, Dr. Kristine Still
Department & Date
Student’s Date of Defense: November 29, 2017
ACKNOWLEDGMENTS
I would like to thank a few people who have helped make this research study possible.
Thank you to my family, which includes my husband Kevin Gorby, children Eden and
Odin, and parents Ray and Joan Kawolics, who have given me the space to spend time working on this project. Thank you to my advisor, Dr. Kathleen Little, who made this research possible by not only finding a way to fund this study, but by also spending a considerable amount of time guiding me through designing and implementing this project, and by teaching me how to analyze data and interpret results. I would have not been able to undertake such a detailed research study without your generous guidance.
Your investment in my future has been a priceless gift. Thank you also to my thesis committee, Dr. Emily Kullman and Dr. Sheila M. Patterson, who took the time to review and improve this research study. Your time has been greatly appreciated. Also thank you to Dr. Kenneth Sparks who encouraged me to pursue an additional Masters degree.
Attending classes at Cleveland State University not only greatly improved my knowledge and skills, but also intensified my passion for exercise science and health promotion.
COMPARISON OF DAILY STEPS AND ACTIVE MINUTES USING A FITBIT
DEVICE AS PART OF AN ONLINE COMMUNITY VERSUS TRACKING ALONE
Karen Kawolics
ABSTRACT
Introduction: It is estimated that 80% of American adults are employed in low active or sedentary jobs. Workplace physical activity interventions may be a tool to potentially impact sedentary related diseases. Purpose: The purpose of this study was to compare the average number of daily steps and active minutes of office workers who used a Fitbit Flex device as part of an online community versus office workers tracking steps alone. Methods: An experimental study was conducted using a convenience sample of 29 subjects (mean age = 42 ± 13 years; mean BMI = 34 ± 9) recruited from a
Midwestern medical supply company. After stratification by gender, race, age, and self- reported activity level, subjects were randomly assigned to either a tracking alone group
(TA, n = 15) or a community group (CG, n = 14). Baseline steps and active minutes were collected for one week. During the two-week intervention, all subjects were asked to walk, at minimum, 30 minutes per day, in no less than 10 minute bouts, five days per week. During the intervention, TA subjects were told to view only their own data via their smartphone Fitbit app or the Fitbit website via a personal computer. CG subjects were instructed to join a closed online Fitbit community group and post comments at least three times per week and view website data at least five days per week. CG subjects were also told to view their smartphone Fitbit app as much as desired. Repeated measures ANOVA was conducted to assess intervention effects. Results: Both groups
iv significantly (p = .006) increased mean steps from baseline to completion (TA ∆ = 996;
CG ∆ = 904). Both groups significantly (p = .008) increased active minutes from baseline to week one (TA ∆ = 33; CG ∆ = 12), but not to completion of week two (p =
.128). However, there was no significant group by time interaction effect for steps (p =
.887) or active minutes (p = .418). Conclusion: While both groups significantly increased activity over the course of the intervention, participating in the community group had no significantly greater influence on average daily steps or active minutes when compared to tracking alone.
v
TABLE OF CONTENTS
Page
ABSTRACT………………………………………………………………………...……iv
LIST OF TABLES………………………………………………………….…………....…...…...ix
LIST OF FIGURES…………………………………...………………………………….…...…...x
CHAPTERS
I. INTRODUCTION………………………………………………………...1
1.1 BACKGROUND……………………………………………………...1
1.2 STATEMENT OF THE PROBLEM.………………………………...7
1.3 PURPOSE……………………………….……………………………7
1.4 HYPOTHESES……………………………………………………….7
II. LITERATURE REVIEW………………………………………………....8
III. METHODS………………………………………………………………18
3.1 RESEARCH DESIGN……………………………………………….18
3.2 SUBJECTS…………………………………………………………..18
3.4 PROCEDURES……………………………………………………...19
3.5 DATA ANALYSIS…………………………………………………..23
IV. RESULTS …………………….…………………………………………24
4.1 SUBJECT CHARACTERISTICS…………………………………...24
4.2 AVERAGE DAILY STEPS RESULTS………………………….….26
4.2.1 BASELINE DAILY STEPS RANGE………….………….30
4.2.2 INTERVENTION DAILY STEPS RANGE………………32
vi
4.2.3 DAILY STEPS OUTLIERS…………………………….…34
4.3 AVERAGE DAILY ACTIVE MINUTES RESULTS……………....36
4.3.1 BASELINE DAILY ACTIVE MINUTES RANGE……….40
4.3.2 INTERVENTION DAILY ACTIVE MINUTES RANGE..42
4.3.3 DAILY ACTIVE MINUTES OUTLIERS………………...44
4.4 SMARTPHONE AND FITBIT.COM USAGE RESULTS..…….….46
4.5 IMPACT OF COMMUNITY GROUP INTERACTION…………....48
4.6 SELF-REPORTED ACTIVITY LEVEL……...…….………..……..49
4.7 COMMUNITY GROUP ONLINE POSTING RESULTS….……….51
V. DISCUSSION …………….………………………………….…….…....53
VI. SUMMARY & CONCLUSION…………………………………………59
6.1 SUMMARY…………………….…………………….…………...…59
6.2 CONCLUSION……………………..…………………….……….…60
6.3 LIMITATIONS………………………………………………………60
6.4 FUTURE RESEARCH RECOMMENDATIONS……….………….61
6.5 APPLICATION….…………………………………………………..62
REFERENCES………………………………………………………………………...... 63
APPENDICES…………………………………………………………………………...70
A. IRB STUDY APPROVAL EMAIL………………………………………….71
B. RECRUITMENT FLYER………………………………...……………...... 72
C. INFORMED CONSENT…………………………………………………….73
D. AHA/ACSM PRESCREENING QUESTIONNAIRE ...... 77
vii
E. PRE-STUDY SURVEY …………………………………...….…………….78
F. TRACKING ALONE STUDY INSTRUCTIONS HANDOUT…….……....81
G. COMMUNITY GROUP STUDY INSTRUCTIONS HANDOUT ……...….85
H. POST STUDY SURVEY ….………………………………………………..95
I. FIBIT PERMISSION TO USE PHOTOS………………………..………….98
viii
LIST OF TABLES
Page
I. CHARACTERISTICS OF SUBJECTS…………….……………………....……25
II. COMPARISON OF BASELINE AVERAGE DAILY STEPS.…….…………...26
III. OUTLIERS EXCLUDED, AVERAGE DAILY STEPS...………………………35
IV. COMPARISON OF BASELINE AVERAGE DAILY ACTIVE MINUTES…...36
V. OUTLIERS EXCLUDED, AVERAGE DAILY ACTIVE MINUTES …………45
VI. SMARTPHONE VIEWING, DAYS & TIMES PER WEEK ………….……….47
VII. FTIBIT.COM VIEWING, DAYS & TIMES PER WEEK……………………..48
VIII. PRE & POST STUDY SURVEY SELF-REPORTED ACTIVITY LEVEL….51
ix
LIST OF FIGURES
Page
1. AVERAGE DAILY STEPS, BASELINE TO WEEK 1……………...…....……27
2. AVERAGE DAILY STEPS, BASELINE TO WEEK 2.………...……………...28
3. AVERAGE DAILY STEPS, BASELINE TO COMPLETION………………....29
4. TRACKING ALONE GROUP BASELINE DAILY STEPS RANGE………….31
5. COMMUNITY GROUP BASELINE DAILY STEPS RANGE……………...…31
6. TRACKING ALONE INTERVENTION DAILY STEPS RANGE…………….33
7. COMMUNITY GROUP INTERVENTION DAILY STEPS RANGE…………33
8. AVERAGE DAILY ACTIVE MINUTES, BASELINE TO WEEK 1………….37
9. AVERAGE DAILY ACTIVE MINUTES, BASELINE TO WEEK 2..……..….38
10. AVERAGE DAILY ACTIVE MINUTES, BASELINE TO COMPLETION..…39
11. TRACKING ALONE GROUP BASELINE ACTIVE MINUTES RANGE……41
12. COMMUNITY GROUP BASELINE ACTIVE MINUTES RANGE….……….41
13. TRACKING ALONE GROUP INTERVENTION ACTIVE MINUTES
RANGE…………………………………………………………………………..43
14. COMMUNITY GROUP INTERVENTION ACTIVE MINUTES RANGE……43
x
CHAPTER I
INTRODUCTION
1.1 Background
Persistent sedentary behavior has been associated with reduced life expectancy
(Janssen, Carson, Lee, Katzmarzyk, & Blair, 2013), as well as the incidence of chronic diseases such as cardiovascular and metabolic disorders and certain types of cancer (Lee et al., 2012). To maintain health, a minimum of 150 minutes of weekly moderate to vigorous physical activity (MVPA) in bouts of no less than 10 minutes is recommended for adults according to the Physical Activity Guidelines for Americans (Physical Activity
Guidelines for Americans [PAGA], 2015). Few Americans and others living in developed nations meet these recommendations (Bohannon, 2007; Hallal et al., 2012).
Globally, 3.2 million annual deaths are related to a lack of regular physical activity
(World Health Organization [WHO], 2014). Office-based workers are a population
1 particularly at risk for chronic diseases related to a sedentary lifestyle. One study reported that some office workers sit more than 10 ½ hours per day with few movement breaks during that time (Smith et al., 2015). It has also been reported that office workers who sit longer at work also spend more time sitting at home (Clemes, Patel, Mahon, &
Griffiths, 2014). Since about 80 percent of the United States (US) adult population has a sedentary or very low active job (Church et al., 2011), interventions directed toward office workers could potentially impact public health issues related to physical inactivity.
Accelerometer-based activity trackers and their companion social web applications, which have recently gained popularity (Danova, 2014), may show promise as an effective intervention tool for this population.
Pedometers are the predecessors to consumer accelerometer-based activity monitoring devices (“How does my Fitbit device count steps?,” 2017; Wise & Hongu,
2014). Basic mechanical pedometers measure hip movement taken with each step forward. These devices utilize one plane of movement to estimate step count.
Accelerometer-based activity trackers are a type of pedometer which measures steps on multiple planes of movement. In many brands and models, these planes include sideways, up and down, and vertical movements. Manufacturers of accelerometer-based activity tracking devices use patented algorithms to determine step count and other movement related data (“How does my Fitbit device count steps?,” 2017; Wise & Hongu,
2014).
