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Commercial off-the-shelf activity trackers (eg, Fitbit) allow users to self-monitor their daily physical activity (PA), including the number of steps, type of PA, amount of sleep, and other features. Fitbits have been used as both measurement and intervention tools. However, it is not clear how they are being incorporated into PA intervention studies, and their use in specific age groups across the life course is not well understood.
This narrative review aims to characterize how PA intervention studies across the life course use Fitbit devices by synthesizing and summarizing information on device selection, intended use (intervention vs measurement tool), participant wear instructions, rates of adherence to device wear, strategies used to boost adherence, and the complementary use of other PA measures. This review provides intervention scientists with a synthesis of information that may inform future trials involving Fitbit devices.
We conducted a search of the Fitabase Fitbit Research Library, a database of studies published between 2012 and 2018. Of the 682 studies available on the Fitabase research library, 60 interventions met the eligibility criteria and were included in this review. A supplemental search in PubMed resulted in the inclusion of 15 additional articles published between 2019 and 2020. A total of 75 articles were reviewed, which represented interventions conducted in childhood; adolescence; and early, middle, and older adulthood.
There was considerable heterogeneity in the use of Fitbit within and between developmental stages. Interventions for adults typically required longer wear periods, whereas studies on children and adolescents tended to have more limited device wear periods. Most studies used developmentally appropriate behavior change techniques and device wear instructions. Regardless of the developmental stage and intended Fitbit use (ie, measurement vs intervention tool), the most common strategies used to enhance wear time included sending participants reminders through texts or emails and asking participants to log their steps or synchronize their Fitbit data daily. The rates of adherence to the wear time criteria were reported using varying metrics. Most studies supplemented the use of Fitbit with additional objective or self-reported measures for PA.
Overall, the heterogeneity in Fitbit use across PA intervention studies reflects its relative novelty in the field of research. As the use of monitoring devices continues to expand in PA research, the lack of uniformity in study protocols and metrics of reported measures represents a major issue for comparability purposes. There is a need for increased transparency in the prospective registration of PA intervention studies. Researchers need to provide a clear rationale for the use of several PA measures and specify the source of their main PA outcome and how additional measures will be used in the context of Fitbit-based interventions.
Insufficient physical activity (PA) in all stages of life, from early childhood to older adulthood, is a well-documented public health issue [
Advances in 21st century technology have introduced the use of commercial off-the-shelf activity trackers (eg, Fitbit and Apple Watch) that allow users to self-monitor their daily PA. As one of the top 5 wearable companies based on shipment volume, Fitbit has produced some of the most popular fitness trackers that are currently available on the market [
In the last decade, researchers have begun to take advantage of Fitbit’s public appeal, prominence, and relatively low cost compared with that of other commercial off-the-shelf activity trackers such as the Apple Watch, by incorporating these devices into their studies. This has been facilitated by Fitbit’s open application programming interface (API), which allows programmers to collect and store data across multiple devices [
Early studies involving Fitbit focused on establishing its accuracy as an objective PA measurement tool, especially in comparison with existing gold standard measurement devices [
Given that it serves as a repository of Fitbit-related studies, we first conducted a search of the Fitabase Fitbit Research Library [
The first 2 authors created a standardized form for data extraction by using Microsoft Excel. The items on this form, which were all open-ended, captured (1) general study characteristics (ie, sample size, study design, and intervention description) and (2) Fitbit use (ie, model, wear time and adherence, strategies to boost wear time, and other measures of PA). After finalizing the form, the first author read all the eligible studies and extracted the relevant data. To enhance the reliability of the extracted information, 3 additional coders (RL, MK, and YA) subsequently read the articles and reviewed the extracted data. As part of our protocol, disagreements between authors were resolved through discussion, with the final decision being made by the senior author.
Of the 682 studies available on the Fitabase Fitbit Research Library, 60 interventions met the eligibility criteria for this review. An additional 15 eligible studies resulting from the PubMed search were included. A total of 75 studies were reviewed (n=6 in childhood, n=11 in adolescence, n=20 in early adulthood, n=28 in middle adulthood, and n=10 in older adulthood).
Study selection flow diagram.
General study characteristics.
Developmental stage | Study design and intervention description | Participant characteristics at baseline | ||||||||||||
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Value, N | Age (years), mean (SD) or range | Female, % | Race or ethnicity | Weight status (eg, BMI, weight) | ||||||||
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Evans et al, 2017 [ |
Quasi-experimental design with 3 conditions: (1) Fitbit+intervention, (2) Fitbit only, and (3) control 6-week classroom-based intervention One session per week lasting 40 min and led by teachers and study staff Individual and group-level achievements BCTsa: goal setting, self-monitoring, and rewards |
42 | 12.3 (0.3) | 47b | NRc | 42% overweight or obese | ||||||
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Mackintosh et al, 2016 [ |
Single-group pre-post design 4-week intervention with teams designing and completing week-long missions Teachers equipped with a guide and DVD outlining various missions BCTs: goal setting, self-monitoring, and rewards |
30 | 10.1 (0.3) | 40 | NR | BMI: mean 19.9 (SD 4) kg/m2 | ||||||
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Walther et al, 2018 [ |
Single-group pre-post design 12-week afterschool program with two 60-min sessions per week (24 total) 12 sessions focused on nutrition and increasing PAd and 12 sessions taught safe food preparation while preparing simple, healthful recipes BCTs: shaping knowledge and self-monitoring |
24 | 9.58 (NR) | 83 | 30% White; 29% Black; 25% Hispanic; 16% Native American | NR | ||||||
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Buchele Harris and Chen, 2018 [ |
Quasi-experimental design with 2 conditions: (1) PA engaging the brain+Fitbit challenge (PAEB-C) or (2) Fitbit only 4-week school-based intervention Participants in PAEB-C condition followed a 6-min video once a day BCTs: behavioral rehearsal and self-monitoring |
116 | 10-11 | 49 | 60% reported race other than White, with 30% Blackb | NR | ||||||
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Harris et al, 2018b [ |
Quasi-experimental design with 2 conditions: (1) coordinated-bilateral PA intervention or (2) Fitbit only 4-week school-based intervention Repetitive coordinated-bilateral motor movements performed while following a 6-min video instruction once a day BCTs: behavioral rehearsal and self-monitoring |
