Authors' contributions JY and GS conceived of the study, participated in its design and coordination, and contributed to the drafting and editing of the manuscript. DR scoped and led participant research, prototype design and testing, and drafted the manuscript. BB scoped and contributed to participant research, prototype design and testing, and editing of the manuscript. JL led recruitment and coordination of patient and provider participants, participated in participant research and prototyping, and contributed to drafting of the manuscript. LT participated in the design and coordination of the study and helped draft the manuscript. JG contributed to research and prototyping data synthesis and drafting of the manuscript. MM contributed to the participant research and helped draft the manuscript. DECLARATIONS Ethical approval and consent to participate We obtained informed consent for all participants in this study. The UCSF institutional review board approved this study protocol. The IRB reference number is 15-18282. Consent to publish Not applicable Availability of data and materials The datasets generated and/or analyzed during the current study are not publicly available due to patient confidentiality but are available from the corresponding author on reasonable request.

Objective People with long-term conditions require serial clinical assessments. Digital patient-reported symptoms collected between visits can inform these, especially if integrated into electronic health records (EHRs) and clinical workflows. This systematic review identified and summarized EHR-integrated systems to remotely collect patient-reported symptoms and examined their anticipated and realized benefits in long-term conditions. Materials and Methods We searched Medline, Web of Science, and Embase. Inclusion criteria were symptom reporting systems in adults with long-term conditions; data integrated into the EHR; data collection outside of clinic; data used in clinical care. We synthesized data thematically. Benefits were assessed against a list of outcome indicators. We critically appraised studies using the Mixed Methods Appraisal Tool. Results We included 12 studies representing 10 systems. Seven were in oncology. Systems were technically and functionally heterogeneous, with the majority being fully integrated (data viewable in the EHR). Half of the systems enabled regular symptom tracking between visits. We identified 3 symptom report-guided clinical workflows: Consultation-only (data used during consultation, n = 5), alert-based (real-time alerts for providers, n = 4) and patient-initiated visits (n = 1). Few author-described anticipated benefits, primarily to improve communication and resultant health outcomes, were realized based on the study results, and were only supported by evidence from early-stage qualitative studies. Studies were primarily feasibility and pilot studies of acceptable quality. Discussion and Conclusions EHR-integrated remote symptom monitoring is possible, but there are few published efforts to inform development of these systems. Currently there is limited evidence that this improves care and outcomes, warranting future robust, quantitative studies of efficacy and effectiveness.

Inclusion of patient-reported outcomes is important in SLE clinical trials as they allow capture of the benefits of a proposed intervention in areas deemed pertinent by patients. We aimed to compare the measurement properties of health-related quality of life (HRQoL) measures used in adults with SLE and to evaluate their responsiveness to interventions in randomised controlled trials (RCTs). A systematic review was undertaken using full original papers in English identified from three databases: MEDLINE, EMBASE and PubMed. Studies describing the validation of HRQoL measures in English-speaking adult patients with SLE and SLE drug RCTs that used an HRQoL measure were retrieved. Twenty-five validation papers and 26 RCTs were included in the indepth review evaluating the measurement properties of 4 generic (Medical Outcomes Study Short-Form 36 (SF36), Patient Reported Outcomes Measurement Information System (PROMIS) item-bank, EuroQol-5D, and Functional Assessment of Chronic Illness Therapy-Fatigue) and 3 disease-specific (Lupus Quality of Life (LupusQoL), Lupus Patient Reported Outcomes, Lupus Impact Tracker (LIT)) instruments. All measures had good convergent and discriminant validity. PROMIS provided the strongest evidence for known-group validity and reliability among generic instruments; however, data on its responsiveness have not been published. Across measures, standardised response means were generally indicative of poor-moderate sensitivity to longitudinal change. In RCTs, clinically important improvements were reported in SF36 scores from baseline; however, between-arm differences were frequently non-significant and non-important. SF36, PROMIS, LupusQoL and LIT had the strongest evidence for acceptable measurement properties, but few measures aside from the SF36 have been incorporated into clinical trials. This review highlights the importance of incorporating a broader range of SLE-specific HRQoL measures in RCTs and warrants further research that focuses on longitudinal responsiveness of newer instruments.