Interventions utilizing pedometers to document steps per day have been used in health promotion campaigns and research studies targeted at many different types of sedentary and non-sedentary populations, including office workers (Banks-Wallace &
2
Conn, 2005; Bohannon, 2007; Cao et al., 2014; Kroemeke et al., 2014; Tudor-Locke,
Williams, Reis, & Pluto, 2002). More recently, the development of accelerometer-based activity trackers, such as Fitbit brand devices which incorporate smartphones and internet based applications, has put additional exercise related data in the hands of consumers
(Stackpool, Porcari, Mikat, Gillette, & Foster, 2014; Storm, Heller, & Mazzà, 2015).
Some of this information includes detailed weekly and monthly graphs of steps taken per day, active minutes per day, progress towards weekly goals, and best day of steps taken.
A social component includes the option to join or create various online Fitbit community groups which allow a user to see and comment on others’ progress toward goals and to compete for the highest number of steps within a chosen time frame with other online users (“There's a Fitbit product for everyone,” 2015). Data recorded by accelerometer- based activity trackers has been used to ascertain self-awareness and to make decisions about behavior change (Cadmus-Bertram, Marcus, Patterson, Parker, & Morey, 2015a;
Lyons, Lewis, Mayrsohn, & Rowland, 2014).
Accelerometer-based activity trackers are growing in popularity in the US. From
2010 to 2011, approximately 500,000 tracking devices were sold. Between April 2013 and March 2014, US consumers purchased 3.3 million units. Out of all wearable tracking devices sold between 2010 to 2014, the Fitbit brand was the most popular with approximately 67% of total sales (Danova, 2014). Worldwide, Fitbit popularity has continued to grow. Global sales rose from 60,000 units in 2010 to more than 22 million units sold in 2016 (“Number of Fitbit devices sold worldwide from 2010 to 2016 (in
1,000s),” 2017). Fitbit devices purchased from the manufacturer range in price from $60 to $250 ("Need Help Choosing a Tracker?," 2016; “Number of Fitbit devices sold
3 worldwide from 2010 to 2016 (in 1,000s),” 2017). In part due to the growing widespread interest in activity trackers (Danova, 2014), researchers have sought to determine the validity and reliability of various models offered to consumers. A variety of placement options, such as at the waistband, shoe, wrist or in a pant pocket or attached to a bra, and a variety of brands, have been the topic of various experimental investigations (Dontje,
Groot, Lengton, Schans, & Krijnen, 2015; Evenson, Goto, & Furberg, 2015; Kooiman et al., 2015).
A systematic review by Evenson et al. (2015) of 22 lab and field based validity and reliability studies, including the Fibit and Jawbone brands sold prior to January 2015, found high validity (r > .80) for lab-based studies in regard to step count (Evenson, et al.,
2015). Seven studies using Fitbit models reported high interdevice reliability. For step count, Fitbit interdevice reliability was .76-1.00 [Pearson and Intraclass Correlation
Coefficient (ICC)] (Evenson, et al., 2015).
Similarly, Kooiman et al. (2015) tested 10 different accelerometer-based activity trackers, including the Fitbit Flex, Fitbit Zip, and other brands, worn concurrently on the hip and wrist, for step count validity and reliability in free living and laboratory settings.
To compare all brands and models to a gold standard for validity, the Optogait system was used in the laboratory setting and the ActivPAL was used as the gold standard in the free-living test. The Optogait system is attached to a treadmill and uses beams of light to count steps. The ActivPal, worn on the thigh, was also worn by subjects in the laboratory test. The results showed excellent agreement between the two gold standards (ICC =
1.0). For the laboratory test, 33 healthy adult office workers, 16 males (mean age = 39 ±
13.1 years) and 17 females (mean age of 35 ± 11.2), wore all trackers during two
4 treadmill lab tests conducted one week apart. Each test was 30 minutes in duration at a walking speed of 4.8 km/hour. Comparing test one and two, interdevice reliability was
.81-.92 (ICC) for 6 models which included the Fitbit Flex (.81) and the Fitbit Zip (.90).
Five of the 10 devices had an ICC with the Optogait system of .98 or higher including the
Fitbit zip (.99) demonstrating high validity. Two devices were .59 and .65 and all others were below .22. The Fitbit Flex had low validity with the Optogait (ICC = .22) in the laboratory setting. Compared to the Optogait, the Fitbit Flex underestimated the number of steps by 5.7% (188 steps) (Kooiman et al., 2015).
To examine a free-living environment, 56 subjects (which included 23 from the lab based study), 18 males (mean age = 37.1 ± 10.6 years) and 38 females (mean age =
30 ± 9.5 years), wore the same trackers for one work day. The validity was good to high for all trackers when compared to the ActivPAL. The ICC between eight different activity trackers and the ActivPAL was between .94-1.00 (Fitbit Zip = 1.00; Fitbit Flex =
.96). Two others were .80 and .83. Overall, hip placement had higher step count reliably and validity than wrist placement, in both settings. The Fitbit Zip, worn on the hip, was the most valid (ICC = .99 in laboratory and ICC = 1.00 in free-living study) (Kooiman et al., 2015).
The Fitbit One, designed for hip placement, and the Fitbit Flex, designed to be worn on the non-dominant wrist, have been validated for step count and energy expenditure as a reasonable method of tracking walking and running (Diaz et al., 2015).
In a four stage, six-minute interval treadmill test ranging from walking [1.9 miles per hour (mph)] to jogging (5.2 mph) including 10 healthy males (age range = 20-54 years), both models tracked data strongly correlated with a video recorded step count within all
5 phases of activity (r = .97-.99 hip placement; r = .77-.85 wrist placement) (Diaz et al.,
2015). In addition, Dontje and associates (2015) found good interdevice reliability in one
46-year-old male wearing 10 different Fitbit Ultras on the hip, at the same time, for eight consecutive days. For the 10 devices, the ICC for steps per minute was ≥ .91 and ≥ .99 for both steps per hour and steps per day. Regardless of model, all Fitbit devices utilize the same technology when recording movement ("How Accurate Are Fitbit Trackers?,"
2017).
While many brands of accelerometer-based activity devices have been found to be valid and reliable in regard to recording activity (Diaz et al., 2015; Evenson et al., 2015;
Kooiman et al., 2015), minimal research exists about the influence of sharing, comparing, and commenting on activity level among peers via a website or smartphone application.
Current research is largely limited to the influence of the physical presence of peers in regard to exercise motivation (Carnes & Barkley, 2015; Carron, Hausenblas, & Mack,
1996). In previous research, subjects who performed physical activity together reported enhanced enjoyment (Carnes & Barkley, 2015). In addition, social support has been shown to have a positive influence on adherence to an exercise program (Carron et al.,
1996). Even the presence of a virtual workout partner via a computer monitor has also been shown to positively influence performance (Irwin, Scorniaenchi, Kerr, Eisenmann,
& Feltz, 2012). The influence of viewing peers’ activity volume, as well as the ability to concurrently interact on a social network, on exercise motivation remains unclear. If existing research on the positive influence of social support translates to a virtual community, exploring the feasibility of utilizing this technology with sedentary office workers may positively impact public health. The low cost of this type of intervention
6 could greatly impact the 80% of US adults who have a sedentary or low active job
(Church et al., 2011; "Need Help Choosing a Tracker?," 2016).
1.2 Statement of the Problem
A review of the literature found no peer reviewed published interventions using the Fitbit Flex or any other accelerometer-based activity tracker that explored the effectiveness of incorporating an online Fitbit community group to increase daily step count and therefore, increase physical activity. Exploring the feasibility of using this new technology as a tool to motivate individuals in sedentary jobs to increase activity could greatly impact public health concerns related to obesity and other hypokinetic diseases.
1.3 Purpose
The purpose of this study was to compare the average number of daily steps and active minutes of office workers who used a Fitbit Flex device as part of an online community group versus office workers tracking steps alone.
1.4 Hypothesis
It was hypothesized that office workers participating in an online community activity group with other Fitbit Flex device users as part of a two-week walking program would perform more steps and active minutes per day than office workers using a Fitbit
Flex alone.
7
CHAPTER II
LITERATURE REVIEW
Pedometers have been used in many health promotion campaigns as a means of helping sedentary individuals, such as office workers, meet physical activity recommendations for optimal health (Banks-Wallace & Conn, 2005; Bohannon, 2007;
Cao et al., 2014; Kroemeke et al., 2014; Tudor-Locke, Williams, Reis, & Pluto, 2002).
The recent introduction of accelerometer-based activity trackers which can connect wirelessly to other devices, has enhanced consumers’ ability to monitor multiple parameters of movement such as steps and active minutes over any time period. In addition, a social media component, which accompanies Fitbit brand devices, allows users to view other users’ activity progress and also to communicate with peers and to take part in group fitness challenges online (“There's a Fitbit product for everyone,”
2015). Some research studies have found that peer involvement increased participation and enjoyment of physical activity interventions (Carnes & Barkley, 2015; Leahey et al.,
2010).
8
Minimal research exists as to the impact of the Fitbit devices (Cadmus-Bertram,
Marcus, Patterson, Parker, & Morey, 2015b) in regard to motivation with meeting daily physical activity recommendations. A common goal of 10000 steps per day has been suggested in many wellness campaigns (“10,000 Steps - Shape Up America,” 2015;
“10,000 Steps USA,” n.d.). Pedometer research suggests that much variability exists in regard to establishing a standard that equates with the PAGA (2015) recommendations.
One study found that 7700 to 8000 steps per day equated to 150 minutes per week of
MVPA for 460 male and 480 female normal weight Japanese subjects, ages 20 to 69 years old (Cao et al., 2014). Another study determined that 79 healthy postmenopausal women, ranging from a normal body mass index (BMI) to obese, average age 63.3 ± 5.5 years, needed to walk 12500 steps per day to maintain a normal BMI (Kroemeke et al.,
2014). Other researchers found that 10000 steps per day was an effective recommendation for weight loss for 19 male and 37 female sedentary overweight or obese, but otherwise healthy subjects, average age of 47 ± 7 years (Schneider, Bassett,
Thompson, Pronk, & Bielak, 2006).
No standard step volume recommendation for health benefits exists. However, some studies showed an increase in activity and improvement in health-related outcomes as subjects monitored their daily step counts or exercise minutes over an extended period of time. At a six month follow up assessment (Banks-Wallace & Conn, 2005), a sample of 21 hypertensive African American women, ages 25-68 years, who completed a 12- month pilot walking intervention program utilizing a pedometer, increased daily mean step count by 37% from baseline (3857 to 5582 steps/day). Due to the small sample size, the researchers chose not to conduct statistical analyses. The women also participated in
9 monthly heart health educational meetings and used the Stanford Walking Kit at home.
