Symptoms in first-degree relatives of patients with rheumatoid arthritis: evaluation of cross-sectional data from the symptoms in persons at risk of rheumatoid arthritis (SPARRA) questionnaire in the PRe-clinical EValuation of Novel Targets in RA (PREVeNT-RA) Cohort.

BACKGROUND: First-degree relatives (FDRs) of people with rheumatoid arthritis (RA) have a fourfold increased risk of developing RA. The Symptoms in Persons At Risk of Rheumatoid Arthritis (SPARRA) questionnaire was developed to document symptoms in persons at risk of RA. The aims of this study were (1) to describe symptoms in a cohort of FDRs of patients with RA overall and stratified by seropositivity and elevated CRP and (2) to determine if patient characteristics were associated with symptoms suggestive of RA. METHODS: A cross-sectional study of FDRs of patients with RA, in the PREVeNT-RA study, who completed a study questionnaire, provided a blood sample measured for rheumatoid factor, anti-CCP and CRP and completed the SPARRA questionnaire. Moderate/severe symptoms and symmetrical, small and large joint pain were identified and described. Symptoms associated with both seropositivity and elevated CRP were considered suggestive of RA. Logistic regression was used to determine if symptoms suggestive of RA were associated with patient characteristics. RESULTS: Eight hundred seventy participants provided all data, 43 (5%) were seropositive and 122 (14%) had elevated CRP. The most frequently reported symptoms were sleep disturbances (20.3%) and joint pain (17.9%). Symmetrical and small joint pain were 11.3% and 12.8% higher, respectively, in those who were seropositive and 11.5% and 10.7% higher in those with elevated CRP. In the logistic regression model, seropositivity, older age and feeling depressed were associated with increased odds of small and symmetrical joint pain. CONCLUSIONS: This is the first time the SPARRA questionnaire has been applied in FDRs of patients with RA and has demonstrated that the presence of symmetrical and small joint pain in this group may be useful in identifying people at higher risk of developing RA.

Prognostic factors for musculoskeletal injury identified through medical screening and training load monitoring in professional football (soccer): a systematic review.

OBJECTIVES: To identify prognostic factors and models for spinal and lower extremity injuries in adult professional/elite football players from medical screening and training load monitoring processes. METHODS: The MEDLINE, AMED, EMBASE, CINAHL Plus, SPORTDiscus electronic bibliographic databases and the Cochrane Database of Systematic Reviews were searched from inception to July 2016. Searches were limited to original research, published in peer reviewed journals of any language. The Quality in Prognostic Studies (QUIPS) tool was used for appraisal and the modified GRADE approach was used for synthesis. Prospective and retrospective cohort study designs of spinal and lower extremity injury incidence were found from populations of adult professional/elite football players, between 16 and 40 years. Non-football or mixed sports were excluded. RESULTS: 858 manuscripts were identified. Removing duplications left 551 studies, which were screened for eligibility by title and abstract. Of these, 531 studies were not eligible and were excluded. The full text of the remaining 20 studies were obtained; a further 10 studies were excluded. 10 studies were included for appraisal and analysis, for 3344 participants. CONCLUSIONS: Due to the paucity and heterogeneity of the literature, and shortcomings in methodology and reporting, the evidence is of very low or low quality and therefore cannot be deemed robust enough to suggest conclusive prognostic factors for all lower limb musculoskeletal injury outcomes identified. No studies were identified that examined spinal injury outcomes or prognostic models.

Using Smartphones for Research Outside Clinical Settings: How Operating Systems, App Developers, and Users Determine Geolocation Data Quality in mHealth Studies.

Smartphones that collect user geolocation provid opportunities for mobile Health (mHealth). Although granularity of geolocation data may be high, data completeness depends on the device's operating system, application developer decisions, and user actions. We investigate completeness of geolocation data collected via smartphones of 5601 people that self-reported daily chronic pain symptoms on 349,293 days. On 17% of these days, hourly geolocation data is reported, but days with 0 (16%), 1 (14%) and 2 (13%) geolocations are common. Android phones collect geolocation more often than iPhones (median 17 versus 2 times a day). Factors on operating system level and individual user level influence completeness of geolocation data collected with smartphones. mHealth researchers should be aware of these factors when designing their studies. The mHealth research community should devise standards for reporting geolocation data quality, analysing systematic differences in data quality between participant groups, and methods for data imputation.