Background: 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.
Objective: 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.
Methods: 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.
Results: 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.
Conclusions: 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 . Between 2001 and 2016, although the levels of insufficient PA decreased marginally globally, high-income Western countries, such as the United States, reported a 5% increase in the prevalence of physical inactivity [ ]. Insufficient PA is associated with increased risk for a variety of chronic diseases including cardiovascular disease, hypertension, and type 2 diabetes [ , ]. Although the current PA guidelines for Americans recommend at least 60 minutes per day of moderate- to vigorous-intensity PA for children and adolescents and 150 minutes per week of moderate-intensity PA for adults, more than 80% of youth and adults do not meet these guidelines [ ].
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 . These devices allow users to track their daily activities, including the number of steps, type of PA, and amount of sleep, among other features [ ]. Fitbit released its first device in 2009 and its first wrist-worn tracker in 2012 [ ]. The brand quickly gained popularity and saw a substantial increase in the use of activity trackers in a relatively short time. In 2014, Fitbit reported only 6.7 million active users compared with 29.6 million in 2019 [ ]. In November 2019, Google announced its purchase of Fitbit for US $2.1 billion and publicly committed to accelerating innovation of these devices [ ].
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 . Fitabase is an example of a company that capitalizes on Fitbit’s open API and works with researchers to collect, manage, and analyze data from participants’ Fitbit devices [ ]. In addition to being a data management platform, Fitabase provides the general public with access to an extensive library containing hundreds of published studies, protocols, and methods papers that report their use of Fitbit devices [ ]. As of January 7, 2021, 682 articles published between 2012 and 2018 were available on the Fitabase research library [ ].
Early studies involving Fitbit focused on establishing its accuracy as an objective PA measurement tool, especially in comparison with existing gold standard measurement devices [, ]. The first study using a Fitbit device to assess PA was published in 2012 and assessed its validity in measuring steps taken during self-paced and prescribed PA [ ]. Overall, there have been mixed findings about the accuracy of Fitbit measurements, with some studies indicating step count accuracy 50% of the time compared with research-grade accelerometers [ ] and others reporting high validity in step count measurements [ , ]. In addition to their ability to serve as a PA measurement tool, Fitbit devices are increasingly being used to support self-monitoring and goal setting as a way of promoting PA in intervention studies across the life course [ - ]. However, it is not clear how these commercially available devices are being incorporated into PA intervention studies. This gap severely hinders the creation of standardized procedures that operationalize Fitbit use in PA intervention studies (eg, wear time protocols, strategies to boost wear time, and analysis implications) [ ]. An overview of the ways in which Fitbit devices can be used to measure or help achieve the desired intervention effects can further contribute to the evidence base. Notably, Fitbit devices have been used in PA interventions targeting children through older adults. However, differences in use protocols across age groups (eg, models and strategies to boost wear time) are not known. In this context, this narrative review aims to characterize how PA intervention studies across the life course use Fitbit in terms of device selection, intended use (intervention vs measurement tool), wear instructions, rates of adherence to device wear, strategies used to boost adherence, and potential use of additional PA measures. This review provides intervention scientists with a synthesis of information that may inform future trials involving Fitbit devices.
Search Strategy and Eligibility Criteria
Given that it serves as a repository of Fitbit-related studies, we first conducted a search of the Fitabase Fitbit Research Library . As of January 7, 2021, the Fitabase research library included studies published between 2012 and 2018 and retrieved from PubMed, Google Scholar, the Association for Computing Machinery, JMIR, Science Direct, and IEEE. Approximately twice a week during this period, the Fitabase team conducted searches of those sources using the keyword Fitbit. The studies identified in the search were then put through a screening process wherein they were deemed eligible for inclusion in the library only if a Fitbit device was used as a key element of the study (ie, for measurement or intervention purposes) [ ]. In the Fitabase library, we applied preexisting filters to limit eligible studies to those that were (1) intervention studies, (2) focused on and reported PA as a main study outcome, and (3) conducted in one of five developmental stages of interest (ie, childhood [9-12 years old], adolescence [13-17 years old], early adulthood [18-40 years old], middle adulthood [41-64 years old], or older adulthood [≥65 years old]). We excluded nonintervention studies, those that did not report a specific target population, and those that did not have full-text articles available. We also excluded intervention studies that used Fitbit devices exclusively to monitor sleep. To capture studies published between 2019 and 2020, we conducted a search of PubMed using the following string search: “(physical activity[Title/Abstract]) AND (Fitbit[Title/Abstract]) AND (intervention*[Title/Abstract]).” In addition to applying the inclusion and exclusion criteria specified earlier, we excluded protocol and review papers and qualitative studies.
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).shows the flow diagram of the study. and show the study characteristics and Fitbit use by developmental stage for included studies, organized by intended Fitbit use (ie, intervention vs measurement).
|Developmental stage||Study design and intervention description||Participant characteristics at baseline|
|Value, N||Age (years), mean (SD) or range||Female, %||Race or ethnicity||Weight status (eg, BMI, weight)|
|Evans et al, 2017 ||42||12.3 (0.3)||47b||NRc||42% overweight or obese|
