University of Alabama at Birmingham, 1720 2nd Avenue South, Birmingham, AL, 35294, USA.
University of Nebraska Medical Center and VA Nebraska-Western Iowa Health Care System, Omaha, NE, USA.
Arthritis Res Ther. 2020 Aug 3;22(1):181. doi: 10.1186/s13075-020-02272-2.
To determine the feasibility and validity of using wearable activity trackers to test associations between gout flares with physical activity and sleep.
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.
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.
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.
NCT, NCT02855437 . Registered 4 August 2016.
确定使用可穿戴活动追踪器来测试痛风发作与身体活动和睡眠之间的关联的可行性和有效性。
招募了经医生诊断患有痛风、高尿酸血症（≥6.8mg/dl）、目前使用智能手机且在过去 6 个月内有≥2 次自报告发作的参与者。从可穿戴活动追踪器（Fitbit Charge HR2）获取身体活动、心率和睡眠数据。日常依从性定义为至少 80%的白天有足够完整的活动数据。通过将与发作相关的值与平均每日睡眠和步数进行比较，来测量每周痛风发作与睡眠和活动之间的关联。我们使用混合线性模型来解释重复观察结果。
共有 44 名参与者入组；33 名参与者符合最小佩戴时间和发作报告标准，有活动追踪器数据的研究天数占总研究天数的 60.5%。平均年龄为 48.8±14.9 岁；85%为男性；15%为黑人；88%服用别嘌醇或非布司他，30%报告在过去 6 个月内有≥6 次发作。活动追踪器共记录了 204（38%）个人发作周和 340（62%）个人无发作周。发作期间平均每日步数（5900±4071）显著低于无发作期间（6972±5214）（p<0.0001）；而睡眠没有差异。
这项研究中的佩戴模式说明了在未来关节炎研究中使用这些设备的合理可行性。使用这些设备来被动测量身体活动模式的变化可能可以估计痛风发作的发生和持续时间。
NCT02855437，于 2016 年 8 月 4 日注册。