116 | NR | 50 | 60% reported race other than White, with 30% Blackb | NR | ||||||
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Hayes and Van Camp, 2015 [ |
Single-group pre-post design 22 sessions of 20 min, 1 to 4 days per week on an elementary school playground during regularly scheduled, unstructured recess BCTs: self-monitoring |
6 | NR | 100 | NR | 66% normal weight | ||||||
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Chen et al, 2017 [ |
RCTe with 2 conditions Phone-based 3-month intervention for adolescents who are overweight and obese 8 modules focused on lifestyle modification, weight management, nutrition, and stress BCTs: shaping knowledge and self-monitoring |
40 | 14.9 (1.7) | 42 | 90% Chinese American | BMI: mean 28.3 (SD 4.7) kg/m2 | ||||||
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Gandrud et al, 2018 [ |
Parallel-group RCT with 2 conditions 6-month intervention using intensive remote therapy for pediatric patients with type 1 diabetes Content focused on recommendations for diabetes management, glucose control, and PA BCT: shaping knowledge and self-monitoring |
117 | 12.7 (2.5) | 54 | NR | BMI z-score: mean 0.5 (SD 0.9) | ||||||
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Mendoza et al, 2017 [ |
Pilot RCT with 2 conditions 10-week intervention for adolescent and young adult survivors of cancer using a wearable device, mobile health app, and Facebook support group for reaching PA goals BCTs: shaping knowledge, self-monitoring, and social support |
60 | 16.6 (1.5) | 59 | 66% non-Hispanic White; 14% Hispanic; 7% non-Hispanic Black; 14% Other | NR | ||||||
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Haegele and Porretta, 2016 [ |
Single-group pre-post design Social cognitive theory–based PA intervention for adolescents with visual impairments 9 lessons delivered during PA classes that included curricular concepts, in-class activities, and homework BCTs: shaping knowledge, behavioral rehearsal, and self-monitoring |
6 | NR | NR | NR | NR | ||||||
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Meng et al, 2018 [ |
Quasi-experimental design 2-year intervention for soccer players delivered by coaches Content focused on addressing exercise, body image, and nutrition BCTs: shaping knowledge and self-monitoring |
388 | 15.3 (1.1) | 58 | 62% non-Latino; 38% Latino | BMI %: mean 62.8 (SD 25.0) | ||||||
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Walther et al, 2018 [ |
Pre-post study design 12-week intervention with fourth and fifth graders that focused on proper nutrition and safe food preparation techniques and promoted PA via interactive games BCTs: self-monitoring, shaping knowledge, and social support |
30 | 9.58 (NR) | 83 | 30% White; 29% Black or African American; 25% Hispanic; 16% Native American | NR | ||||||
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Gaudet et al, 2017 [ |
Quasi-experimental crossover design 7-week classroom-based intervention to increase students’ PA BCTs: self-monitoring, self-regulation, and goal setting |
46 | 13.0 (0.3) | 52% | NR | NR | ||||||
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Pope et al, 2018 [ |
Multiphase mixed methods consisting of an RCT 12-week intervention for high school students where participants assigned to the game group were rewarded based on the number of daily steps taken BCTs: goal setting, self-monitoring, and rewards |
105 | 17.0 (NR) | 71 | 67% White; 16% Black; 12% Hispanic or Latino; 12% Asian; 5% Other | NR | ||||||
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Remmert et al, 2019 [ |
Quasi-experimental pilot study 12-week school-based ABTf intervention to increase PA in adolescents with low activity Weekly sessions conducted by project coordinator consisted of acceptance-based behavioral counseling combined with preferred-intensity exercise for 30 min BCTs: behavioral counseling, behavioral practice, and self-monitoring |
20 | 12.0 (0.0) | 60 | 55% Latino; 25% non-Latino White; 20% Other | BMI: mean 21.7 (SD 3.6) kg/m2 | ||||||
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Short et al, 2018 [ |
RCT with 2 conditions 48-week exercise intervention subdivided into 3 consecutive 16-week phases Tested how different incentive schemes influence exercise frequency and duration among youth Self-monitoring and rewards |
77 | 14.0 (2.2) | NR | 100% American Indian | BMI%: mean 98 (SD 3) | ||||||
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Van Woudenberg et al, 2018 [ |
RCT with 2 conditions 7-day classroom-based intervention that used a social network model to select and train influential adolescents (using smartphones) BCTs: social facilitation, behavior modeling, impression management, and self-persuasion |
190 | 12.2 (0.5) | 54 | NR | NR | ||||||
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Bang et al, 2017 [ |
Quasi-experimental design 6-week campus-based program with one session per week during lunch Participants walked together through the campus forest for approximately 40 min and received one lecture on stress management Encouraged to walk at least once per week at their leisure BCTs: self-monitoring, behavioral practice, and social support |
99 | 24.8 (4.7)b | 49b | NR | BMI: mean 21.9 (SD 2.9) kg/m2b | ||||||
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Baruth et al, 2019 [ |
Quasi-experimental pilot study with 2 conditions: (1) intervention and (2) control Weekly PA intervention for pregnant women until 35-week gestation BCTs: goal setting, behavior counseling, self-monitoring, and social support |
45 | 28.4 (4.5)b | 100 | 81.8% Whiteb | BMI: mean 26.9 (SD 7.2) kg/m2b | ||||||
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Losina et al, 2017 [ |
Single condition feasibility study 6-month workplace program to increase PA among sedentary hospital employees through individual and team-based financial incentives BCTs: self-monitoring, goal setting, and rewards |
292 | 38.0 (11.0) | 83 | 62% White; 14% Black; 10% Asian; 7% Hispanic; 7% Other | 32% normal weight; 30% overweight; 38% obese | ||||||
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Mahar et al, 2015 [ |
RCT with 2 conditions: (1) Fitbit and (2) no Fitbit 10-week intervention examined effects of movement technology on college students’ PA BCTs: self-monitoring |
75 | 19.4 (1.2) | NR | NR | NR | ||||||
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Chen and Pu, 2014 [ |
RCT with 3 conditions: (1) competition, (2) cooperation or (3) hybrid One-week mobile app intervention to help promote exercise in pairs and earn badges based on performance BCTs: self-monitoring, social support, goal setting, and rewards |
36 | 20-30 | 58 | NR | 2.8% underweight, 94% normal weight, 2.8% obese | ||||||
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Pagkalos et al, 2017 [ |
RCT with 2 conditions: (1) intervention and (2) control 5-week pilot study to monitor young adults’ exercise via a custom-built Facebook app for activity self-reporting BCTs: self-monitoring and social support |
49 | 24.0 (7.0) | NR | NR | BMI: mean 22.5 (SD 3.0) kg/m2 | ||||||
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Ptomey et al, 2018 [ |
RCT with 2 conditions: (1) exercise once a week and (2) exercise twice a week 12-week at-home intervention to increase MVPAg using videoconferencing for groups of adults with Down syndrome BCTs: self-monitoring, behavioral practice, and social support |
27 | 27.9 (7.1) | 41 | 10% ethnic minorities | Group 1 BMI: mean 35.4 (SD 9.7) kg/m2; Group 2 BMI: mean 31.4 (SD 6.8) kg/m2 | ||||||
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Walsh and Golbeck, 2014 [ |
