@Article{info:doi/10.2196/67981,
author="Sebo, Paul
and Tudrej, Benoit
and Bernard, Augustin
and Delaunay, Bruno
and Dupuy, Alexandra
and Malavergne, Claire
and Maisonneuve, Hubert",
title="Increasing Participation and Completion Rates in Questionnaire Surveys of Primary Care Patients: Cluster-Randomized Study",
journal="Interact J Med Res",
year="2025",
month="Feb",
day="25",
volume="14",
pages="e67981",
keywords="completion rate",
keywords="missing data",
keywords="mixed mode",
keywords="web-based",
keywords="participation rate",
keywords="primary care",
keywords="questionnaire",
keywords="QR code",
keywords="tablet",
keywords="survey",
keywords="primary care patients",
keywords="randomized study",
abstract="Background: Participation and completion rates in questionnaire-based surveys are often low. Objective: This study aims to assess participation and completion rates for a survey using paper and mixed mode questionnaires with patients recruited by research assistants in primary care waiting rooms. Methods: This cluster-randomized study, conducted in 2023 in France, involved 974 patients from 39 practices randomized into 4 groups: ``paper with incentive'' (n=251), ``paper without incentive'' (n=368), ``mixed mode with tablet'' (n=187), and ``mixed mode with QR code'' (n=168). Analyses compared the combined paper group with the 2 mixed mode groups and the ``paper with incentive'' and ``paper without incentive'' groups. Logistic regressions were used to analyze participation and completion rates. Results: Of the 974 patients recruited, 822 (women: 536/821, 65.3\%; median age 52, IQR 37-68 years) agreed to participate (participation rate=84.4\%), with no significant differences between groups. Overall, 806 patients (98.1\%) answered all 48 questions. Completion rates were highest in the combined paper group (99.8\%) compared to mixed mode groups (96.8\% for paper or tablet, 93.3\% for paper or QR code; P<.001). There was no significant difference in completion rates between the ``paper with incentive'' and ``paper without incentive'' groups (100\% vs 99.7\%). Conclusions: Recruiting patients in waiting rooms with research assistants resulted in high participation and completion rates across all groups. Mixed mode options did not enhance participation or completion rates but may offer logistical advantages. Future research should explore incentives and mixed-mode strategies in diverse settings. ",
doi="10.2196/67981",
url="https://www.i-jmr.org/2025/1/e67981"
}


@Article{info:doi/10.2196/54609,
author="Kumar, Naresh
and Hui, Jian Si
and Lee, Renick
and Athia, Sahil
and Tan, Hao Joel Yong
and Tan, Hao Jonathan Jiong",
title="Evaluation of the Feasibility of Transfusing Leukocyte Depletion Filter--Processed Intraoperative Cell Salvage Blood in Metastatic Spine Tumor Surgery: Protocol for a Non--Randomized Study",
journal="JMIR Res Protoc",
year="2025",
month="Jan",
day="17",
volume="14",
pages="e54609",
keywords="blood transfusion",
keywords="autologous blood transfusion",
keywords="operative blood salvage",
keywords="leukocyte reduction filtration",
keywords="intraoperative blood cell salvage",
keywords="extramedullary spinal cord compression",
keywords="metastases",
keywords="tumors",
keywords="leukocytes",
abstract="Background: Metastatic spine tumor surgery (MSTS) is often complex and extensive leading to significant blood loss. Allogeneic blood transfusion (ABT) is the mainstay of blood replenishment but with immune-mediated postoperative complications. Alternative blood management techniques (salvaged blood transfusion [SBT]) allow us to overcome such complications. Despite widespread use of intraoperative cell salvage (IOCS) in oncological and nononcological surgical procedures, surgeons remain reluctant to use IOCS in MSTS. Objective: This study aims to analyze safety of IOCS-leukocyte depletion filter (LDF)--processed blood transfusion for patients undergoing MSTS by assessing clinical outcomes---disease progression: tumor progression and overall survival. This study evaluates whether reinfusion of IOCS-LDF--processed blood reduces ABT rates in patients undergoing MSTS by sorting patients undergoing MSTS who require ABT into patients who consent to receive or not receive SBT. Methods: We aim to recruit a minimum of 90 patients---30 patients for SBT, 30 patients for ABT, and 30 patients with no blood transfusion. SBT and ABT form the 2 experimental arms, whereas no blood transfusion forms the control cohort. Available patient data will be reviewed to determine tumor burden secondary to metastasis and postoperative survival and disease progression, improvement in pain, and neurological and ambulatory status. Data collected will be studied postoperatively at 3, 6, 12, 24, 36, and 48 months or until demise, whichever occurs first. Outcomes of the experimental groups will be compared with those of the control group. Outcomes will be analyzed using 1-way ANOVA and Fisher exact test. The Kaplan-Meier curve and a log-rank test will be used to study overall survival. A multivariate and competing risk analysis will be used to study the association between blood transfusion type and tumor progression. All statistical analyses will be done using Stata Special Edition 14.0 (StataCorp LP). Results: