The Effects of a Mobile Wellness Intervention with Fitbit Use and Goal Setting for Workers

Abstract

Background and Introduction: There is strong evidence that worksite wellness programs can significantly improve the health profile of participating workers. To date, little is known about research on the effects of mobile wellness interventions in worksite settings. Furthermore, no studies have been conducted to evaluate mobile wellness interventions with activity trackers and tailoring strategies for physically inactive workers in manufacturing companies. This study aimed to examine the effects of a mobile wellness intervention with Fitbit^® and goal setting using brief counseling and text messaging among workers.

Materials and Methods: A total of 79 (n = 79) workers from large manufacturing companies were allocated into an experimental group (n = 41) and a control group (n = 38). All participants were asked to wear an activity tracker (Fitbit Charger HR^®) during all waking hours for 5 weekdays. Participants in the experimental group received Fitbit, daily motivational text messaging, and biweekly counseling with a specifically designed workbook for 12 weeks, whereas Fitbit was only provided to the control group.

Results: At the 12-week measurement, there were significant differences between the experimental group and control group on wellness (p < 0.001), physical activity behavior (p < 0.001), daily walking steps (p < 0.001), and physical activity self-efficacy (p < 0.001).

Discussion and Conclusions: Although Fitbit facilitates an individual's activities by providing information about daily steps, the tracker itself, without additional goal-setting techniques, may be insufficient to encourage behavior change. These findings indicate that the mobile wellness intervention with Fitbit and goal setting using brief counseling and tailored text messaging is more effective for physically inactive workers.

Introduction

The demand for skilled workers at the workplace has increased globally, while the prevalence of various chronic diseases, such as cardiovascular diseases, diabetes, and cancer, has not been decreasing in workers.¹ These chronic health conditions in workers have become a major burden, as they lead to decreases in individual's wellness, company's productivity, and country's competitiveness due to workers' absenteeism.^1,2 Since there is a growing recognition that workplace wellness programs afford a good opportunity to positively impact the health profile of working populations in a cost-effective manner,^3
–5 employers are interested in workplace wellness programs to change workers' health behavior.⁶

As most full-time workers spend more than one-third of their time at the workplace, the worksite is considered a fruitful setting for improving the wellness of the working population by targeting physical activity.⁷ The 2018 Physical Activity Guidelines report that regular physical activity not only provides a variety of health benefits but also reduces the risks of a large number of chronic diseases.⁸ Despite such positive effects of engaging physical activity, current national surveillance data continue to show that the physical activity levels of most individuals in the United States and Korea remain insufficient to attain the benefits of an active lifestyle. For example, only 29.9% of Korean adults engaged in moderate to vigorous physical activity (or 150–300 min per week of moderate-intensity physical activity),⁹ and 49.8% of US adults reported levels of moderate to vigorous physical activity with federal guidelines for Americans in 2015.¹⁰ Given that most of the adult population are working population, tailoring workplace wellness programs with specific strategies should be provided to promote physical activity for workers.

Contemporary technology has led to the use of wearable devices such as activity trackers to promote sustainable physical activity.¹¹ Various systematic reviews have shown that activity trackers have been used to measure changes of daily steps and heart rate in physical activity interventions, which can help people achieve sufficient physical activity increases by self-monitoring their activity easily.^12

–16 Integrating activity trackers into the workplace wellness program could be a key contributing factor to engage workers in physical activity and increase their wellness.^14,17 The activity trackers may be a viable way to motivate participants, but those may not be effective for working adults with lack of time or for less motivated individuals such as sedentary workers. Thus, the addition of another motivational strategies could be needed to increase the physical activity of working adults.