Within-subject crossover study with 3 conditions: (1) social game using Fitbit steps as currency, (2) social interaction experience, and (3) control 30-day web-based intervention Participants in the social interaction could interact or communicate and share their PA levels with friends BCTs: self-monitoring, social support, and social comparison |
74 | 37.7 (10.2) | 59 | NR | NR | ||||||
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Yoon et al, 2018 [ |
RCT with 2 conditions: (1) intervention and (2) control Observational PA data collected from participants over first 6 months Participants were sent a personalized email message about their activity to inform them of current PA levels and encourage increase in the last 6 months BCTs: self-monitoring and feedback on behavior |
79 | 31.9 (9.6) | 59 | 29.2% Hispanic | NR | ||||||
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Choi, 2016 [ |
RCT with 2 conditions: (1) intervention mobile app+Fitbit and (2) Fitbit 12-week intervention with pregnant women between 10 and 20 weeks of gestation After an initial 30-min in-person intervention session, participants received daily message or video, encouragement, and activity diary through the app BCTs: self-monitoring, shaping knowledge, and written persuasion to boost self-efficacy |
30 | 33.7 (2.6) | 100 | 43% White; 40% Asian; 10% Hispanic; 7% Black | BMI (prepregnancy): mean 27.7 (SD 3.7) kg/m2 | ||||||
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Chung et al, 2017 [ |
Single-group pre-post design stratified into 2 groups: (1) overweight or obese group and (2) healthy weight group 2-month intervention where participants received Twitter messages to encourage PA and healthy eating, photo-based messages, infographics, and website links related to healthy lifestyle behaviors BCTs: self-monitoring, shaping knowledge, and written persuasion to boost self-efficacy |
12 | 19-20 | 67 | 50% White; 33% Black; 8% Asian; 8% American Indian | Group 1 BMI range: 25-35 kg/m2; Group 2 BMI range: 22-24.9 kg/m2 | ||||||
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Gilmore et al, 2017 [ |
RCT for postpartum women with 2 conditions: (1) WICh standard care (WIC Moms) and (2) WIC standard care and personalized weight management via a smartphone (E-Moms) E-Moms group was given access to the SmartLoss SmartPhone app that included near real-time weight and activity monitoring, scheduled delivery of health information, and interventionist feedback BCTs: self-monitoring, feedback on behavior |
35 | 26.0 (5.4) | 100 | 74% African American | BMI: mean 32 (SD 3) kg/m2 (range 25.6-37.0 kg/m2) | ||||||
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Halliday et al, 2017 [ |
Pre-post study design A goal-focused exercise program that included weekly phone or face-to-face coaching to reinforce walking goals, as well as an optional 1-h supervised group walk on 2 occasions per week BCTs: self-monitoring, social support, behavioral practice, behavior counseling, goal setting |
15 | 38.3 (6.4) | 60 | 80% Caucasian | BMI: mean 30.4 (SD 6.4) kg/m2 | ||||||
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Florence et al, 2016 [ |
RCT with 3 conditions: (1) group 1 (Fitbit+modules), (2) group 2 (Fitbit+modules+a social media-based game), (3) control group with just educational modules 14-week intervention for first-year medical students where daily steps and sleep hours were monitored in groups 1 and 2 during weeks 1-8 From week 9, all 3 groups had access to Fitbit Flex and the game platform, and students’ daily steps and sleep time were monitored until week 14 by Fitbit Flex BCTs: self-monitoring and social support |
300 | 18-19 | 58 | NR | NR | ||||||
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Miragall et al, 2017 [ |
RCT with 3 conditions: (1) IMIi+PED condition (access to IMI and use of a pedometer), (2) IMI condition (access to IMI and use of a blinded pedometer), and (3) control condition (use of a blinded pedometer) 3-week IMI conducted with sedentary or low-active students to increase motivation and set individualized PA goals BCTs: self-monitoring, goal setting, and verbal persuasion about self-efficacy |
76 | 22.2 (3.7) | 86 | NR | BMI: mean 21.7 (SD 3.2) kg/m2 | ||||||
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Schrager et al, 2017 [ |
Pre-post cohort study 1-month intervention where emergency medicine residents were asked to wear a Fitbit to assess its effects on their PA levels BCTs: self-monitoring |
30 | Median age: 28 | 47 | NR | NR | ||||||
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Thorndike et al, 2014 [ |
2-phase intervention: phase 1 was a 6-week RCT and phase 2 was a 6-week nonrandomized team steps competition 12-week intervention that provided medical residents with free access to a fitness center, weekly one-hour personal training sessions, and up to 2 individual appointments with a Be Fit staff nutritionist BCTs: self-monitoring and shaping knowledge |
108 | 29 (23-37) | 54 | 66% White | BMI: mean 24.1 (range 17.8-35.6) kg/m2 | ||||||
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Washington et al, 2014 [ |
Pre-post study design 3-week intervention in which participants won prizes for wearing their Fitbit and meeting experimenter-determined step criteria BCTs: self-monitoring, goal setting, and rewards |
13 | 18-26 | 67 | NR | NR | ||||||
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West et al, 2016 [ |
Quasi-experimental study design 9-week intervention where undergraduate students were assigned to either (1) a behavioral weight gain prevention intervention (healthy weight) or (2) an HPVj awareness intervention 8 lessons on behavioral strategies to maintain weight and avoid obesity were delivered via electronic newsletters and Facebook postings BCTs: self-monitoring and shaping knowledge |
58 | 21.6 (2.2) | 81 | 90% White | BMI: mean 24.0 (SD 5.1) kg/m2 | ||||||
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Zhang and Jemmott, 2019 [ |
Pilot RCT with 2 conditions: (1) intervention and (2) control 3-month intervention in small groups with mobile app to track group’s PA data and engage with others BCTs: self-monitoring, social support, and social comparison |
91 | 26.8 (5.1) | 100 | 100% African American | BMI: mean 31.6 (SD 8.2) kg/m2 | ||||||
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Amorim et al, 2019 [ |
Pilot RCT with 2 conditions: (1) intervention and (2) control 6-month intervention with PA booklet, health coaching sessions, app, and Fitbit BCTs: self-monitoring, behavioral counseling, and shaping knowledge |
68 | 58.4 (13.4) | 50 | NR | BMI: mean 28 (SD 5.5) kg/m2 |
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Butryn et al, 2014 [ |
Single-group pre-post design 6 months group-based intervention with a web platform component to facilitate social connectivity BCTs: self-monitoring and social support |
36 | 54 (7.18) | 100 | 62% Caucasian | BMI: mean 32.7 (SD 7.32) kg/m2 | ||||||
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Cadmus-Bertram al et, 2015 [ |
RCT with 2 conditions: (1) intervention (2) comparison (standard pedometer only) 16-week web-based self-monitoring intervention for inactive, postmenopausal women Content combined self-monitoring with self-regulatory skills, such as goal setting and frequent feedback BCTs: self-monitoring, knowledge shaping, self-regulation, goal setting, and feedback |
51 | 60.0 (7.1) | 100 | 92% non-Hispanic Whiteb | BMI: mean 29.2 (SD 3.5) kg/m2 | ||||||
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Cadmus-Bertram et al, 2019 [ |
Pilot RCT with 2 conditions: (1) intervention and (2) comparison 12-week multi-component intervention for cancer survivors and support partners with Fitbit linked to electronic health records BCTs: self-monitoring and social support |
50 | 54.4 (11.2) | 96 | 94% non-Hispanic White; 2% Hispanic; 2% Black; 2% Multiracial | BMI: mean 32.2 (SD 7.4) kg/m2 | ||||||
