Physical activity measured using wearable activity tracking devices associated with gout flares

Nada Elmagboul, Brian W Coburn, Jeffrey Foster, Amy Mudano, Joshua Melnick, Debra Bergman, Shuo Yang, Lang Chen, Cooper Filby, Ted R Mikuls, Jeffrey R Curtis, Kenneth Saag, Nada Elmagboul, Brian W Coburn, Jeffrey Foster, Amy Mudano, Joshua Melnick, Debra Bergman, Shuo Yang, Lang Chen, Cooper Filby, Ted R Mikuls, Jeffrey R Curtis, Kenneth Saag

Abstract

Objective: To determine the feasibility and validity of using wearable activity trackers to test associations between gout flares with physical activity and sleep.

Methods: Participants with physician-diagnosed gout, hyperuricemia (≥ 6.8 mg/dl), current smartphone use, and ≥ 2 self-reported flares in the previous 6 months were enrolled. Physical activity, heart rate, and sleep data were obtained from wearable activity trackers (Fitbit Charge HR2). Daily compliance was defined by the availability of sufficiently complete activity data at least 80% of the day. Associations of weekly gout flares with sleep and activity were measured by comparing flare-related values to average sleep and steps per day. We used mixed linear models to account for repeated observations.

Results: Forty-four participants enrolled; 33 met the criteria for minimal wear time and flare reporting, with activity tracker data available for 60.5% of all total study days. Mean ± SD age was 48.8 ± 14.9 years; 85% were men; 15% were black; 88% were on allopurinol or febuxostat, and 30% reported ≥ 6 flares in the prior 6 months. Activity trackers captured 204 (38%) person-weeks with flares and 340 (62%) person-weeks without flares. Mean ± SD daily step count was significantly lower (p < 0.0001) during weeks with gout flares (5900 ± 4071) than during non-flare periods (6972 ± 5214); sleep however did not differ.

Conclusion: The pattern of wear in this study illustrates reasonable feasibility of using such devices in future arthritis research. The use of these devices to passively measure changes in physical activity patterns may provide an estimate of gout flare occurrence and duration.

Trial registration: NCT, NCT02855437 . Registered 4 August 2016.

Keywords: Gout flares; Interactive voice response system; Smartphone application.

Conflict of interest statement

T. Mikuls: research support—Ironwood/Astra Zeneca, Horizon.

K. Saag: research support—Horizon, Takeda; consulting—Horizon, Kowa, LG Chem, Takeda.

Others: none.

Figures

Fig. 1
Fig. 1
Heat map showing daily compliance with wearing the health tracker device. Compliance analysis for each participant is shown with data in each column reflecting a participant; each row is a person-day in the study. Red = compliant wear (≥ 80%) with sleep data; green = compliant wear (> 80%) without sleep data; blue = partial wear; white = not wearing