Goal setting is an effective strategy for engaging a physically active lifestyle.¹⁸ Evidences on goal setting to increase physical activity have shown that interventions with specific goal setting and frequent goal progress tracking can facilitate physical activity behavior change in adults.¹⁸ With the use of text messages as a useful tool to track goal achievement progress, researchers have incorporated a strategy of goal setting using text messaging into physical activity interventions.¹⁹ For example, a systematic review on the effects of wellness programs using text messaging has revealed that text messaging is effective in promoting health and managing chronic diseases by consistently motivating people to sustain healthy behaviors, such as exercise, managing blood sugar levels, and controlling weight.^19,20 Despite the increasing prevalence of text messaging interventions, there is still a paucity of evidence on mobile wellness interventions with goal setting using text messaging for physically inactive workers. Although previous research on workplace wellness interventions has concluded that the participation in regular physical activity can have positive effects on workers' health and job performance,^21,22 even less is known about the mobile wellness interventions for sedentary workers. Additionally, no studies have been conducted to evaluate mobile wellness interventions with activity trackers and tailoring strategies for physically inactive workers in manufacturing companies. Therefore, this study aimed to examine the effects of a mobile wellness intervention with Fitbit^® (Fitbit, Inc., San Francisco, CA) and goal-setting strategies consisting of brief counseling and motivational text messaging to increase the physical activity of workers in manufacturing companies.

Materials and Methods

Study Design

This quasi-experimental study used a pre- and post-test nonequivalent control group design to examine the effects of a mobile wellness intervention using Fitbit and goal setting using brief counseling and motivational text messaging for workers.

Setting and Participants

This study performed convenient sampling with workers in two large manufacturing companies consisting of more than 2000 full-time workers. The eligibility criteria included participants aged 19–60 years who used a Fitbit-compatible smartphone to download the Fitbit app and to receive text messages, and not meeting the recommended physical activity guidelines for adults (<150 min/week moderate- to vigorous-intensity physical activity). We excluded workers who were diagnosed with active cardiovascular diseases and musculoskeletal diseases that restrict participation in the wellness programs, and those who participated in any exercise intervention in the previous 6 months. Participants were assigned into two groups: (1) the control group was given only a self-monitoring activity tracker, Fitbit; (2) the experimental group received Fitbit, a brief face-to-face counseling to track goal achievement progress,, and daily motivational text messages including goal-setting messages.

Sample size estimation was calculated a priori to test the primary hypothesis that the experimental group receiving Fitbit, a brief counseling, and goal-setting text messages would have a significant increase in daily steps and wellness than the control group with Fitbit only. Based on a previous study,²³ to increase physical activity level through mobile health coaching, an effect size of 0.6 was determined. We assumed a priori sample size determination that a total of 72 participants (36 people per group) was estimated to provide 80% statistical power at a significance level (α) of 0.05 and effect size 0.60 for a two-sided two-sample t test to detect differences in wellness and physical activity between the two groups at week 12. Allowing for 15% attrition, we recruited a total of 82 participants (41 people per group). Of 82 participants, three persons in the control group withdrew from the study due to frequent business trips. Finally, 79 persons participated in the study (Fig. 1).

Fig. 1.

Flowchart of participant recruitment.

Instruments

The general characteristics that participants were asked about included age, marital status, educational level, occupational class, and subjective health status.

Daily walking step

Daily walking steps were objectively measured using a triaxial accelerometer (Fitbit Charger HR^®; Fitbit, Inc., San Francisco, CA), which was a valid and reliable measure of physical activity in adults.^24

–27 Multiple systematic reviews to examine the accuracy of Fitbit devices have found that Fitbit devices are most likely to provide an accurate measure of steps in adults.^26,27 Participants were asked to wear Fitbit during all waking hours for 5 weekdays to measure their daily walking steps. The activity data were automatically uploaded to the Fitbit app on the user's smartphone and then downloaded by the research team through a database called Wellness Portal (DGIST Convergence Research Center for Wellness, Daegu, Korea), which allows for collecting data at the minute level. Data were considered as valid if the participant wore the device for ≥10 h per day and for ≥4 days during 12 weeks.^13,28,29 We represented daily walking steps as a mean value in total walking steps, calculated as the sum of daily walking steps divided by the number of weekdays.

Physical activity behavior

Physical activity subscale of the Health-Promoting Lifestyle Profile II questionnaire was used to measure the extent to which persons engaged in a physical activity.³⁰ The instrument is a eight-item questionnaire with a four-point Likert scale ranging from 1 (never) to 4 (routinely). The scale showed good psychometric properties by providing good reliability, construct validity, content validity, and criterion-related validity³⁰ and has been used in many physical activity interventions. A score for physical activity behavior was estimated by calculating a mean value of the individual's responses to all items. Higher scores represent higher intensity of physical activity. The Cronbach's α for this study was 0.78 for the physical activity subscale.

