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

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.

Assessing Rheumatoid Arthritis Disease Activity With Patient-Reported Outcomes Measurement Information System Measures Using Digital Technology.

OBJECTIVE: Health information technology has enabled efficient measurement of patient-reported outcomes (PROs). The National Institutes of Health Patient-Reported Outcomes Measurement Information System (PROMIS) is becoming more widely adopted for research and routine care, and some PROMIS instruments might be substituted for lengthier, legacy PRO instruments. METHODS: Four PROMIS computer-adaptive testing (CAT) instruments (pain interference, physical function, sleep disturbance, and fatigue) and the Routine Assessment of Patient Index Data 3 (RAPID3), along with pain intensity and patient global assessment score, were administered to participants in the ArthritisPower registry. The RAPID3 was predicted using different combinations of these variables to create a new score (CAT-PROMIS RAPID3). Kappa statistics and Bland-Altman 95% limits of agreement were used to measure agreement between the observed versus predicted RAPID3. RESULTS: A total of 6,154 eligible patients contributed 11,275 observations. The mean ± SD age was 52.7 ± 10.5 years, and 93% of patients were women. The median assessment times ranged from 29 seconds (PROMIS sleep disturbance) to 116 seconds (RAPID3). As single pairwise comparisons, the PROMIS CATs examined were modestly correlated (r approximately 0.4-0.7) to one other and RAPID3. Together with the pain intensity and patient global assessment, the PROMIS instruments explained a high fraction of total variance (R2 = 0.97) of the RAPID3 score. In the model with the highest agreement (κ = 0.93) between the observed RAPID3 and the CAT-PROMIS predicted RAPID3, Bland-Altman 95% limits of agreement showed minimal residual differences and no systematic biases. CONCLUSION: There was excellent agreement between the observed RAPID3 and predicted RAPID3 scores estimated using several PROMIS instruments. The Multidimensional Health Assessment Questionnaire and patient global assessment components of RAPID3 may be unnecessary if PROMIS scores are available.

Comparison of an interactive voice response system and smartphone application in the identification of gout flares.

OBJECTIVE: To examine the feasibility, preference, and satisfaction of an interactive voice response (IVR) system versus a customized smartphone application (StudyBuddy) to capture gout flares METHODS: In this 24-week prospective, randomized, crossover, open-label pilot study, 44 gout patients were randomized to IVR vs. StudyBuddy and were crossed over to the other technology after 12 weeks. Flares were reported via weekly (and later daily) scheduled StudyBuddy or IVR queries. Feasibility was ascertained via response rate to scheduled queries. At 12 and 24 weeks, participants completed preference/satisfaction surveys. Preference and satisfaction were assessed using dichotomous or ordinal questions. Sensitivity was assessed by the frequency of flare reporting with each approach. RESULTS: Thirty-eight of 44 participants completed the study. Among completers, feasibility was similar for IVR (81%) and StudyBuddy (80%). Conversely, most (74%) preferred StudyBuddy. Measures of satisfaction (ease of use, preference over in-person clinic visits, and willingness for future use) were similar between the IVR and StudyBuddy; however, more participants deemed the StudyBuddy as convenient (95% vs. 73%, P = 0.01) and less disruptive (97% vs. 82%, P = 0.03). Although the per patient number of weeks in flare was not significantly different (mean 3.4 vs. 2.6 weeks/patient, P = 0.15), the StudyBuddy captured more of the total flare weeks (35%) than IVR (27%, P = 0.02). CONCLUSION: A smartphone application and IVR demonstrated similar feasibility but overall sensitivity to capture gout flares and participant preference were greater for the smartphone application. Participant preference for the smartphone application appeared to relate to perceptions of greater convenience and lower disruption. TRIAL REGISTRATION: NCT, NCT02855437 . Registered 4 August 2016.