|Mackintosh et al, 2016 ||30||10.1 (0.3)||40||NR||BMI: mean 19.9 (SD 4) kg/m2|
|Walther et al, 2018 ||24||9.58 (NR)||83||30% White; 29% Black; 25% Hispanic; 16% Native American||NR|
|Intervention and measurement|
|Buchele Harris and Chen, 2018 ||116||10-11||49||60% reported race other than White, with 30% Blackb||NR|
|Harris et al, 2018b ||116||NR||50||60% reported race other than White, with 30% Blackb||NR|
|Hayes and Van Camp, 2015 ||6||NR||100||NR||66% normal weight|
|Chen et al, 2017 ||40||14.9 (1.7)||42||90% Chinese American||BMI: mean 28.3 (SD 4.7) kg/m2|
|Gandrud et al, 2018 ||117||12.7 (2.5)||54||NR||BMI z-score: mean 0.5 (SD 0.9)|
|Mendoza et al, 2017 ||60||16.6 (1.5)||59||66% non-Hispanic White; 14% Hispanic; 7% non-Hispanic Black; 14% Other||NR|
|Haegele and Porretta, 2016 ||6||NR||NR||NR||NR|
|Meng et al, 2018 ||388||15.3 (1.1)||58||62% non-Latino; 38% Latino||BMI %: mean 62.8 (SD 25.0)|
|Walther et al, 2018 ||30||9.58 (NR)||83||30% White; 29% Black or African American; 25% Hispanic; 16% Native American||NR|
|Intervention and measurement|
|Gaudet et al, 2017 ||46||13.0 (0.3)||52%||NR||NR|
|Pope et al, 2018 ||105||17.0 (NR)||71||67% White; 16% Black; 12% Hispanic or Latino; 12% Asian; 5% Other||NR|
|Remmert et al, 2019 ||20||12.0 (0.0)||60||55% Latino; 25% non-Latino White; 20% Other||BMI: mean 21.7 (SD 3.6) kg/m2|
|Short et al, 2018 ||77||14.0 (2.2)||NR||100% American Indian||BMI%: mean 98 (SD 3)|
|Van Woudenberg et al, 2018 ||190||12.2 (0.5)||54||NR||NR|
|Early adulthood (18-40 years)|
|Bang et al, 2017 ||99||24.8 (4.7)b||49b||NR||BMI: mean 21.9 (SD 2.9) kg/m2b|
|Baruth et al, 2019 ||45||28.4 (4.5)b||100||81.8% Whiteb||BMI: mean 26.9 (SD 7.2) kg/m2b|
|Losina et al, 2017 ||292||38.0 (11.0)||83||62% White; 14% Black; 10% Asian; 7% Hispanic; 7% Other||32% normal weight; 30% overweight; 38% obese|
|Mahar et al, 2015 ||75||19.4 (1.2)||NR||NR||NR|
|Chen and Pu, 2014 ||36||20-30||58||NR||2.8% underweight, 94% normal weight, 2.8% obese|
|Pagkalos et al, 2017 ||49||24.0 (7.0)||NR||NR||BMI: mean 22.5 (SD 3.0) kg/m2|
|Ptomey et al, 2018 ||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|
|Walsh and Golbeck, 2014 ||74||37.7 (10.2)||59||NR||NR|
|Yoon et al, 2018 ||79||31.9 (9.6)||59||29.2% Hispanic||NR|
|Intervention and measurement|
|Choi, 2016 ||30||33.7 (2.6)||100||43% White; 40% Asian; 10% Hispanic; 7% Black||BMI (prepregnancy): mean 27.7 (SD 3.7) kg/m2|
|Chung et al, 2017 ||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|
|Gilmore et al, 2017 ||35||26.0 (5.4)||100||74% African American||BMI: mean 32 (SD 3) kg/m2 (range 25.6-37.0 kg/m2)|
|Halliday et al, 2017 ||15||38.3 (6.4)||60||80% Caucasian||BMI: mean 30.4 (SD 6.4) kg/m2|
|Florence et al, 2016 ||300||18-19||58||NR||NR|
|Miragall et al, 2017 ||76||22.2 (3.7)||86||NR||BMI: mean 21.7 (SD 3.2) kg/m2|
|Schrager et al, 2017 ||30||Median age: 28||47||NR||NR|
|Thorndike et al, 2014 ||108||29 (23-37)||54||66% White||BMI: mean 24.1 (range 17.8-35.6) kg/m2|
|Washington et al, 2014 ||13||18-26||67||NR||NR|
|West et al, 2016 ||58||21.6 (2.2)||81||90% White||BMI: mean 24.0 (SD 5.1) kg/m2|
|Zhang and Jemmott, 2019 ||91||26.8 (5.1)||100||100% African American||BMI: mean 31.6 (SD 8.2) kg/m2|
|Middle adulthood (41-64 years)|
|Amorim et al, 2019 ||68||58.4 (13.4)||50||NR||BMI: mean 28 (SD 5.5) kg/m2|
|Butryn et al, 2014 ||36||54 (7.18)||100||62% Caucasian||BMI: mean 32.7 (SD 7.32) kg/m2|
|Cadmus-Bertram al et, 2015 ||51||60.0 (7.1)||100||92% non-Hispanic Whiteb||BMI: mean 29.2 (SD 3.5) kg/m2|
|Cadmus-Bertram et al, 2019 ||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|
|Dean et al, 2018 ||40||46.9 (9.8)||0||100% African American||67% obese|
|Duncan et al, 2020 ||116||44.5 (10.5)||70.7||NR||BMI: mean 31.7 (SD 3.9) kg/m2|
|Ellingson et al, 2019 ||91||41.7 (9.3)||53||79% White||BMI: mean 29.6 (SD 6.3) kg/m2|
|Kandula et al, 2017 ||30||40 (5)||100||100% South Asian||BMI: mean 30 (SD 3) kg/m2|
|Ross and Wing, 2016 ||80||51.1 (11.7)||86||84% Non-Hispanic White||BMI: mean 33 (SD 3.4) kg/m2|
|Singh et al, 2020 ||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|
|Van Blarigan et al, 2019 ||42||54 (11)||59||73% White, 12% Asian, 12% Native American or other, 2% Black||BMI: mean 28.4 (SD 5.9) kg/m2|
|Patel et al, 2017 ||200||55.4 (NR)||56||100% Caucasian||BMI: mean 27.2 (SD 5.1) kg/m2b|
|Robinson et al, 2019 ||63||49.4 (8.3)||72.6||NR||NR|
|Schumacher et al, 2017 ||20||50 (7.2)||100||95% Caucasian||BMI: mean 30.9 (SD 8.9) kg/m2|
|Intervention and measurement|
|Adams et al, 2017 ||96||41 (9.5)||77||81.3% Caucasian||BMI: mean 34.1 (SD 6.18) kg/m2|
|Arigo, 2015 ||12||46 (13.1)||100||75% Caucasian||BMI: mean 32.6 (SD 5.7) kg/m2|
|Arigo et al, 2015b ||20||50 (7.2)||100||90% Caucasian||BMI: mean 30.9 (SD 8.9) kg/m2|
|Finkelstein et al, 2015 ||27||52 (12.0)||100||47% White; 47% African American||BMI: mean 37.0 (SD 6.0) kg/m2|
|Fukuoka et al, 2018 ||54||45.3 (10.8)||68.5||100% Latino||BMI: mean 31.4 (SD 4.1) kg/m2|
|Gell et al, 2020 ||59||61.4 (9)||81||98.5% non-Hispanic White, 1.2% Black or Hispanic||BMI: mean 30.4 (SD 7) kg/m2|
|Gremaud et al, 2018 ||146||40.6 (11.7)b||79.2b||91.7% Caucasianb||BMI: mean 29.9 (SD 6.6) kg/m2b|
|Grossman et al, 2017 ||11||59.53 (11.7)||100||NR||BMI: mean 32 (SD 2.53) kg/m2|
|Linke et al, 2019 ||15||45 (9.7)||13||60% non-Hispanic White, 27% Black, 13% Hispanic||NR|
|Meints et al, 2019 ||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|
|Painter et al, 2017 ||2113||44.54 (10.72)||59||NR||BMI: mean 33.8 (SD 6.8) kg/m2|
|Reed et al, 2019 ||59||48 (NR)||79.3b||93.2% Whiteb||Weight: mean 92.47 (SD 22.8) kgb|
|Wang et al, 2015 ||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|
|Willis et al, 2017 ||70||47 (12.4)||84||24.3% minorities||BMI: mean 36.2 (SD 4) kg/m2|
|Older adulthood (≥65 years)|
|Ashe et al, 2015 ||25||64.1 (4.6)||100||NR||BMI: mean 26.9 (SD 6.8) kg/m2b|
|Christiansen et al, 2020 ||43||67 (7)||53.4||91% White||BMI: mean 31.5 (SD 5.9) kg/m2|
|Kenfield et al, 2019 ||76||65 (NR)||0||84% White||41% overweight, 35% with obesity|
|Thompson et al, 2014 ||48||79.5 (7.0)||81||NR||Weight: mean 75.7 (SD 13.4) kgb|
|Rossi et al, 2018 ||25||62 (9)||100||36% non-Hispanic White; 36% Hispanic; 16% non-Hispanic Black; 12% Asian||BMI: mean 32 (SD 9) kg/m2|
|Schmidt et al, 2018 ||40||66.3 (3.19)||62.5||NR||BMI: mean 25.19 (SD 3.52) kg/m2|
|Streber et al, 2017 ||87||76 (9.2)||78||NR||NR|
|Intervention and measurement|
|Harkins et al, 2017 ||94||80.3||74||98% Caucasian||NR|
|McMahon et al, 2017 ||102||79 (NR)||75||75% White; 25% Black||NR|
|Vidoni et al, 2016 ||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.