Self-efficacy for physical activity

Self-efficacy for physical activity refers to the self-confidence in the ability to persist physical activities in various situations, such as being in a bad mood, not having a time, and experiencing bad weather.³¹ The eight-item scale developed by Kang and Gu measured self-efficacy for physical activity with a five-point Likert scale ranging from 1 (very uncertain) to 5 (very certain).³¹ This scale is considered valid and reliable for assessing physical activity self-efficacy by offering acceptable reliability, construct validity, and content validity.³¹ A score for physical activity self-efficacy was obtained by calculating a mean of the individual's responses to eight items. Higher values represent higher levels of self-efficacy for physical activity. The Cronbach's α for this study was 0.80.

Wellness

The World Health Organization defines that wellness refers to the pursuit of an optimal state of health of individuals and groups.³² Wellness involves several dimensions, including physical, emotional, social, intellectual, spiritual, occupational, and environmental wellness.³³ Wellness was measured with the wellness index for workers.³⁴ The wellness index contains 18 items with a five-point Likert scale ranging from 1 (very disagree) to 5 (very agree). This scale is grouped into five dimensions of wellness, each containing three to five items: (1) physical wellness, (2) emotional·spiritual wellness, (3) social wellness, (4) intellectual wellness, and (5) occupational wellness. The scale has shown both good reliability and validity among different working populations.³⁴ Each domain score was combined to provide a measure of overall wellness. A score for wellness was obtained by calculating a mean of the individual's responses to 18 items. Higher values represent higher levels of wellness. The Cronbach's α for this study was 0.80.

Procedures

A mobile wellness intervention was provided to the experimental group for 12 weeks from June to October 2016. The intervention consisted of the provision of Fitbit, a face-to-face counseling for 5 min with a workbook to track goal achievement progress, and receiving motivational messages daily. The goal during first to sixth week of the mobile wellness intervention was to gradually increase the daily walking steps of individual workers by achieving a daily average number of 10,000 steps or more. In the 7th–12th week of the mobile wellness intervention, the goal was to maintain an increased level of physical activity and the intensity of exercise at 50–60% of the target heart rate.

All participants were given a Fitbit Charger HR, which is a small, wearable, triaxial accelerometer. To minimize the potential barriers to navigating the technology, the project coordinator (1) set up the Fitbit account for each participant, (2) demonstrated how to download and install the Fitbit software, (3) trained the participant on self-monitoring, (4) asked the participant to wear Fitbit at all times for 12 weeks, and (5) provided the participant with a workbook with goal setting, detailed instruction, and information about health-enhancing physical activity. The Fitbit measures steps on a minute-by-minute frequency, and the data collected are automatically uploaded to the Fitbit website via a Bluetooth connection with an app on the participant's smartphone. The Fitbit app provides users with activity summaries and retains historical information on physical activity. Our research team developed a website named Wellness Portal (DGIST Convergence Research Center for Wellness), where we could monitor workers' overall physical activity, including daily walking steps, in real time. We accessed the workers' activity data collected by each participant's Fitbit device via the DGIST Wellness Portal. Once the data are uploaded to the Fitbit Cloud servers, the real-time walking steps of the workers are accessible and could be analyzed within the DGIST Wellness Portal.

Study goals were over 10,000 steps/day, but each participant's goal could be higher. Based on the analysis of workers' activity data from the Fitbit device, biweekly face-to-face counseling was provided regarding the physical activity level of the worker. The topics of brief counseling included checking daily walking steps, assessing weekly activity goal, re-establishing the strategy to meet the goal, increasing one's daily walking steps by 15–20%, checking the target heart rate with Fitbit, recognizing the benefits of aerobic exercise, and perceiving the obstacles to perform sufficient physical activity.

