Accuracy of the FitBit at walking speeds and cadences relevant to clinical rehabilitation populations

BackgroundEmerging interventions that rely on and harness variability in behavior to adapt to individual performance over time may outperform interventions that prescribe static goals (e.g., 10,000 steps/day). The purpose of this factorial trial was to compare adaptive vs. static goal setting and immediate vs. delayed, non-contingent financial rewards for increasing free-living physical activity (PA).MethodsA 4-month 2 × 2 factorial randomized controlled trial tested main effects for goal setting (adaptive vs. static goals) and rewards (immediate vs. delayed) and interactions between factors to increase steps/day as measured by a Fitbit Zip. Moderate-to-vigorous PA (MVPA) minutes/day was examined as a secondary outcome.ResultsParticipants (N = 96) were mainly female (77%), aged 41 ± 9.5 years, and all were insufficiently active and overweight/obese (mean BMI = 34.1 ± 6.2). Participants across all groups increased by 2389 steps/day on average from baseline to intervention phase (p < .001). Participants receiving static goals showed a stronger increase in steps per day from baseline phase to intervention phase (2630 steps/day) than those receiving adaptive goals (2149 steps/day; difference = 482 steps/day, p = .095). Participants receiving immediate rewards showed stronger improvement (2762 step/day increase) from baseline to intervention phase than those receiving delayed rewards (2016 steps/day increase; difference = 746 steps/day, p = .009). However, the adaptive goals group showed a slower decrease in steps/day from the beginning of the intervention phase to the end of the intervention phase (i.e. less than half the rate) compared to the static goals group (−7.7 steps vs. -18.3 steps each day; difference = 10.7 steps/day, p < .001) resulting in better improvements for the adaptive goals group by study end. Rate of change over the intervention phase did not differ between reward groups. Significant goal phase x goal setting x reward interactions were observed.ConclusionsAdaptive goals outperformed static goals (i.e., 10,000 steps) over a 4-month period. Small immediate rewards outperformed larger, delayed rewards. Adaptive goals with either immediate or delayed rewards should be preferred for promoting PA.Trial RegistrationClinicalTrials.gov ID: NCT02053259 registered prospectively on January 31, 2014.

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“…[50]. The Fitbit Zip is also valid for measuring cadence (steps/min) faster than 0.7 m/s (1.57 miles per hour) [51]. …”

Section: Methodsmentioning

confidence: 99%

Adaptive goal setting and financial incentives: a 2 × 2 factorial randomized controlled trial to increase adults’ physical activity

et al. 2017

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“…In 24-hour measurements, activities other than walking activity will always be recorded 37. A study by Singh et al revealed a high accuracy at slow speeds when the Zips were placed on the lower limbs 38. This is an interesting finding for a cardiac population, even though placing a Zip on a lower limb might be difficult, as the device is not designed for this placement.…”

Section: Discussionmentioning

confidence: 99%

“…38 A complete 24-hour activity record provides a picture of a person's daily life, providing the possibility to tailor activity recommendations and integrate them into a person's daily life. Despite inaccuracies, the long-term use of these devices is expected to demonstrate an increase or decrease in walking over time 46.…”

Section: Discussionmentioning

confidence: 99%

Accuracy of a step counter during treadmill and daily life walking by healthy adults and patients with cardiac disease

et al. 2017

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BackgroundStep counters have been used to observe activity and support physical activity, but there is limited evidence on their accuracy.ObjectiveThe purpose was to investigate the step accuracy of the Fitbit Zip (Zip) in healthy adults during treadmill walking and in patients with cardiac disease while hospitalised at home.MethodsTwenty healthy adults aged 39±13.79 (mean ±SD) wore four Zips while walking on a treadmill at different speeds (1.7–6.1 km/hour), and 24 patients with cardiac disease (age 67±10.03) wore a Zip for 24 hours during hospitalisation and for 4 weeks thereafter at home. A Shimmer3 device was used as a criterion standard.ResultsAt a treadmill speed of 3.6 km/hour, the relative error (±SD) for the Zips on the upper body was −0.02±0.67 on the right side and −0.09 (0.67) on the left side. For the Zips on the waist, this was 0.08±0.71 for the right side and -0.08 (0.47) on the left side. At a treadmill speed of 3.6 km/hour and higher, the average per cent of relative error was <3%. The 24-hour test for the hospitalised patients showed a relative error of −47.15±24.11 (interclass correlation coefficient (ICC): 0.60), and for the 24-hour test at home, the relative error was −27.51±28.78 (ICC: 0.87). Thus, none of the 24-hour tests had less than the expected 20% error. In time periods of evident walking during the 24 h test, the Zip had an average per cent relative error of <3% at 3.6 km/hour and higher speeds.ConclusionsA speed of 3.6 km/hour or higher is required to expect acceptable accuracy in step measurement using a Zip, on a treadmill and in real life. Inaccuracies are directly related to slow speeds, which might be a problem for patients with cardiac disease who walk at a slow pace.

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“…Indeed, studies have found that consumer fitness trackers have greater estimation errors for older adults with health problems or slow walking speeds and some of the different results could also be due to variations in algorithms across instruments (71, 72). There is also debate over the best locations for placement on the body, with some areas more accurate than others, and recommended placements may change with age (16, 71, 73). …”

Section: Accuracy Of Fitness Technology For Measuring Physical Activitymentioning

confidence: 99%

Behavior Change with Fitness Technology in Sedentary Adults: A Review of the Evidence for Increasing Physical Activity

Sullivan

Lachman

2017

Front. Public Health

360

298

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Physical activity is closely linked with health and well-being; however, many Americans do not engage in regular exercise. Older adults and those with low socioeconomic status are especially at risk for poor health, largely due to their sedentary lifestyles. Fitness technology, including trackers and smartphone applications (apps), has become increasingly popular for measuring and encouraging physical activity in recent years. However, many questions remain regarding the effectiveness of this technology for promoting behavior change. Behavior change techniques such as goal setting, feedback, rewards, and social factors are often included in fitness technology. However, it is not clear which components are most effective and which are actually being used by consumers. We discuss additional strategies not typically included in fitness technology devices or apps that are promising for engaging inactive, vulnerable populations. These include action planning, restructuring negative attitudes, enhancing environmental conditions, and identifying other barriers to regular physical activity. We consider which strategies are most conducive to motivating behavior change among sedentary adults. Overall, fitness technology has the potential to significantly impact public health, research, and policies. We suggest ways in which app developers and behavior change experts can collaborate to develop successful apps. Advances are still needed to help inactive individuals determine how, when, where, and with whom they can increase their physical activity.

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Accuracy of the FitBit at walking speeds and cadences relevant to clinical rehabilitation populations

Cited by 44 publications

References 21 publications

Adaptive goal setting and financial incentives: a 2 × 2 factorial randomized controlled trial to increase adults’ physical activity

Adaptive goal setting and financial incentives: a 2 × 2 factorial randomized controlled trial to increase adults’ physical activity

Accuracy of a step counter during treadmill and daily life walking by healthy adults and patients with cardiac disease

Behavior Change with Fitness Technology in Sedentary Adults: A Review of the Evidence for Increasing Physical Activity

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