|Study||Fitbit||Wear instructions||Fitbit use adherence||Fitbit used in comparison group?||Other PAa measures|
|Minimum wear time criteria||Rate||Strategies to boost adherence|
|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|
|Mackintosh et al, 2016 ||Zip||Duration of intervention||Entire duration of session||100% adherence (with staff monitoring)||NRc||N/Ad||Accelerometry|
|Walther et al, 2018 ||Charge HR||24 h for 7 days, including one weekend||NR||NR||NR||N/A||Self-reporting|
|Intervention and measurement|
|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|
|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|
|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|
|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|
|Gandrud et al, 2018 ||NR||NR||NR||NR||Weekly reminders sent to upload data||Yes||NR|
|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|
|Haegele and Porretta, 2016 ||Zip||NR||NR||NR||NR||N/A||NR|
|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|
|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|
|Intervention and measurement|
|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|
|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|
|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|
|Short et al, 2018 ||Zip||Daily for 7 days||NR||NR||NR||Yes||NR|
|Van Woudenberg et al, 2018 ||Flex||Daily for 7 days||Minimum of 1000 steps/day||Days participants were adherent: 73.4%||NR||Yes||NR|
|Early adulthood (18-40 years)|
|Bang et al, 2017 ||Zip||NR||NR||NR||NR||No||IPAQe|
|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|
|Losina et al, 2017 ||Flex||Daily for duration of intervention||Minimum of 10 h/day||NR||NR||N/A||Self-reporting|
|Mahar et al, 2015 ||Flex||Daily for duration of intervention||NR||NR||NR||No||Self-reporting|
|Chen and Pu, 2014 ||Ultra and One||Daily for 2 weeks||NR||NR||Daily reminder to share experience of wearing Fitbit||No||NR|
|Pagkalos et al, 2017 ||Zip||Daily for duration of intervention||NR||NR||NR||No||Self-reporting|
|Ptomey et al, 2018 ||Charge HR||During intervention sessions||NR||100% (with staff supervision)||NR||No||NR|
|Walsh and Golbeck, 2014 ||Classic||Daily for 10 days||NR||73% of participants were adherent||NR||Yes; same use||IPAQ|
|Yoon et al, 2018 ||Flex||Daily for duration of intervention||NR||Days participants were adherent: 66%||NR||Yes; same use||Self-reporting|
|Intervention and measurement|
|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|
|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|
|Gilmore et al, 2017 ||Zip||Daily||NR||NR||NR||No||NR|
|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|
|Florence et al 2016 ||Flex||Daily for duration of intervention||NR||NR||NR||Yes; control group started Fitbit Flex on week 8||IPAQ|
|Miragall et al, 2017 ||One||Daily for duration of intervention||NR||N/A||N/A||Yes; blinded||NR|
|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|
|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|
|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|
|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|
|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|
|Middle adulthood (41-64 years)|
|Amorim et al, 2019 ||NR||Daily||N/A||96% reported wearing every day or most days||NR||No||Accelerometry and IPAQ|
|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|
|Cadmus-Bertram et al, 2015 ||One||Daily for duration of intervention||Minimum of 2000 steps/day||NR||NR||No||Accelerometry|
|Cadmus-Bertram et al, 2019 ||Charhe HR or Charge 2||Daily||N/A||NR||In-person instruction on Fitbit use||No||Accelerometry|
|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|
|Duncan et al, 2020 ||Alta||NR||NR||NR||NR||Yes, for both intervention groups; no, for control group||Accelerometry and Active Australia Survey|
|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|
|Kandula et al, 2017 ||Zip||Daily||NR||NR||NR||N/A||Actigraph Accelerometer and self-reported questionnaire|
|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|
|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|
|Van Blarigan et al, 2019 ||Flex||Daily||NR||Participants wore Fitbit for 88% of study days||N/A||No||Accelerometry|
|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|
|Robinson et al, 2019 ||Zip||Daily during waking hours||NR||NR||Participants asked to sync Fitbit data daily||Yes; same use||NR|
|Schumacher et al, 2017 ||Flex||Daily||Minimum of 100 steps/day||97% adherent to wear time criteria||NR||N/A||NR|
|Intervention and measurement|
|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|
|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|
|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|
|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|
|Fukuoka et al, 2018 ||Zip||Daily||Minimum of 8 h/day||NR||NR||N/A||IPAQ short version|
|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|
|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|
|Grossman, et al 2017 ||Charge HR||Duration of intervention||NR||NR||NR||Yes||NR|
|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|
|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|
|Painter et al, 2017 ||NR||Daily use||NR||NR||NR||NR||NR|
|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|
|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|
|Willis et al, 2017 ||Flex||Daily||NR||NR||NR||Yes||Accelerometry and self-reporting|
|Older adulthood (≥65 years)|
|Ashe et al, 2015 ||One||Daily for 26 weeks||NR||NR||NR||No||Accelerometry|
|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|
|Kenfield et al, 2019 ||One||Duration of intervention||NR||Fitbits worn 98% of days during intervention||NR||No||Accelerometry and self-reporting|
|Thompson et al, 2014 ||NR||Daily for 48 weeks||NR||NR||NR||Yes; same use||Accelerometry|
|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|
|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|
|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|
|Intervention and measurement|
|Harkins et al, 2017 ||Ultra||Daily||NR||NR||Daily email or text message and financial incentives for meeting goal||Yes; same use||Self-reporting|
|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|
|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.
Childhood (9-12 Years)
General Study Characteristics
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 [, , , ]. A total of 3 studies used Fitbits for both intervention and measurement purposes, 2 for intervention only, and 1 for measurement only. Participants in the comparison condition used Fitbit devices in only one of the 3 quasi-experimental studies.
Wear Time and Adherence
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 2 interventions instructed participants to wear their Fitbits only during school days for the duration of the intervention [ , ]. In one study, participants were asked to wear the device for 24 hours during a 7-day period [ ]. Applying a wear time criterion of 8 hours per day, one study reported that participants were adherent on 65%-73% of intervention days [ ].
Adolescence (13-17 Years)
General Study Characteristics
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 [, , , ] and 1 that used Facebook as a web-based platform to encourage interactions between participants [ ]. A total of 7 studies were delivered in a school setting [ , , - , ]. Across all studies, the most commonly used behavioral change techniques were goal setting, self-monitoring, and knowledge shaping. The study duration varied between 4 weeks and 24 months.
The most commonly used Fitbit model was the Fitbit Flex, which was used in 5 of the 12 interventions [, , , , ]. The Fitbit Zip was the second most commonly used device (in 3 studies [ , , ]). A total of 5 studies used Fitbits for both intervention and measurement purposes, 3 for intervention only, and 3 for measurement only. In 7 of the 10 studies with multiple conditions, participants in the comparison condition used Fitbit devices.
Wear Time and Adherence
Overall, 5 studies instructed participants to wear the device daily for the entire duration of the study [, , , , ], 4 studies instructed participants to wear the device for 7-day data collection periods only [ , , , ], and the remaining 2 studies did not report wear instructions [ , , ]. Moreover, 5 studies used a minimum wear time criterion that was defined by either the number of hours (eg, 8 hours, 10 hours, or 24 hours per day) or steps (eg, 500 or 1000 steps per day) [ , , , , ]. In addition, 3 studies reported the percentage of intervention days on which a specific minimum wear criterion was met (67.3% [ ], 71.5% [ ], and 73.4% [ ]). One study excluded participants from the analysis who did not meet the wear time criterion [ ]. One intervention that did not use the minimum wear time criterion was able to report an average number of days of valid Fitbit wear of 78.1 (SD 8.6; of a maximum of 84 days) for intervention participants [ ]. Another study without a minimum wear time criterion reported that 36% of participants never wore their Fitbit [ ].
Strategies to Boost Wear Time
Strategies to boost wear time included providing participants with oral and written instructions for Fitbit use [, ]. Some studies also sent participants daily or weekly text messages or emails to encourage consistent use, meeting PA goals, or data upload [ - , ]. In one study, a weekly lottery was used to reward participants with gift cards [ ].
Other Measures of PA
Furthermore, 3 studies assessed PA with accelerometers at data collection time points [, , ], and 3 studies used self-report measures of PA [ , , ].
Early Adulthood (18-40 Years)
General Study Characteristics
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 [- , - , , ]. Of these studies, 3 encouraged web-based interactions between participants [ , , ]. In total, 8 of the 20 studies used a campus- or workplace-based approach to intervention delivery [ , , , - , ]. Strategies for behavioral change included competition or challenges, both at the individual and group levels, and self-monitoring, social support, and goal setting. The study duration ranged from 1 week to 12 months.
The most commonly used Fitbit models were Fitbit Zip and Flex, which were used in 11 of the 20 studies [, , , , , , , , , , ]. Furthermore, 10 studies used Fitbits for both intervention and measurement purposes, 4 for intervention only, and 5 for measurement only. In 6 of the 15 studies with multiple conditions, participants in the comparison condition used Fitbit devices.
Wear Time and Adherence
All but 3 studies [, , ] instructed participants to wear the device daily, either at all times or during waking hours, for the duration of the intervention. Furthermore, 2 studies instructed participants to wear the device for a specific data collection period [ , ]. Different metrics were used to report adherence to daily wear instructions. There were 3 studies that reported the percentage of intervention days in which participants were adherent: 66% [ ], 73% [ ], and 78%-99% [ ]. Another study reported that, on average, participants were adherent on 23.7 (SD 15.2) days (of 63 days) [ ]. One study instructed participants to wear the device only during intervention sessions, and 100% of the participants were adherent [ ]. Minimum wear time criteria were also used to report adherence. One study with a minimum wear time criterion of 1000 steps per day reported that participants met the criterion on 78% of intervention days [ ], whereas another study in which the minimum wear time criterion was set at 500 steps per day reported that participants met the criterion on 60%-70% of intervention days [ ]. A minimum criterion of 100 steps per day allowed one study to report a median number of 27.5 days (of 30) on which participants were adherent [ ]. Another study with the same minimum wear time criterion reported that 51%-83% of participants were adherent [ ]. With a minimum wear criterion of one day per week, one study reported that participants were adherent on 93% of intervention weeks on average [ ].
Strategies to Boost Wear Time
Strategies to boost wear time included sending daily emails to inquire about Fitbit use experience , prompting participants to enter daily Fitbit data into an app [ ], asking participants to share Fitbit data publicly [ ], or sending daily reminder messages and instructions on Fitbit use [ ]. Some studies provided participants with opportunities to win incentives based on compliance rates [ , ].
