BACKGROUND: There has been a rapid expansion of digital health care services, making the need for measuring and improving digital health readiness a priority. In response, our study team developed the Mobile-Centered Digital Health Readiness: Health Literacy and Equity Scale (mDiHERS) to measure digital health readiness.
OBJECTIVE: We aim to develop and validate a scale that assesses digital health readiness, encompassing literacy and equity, and to ensure the effective use of mobile-centered digital health services.
METHODS: This study was conducted from October 2021 to October 2022 to develop and validate the mDiHERS. Participants included patients with inflammatory bowel disease, which is a chronic condition requiring continuous management, and experts in medical and nursing informatics. The scale development involved a literature review, focus group interviews, and content validity evaluations. A total of 440 patients with inflammatory bowel disease were recruited for the validation phase, with 403 completing the survey. The scale\'s validity and reliability were assessed through exploratory factor analysis and Cronbach α. The scale was translated into English by translators and bilingual and native researchers, ensuring its applicability in diverse settings.
RESULTS: The mDiHERS consists of 36 items across 6 domains, with a 5-point Likert scale for responses. The validation process confirmed the scale\'s construct validity, with 4 factors explaining 65.05% of the total variance. The scale\'s reliability was established with Cronbach α values ranging from 0.84 to 0.91. The scale\'s development considered the technical proficiency necessary for engaging with health mobile apps and devices, reflecting the importance of subjective confidence and objective skills in digital health literacy.
CONCLUSIONS: The mDiHERS is a validated tool for measuring patients\' readiness and ability to use digital health services. The mDiHERS assesses user characteristics, digital accessibility, literacy, and equity to contribute to the effective use of digital health services and improve accessibility. The development and validation of the mDiHERS emphasize the importance of confidence and competence in managing health digitally. Continuous improvements are necessary to ensure that all patients can benefit from digital health care.

OBJECTIVE: The morbidity and mortality of heart failure with preserved ejection fraction (HFpEF) continue to increase with the accelerating global aging process. During the past decade, the pathophysiology, diagnostic methods, and prognostic prediction of HFpEF have been revolutionized, resulting in new and effective management strategies. Dynamic prognostic assessment facilitates systematic clinical management of patients, and the aim of this study was to investigate the risk factors for mortality in patients with HFpEF and to develop a risk prediction assessment model.
UNASSIGNED: Data for the derivation cohort were obtained from three databases, PubMed, Embase, and Cochrane. The validation cohort was obtained from the Chinese Heart Failure Center database. The β-coefficient was calculated based on the risk ratio (RR) and 95% confidence intervals (CI) corresponding to each risk factor to construct a mortality risk assessment model. A total of 30 studies were included in the meta-analysis: 22 prospective cohort studies and 8 retrospective cohort studies, including 34 196 HFpEF patients. Seven predictors of all-cause mortality in HFpEF patients were derived. Considering the need for feasibility in clinical practice, we performed subgroup and sensitivity analyses and determined the following cutoff values: age > 75 years (RR: 2.07, 95% CI: 1.83-2.35; P < 0.001), male sex (RR: 1.36, 95% CI: 1.17-1.59; P < 0.001), DM (RR: 1.23, 95% CI: 1.11-1.36; P < 0.001), anaemia (RR: 1.53, 95% CI: 1.41-1.67; P < 0.001), albumin concentration < 3.2 g/dL (RR: 1.29, 95% CI: 1.14-1.47; P < 0.001), AF (RR: 1.27, 95% CI: 1.12-1.43; P < 0.001), and NYHA class III/IV (RR: 1.63, 95% CI: 1.43-1.87; P < 0.001). The area under the receiver operating characteristic (ROC) curve (AUC) for this model was 71.3% (95% CI: 0.696-0.736), with an optimal cut-off value of 10.75. The sensitivity and specificity were 0.778 and 0.566, respectively. According to this risk score, we divided patients into three risk classes (low, moderate, and high risk), the numbers of patients who died by the end of the 1-year follow-up were 23 (1.87%), 82 (5.62%), and 382 (15.52%) in these three groups, and the 5-year mortality rates were 9.82%, 20.68%, and 43.28%, respectively.
CONCLUSIONS: This study developed an HF-DANAS scoring system for the HFpEF mortality risk containing seven predictors, providing clinicians with a simple assessment tool that can help improve clinical management.