This is the largest clinical study on use of IOCS in MSTS from various primary malignancies to date. It will provide significant clinical evidence regarding the safety and applicability of IOCS in MSTS. It will help reduce use of ABT, improving overall blood management of patients undergoing MSTS. A limitation of this study is that not all patients undergoing MSTS will survive for the follow-up period (4 years), theoretically leading to underreporting of disease progression. Study commenced in 2016 and patient recruitment continued till 2019. As of September 2019, we have collected operative data on 140 patients. However, the 2-year outcomes of about 40.0\% (56/140) of patients are in the process of collection. The study is aimed to be published in the years 2023-2024. Conclusions: Results will be disseminated via peer-reviewed publications, paving the way for future studies. International Registered Report Identifier (IRRID): DERR1-10.2196/54609 ",
doi="10.2196/54609",
url="https://www.researchprotocols.org/2025/1/e54609"
}


@Article{info:doi/10.2196/60022,
author="Atchison, J. Christina
and Gilby, Nicholas
and Pantelidou, Galini
and Clemens, Sam
and Pickering, Kevin
and Chadeau-Hyam, Marc
and Ashby, Deborah
and Barclay, S. Wendy
and Cooke, S. Graham
and Darzi, Ara
and Riley, Steven
and Donnelly, A. Christl
and Ward, Helen
and Elliott, Paul",
title="Strategies to Increase Response Rate and Reduce Nonresponse Bias in Population Health Research: Analysis of a Series of Randomized Controlled Experiments during a Large COVID-19 Study",
journal="JMIR Public Health Surveill",
year="2025",
month="Jan",
day="9",
volume="11",
pages="e60022",
keywords="study recruitment",
keywords="response rate",
keywords="population-based research",
keywords="COVID-19",
keywords="SARS-CoV-2",
keywords="web-based questionnaires",
abstract="Background: High response rates are needed in population-based studies, as nonresponse reduces effective sample size and bias affects accuracy and decreases the generalizability of the study findings. Objective: We tested different strategies to improve response rate and reduce nonresponse bias in a national population--based COVID-19 surveillance program in England, United Kingdom. Methods: Over 19 rounds, a random sample of individuals aged 5 years and older from the general population in England were invited by mail to complete a web-based questionnaire and return a swab for SARS-CoV-2 testing. We carried out several nested randomized controlled experiments to measure the impact on response rates of different interventions, including (1) variations in invitation and reminder letters and SMS text messages and (2) the offer of a conditional monetary incentive to return a swab, reporting absolute changes in response and relative response rate (95\% CIs). Results: Monetary incentives increased the response rate (completed swabs returned as a proportion of the number of individuals invited) across all age groups, sex at birth, and area deprivation with the biggest increase among the lowest responders, namely teenagers and young adults and those living in more deprived areas. With no monetary incentive, the response rate was 3.4\% in participants aged 18?22 years, increasing to 8.1\% with a {\textsterling}10 (US \$12.5) incentive, 11.9\% with {\textsterling}20 (US \$25.0), and 18.2\% with {\textsterling}30 (US \$37.5) (relative response rate 2.4 [95\% CI 2.0-2.9], 3.5 [95\% CI 3.0-4.2], and 5.4 [95\% CI 4.4-6.7], respectively). Nonmonetary strategies had a modest, if any, impact on response rate. The largest effect was observed for sending an additional swab reminder (SMS text message or email). For example, those receiving an additional SMS text message were more likely to return a completed swab compared to those receiving the standard email-SMS approach, 73.3\% versus 70.2\%: percentage difference 3.1\% (95\% CI 2.2\%-4.0\%). Conclusions: Conditional monetary incentives improved response rates to a web-based survey, which required the return of a swab test, particularly for younger age groups. Used in a selective way, incentives may be an effective strategy for improving sample response and representativeness in population-based studies. ",
doi="10.2196/60022",
url="https://publichealth.jmir.org/2025/1/e60022"
}


@Article{info:doi/10.2196/60231,
author="Silvey, Scott
and Liu, Jinze",
title="Sample Size Requirements for Popular Classification Algorithms in Tabular Clinical Data: Empirical Study",
journal="J Med Internet Res",
year="2024",
month="Dec",
day="17",
volume="26",
pages="e60231",
keywords="medical informatics",
keywords="machine learning",
keywords="sample size",
keywords="research design",
keywords="decision trees",
keywords="classification algorithm",
keywords="clinical research",
keywords="learning-curve analysis",
keywords="analysis",
keywords="analyses",
keywords="guidelines",
keywords="ML",
keywords="decision making",
keywords="algorithm",
keywords="curve analysis",
keywords="dataset",