Other Measures of PA
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 [, , , , - , - , - ]. Only 1 study used an additional objective measure of PA (ie, accelerometer [ ]).
Middle Adulthood (41-64 Years)
General Study Characteristics
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 common behavior change techniques used were self-monitoring, social support, behavioral counseling, and goal setting. The study duration ranged from 4 weeks to 6 months.
The most commonly used device was the Fitbit Flex, which was used in 9 studies [, , , , , , , , ]. There were 14 studies that used Fitbit for both intervention and measurement purposes, 11 for intervention only, and 3 for measurement only. Of the 18 studies with multiple conditions, 13 provided participants in the comparison condition with Fitbit devices.
Wear Time and Adherence
All but 3 studies [, , ] instructed participants to wear the device daily, either at all times or during waking hours, for the duration of the intervention. Among them, 2 studies reported the percentage of participants who were adherent to daily wear instructions: 96% [ ] and 70% [ ]. Other studies reported the percentage of days on which participants were adherent to wear instructions: 86% [ ], 88% [ ], 97% [ ], 93% [ ], and 76%-86% [ ]. Furthermore, 9 studies also used a minimum wear time criterion defined by either the number of hours (eg, 8 or 10 hours per day) or steps (eg, 100 or 2000 steps per day) [ , , , , , , , , ]. With a minimum wear time criterion of 100 steps per day, 1 study reported that 97% of the participants were adherent [ ]. A minimum wear criterion of 10 hours per day allowed another study to report 18 of 26 average valid weeks of Fitbit wear [ ], whereas another study used the same criterion to report that participants were adherent to the criterion on 6 days per week on average [ ]. A minimum criterion of 10 hours per day was also used in another study to report 5%-9% of days on which participants did not meet the criterion on average [ ]. Similarly, with a minimum wear time criterion of 1000 steps per day, another study reported 10.1%-12.7% of missing observation days [ ]. Allowing participants to self-monitor PA as desired, one study reported the average hours worn of 17.3 (SD 5.7) hours per 6.1 (SD 0.8) days per week [ ]. Another study excluded 3 participants who provided no Fitbit data [ ].
Strategies to Boost Wear Time
Various strategies were used to promote Fitbit wear, including weekly texts to encourage PA based on Fitbit data , weekly emails providing activities’ progress summaries [ ], asking participants to sync Fitbit data daily [ ], providing incentives for wearing Fitbit regularly [ ], public display of Fitbit data [ , ], and instructions on device use [ , ].
Other Measures of PA
Objective measures to assess PA were used in 12 studies [- , , , , , ], whereas self-reported measures were used in 11 studies [ , , , , , , , , , , ].
General Study Characteristics
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 [, , ]. One study provided participants with access to a study website and used text messages for intervention delivery [ ]. Interventions providing PA education were often delivered in a group setting through a community-based approach, which allowed for the use of social support as a behavioral change technique [ , , ]. Other behavioral change techniques included goal setting, behavioral counseling, and self-monitoring.
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.
Wear Time and Adherence
All but 2 studies [, ] instructed participants to wear the device daily, either at all times or during waking hours, for the duration of the intervention. Using daily wear instructions, the number of days the device worn was commonly reported either as an average (6.6, SD 1.1 over 7 days) [ ] or as a median (93% over 30 days) [ ]. One study reported that 60% of participants in the intervention group used Fitbit at least 80% of the study time [ ], whereas another study simply reported that Fitbit was worn on 98% of days during the intervention [ ]. One study used a minimum wear time criterion (8 hours per day) but did not report adherence to the criterion [ ]. One study excluded 2 participants who did not wear the device for at least half of the instructed wear period (14 days) [ ].
Strategies to Boost Wear Time
Strategies used to promote wear time adherence included providing participants with wear instructions and reminders via phone calls and text messages [, , , ]. Some studies also asked participants to upload PA data on a daily basis or to document the device wear time and day [ , ].
Other Measures of PA
All but one study  used an additional measure of PA. Although self-reporting (using different scales) was the most common measure, which was used in 6 studies [ , - , ], accelerometers were used in 4 studies [ , - ]. One study used a physical performance test along with a walk test [ ].
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 , the most common behavior change techniques used were self-monitoring and goal setting, regardless of the intended device use. This aligns with previous findings indicating goal setting and self-monitoring as the most commonly used behavior change techniques in studies with activity trackers [ ]. As a self-monitoring technology, Fitbit devices provide real-time feedback that has the potential to stimulate behavior change. Self-monitoring allows participants to establish and track goals that were commonly operationalized through individual or group step count challenges. For example, a classroom-based study in children used individual step goals consistent with achieving 60 minutes of moderate-to-vigorous physical activity (MVPA) per day [ ]. Additional behavioral change techniques appeared to be developmentally targeted. For example, among children, rewards for meeting step goals were often provided (eg, accruing points toward gift card balance). Through the use of social media platforms, adolescents and adults were provided with performance-based, web-based badges [ , ]. Among older adults, group-based PA education along with individual PA coaching or counseling provided social support to encourage the initiation and maintenance of behavior change [ ].
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 [, ]. As such, limited device wear time in children and adolescents could potentially be a strategy that aims at capitalizing on wear patterns and usability trends in these groups. Studies conducted during early and middle adulthood tended to specify a minimum wear time criterion for inclusion in analyses based on specific numbers of steps or hours, in addition to daily wear instructions. However, studies conducted in older adults did not set minimum wear time criteria and instructed participants to wear the device daily during waking hours. The less rigid guidelines for device wear adherence among older adults could potentially be a way of increasing feasibility in populations who are less able to meet strict criteria and are less proficient in the use of technology [ ].
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 within developmental stages. A systematic review that examined the length of device wear time required in PA interventions found that most studies conducted among adults did not report minimum device wear and that there was significant variation among studies reporting these criteria . Corresponding to the lack of uniformity in wear time criteria, different metrics (eg, percentage, mean, and median) were used to report rates of adherence to wear instructions. If not met, the wear time criterion was sometimes used to exclude participants from the data analysis. However, many studies used the wear time criteria to report different metrics of adherence. Overall, the absence of clear reporting with standardized metrics significantly impaired efforts to assess overall adherence rates within developmental stages.
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 , approximately 2% of study participants stopped using their devices each week altogether [ ], and study participants also reported using their Fitbit less than 10% of the time following the end of wear-based incentives [ ]. Therefore, these strategies are particularly essential given the evidence regarding decrease in Fitbit wear adherence over time in users and the need for reminder strategies to boost wear time [ ].
Despite questions regarding the validity of Fitbits for assessing PA , most interventions in this review used Fitbit devices for both intervention and measurement purposes (39/75, 52%) or for data measurement purposes exclusively (15/75, 20%). Most studies (45/75, 63%) that were reviewed supplemented the use of Fitbit with additional objective (eg, accelerometers) or self-reported (eg, International PA Questionnaire) measures of PA. It is possible that the addition of other PA measures, even in studies that used Fitbit devices primarily as a measurement or data collection tool, was because of concerns about the uncertainty around the accuracy of measures provided by Fitbit devices [ ]. In addition, the use of other measures (ie, accelerometry or self-reporting) to collect baseline or habitual activity [ ] could also point to the perceived inaccuracy of data collected from commercially available trackers, which could have a potential impact on activity. Previous studies have also shown that commercially available trackers such as Fitbit devices often overestimate the time spent in MVPA compared with research-grade monitors [ , , ].
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 . With the expansion of the use of Fitbit devices in PA intervention studies, previous studies have raised issues regarding their inability to capture PA constructs such as nonambulatory activities or energy expenditure [ ]. In a recently published paper, Balbim et al [ ] summarized the challenges and possible solutions to use Fitbit devices in mobile health intervention research. They described challenges and solutions at four different study phases: preparation, intervention delivery, data collection and analysis, and study closeout. For example, during the data collection phase, they point to the inaccuracy or unavailability of wear time data through Fitbit’s web API. They then discussed the potential solution of using heart rate data and pre-established rules for determining wear time and manually identifying gaps in heart rate data, indicating nonwear time. They also highlight the tedious and challenging nature of such an endeavor [ ]. Thus, the use of additional PA measures (objective and subjective), despite increased burden on participants, allows for the efficient collection of different types of data, including valid wear time, information about body positions, sedentary behaviors, postural allocation, and the type of activity being performed [ , - ].