Background Consistent evidence suggests a relationship between lower educational attainment and total obesity defined using body mass index (BMI); however, a comparison of the relationships between educational attainment and total obesity (BMI ≥30 kg/m 2 ) and central obesity (waist circumference (WC) > 102 cm for men and WC > 88 cm for women) has yet to be carried out. This systematic literature review (SLR) and meta-analyses aimed to understand whether i) the associations between education and obesity are different depending on the measures of obesity used (BMI and WC), and ii) to explore whether these relationships differ by gender and region. Methods Medline, Embase and Web of Science were searched to identify studies investigating the associations between education and total and central obesity among adults in the general population of countries in the Organisation for Economic Co-operation and Development (OECD). Meta-analyses and meta-regression were performed in a subset of comparable studies (n=36 studies; 724,992 participants). Results 86 eligible studies (78 cross-sectional and eight longitudinal) were identified. Among women, most studies reported an association between a lower education and total and central obesity. Among men, there was a weaker association between lower education and central than total obesity (OR central vs total obesity in men 0.79 (95% CI 0.60, 1.03)). The association between lower education and obesity was stronger in women compared with men (OR women vs men 1.66 (95% CI 1.32, 2.08)). The relationship between lower education and obesity was less strong in women from Northern than Southern Europe (OR Northern vs Southern Europe in women 0.37 (95% CI 0.27, 0.51)), but not among men. Conclusions Associations between education and obesity differ depending on whether total or central obesity is used among men, but not in women. These associations are stronger among women than men, particularly in Southern European countries.

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Objective. Applying treat-to-target strategies in the care of patients with rheumatoid arthritis (RA) is critical for improving outcomes, yet electronic health records (EHRs) have few features to facilitate this goal. We undertook this study to evaluate the effect of 3 health information technology (health-IT) initiatives on the performance of RA disease activity measures and outcomes in an academic rheumatology clinic.Methods. We implemented the 3 following initiatives designed to facilitate performance of the Clinical Disease Activity Index (CDAI): an EHR flowsheet to input scores, peer performance reports, and an EHR SmartForm including a CDAI calculator. We performed an interrupted time-series trial to assess effects on the proportion of RA visits with a documented CDAI. Mean CDAI scores before and after the last initiative were compared using t-tests. Additionally, we measured physician satisfaction with the initiatives.Results. We included data from 995 patients with 8,040 encounters between 2012 and 2017. Over this period, electronic capture of CDAI scores increased from 0% to 64%. Performance remained stable after peer reporting and the SmartForm were introduced. We observed no meaningful changes in disease activity levels. However, physician satisfaction increased after SmartForm implementation.Conclusion. Modifications to the EHR, provider culture, and clinical workflows effectively improved capture of RA disease activity scores and physician satisfaction, but parallel gains in disease activity levels were missing. This study illustrates how a series of health-IT initiatives can evolve to enable sustained changes in practice. However, capture of RA outcomes alone may not be sufficient to improve levels of disease activity without a comprehensive treat-to-target program.The content herein is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.

While the PF10a has good measurement properties and is both practical and acceptable for implementation in routine clinical practice, we also found important differences across racial/ethnic groups and those with limited English proficiency that warrant further investigation. This article is protected by copyright. All rights reserved.

Objective To explore the frequency of self-reported flares and their association with preceding symptoms collected through a smartphone app by people with rheumatoid arthritis (RA). Methods We used data from the Remote Monitoring of Rheumatoid Arthritis (REMORA) study, where patients tracked their daily symptoms and weekly flares on an app. We summarised the number of self-reported flare weeks. For each week preceding a flare question, we calculated three summary features for daily symptoms: mean, variability and slope. Mixed effects logistic regression models quantified associations between flare weeks and symptom summary features. Pain was used as an example symptom for multivariate modelling. Results Twenty patients tracked their symptoms for a median of 81 days (interquartile range 80, 82). 15/20 participants reported at least one flare week, adding up to 54 flare weeks out of 198 participant weeks in total. Univariate mixed effects models showed that higher mean and steeper upward slopes in symptom scores in the week preceding the flare increased the likelihood of flare occurrence, but the association with variability was less strong. Multivariate modelling showed that for pain, mean scores and variability were associated with higher odds of flare, with odds ratios 1.83 (95% confidence interval, 1.15–2.97) and 3.12 (95% confidence interval, 1.07–9.13), respectively. Conclusion Our study suggests that patient-reported flares are common and are associated with higher daily RA symptom scores in the preceding week. Enabling patients to collect daily symptom data on their smartphones may ultimately facilitate prediction and more timely management of imminent flares.