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Dean et al, 2018 [ |
Quasi-experimental pilot study 8 weekly small group sessions Each 90-min session had a group discussion and an exercise component BCTs: self-monitoring, knowledge shaping, and social support |
40 | 46.9 (9.8) | 0 | 100% African American | 67% obese | ||||||
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Duncan et al, 2020 [ |
RCT with 3 conditions: (1) enhanced, (2) traditional, and (3) control 6-month intervention for adults with overweight or obesity delivered via the app with educational content, dietary consultation, Fitbit, and scales Enhanced group received additional sleep intervention content via the app BCTs: self-monitoring, knowledge shaping, goal setting, and behavioral counseling |
116 | 44.5 (10.5) | 70.7 | NR | BMI: mean 31.7 (SD 3.9) kg/m2 | ||||||
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Ellingson et al, 2019 [ |
Randomized feasibility trial with 2 conditions: (1) intervention with Fitbit and (2) Fitbit only 12-week intervention with motivational interviewing, habit education, and Fitbit BCTs: self-monitoring and verbal persuasion to boost self-efficacy |
91 | 41.7 (9.3) | 53 | 79% White | BMI: mean 29.6 (SD 6.3) kg/m2 | ||||||
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Kandula et al, 2017 [ |
16-week community-based, pre-post intervention Twice weekly group exercise classes, Fitbit Zip and web-based platform, goal setting, and classes on healthy eating BCTs: self-monitoring, social support, goal setting, and knowledge shaping |
30 | 40 (5) | 100 | 100% South Asian | BMI: mean 30 (SD 3) kg/m2 | ||||||
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Ross and Wing, 2016 [ |
Randomized pilot trial with 3 conditions: (1) tech, (2) tech+phone, and (3) self-monitoring 6-month intervention with one group receiving self-monitoring tools (eg, booklets or scale) Tech group received Fitbit and tracked caloric intake through Fitbit app Tech+phone group received same materials along with 14 calls regarding behavioral weight loss techniques BCTs: self-monitoring, behavioral counseling, and knowledge shaping |
80 | 51.1 (11.7) | 86 | 84% Non-Hispanic White | BMI: mean 33 (SD 3.4) kg/m2 | ||||||
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Singh et al, 2020 [ |
RCT with 2 conditions: (1) PA counseling, (2) PA counseling and Fitbit 12-week intervention for women with breast cancer that included a PA counseling session with exercise physiologist and educational booklet BCTs: self-monitoring, behavioral counseling, and knowledge shaping |
52 | Group 1: 52.8 (9.5); Group 2: 49.5 (8.6) | 100 | NR | Group 1: BMI: mean 28.5 (SD 5.2) kg/m2; Group 2: BMI: mean 28.7 (SD 6) kg/m2 | ||||||
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Van Blarigan et al, 2019 [ |
Pilot RCT with 2 conditions: (1) intervention and (2) control 12-week intervention for cancer survivors with daily text messaging BCTs: self-monitoring and cues |
42 | 54 (11) | 59 | 73% White, 12% Asian, 12% Native American or other, 2% Black | BMI: mean 28.4 (SD 5.9) kg/m2 | ||||||
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Patel et al, 2017 [ |
12-week family-based RCT intervention On the basis of behavioral economics and gamification principles, the intervention used points and levels (bronze, silver, gold, and platinum) to encourage families to change their behavior and increase their PA levels BCTs: self-monitoring, rewards, and social support |
200 | 55.4 (NR) | 56 | 100% Caucasian | BMI: mean 27.2 (SD 5.1) kg/m2b | ||||||
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Robinson et al, 2019 [ |
Pilot RCT with 2 conditions: (1) intervention and (2) control 5-week study using implementation intentions to establish PA habits using personalized materials BCTs: self-monitoring and knowledge shaping |
63 | 49.4 (8.3) | 72.6 | NR | NR | ||||||
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Schumacher et al, 2017 [ |
Single-group pre-post trial study Partner-based PA program for women examining PA lapses, cognitive-affective responses to lapses, and the role of social support in PA BCTs: self-monitoring and social support |
20 | 50 (7.2) | 100 | 95% Caucasian | BMI: mean 30.9 (SD 8.9) kg/m2 | ||||||
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Adams et al, 2017 [ |
2×2 factorial, 4-month RCT with goal setting (adaptive vs static goals) and rewards (immediate vs delayed) WalkIT trial delivered intervention components by SMS text messages on a daily basis with prompt-to-action messages (eg, tips, questions, or motivational or inspirational messages) BCTs: self-monitoring, goal setting, shaping knowledge, persuasion to boost self-efficacy, and cues |
96 | 41 (9.5) | 77 | 81.3% Caucasian | BMI: mean 34.1 (SD 6.18) kg/m2 | ||||||
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Arigo, 2015 [ |
Single-group pre-post design 4-week web-based intervention in pairs Participants have access to web-based modules and worksheets guiding them through seeking support and setting weekly PA goals BCTs: self-monitoring, social support, and goal setting |
12 | 46 (13.1) | 100 | 75% Caucasian | BMI: mean 32.6 (SD 5.7) kg/m2 | ||||||
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Arigo et al, 2015b [ |
Single-group pre-post design 6-week program predominantly web-based with a single face-to-face session introducing PA promotion skills Participants were encouraged to communicate with their PA dyad partner and other participants BCTs: self-monitoring, goal setting, and social support |
20 | 50 (7.2) | 100 | 90% Caucasian | BMI: mean 30.9 (SD 8.9) kg/m2 | ||||||
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Finkelstein et al, 2015 [ |
Randomized crossover design with 2 conditions: (1) message-on and (2) message-off 4-week web-based intervention targeted inactivity level with tailored text messages about sedentary time BCTs: self-monitoring and cues |
27 | 52 (12.0) | 100 | 47% White; 47% African American | BMI: mean 37.0 (SD 6.0) kg/m2 | ||||||
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Fukuoka et al, 2018 [ |
Single-group pre-post trial, uncontrolled pilot study 8-week weight loss program for Latino Participants were provided with 2 in-person counseling sessions, Fitbit, use of the Fitbit app, and a Facebook group and were asked to track diet daily and weight twice per week BCTs: self-monitoring, behavioral practice, and social support |
54 | 45.3 (10.8) | 68.5 | 100% Latino | BMI: mean 31.4 (SD 4.1) kg/m2 | ||||||
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Gell et al, 2020 [ |
Pilot RCT with 2 conditions: (1) intervention and (2) control with Fitbit 8-week intervention for cancer survivors with health coaching, text messaging, and Fitbit BCTs: self-monitoring, behavioral counseling, and cues |
59 | 61.4 (9) | 81 | 98.5% non-Hispanic White, 1.2% Black or Hispanic | BMI: mean 30.4 (SD 7) kg/m2 | ||||||
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Gremaud et al, 2018 [ |
10-week RCT intervention comparing 2 arms: (1) Fitbit only and (2) Fitbit+MapTrek MapTrek, mobile phone–based walking game leverages Fitbit to track users’ PA and motivate users to engage in virtual walking races in numerous places around the globe BCTs: self-monitoring and feedback |
146 | 40.6 (11.7)b | 79.2b | 91.7% Caucasianb | BMI: mean 29.9 (SD 6.6) kg/m2b | ||||||
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Grossman et al, 2017 [ |
16-week behavioral pre-post pilot program for postmenopausal women The program consisted of face-to-face group meetings every month, weekly weigh-ins, electronic check-ins, calorie-restricted diet, and high-intensity interval training BCTs: self-monitoring, social support, and behavioral practice |
11 | 59.53 (11.7) | 100 | NR | BMI: mean 32 (SD 2.53) kg/m2 | ||||||