Strengths and Limitations
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 . The prominence and relatively low cost of Fitbit devices have increased their use by the public and researchers as PA trackers. Although behavior change techniques and strategies to boost Fitbit wear time were similar across all studies reviewed, our findings indicate significant differences in wear instructions and metrics for reporting adherence rates. Although between-stage differences appear to be based on developmental considerations that aim to maximize device use in each age group, within-group differences appear to result from a lack of uniformity in metrics used to report rates of adherence and minimum wear time criteria. The use of additional PA data collection tools in most studies that were reviewed points to the accuracy issues raised by previous research focusing on Fitbits in PA interventions [ , ] and a reluctance to rely on Fitbits as the primary measurement device or for the assessment of habitual activity. However, additional PA measures are also used to capture PA constructs not measured by Fitbit devices (eg, MVPA, sedentary behaviors, and types of activity). As the use of monitoring devices continues to expand in the field of PA research, the lack of uniformity in study protocols and metrics of reported measures represents a major issue for purposes of comparison [ ]. Given that clinical trial registries serve as a repository for researchers [ ], there is a need for increased transparency in the prospective registration of PA intervention studies. This paper serves as a call for researchers using Fitbit devices to provide a clear rationale for the use of several PA measures and to specify the metrics that will be reported for each. By providing researchers with a synthesis of information on the use of Fitbit devices in PA intervention studies across the life course, this narrative review serves as a resource that may be used to inform the design of future trials involving Fitbit devices.
Conflicts of Interest
- Jake-Schoffman D, Baskin M. Promoting physical activity across the life span: progress and promise. J Public Health Manag Pract 2019;25(1):30-31. [CrossRef] [Medline]
- Guthold R, Stevens G, Riley L, Bull F. Worldwide trends in insufficient physical activity from 2001 to 2016: a pooled analysis of 358 population-based surveys with 1·9 million participants. Lancet Glob Health 2018 Oct;6(10):1077-1086. [CrossRef]
- Booth F, Roberts C, Laye M. Lack of exercise is a major cause of chronic diseases. Compr Physiol 2012 Apr;2(2):1143-1211 [FREE Full text] [CrossRef] [Medline]
- Chen Y, Sloan FA, Yashkin AP. Adherence to diabetes guidelines for screening, physical activity and medication and onset of complications and death. J Diabetes Complicat 2015 Nov;29(8):1228-1233. [CrossRef]
- Piercy KL, Troiano RP, Ballard RM, Carlson SA, Fulton JE, Galuska DA, et al. The physical activity guidelines for Americans. J Am Med Assoc 2018 Nov 20;320(19):2020-2028. [CrossRef] [Medline]
- Worldwide quaterly wearable device tracker. IDC Corporate. URL: https://www.idc.com/tracker/showproductinfo.jsp?containerId=IDC_P31315 [accessed 2021-05-04]
- Fitbit. URL: https://www.fitbit.com/us/home [accessed 2019-12-09]
- Vailshery LS. Fitbit unit sales worldwide 2010-2019. Statista. 2021. URL: https://www.statista.com/statistics/472591/fitbit-devices-sold/ [accessed 2021-05-05]
- Fitbit reports 2019 fourth quarter and full year results. Fitbit, Inc. 2020. URL: https://investor.fitbit.com/press-releases/press-release-details/2020/Fitbit-Reports-2019-Fourth-Quarter-and-Full-Year-Results/default.aspx [accessed 2021-05-05]
- Brinton JE, Keating MD, Ortiz AM, Evenson KR, Furberg RD. Establishing linkages between distributed survey responses and consumer wearable device datasets: a pilot protocol. JMIR Res Protoc 2017 Apr 27;6(4):e66 [FREE Full text] [CrossRef] [Medline]
- Fitabase. URL: https://www.fitabase.com/ [accessed 2019-12-10]
- Guo F, Li Y, Kankanhalli M, Brown M. An evaluation of wearable activity monitoring devices. In: Proceedings of the 1st ACM international workshop on Personal data meets distributed multimedia. 2013 Presented at: MM '13: ACM Multimedia Conference; October, 2013; Barcelona Spain p. 31-34. [CrossRef]
- Dannecker K, Sazonova N, Melanson E, Sazonov E, Browning R. A comparison of energy expenditure estimation of several physical activity monitors. Med Sci Sports Exerc 2013 Nov;45(11):2105-2112 [FREE Full text] [CrossRef] [Medline]
- McArdle WD, Katch FI, Katch VL. Accuracy of the Fitbit pedometer for self-pacedprescribed physical activity. In: Ramirez E, Peterson C, Wu W, Norman G, editors. Sports and Exercise Nutrition 4th Edition. Philadelphia, Pennsylvania, United States: Lippincott Williams & Wilkins; 2012:1-681.
- Feehan LM, Geldman J, Sayre EC, Park C, Ezzat AM, Yoo JY, et al. Accuracy of Fitbit devices: systematic review and narrative syntheses of quantitative data. JMIR Mhealth Uhealth 2018 Aug 09;6(8):e10527 [FREE Full text] [CrossRef] [Medline]
- Evenson KR, Goto MM, Furberg RD. Systematic review of the validity and reliability of consumer-wearable activity trackers. Int J Behav Nutr Phys Act 2015 Dec 18;12(1):159 [FREE Full text] [CrossRef] [Medline]
- Takacs J, Pollock CL, Guenther JR, Bahar M, Napier C, Hunt MA. Validation of the Fitbit One activity monitor device during treadmill walking. J Sci Med Sport 2014 Sep;17(5):496-500. [CrossRef] [Medline]
- Buchele Harris H, Chen W. Technology-enhanced classroom activity breaks impacting children's physical activity and fitness. J Clin Med 2018 Jun 29;7(7):165 [FREE Full text] [CrossRef] [Medline]
- Gaudet J, Gallant F, Bélanger M. A Bit of Fit: minimalist intervention in adolescents based on a physical activity tracker. JMIR Mhealth Uhealth 2017 Jul 06;5(7):e92 [FREE Full text] [CrossRef] [Medline]
- Dean DA, Griffith DM, McKissic SA, Cornish EK, Johnson-Lawrence V. Men on the move-nashville: feasibility and acceptability of a technology-enhanced physical activity pilot intervention for overweight and obese middle and older age African American men. Am J Mens Health 2018 Jul 19;12(4):798-811 [FREE Full text] [CrossRef] [Medline]
- Thompson WG, Kuhle CL, Koepp GA, McCrady-Spitzer SK, Levine JA. "Go4Life" exercise counseling, accelerometer feedback, and activity levels in older people. Arch Gerontol Geriatr 2014 May;58(3):314-319. [CrossRef] [Medline]
- Jake-Schoffman DE, Silfee VJ, Sreedhara M, Rosal MC, May CN, Lopez-Cepero A, et al. Reporting of physical activity device measurement and analysis protocols in lifestyle interventions. Am J Lifestyle Med 2019 Jul 17:155982761986217. [CrossRef]
- Evans E, Abrantes A, Chen E, Jelalian E. Using novel technology within a school-based setting to increase physical activity: a pilot study in school-age children from a low-income, urban community. Biomed Res Int 2017;2017(4271483) [FREE Full text] [CrossRef] [Medline]
- Mackintosh K. Mission possible: using ubiquitous social goal sharing technology to promote physical activity in children. Malaysian J Move, Health Exerc 2016 Oct 18;5(2). [CrossRef]
- Walther A, Dunker T, Franzen-Castle L, Krehbiel M. Increasing at-risk youth self-reported and objectively measured physical activity in an afterschool program. J Fam Consum Sci 2018 Mar 01;110(1):59-63. [CrossRef]
- Harris HB, Cortina KS, Templin T, Colabianchi N, Chen W. Impact of coordinated-bilateral physical activities on attention and concentration in school-aged children. Biomed Res Int 2018 May 28;2018:2539748 [FREE Full text] [CrossRef] [Medline]