The home-based program provided instructions on how to gradually increase walking over the course of six weeks, instructed participants to walk with a partner two times per week, and to also discuss with their partner barriers to walking. The program also provided problem-solving techniques to overcome physical activity barriers (Banks-
Wallace & Conn, 2005).
Similarly, 18 overweight or obese women, ages 40-65 years, significantly increased their daily step count by 83% (4972 to 9213 steps/day) after completing an eight-week walking program utilizing a pedometer with the goal of 10000 steps per day
(Swartz et al., 2003). These subjects, who had a family history of diabetes, also showed significant improvements from baseline in two hour postload glucose levels, which decreased by 11%, and systolic and diastolic blood pressure, which decreased by 4.7% and 6.7% respectively, although no dietary changes were made (Swartz et al., 2003).
Furthermore, some research indicates that on demand access to step count has a propensity toward lasting change (Mansi et al., 2015). Mansi and associates (2015) studied 24 male and 34 female meat packing workers, average age of 42 years (range =
18-65 years), for 12 weeks and at a three month follow up. Over the course of the 12- week randomized controlled study, the control group wore a pedometer only during the first and last week of the study. Both the control (n=29) and experimental (n=29) groups were given materials about walking and received a weekly email encouraging them to increase activity based on baseline data collected. In addition to wearing the pedometer for the duration of the study, the experimental group also kept a written log of activity and received an additional personalized weekly email with specifics regarding progress.
10
The intervention group significantly increased average step count from 5993 to 9792 steps per day, which was maintained at a three month follow up. The increase in steps from baseline at the three month follow up was 59%. The control group significantly increased average step count from baseline to 12 weeks (5788 to 6551 steps per day), but this was not maintained at a three month follow up. The researchers concluded that self- monitoring may increase workplace movement in the short term (Mansi et al., 2015).
In addition to the use of a pedometer, many walking based intervention studies have incorporated supplementary tools to examine their effects on activity (Banks-
Wallace & Conn, 2005; Watson, Bickmore, Cange, Kulshreshtha, & Kvedar, 2012). In some cases, computer based interaction has significantly impacted activity. Watson et al.
(2012) compared mean daily steps taken over the course of 12 weeks between subjects who had access to an animated, automated, virtual coach and those who did not. Sixty- two mostly White, female, overweight or obese, college educated participants, average age of 42 years, wore a pedometer and had access to a website to track daily step count.
In addition, the experimental group (n=31) was asked to meet with a virtual coach to set goals and gain feedback three times per week for five to 10 minutes at a time. The experimental group maintained mean step count while the control group decreased over the course of 12 weeks. The experimental group’s mean daily step count did not significantly change from baseline (6943) to 12 weeks (7024), while the control group’s mean daily step count significantly decreased from 7174 to 6149 steps per day. Percent changes between the two groups were calculated every three weeks and showed that the experimental group significantly improved over the control group at every point in time.
In addition, 87% of experimental subjects reported guilt for missing an appointment with
11 the computer-generated coach, and 58% also said they were motivated by this type of interaction. The researchers concluded that computer interaction with a virtual human may motivate subjects to maintain activity (Watson et al., 2012).
Cadmus-Bertram et al. (2015a) compared MVPA minutes and steps taken by 51 overweight postmenopausal women, average age of 60±7.1 years, who were randomly assigned to wear either the Fibit One (n=25) (a predecessor to the Fitbit Flex worn on the waist band) and use its companion web application, or wear a pedometer (n=26).
Subjects were asked to complete 150 minutes of MVPA per week and 10000 steps per day (Cadmus-Bertram et al., 2015a). The Fitbit group was shown how to download and interact with the Fitbit web application. The pedometer group was provided written materials in regard to steps per day goals. At 16 weeks, the Fitbit group significantly increased mean weekly exercise MVPA minutes from baseline by 62 minutes (172 to
234), and significantly increased the mean number of steps per day by 789 steps (5906 to
6695). The pedometer group had no significant increases from baseline; mean weekly exercise MVPA minutes increased by 13 minutes (176 to 189) and mean daily steps increased by only 362 steps (5827 to 6188). Eighty-eight percent of the Fitbit subjects said they used the website during the intervention. Fifty-two percent reported viewing the website two to three days per week. The researchers concluded that incorporating tracking devices, such as the Fitbit, may enhance wellness programming interventions
(Cadmus-Bertram et al., 2015a).
In contrast, Staudter, Dramiga, Webb, Hernandez, & Cole (2011) found no significant differences in regard to an increase in average daily steps and/or other health related outcomes (weight, waist circumference, BMI, percent body fat, and heart rate)
12 between overweight or obese, active duty military or military related (retired military and military spouses) subjects (N=89), mean age 50 ± 9.3 years, who were randomly assigned to either a pedometer (PED, n = 45) using an interactive website, or a sealed pedometer
(SPED, n = 44) and instructed to make no lifestyle changes, over the course of a 12-week study and a five month follow up. PED subjects concurrently used an interactive website with investigator interaction for 12 weeks, and were also encouraged to set step goals.
PED subjects were also encouraged to post online messages to other participants during the 12-week study and to continue posting until completion of the five month follow up.
SPED wore a sealed pedometer only during four data collection days at baseline, six weeks, 12 weeks, and at a five month follow up, and were told to maintain current physical activity throughout the study and follow up. PED also wore a sealed pedometer at the same data collection points, however unsealed pedometers were returned after data was collected and subjects were instructed to continue to wear pedometers daily. To control for diet, both groups were given 30 minutes of nutrition education at the same four times data was collected (Staudter et al., 2011).
While both groups significantly increased mean daily steps (~ 3000) from baseline to 12 weeks, and maintained mean daily steps at a five month follow up, there was no significant group by time interaction. In addition, both groups significantly improved some health outcomes from baseline to 12 weeks (weight, waist circumference,
BMI, percent body fat, and heart rate). Systolic and diastolic blood pressure were the only outcomes that did not significantly decrease in either group. Researchers suggested the lack of an intervention effect may have been due to investigator interaction, the presence of a pedometer whether sealed or not, and/or workplace interaction between
13 subjects from both groups. Researchers also suggested that control group subjects’ interaction with investigators, even though it was not online, may have positively influenced daily activity. They concluded that involvement in an interactive website did not increase activity or improve health when comparing these two groups. Researchers suggested future studies control for experimental and control group subject contact, as well as subject and investigator interaction, in order to assess motivating or non- motivating factors (Staudter et al., 2011).
While a review of the literature could find no studies regarding the influence of
Fitbit community group participation on steps or exercise active minutes, other studies have shown the involvement of peers influenced physical activity level. Irwin et al.
(2012) compared cycling duration between subjects who exercised on a stationary bike with a virtual partner and were told their exercise minutes would be tallied together as part of a team score, to those who cycled with a virtual partner but were not told they were part of a team, and those who cycled alone. Fifty-eight females, average age of 20.5
± 1.9 years, who described their fitness level as average or good, were randomized into three groups which included a team group, a non-team group, and a control group. The team and non-team intervention groups cycled with a partner who they observed via a computer monitor. Subjects were unaware that the virtual partner was a recorded video; they were told it was a live person. No virtual partner cycled with the control group. All groups were instructed to cycle at 65% of their heart rate reserve selecting a gear which allowed them to maintain 66-74 revolutions per minute. If they dropped below these parameters for five seconds or longer on three occurrences, they were told to stop. In addition, the non-team group was told they could also stop at any point. The maximum
14 duration permitted for the baseline test and five sessions, which took place over the course of four weeks, was 60 minutes. At a pretest trial, the intervention groups were given false feedback that the virtual partner cycled longer than them. Combining all sessions, the team group cycled significantly longer than the other two groups. The team group cycled 21.9 ± 10.1 minutes; the non-team group cycled for 19.8 ± 9.0 minutes; the control group cycled 10.6 ± 5.8 minutes. The researchers concluded that aerobic exercise in a group setting where the results of team performance are combined, may increase activity motivation (Irwin et al., 2012).
Similarly, Leahey and colleagues (2010) examined the relationship between viewing teammates’ step progress and activity level using an online website over the course of a 16-week intervention. As part of the statewide program, Shape Up Rhode
Island, 5333 adults (age = 42.6 ± 11 years; BMI = 29.3 ± 6.4) were divided into 652 different teams who wore a pedometer and logged step activity online. Sixteen percent of subjects were males. Participants were able to view their fellow team members’ step values. Subjects significantly increased mean daily step count from 7029 ± 3915 to 9393
± 5976 by the end of the program. In addition, the researchers found that individuals who were on a team of members who identified themselves as active at baseline, were significantly associated with completing more steps by the end of the program than those who described themselves as less active at baseline. Many subjects also became more active as a result of the program. At baseline, 16% of teams described themselves as active. By the end of the 16 weeks, active subjects significantly increased to 40%.
Active was described as taking more than 10000 steps per day. The researchers
15 concluded that the activity level of team members may be positively associated with individual activity outcomes (Leahey et al., 2010).
While not all research studies have found that exercising with peers increased performance, Carnes & Barkley (2001) found that male college athletes reported a greater degree of enjoyment in running with teammates versus running alone. Twelve competitive college distance runners, age 20.5 ± 2.0 years, were instructed to complete a
6.4 km outdoor run in three different scenarios over the course of three days. The subjects ran alone, with one peer, and then in a group of three peers. The trial completion order was randomly selected. While subjects ran significantly faster alone (14.6 ± 1.4 km/hr) than with one peer (13.7 ± 1.3 km/hr) or with a group of peers (13.6 ± 1.8 km/hr), participants reported liking running with a peer and a group of peers more than liking running alone. At the end of each run, a visual analog scale was used to assess each subject’s degree of enjoyment. Subjects were instructed to touch a point on a 100 millimeter string that corresponded to their feelings about each run. The left end of the string indicated a dislike for a run and the right end indicated liking a run very much.
Both the peer (70.3 ± 3.6 mm) and group (78.4 ± 8.9 mm) scenarios were liked significantly greater than the individual run (63.0 ± 14.2 mm). The researchers concluded that these male college distance runners preferred running with peers over running alone in regard to enjoyment (Carnes & Barkley, 2015). Because the enjoyment of an exercise may motivate one to exercise, these results may suggest that peer involvement plays a role in greater adherence to physical activity programs.
In summary, Fitbit devices provide a method for consumers to track activity and connect online socially with other users ("Need Help Choosing a Tracker?," 2016).