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Linke et al, 2019 [ |
One-arm pilot study 12-week intervention for veterans recovering from substance use disorder that included psychoeducation classes, gym membership, and Fitbit BCTs: self-monitoring, social support, and knowledge shaping |
15 | 45 (9.7) | 13 | 60% non-Hispanic White, 27% Black, 13% Hispanic | NR | ||||||
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Meints et al, 2019 [ |
Prospective cohort study 26-week intervention for hospital employees to increase PA with financial incentives Groups of 3 were formed and financial incentives were given if team members met goals BCTs: self-monitoring, social support, rewards, and goal setting |
225 | Black participants: 43 (10); White participants: 39 (12) | 84 | 81% White; 19% Black | Black participants: 84% had overweight or obesity; White participants: 68% had overweight or obesity | ||||||
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Painter et al, 2017 [ |
Retrospective analyses of 6 weight loss programs Participants were taught self-management strategies and were given a Fitbit, Wi-Fi-enabled scale, digital food and exercise log, and access to expert coach via electronic messages BCTs: self-monitoring and behavioral counseling |
2113 | 44.54 (10.72) | 59 | NR | BMI: mean 33.8 (SD 6.8) kg/m2 | ||||||
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Reed et al, 2019 [ |
Randomized repeated-measures study with 2 conditions: (1) intervention and (2) control 12-week intervention with self-regulatory PA strategies, weekly text messaging, and Fitbit BCTs: self-monitoring, self-regulation, and cues |
59 | 48 (NR) | 79.3b | 93.2% Whiteb | Weight: mean 92.47 (SD 22.8) kgb | ||||||
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Wang et al, 2015 [ |
RCT with 2 conditions: (1) text messaging+Fitbit and (2) Fitbit only 6-week intervention for adults with overweight and obesity receiving Fitbit and 3 daily SMS text messages prompting PA BCTs: self-monitoring and cues |
67 | 48.2 (11.7) | 91 | 67% White; 16% Hispanic; 4% African American; 3% Asian; 3% Other | BMI: mean 31 (SD 3.7) kg/m2 | ||||||
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Willis et al, 2017 [ |
Randomized feasibility study with 2 conditions: (1) web-based social network delivery and (2) conference call delivery 6-month weight loss intervention Web-based social network condition had 24 weekly web-based modules led by health educators Conference call condition consisted of 24 weekly 60-min phone conferences BCTs: self-monitoring, social support, and knowledge shaping |
70 | 47 (12.4) | 84 | 24.3% minorities | BMI: mean 36.2 (SD 4) kg/m2 | ||||||
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Ashe et al, 2015 [ |
Randomized pilot trial with 2 conditions: (1) intervention and (2) comparison (educational sessions) 6-month intervention to increase PA through social support, group-based education, and individualized PA prescription BCTs: self-monitoring, knowledge shaping, and social support |
25 | 64.1 (4.6) | 100 | NR | BMI: mean 26.9 (SD 6.8) kg/m2b | ||||||
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Christiansen et al, 2020 [ |
RCT with 2 conditions: (1) intervention and (2) control 6-month intervention for total knee replacement patients that included physical therapy, Fitbit, step goals, and monthly call with physical therapist BCTs: self-monitoring, goal setting, and behavioral counseling |
43 | 67 (7) | 53.4 | 91% White | BMI: mean 31.5 (SD 5.9) kg/m2 | ||||||
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Kenfield et al, 2019 [ |
Pilot RCT with 2 conditions: (1) intervention and (2) control 12-week intervention for men with prostate cancer that included personalized health recommendations, Fitbit, study website, and text messages BCTs: self-monitoring, knowledge shaping and cues |
76 | 65 (NR) | 0 | 84% White | 41% overweight, 35% with obesity | ||||||
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Thompson et al, 2014 [ |
Randomized controlled crossover trial with 2 conditions: (1) immediate intervention and (2) delayed intervention 48-week total: 24-week intervention that combined accelerometers with exercise counseling and 24 weeks without intervention Content included materials on exercise, goal setting, and tracking PA BCTs: self-monitoring, goal setting, behavioral counseling, and knowledge shaping |
48 | 79.5 (7.0) | 81 | NR | Weight: mean 75.7 (SD 13.4) kgb | ||||||
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Rossi et al, 2018 [ |
Single-group study (survey and qualitative interviews) Participants wore Fitbit for 30 days to evaluate acceptability and validity of the device in diverse cancer survivors BCTs: self-monitoring |
25 | 62 (9) | 100 | 36% non-Hispanic White; 36% Hispanic; 16% non-Hispanic Black; 12% Asian | BMI: mean 32 (SD 9) kg/m2 | ||||||
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Schmidt et al, 2018 [ |
Single-group study Participants wore Fitbit for 14 consecutive days and social cognitive factors, health issues, and views on aging were assessed BCTs: self-monitoring |
40 | 66.3 (3.19) | 62.5 | NR | BMI: mean 25.19 (SD 3.52) kg/m2 | ||||||
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Streber et al, 2017 [ |
RCT with 2 conditions: (1) intervention and (2) control with weekly gymnastics or cognitive training 12-week intervention with 90-min weekly sessions including PA program with social and cognitive activities and PA coaching program BCTs: self-monitoring, social support, knowledge shaping, and behavioral counseling |
87 | 76 (9.2) | 78 | NR | NR | ||||||
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Harkins et al, 2017 [ |
RCT with 4 conditions: (1) financial incentive, (2) social goals, (3) combined, and (4) control 16-week intervention to test use of financial incentives and donations on PA increase with 4-week follow-up that included pedometer, goal setting, and weekly feedback on goal attainment BCTs: self-monitoring, rewards, goal setting, and feedback |
94 | 80.3 | 74 | 98% Caucasian | NR | ||||||
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McMahon et al, 2017 [ |
2×2 randomized factorial experiment with 4 conditions receiving PA protocol and Fitbit: (1) interpersonal BCSk, (2) intrapersonal BCS, (3) interpersonal and intrapersonal BCS, and (4) control based on receipt of interpersonal and intrapersonal behavior change strategies 8-week intervention with weekly 90-min meetings with all conditions receiving PA protocol, Fitbit, and workbook BCTs: self-monitoring, knowledge shaping, and social support |
102 | 79 (NR) | 75 | 75% White; 25% Black | NR | ||||||
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Vidoni et al, 2016 [ |
Randomized crossover trial with 2 conditions: (1) immediate intervention and (2) delayed intervention 16-week trial divided into 8-week intervention and 8-week baseline or maintenance phase data collection Intervention included the use of a Fitbit device and PA prescription BCTs: self-monitoring and goal setting |
30 | With cognitive impairment: 72.3 (5.2); without cognitive impairment: 69.6 (5.8) | With cognitive impairment: 43; without cognitive impairment: 89 | With cognitive impairment: 90% White; 10% African- American; without cognitive impairment: 100% White | BMI (with cognitive impairment): mean 29.4 (SD 3.8) kg/m2; BMI (without cognitive impairment): mean 27.8 (SD 4.3) kg/m2 |
aBCT: behavior change technique.
bOnly intervention condition data reported.
cNR: not reported.
dPA: physical activity.
eRCT: randomized controlled trial.
fABT: acceptance-based therapy.
gMVPA: moderate-to-vigorous physical activity.
hWIC: women, infants, and children.
iIMI: internet-based motivational intervention.
jHPV: human papillomavirus.
kBCS: behavior change strategy.
Description of Fitbit use.
