BACKGROUND: Urinary Tract Infections (UTIs) are among the most severe complications for users of intermittent catheterisation (IC), with numerous risk factors contributing to their occurrence. The aim of this study was to develop a tool to assess UTI risk factors among IC users in a systematic way that considers the perspective of the individual user.

METHODS: The Design Thinking Process was used to guide the development of the content and format of the tool. The UTI Risk Factors model by Kennelly et al. was used as a basis for developing the content. Insights on the appropriate content and format were collected via the Coloplast Nurse Advisory Boards and by conducting a qualitative evidence synthesis on user perspectives and practices in relation to UTIs.

RESULTS: The literature search identified a total of 3544 articles, out of which 22 met the inclusion criteria. Additionally, three rounds of meetings were conducted with approximately 90 nurses from the Nurse Advisory Boards across Europe. The qualitative evidence synthesis showed that users describe their UTI symptoms in different terms and that personal needs and priorities impact their adherence and catheter selection. Furthermore, some users lack relevant and updated knowledge about IC and UTIs. The nurses described that correct UTI diagnosis is essential. They pointed that they would assess the user's general condition, adherence, technique, and catheter type as potential areas of risk factors and emphasised the importance of adequate support for users. The study resulted in the development of the UTI assessment tool for intermittent catheter users, which comprises three elements: a guide for healthcare professionals, a dialogue board, and a notepad. The tool starts with a confirmation of the UTI incidence, and then assesses risk factors via questions on health, adherence, technique, and catheter, and concludes with a support section.

CONCLUSIONS: The UTI assessment tool for intermittent catheter users is designed to help healthcare professionals assess UTI risk factors in a systematic way, while engaging users and taking their perspective into account. By identifying the relevant risk factors, the use of this tool has the potential to reduce the occurrence of UTIs for the individual IC user.

OBJECTIVE: To identify the core components of digital behaviour change interventions for weight loss maintenance targeting physical activity, in terms of: (i) behaviour change techniques, (ii) mechanisms of action, (iii) modes of delivery, (iv) dose and (v) tailoring/personalization. In addition, the links between these components were investigated.

METHODS: A literature search was performed in five electronic databases: PubMed, Embase, CINHAL, PsycINFO and Web of Science. Two reviewers independently screened the identified articles and extracted data related with the study characteristics and behaviour change techniques, mechanism of action, mode of delivery, dose, and tailoring, using standardized classifications whenever available (e.g. behaviour change techniques taxonomy).

RESULTS: Seventeen articles reporting 11 original studies were selected. Two studies were protocols, 9 studies presented results for weight change and all but one showed no significant differences between the intervention and control groups. Eight studies (73%) provided adequate information on behaviour change techniques. Five studies (45%) provided partial information about how the behaviour change techniques were linked to mechanisms of action, and only one study (0.9%) described these links for all the techniques. Around half of the studies reported the modes through which behaviour change techniques were delivered. Descriptions of dose were present in most studies, but with minimal information. The use of tailoring or personalization approaches was mentioned in eight studies (73%), but descriptions of what was tailored and how were minimal.

CONCLUSIONS: The compilation of information regarding intervention components was difficult due to the lack of information and systematization in reporting across papers. This is particularly true for the reporting of the links between behaviour change techniques and the other core intervention components. This information is crucial to help us understand in the context of behaviour change interventions what works or does not work, how it works and why.

OBJECTIVES: The COVID-19 pandemic has changed the working environment, how we think of it and how it stands to develop into the future. Knowledge about how people have continued to work on-site and adjusted to working from home during the COVID-19 lockdown will be vital for planning work arrangements in the post-pandemic period. Our primary objective was to investigate experiences of working from home or having colleagues working from home during a late stage of the COVID-19 lockdown among researchers and healthcare providers in a hospital research setting. Second, we aimed to investigate researchers' productivity through changes in various proxy measures during lockdown as compared with pre-lockdown.

DESIGN: Mixed-method participatory Group Concept Mapping (GCM).

SETTING AND PARTICIPANTS: GCM, based on a mixed-method participatory approach, was applied involving researchers' and healthcare providers' online sorting and rating experiences working from home during the COVID-19 pandemic. At a face-to-face meeting, participants achieved consensus on the number and labelling of domains-the basis for developing a conceptual model.

RESULTS: Through the GCM approach, 47 participants generated 125 unique statements of experiences related to working from home, which were organised into seven clusters. Using these clusters, we developed a conceptual model that illustrated the pros and cons of working from home.

CONCLUSION: The future work setting, the role of the office and the overall work environment need to respond to workers' increased wish for flexible work arrangements and co-decision.

OBJECTIVE: To develop and validate Clinical Diversity In Meta-analyses (CDIM), a new tool for assessing clinical diversity between trials in meta-analyses of interventions.

STUDY DESIGN AND SETTING: The development of CDIM was based on consensus work informed by empirical literature and expertise. We drafted the CDIM tool, refined it, and validated CDIM for interrater scale reliability and agreement in three groups.

RESULTS: CDIM measures clinical diversity on a scale that includes four domains with 11 items overall: setting (time of conduct/country development status/units type); population (age; sex; patient inclusion criteria/baseline disease severity, comorbidities); interventions (intervention intensity/strength/duration of intervention; timing; control intervention; cointerventions);and outcome (definition of outcome; timing of outcome assessment). The CDIM is completed in two steps: first two authors independently assess clinical diversity in the four domains. Second, after agreeing upon scores of individual items a consensus score is achieved. Interrater scale reliability and agreement ranged from moderate to almost perfect depending on the type of raters.

CONCLUSION: CDIM is the first tool developed for assessing clinical diversity in meta-analyses of interventions. We found CDIM to be a reliable tool for assessing clinical diversity among trials in meta-analysis.