16
Previous studies have shown that self-monitoring movement significantly increased the number of daily steps and active minutes (Mansi et al., 2015; Swartz et al., 2003). In addition, in some studies, exercising with peers has been shown to be more enjoyable with increased adherence (Carnes & Barkley, 2015; Carron et al., 1996). While Fitbit devices provide consumers with an opportunity for both self-monitoring and peer involvement ("Need Help Choosing a Tracker?," 2016), it is unclear if a virtual connection provides the same motivation as an actual person. If a positive influence exists, the low cost of this type of intervention could impact the large population of sedentary and low active US office workers.
17
CHAPTER III
METHODS
3.1 Research Design
An experimental study was conducted over a two-week intervention.
Participation in a Fitbit online community activity group versus using the device alone was the independent variable. The average number of daily steps and active minutes accumulated over the two-week period were the dependent variables. This study was delimited to office workers who sit at their desk at least five hours per day.
3.2 Subjects
After the study was approved by the Cleveland State University Institutional
Review Board (Appendix A), a convenience sample of 30 office workers, 23-68 years old, from a Midwestern medical supply company were recruited via email. A total of 853 employees received an email containing an informational flyer (Appendix B) regarding study participation. Forty-four office workers responded expressing interest. Potential
18 subjects filled out an informed consent form approved by the Cleveland State University
Institutional Review Board (Appendix C), were given the American Heart
Association/American College of Sports Medicine (AHA/ACSM) Facility
Preparticipation Screening Questionnaire (Appendix D), and took a Pre-Study Survey
(Appendix E) to determine eligibility. High risk subjects or those with any medical conditions that would prevent them from walking daily were not eligible. No potential subjects were high risk. All potential subjects were required to own a smartphone with texting and internet capabilities and to have a home internet connection which could be accessed by a personal computer. All potential subjects met this requirement. Potential subjects were asked to self-report fitness level during the previous six months based on a scale which included the number of days spent exercising per week and perceived intensity of exercise.
Thirty subjects were randomly selected. To control for gender, race, age, and fitness level bias, subjects were equally stratified by these characteristics and then randomly assigned to either a control (tracking alone) or experimental (community) group. One subject withdrew from the study after completing the baseline week due to the death of a friend. The final total sample had 29 subjects with 15 participants in the tracking alone group and 14 participants in the community group.
3.3 Procedures
Prior to meeting with subjects individually, the researcher set up one of the subject’s Fitbit accounts and used that information to create a closed community group which was named Workplace Fitbit Group on the Fitbit website. This was so the
19 researcher would not formally be a part of the community group but still have access to download all data from the intervention and to invite subjects to join the closed group.
Community group subjects did not receive an invite to join this group until the evening before the first day of the two-week intervention. Subjects were aware that the researcher had access to the Fitbit community group.
Prior to the start of the study, each subject met individually with the researcher for approximately 30 minutes to receive a Fitbit Flex device, a handout with study instructions specific to their assigned group (Appendix F; Appendix G), and a demonstration on how to operate the Fitbit Flex and access the Fitbit website and smartphone app. During the meeting, weight and height of all subjects were taken and
BMI was calculated. Subjects were asked to provide their cellular phone number to the researcher and asked what would be a good time of day to receive a daily text reminder.
Subjects were next provided a Fitbit Flex device and assigned a gmail account and password which the researcher used to create a Fitbit account for each subject. For simplicity, the same username and password were used for both a subject’s gmail and
Fitbit accounts. The researcher kept a list of all usernames and passwords for the purposes of collecting data. Subjects were instructed not to change their username and password until three weeks after the study was completed. To maintain anonymity, participants were also told not to reveal their username to any coworkers.
Next, the researcher assisted subjects who did not already have the Fitbit app, with downloading the application to their smartphones. Once subjects were able to open the Fitbit app on their smartphones, the researcher assisted with syncing assigned devices to subjects’ individual smartphones. With each subject, the researcher demonstrated how
20 to charge and operate the Fitbit and also how to view their data via both a smartphone and a personal computer.
A handout with operating information and study instructions was provided for both groups (Appendix F; Appendix G). For one week prior to the intervention, all participants were instructed to continue with their normal activity while wearing the
Fitbit Flex for nearly 24 hours a day with the exception of removing it while swimming, bathing, submerging their hand in water, and charging it overnight as needed. During the baseline week, all subjects were also instructed to only view their own data via the Fitbit app on their smartphones or the Fitbit website via a personal computer, and to avoid joining any online Fitbit challenges or adding any Fitbit users to their accounts as friends.
They were also instructed not to join any wellness or fitness challenges during the entire three weeks. Subjects were told they would receive a daily text reminding them to wear their Fitbit.
During this meeting, subjects were also given instructions to follow during the intervention period (Appendix F; Appendix G). Both groups were told to walk at least 30 minutes a day, five days a week, in no less than 10 minute bouts at a time, for a two-week period. Subjects were instructed to walk outside or inside a building. In the event of unfavorable weather, treadmill use was permitted. The tracking alone group was also instructed to continue monitoring their own data only, as much as desired, either on their smartphone and/or the Fitbit website via a personal computer. They were also told to continue avoiding any online interaction with other Fitbit users during the two-week period. All subjects continued to receive a daily text reminding them to wear their Fitbit.
21
The evening before the first day of the intervention, the community group was instructed to join an online closed Fitbit community group called Workplace Fitbit
Group. Because it was a closed group, the researcher used the same subject’s account that was used to create the group, to invite all other subjects to join the Workplace Fitbit
Group and also to add the 14 subjects as Fitbit friends. Since Fitbit community groups cannot be accessed from a smartphone, subjects were instructed to login from their home or work computers. Subjects were told to post a comment on the community group at least three days per week and to view the community group at least five days per week.
Besides a comments page, when accessing the Workplace Fitbit Group, subjects could also see a leaderboard which ranked participants in order of greatest daily steps, distance, and active minutes. The researcher monitored website interaction and collected data by using subject login information. Subjects in the community group were also instructed to view their own Fitbit data and fellow subjects’ data using their smartphone app as often as they desired. During the intervention, the community group was instructed to only interact with the 14 subjects in the Workplace Fitbit Group.
Experienced users in both groups, who may have been a part of other community groups previously, were asked to remove themselves from those groups during the three- week study. Step count and active minute data were collected for the baseline and two- week intervention. Throughout the three weeks of the study, all subjects received a morning text from the researcher reminding them to wear their Fitbit each day. After the baseline week was complete, the community group subjects’ text message also reminded subjects to post in the community group. After completing the study, subjects were asked
22 to complete an anonymous Post Study Survey (Appendix H). As compensation, all subjects were allowed to keep their Fitbit Flex device.
Fitbit Health Solutions gave permission (Appendix I) to include screenshots from their website in Appendices F and G.
3.4 Data Analysis
Descriptive statistics were obtained. An independent t-test was used to compare baseline age, height, weight, BMI, length of workday, and time spent sitting between the two groups. A Chi square test of independence was used to determine group differences in nominal variables including gender, race, self-reported activity level, job type, and previous use of a fitness tracking device. Repeated measures ANOVA was used to assess treatment differences due to the independent variable, community group versus tracking alone, on the dependent variables, average steps and average active minutes per day. For all tests, a p value of less than .05 was the criterion used to determine significance. SPSS
(version 22) was used for all analyses.
23
CHAPTER IV
RESULTS
4.1 Subject Characteristics
Twenty-nine subjects completed the study (Tracking Alone group, n = 15;
Community group, n = 14). Age, height, and weight were obtained and BMI was calculated. Workday length (hours) and sedentary time (hours) were obtained from the
Pre-Study Survey (Appendix E). An independent t-test revealed no significant differences (p ≥ .05) between groups (Table 1). Gender, race, activity level, job type, and previous use of an activity tracker were also self-reported in the Pre-Study Survey
(Appendix E). A Chi-square test of independence indicated no significant difference (p ≥
.05) between groups (Table 1).
24
Table 1. Characteristics of Subjects.
Tracking Alone (n = 15) Community (n = 14) p value
Age (years) 42.7 ± 12.8 41.2 ± 13.6 0.769
Height (cm) 168.1 ± 7.4 165.7 ± 9.4 0.455
Weight (kg) 100.5 ± 21.8 89.9 ± 22.2 0.207
Body Mass Index 0.356 35.9 ± 9.2 32.9 ± 8.2 (kg/m²)
Length of 8.4 ± 0.6 8.4 ± 0.5 0.930 workday (hours) Time spent sitting 7.4 ± 1.0 7.3 ± 0.8 0.823 during workday (hours) Gender (n, %) Female, 11 (73.3) Female, 11 (78.6) 0.742
Male, 4 (26.7) Male, 3 (21.4)
Race (n, %) White, 11 (73.3) White, 11 (78.6) 0.742
Black, 4 (26.7) Black, 3 (21.4)
Self-Reported Sedentary, 4 (26.7) Sedentary, 3 (21.4) 0.991 Activity Level (n, %) Lightly Active, 3 (20.0) Lightly Active, 3 (21.4)
Moderately Active, 7 Moderately Active, 7
(46.7) (50.0)
Very Active, 1 (6.7) Very Active, 1 (7.1)
Extra Active, 0 (0) Extra Active, 0 (0)
Job Type (n, %) Hourly, 9 (60.0) Hourly, 11 (78.6) 0.280
Salary, 6 (40.0) Salary, 3 (21.4)
Previous use, any 5 (33.0) 3 (21.4) 0.474 activity tracker (n, %)
25
4.2 Average Daily Steps Results
A repeated measures ANOVA showed that the tracking alone and community groups significantly (p = .006) increased average daily steps from baseline to completion of the two-week intervention (Table 2). When comparing individual weeks, both groups significantly (p = .002) increased average daily steps the first week, but decreased in week two. There was no significant interaction effect (p ≥ .05) when comparing average daily steps from baseline to week one, week two, or the two-week average. Participating in the community group had no significantly greater influence on average daily steps when compared to tracking alone (Figures 1-3).
Table 2. Comparison of Baseline Average Daily Steps with Week 1, Week 2, and the 2-
Week Average (Mean + SD).
Tracking Alone Community Time Interaction (n = 15) (n = 14) p value p value
Baseline 6488 ± 3535 6778 ± 2770 Daily Steps
Week 1 7933 ± 3487 7950 ± 3682 0.002* 0.718 Daily Steps ∆ = 1445 ∆ = 1172
Week 2 7045 ± 2951 7330 ± 4138 0.177 0.995 Daily Steps ∆ = 557 ∆ = 552
Daily Steps, 7484 ± 2990 7682 ± 3754 0.006* 0.887 2-Week Average ∆ = 996 ∆ = 904
* Significant difference (p < .05); ∆ = change from baseline.