abstract="Background: The performance of a classification algorithm eventually reaches a point of diminishing returns, where the additional sample added does not improve the results. Thus, there is a need to determine an optimal sample size that maximizes performance while accounting for computational burden or budgetary concerns. Objective: This study aimed to determine optimal sample sizes and the relationships between sample size and dataset-level characteristics over a variety of binary classification algorithms. Methods: A total of 16 large open-source datasets were collected, each containing a binary clinical outcome. Furthermore, 4 machine learning algorithms were assessed: XGBoost (XGB), random forest (RF), logistic regression (LR), and neural networks (NNs). For each dataset, the cross-validated area under the curve (AUC) was calculated at increasing sample sizes, and learning curves were fit. Sample sizes needed to reach the observed full--dataset AUC minus 2 points (0.02) were calculated from the fitted learning curves and compared across the datasets and algorithms. Dataset--level characteristics, minority class proportion, full--dataset AUC, number of features, type of features, and degree of nonlinearity were examined. Negative binomial regression models were used to quantify relationships between these characteristics and expected sample sizes within each algorithm. A total of 4 multivariable models were constructed, which selected the best-fitting combination of dataset--level characteristics. Results: Among the 16 datasets (full-dataset sample sizes ranging from 70,000-1,000,000), median sample sizes were 9960 (XGB), 3404 (RF), 696 (LR), and 12,298 (NN) to reach AUC stability. For all 4 algorithms, more balanced classes (multiplier: 0.93-0.96 for a 1\% increase in minority class proportion) were associated with decreased sample size. Other characteristics varied in importance across algorithms---in general, more features, weaker features, and more complex relationships between the predictors and the response increased expected sample sizes. In multivariable analysis, the top selected predictors were minority class proportion among all 4 algorithms assessed, full--dataset AUC (XGB, RF, and NN), and dataset nonlinearity (XGB, RF, and NN). For LR, the top predictors were minority class proportion, percentage of strong linear features, and number of features. Final multivariable sample size models had high goodness-of-fit, with dataset--level predictors explaining a majority (66.5\%-84.5\%) of the total deviance in the data among all 4 models. Conclusions: The sample sizes needed to reach AUC stability among 4 popular classification algorithms vary by dataset and method and are associated with dataset--level characteristics that can be influenced or estimated before the start of a research study. ",
doi="10.2196/60231",
url="https://www.jmir.org/2024/1/e60231"
}


@Article{info:doi/10.2196/53622,
author="Camirand Lemyre, F{\'e}lix
and L{\'e}vesque, Simon
and Domingue, Marie-Pier
and Herrmann, Klaus
and Ethier, Jean-Fran{\c{c}}ois",
title="Distributed Statistical Analyses: A Scoping Review and Examples of Operational Frameworks Adapted to Health Analytics",
journal="JMIR Med Inform",
year="2024",
month="Nov",
day="14",
volume="12",
pages="e53622",
keywords="distributed algorithms",
keywords="generalized linear models",
keywords="horizontally partitioned data",
keywords="GLMs",
keywords="learning health systems",
keywords="distributed analysis",
keywords="federated analysis",
keywords="data science",
keywords="data custodians",
keywords="algorithms",
keywords="statistics",
keywords="synthesis",
keywords="review methods",
keywords="searches",
keywords="scoping",
abstract="Background: Data from multiple organizations are crucial for advancing learning health systems. However, ethical, legal, and social concerns may restrict the use of standard statistical methods that rely on pooling data. Although distributed algorithms offer alternatives, they may not always be suitable for health frameworks. Objective: This study aims to support researchers and data custodians in three ways: (1) providing a concise overview of the literature on statistical inference methods for horizontally partitioned data, (2) describing the methods applicable to generalized linear models (GLMs) and assessing their underlying distributional assumptions, and (3) adapting existing methods to make them fully usable in health settings. Methods: A scoping review methodology was used for the literature mapping, from which methods presenting a methodological framework for GLM analyses with horizontally partitioned data were identified and assessed from the perspective of applicability in health settings. Statistical theory was used to adapt methods and derive the properties of the resulting estimators. Results: From the review, 41 articles were selected and 6 approaches were extracted to conduct standard GLM-based statistical analysis. However, these approaches assumed evenly and identically distributed data across nodes. Consequently, statistical procedures were derived to accommodate uneven node sample sizes and heterogeneous data distributions across nodes. Workflows and detailed algorithms were developed to highlight information sharing requirements and operational complexity. Conclusions: This study contributes to the field of health analytics by providing an overview of the methods that can be used with horizontally partitioned data by adapting these methods to the context of heterogeneous health data and clarifying the workflows and quantities exchanged by the methods discussed. Further analysis of the confidentiality preserved by these methods is needed to fully understand the risk associated with the sharing of summary statistics. ",