Study | Fitbit | Wear instructions | Fitbit use adherence | Fitbit used in comparison group? | Other PAa measures | |||||||||||||
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Minimum wear time criteria | Rate | Strategies to boost adherence |
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Evans et al, 2017 [ |
Zip (phase 1) and charge (phase 2) | Phase 1: all waking hours 7 days/week; phase 2: 24 h, 7 days/week | Minimum of 8 h/day | Days participants were adherent in phase 1: 64.8%; days participants were adherent in phase 2: 73.4%b | After-session meetings with study staff to sync their Fitbit data | Yes; same for Fitbit-only comparison condition; no device for control group | Sensewear, Armband Mini, and Jawbone | |||||||||
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Mackintosh et al, 2016 [ |
Zip | Duration of intervention | Entire duration of session | 100% adherence (with staff monitoring) | NRc | N/Ad | Accelerometry | |||||||||
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Walther et al, 2018 [ |
Charge HR | 24 h for 7 days, including one weekend | NR | NR | NR | N/A | Self-reporting | |||||||||
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Buchele Harris and Chen, 2018 [ |
Charge HR | Daily; 5 school days/week for 4 weeks | Minimum of 14 h/day | Average loss of 1-day data per person per week | Log sheets record PA | No | NR | |||||||||
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Harris et al, 2018b [ |
Charge HR | Daily; 5 school days/week for 4 weeks | NR | NR | Devices were charged at the end of the week | Yes; same use | NR | |||||||||
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Hayes and Van Camp, 2015 [ |
Classic | Duration of intervention recess session | Entire duration of 20-min recess session | 100% adherence (with staff monitoring) | NR | N/A | Second Fitbit | |||||||||
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Chen et al, 2017 [ |
Flex | Daily for 3 months | NR | NR | Weekly text reminders and phone calls | No | Self-reporting of PA using the California Health Interview Survey | |||||||||
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Gandrud et al, 2018 [ |
NR | NR | NR | NR | Weekly reminders sent to upload data | Yes | NR | |||||||||
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Mendoza et al, 2017 [ |
Flex | Daily for 10 weeks | Minimum of 500 steps/day | Days participants were adherent: 72% | Text reminders sent every other day to encourage PA goals | No | Accelerometry | |||||||||
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Haegele and Porretta, 2016 [ |
Zip | NR | NR | NR | NR | N/A | NR | |||||||||
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Meng et al, 2018 [ |
Zip | 7 days/week at baseline and post measures | Minimum of 8 h/day | NR | Daily texts or email reminders | Yes; device masked with duct-tape | NR | |||||||||
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Walther et al, 2018 [ |
Charge | Wear on the 2nd and 10th week of the intervention for 7 days, including 1 weekend | 24 h | NR | NR | N/A | Self-reported days of 60-min PA | |||||||||
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Gaudet et al, 2017 [ |
Charge HR | Daily for 7 weeks | Minimum of 10 h/day | Median participant adherent 67% of intervention days | NR | Yes | Accelerometry and self-reporting | |||||||||
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Pope et al, 2018 [ |
Flex | Daily for 12 weeks | NR | 15% of students wore their Fitbit for <10 days; 36% never wore their Fitbit | Weekly lottery to win US $10 Amazon gift cards, weekly email reminders, and in-person troubleshooting at school once a week | Yes | NR | |||||||||
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Remmert et al, 2019 [ |
Flex 2 | Daily for 12 weeks | NR | Average number of days of valid Fitbit wear: 78 (out of 84 days)b | NR | Yes | Accelerometry | |||||||||
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Short et al, 2018 [ |
Zip | Daily for 7 days | NR | NR | NR | Yes | NR | |||||||||
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Van Woudenberg et al, 2018 [ |
Flex | Daily for 7 days | Minimum of 1000 steps/day | Days participants were adherent: 73.4% | NR | Yes | NR | |||||||||
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Bang et al, 2017 [ |
Zip | NR | NR | NR | NR | No | IPAQe | |||||||||
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Baruth et al, 2019 [ |
Charge | Daily for duration of intervention | Minimum one day per week | Fitbit worn on 93% of intervention weeks | NR | No | Accelerometry | |||||||||
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Losina et al, 2017 [ |
Flex | Daily for duration of intervention | Minimum of 10 h/day | NR | NR | N/A | Self-reporting | |||||||||
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Mahar et al, 2015 [ |
Flex | Daily for duration of intervention | NR | NR | NR | No | Self-reporting | |||||||||
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Chen and Pu, 2014 [ |
Ultra and One | Daily for 2 weeks | NR | NR | Daily reminder to share experience of wearing Fitbit | No | NR | |||||||||
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Pagkalos et al, 2017 [ |
Zip | Daily for duration of intervention | NR | NR | NR | No | Self-reporting | |||||||||
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Ptomey et al, 2018 [ |
Charge HR | During intervention sessions | NR | 100% (with staff supervision) | NR | No | NR | |||||||||
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Walsh and Golbeck, 2014 [ |
Classic | Daily for 10 days | NR | 73% of participants were adherent | NR | Yes; same use | IPAQ | |||||||||
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Yoon et al, 2018 [ |
Flex | Daily for duration of intervention | NR | Days participants were adherent: 66% | NR | Yes; same use | Self-reporting | |||||||||
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Choi et al, 2016 [ |
Ultra | Daily for at least 10 h | Minimum of 1000 steps/day | Days participants were adherent: intervention: 78%; comparison: 80% | Participants entered steps into their daily activity diary | Yes; same use | Self-reporting | |||||||||
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Chung et al, 2016 [ |
Zip | Daily for duration of intervention | NR | Days participants were adherent: overweight group: 99%; normal weight group: 78% | Study team sent Twitter message reminders | N/A | NR | |||||||||
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Gilmore et al, 2017 [ |
Zip | Daily | NR | NR | NR | No | NR | |||||||||
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Halliday et al, 2017 [ |
NR | Daily for duration of intervention | 100 or more steps per day | 50.5%-82.9% of participants adhered to wearing Fitbit on a weekly basis | Participants were invited to join a private group on the Fitbit website that allowed for data sharing | N/A | NR | |||||||||
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Florence et al 2016 [ |
Flex | Daily for duration of intervention | NR | NR | NR | Yes; control group started Fitbit Flex on week 8 | IPAQ | |||||||||
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Miragall et al, 2017 [ |
One | Daily for duration of intervention | NR | N/A | N/A | Yes; blinded | NR | |||||||||
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Schrager et al, 2017 [ |
Flex | Daily for duration of intervention | 100 or more steps per day | Median number of eligible days where the participant recorded at least 100 steps was 27.5 (IQR 8) | Participants were given a 2-week acclimatization period to wear and use the device | N/A | Self-reporting of PA | |||||||||
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Thorndike et al, 2014 [ |
Classic | Duration of intervention | 500 or more steps/day | Percentage of worn days in each phase: 77% in phase 1 and 60% in phase 2 | Weekly reminder emails to charge device and monetary incentives for high compliance rates | Yes; blinded | NR | |||||||||
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Washington et al, 2014 [ |
Classic | Daily for duration of intervention | NR | 2 subjects had missing Fitbit data | Participants earned opportunities to draw prizes and brought the device to the lab 3 times a week for charging and retrieving data | N/A | Self-reporting of PA | |||||||||
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West et al, 2016 [ |
Zip and Aria | Daily for duration of intervention | NR | Students used their Fitbit for an average of 23.7 days (SD 15.2 days) | NR | No | NR | |||||||||
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Zhang and Jemmott, 2019 [ |
Zip | Daily for duration of intervention | NR | 16% of Fitbit data were missing during intervention period | Daily notifications to wear Fitbit and log PA | Yes; same use | NR | |||||||||
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Amorim et al, 2019 [ |
NR | Daily | N/A | 96% reported wearing every day or most days | NR | No | Accelerometry and IPAQ | |||||||||
