Efficacy of a Community-Based Technology-Enabled Physical Activity Counseling Program for People With Knee Osteoarthritis: Proof-of-Concept Study

Li, Linda; Sayre, Eric C.; Xie, Hui; Falck, Ryan S.; Best, John R.; Liu-Ambrose, Teresa; Grewal, Navi; Hoens, Alison M.; Noonan, Gregory O.; Feehan, Lynne M.

doi:10.2196/jmir.8514

Cited by 52 publications

(71 citation statements)

References 47 publications

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“…As well, given the ability for Fitbit to tap into metadata from millions of users worldwide and apply advanced algorithms to better identify complex patterns of motion, it is likely that evolving software upgrades will also lead to improved measurement accuracy. Furthermore, our findings do not negate the value of using Fitbit activity trackers in the manner for which the devices were intended, which is for self-monitoring of physical activity patterns and motivating individuals to achieve their physical activity goals [ 88 - 91 ].…”

Section: Discussionmentioning

confidence: 85%

Accuracy of Fitbit Devices: Systematic Review and Narrative Syntheses of Quantitative Data

Feehan¹,

Geldman²,

Sayre³

et al. 2018

JMIR Mhealth Uhealth

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460

323

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BackgroundAlthough designed as a consumer product to help motivate individuals to be physically active, Fitbit activity trackers are becoming increasingly popular as measurement tools in physical activity and health promotion research and are also commonly used to inform health care decisions.ObjectiveThe objective of this review was to systematically evaluate and report measurement accuracy for Fitbit activity trackers in controlled and free-living settings.MethodsWe conducted electronic searches using PubMed, EMBASE, CINAHL, and SPORTDiscus databases with a supplementary Google Scholar search. We considered original research published in English comparing Fitbit versus a reference- or research-standard criterion in healthy adults and those living with any health condition or disability. We assessed risk of bias using a modification of the Consensus-Based Standards for the Selection of Health Status Measurement Instruments. We explored measurement accuracy for steps, energy expenditure, sleep, time in activity, and distance using group percentage differences as the common rubric for error comparisons. We conducted descriptive analyses for frequency of accuracy comparisons within a ±3% error in controlled and ±10% error in free-living settings and assessed for potential bias of over- or underestimation. We secondarily explored how variations in body placement, ambulation speed, or type of activity influenced accuracy.ResultsWe included 67 studies. Consistent evidence indicated that Fitbit devices were likely to meet acceptable accuracy for step count approximately half the time, with a tendency to underestimate steps in controlled testing and overestimate steps in free-living settings. Findings also suggested a greater tendency to provide accurate measures for steps during normal or self-paced walking with torso placement, during jogging with wrist placement, and during slow or very slow walking with ankle placement in adults with no mobility limitations. Consistent evidence indicated that Fitbit devices were unlikely to provide accurate measures for energy expenditure in any testing condition. Evidence from a few studies also suggested that, compared with research-grade accelerometers, Fitbit devices may provide similar measures for time in bed and time sleeping, while likely markedly overestimating time spent in higher-intensity activities and underestimating distance during faster-paced ambulation. However, further accuracy studies are warranted. Our point estimations for mean or median percentage error gave equal weighting to all accuracy comparisons, possibly misrepresenting the true point estimate for measurement bias for some of the testing conditions we examined.ConclusionsOther than for measures of steps in adults with no limitations in mobility, discretion should be used when considering the use of Fitbit devices as an outcome measurement tool in research or to inform health care decisions, as there are seemingly a limited number of situations where the device is likely to provide accurate measurement.

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Section: Discussionmentioning

confidence: 85%

Accuracy of Fitbit Devices: Systematic Review and Narrative Syntheses of Quantitative Data

Feehan¹,

Geldman²,

Sayre³

et al. 2018

JMIR Mhealth Uhealth

Self Cite

460

323

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“…Only one study included adolescents [ 93 ]. Thirteen studies included young adults (age 18-43.9 years) [ 73 , 77 , 79 , 81 - 83 , 86 , 90 , 91 , 94 , 97 , 99 , 102 ], 17 studies included middle-aged adults (age 44-64.9 years) [ 48 , 49 , 57 , 67 - 72 , 78 , 84 , 87 - 89 , 96 , 103 , 104 ], and six studies included older adults (age ≥65 years) [ 76 , 85 , 92 , 95 , 98 , 101 ].…”

Section: Resultsmentioning

confidence: 99%

“…Seven studies did not specify which model was used [ 49 , 67 , 69 , 70 , 95 , 101 , 102 ]. The remaining studies used wrist-worn devices, such a Fitbit Flex [ 57 , 71 , 73 , 77 , 86 , 89 , 91 , 93 , 103 , 104 ] and Fitbit Charge [ 72 , 78 , 99 ]. Use patterns with these devices were mentioned in 35% (13/37) of the studies.…”

Section: Resultsmentioning

confidence: 99%

“…Other studies involved usual care [ 48 , 76 , 78 , 83 , 85 , 88 , 93 , 96 - 98 ], financial incentives [ 68 , 82 ], blinded wearables [ 94 , 101 ], or no intervention at all [ 77 , 79 , 81 , 91 , 92 ]. Participants in some control groups were put on a waiting list to receive the same intervention following a delay [ 57 , 69 , 71 , 89 ]. Two studies included a control group to which overlapping intervention components were allocated [ 102 , 104 ].…”