References

    1. Schumacher HR, Jr, et al. Rilonacept (Interleukin-1 Trap) in the prevention of acute gout flares during initiation of urate-lowering therapy: results of a phase II randomized, double-blind, placebo-controlled trial. Arthritis Rheumatism. 2012;64(3):876–884. doi: 10.1002/art.33412.
    1. Becker MA, et al. Febuxostat compared with allopurinol in patients with hyperuricemia and gout. N Engl J Med. 2005;353(23):2450–2461. doi: 10.1056/NEJMoa050373.
    1. Sigurdardottir V, et al. Work disability in gout: a population-based case–control study. Ann Rheum Dis. 2018;77(3):399–404. doi: 10.1136/annrheumdis-2017-212063.
    1. Khanna PP, et al. Tophi and frequent gout flares are associated with impairments to quality of life, productivity, and increased healthcare resource use: results from a cross-sectional survey. Health Qual Life Outcomes. 2012;10(1):117. doi: 10.1186/1477-7525-10-117.
    1. Singh JA. Any sleep is a dream far away: a nominal group study assessing how gout affects sleep. Rheumatology. 2018;57(11):1925–1932. doi: 10.1093/rheumatology/kex535.
    1. Gaffo AL, et al. Brief report: validation of a definition of flare in patients with established gout. Arthritis Rheumatol. 2018;70(3):462–467. doi: 10.1002/art.40381.
    1. Elmagboul, N., et al. Comparison of an interactive voice response (IVR) to smart phone app to determine patient preference for reporting gout flares. in ARTHRITIS & RHEUMATOLOGY. 2018. WILEY 111 RIVER ST, HOBOKEN 07030–5774, NJ USA.
    1. Davergne T, et al. Use of wearable activity trackers to improve physical activity behavior in patients with rheumatic and musculoskeletal diseases: a systematic review and meta‐analysis. Arthritis Care Res. 2019;71(6):758–767. doi: 10.1002/acr.23752.
    1. Jacquemin C, et al. Flares assessed weekly in patients with rheumatoid arthritis or axial spondyloarthritis and relationship with physical activity measured using a connected activity tracker: a 3-month study. RMD open. 2017;3(1):e000434. doi: 10.1136/rmdopen-2017-000434.
    1. Fitbit Reports $571 Million Q4’18 Revenue and $1.51 Billion FY’18 Revenue. 2019; Available from: . Accessed 5 Oct 2019.
    1. Fitabase - research device data and analytics. 2019; Available from: . Accessed 5 Oct 2019.
    1. Henriksen A, et al.Using fitness trackers and smartwatches to measure physical activity in research: analysis of consumer wrist-worn wearables. J Med Int Res. 2018;20(3):e110.
    1. 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;12(1):159.
    1. Schneider M, Chau L. Validation of the Fitbit Zip for monitoring physical activity among free-living adolescents. BMC Res Notes. 2016;9(1):448. doi: 10.1186/s13104-016-2253-6.
    1. Ferguson T, et al. The validity of consumer-level, activity monitors in healthy adults worn in free-living conditions: a cross-sectional study. Int J Behav Nutr Phys Act. 2015;12(1):42. doi: 10.1186/s12966-015-0201-9.
    1. Reid RE, et al. Validity and reliability of Fitbit activity monitors compared to ActiGraph GT3X+ with female adults in a free-living environment. J Sci Med Sport. 2017;20(6):578–582. doi: 10.1016/j.jsams.2016.10.015.
    1. Cadmus-Bertram L, et al. 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 and uHealth. 2015;3(4):e96. doi: 10.2196/mhealth.4229.
    1. Heale LD, et al. A wearable activity tracker intervention for promoting physical activity in adolescents with juvenile idiopathic arthritis: a pilot study. Pediatr Rheumatol. 2018;16(1):66. doi: 10.1186/s12969-018-0282-5.
    1. Paxton RJ, et al. A feasibility study for improved physical activity after total knee arthroplasty. J Aging Phys Act. 2018;26(1):7–13. doi: 10.1123/japa.2016-0268.
    1. Jerome G, et al. Assessing the impact of a novel smartphone application compared with standard follow-up on mobility of patients with knee osteoarthritis following treatment with Hylan G-F 20: a randomized controlled trial, in JMIR Mhealth Uhealth. 2017.
    1. Tully MA, et al. The validation of Fitbit Zip™ physical activity monitor as a measure of free-living physical activity. BMC Res Notes. 2014;7(1):952. doi: 10.1186/1756-0500-7-952.
    1. Case MA, et al. Accuracy of smartphone applications and wearable devices for tracking physical activity data. JAMA. 2015;313(6):625–626. doi: 10.1001/jama.2014.17841.
    1. Brage S, et al. Features of the metabolic syndrome are associated with objectively measured physical activity and fitness in Danish children: the European Youth Heart Study (EYHS) Diabetes Care. 2004;27(9):2141–2148. doi: 10.2337/diacare.27.9.2141.
    1. Cradock AL, et al. Youth recall and TriTrac accelerometer estimates of physical activity levels. Med Sci Sports Exerc. 2004;36(3):525–532. doi: 10.1249/01.MSS.0000117112.76067.D3.
    1. Pate RR, et al. Physical activity among children attending preschools. Pediatrics. 2004;114(5):1258–1263. doi: 10.1542/peds.2003-1088-L.
    1. Steele BG, et al. Monitoring daily activity during pulmonary rehabilitation using a triaxial accelerometer. J Cardiopulm Rehabil Prev. 2003;23(2):139–142. doi: 10.1097/00008483-200303000-00011.
    1. Masse LC, et al. Accelerometer data reduction: a comparison of four reduction algorithms on select outcome variables. Med Sci Sports Exerc. 2005;37(11 Suppl):S544–S554. doi: 10.1249/01.mss.0000185674.09066.8a.
    1. Gossec L, et al. Detection of flares by decrease in physical activity, collected using wearable activity trackers, in rheumatoid arthritis or axial spondyloarthritis: an application of machine-learning analyses in rheumatology. Arthritis Care Res. 2019;71(10):1336–1343. doi: 10.1002/acr.23768.
    1. Feehan LM, et al. Accuracy of Fitbit devices: systematic review and narrative syntheses of quantitative data. JMIR mHealth and uHealth. 2018;6(8):e10527.
    1. Nowell WB, et al. Digital tracking of rheumatoid arthritis longitudinally (DIGITAL) using biosensor and patient-reported outcome data: protocol for a realworld study. JMIR Res Protocols. 2019;8(9):e14665.

Source: PubMed

Sponsor e collaboratori

Condizioni mediche

Interventi farmacologici

3
Sottoscrivi