doi="10.2196/53622",
url="https://medinform.jmir.org/2024/1/e53622"
}


@Article{info:doi/10.2196/54841,
author="Calderon Ramirez, Lucrecia Claudia
and Farmer, Yanick
and Downar, James
and Frolic, Andrea
and Opatrny, Lucie
and Poirier, Diane
and Bravo, Gina
and L'Esp{\'e}rance, Audrey
and Gaucher, Nathalie
and Payot, Antoine
and Dahine, Joseph
and Tanuseputro, Peter
and Rousseau, Louis-Martin
and Dumez, Vincent
and Desc{\^o}teaux, Annie
and Dallaire, Clara
and Laporte, Karell
and Bouthillier, Marie-Eve",
title="Assessing the Quality of an Online Democratic Deliberation on COVID-19 Pandemic Triage Protocols for Access to Critical Care in an Extreme Pandemic Context: Mixed Methods Study",
journal="J Particip Med",
year="2024",
month="Nov",
day="11",
volume="16",
pages="e54841",
keywords="quality assessment",
keywords="online democratic deliberation",
keywords="COVID-19 triage or prioritization",
keywords="critical care",
keywords="clinical ethics",
abstract="Background: Online democratic deliberation (ODD) may foster public engagement in new health strategies by providing opportunities for knowledge exchange between experts, policy makers, and the public. It can favor decision-making by generating new points of view and solutions to existing problems. Deliberation experts recommend gathering feedback from participants to optimize future implementation. However, this online modality has not been frequently evaluated. Objective: This study aims to (1) assess the quality of an ODD held in Quebec and Ontario, Canada, on the topic of COVID-19 triage protocols for access to critical care in an extreme pandemic context and (2) determine its transformative aspect according to the perceptions of participants. Methods: We conducted a simultaneous ODD in Quebec and Ontario on May 28 and June 4, 2022, with a diversified target audience not working in the health care system. We used a thematic analysis for the transcripts of the deliberation and the written comments of the participants related to the quality of the process. Participants responded to a postdeliberation questionnaire to assess the quality of the ODD and identify changes in their perspectives on COVID-19 pandemic triage protocols after the deliberation exercise. Descriptive statistics were used. An index was calculated to determine equality of participation. Results: The ODD involved 47 diverse participants from the public (n=20, 43\% from Quebec and n=27, 57\% from Ontario). Five themes emerged: (1) process appreciation, (2) learning experience, (3) reflecting on the common good, (4) technological aspects, and (5) transformative aspects. A total of 46 participants responded to the questionnaire. Participants considered the quality of the ODD satisfactory in terms of process, information shared, reasoning, and videoconferencing. A total of 4 (80\%) of 5 participants reported at least 1 change of perspective on some of the criteria and values discussed. Most participants reported that the online modality was accessible and user-friendly. We found low polarization when calculating equal participation. Improvements identified were measures to replace participants when unable to connect and optimization of time during discussions. Conclusions: Overall, the participants perceived the quality of ODD as satisfactory. Some participants self-reported a change of opinion after deliberation. The online modality may be an acceptable alternative for democratic deliberation but with some organizational adaptations. ",
doi="10.2196/54841",
url="https://jopm.jmir.org/2024/1/e54841"
}


@Article{info:doi/10.2196/56343,
author="Mollalo, Abolfazl
and Hamidi, Bashir
and Lenert, A. Leslie
and Alekseyenko, V. Alexander",
title="Application of Spatial Analysis on Electronic Health Records to Characterize Patient Phenotypes: Systematic Review",
journal="JMIR Med Inform",
year="2024",
month="Oct",
day="15",
volume="12",
pages="e56343",
keywords="clinical phenotypes",
keywords="electronic health records",
keywords="geocoding",
keywords="geographic information systems",
keywords="patient phenotypes",
keywords="spatial analysis",
abstract="Background: Electronic health records (EHRs) commonly contain patient addresses that provide valuable data for geocoding and spatial analysis, enabling more comprehensive descriptions of individual patients for clinical purposes. Despite the widespread use of EHRs in clinical decision support and interventions, no systematic review has examined the extent to which spatial analysis is used to characterize patient phenotypes. Objective: This study reviews advanced spatial analyses that used individual-level health data from EHRs within the United States to characterize patient phenotypes. Methods: We systematically evaluated English-language, peer-reviewed studies from the PubMed/MEDLINE, Scopus, Web of Science, and Google Scholar databases from inception to August 20, 2023, without imposing constraints on study design or specific health domains. Results: A substantial proportion of studies (>85\%) were limited to geocoding or basic mapping without implementing advanced spatial statistical analysis, leaving only 49 studies that met the eligibility criteria. These studies used diverse spatial methods, with a predominant focus on clustering techniques, while spatiotemporal analysis (frequentist and Bayesian) and modeling were less common. A noteworthy surge (n=42, 86\%) in publications was observed after 2017. The publications investigated a variety of adult and pediatric clinical areas, including infectious disease, endocrinology, and cardiology, using phenotypes defined over a range of data domains such as demographics, diagnoses, and visits. The primary health outcomes investigated were asthma, hypertension, and diabetes. Notably, patient phenotypes involving genomics, imaging, and notes were limited. Conclusions: This review underscores the growing interest in spatial analysis of EHR-derived data and highlights knowledge gaps in clinical health, phenotype domains, and spatial methodologies. We suggest that future research should focus on addressing these gaps and harnessing spatial analysis to enhance individual patient contexts and clinical decision support. ",
doi="10.2196/56343",
url="https://medinform.jmir.org/2024/1/e56343"
}


@Article{info:doi/10.2196/54934,
author="Gonz{\'a}lez-Castro, Ana
and Leir{\'o}s-Rodr{\'i}guez, Raquel
and Prada-Garc{\'i}a, Camino
and Ben{\'i}tez-Andrades, Alberto Jos{\'e}",
title="The Applications of Artificial Intelligence for Assessing Fall Risk: Systematic Review",
journal="J Med Internet Res",
year="2024",
month="Apr",
day="29",
volume="26",
pages="e54934",
keywords="machine learning",
keywords="accidental falls",
keywords="public health",
keywords="patient care",
keywords="artificial intelligence",
keywords="AI",
keywords="fall risk",
abstract="Background: Falls and their consequences are a serious public health problem worldwide. Each year, 37.3 million falls requiring medical attention occur. Therefore, the analysis of fall risk is of great importance for prevention. Artificial intelligence (AI) represents an innovative tool for creating predictive statistical models of fall risk through data analysis. Objective: The aim of this review was to analyze the available evidence on the applications of AI in the analysis of data related to postural control and fall risk. Methods: A literature search was conducted in 6 databases with the following inclusion criteria: the articles had to be published within the last 5 years (from 2018 to 2024), they had to apply some method of AI, AI analyses had to be applied to data from samples consisting of humans, and the analyzed sample had to consist of individuals with independent walking with or without the assistance of external orthopedic devices. Results: We obtained a total of 3858 articles, of which 22 were finally selected. Data extraction for subsequent analysis varied in the different studies: 82\% (18/22) of them extracted data through tests or functional assessments, and the remaining 18\% (4/22) of them extracted through existing medical records. Different AI techniques were used throughout the articles. All the research included in the review obtained accuracy values of >70\% in the predictive models obtained through AI. Conclusions: The use of AI proves to be a valuable tool for creating predictive models of fall risk. The use of this tool could have a significant socioeconomic impact as it enables the development of low-cost predictive models with a high level of accuracy. Trial Registration: PROSPERO CRD42023443277; https://tinyurl.com/4sb72ssv ",
doi="10.2196/54934",
url="https://www.jmir.org/2024/1/e54934",
url="http://www.ncbi.nlm.nih.gov/pubmed/38684088"
}


@Article{info:doi/10.2196/54704,
author="Sallam, Malik
and Barakat, Muna
and Sallam, Mohammed",
title="A Preliminary Checklist (METRICS) to Standardize the Design and Reporting of Studies on Generative Artificial Intelligence--Based Models in Health Care Education and Practice: Development Study Involving a Literature Review",
journal="Interact J Med Res",
year="2024",
month="Feb",
day="15",
volume="13",
pages="e54704",
keywords="guidelines",
keywords="evaluation",
keywords="meaningful analytics",
keywords="large language models",
keywords="decision support",
abstract="Background: Adherence to evidence-based practice is indispensable in health care. Recently, the utility of generative artificial intelligence (AI) models in health care has been evaluated extensively. However, the lack of consensus guidelines on the design and reporting of findings of these studies poses a challenge for the interpretation and synthesis of evidence. Objective: This study aimed to develop a preliminary checklist to standardize the reporting of generative AI-based studies in health care education and practice. Methods: A literature review was conducted in Scopus, PubMed, and Google Scholar. Published records with ``ChatGPT,'' ``Bing,'' or ``Bard'' in the title were retrieved. Careful examination of the methodologies employed in the included records was conducted to identify the common pertinent themes and the possible gaps in reporting. A panel discussion was held to establish a unified and thorough checklist for the reporting of AI studies in health care. The