Dataset Information

Development and usability of a web-based patient-tailored tool to support adherence to urate-lowering therapy in gout.

ABSTRACT:

Background

The aim of this study is to develop and assess usability of a web-based patient-tailored tool to support adherence to urate-lowering therapy (ULT) among gout patients in a clinical setting.

Methods

The content of the tool was based on the Integrated Change (I-Change) model. This model combines various socio-cognitive theories and assumes behavioral change is a result of becoming aware of the necessity of change by integrating pre-motivational, motivational, and post-motivational factors. An expert group (five gout experts, three health services researchers, and one health behavior expert) was assembled that decided in three meetings on the tool's specific content (assessments and personalized feedback) using information from preparatory qualitative studies and literature reviews. Usability was tested by a think aloud approach and validated usability questionnaires.

Results

The I-Change Gout tool contains three consecutive sessions comprising 80 questions, 66 tailored textual feedback messages, and 40 tailored animated videos. Navigation through the sessions was determined by the patients' intention to adapt suboptimal ULT adherence. After the sessions, patients receive an overview of the personalized advices and plans to support ULT adherence. Usability testing among 20 gout patients that (ever) used ULT and seven healthcare professionals revealed an overall score for the tool of 8.4 ± 0.9 and 7.7 ± 1.0 (scale 1-10). Furthermore, participants reported a high intention to use and/or recommend the tool to others. Participants identified some issues for further improvement (e.g. redundant questions, technical issues, and text readability). If relevant, these were subsequently implemented in the I-Change Gout tool, to allow further testing among the following participants.

Conclusion

This study provides initial support for the usability by patients and healthcare professionals of the I-Change Gout tool to support ULT adherence behavior.

SUBMITTER: Te Kampe R

PROVIDER: S-EPMC8991610 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Development and usability of a web-based patient-tailored tool to support adherence to urate-lowering therapy in gout.

Te Kampe Ritch R Boonen Annelies A Jansen Tim L TL Elling Jan Mathis JM Flendrie Marcel M van Eijk-Hustings Yvonne Y Janssen Matthijs M van Durme Caroline C de Vries Hein H

BMC medical informatics and decision making 20220407 1

<h4>Background</h4>The aim of this study is to develop and assess usability of a web-based patient-tailored tool to support adherence to urate-lowering therapy (ULT) among gout patients in a clinical setting.<h4>Methods</h4>The content of the tool was based on the Integrated Change (I-Change) model. This model combines various socio-cognitive theories and assumes behavioral change is a result of becoming aware of the necessity of change by integrating pre-motivational, motivational, and post-mot ...[more]

PMID: 35392890

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Project description:BackgroundPrevious studies have noted significant variation in serum urate (sUA) levels, and it is unknown how this influences the accuracy of hyperuricemia classification based on single data points. Despite this known variability, hyperuricemic patients are often used as a control group in gout studies. Our objective was to determine the accuracy of hyperuricemia classifications based on single data points versus multiple data points given the degree of variability observed with serial measurements of sUA.MethodsData was analyzed from a cross-over clinical trial of urate-lowering therapy in young adults without a gout diagnosis. In the control phase, sUA levels used for this analysis were collected at 2-4 week intervals. Mean coefficient of variation for sUA was determined, as were rates of conversion between normouricemia (sUA ≤6.8 mg/dL) and hyperuricemia (sUA > 6.8 mg/dL).ResultsMean study participant (n = 85) age was 27.8 ± 7.0 years, with 39% female participants and 41% African-American participants. Mean sUA coefficient of variation was 8.5% ± 4.9% (1 to 23%). There was no significant difference in variation between men and women, or between participants initially normouricemic and those who were initially hyperuricemic. Among those initially normouricemic (n = 72), 21% converted to hyperuricemia during at least one subsequent measurement. The subgroup with initial sUA < 6.0 (n = 54) was much less likely to have future values in the range of hyperuricemia compared to the group with screening sUA values between 6.0-6.8 (n = 18) (7% vs 39%, p = 0.0037). Of the participants initially hyperuricemic (n = 13), 46% were later normouricemic during at least one measurement.ConclusionSingle sUA measurements were unreliable in hyperuricemia classification due to spontaneous variation. Knowing this, if a single measurement must be used in classification, it is worth noting that those with an sUA of < 6.0 mg/dL were less likely to demonstrate future hyperuricemic measurements and this could be considered a safer threshold to rule out intermittent hyperuricemia based on a single measurement point.Trial registrationData from parent study ClinicalTrials.gov Identifier: NCT02038179 .