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Butryn et al, 2014 [ |
Flex | Daily for duration of intervention | NR | Participants wore 86% of days during intervention | Public display of PA data | N/A | GT3X+accelerometers | |||||||||
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Cadmus-Bertram et al, 2015 [ |
One | Daily for duration of intervention | Minimum of 2000 steps/day | NR | NR | No | Accelerometry | |||||||||
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Cadmus-Bertram et al, 2019 [ |
Charhe HR or Charge 2 | Daily | N/A | NR | In-person instruction on Fitbit use | No | Accelerometry | |||||||||
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Dean et al, 2018 [ |
Flex | Daily; duration of intervention | NR | Participants who were adherent to wear instructions: 70% | Participants received 3 text messages weekly | N/A | Community Health Activities Model Program for Seniors Questionnaire | |||||||||
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Duncan et al, 2020 [ |
Alta | NR | NR | NR | NR | Yes, for both intervention groups; no, for control group | Accelerometry and Active Australia Survey | |||||||||
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Ellingson et al, 2019 [ |
Charge | Use at participants’ discretion for duration of intervention | Minimum of 10 h/day | NR | Intervention group determined cues to remember to wear Fitbit and check data | Yes; same use | Accelerometry | |||||||||
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Kandula et al, 2017 [ |
Zip | Daily | NR | NR | NR | N/A | Actigraph Accelerometer and self-reported questionnaire | |||||||||
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Ross and Wing, 2016 [ |
Zip and Aria | Daily | NR | Days participants were adherent: Tech: 76%; Tech+phone: 86% | Fitbit sent weekly emails updating progress | Fitbit used in one comparison group but not the other (pedometer used) | NR | |||||||||
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Singh et al, 2020 [ |
Charge | As desired to self-monitor and manage PA | NR | Average h worn: 17.3 h (SD 5.7 h) per 6.1 days (SD 0.8 days) per week | Basic instruction on using and setting up Fitbit | No | Accelerometry and Active Australia Survey | |||||||||
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Van Blarigan et al, 2019 [ |
Flex | Daily | NR | Participants wore Fitbit for 88% of study days | N/A | No | Accelerometry | |||||||||
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Patel et al, 2017 [ |
Flex | Daily | At least 1000 steps/day | 10.1% of missing observation days in intervention arm and 12.7% in control arm | NR | Yes | NR | |||||||||
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Robinson et al, 2019 [ |
Zip | Daily during waking hours | NR | NR | Participants asked to sync Fitbit data daily | Yes; same use | NR | |||||||||
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Schumacher et al, 2017 [ |
Flex | Daily | Minimum of 100 steps/day | 97% adherent to wear time criteria | NR | N/A | NR | |||||||||
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Adams et al, 2017 [ |
Zip | Daily during waking hours | NR | NR | Text step counts daily and random selection for monthly incentives for wearing their Fitbit regularly | Yes | IPAQ | |||||||||
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Arigo, 2015 [ |
Flex | Daily; duration of intervention | NR | Days participants were adherent: 93% | Badges for achieving PA milestones; participants were advised to check step progress daily | N/A | NR | |||||||||
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Arigo et al, 2015b [ |
Flex | Daily for duration of intervention | Defined as >100 steps in a day | Participants wore 97% of days during intervention | Instructions on device use, public display of steps data, and PA partner accountability | NA | NR | |||||||||
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Finkelstein et al, 2015 [ |
One | Daily | NR | 3 participants did not provide Fitbit data | Instructions and use of device before study for comfort and familiarity | Yes | Self-reporting | |||||||||
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Fukuoka et al, 2018 [ |
Zip | Daily | Minimum of 8 h/day | NR | NR | N/A | IPAQ short version | |||||||||
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Gell et al, 2020 [ |
One | Daily for duration of intervention | Minimum of 10 h/day | Average days participants were adherent: 6 days/week | NR | Yes; same use | Accelerometry | |||||||||
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Gremaud et al, 2018 [ |
Zip | Daily during waking hours | NR | 64.6% wear time in Fitbit arm with a 16.5% increase for Fitbit+Map Trek arm | Reminder system, which prompted each user to wear their Fitbit following nonwear days | Yes | NR | |||||||||
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Grossman, et al 2017 [ |
Charge HR | Duration of intervention | NR | NR | NR | Yes | NR | |||||||||
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Linke et al, 2019 [ |
Charge HR | Daily for duration of intervention | NR | NR | Participants met with study team to sync Fitbit weekly and problem-solve Fitbit-related issues | N/A | Godin Leisure-Time Exercise Questionnaire | |||||||||
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Meints et al, 2019 [ |
Flex | Duration of intervention | Minimum of 10 h/day and 4 days/week | 18 (out of 26) average valid weeks of Fitbit wear | Participants earned monetary reward for accurate use of Fitbit during first 2 weeks | N/A | NR | |||||||||
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Painter et al, 2017 [ |
NR | Daily use | NR | NR | NR | NR | NR | |||||||||
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Reed et al, 2019 [ |
Charge 2 | Daily during waking hours | NR | NR | Basic instruction on using and setting up Fitbit | Yes; same use | Godin Leisure-Time Exercise Questionnaire | |||||||||
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Wang et al, 2015 [ |
One | Duration of intervention | Minimum of 10 h/day | Nontypical days (not meeting wear time criteria) ranged from 5%-9% | NR | Yes | Accelerometry | |||||||||
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Willis et al, 2017 [ |
Flex | Daily | NR | NR | NR | Yes | Accelerometry and self-reporting | |||||||||
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Ashe et al, 2015 [ |
One | Daily for 26 weeks | NR | NR | NR | No | Accelerometry | |||||||||
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Christiansen et al, 2020 [ |
Zip | Daily during waking hours | NR | 60% of intervention group monitored steps at least 80% of study time | In-person instruction of Fitbit use | No | Accelerometry | |||||||||
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Kenfield et al, 2019 [ |
One | Duration of intervention | NR | Fitbits worn 98% of days during intervention | NR | No | Accelerometry and self-reporting | |||||||||
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Thompson et al, 2014 [ |
NR | Daily for 48 weeks | NR | NR | NR | Yes; same use | Accelerometry | |||||||||
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Rossi et al, 2018 [ |
Alta | At all times for 30 days; remove only for bathing and sleeping | NR | Participants wore median of 93% of 30 days | Staff called participants after 1 week | N/A | Godin Leisure-Time Exercise Questionnaire | |||||||||
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Schmidt et al, 2018 [ |
Charge HR | 14 consecutive days during waking hours | NR | 2 participants excluded for not wearing the device for a week | 3 home visits | N/A | NR | |||||||||
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Streber et al, 2017 [ |
Zip | During waking hours for 7 consecutive days | Minimum of 8 h/day | NR | No charging and no turning off and on | Yes; same use | Self-reporting | |||||||||
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Harkins et al, 2017 [ |
Ultra | Daily | NR | NR | Daily email or text message and financial incentives for meeting goal | Yes; same use | Self-reporting | |||||||||
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McMahon et al, 2017 [ |
One | During waking hours for 7 consecutive days | NR | Average hours worn at baseline: 13.01 (SD 1.87) | Participants asked to document days or times monitor was used; staff reviewed documentation and data | Yes; same use | Community Health Activities Model Program for Seniors Questionnaire |
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Vidoni et al, 2016 [ |
Zip | During waking hours | NR | NR | Staff made biweekly phone calls and additional calls if no activity for 3 days | Yes; device masked for 8 weeks versus 1 week | 6-min walk test, mini-physical performance test, and battery of timed physical tasks |
aPA: physical activity.
bOnly the reported intervention condition data.
cNR: not reported.
dN/A: not applicable.
eIPAQ: International Physical Activity Questionnaire.
