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Wearable fitness monitors are increasingly popular but the actual utility of these devices for promoting physical activity behavior is unknown. The purpose is to examine the efficacy of the Fitbit monitor for previously inactive individuals when used alone or following brief training in behavior change strategies and techniques. Psychosocial factors will be assessed and changes in physical activity will be monitored over three months to determine the efficacy of this intervention and to better understand individual differences in effectiveness.
The objective for this pilot study is to determine the efficacy of the Fitbit Charge wearable fitness monitor alone or in combination with additional behavior change strategies for increasing physical activity in inactive adults. A secondary objective is to assess the influence of psychosocial factors (e.g. self-efficacy, self-regulation, habit formation) on the effectiveness of this type of behavior change intervention. The central hypothesis is that use of the Fitbit will increase physical activity from baseline and that adding additional strategies will enhance this effect. This hypothesis is based on previous research demonstrating that continuous self- monitoring (using wearable technology) is effective in promotion of weight-loss in overweight and obese adults. This objective will be addressed through pursuing the following specific aims.
Aim 1: To determine the efficacy of using the Fitbit to increase physical activity behaviors and improve health markers in inactive adults. The working hypothesis is that wearing a Fitbit for 3 months will increase physical activity and improve health markers from baseline to follow-up in inactive adults.
Aim 2: To compare the efficacy of the Fitbit alone to the Fitbit in combination with behavior change strategies for increasing physical activity and improving psychosocial factors in inactive adults. The working hypothesis is that using the Fitbit along with behavior change strategies will lead to greater improvements in physical activity and psychosocial factors (self- motivation, self-regulation, self-efficacy, and social support) than using the Fitbit alone.
Aim 3: To assess the influence of individual differences in psychosocial variables on changes in physical activity over the intervention. The working hypothesis is that higher levels of self- motivation, self-regulation, self-efficacy, and social support at baseline will be predictive of greater improvements in physical activity over the intervention, regardless of group assignment.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Activity Tracker with Health Coaching | Experimental | Participants assigned to this condition will receive a Fitbit activity tracker to use for 3 months. Also, they will be asked to come in for a visit approximately one week following baseline assessments. Using the principles of Motivational Interviewing and Habit Formation, participants will discuss their perceived benefits and barriers of becoming more physically active with a member of the research team. They will also be encouraged to set a goal related to using their Fitbit to increase their physical activity. Lastly, they will be given information regarding habit formation and encouraged to identify one or more cues that regularly occur in their daily life to check their Fitbit data as a prompt to engage in physical activity. |
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| Activity Tracker alone | Active Comparator | Participants assigned to this condition will use their Fitbit on their own for the duration of 3- month intervention, similar to the experience of participants buying the device off-the-shelf. |
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| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Activity tracker and health coaching | Behavioral | Use of an activity tracker alone or in combination with health coaching on physical activity behaviors. |
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| Measure | Description | Time Frame |
|---|---|---|
| Steps | Average steps accumulated per day assessed via the activPAL | 3 months |
| Moderate and Vigorous Physical Activity | Average minutes per day assessed via a combination of activPAL and ActiGraph | 3 months |
| Measure | Description | Time Frame |
|---|---|---|
| Habit Development | Measured via the Automaticity Index of the Self-Reported Habit Index. The Self-Reported Habit Index consists of 12 items and the Automaticity Subscale includes 4 of these 12. Each item is scored on a 5-point (0-4) Likert scale with anchors ranging from strongly disagree (0) to strongly agree (4). As such, scores for the Automaticity Index range from 0-16 with higher scores indicating stronger habits. |
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Inclusion Criteria:
Exclusion Criteria:
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| Name | Affiliation | Role |
|---|---|---|
| Laura D Ellingson-Sayen, PhD | Iowa State University | Principal Investigator |
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| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 30762582 | Derived | Ellingson LD, Lansing JE, DeShaw KJ, Peyer KL, Bai Y, Perez M, Phillips LA, Welk GJ. Evaluating Motivational Interviewing and Habit Formation to Enhance the Effect of Activity Trackers on Healthy Adults' Activity Levels: Randomized Intervention. JMIR Mhealth Uhealth. 2019 Feb 14;7(2):e10988. doi: 10.2196/10988. |
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| ID | Term |
|---|---|
| D009043 | Motor Activity |
| D006184 | Habits |
| ID | Term |
|---|---|
| D001519 | Behavior |
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| ID | Term |
|---|---|
| D000072936 | Fitness Trackers |
| ID | Term |
|---|---|
| D019719 | Diagnostic Equipment |
| D004864 | Equipment and Supplies |
| D000076251 | Wearable Electronic Devices |
| D055615 | Electrical Equipment and Supplies |
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Participants are randomized to one of two groups. One group received an activity tracker and the other received an activity tracker in combination with Health Coaching.
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Individuals processing outcome data are blinded to experimental condition.
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| 3 months |