finalized checklist was used to evaluate the included records by 2 independent raters. Cohen $\kappa$ was used as the method to evaluate the interrater reliability. Results: The final data set that formed the basis for pertinent theme identification and analysis comprised a total of 34 records. The finalized checklist included 9 pertinent themes collectively referred to as METRICS (Model, Evaluation, Timing, Range/Randomization, Individual factors, Count, and Specificity of prompts and language). Their details are as follows: (1) Model used and its exact settings; (2) Evaluation approach for the generated content; (3) Timing of testing the model; (4) Transparency of the data source; (5) Range of tested topics; (6) Randomization of selecting the queries; (7) Individual factors in selecting the queries and interrater reliability; (8) Count of queries executed to test the model; and (9) Specificity of the prompts and language used. The overall mean METRICS score was 3.0 (SD 0.58). The tested METRICS score was acceptable, with the range of Cohen $\kappa$ of 0.558 to 0.962 (P<.001 for the 9 tested items). With classification per item, the highest average METRICS score was recorded for the ``Model'' item, followed by the ``Specificity'' item, while the lowest scores were recorded for the ``Randomization'' item (classified as suboptimal) and ``Individual factors'' item (classified as satisfactory). Conclusions: The METRICS checklist can facilitate the design of studies guiding researchers toward best practices in reporting results. The findings highlight the need for standardized reporting algorithms for generative AI-based studies in health care, considering the variability observed in methodologies and reporting. The proposed METRICS checklist could be a preliminary helpful base to establish a universally accepted approach to standardize the design and reporting of generative AI-based studies in health care, which is a swiftly evolving research topic. ",
doi="10.2196/54704",
url="https://www.i-jmr.org/2024/1/e54704",
url="http://www.ncbi.nlm.nih.gov/pubmed/38276872"
}


@Article{info:doi/10.2196/45898,
author="Zhu, Zhihui
and Li, Yuehuan
and Zhang, Fan
and Steiger, Stefanie
and Guo, Cheng
and Liu, Nan
and Lu, Jiakai
and Fan, Guangpu
and Wu, Wenbo
and Wu, Mingying
and Wang, Huaibin
and Xu, Dong
and Chen, Yu
and Zhu, Junming
and Meng, Xu
and Hou, Xiaotong
and Anders, Hans-Joachim
and Ye, Jian
and Zheng, Zhe
and Li, Chenyu
and Zhang, Haibo",
title="Prediction of Male Coronary Artery Bypass Grafting Outcomes Using Body Surface Area Weighted Left Ventricular End-diastolic Diameter: Multicenter Retrospective Cohort Study",
journal="Interact J Med Res",
year="2023",
month="Mar",
day="23",
volume="12",
pages="e45898",
keywords="body surface area",
keywords="BSA",
keywords="left ventricular end-diastolic diameter",
keywords="LVEDD",
keywords="coronary artery bypass grafting",
keywords="CABG",
keywords="outcomes",
abstract="Background: The presence of a high left ventricular end-diastolic diameter (LVEDD) has been linked to a less favorable outcome in patients undergoing coronary artery bypass grafting (CABG) procedures. However, by taking into consideration the reference of left ventricular size and volume measurements relative to the patient's body surface area (BSA), it has been suggested that the accuracy of the predicting outcomes may be improved. Objective: We propose that BSA weighted LVEDD (bLVEDD) is a more accurate predictor of outcomes in patients undergoing CABG compared to simply using LVEDD alone. Methods: This study was a comprehensive retrospective cohort study that was conducted across multiple medical centers. The inclusion criteria for this study were patients who were admitted for treatment between October 2016 and May 2021. Only elective surgery patients were included in the study, while those undergoing emergency surgery were not considered. All participants in the study received standard care, and their clinical data were collected through the institutional registry in accordance with the guidelines set forth by the Society of Thoracic Surgeons National Adult Cardiac Database. bLVEDD was defined as LVEDD divided by BSA. The primary outcome was in-hospital all-cause mortality (30 days), and the secondary outcomes were postoperative severe adverse events, including use of extracorporeal membrane oxygenation, multiorgan failure, use of intra-aortic balloon pump, postoperative stroke, and postoperative myocardial infarction. Results: In total, 9474 patients from 5 centers under the Chinese Cardiac Surgery Registry were eligible for analysis. We found that a high LVEDD was a negative factor for male patients' mortality (odds ratio 1.44, P<.001) and secondary outcomes. For female patients, LVEDD was associated with secondary outcomes but did not reach statistical differences for morality. bLVEDD showed a strong association with postsurgery mortality (odds ratio 2.70, P<.001), and secondary outcomes changed in parallel with bLVEDD in male patients. However, bLVEDD did not reach statistical differences when fitting either mortality or severer outcomes in female patients. In male patients, the categorical bLVEDD showed high power to predict mortality (area under the curve [AUC] 0.71, P<.001) while BSA (AUC 0.62) and LVEDD (AUC 0.64) both contributed to the risk of mortality but were not as significant as bLVEDD (P<.001). Conclusions: bLVEDD is an important predictor for male mortality in CABG, removing the bias of BSA and showing a strong capability to accurately predict mortality outcomes. Trial Registration: ClinicalTrials.gov NCT02400125; https://clinicaltrials.gov/ct2/show/NCT02400125 ",