A pool of five types of text messages was developed, and text messages were sent via the server by a commercial web service^*. The types of text messages included (1) motivation (“Goal-setting today! Your goal of daily steps in this week for [name of the participant] is [the goal set by each participant] steps. How about walking today to achieve this goal?”); (2) health-related information (“Your cholesterol level will improve as soon as you exercise for more than 20 min, until you feel a bit out of breath.”); (3) emotional support and encouragement (“Isn't it hard to start exercising because it is hot today? Well begin and you are halfway there! You can start now to meet the goal.”); (4) support for health behaviors (“How about walking around the cafeteria twice after having lunch with your teammate?”); and (5) problem solving (“Be careful on Friday not to be tempted to drink or overeat! Try following a healthy diet and exercising for your health!”). Participants in the experimental group received motivational texts on Monday regarding the daily steps of previous week, and were asked to set a step goal for the current day and week. For example, the motivational text sent to the workers on Monday was: “You achieved 7,119 steps in previous week. Your goal is 8,000 steps in this week.” To increase physical activity self-efficacy, workers were instructed to set a long-term goal as well as a short-term goal, which they wanted to meet by the end of the 12-week intervention.

Data Collection

Posters and banners regarding the mobile wellness intervention were posted in the cafeterias and at the companies' gates. Data were collected from two companies from June 2016 to October 2016. Data collectors were blinded to ensure unbiased ascertainment of findings. Those collectors conducted the pre- and post-surveys without knowing which workers belonged to the experimental and control groups. Data were collected immediately before the start of intervention as pre-test and post-test at 12 weeks at the end of intervention.

Data Analysis

The data were analyzed using the SAS 9.4 program. First, baseline characteristics were analyzed using descriptive statistics, namely, frequency for categorical data and means and standard deviations for continuous data. Second, a homogeneity test was performed using an independent sample t-test or chi-square test. For subgroup analysis, the participants were classified into two groups: one with a low level of physical activity (<10,000 steps/day) and another with a high level of physical activity (>10,000 steps/day). Third, the effects of the intervention at 12 weeks were conducted using an independent sample t-test and paired t-test.

Results

Homogeneity of General Characteristics of Participants

There were no significant differences between the two groups in any of the general characteristics: age, marital status, educational level, occupational class, and subjective health status, indicating that the two groups were homogeneous. There were no significant differences between the two groups in daily walking steps, physical activity behavior, physical activity self-efficacy, and wellness (Table 1).

Table 1.

Homogeneity Test of the General Characteristics of the Participants (N = 79)

	EXP. (N = 41)	CONT. (N = 38)
CHARACTERISTICS	N (%) OR M ± SD	N (%) OR M ± SD	t /χ ² ( p )
Age (year)	37.68 ± 9.31	34.42 ± 4.16	1.98 (0.051)
Marital status
Married	14 (34.1)	12 (31.6)	0.06 (0.499)
Single	27 (65.9)	26 (68.4)
Educational level
High school	9 (22.0)	5 (13.2)	6.89 (0.075)
≤College	9 (22.0)	19 (50.0)
≥University	23 (56.0)	14 (36.8)
Occupational class
Blue collar	18 (43.9)	19 (50.0)	0.29 (0.655)
White collar	23 (56.1)	19 (50.0)
Subjective health status
Unhealthy	9 (22.0)	9 (23.7)	0.14 (0.931)
Moderate	19 (46.3)	16 (42.1)
Healthy	13 (31.7)	13 (34.2)
Daily walking steps
5,000–9,999^a	16 (39.0)	16 (42.1)	0.82 (0.480)
≥10,000^b	25 (61.0)	22 (57.9)
Physical activity behavior	2.18 ± 0.63	2.03 ± 0.53	1.12 (0.266)
Physical activity self-efficacy	3.38 ± 0.67	3.12 ± 0.52	1.98 (0.051)
Wellness	3.38 ± 0.42	3.23 ± 0.40	1.69 (0.096)

Exp. (n = 25), Cont. (n = 21).

Exp. (n = 14), Cont. (n = 19).

Exp., experimental group; Cont., control group; M, mean; SD, standard deviation.

Effects of the Mobile Wellness Intervention

The effects of the mobile wellness intervention on daily walking steps, physical activity behavior, physical activity self-efficacy, and wellness are shown in Table 2. There were statistically significant differences between the two groups: daily walking steps (t = 2.00; p = 0.049), physical activity behavior (t = 2.09; p = 0.039), physical activity self-efficacy (t = 2.03; p = 0.045), and wellness (t = 2.01; p = 0.048). In addition, there was a statistically significant difference on daily walking steps of 5,000–9,999 (t = 2.34; p = 0.025), whereas there was no significant difference on daily walking steps of over 10,000 (t = 1.36; p = 0.182).

Table 2.