26
AverageDaily Steps
Baseline Week 1
Figure 1. Both groups significantly increased (p = .002) average daily steps from baseline to week one. However, there was no significant interaction effect (p = .718).
27
AverageDaily Steps
Baseline Week 2
Figure 2. Both groups increased average daily steps from baseline to week two, but it was not significant (p = .177). There was also no significant interaction effect (p =
.995).
28
AverageDaily Steps
Baseline 2-Week Average
Figure 3. Both groups significantly (p = .006) increased average daily steps from baseline over the two-week intervention. However, there was no significant interaction effect (p = .887).
29
4.2.1 Baseline Daily Steps Range
During the baseline week, the range of mean daily steps was similar for both groups with a difference of only 19 steps more for the CG group. For the TA group, mean daily steps varied from 5252 to 7287 (Figure 4). A trendline shows an increase in mean steps over the course of the baseline week for the TA group (Figure 4). The baseline week average was 6488 ± 3535 steps per day for the TA group (Table 2).
During the baseline week, mean daily steps varied from 5990 to 8044 for the CG
(Figure 5). A trendline shows an increase in mean steps over the course of the baseline week for the CG (Figure 5). The CG week average was 6778 ± 2770 steps per day
(Table 2).
30
10,000
9,000
8,000 7,287 7,243 7,097 6,774 7,000 6,497
6,000
Average Daily Daily Steps Average 5,264 5,252 5,000
4,000 1 2 3 4 5 6 7 Tracking Alone Group Baseline Days
Figure 4. TA average daily steps range was 5252 to 7287 steps; the step mean was
6488 ± 3535 steps during the baseline week. A trendline shows an increase in steps.
10,000
9,000
7,900 8,044 8,000
7,000 6,577 6,477 6,368 5,990 6,090
6,000 Average Daily Daily Steps Average
5,000
4,000 1 2 3 4 5 6 7 Community Group Baseline Days
Figure 5. CG average daily steps range was 5990 to 8044 steps; the daily step mean was 6778 ± 2770 steps during the baseline week. A trendline shows an increase in steps.
31
4.2.2 Intervention Daily Steps Range
During the two-week intervention, the range of mean daily steps was similar for both groups with a difference of only 22 steps more for the CG. For the TA group, mean daily steps varied from 4967 to 8917 (Figure 6). A trendline shows a decrease in mean steps over the course of the two-week intervention for the TA group (Figure 6). The TA group two-week average was 7484 ± 2990 steps per day (Table 2).
During the two-week intervention, mean daily steps varied from 5744 to 9716 for the CG (Figure 7). A trendline shows a decrease in mean steps over the course of the two-week intervention for the CG (Figure 7). The two-week average was 7682 ± 3754 steps per day for CG (Table 2).
32
10,000
8,836 8,917 9,000 8,587 8,480 8,627 8,185 8,000 7,718 7,662 7,017 7,069 7,000 6,708 6,266 5,917
6,000 Average Daily Daily Steps Average 4,967 5,000
4,000 1 2 3 4 5 6 7 8 9 10 11 12 13 14 Tracking Alone Group Intervention Days
Figure 6. TA average daily step range was 4967 to 8917 steps; the mean was 7484 ±
2990 steps during the two-week intervention. A trendline shows a decrease in steps.
10,000 9,714 9,716 9,251 9,000 8,582 8,358 8,123 8,000 7,631 7,356 7,316 7,102 7,000 6,571 6,458 5,821
6,000 5,744 Average Daily Daily Steps Average
5,000
4,000 1 2 3 4 5 6 7 8 9 10 11 12 13 14 Community Group Intervention Days
Figure 7. CG average daily step range was 5744 to 9716 steps; the mean was 7682 ±
3754 steps during the two-week intervention. A trendline shows a slight decrease in steps.
33
4.2.3 Daily Steps Outliers
The results of the analyses were repeated excluding days for a few subjects that appeared to have not worn their Fitbit (few or no steps). A total of eight days from TA and nine days from CG were removed and averaged over a shorter period of time for outliers. During the baseline week, three TA subject days recorded only a few steps (62,
510, 712 steps) while two CG subject days showed zero steps. During the two-week intervention, five TA subject days recorded only a few steps (range = 11 - 473), while seven CG subject days showed zero to 473 steps.
Statistical analyses were repeated excluding outliers and produced no change in significance at any point (Table 3). At the time data was analyzed, the causes of these outliers, whether technical (uncharged battery, faulty device), unintentional (forgot to wear device) or intentional (lack of motivation), were unknown. Subjects received a daily text from the researcher reminding them to wear the device. No subjects reported any difficulties to the researcher during the study.
34
Table 3. Comparison of Baseline Average Daily Steps with Week 1, Week 2, and the 2-
Week Average (Mean + SD) Excluding Outliers.
Tracking Alone Community Time Interaction (n = 15) (n = 14) p value p value
Baseline 6569 ± 3446 6929 ± 2884 Daily Steps
Week 1 7970 ± 3439 8004 ± 3633 0.003* 0.669 Daily Steps ∆ = 1401 ∆ = 1075
Week 2 7231 ± 2804 7592 ± 3911 0.081 1.000 Daily Steps ∆ = 662 ∆ = 663
Daily Steps, 7611 ± 2954 7875 ± 3619 0.004* 0.879 2-Week Average ∆ = 1042 ∆ = 946
* Significant difference (p < .05); ∆ = change from baseline.
35
4.3 Average Daily Active Minutes Results
Neither group significantly increased average daily active minutes from baseline to completion of the two-week intervention (Table 4). When comparing individual weeks, both groups significantly (p = .008) increased average daily active minutes the first week, but did not increase significantly (p = .874) week two. There was no significant interaction effect (p ≥ .05) when comparing average daily active minutes from baseline to week one, week two, or the two-week average. Participating in the community group had no significantly greater influence on average daily active minutes
(Figures 8-10).
Table 4. Comparison of Baseline Average Daily Active Minutes with Week 1, Week 2, and the 2-Week Average (Mean ± SD)
Tracking Alone Community Time Interaction (n = 15) (n = 14) p value p value
Baseline 231 ± 98 203 ± 57 Daily Active Minutes
Week 1 264 ± 100 215 ± 57 0.008* 0.189 Daily Active Minutes ∆ = 33 ∆ = 12
Week 2 230 ± 66 201 ± 72 0.874 0.956 Daily Active Minutes ∆ = -1 ∆ = -2
Daily Active 246 ± 76 208 ± 62 0.128 0.418 Minutes, 2- Week ∆ = 15 ∆ = 5 Average
* Significant difference (p <. 05); ∆ = change from baseline.
36
Active Minutes Active AverageDaily
Baseline Week 1 Figure 8. Both groups significantly (p = .008) increased average daily active minutes from baseline to week one. However, there was no significant interaction effect (p = .189).
37
Figure 9. Neither group significantly (p = .874) increased average daily active minutes from baseline to week two. Both groups decreased back to baseline. There was also no significant interaction effect (p = .956).
38
Baseline 2-Week Average
Figure 10. Neither group significantly (p = .128) increased average daily active minutes from baseline over the two-week intervention. There was also no significant interaction effect (p = .418).
39
4.3.1 Baseline Daily Active Minutes Range
During the baseline week, the TA group had a greater range of average daily active minutes (32 more minutes) than the community group. For the TA group, mean daily active minutes varied from 209 to 292 (Figure 11). A trendline shows an increase in mean daily active minutes over the course of the baseline week for the TA group
(Figure 11). The baseline week average was 231 ± 98 minutes per day for the TA group
(Table 4).
During the baseline week, average daily active minutes varied from 178 to 229 for the CG (Figure 12). A trendline shows an increase in mean daily active minutes over the course of the baseline week for CG (Figure 12). CG week average was 203 ± 57 minutes per day (Table 4).
40
350
325
300 292 274 275 247 249 250 224 216 225 209 200
Average Daily Minutes Daily Active Average 175
150 1 2 3 4 5 6 7 Tracking Alone Group Baseline Days
Figure 11. TA average daily active minutes range was 209 to 292 minutes; the daily active minutes mean was 231 ± 98 minutes during the baseline week. A trendline shows an increase in active minutes.
350 325 300 275 250 229 223 225 202 207 194 200 190 178
Average Daily Minutes Daily Active Average 175 150 1 2 3 4 5 6 7 Community Group Baseline Days
Figure 12. CG average daily active minutes range was 178 to 229 minutes; the daily active minutes mean was 203 ± 57 minutes during the baseline week. A trendline shows an increase in active minutes.
41
4.3.2 Intervention Daily Active Minutes Range
During the two-week intervention, the TA group had a greater range of average daily active minutes (89 more steps) than the CG. For the TA group, mean daily active minutes varied from 191 to 353 (Figure 13). A trendline shows a decrease in mean daily active minutes over the course of the two-week intervention for the TA group (Figure
13). The two-week average was 246 ± 76 minutes per day for the TA group (Table 4).
During the two-week intervention mean daily active minutes varied from 180 to
253 for the CG (Figure 14). A trendline shows a slight decrease in mean daily active minutes over the course of the two-week intervention for the CG (Figure 14). The CG two-week average was 208 ± 62 minutes per day (Table 4).
42
353 350 334 325 302 297 302 300 286 288 277 269 275 264 267 264
250 222 225
200 191
Average Daily Minutes Daily Active Average 175
150 1 2 3 4 5 6 7 8 9 10 11 12 13 14 Tracking Alone Group Intervention Days
Figure 13. TA average daily active minutes range was 191 to 353 minutes; the daily active minutes mean was 246 ± 76 minutes during the two-week intervention. A trendline shows a decrease in active minutes.
350
325
300
275 253 244 250 225 224 225 210 204 209 203 208 195 200 187 188 180 183
Average Daily Minutes Daily Active Average 175
150 1 2 3 4 5 6 7 8 9 10 11 12 13 14 Community Group Internvention Days
Figure 14. CG average daily active minutes range was 180 to 253 minutes; the daily active minutes mean was 208 ± 62 minutes during the two-week intervention. A trendline shows a slight decrease in active minutes.
43
4.3.3 Daily Active Minutes Outliers
In both groups, a few subjects appeared to have not worn their Fitbit (few or no steps and minutes recorded) during the three-week study. Eight total few or no step days and corresponding active minutes were removed from TA data. Similarly, nine days were removed from CG data.