doi="10.2196/45898",
url="https://www.i-jmr.org/2023/1/e45898",
url="http://www.ncbi.nlm.nih.gov/pubmed/36951893"
}


@Article{info:doi/10.2196/26825,
author="R{\"o}hling, Marie Hanna
and Althoff, Patrik
and Arsenova, Radina
and Drebinger, Daniel
and Gigengack, Norman
and Chorschew, Anna
and Kroneberg, Daniel
and R{\"o}nnefarth, Maria
and Ellermeyer, Tobias
and Rosenkranz, Cath{\'e}rine Sina
and Heesen, Christoph
and Behnia, Behnoush
and Hirano, Shigeki
and Kuwabara, Satoshi
and Paul, Friedemann
and Brandt, Ulrich Alexander
and Schmitz-H{\"u}bsch, Tanja",
title="Proposal for Post Hoc Quality Control in Instrumented Motion Analysis Using Markerless Motion Capture: Development and Usability Study",
journal="JMIR Hum Factors",
year="2022",
month="Apr",
day="1",
volume="9",
number="2",
pages="e26825",
keywords="instrumented motion analysis",
keywords="markerless motion capture",
keywords="visual perceptive computing",
keywords="quality control",
keywords="quality reporting",
keywords="gait analysis",
abstract="Background: Instrumented assessment of motor symptoms has emerged as a promising extension to the clinical assessment of several movement disorders. The use of mobile and inexpensive technologies such as some markerless motion capture technologies is especially promising for large-scale application but has not transitioned into clinical routine to date. A crucial step on this path is to implement standardized, clinically applicable tools that identify and control for quality concerns. Objective: The main goal of this study comprises the development of a systematic quality control (QC) procedure for data collected with markerless motion capture technology and its experimental implementation to identify specific quality concerns and thereby rate the usability of recordings. Methods: We developed a post hoc QC pipeline that was evaluated using a large set of short motor task recordings of healthy controls (2010 recordings from 162 subjects) and people with multiple sclerosis (2682 recordings from 187 subjects). For each of these recordings, 2 raters independently applied the pipeline. They provided overall usability decisions and identified technical and performance-related quality concerns, which yielded respective proportions of their occurrence as a main result. Results: The approach developed here has proven user-friendly and applicable on a large scale. Raters' decisions on recording usability were concordant in 71.5\%-92.3\% of cases, depending on the motor task. Furthermore, 39.6\%-85.1\% of recordings were concordantly rated as being of satisfactory quality whereas in 5.0\%-26.3\%, both raters agreed to discard the recording. Conclusions: We present a QC pipeline that seems feasible and useful for instant quality screening in the clinical setting. Results confirm the need of QC despite using standard test setups, testing protocols, and operator training for the employed system and by extension, for other task-based motor assessment technologies. Results of the QC process can be used to clean existing data sets, optimize quality assurance measures, as well as foster the development of automated QC approaches and therefore improve the overall reliability of kinematic data sets. ",
doi="10.2196/26825",
url="https://humanfactors.jmir.org/2022/2/e26825",
url="http://www.ncbi.nlm.nih.gov/pubmed/35363150"
}


@Article{info:doi/10.2196/28692,
author="Staffini, Alessio
and Fujita, Kento
and Svensson, Kishi Akiko
and Chung, Ung-Il
and Svensson, Thomas",
title="Statistical Methods for Item Reduction in a Representative Lifestyle Questionnaire: Pilot Questionnaire Study",
journal="Interact J Med Res",
year="2022",
month="Mar",
day="18",
volume="11",
number="1",
pages="e28692",
keywords="item reduction",
keywords="surveys and lifestyle questionnaires",
keywords="feedback measures",
keywords="questionnaire design",
keywords="variance inflation factor",
keywords="factor analysis",
keywords="mobile phone",
abstract="Background: Reducing the number of items in a questionnaire while maintaining relevant information is important as it is associated with advantages such as higher respondent engagement and reduced response error. However, in health care, after the original design, an a posteriori check of the included items in a questionnaire is often overlooked or considered to be of minor importance. When conducted, this is often based on a single selected method. We argue that before finalizing any lifestyle questionnaire, a posteriori validation should always be conducted using multiple approaches to ensure the robustness of the results. Objective: The objectives of this study are to compare the results of two statistical methods for item reduction (variance inflation factor [VIF] and factor analysis [FA]) in a lifestyle questionnaire constructed by combining items from different sources and analyze the