This report describes the knowledge mobilization and translation outcomes of the Canadian-funded portion of a large, international project called the Food Biomarker Alliance (FoodBAll), which ran from 2015 to 2019. This remarkably successful project led to a large number of important findings, outputs, and impacts. In particular, FoodBAll unequivocally demonstrated that metabolomics could be used to not only discover biomarkers of food intake (BFIs), but also to measure diet in a more objective manner. FoodBAll also created standards for assessing and validating BFIs, papers and databases describing BFIs, and kits for measuring BFIs and it laid the groundwork for many global studies exploring food composition and precision nutrition.

BACKGROUND: With the progressive increase in aging populations, the use of opportunistic computed tomography (CT) scanning is increasing, which could be a valuable method for acquiring information on both muscles and bones of aging populations.
OBJECTIVE: The aim of this study was to develop and externally validate opportunistic CT-based fracture prediction models by using images of vertebral bones and paravertebral muscles.
METHODS: The models were developed based on a retrospective longitudinal cohort study of 1214 patients with abdominal CT images between 2010 and 2019. The models were externally validated in 495 patients. The primary outcome of this study was defined as the predictive accuracy for identifying vertebral fracture events within a 5-year follow-up. The image models were developed using an attention convolutional neural network-recurrent neural network model from images of the vertebral bone and paravertebral muscles.
RESULTS: The mean ages of the patients in the development and validation sets were 73 years and 68 years, and 69.1% (839/1214) and 78.8% (390/495) of them were females, respectively. The areas under the receiver operator curve (AUROCs) for predicting vertebral fractures were superior in images of the vertebral bone and paravertebral muscles than those in the bone-only images in the external validation cohort (0.827, 95% CI 0.821-0.833 vs 0.815, 95% CI 0.806-0.824, respectively; P<.001). The AUROCs of these image models were higher than those of the fracture risk assessment models (0.810 for major osteoporotic risk, 0.780 for hip fracture risk). For the clinical model using age, sex, BMI, use of steroids, smoking, possible secondary osteoporosis, type 2 diabetes mellitus, HIV, hepatitis C, and renal failure, the AUROC value in the external validation cohort was 0.749 (95% CI 0.736-0.762), which was lower than that of the image model using vertebral bones and muscles (P<.001).
CONCLUSIONS: The model using the images of the vertebral bone and paravertebral muscle showed better performance than that using the images of the bone-only or clinical variables. Opportunistic CT screening may contribute to identifying patients with a high fracture risk in the future.

BACKGROUND: Warfarin dosing in cardiac surgery patients is complicated by a heightened sensitivity to the drug, predisposing patients to adverse events. Predictive algorithms are therefore needed to guide warfarin dosing in cardiac surgery patients.
OBJECTIVE: This study aimed to develop and validate an algorithm for predicting the warfarin dose needed to attain a therapeutic international normalized ratio (INR) at the time of discharge in cardiac surgery patients.
METHODS: We abstracted variables influencing warfarin dosage from the records of 1031 encounters initiating warfarin between April 1, 2011, and November 29, 2019, at St Michael\'s Hospital in Toronto, Ontario, Canada. We compared the performance of penalized linear regression, k-nearest neighbors, random forest regression, gradient boosting, multivariate adaptive regression splines, and an ensemble model combining the predictions of the 5 regression models. We developed and validated separate models for predicting the warfarin dose required for achieving a discharge INR of 2.0-3.0 in patients undergoing all forms of cardiac surgery except mechanical mitral valve replacement and a discharge INR of 2.5-3.5 in patients receiving a mechanical mitral valve replacement. For the former, we selected 80% of encounters (n=780) who had initiated warfarin during their hospital admission and had achieved a target INR of 2.0-3.0 at the time of discharge as the training cohort. Following 10-fold cross-validation, model accuracy was evaluated in a test cohort comprised solely of cardiac surgery patients. For patients requiring a target INR of 2.5-3.5 (n=165), we used leave-p-out cross-validation (p=3 observations) to estimate model performance. For each approach, we determined the mean absolute error (MAE) and the proportion of predictions within 20% of the true warfarin dose. We retrospectively evaluated the best-performing algorithm in clinical practice by comparing the proportion of cardiovascular surgery patients discharged with a therapeutic INR before (April 2011 and July 2019) and following (September 2021 and May 2, 2022) its implementation in routine care.
RESULTS: Random forest regression was the best-performing model for patients with a target INR of 2.0-3.0, an MAE of 1.13 mg, and 39.5% of predictions of falling within 20% of the actual therapeutic discharge dose. For patients with a target INR of 2.5-3.5, the ensemble model performed best, with an MAE of 1.11 mg and 43.6% of predictions being within 20% of the actual therapeutic discharge dose. The proportion of cardiovascular surgery patients discharged with a therapeutic INR before and following implementation of these algorithms in clinical practice was 47.5% (305/641) and 61.1% (11/18), respectively.
CONCLUSIONS: Machine learning algorithms based on routinely available clinical data can help guide initial warfarin dosing in cardiac surgery patients and optimize the postsurgical anticoagulation of these patients.