Effects of the Mobile Wellness Intervention (N = 79)

		PRE-TEST	POST-TEST	EFFECT BY POINT	INTERGROUP EFFECT
VARIABLES	GROUP	M ± SD	M ± SD	t ( p )	t ( p )
Daily walking steps	Exp.	11,056 ± 4,029	12,471 ± 6,977	−1.28 (0.210)	2.00 (0.049)
Daily walking steps	Cont.	10,457 ± 2,976	9,354 ± 2,932	2.13 (0.040)
5,000–9,999^a	Exp.	7,756 ± 1,850	9,718 ± 1,639	2.25 (0.031)	2.34 (0.025)
5,000–9,999^a	Cont.	7,919 ± 1,726	7,953 ± 1,883	−0.05 (0.960)
≥10,000^b	Exp.	13,787 ± 3,018	14,625 ± 8,683	−0.43 (0.669)	1.36 (0.182)
≥10,000^b	Cont.	12,741 ± 1,730	10,615 ± 3,167	−2.99 (0.007)
Physical activity behavior	Exp.	2.18 ± 0.63	2.44 ± 0.54	−2.16 (0.036)	2.09 (0.039)
Physical activity behavior	Cont.	2.03 ± 0.53	1.97 ± 0.39	−0.19 (0.854)
Physical activity self-efficacy	Exp.	3.38 ± 0.67	3.60 ± 0.46	−2.02 (0.050)	2.03 (0.045)
Physical activity self-efficacy	Cont.	3.12 ± 0.52	2.95 ± 0.76	1.04 (0.303)
Wellness	Exp.	3.38 ± 0.42	3.51 ± 0.48	−2.09 (0.043)	2.01 (0.048)
Wellness	Cont.	3.23 ± 0.40	3.12 ± 0.49	−0.04 (0.970)

Exp. (n = 25), Cont.(n = 21).

Exp. (n = 14), Cont. (n = 19).

Discussion

This was the first study to conduct a mobile wellness intervention with a wearable activity tracker, such as Fitbit, and goal setting using brief counseling and text messaging for workers in manufacturing companies, and to discuss how this intervention relates to changes in workers' physical activity. The wellness and productivity of workers is becoming more important due to a growing number of workers being affected with chronic diseases and mental illnesses, and an aging workforce is more likely to be affected by these conditions.^1,21,22 The increasing awareness and rising health care costs associated with chronic diseases have resulted in a significant increase in the demand for workplace wellness interventions. Based on the accelerometry data collected as part of the 2005–2006 National Health and Nutrition Examinations Survey, 5,000–9,999 steps/day has been classified as low to somewhat active and ≥10,000 steps/day as active to highly active.³⁵ Approximately 60% of the workers in this study could be classified as low to somewhat active (5,000–9,999 steps/day). Systematic reviews on the step-based recommendations of physical activity guidelines have shown that 10,000 steps/day is a reasonable target for healthy adults.³⁶ Since walking is generally an appropriate activity for individuals with a low level of physical activity, integrating activity trackers into physical activity interventions can be used to motivate and monitor progress toward step-based goals in increasing physical activity. That is why such a mobile wellness intervention with Fitbit use and goal setting was needed for physically inactive workers to increase their activity and improve their wellness.

Wearable activity trackers such as Fitbit are known to increase daily steps in physical activity interventions because they can easily self-monitor physical activity and provide objective feedback on daily steps.^37,38 The daily steps in the experimental group participating in our mobile wellness intervention increased significantly by 1415 steps/day, while it decreased by 1103 steps/day in the control group, showing a significant difference between the two groups. The results of previous studies on the relationships between Fitbit use and daily steps were null or very mixed. Some studies have suggested that a strong adherence to Fitbit use is associated with improved physical activity levels.^29,38

–41 Similarly, a recent review has found that a consistent use of activity trackers is associated with higher activity levels.³⁸ However, some research has found no significant differences between the experimental and control groups even though both groups' daily steps increased by 500–1500 steps/day^13,42 This indicates that self-monitoring of activity with Fitbit itself may not be adequate for increasing individual's physical activity. Motivating factors for increasing the physical activity in the workplace wellness intervention would be needed as well as self-monitoring of one's own activity levels.