Project description:ObjectiveGout is a common inflammatory arthritis and lipid metabolism plays a crucial role in urate and gout. Potential associations between urate and gout and lipid-lowering drugs have been revealed in observational studies. However, the effects of lipid-lowering drugs on urate and gout remain controversial. The aim of this study was to investigate the genetic association between lipid-lowering drugs and urate and gout.MethodsIn this study, two genetic proxies were employed to approximate lipid-lowering drug exposure: expression quantitative trait loci (eQTL) associated with drug-target genes and genetic variations proximal to or within genes targeted by these drugs, which are linked to low-density lipoprotein cholesterol (LDL-C) levels. The study's exposures encompassed genetic variants within drug target genes (HMGCR, PCSK9, NPC1L1), each representing distinct lipid-lowering mechanisms. Causal effects were estimated using the inverse variance weighting (IVW) method, while the Summary Data-based Mendelian Randomization (SMR) method, leveraging pooled data, was applied to compute effect estimates. These estimates were further refined through various approaches including MR-Egger, the weighted median method, simple and weighted models, and leave-one-out analyses to conduct sensitivity analyses.ResultThe analytical outcomes utilizing the IVW method indicated that inhibitors of HMGCR were correlated with an elevated risk of developing gout (IVW: OR [95%CI] = 1.25 [1.03, 1.46], p=0.0436), while PCSK9 inhibitors were linked to heightened levels of urate (IVW: OR [95%CI] = 1.06 [1.01,1.10], p=0.0167). Conversely, no significant correlation between NPC1L1 inhibitors and the levels of urate or the risk of gout was established. Furthermore, the SMR analysis failed to identify significant associations between the expression levels of the HMGCR, PCSK9, and NPC1L1 genes and the risk of gout or elevated urate levels (SMR method: all P values >0.05). Sensitivity analyses further confirmed the robustness of these results, with no significant heterogeneity or pleiotropy found.ConclusionThis study furnishes novel causal evidence supporting the potential genetic correlation between the use of lipid-lowering drugs and the incidence of gout as well as urate levels. The findings indicate that inhibitors targeting HMGCR may elevate the risk associated with the development of gout, while inhibitors targeting PCSK9 are likely to increase urate concentrations.

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Project description:BackgroundGout has an increasing global prevalence. Underutilization of urate-lowering therapy (ULT) is thought to be common, via both suboptimal dosing and poor medication adherence. The aims of this study were to determine the prevalence of self-reported gout and the key predictors of ULT use in those with gout in a representative population survey in South Australia.MethodsData were obtained from the Spring 2015 South Australian Health Omnibus Survey, a multilevel, systematic, survey in a representative population sample involving face-to-face interviews (n = 3005). This study analyzed responses from respondents aged ≥ 25 years (n = 2531) about self-reported gout, ULT use, sociodemographic factors, lifestyle factors, and comorbidities, using survey weighting. Univariate and subsequent adjusted logistic regression analyses on self-reported gout were performed. ULT use was divided into three categories (never use, prior use, and current use) and these data were analyzed using a multinomial logistic regression model.ResultsSelf-reported gout prevalence was 6.8% (95% CI 5.8, 7.9). The mean age of respondents with gout was 64 years (standard deviation 16) and 82% were male. As expected, older age, male gender, lower socioeconomic status (SES), and higher body mass index (BMI) were associated with gout, as were high alcohol consumption, current smoking, other forms of arthritis, and hypertension or hypercholesterolemia medication, after adjustment for sociodemographic variables. Two thirds of respondents with gout reported ULT use (36% current; 29% previous) with only 55% continuing treatment. Predictors of ULT use included male gender, low SES, and concomitant cholesterol-lowering therapy. Respondents with gout with a higher BMI were more likely to remain on ULT.ConclusionsDespite gout being a common, potentially disabling joint disease, only 55% of respondents with gout in this study adhered to ULT. Identification of key predictors of ULT use will provide guidance on prescribing strategy in clinical practice and on the quality of gout care in the community.

Dataset Information

Development and usability of a web-based patient-tailored tool to support adherence to urate-lowering therapy in gout.

Background

Methods

Results

Conclusion

Publications

Development and usability of a web-based patient-tailored tool to support adherence to urate-lowering therapy in gout.

Similar Datasets

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