OBJECTIVE: The aim of this study was to investigate the linear and nonlinear dose-response associations of animal-based dietary protein intake and risk of developing rheumatoid arthritis (RA).

METHODS: A systematic search of MEDLINE, Scopus and Embase was conducted up to October 2020. Observational studies that report risk estimates of RA for animal-based protein consumption were included. We calculated pooled relative risks (RRs) by using a random-effects model. Linear and non-linear dose-response analyses were performed to examine the dose-response relations between animal-based protein consumption and RA.

RESULTS: Seven cohort studies (n = 457,554) with 3545 incident cases and six case-control studies with 3994 cases and 5252 controls were identified. Highest compared with the lowest category of fish consumption was inversely associated with risk of RA (RR: 0.89; 95% CI, 0.80 to 0.99; I2 = 0%, n = 10). Also, a 100 g/day increment in fish intake was associated with a 15% decreased risk of RA. Dose-response analysis showed a modest U-shaped association between fish consumption and incidence of RA, with the lowest risk at a fish intake of 20-30 g/day (Pnon-linearity = 0.04). We found no significant association between consumption of red meat, poultry or dairy and the risk of RA.

CONCLUSION: The present study revealed a significant reverse association between fish consumption and risk of RA. While we observed no association between red meat, dairy or poultry consumption and risk of RA. Further well-designed prospective studies are needed to support our findings.

BACKGROUND: Many weight loss programs show short-term effectiveness, but subsequent weight loss maintenance is difficult to achieve. Digital technologies offer a promising means of delivering behavior change approaches at low costs and on a wide scale. The Navigating to a Healthy Weight (NoHoW) project, which was funded by the European Union's Horizon 2020 research and innovation program, aimed to develop, test, and evaluate a digital toolkit designed to promote successful long-term weight management. The toolkit was tested in an 18-month, large-scale, international, 2×2 factorial (motivation and self-regulation vs emotion regulation) randomized controlled trial that was conducted on adults with overweight or obesity who lost ≥5% of their body weight in the preceding 12 months before enrollment into the intervention.

OBJECTIVE: This paper aims to describe the development of the NoHoW Toolkit, focusing on the logic models, content, and specifications, as well as the results from user testing.

METHODS: The toolkit was developed by using a systematic approach, which included the development of the theory-based logic models, the selection of behavior change techniques, the translation of these techniques into a web-based app (NoHoW Toolkit components), technical development, and the user evaluation and refinement of the toolkit.

RESULTS: The toolkit included a set of web-based tools and inputs from digital tracking devices (smart scales and activity trackers) and modules that targeted weight, physical activity, and dietary behaviors. The final toolkit comprised 34 sessions that were distributed through 15 modules and provided active content over a 4-month period. The motivation and self-regulation arm consisted of 8 modules (17 sessions), the emotion regulation arm was presented with 7 modules (17 sessions), and the combined arm received the full toolkit (15 modules; 34 sessions). The sessions included a range of implementations, such as videos, testimonies, and questionnaires. Furthermore, the toolkit contained 5 specific data tiles for monitoring weight, steps, healthy eating, mood, and sleep.

CONCLUSIONS: A systematic approach to the development of digital solutions based on theory, evidence, and user testing may significantly contribute to the advancement of the science of behavior change and improve current solutions for sustained weight management. Testing the toolkit by using a 2×2 design provided a unique opportunity to examine the effect of motivation and self-regulation and emotion regulation separately, as well as the effect of their interaction in weight loss maintenance.

OBJECTIVE: Numerous patient-reported outcome measures (PROM) exist for the measurement of physical function for psoriatic arthritis (PsA), but only a few are validated comprehensively. The objective of this project was to prioritize PROM for measuring physical function for potential incorporation into a standardized outcome measurement set for PsA.

METHODS: A working group of 13 members including 2 patient research partners was formed. PROM measuring physical function in PsA were identified through a systematic literature review and recommendations by the working group. The rationale for inclusion and exclusion from the original list of existing PROM was thoroughly discussed and 2 rounds of Delphi exercises were conducted to achieve consensus.

RESULTS: Twelve PROM were reviewed and discussed. Six PROM were prioritized: Health Assessment Questionnaire (HAQ) and 4 modifications (HAQ-Disability Index, HAQ-Spondyloarthritis, modified HAQ, multidimensional HAQ), Medical Outcomes Study 36-item Short Form survey physical functioning domain, and the Patient-Reported Outcomes Measurement Information System (PROMIS) physical functioning module.

CONCLUSION: Through discussion and Delphi exercises, we achieved consensus to prioritize 6 physical function PROM for PsA. These 6 PROM will undergo further appraisal using the Outcome Measures in Rheumatology (OMERACT) Filter 2.1.

Non-negative matrix factorization is a relatively new method of matrix decomposition which factors an m×n data matrix X into an m×k matrix W and a k×n matrix H, so that X≈W×H. Importantly, all values in X, W, and H are constrained to be non-negative. NMF can be used for dimensionality reduction, since the k columns of W can be considered components into which X has been decomposed. The question arises: how does one choose k? In this paper, we first assess methods for estimating k in the context of NMF in synthetic data. Second, we examine the effect of normalization on this estimate's accuracy in empirical data. In synthetic data with orthogonal underlying components, methods based on PCA and Brunet's Cophenetic Correlation Coefficient achieved the highest accuracy. When evaluated on a well-known real dataset, normalization had an unpredictable effect on the estimate. For any given normalization method, the methods for estimating k gave widely varying results. We conclude that when estimating k, it is best not to apply normalization. If underlying components are known to be orthogonal, then Velicer's MAP or Minka's Laplace-PCA method might be best. However, when orthogonality of the underlying components is unknown, none of the methods seemed preferable.