Statistical analyses were repeated excluding outliers. No significant group by time interaction effect occurred for active minutes. However, after removing outliers, both groups significantly (p = .046) increased mean daily active minutes from baseline to the two-week average (Table 5). This did not occur with the original data (p = .128)
(Table 4). It should be noted that the intensity of movement, represented by the number of active minutes accumulated, is not proportional to daily steps. Fitbit devices only record active minutes which occur at a 3.0 metabolic equivalent (MET) or greater sustained for at least 10 minutes.
44
Table 5. Comparison of Baseline Average Daily Active Minutes with Week 1, Week 2, and the 2-Week Average (Mean ± SD) Excluding Outliers.
Tracking Alone Community Time Interaction (n = 15) (n = 14) p value p value
Baseline 235 ± 94 206 ± 55 Daily Active Minutes
Week 1 265 ± 98 217 ± 55 0.008* 0.159 Daily Active Minutes ∆ = 30 ∆ = 11
Week 2 237 ± 61 209 ± 63 0.746 0.987 Daily Active Minutes ∆ = 2 ∆ = 3
Daily Active 252 ± 76 214 ± 57 0.046* 0.390 Minutes, 2- Week ∆ = 17 ∆ = 8 Average
* Significant difference (p <. 05); ∆ = change from baseline.
45
4.4 Smartphone and Fitbit.com Usage Results
The TA group subjects were instructed to monitor data on their smartphone and/or the Fitbit.com website via a personal computer as much as desired. The CG subjects were instructed to post on the Fitbit community group page via a personal computer at least three days each week, and to view the group page a minimum of five days per week.
Subjects were told the three days they posted comments on the CG page could also count toward the five viewing days. They were also told to utilize their smartphone Fitbit app as much as desired to look at their own data or other subjects’ data.
In an anonymous Post Study Survey (Appendix H), subjects were asked how many days per week and times per day they utilized their smartphone and/or Fitbit.com via a personal computer to view their own data (Tables 6 &7). During week one of the intervention, 80% (n = 12) of the TA group (n = 15) viewed their own data on a smartphone all seven days, while three subjects (20%) viewed their phones either three, five, or six days. In the CG (n = 14) during the first week, 71.4% (n = 10) viewed their own data all seven days of the week on their smartphone, 21.4% (n = 3) viewed their smartphone six days of the week, and one (7%) subject viewed their smartphone data three days out of the week.
During week two of the intervention, 73.3% (n = 11) of the TA group viewed their smartphone data seven days of the week, while another 13.3% (n = 2) viewed their phones five days of the week, and the remaining 13.3 % (n = 2) viewed their phones three and four days during week two. In the CG, 64.3% (n = 9) viewed their own smartphone data seven days during week two, while 28.6% (n = 4) viewed their phones six days out of week two, and one subject looked at their smartphone data three days during week
46 two. Subjects in both groups viewed their smartphone data an average of 4.1 times per day (Table 6).
Table 6. Average Number of Days Per Week and Times Per Day Data Was Viewed on a Smartphone.
Tracking Alone Community Group (n = 15) (n = 14)
Week 1 6.5 6.5 (Days per week)
Week 2 6.3 6.4 (Days per week)
2-Week Average 4.1 4.1 (Times per day)
Throughout the two-week intervention, Fitbit.com was utilized within each group consistently by subjects to view their own data (Table 7). The average days per week the
TA group logged on to Fitbit.com to view their own data was 0.9 days, and the average number of times per day the group logged on was 0.4 times. The average days per week the CG logged on to Fitbit.com to view their own data was 4.5 days, and the average number of times per day the CG logged on was 1.2 times (Table 7).
47
Table 7. Average Number of Days Per Week and Times Per Day Subjects Viewed
Their Own Data on a Personal Computer via Fitbit.Com.
Tracking Alone Community Group (n = 15) (n = 14)
Week 1 0.9 4.5 (Days per week)
Week 2 0.9 4.5 (Days per week)
2-Week Average 0.4 1.2 (Times per day)
The CG was also asked how many days per week they logged onto the CG page on Fitbit.com to view any data and/or post on the community board. Subjects reported logging on an average of 4.5 times during week one and 4.6 times during week two.
Over the course of the two week intervention, 64.2% (n = 9) of the CG said they logged on to the website five times or more each week.
4.5 Impact of the Community Group Interaction on Activity
In an anonymous Post Study Survey (Appendix H), the CG (n = 14) was asked if observing other subjects’ daily steps and active minutes had any influence on their own activity. Three subjects (21.4%) said it had no influence, while 11 subjects (78.6%) said it influenced them to be more active. No subject said it decreased their activity. Subjects were asked the same question in regard to the influence of reading posts on the CG page on activity level. Eight subjects (57.1%) said it had no influence on their activity level, while six subjects (42.9%) said it motivated them to increase their activity. No subject said it decreased their activity.
48
4.6 Self-Reported Activity Level
In a Pre-Study Survey (Appendix E), subjects were asked to report their activity level based on a scale which included the number of days of exercise and intensity of exercise they perceived themselves doing on a regular basis within the previous six months. In an anonymous Post Study Survey (Appendix H), subjects were asked the same question in regard to their activity level during the two-week intervention. A number value was assigned to each activity level [1 = sedentary (little or no exercise), 2 = lightly activity (light exercise/sports 1-3 days/week), 3 = moderately activity (moderate exercise/sports 3-5 days/week), 4 = very active (hard exercise/sports 6-7 days a week), 5
= extra active (very hard exercise/sports and physical job or training intensely two times per day)].
As shown in Table 8, in the Pre-Study Survey (Appendix E), self-reported activity level was similar for both groups. The TA group’s (n = 15) mean activity level was 2.3, indicating on average, that subjects’ self-perception was slightly above lightly active.
The majority of TA group subjects, 46.7% (n = 7), rated their activity level as moderate;
46.7% (n = 7) rated themselves as sedentary or lightly active. One subject (6.7%) rated themself as active. In the Pre-Study Survey (Appendix E), the CG mean activity level was 2.4, indicating on average that subjects’ self-perception was also above lightly active. The majority of subjects (50%, n = 7) rated their activity level as moderate and
42.8% (n = 6) rated themselves as sedentary or lightly active. One subject (7.1%) rated themself as active.
As shown in Table 8, in the anonymous Post Study Survey (Appendix H), both groups’ average self-perception of activity level increased, and more subjects in the CG
49 rated themselves at a higher activity level than subjects in the TA group. The TA group average was 2.7, indicating just below moderately active, while the CG average was 3.0, indicating moderately active. In both groups, the sedentary category decreased by three subjects (~20%). After the intervention, only one subject in the TA group rated themself as sedentary, while in the CG, no subject rated themself as sedentary. After the intervention, more subjects in the CG rated themselves at a higher activity level than the
TA group. Ten subjects in the CG rated themselves as moderately active and two subjects rated themselves as active. In the TA group, seven subjects perceived their activity level as moderate, and two subjects identified themselves as active (Table 8).
50
Table 8. Pre and Post Study Survey: Self-Reported Activity Level.
Track Alone Community Group (n = 15) (n = 14) % (n) % (n)
Pre-Study Survey Responses: Activity level
1 Sedentary 26.7 (4) 21.4 (3) 2 Lightly active 20.0 (3) 21.4 (3) 3 Moderately Active 46.7 (7) 50.0 (7) 4 Very Active 6.7 (1) 7.1 (1) 5 Extra Active 0 0
Post Study Survey % (n) ∆% (∆n) % (n) ∆% (∆n) Responses: Activity level
1 Sedentary 14.7(1) -20.0 (-3) 0 (0) 21.4 (-3) 2 Lightly active 33.3 (5) 13.3 (2) 14.3 (2) -7.1 (-1) 3 Moderately Active 46.7 (7) 0 (0) 71.4 (10) 21.4 (3) 4 Very Active 13.3 (2) 6.6(1) 14.3 (2) 7.2 (1) 5 Extra Active 0 (0) 0 (0) 0 (0) 0 (0)
∆% = Percent of group change in perception of activity level from baseline to completion of study; ∆n = Number of subjects change in perception of activity level from baseline to completion of study.
4.7 Community Group Online Posting Results
During the two-week intervention, a total of 117 posts, with each subject posting an average of 8.3 times (range = 3-15 comments per participant), including 36 different topics were created by CG subjects. Topic themes included an initial introduction to others in the group, comments about the weather, preferences to walk outside over walking on a treadmill, types of activity in which subjects were accumulating steps such
51 as participating in a paintball game, running, hiking, or shopping in a mall, the accuracy of Fitbit devices, how to use the sleep tracker, comments about healthy snacking, getting off to a slow start with the program, walking for stress relief, and seasonal holiday and sports themed comments. During the intervention, the average number of times subjects commented on one topic was 3.3 times. A thread started by one subject who wrote that the Fitbit was not recording all activity minutes received the most comments (nine total, including the initial response) during the two-week intervention. Three threads received eight comments each during the intervention. These included participants introducing themselves to the group, comments about walking being a good stress reliever, and one subject asking for food suggestions related to healthy snacking during the workday.
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CHAPTER V
DISCUSSION
During this 2-week intervention, both groups significantly increased average daily steps from baseline to completion and also significantly increased active minutes from baseline to week one. The results of this study suggest that utilizing an accelerometer- based activity tracker may motivate office workers to increase daily steps and active minutes. However, whether subjects participated in an online Fitbit community group or tracked steps alone showed no significant difference in the number of steps or active minutes accumulated over the course of the two-week intervention.
These results support research which suggests using a pedometer to self-monitor daily steps has been significantly associated with increased activity in a variety of scenarios (Bravata et al., 2007; Staudter et al., 2011). In a 2007 meta-analysis (Bravata et al., 2007), researchers examined 26 studies involving more than 2700 subjects. Data from eight randomized controlled trials (RCT) and 18 observational studies (OS) were
53 analyzed. In both cases, pedometer users significantly increased average daily steps over control participants (RCT= 2491 steps, OS =2183 steps) (Bravata et al., 2007). However, a key difference between the meta-analysis (Bravata et al., 2007) and the current study was intervention length. The average intervention for studies analyzed in the meta- analysis was 16 weeks. It is unknown if in the current study an intervention beyond two weeks would have maintained significance or yielded an interaction effect. A commonality between the meta-analysis and the current study was goal setting. Bravata and colleagues (2007) examined various interventions which included individualized goals, a goal of 10,000 steps per day, no goal, keeping an activity diary, educational materials, and/or individual or group counseling sessions. Researchers found that subjects who were given a specific goal significantly increased mean daily steps over those who were given no goal (Bravata et al., 2007). In a review of 15 published studies,
Schroer, Haupt, & Pieper (2013) also found interventions with specific goals were inclined to be more effective than those with no goal. The findings of the current study agree, as subjects in both the tracking alone and community groups were given the goal of 30 minutes of walking, in no less than 10 minute bouts, five days per week.