BACKGROUND: Clinical trials often use digital technologies to collect data continuously outside the clinic and use the derived digital endpoints as trial endpoints. Digital endpoints are also being developed to support diagnosis, monitoring, or therapeutic interventions in clinical care. However, clinical validation stands as a significant challenge, as there are no specific guidelines orienting the validation of digital endpoints.
OBJECTIVE: This paper presents the protocol for a scoping review that aims to map the existing methods for the clinical validation of digital endpoints.
METHODS: The scoping review will comprise searches from the electronic literature databases MEDLINE (PubMed), Scopus (including conference proceedings), Embase, IEEE (Institute of Electrical and Electronics Engineers) Xplore, ACM (Association for Computing Machinery) Digital Library, CENTRAL (Cochrane Central Register of Controlled Trials), Web of Science Core Collection (including conference proceedings), and Joanna Briggs Institute Database of Systematic Reviews and Implementation Reports. We will also include various sources of gray literature with search terms related to digital endpoints. The methodology will adhere to the Joanna Briggs Institute Scoping Review and the Guidance for Conducting Systematic Scoping Reviews.
RESULTS: A search for reviews on the existing evidence related to this topic was conducted and has shown that no such review was previously undertaken. This review will provide a systematic assessment of the literature on methods for the clinical validation of digital endpoints and highlight any potential need for harmonization or reporting of methods. The results will include the methods for the clinical validation of digital endpoints according to device, digital endpoint, and clinical application goal of digital endpoints. The study started in January 2023 and is expected to end by December 2023, with results to be published in a peer-reviewed journal.
CONCLUSIONS: A scoping review of methodologies that validate digital endpoints is necessary. This review will be unique in its breadth since it will comprise digital endpoints collected from several devices and not focus on a specific disease area. The results of our work should help guide researchers in choosing validation methods, identify potential gaps in the literature, or inform the development of novel methods to optimize the clinical validation of digital endpoints. Resolving these gaps is the key to presenting evidence in a consistent way to regulators and other parties and obtaining regulatory acceptance of digital endpoints for patient benefit.
UNASSIGNED: PRR1-10.2196/47119.

BACKGROUND: The Box and Block Test (BBT) measures unilateral gross manual dexterity and is widely used in clinical settings with a wide range of populations, including older people and clients with neurological disorders.
OBJECTIVE: In this study, we present a newly developed digitized version of the BBT, called the digital BBT (dBBT). The physical design is similar to the original BBT, but the dBBT contains digital electronics that automate the test procedure, timing, and score measurement. The aim of this study is to investigate the validity and reliability of the dBBT.
METHODS: We performed measurements at 2 time points for 29 healthy participants. BBT and dBBT were used at the first measurement time point, and dBBT was used again at the second measurement time point. Concurrent validity was assessed using the correlation between BBT and dBBT, the paired t test, and the Bland-Altman analysis. Test-retest reliability and interrater reliability were examined using the interclass correlation coefficient (ICC) by repeated measures with the dBBT within an interval of 10 days.
RESULTS: Our results showed moderate concurrent validity (r=0.48, P=.008), moderate test-retest reliability (ICC 0.72, P<.001), a standard error of measurement of 3.1 blocks, and the smallest detectable change at a 95% CI of 8.5 blocks. Interrater reliability was moderate with an ICC of 0.67 (P=.02). The Bland-Altman analysis showed sufficient accuracy of the dBBT in comparison with the conventional BBT.
CONCLUSIONS: The dBBT can contribute to objectifying the measurement of gross hand dexterity without losing its important characteristics and is simple to implement.

UNASSIGNED: Case finding for low mood is essential in primary care, but it is time-consuming using current depression inventories. The Burns Depression Scale Today (BDST) is a short, simple inventory which assesses mood for today, and we aimed to validate it in this study.
UNASSIGNED: Consecutive patients with emotional distress seen in a single primary care clinic by one of the authors over 22 months were eligible for this retrospective audit (N = 160). Multiple visits (N = 421) from the same patient were included in the study. The index test was BDST, which assesses the patient\'s mood for today. The reference standard was the 9-item Patient Health Questionnaire (PHQ-9), which assesses mood over the past 2 weeks. PHQ-9 had a cut-off point of ≥10 and BDST had a cut-off point of ≥6 for a significant mood issue.
UNASSIGNED: The median age of patients was 35 years, and 63% of the cohort were women. The median BDST score was 8, indicative of moderately low mood, and the median PHQ-9 score was 15, indicative of moderately severe depression. For patients with a BDST score ≥6, the likelihood ratio of a positive test was 2.67. The sensitivity was 85% (95% confidence interval [CI]: 89%-96%) and the specificity was 68% (95% CI: 60%-76%). The area under the curve was 84% (95% CI: 80%-87%).
UNASSIGNED: This audit validates BDST against PHQ-9 and finds it an excellent case-finding tool compared to PHQ-9. This is the first validation study of BDST.