The 6 childhood studies had sample sizes ranging from 6 to 116 participants and were either single-group (n=3) or quasi-experimental designs (n=3). All studies were conducted in a school setting, and when appropriate, tried to integrate the intervention sessions into regular, daily school activities, including class sessions and recess periods. The most commonly used behavior change techniques were goal setting (through individual and group challenges) and positive reinforcement (through rewards). The duration of the intervention ranged between 4 and 12 weeks.
The most commonly used Fitbit model was the Fitbit Charge, which was used in 4 of the 6 interventions [
In total, 5 of the 6 interventions instructed participants to wear the device for a specific period. A total of 2 studies restricted device wear time to in-school supervised intervention sessions and reported that 100% of participants adhered to the device wear protocol, largely because of study staff monitoring [
The 11 adolescent studies had sample sizes ranging from 6 to 388 participants. In total, 6 of the interventions used a randomized controlled trial design, 3 were quasi-experimental, and 2 used a single-group design. In total, 4 studies used an electronic or web-based platform for intervention delivery, including 3 that used mobile apps for data collection and the delivery of intervention content [
The most commonly used Fitbit model was the Fitbit Flex, which was used in 5 of the 12 interventions [
Overall, 5 studies instructed participants to wear the device daily for the entire duration of the study [
Strategies to boost wear time included providing participants with oral and written instructions for Fitbit use [
Furthermore, 3 studies assessed PA with accelerometers at data collection time points [
The 20 eligible studies for adults aged 18-40 years had a range of sample sizes of participants. Randomized controlled trials (RCTs) were the most commonly used study design (11/20, 55% studies), followed by single-group study designs (5/20, 25% studies). In total, 12 of the 20 studies used mobile apps, web-based platforms, emails, or text messages for intervention delivery [
The most commonly used Fitbit models were Fitbit Zip and Flex, which were used in 11 of the 20 studies [
All but 3 studies [
Strategies to boost wear time included sending daily emails to inquire about Fitbit use experience [
A total of 10 studies asked participants to self-report their PA using instruments such as the International PA Questionnaire, the Stanford Brief PA Survey, and the 30-day PA Recall [
The sample sizes in the 28 middle adulthood studies ranged from 11 to 2113 participants. Most of the studies were RCTs (17/28, 61%), and 20 interventions used technology (eg, texts, apps, and social media) for intervention delivery [
The most commonly used device was the Fitbit Flex, which was used in 9 studies [
All but 3 studies [
Various strategies were used to promote Fitbit wear, including weekly texts to encourage PA based on Fitbit data [
Objective measures to assess PA were used in 12 studies [
The 10 older adulthood studies had sample sizes ranging from 25 to 102 participants, and most (8/10, 80%) were RCTs. Studies with older adults used individual and group-based approaches for intervention delivery. In addition to encouraging individualized PA goal setting or prescribing exercises, 3 studies involved regular phone calls made by study counselors or coaches [
Different Fitbit devices were used across studies, including Classic, Zip, Ultra, Charge HR, and One, with none being predominant. In addition, 3 studies used Fitbit for both intervention and measurement purposes, 4 for intervention only, and 3 for measurement only. Of the 8 studies with multiple conditions, 5 provided participants in the comparison condition with Fitbit devices.
All but 2 studies [
Strategies used to promote wear time adherence included providing participants with wear instructions and reminders via phone calls and text messages [
All but one study [
This study reviewed the use of Fitbit devices in PA intervention studies across the life course. In addition to differences in study designs and intervention delivery methods, our results indicate considerable heterogeneity in Fitbit use within and between developmental stages. From early to older adulthood, most studies instructed participants to wear their Fitbit daily, either at all times or during waking hours, for the duration of the intervention. Studies conducted among children and adolescents tended to specify more limited device wear periods (eg, 24 hours for 7 days). Within developmental stages, our findings also suggest a lack of consistency in the definition of wear time criteria, which sometimes were used to report different adherence metrics or to exclude incomplete data from study analyses. A total of 8 different types of Fitbit devices were used across all age groups, with Fitbit Flex and Zip being the most predominant and some seemingly discontinuing use as newer devices became available. Regardless of intended Fitbit use (ie, measurement vs intervention tool), strategies to boost wear time were similar across stages, and the most commonly used strategies included sending participants reminders through texts or emails and asking participants to log their steps or sync their Fitbit data daily. Overall, the heterogeneity in Fitbit use across PA intervention studies reflects its relative novelty in the field of research.
Across all stages, based on the taxonomy developed by Lyons et al [
Similar to behavior change techniques, the heterogeneity we observed regarding wear instructions and criteria also seemed to be because of developmental considerations. Most studies conducted among children and adolescents opted for instructions that required the device to be worn daily (8-24 hours) for a set data collection period (5-14 days); these studies did not set specific wear time criteria for inclusion in the analyses. Our findings align with previous results indicating a considerable reduction in the use of wearable trackers in youth following the first 2 weeks [
Despite the importance of meeting a minimum threshold of wear time criteria to calculate a reliable estimate of PA, the results from this review also indicated a lack of consistency in the criteria used to define adherence to device wear
The most common pattern that emerged across studies was the use of reminder strategies to boost wear time, which did not differ by the intended device use (ie, intervention or measurement). Generally, texts and emails were sent on a daily or weekly basis as PA and Fitbit wear reminders. Manually logging or syncing Fitbit data on a daily basis was also a strategy to indirectly promote Fitbit wear on a daily basis. Results from previous studies indicate that, in addition to forgetting to wear their trackers [
Despite questions regarding the validity of Fitbits for assessing PA [
However, the use of additional PA measures is not limited to addressing the accuracy issues. Results from a recent systematic review and meta-analysis of Fitbit-based interventions highlighted that the use of accelerometers and self-report, in addition to Fitbit, is often done to capture PA outcomes other than steps [
The primary limitation of this review is that the search for articles was restricted to articles available in the Fitabase library between 2012 and 2018 or on PubMed between 2019 and 2020. Given that the Fitabase library uses the systematic searching procedures of several databases (eg, PubMed, Google Scholar, and Science Direct), searching only PubMed for articles from 2019 to 2020 could have resulted in missed literature. In addition, this review was limited to intervention studies published in English and likely missed formative work that could provide important information regarding the design of Fitbit-based studies. Despite these limitations, this review provides insight into the current state of affairs in Fitbit use in research by focusing on different developmental stages and how the use of the device differs across those stages. Describing both study characteristics and the use of Fitbit devices provides insight into PA study designs across the lifespan and the different ways in which these monitoring devices are used.
Insufficient PA across the lifespan is associated with an increased risk of numerous chronic diseases and is a major public health issue [
application programming interface
moderate-to-vigorous physical activity
physical activity
randomized controlled trial
None declared.