The results showed that participating in the community group had no significantly greater influence on average daily steps or active minutes when compared to tracking alone. Leahey and colleagues (2010) found a significant increase in mean daily step count from baseline to 16-weeks in a study in which subjects viewed fellow teammates’ progress online. In addition, Cadmus-Bertram et al. (2015a) found that overweight post- menopausal women who wore a Fitbit One and utilized the Fitbit website to view their own data, significantly increased physical activity from baseline, while the group who
54 wore a pedometer and were given handouts did not significantly increase activity over the course of a 16-week intervention. In contrast, Staudter and associates (2011) found no significant difference in mean daily step count and health related outcomes between subjects assigned to wear a pedometer, interact on a website with other subjects, and encouraged to set step goals versus those assigned to wear a sealed pedometer with no interaction and instructed to maintain activity over the course of a 12-week study and five month follow up. These studies suggest that incorporation of an online component may warrant additional investigation. This is further supported by the Post Study Survey results in the current study, which indicated some benefit from online interaction. Nearly
79% of community group subjects reported that observing other Fitbit users’ steps influenced them to be more active, and 42.9% reported that reading posts in the community group influenced them to be more active. No subjects said either of these activities decreased their desire to be active.
While comments posted online in the Fitbit community group did not indicate any difficulty regarding subjects’ ability to access the Fitbit.com website or to utilize the smartphone app, it appears for some community group participants, a technical barrier existed with the operation of the Fitbit, which may have influenced accurate step and active minute tracking. The most popular thread (nine comments) during the two-week intervention indicated that some of the subjects had difficulty operating the Fitbit device.
A few mentioned that the device went into sleep mode during activity. Others were concerned that hand placement during activity lead to an inaccuracy with step and active minute recording. One subject mentioned that few steps were recorded while mowing the lawn for an hour. Another subject who went on a 45-minute walk said only 10 minutes
55 were recorded, and thought it was likely due to hand placement inside a “hoodie” front pocket. Fitbit wrist worn trackers will record steps even if arms are not moving, such as with pushing a stroller. However the manufacturer says keeping hands stationary might be slightly less accurate than freely moving arms ("How Accurate Are Fitbit Trackers?,"
2017). In a separate thread, another subject who took part in a paintball game also indicated that the Fitbit Flex went into sleep mode several times during the activity.
While the Fitbit Flex was found to have high validity in a free-living environment
(Kooiman et al., 2015), and also high interdevice reliability (Evenson, et al., 2015), and good validity and reliability (Diaz et al., 2015) in other studies, the possibility exists that operational issues may have led to a deficit in the number of steps and active minutes recorded for the community group. It is unknown if the tracking alone group had similar challenges.
Despite no significant effect of the intervention, other community group comments indicated a desire to adapt healthy behaviors during the workday. A thread started by one subject who requested healthy workday snack ideas, and another thread which mentioned experiencing stress reduction while walking during the workday or after work, each received eight comments. Other thread topics focused on types of activities subjects participated in to accumulate steps and active minutes, such as running, hiking, and shopping.
Questions remain whether the starting activity level of the subjects in this study influenced the results. Previous research suggests that sedentary individuals may increase activity to a greater degree from baseline than those who are active (Bravata et al., 2007). At baseline, the average self-reported activity level was somewhat above
56 lightly active for both groups, and in addition, more than 50% of subjects (eight in both groups) identified themselves as moderately active or very active. From baseline to completion, tracking alone subjects increased mean daily steps by 996 steps, while the community group increased by 904 steps. This change was less than that reported in the meta-analysis by Bravata et al. (2007) of 2000 to 2500 steps. While both groups significantly increased steps over the two-week intervention and active minutes during the first week of the intervention, the possibility exists that a greater number of active of subjects at baseline may have contributed to the lack of the intervention effect. It is unknown if a lack of time in the day affected “already active” subjects’ ability to do more. Interestingly, in an anonymous Post Study Survey, the community group identified themselves as more active than the tracking alone group, although the tracking alone group consistently outperformed the community group in both steps and active minutes. The largest difference was from baseline to week one. During this week, the tracking alone group logged 273 more mean daily steps and 21 more average daily active minutes than the community group. More research is needed to see if this discrepancy in perception may have been influenced by community group subjects monitoring what others were doing during the intervention.
A strength of this study was that there were no significant differences in baseline group demographics. Subjects were relatively similar in average age, BMI, activity level, and job type. In addition, the gender and racial makeup of the two groups showed no significant difference. While no data was available on the racial and gender demographics of the midwestern medical supply company from which subjects were recruited, a visual observation from the researcher estimated that about 60% of
57 employees were female, and about 70% were Caucasian, 20% African American, and the remaining 10% of various ethnic groups. These demographics were similar to the sample used in this study.
58
CHAPTER VI
SUMMARY AND CONCLUSION
6.1 Summary
Sedentary and low active office workers are a population at risk for chronic diseases related to inactivity. Accelerometer-based activity trackers provide a method for self-monitoring movement. The effectiveness of utilizing this technology to increase daily activity has been supported by this study and previous research. While this study did not show that involvement in an online Fitbit community group significantly increased steps or active minutes versus those tracking steps alone, previous research has suggested group interaction may motivate increases in physical activity. To the knowledge of this researcher, since this is the first study examining an online Fitibit community group, further research may be warranted to shed light on the effectiveness of this low-cost intervention.
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6.2 Conclusion
The results of this study indicate that the 29 office workers who utilized a Fitbit activity tracker with the assigned goal of walking at least 30 minutes per day, in bouts of no less than 10 minutes, five days out of the week, significantly increased mean daily steps from baseline to completion and significantly increased mean active minutes from baseline to week one. However, the involvement in an online Fitbit community group elicited no greater influence than tracking alone on mean steps and/or mean active minutes accumulated during the intervention. The hypothesis that the community group would be more active than the tracking alone group was not supported.
6.3 Limitations
The short duration of this study may have been a limitation. Some previous meta- analyses and review articles indicated 16 weeks was the average intervention time. It is not possible to predict if results would have been the same if the study was extended beyond three weeks. Another limitation was that the Post Study Survey was anonymous while the Pre-Study Survey was not. Since the researcher was also a coworker of the subjects, Post Study Survey anonymity was chosen in order to avoid the potential of subjects answering questions in a way that could be perceived desirable to the researcher.
The small sample size and compliance may have also affected results. It is possible that a few outliers may have influenced outcomes. During the three-week study, eight tracking alone and nine community group days had either zero or few steps and corresponding active minutes recorded. No subject expressed to the researcher technical difficulties with the Fitbit and all subjects received a daily text reminder to wear their
Fitbit. At the time of analysis, the cause of these outliers was unknown. Removing
60 outliers did not significantly alter mean daily steps or impact the overall effect of the intervention. However, both groups significantly increased mean daily active minutes from baseline to the two-week average. This did not occur with the original data. Fitbit devices do not correlate steps with active minutes. Fitbit active minutes are a measure of intensity of activity. Fitbit devices only count active minutes after a subject has performed at an intensity of 3 METs for 10 minutes. Fitbit algorithms used to determine intensity are proprietary information ("What Are Active Minutes?," 2017). Due to the 10 minute criteria, the importance of group comparison of active minutes is unclear.
A key limitation was the requirement of a personal computer to access the Fitbit community group. At the time of this study, the Fitbit manufacturer did not make online community groups available via a smartphone app. While community group subjects were able to view their fellow subjects’ steps on their smartphone app, they were not able to access the other features provided by the Fitbit website, unless they were on a personal computer outside of the workplace. All subjects in this study owned smartphones but could not access the Fitbit website via a personal computer during the workday. It remains to be seen if results would have been different had subjects been able to communicate with other members during the workday.
6.4 Future Research Recommendations
Since, to the knowledge of the researcher, this is the first study examining the influence of an online Fitbit community group, additional studies with larger samples of varying demographics may be warranted to determine the usefulness of this type of intervention.
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6.5 Application
The results of this study indicated that in this sample of office workers simply utilizing a fitness tracker with a set goal to walk at least 30 minutes a day, in bouts of at least 10 minutes, five days a week, increased activity during a two-week intervention.
Because the Fitbit is a low-cost device, fitness professionals may find value in incorporating similar health promotions in the workplace.
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APPENDICES
70
APPENDIX A
IRB Study Approval Email
71
APPENDIX B
Recruitment Flyer
Photo Permission: Appendix I
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APPENDIX C
Informed Consent
Comparison of Daily Steps and Activity Minutes Using a Fitbit Device as Part of an Online Community Versus Tracking Alone
You have been invited to participate in a research study. This study will be conducted by Exercise Science graduate student, Karen Kawolics, under the supervision of Dr. Kathleen Little, Associate Professor in the Department of Health and Human Performance. Purpose of the Study The purpose of this study is to compare the average number of daily steps and activity minutes of office workers who use a Fitbit device as part of an online community group versus office workers tracking steps alone. The study will take place over the course of three weeks. Procedures I understand that I will complete a questionnaire. I will also complete a brief medical history. These will be used to determine my eligibility for participation in the study. I understand that as part of this research study, I will be provided a Fitbit device. I will use this device over the course of the three-week study. I agree to the following procedures:
1. I understand that the researcher will measure and record my weight and height. This will take place prior to the start of the study. 2. I understand that as part of this research study I will be asked to create a free Gmail account. This will only be used to set up the Fitbit device. I understand that I will provide this Gmail username and password to the researcher. This is solely for purposes related to data collection. 3. I understand that after being provided a Fitbit device, I will provide my Fitbit username and password to the researcher. This is for the purpose of data collection only. 4. Four weeks after the completion of the study, I understand that I will change my Fitbit and Gmail passwords. At this point, the researcher will no longer have access to my information.