- Hayes LB, Van Camp CM. Increasing physical activity of children during school recess. J Appl Behav Anal 2015 Sep 29;48(3):690-695. [CrossRef] [Medline]
- Chen J, Guedes CM, Cooper BA, Lung AE. Short-term efficacy of an innovative mobile phone technology-based intervention for weight management for overweight and obese adolescents: pilot study. Interact J Med Res 2017 Aug 02;6(2):e12 [FREE Full text] [CrossRef] [Medline]
- Gandrud L, Altan A, Buzinec P, Hemphill J, Chatterton J, Kelley T, et al. Intensive remote monitoring versus conventional care in type 1 diabetes: a randomized controlled trial. Pediatr Diabetes 2018 Feb 21;19(6):1086-1093. [CrossRef] [Medline]
- Mendoza JA, Baker KS, Moreno MA, Whitlock K, Abbey-Lambertz M, Waite A, et al. A Fitbit and Facebook mHealth intervention for promoting physical activity among adolescent and young adult childhood cancer survivors: a pilot study. Pediatr Blood Cancer 2017 Dec 15;64(12):e26660. [CrossRef] [Medline]
- Haegele J, Porretta D. A leisure-time physical activity intervention for adolescents with visual impairments. Res Q Exerc Sport. 2016. URL: https://search.proquest.com/openview/e1e94139646d164224809faee1a60f4c/1?pq-origsite=gscholar%7B%5C&%7Dcbl=40785 [accessed 2021-05-07]
- Meng Y, Manore M, Schuna J, Patton-Lopez M, Branscum A, Wong S. Promoting healthy diet, physical activity, and life-skills in high school athletes: results from the WAVE ripples for change childhood obesity prevention two-year intervention. Nutrients 2018 Jul 23;10(7):947 [FREE Full text] [CrossRef] [Medline]
- Pope L, Garnett B, Dibble M. Lessons learned through the implementation of an eHealth physical activity gaming intervention with high school youth. Games Health J 2018 Apr;7(2):136-142. [CrossRef] [Medline]
- Remmert JE, Woodworth A, Chau L, Schumacher LM, Butryn ML, Schneider M. Pilot trial of an acceptance-based behavioral intervention to promote physical activity among adolescents. J Sch Nurs 2019 Dec 13;35(6):449-461 [FREE Full text] [CrossRef] [Medline]
- Short KR, Chadwick JQ, Cannady TK, Branam DE, Wharton DF, Tullier MA, et al. Using financial incentives to promote physical activity in American Indian adolescents: a randomized controlled trial. PLoS ONE 2018 Jun 1;13(6):e0198390. [CrossRef]
- van Woudenberg TJ, Bevelander KE, Burk WJ, Smit CR, Buijs L, Buijzen M. A randomized controlled trial testing a social network intervention to promote physical activity among adolescents. BMC Public Health 2018 Apr 23;18(1):542 [FREE Full text] [CrossRef] [Medline]
- Bang K, Lee I, Kim S, Lim CS, Joh H, Park B, et al. The effects of a campus forest-walking program on undergraduate and graduate students' physical and psychological health. Int J Environ Res Public Health 2017 Jul 05;14(7):728 [FREE Full text] [CrossRef] [Medline]
- Baruth M, Schlaff RA, Deere S, Walker JL, Dressler BL, Wagner SF, et al. The feasibility and efficacy of a behavioral intervention to promote appropriate gestational weight gain. Matern Child Health J 2019 Dec 20;23(12):1604-1612. [CrossRef] [Medline]
- Losina E, Smith SR, Usiskin IM, Klara KM, Michl GL, Deshpande BR, et al. Implementation of a workplace intervention using financial rewards to promote adherence to physical activity guidelines: a feasibility study. BMC Public Health 2017 Dec 01;17(1):921 [FREE Full text] [CrossRef] [Medline]
- Mahar M, Nanney L, Das B, Raedeke T, Vick G, Rowe D. Effects of an intervention using movement technology in a University physical activity class. Med Sci Sports Exerc 2015:522. [CrossRef]
- Chen Y, Pu P. HealthyTogether: exploring social incentives for mobile fitness applications. In: Proceedings of the Second International Symposium of Chinese CHI. 2014 Presented at: Chinese CHI '14: The Second International Symposium of Chinese CHI; April, 2014; Toronto Ontario Canada p. 25-34. [CrossRef]
- Pagkalos I, Kokkinopoulou A, Weal M, Petrou L, Hassapidou M. Exercise monitoring of young adults using a Facebook application. Digit Health 2017 May 22;3:2055207617711286 [FREE Full text] [CrossRef] [Medline]
- Ptomey LT, Szabo AN, Willis EA, Gorczyca AM, Greene JL, Danon JC, et al. Changes in cognitive function after a 12-week exercise intervention in adults with Down syndrome. Disabil Health J 2018 Jul;11(3):486-490 [FREE Full text] [CrossRef] [Medline]
- Walsh G, Golbeck J. StepCity: a preliminary investigation of a personal informatics-based social game on behavior change. In: Proceedings of the CHI '14 Extended Abstracts on Human Factors in Computing Systems. 2014 Presented at: CHI '14: CHI Conference on Human Factors in Computing Systems; April, 2014; Toronto Ontario Canada p. 2371-2376. [CrossRef]
- Yoon S, Schwartz JE, Burg MM, Kronish IM, Alcantara C, Julian J, et al. Using behavioral analytics to increase exercise: a randomized N-of-1 study. Am J Prev Med 2018 Apr;54(4):559-567 [FREE Full text] [CrossRef] [Medline]
- Choi J, Lee JH, Vittinghoff E, Fukuoka Y. mHhealth physical activity intervention: a randomized pilot study in physically inactive pregnant women. Matern Child Health J 2016 May 9;20(5):1091-1101 [FREE Full text] [CrossRef] [Medline]
- Chung AE, Skinner AC, Hasty SE, Perrin EM. Tweeting to health: a novel mHealth intervention using Fitbits and Twitter to foster healthy lifestyles. Clin Pediatr (Phila) 2017 Jan 19;56(1):26-32. [CrossRef] [Medline]
- Gilmore LA, Klempel MC, Martin CK, Myers CA, Burton JH, Sutton EF, et al. Personalized mobile health intervention for health and weight loss in postpartum women receiving women, infants, and children benefit: a randomized controlled pilot study. J Womens Health (Larchmt) 2017 Jul;26(7):719-727 [FREE Full text] [CrossRef] [Medline]
- Halliday GC, Miles GC, Marsh JA, Kotecha RS, Alessandri AJ. Regular exercise improves the well-being of parents of children with cancer. Pediatr Blood Cancer 2017 Dec 19;64(12):e26668. [CrossRef] [Medline]
- H-Jennings F, Clément M, Brown M, Leong B, Shen L, Dong C. Promote students’ healthy behavior through sensor and game: a randomized controlled trial. Med Sci Educ 2016 May 3;26(3):349-355. [CrossRef]
- Miragall M, Domínguez-Rodríguez A, Navarro J, Cebolla A, Baños RM. Increasing physical activity through an Internet-based motivational intervention supported by pedometers in a sample of sedentary students: a randomised controlled trial. Psychol Health 2018 Apr 07;33(4):465-482. [CrossRef] [Medline]
- Schrager JD, Shayne P, Wolf S, Das S, Patzer RE, White M, et al. Assessing the influence of a Fitbit physical activity monitor on the exercise practices of emergency medicine residents: a pilot study. JMIR Mhealth Uhealth 2017 Jan 31;5(1):e2 [FREE Full text] [CrossRef] [Medline]
- Thorndike AN, Mills S, Sonnenberg L, Palakshappa D, Gao T, Pau CT, et al. Activity monitor intervention to promote physical activity of physicians-in-training: randomized controlled trial. PLoS One 2014 Jun 20;9(6):e100251 [FREE Full text] [CrossRef] [Medline]
- Washington WD, Banna KM, Gibson AL. Preliminary efficacy of prize-based contingency management to increase activity levels in healthy adults. J Appl Behav Anal 2014 Apr 17;47(2):231-245. [CrossRef] [Medline]
- West DS, Monroe CM, Turner-McGrievy G, Sundstrom B, Larsen C, Magradey K, et al. A technology-mediated behavioral weight gain prevention intervention for college students: controlled, quasi-experimental study. J Med Internet Res 2016 Jun 13;18(6):e133 [FREE Full text] [CrossRef] [Medline]
- Zhang J, Iii JB. Mobile app-based small-group physical activity intervention for young African American women: a pilot randomized controlled trial. Prev Sci 2019 Aug 20;20(6):863-872. [CrossRef] [Medline]