Many people with harmful addictive behaviors may not meet formal diagnostic thresholds for a disorder. A dimensional approach, by contrast, including clinical and community samples, is potentially key to early detection, prevention, and intervention. Importantly, while neurocognitive dysfunction underpins addictive behaviors, established assessment tools for neurocognitive assessment are lengthy and unengaging, difficult to administer at scale, and not suited to clinical or community needs. The BrainPark Assessment of Cognition (BrainPAC) Project sought to develop and validate an engaging and user-friendly digital assessment tool purpose-built to comprehensively assess the main consensus-driven constructs underpinning addictive behaviors.
The purpose of this study was to psychometrically validate a gamified battery of consensus-based neurocognitive tasks against standard laboratory paradigms, ascertain test-retest reliability, and determine their sensitivity to addictive behaviors (eg, alcohol use) and other risk factors (eg, trait impulsivity).
Gold standard laboratory paradigms were selected to measure key neurocognitive constructs (Balloon Analogue Risk Task [BART], Stop Signal Task [SST], Delay Discounting Task [DDT], Value-Modulated Attentional Capture [VMAC] Task, and Sequential Decision-Making Task [SDT]), as endorsed by an international panel of addiction experts; namely, response selection and inhibition, reward valuation, action selection, reward learning, expectancy and reward prediction error, habit, and compulsivity. Working with game developers, BrainPAC tasks were developed and validated in 3 successive cohorts (total N=600) and a separate test-retest cohort (N=50) via Mechanical Turk using a cross-sectional design.
BrainPAC tasks were significantly correlated with the original laboratory paradigms on most metrics (r=0.18-0.63, P<.05). With the exception of the DDT k function and VMAC total points, all other task metrics across the 5 tasks did not differ between the gamified and nongamified versions (P>.05). Out of 5 tasks, 4 demonstrated adequate to excellent test-retest reliability (intraclass correlation coefficient 0.72-0.91, P<.001; except SDT). Gamified metrics were significantly associated with addictive behaviors on behavioral inventories, though largely independent of trait-based scales known to predict addiction risk.
A purpose-built battery of digitally gamified tasks is sufficiently valid for the scalable assessment of key neurocognitive processes underpinning addictive behaviors. This validation provides evidence that a novel approach, purported to enhance task engagement, in the assessment of addiction-related neurocognition is feasible and empirically defensible. These findings have significant implications for risk detection and the successful deployment of next-generation assessment tools for substance use or misuse and other mental disorders characterized by neurocognitive anomalies related to motivation and self-regulation. Future development and validation of the BrainPAC tool should consider further enhancing convergence with established measures as well as collecting population-representative data to use clinically as normative comparisons.

Thermally-enhanced soil vapor extraction (T-SVE) remediation technology is widely used in organic-contaminated sites due to its high efficiency, short remediation period and controllable secondary contamination. However, the remediation efficiency is affected by the complex site factors, which leads to the uncertainty of the remediation process and energy waste. Thus, it is necessary to optimize T-SVE systems to accurately remediate the sites. In this work, a pilot site of reagent factory in Tianjin was taken as the research object to validate the model, and the T-SVE process parameters of a VOCs-contaminated sites were predicted by this simulation method. The simulation results showed that the Nash efficiency coefficient E of the measured and simulated temperature rise data in the study area was 0.885, and the linear correlation coefficient R of the measured and simulated concentrations of cis-1,2-dichloroethylene after remediation was 0.877, indicating that this simulation method is highly reliable. Based on this numerical simulation method, some parameters of the T-SVE process at the VOCs-contaminated site of an insulation plant in Harbin were simulated and optimized. Included a heating well spacing of 3.0 m, extraction pressure of 40 Kpa, extraction well influence radius of 4.35 m, extraction flow rate of 2.97 × 10-4 m3/s, and a theoretical number of 25 extraction wells (adjusted to 29 wells in practice), and the corresponding extraction well layout has been designed. The results can provide a technical reference for the future application of T-SVE in the remediation of organic-contaminated sites.