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5. I understand that by agreeing to participate in the study, I am agreeing to wear a Fitbit device. I agree to wear it on my non-dominant wrist for three weeks. I understand that I will remove the Fitbit device as needed for charging. This is ideally during my sleeping hours. I will not wear it while swimming or put it under water. I understand that I will wear my Fitbit device all other times. I understand that this Fitbit device can only be charged by attaching it to a personal computer. I understand that if my device is not working properly, I will immediately contact the researcher. 6. I understand that during the first week of the study, I will wear my Fitbit device during my normal activities. I understand that during the second and third weeks of the study, I will be asked to walk at least 30 minutes per day, on five days per week. 7. I agree to receive a daily reminder text from the researcher. 8. If I am in the Experimental group, I agree to the following procedures: a. I will not “add” or “accept” any friends to my Fitbit account during the first week of the study. b. During the last two weeks of the study, I understand that I will interact with a closed Fitbit Online Community Group via my personal computer. I understand that this group has been created by the researcher on www.Fitbit.com. I agree to access and view information on this community group 5 days per week. I also agree to post a minimum of 3 comments on 3 separate days of the week. I understand that this community group cannot be accessed by a Smartphone. I understand that it can only be accessed via a personal computer connected to the Internet. c. I understand that the researcher will provide me a list of all of the other members of this group. I agree that these users are the only friends I will “add” or “accept” as friends. I understand that this will take place on the first day of week 2 of the study. I agree to keep these friends until the study is completed. d. I understand that I will download the Fitbit application to my Smartphone. I will view my information and my friends’ information on my Smartphone as desired. e. I understand that I will not participate in any Fitbit challenges or other fitness or wellness programs during the three week study period. f. I agree to fill out an anonymous post study survey. 9. If am in the Control group, I agree to the following procedures: a. I understand that I will download the Fitbit application to my Smartphone. I will view my information on my Smartphone as much as I desire. I understand that I may also access the Fitbit website at www.fibit.com and access my personal dashboard as much as I desire.
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b. I understand that I will not “friend” any other users during the three week study period. I agree to only view my information during the three week study period. c. I understand that I will not participate in any Fitbit challenges or other fitness or wellness programs during the three week study period. d. I agree to fill out an anonymous post study survey. 10. If my employment with Cardinal Health is terminated during the three week period, I agree to continue with the study. I will also provide all required information to the researcher.
Risks I understand that the risks associated with this study are minimal. I understand that they are no different than my normal activities of daily living. Benefits
I understand that if I complete the three week study, I will be allowed to keep the Fitbit device. However, if I quit the study, I will return the Fitbit device to the researcher. The results of this study will help determine if online community groups motivate people to be active.
Confidentiality I understand that all information obtained during my participation will be confidential. It will not be disclosed to anyone without my consent. However, I agree that my data may be used for research purposes in group form, without my name used. I understand that I will create a “fake” Fitbit username which will ensure that I will not be personally identifiable to other participants. In order to assure this, I agree not to share my Fitbit user name with anyone else for the study duration. Freedom of Consent I understand that participation in this project is voluntary and that I have the right to quit at any time. I understand that if I have any questions about my rights as a participant, I can contact Cleveland State University’s Review Board at (216) 687-3630.
If I have any questions about the study procedures, I can contact graduate student Karen Kawolics at [email protected] or 440-823-6123, or Dr. Kathleen Little at [email protected] or 216-687-4877.
Participant Acknowledgement
I confirm that I am 18 years of age or older and that I have no known health problems that could prevent me from successfully participating in the study. The purpose, procedures, known discomforts and risks, possible benefits to me and to others have been explained to me. I have read the consent form or it has been read to me, and I understand it.
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Signature: Date:
Print Name:
Witness Signature: Date:
Print Name:
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APPENDIX D
AHA/ACSM Prescreening Questionnaire
Name: ______
Check all true statements below.
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APPENDIX E
Pre-Study Survey
Thank you for considering participation in this research study. The purpose of this study is to compare the average number of daily steps and activity minutes of office workers who use a Fitbit device as part of an online community versus office workers tracking steps alone. This study will take place over the course of three weeks. We are seeking to study a sample of office workers which reflect the general population of office workers in regard to age, race, sex, activity level, and comfort with technology. All information will be kept confidential and will be used to select those eligible for this study. Eligible participants will be randomly assigned to one of two groups.
1. Name______2. Email______3. Cellular Phone ______4. If chosen, are you able and willing to accept a daily text from the researcher? Yes or No (Circle) 5. Do you own a Smartphone, such as an I-phone, Android or other? Yes or No (Circle) 6. Do you have the ability to download and access an application to your Smartphone such as the Fitbit application? Yes or No (Circle) 7. If you answered yes to questions 5 and 6, would you be willing to use your Smartphone as part of this study? Yes or No (Circle) 8. Do you own or have access to a personal computer with access to the Internet? Yes or No (Circle) a. If you answered yes to question 7, would you be able and willing to use this personal computer to access www.Fitbit.com and also charge a Fitbit device? Yes or No (Circle) b. What do you consider your level of comfort with using a Smartphone? c. Not comfortable at all d. Somewhat uncomfortable e. Neutral f. Somewhat comfortable g. Very comfortable 9. What do you consider your level of comfort with using the internet on a personal computer? a. Not comfortable at all b. Somewhat uncomfortable c. Neutral
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d. Somewhat comfortable e. Very comfortable 10. Race a. Asian b. Black c. Hispanic d. Native American e. White (non-Hispanic) f. Other ______11. Gender: a. Female b. Male c. Transgender 12. Date of Birth ______Age ______13. Age Range a. 18-24 b. 25-34 c. 35-44 d. 45-54 e. 55-64 f. 65-74 g. 74-100 14. Are you a Cardinal Health Fitness Center Member? Yes or No (Circle) 15. If you answered yes to question 16, how long have you been a member? ______16. Do you belong to any other type of Fitness Facility? Yes or No (Circle) 17. If you answered yes to question 18, how long have you been a member of any gym? ______18. What department do you work in at Cardinal Health? ______19. What is your job title? ______20. What type of employee are you? a. Hourly b. Salary c. Contractor d. Intern 21. How many hours is your average workday at Cardinal Health? ______
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22. How many hours per work day do you spend sitting or are sedentary at Cardinal Health? ______23. What do you consider your activity level to have been within the past 6 months? a. Sedentary (little or no exercise) b. Lightly activity (light exercise/sports 1-3 days/week) c. Moderately activity (moderate exercise/sports 3-5 days/week) d. Very active (hard exercise/sports 6-7 days a week) e. Extra active (very hard exercise/sports & physical job or training intensely two times per day) 24. Do you currently own a Fitbit? Yes or No (Circle) If yes, which model ______25. Do you currently own or have every used another brand of tracking device similar to a Fitbit? Yes or No (Circle) If yes, which brand and model ______26. Circle any of the following in which you have participated in. a. Any Fitbit Challenges via a Smartphone b. Fitbit Community / Activity Group membership or involvement c. None
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APPENDIX F
Tracking Alone Study Instructions Handout
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USB cable and port
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USB cable photo: Taken by researcher
PhotosOther p hotopermission:s: See permission Appendix in Appendix I I
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APPENDIX G
Community Group Study Instructions Handout
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USB cable and port
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Your page will have the name:
Workplace Fitbit Group
Test (Title of Post)
Create a topic
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Type your comment here.
After your comment is complete, select Post.
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View Full Leaderboard
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USB cable photo: Taken by researcher
Other photos: See permission in Appendix I
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Appendix H
Post Study Survey
Thank you for your participation in this research study. The purpose of this study was to compare the average number of daily steps and activity minutes of office workers who use a Fitbit device as part of an online community versus office workers tracking steps alone. Please answer the following questions. All data will be kept confidential and will only be used for the purpose of this study. Do not put your name on this form.
1. Circle which study group you were a part of: a. Group A (Community Group Involvement – posting comments via www.Fitbit.com) b. Group B (Tracking Alone – viewing only your own information) 2. During the last 2 weeks of the study, on average how many days per week did you look at your own data? (number should not exceed 7 days in each category. If you never looked at your own data mark zero) a. On your Smartphone i. Week 1 ______days per week (Oct. 24 – Oct. 30) ii. Week 2______days per week (Oct. 31 – Nov. 6) b. On your personal computer via www.Fitbit.com i. Week 1 ______days per week (Oct. 24 – Oct. 30) ii. Week 2______days per week (Oct. 31 – Nov. 6) 3. During the last 2 weeks of the study, on average how times per day did you look at your own data? a. On your Smartphone ______times per day b. On your personal computer via www.Fitbit.com ______times per day 4. If you were part of Group A (Community group involvement), on average how many days per week did you log on to the Community group page. To either view the page and/or post on the page. (Skip this question if part of Group B- Tracking Alone) i. Week 1 ______days per week (Oct. 24 – Oct. 30) ii. Week 2______days per week (Oct. 31 – Nov. 6) 5. If you were part of Group A (Community Group Involvement), how did observing your friends daily steps and activity minutes influence your activity level? Please select A, B or C. (Skip this question if part of Group B – Tracking Alone) a. No influence at all
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b. It motivated me to increase my daily activity and daily steps c. It motivated me to decrease my daily activity and daily steps Please provide any additional comments you may have about this question. (Optional) ______6. If you were part of Group A (Community Group Involvement), how did observing your friends posts on the community group page influence your activity level? Please select A, B or C. (Skip this question if part of Group B – Tracking Alone) a. No influence at all b. It motivated me to increase my daily activity and daily steps c. It motivated me to decrease my daily activity and daily steps Please provide any additional comments you may have about this question. (Optional) ______7. What do you consider your level of comfort with using a Smartphone? a. Not comfortable at all b. Somewhat uncomfortable c. Neutral d. Somewhat comfortable e. Very comfortable 8. What do you consider your level of comfort with using the internet on a personal computer? a. Not comfortable at all b. Somewhat uncomfortable c. Neutral d. Somewhat comfortable e. Very comfortable 9. Race a. Asian b. Black c. Hispanic d. Native American e. White (non-Hispanic) f. Other ______
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10. Gender: a. Female b. Male c. Transgender 11. Age ______12. Age Range a. 18-24 b. 25-34 c. 35-44 d. 45-54 e. 55-64 f. 65-74 g. 74-100 13. How many hours is your average workday at Cardinal Health? ______14. How many hours per work day do you spend sitting or are sedentary at Cardinal Health? ______15. What do you consider your activity level to have been within the past 2 weeks? a. Sedentary (little or no exercise) b. Lightly activity (light exercise/sports 1-3 days/week) c. Moderately activity (moderate exercise/sports 3-5 days/week) d. Very active (hard exercise/sports 6-7 days a week) e. Extra active (very hard exercise/sports & physical job or training intensely two times per day) 16. Please list any other comments about your involvement in this study. ______
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APPENDIX I
Fitbit Permission to Use Photos
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