Although Fitbit facilitates activity feedback by providing information about daily steps, the tracker itself, without additional goal-setting techniques, may be insufficient to encourage behavior change.^18,43,44 In this study, workers in the experimental group received a self-monitoring activity tracker and goal settings with motivational messages designed to encourage physical activity and to track goal progress. As a result, subgroup analysis showed that daily walking steps of the workers with a low level of physical activity in the experimental group increased significantly by 1962 steps/day, while those of the workers in the control group increased slightly by 34 steps/day. No significant difference was observed in daily walking steps of workers with high physical activity between two groups which maintained their activity level regardless of our mobile wellness intervention. This finding is consistent with previous studies that have found greater benefit from combining a self-monitoring activity tracker with additional motivating strategies rather than using an activity tracker alone.^41,45 What is noteworthy about this study is that the daily walking steps of the experimental group with a low level of physical activity increased significantly by a large margin, while the daily walking steps of the control group increased only slightly. This indicates that Fitbit-only is not enough to motivate workers to improve physical activity, and this may be explained by the additional motivating strategies such as goal setting using face-to-face counseling and tailored text messaging that was provided in this study. It is also possible that our intervention supports the process of goal setting by incorporating various strategies for achieving goals such as feedback messaging, rewards for achieving 10,000 steps/day, and barrier counseling. This finding highlights that goal-setting strategies consisting of self-monitoring Fitbit, brief counseling, and tailored text messages can be helpful to physically inactive workers.

Self-efficacy is an important factor in people's desire to conduct and sustain health-promoting behaviors, and it is important not only for exercise initiation but also for exercise maintenance and habituation.⁴⁶ In this study, physical activity self-efficacy of the experimental group showed a large increase after they participated in the mobile wellness intervention, while the control group showed little change; thus, a statistically significant difference between the two groups was found. This is generally consistent with previous studies showing increased physical activity self-efficacy after wellness programs or physical activity interventions.¹³ A systematic review to identify the influencing factors of physical activity has found that self-efficacy is the strongest factor for participation in physical activity.⁴⁷ Self-efficacy, which motivates people to sustain health-related behaviors, can be reinforced with enactive mastery experiences, verbal persuasion, and vicarious experience.⁴⁶ The reason why physical activity self-efficacy increased in this study might be that the workers set short- and long-term goals for walking and received tailored text messaging, because previous studies have reported that physical activity self-efficacy could be directly influenced by goal setting and tailoring health information.⁴⁸ Additionally, goal setting is a critical factor for many health interventions, because goals motivate individuals to decrease discrepancy between the current and desired states.⁴⁹ Those strategies for enhancing physical activity self-efficacy will help workers meet their activity goals and experience a sense of accomplishment.

Although this study of the mobile wellness intervention with Fitbit and goal setting using brief counseling and text messaging is an important addition to our understanding of Fitbit and short- and long-term goal setting to motivate and increase individuals' daily steps, some limitations should be noted. First, this study comprised a small sample of a relatively homogenous group of workers from the two companies, and the results may not be generalizable. Second, the intervention period was relatively a short-term follow-up of 12 weeks; long-term use of an activity tracker and its relationship with increasing physical activity could not be assessed. Given that some research has found linear decreases in Fitbit use over a year,⁵⁰ a longer period may have been beneficial for regular physical activity habits. Finally, random sampling was not used in this study, so the results might be influenced by extraneous variables. Future studies using randomization should be considered.

Conclusions

The mobile wellness intervention with Fitbit use and goal setting using brief counseling and text messaging positively affected workers' wellness, daily walking steps, and self-efficacy for physical activity. The daily steps of workers with a low level of physical activity increased to over 13,000 by setting and checking achievable short- and long-term goals, implementing self-monitoring using wearable devices, and meeting their activity goals through face-to-face counseling and motivational messaging. It is proved that workplace mobile intervention may be a cost-effective approach for improving the overall health and wellness of working adults. Thus, if the program takes root as a workplace wellness program, it has the potential to create a healthier and happier environment for workers.

Footnotes

Acknowledgments

The project was funded by the DGIST R&D Program of the Ministry of Education, Science and Technology of Korea (18-IT-02), and the R&D program of Ministry of Trade, Industry and Energy of Korea (10044353).

Disclosure Statement

No competing financial interests exist.

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