Section: Resultsmentioning

confidence: 99%

“…This was also the case for sedentary behavior outcomes. These behaviors were mostly reported in min/day [ 68 , 89 , 93 , 103 , 104 ], while few studies reported them as prolonged sedentary 30-minute bouts (%/day) [ 100 ] or sedentary activity (<5000 steps per day, %) [ 87 ].…”

Section: Resultsmentioning

confidence: 99%

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Fitbit-Based Interventions for Healthy Lifestyle Outcomes: Systematic Review and Meta-Analysis

Ringeval¹,

Wagner²,

Denford³

et al. 2020

J Med Internet Res

145

113

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Background Unhealthy behaviors, such as physical inactivity, sedentary lifestyle, and unhealthful eating, remain highly prevalent, posing formidable challenges in efforts to improve cardiovascular health. While traditional interventions to promote healthy lifestyles are both costly and effective, wearable trackers, especially Fitbit devices, can provide a low-cost alternative that may effectively help large numbers of individuals become more physically fit and thereby maintain a good health status. Objective The objectives of this meta-analysis are (1) to assess the effectiveness of interventions that incorporate a Fitbit device for healthy lifestyle outcomes (eg, steps, moderate-to-vigorous physical activity, and weight) and (2) to identify which additional intervention components or study characteristics are the most effective at improving healthy lifestyle outcomes. Methods A systematic review was conducted, searching the following databases from 2007 to 2019: MEDLINE, EMBASE, CINAHL, and CENTRAL (Cochrane). Studies were included if (1) they were randomized controlled trials, (2) the intervention involved the use of a Fitbit device, and (3) the reported outcomes were related to healthy lifestyles. The main outcome measures were related to physical activity, sedentary behavior, and weight. All the studies were assessed for risk of bias using Cochrane criteria. A random-effects meta-analysis was conducted to estimate the treatment effect of interventions that included a Fitbit device compared with a control group. We also conducted subgroup analysis and fuzzy-set qualitative comparative analysis (fsQCA) to further disentangle the effects of intervention components. Results Our final sample comprised 41 articles reporting the results of 37 studies. For Fitbit-based interventions, we found a statistically significant increase in daily step count (mean difference [MD] 950.54, 95% CI 475.89-1425.18; P<.001) and moderate-to-vigorous physical activity (MD 6.16, 95% CI 2.80-9.51; P<.001), a significant decrease in weight (MD −1.48, 95% CI −2.81 to −0.14; P=.03), and a nonsignificant decrease in objectively assessed and self-reported sedentary behavior (MD −10.62, 95% CI −35.50 to 14.27; P=.40 and standardized MD −0.11, 95% CI −0.48 to 0.26; P=.56, respectively). In general, the included studies were at low risk for bias, except for performance bias. Subgroup analysis and fsQCA demonstrated that, in addition to the effects of the Fitbit devices, setting activity goals was the most important intervention component. Conclusions The use of Fitbit devices in interventions has the potential to promote healthy lifestyles in terms of physical activity and weight. Fitbit devices may be useful to health professionals for patient monitoring and support. Trial Registration PROSPERO International Prospective Register of Systematic Reviews CRD42019145450; https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42019145450

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Efficacy of a Physical Activity Counseling Program With Use of a Wearable Tracker in People With Inflammatory Arthritis: A Randomized Controlled Trial

Feehan

Xie

et al. 2020

Arthritis Care & Research

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Objective. To assess the efficacy of a multifaceted counseling intervention at improving physical activity participation and patient outcomes. Methods. We recruited people with rheumatoid arthritis (RA) or systemic lupus erythematosus (SLE). In weeks 1-8, the immediate group received education and counseling by a physical therapist, used a Fitbit and a web application to obtain feedback about their physical activity, and received 4 follow-up calls from the physical therapist. The delay group received the same intervention in weeks 10-17. Participants were assessed at baseline and at weeks 9, 18, and 27. The primary outcome was time spent in moderate/vigorous physical activity (MVPA; in bouts of ≥10 minutes) measured with a SenseWear device. Secondary outcomes included step count, time in sedentary behavior, pain, fatigue, mood, self-management capacity, and habitual behaviors. Results. A total of 118 participants enrolled. The adjusted mean difference in MVPA was 9.4 minutes/day (95% confidence interval [95% CI]-0.5, 19.3, P = 0.06). A significant effect was found in pain (-2.45 [95% CI-4.78,-0.13], P = 0.04) and perceived walking habit (0.54 [95% CI 0.08, 0.99], P = 0.02). The remaining secondary outcomes improved but were not statistically significant. Post hoc analysis revealed a significant effect in MVPA (14.3 minutes/ day [95% CI 2.3, 26.3]) and pain (-4.05 [95% CI-6.73,-1.36]) in participants with RA but not in those with SLE. Conclusion. Counseling by a physical therapist has the potential to improve physical activity in people with inflammatory arthritis, but further study is needed to understand the intervention effect on different diseases. We found a significant improvement in pain, suggesting that the intervention might have a positive effect on symptom management. ClinicalTrials.gov identifier: NCT02554474.

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Efficacy of a Community-Based Technology-Enabled Physical Activity Counseling Program for People With Knee Osteoarthritis: Proof-of-Concept Study

Cited by 52 publications

References 47 publications

Accuracy of Fitbit Devices: Systematic Review and Narrative Syntheses of Quantitative Data

Accuracy of Fitbit Devices: Systematic Review and Narrative Syntheses of Quantitative Data

Fitbit-Based Interventions for Healthy Lifestyle Outcomes: Systematic Review and Meta-Analysis

Efficacy of a Physical Activity Counseling Program With Use of a Wearable Tracker in People With Inflammatory Arthritis: A Randomized Controlled Trial

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