- Amorim AB, Pappas E, Simic M, Ferreira ML, Jennings M, Tiedemann A, et al. Integrating Mobile-health, health coaching, and physical activity to reduce the burden of chronic low back pain trial (IMPACT): a pilot randomised controlled trial. BMC Musculoskelet Disord 2019 Feb 11;20(1):71 [FREE Full text] [CrossRef] [Medline]
- Butryn ML, Arigo D, Raggio GA, Colasanti M, Forman EM. Enhancing physical activity promotion in midlife women with technology-based self-monitoring and social connectivity: a pilot study. J Health Psychol 2016 Aug 10;21(8):1548-1555. [CrossRef] [Medline]
- Cadmus-Bertram LA, Marcus BH, Patterson RE, Parker BA, Morey BL. Randomized trial of a Fitbit-based physical activity intervention for women. Am J Prev Med 2015 Sep;49(3):414-418 [FREE Full text] [CrossRef] [Medline]
- Cadmus-Bertram L, Tevaarwerk AJ, Sesto ME, Gangnon R, Van Remortel B, Date P. Building a physical activity intervention into clinical care for breast and colorectal cancer survivors in Wisconsin: a randomized controlled pilot trial. J Cancer Surviv 2019 Aug 1;13(4):593-602 [FREE Full text] [CrossRef] [Medline]
- Duncan M, Fenton S, Brown W, Collins C, Glozier N, Kolt G, et al. Efficacy of a multi-component m-health weight-loss intervention in overweight and obese adults: a randomised controlled trial. Int J Environ Res Public Health 2020 Aug 26;17(17):6200 [FREE Full text] [CrossRef] [Medline]
- Ellingson LD, Lansing JE, DeShaw KJ, Peyer KL, Bai Y, Perez M, et al. 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 [FREE Full text] [CrossRef] [Medline]
- Kandula NR, Dave S, De Chavez PJ, Marquez DX, Bharucha H, Mammen S, et al. An exercise intervention for South Asian mothers with risk factors for diabetes. Transl J Am Coll Sports Med 2016 Jun 15;1(6):52-59 [FREE Full text] [Medline]
- Ross KM, Wing RR. Impact of newer self-monitoring technology and brief phone-based intervention on weight loss: a randomized pilot study. Obesity (Silver Spring) 2016 Aug 01;24(8):1653-1659 [FREE Full text] [CrossRef] [Medline]
- Singh B, Spence RR, Sandler CX, Tanner J, Hayes SC. Feasibility and effect of a physical activity counselling session with or without provision of an activity tracker on maintenance of physical activity in women with breast cancer - a randomised controlled trial. J Sci Med Sport 2020 Mar;23(3):283-290. [CrossRef] [Medline]
- Van Blarigan EL, Chan H, Van Loon K, Kenfield SA, Chan JM, Mitchell E, et al. Self-monitoring and reminder text messages to increase physical activity in colorectal cancer survivors (Smart Pace): a pilot randomized controlled trial. BMC Cancer 2019 Mar 11;19(1):218 [FREE Full text] [CrossRef] [Medline]
- Patel MS, Benjamin EJ, Volpp KG, Fox CS, Small DS, Massaro JM, et al. Effect of a game-based intervention designed to enhance social incentives to increase physical activity among families: the BE FIT randomized clinical trial. JAMA Intern Med 2017 Nov 01;177(11):1586-1593 [FREE Full text] [CrossRef] [Medline]
- Robinson SA, Bisson AN, Hughes ML, Ebert J, Lachman ME. Time for change: using implementation intentions to promote physical activity in a randomised pilot trial. Psychol Health 2019 Feb 30;34(2):232-254 [FREE Full text] [CrossRef] [Medline]
- Schumacher LM, Arigo D, Thomas C. Understanding physical activity lapses among women: responses to lapses and the potential buffering effect of social support. J Behav Med 2017 Oct 5;40(5):740-749. [CrossRef] [Medline]
- Adams MA, Hurley JC, Todd M, Bhuiyan N, Jarrett CL, Tucker WJ, et al. Adaptive goal setting and financial incentives: a 2 × 2 factorial randomized controlled trial to increase adults' physical activity. BMC Public Health 2017 Mar 29;17(1):1-16 [FREE Full text] [CrossRef] [Medline]
- Arigo D. Promoting physical activity among women using wearable technology and online social connectivity: a feasibility study. Health Psychol Behav Med 2015 Dec 31;3(1):391-409. [CrossRef]
- Arigo D, Schumacher LM, Pinkasavage E, Butryn ML. Addressing barriers to physical activity among women: a feasibility study using social networking-enabled technology. Digit Health 2015 May 05;1:2055207615583564 [FREE Full text] [CrossRef] [Medline]
- Finkelstein J, Bedra M, Li X, Wood J, Ouyang P. Mobile app to reduce inactivity in sedentary overweight women. Stud Health Technol Inform 2015;216:89-92. [Medline]
- Fukuoka Y, Vittinghoff E, Hooper J. A weight loss intervention using a commercial mobile application in Latino Americans-Adelgaza Trial. Transl Behav Med 2018 Sep 08;8(5):714-723 [FREE Full text] [CrossRef] [Medline]
- Gell NM, Grover KW, Savard L, Dittus K. Outcomes of a text message, Fitbit, and coaching intervention on physical activity maintenance among cancer survivors: a randomized control pilot trial. J Cancer Surviv 2020 Feb 27;14(1):80-88. [CrossRef] [Medline]
- Gremaud AL, Carr LJ, Simmering JE, Evans NJ, Cremer JF, Segre AM, et al. Gamifying accelerometer use increases physical activity levels of sedentary office workers. J Am Heart Assoc 2018 Jul 03;7(13):e007735. [CrossRef]
- Grossman J, Arigo D, Bachman J. Meaningful weight loss in obese postmenopausal women: a pilot study of high-intensity interval training and wearable technology. Menopause 2018 Apr;25(4):465-470. [CrossRef] [Medline]
- Linke SE, Hovsepians R, Schnebly B, Godfrey K, Noble M, Strong DR, et al. The Go-VAR (Veterans Active Recovery): an adjunctive, exercise-based intervention for veterans recovering from substance use disorders. J Psychoactive Drugs 2019 Jan 17;51(1):68-77. [CrossRef] [Medline]
- Meints SM, Yang HY, Collins JE, Katz JN, Losina E. Race differences in physical activity uptake within a workplace wellness program: a comparison of black and white employees. Am J Health Promot 2019 Jul 26;33(6):886-893 [FREE Full text] [CrossRef] [Medline]
- Painter SL, Ahmed R, Hill JO, Kushner RF, Lindquist R, Brunning S, et al. What matters in weight loss? An in-depth analysis of self-monitoring. J Med Internet Res 2017 May 12;19(5):e160 [FREE Full text] [CrossRef] [Medline]
- Reed J, Estabrooks P, Pozehl B, Heelan K, Wichman C. Effectiveness of the 5A's model for changing physical activity behaviors in rural adults recruited from primary care clinics. J Phys Act Health 2019 Dec 01;16(12):1138-1146. [CrossRef] [Medline]
- Wang JB, Cadmus-Bertram LA, Natarajan L, White MM, Madanat H, Nichols JF, et al. Wearable sensor/device (Fitbit One) and SMS text-messaging prompts to increase physical activity in overweight and obese adults: a randomized controlled trial. Telemed J E Health 2015 Oct;21(10):782-792 [FREE Full text] [CrossRef] [Medline]
- Willis EA, Szabo-Reed AN, Ptomey LT, Steger FL, Honas JJ, Al-Hihi EM, et al. Distance learning strategies for weight management utilizing online social networks versus group phone conference call. Obes Sci Pract 2017 Jun 05;3(2):134-142 [FREE Full text] [CrossRef] [Medline]
- Ashe MC, Winters M, Hoppmann CA, Dawes MG, Gardiner PA, Giangregorio LM, et al. "Not just another walking program": Everyday Activity Supports You (EASY) model-a randomized pilot study for a parallel randomized controlled trial. Pilot Feasibility Stud 2015 Jan 12;1(1):4 [FREE Full text] [CrossRef] [Medline]
- Christiansen MB, Thoma LM, Master H, Voinier D, Schmitt LA, Ziegler ML, et al. Feasibility and preliminary outcomes of a physical therapist-administered physical activity intervention after total knee replacement. Arthritis Care Res (Hoboken) 2020 May 08;72(5):661-668. [CrossRef] [Medline]
- Kenfield SA, Van Blarigan EL, Ameli N, Lavaki E, Cedars B, Paciorek AT, et al. Feasibility, acceptability, and behavioral outcomes from a technology-enhanced behavioral change intervention (Prostate 8): a pilot randomized controlled trial in men with prostate cancer. Eur Urol 2019 Jun;75(6):950-958. [CrossRef] [Medline]
- Rossi A, Frechette L, Miller D, Miller E, Friel C, Van Arsdale A, et al. Acceptability and feasibility of a Fitbit physical activity monitor for endometrial cancer survivors. Gynecol Oncol 2018 Jun;149(3):470-475. [CrossRef] [Medline]
- Schmidt LI, Gabrian M, Jansen CP, Wahl HW, Sieverding M. Extending research on self-regulation of physical activity in older age: role of views on aging within an intensive ambulatory assessment scheme. J Self-regul Regul 2018;4:43-59. [CrossRef]
- Streber A, Abu-Omar K, Hentschke C, Rütten A. A multicenter controlled study for dementia prevention through physical, cognitive and social activities - GESTALT-kompakt. Clin Interv Aging 2017 Dec;12:2109-2121 [FREE Full text] [CrossRef] [Medline]
- Harkins KA, Kullgren JT, Bellamy SL, Karlawish J, Glanz K. A trial of financial and social incentives to increase older adults' walking. Am J Prev Med 2017 May;52(5):123-130. [CrossRef] [Medline]
- McMahon SK, Lewis B, Oakes JM, Wyman JF, Guan W, Rothman AJ. Assessing the effects of interpersonal and intrapersonal behavior change strategies on physical activity in older adults: a factorial experiment. Ann Behav Med 2017 Jun 10;51(3):376-390 [FREE Full text] [CrossRef] [Medline]
- Vidoni ED, Watts AS, Burns JM, Greer CS, Graves RS, Van Sciver A, et al. Feasibility of a memory clinic-based physical activity prescription program. J Alzheimer's Dis 2016 Jun 22;53(1):161-170. [CrossRef]
- Cadmus-Bertram L, Marcus BH, Patterson RE, Parker BA, Morey BL. Use of the Fitbit to measure adherence to a physical activity intervention among overweight or obese, postmenopausal women: self-monitoring trajectory during 16 weeks. JMIR Mhealth Uhealth 2015 Nov 19;3(4):e96 [FREE Full text] [CrossRef] [Medline]
- Frechette L, Miller D, Rossi A, Miller E, Van Arsdale A, Viswanathan S, et al. Acceptability and feasibility of wearable fitness technology for endometrial cancer survivors. Gynecol Oncol 2018 Jun;149:223. [CrossRef]
- Lyons EJ, Lewis ZH, Mayrsohn BG, Rowland JL. Behavior change techniques implemented in electronic lifestyle activity monitors: a systematic content analysis. J Med Internet Res 2014 Aug 15;16(8):e192 [FREE Full text] [CrossRef] [Medline]
- Chia GL, Anderson A, McLean LA. Behavior change techniques incorporated in fitness trackers: content analysis. JMIR Mhealth Uhealth 2019 Jul 23;7(7):e12768 [FREE Full text] [CrossRef] [Medline]
- Pumpera MA, Mendozaa JA, Arseniev-Koehlera A, Holma M, Waitea A, Morenoa MA. Using a Facebook group as an adjunct to a pilot ealth physical activity intervention: a mixed methods approach. In: Annual Review of Cybertherapy and Telemedicine. San Diego, California: Interactive Media Institute; 2015:97-101.
- Goodyear VA, Kerner C, Quennerstedt M. Young people’s uses of wearable healthy lifestyle technologies; surveillance, self-surveillance and resistance. Sport Educ Soc 2017 Sep 22;24(3):212-225. [CrossRef]
- Kononova A, Li L, Kamp K, Bowen M, Rikard R, Cotten S, et al. The use of wearable activity trackers among older adults: focus group study of tracker perceptions, motivators, and barriers in the maintenance stage of behavior change. JMIR Mhealth Uhealth 2019 Apr 05;7(4):e9832 [FREE Full text] [CrossRef] [Medline]
- Ledger D, McCaffrey D. Inside wearables: how the science of human behavior change offers the secret to long-term engagement. Endeavour Partners. 2014. URL: https://archives.yegii.com/asset/inside-wearableshow-science-human-behavior-change-offers-secret-long-term-engagement-1513 [accessed 2021-05-07]
- Ridgers ND, Timperio A, Brown H, Ball K, Macfarlane S, Lai SK, et al. Wearable activity tracker use among Australian adolescents: usability and acceptability study. JMIR Mhealth Uhealth 2018 Apr 11;6(4):e86 [FREE Full text] [CrossRef] [Medline]
- Finkelstein EA, Haaland BA, Bilger M, Sahasranaman A, Sloan RA, Nang EE, et al. Effectiveness of activity trackers with and without incentives to increase physical activity (TRIPPA): a randomised controlled trial. Lancet Diabetes Endocrinol 2016 Dec;4(12):983-995. [CrossRef]
- Polgreen LA, Anthony C, Carr L, Simmering JE, Evans NJ, Foster ED, et al. The effect of automated text messaging and goal setting on pedometer adherence and physical activity in patients with diabetes: a randomized controlled trial. PLoS One 2018 May 2;13(5):e0195797 [FREE Full text] [CrossRef] [Medline]
- Imboden MT, Nelson MB, Kaminsky LA, Montoye AH. Comparison of four Fitbit and Jawbone activity monitors with a research-grade ActiGraph accelerometer for estimating physical activity and energy expenditure. Br J Sports Med 2018 Jul 08;52(13):844-850. [CrossRef] [Medline]
- Gomersall SR, Ng N, Burton NW, Pavey TG, Gilson ND, Brown WJ. Estimating physical activity and sedentary behavior in a free-living context: a pragmatic comparison of consumer-based activity trackers and ActiGraph accelerometry. J Med Internet Res 2016 Sep 07;18(9):e239 [FREE Full text] [CrossRef] [Medline]
- Ringeval M, Wagner G, Denford J, Paré G, Kitsiou S. Fitbit-based interventions for healthy lifestyle outcomes: systematic review and meta-analysis. J Med Internet Res 2020 Oct 12;22(10):e23954 [FREE Full text] [CrossRef] [Medline]
- McClung HL, Ptomey LT, Shook RP, Aggarwal A, Gorczyca AM, Sazonov ES, et al. Dietary intake and physical activity assessment: current tools, techniques, and technologies for use in adult populations. Am J Prev Med 2018 Oct;55(4):93-104 [FREE Full text] [CrossRef] [Medline]
- Balbim G, Marques I, Marquez D, Patel D, Sharp L, Kitsiou S, et al. Using Fitbit as an mHealth intervention tool to promote physical activity: potential challenges and solutions. JMIR Mhealth Uhealth 2021 Mar 01;9(3):e25289 [FREE Full text] [CrossRef] [Medline]
- Ainsworth BE, Keller C, Herrmann S, Belyea M, Records K, Nagle-Williams A, et al. Physical activity and sedentary behaviors in postpartum Latinas: Madres para la Salud. Med Sci Sports Exerc 2013 Jul;45(7):1298-1306 [FREE Full text] [CrossRef] [Medline]
- Owen N, Healy G, Matthews C, Dunstan D. Too much sitting: the population health science of sedentary behavior. Exerc Sport Sci Rev 2010 Jul;38(3):105-113 [FREE Full text] [CrossRef] [Medline]
- Hamilton MT, Healy GN, Dunstan DW, Zderic TW, Owen N. Too little exercise and too much sitting: inactivity physiology and the need for new recommendations on sedentary behavior. Curr Cardiovasc Risk Rep 2008 Jul 17;2(4):292-298 [FREE Full text] [CrossRef] [Medline]
- Silfee VJ, Haughton CF, Jake-Schoffman DE, Lopez-Cepero A, May CN, Sreedhara M, et al. Objective measurement of physical activity outcomes in lifestyle interventions among adults: a systematic review. Prev Med Rep 2018 Sep;11:74-80 [FREE Full text] [CrossRef] [Medline]
- Zarin DA, Tse T, Williams RJ, Rajakannan T. Update on trial registration 11 years after the ICMJE Policy was established. N Engl J Med 2017 Jan 26;376(4):383-391. [CrossRef]
|API: application programming interface|
|MVPA: moderate-to-vigorous physical activity|
|PA: physical activity|
|RCT: randomized controlled trial|
Edited by L Buis; submitted 11.08.20; peer-reviewed by K Glanz, N Ridgers, J Alvarez Pitti; comments to author 22.10.20; revised version received 31.01.21; accepted 06.04.21; published 28.05.21Copyright
©Ruth Gaelle St Fleur, Sara Mijares St George, Rafael Leite, Marissa Kobayashi, Yaray Agosto, Danielle E Jake-Schoffman. Originally published in JMIR mHealth and uHealth (https://mhealth.jmir.org), 28.05.2021.
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