different results obtained from the 2 methods and the conclusions that can be made about the original items. Methods: Data were collected from 79 participants (heterogeneous in age and sex) with a high risk of metabolic syndrome working in a financial company based in Tokyo. The lifestyle questionnaire was constructed by combining items (asked with daily, weekly, and monthly frequency) from multiple validated questionnaires and other selected questions. Item reduction was conducted using VIF and exploratory FA. Adequacy tests were used to check the data distribution and sampling adequacy. Results: Among the daily and weekly questions, both VIF and FA identified redundancies in sleep-related items. Among the monthly questions, both approaches identified redundancies in stress-related items. However, the number of items suggested for reduction often differed: VIF suggested larger reductions than FA for daily questions but fewer reductions for weekly questions. Adequacy tests always confirmed that the structural detection was adequate for the considered items. Conclusions: As expected, our analyses showed that VIF and FA produced both similar and different findings, suggesting that questionnaire designers should consider using multiple methods for item reduction. Our findings using both methods indicate that many questions, especially those related to sleep, are redundant, indicating that the considered lifestyle questionnaire can be shortened. ",
doi="10.2196/28692",
url="https://www.i-jmr.org/2022/1/e28692",
url="http://www.ncbi.nlm.nih.gov/pubmed/35302507"
}


@Article{info:doi/10.2196/29506,
author="Yang, Hsuan-Chia
and Rahmanti, Ristya Annisa
and Huang, Chih-Wei
and Li, Jack Yu-Chuan",
title="How Can Research on Artificial Empathy Be Enhanced by Applying Deepfakes?",
journal="J Med Internet Res",
year="2022",
month="Mar",
day="4",
volume="24",
number="3",
pages="e29506",
keywords="artificial empathy",
keywords="deepfakes",
keywords="doctor-patient relationship",
keywords="face emotion recognition",
keywords="artificial intelligence",
keywords="facial recognition",
keywords="facial emotion recognition",
keywords="medical images",
keywords="patient",
keywords="physician",
keywords="therapy",
doi="10.2196/29506",
url="https://www.jmir.org/2022/3/e29506",
url="http://www.ncbi.nlm.nih.gov/pubmed/35254278"
}


@Article{info:doi/10.2196/25743,
author="Aguayo, A. Gloria
and Goetzinger, Catherine
and Scibilia, Renza
and Fischer, Aur{\'e}lie
and Seuring, Till
and Tran, Viet-Thi
and Ravaud, Philippe
and Bereczky, Tam{\'a}s
and Huiart, Laetitia
and Fagherazzi, Guy",
title="Methods to Generate Innovative Research Ideas and Improve Patient and Public Involvement in Modern Epidemiological Research: Review, Patient Viewpoint, and Guidelines for Implementation of a Digital Cohort Study",
journal="J Med Internet Res",
year="2021",
month="Dec",
day="23",
volume="23",
number="12",
pages="e25743",
keywords="patient and public involvement",
keywords="workshops",
keywords="surveys",
keywords="focus groups",
keywords="co-design",
keywords="digital cohort study",
keywords="digital epidemiology",
keywords="social media",
keywords="mobile phone",
abstract="Background: Patient and public involvement (PPI) in research aims to increase the quality and relevance of research by incorporating the perspective of those ultimately affected by the research. Despite these potential benefits, PPI is rarely included in epidemiology protocols. Objective: The aim of this study is to provide an overview of methods used for PPI and offer practical recommendations for its efficient implementation in epidemiological research. Methods: We conducted a review on PPI methods. We mirrored it with a patient advocate's viewpoint about PPI. We then identified key steps to optimize PPI in epidemiological research based on our review and the viewpoint of the patient advocate, taking into account the identification of barriers to, and facilitators of, PPI. From these, we provided practical recommendations to launch a patient-centered cohort study. We used the implementation of a new digital cohort study as an exemplary use case. Results: We analyzed data from 97 studies, of which 58 (60\%) were performed in the United Kingdom. The most common methods were workshops (47/97, 48\%); surveys (33/97, 34\%); meetings, events, or conferences (28/97, 29\%); focus groups (25/97, 26\%); interviews (23/97, 24\%); consensus techniques (8/97, 8\%); James Lind Alliance consensus technique (7/97, 7\%); social media analysis (6/97, 6\%); and experience-based co-design (3/97, 3\%). The viewpoint of a patient advocate showed a strong interest in participating in research. The most usual PPI modalities were research ideas (60/97, 62\%), co-design (42/97, 43\%), defining priorities (31/97, 32\%), and participation in data analysis (25/97, 26\%). We identified 9 general recommendations and 32 key PPI-related steps that can serve as guidelines to increase the relevance of epidemiological studies. Conclusions: PPI is a project within a project that contributes to improving knowledge and increasing the relevance of research. PPI methods are mainly used for idea generation. On the basis of our review and case study, we recommend that PPI be included at an early stage and throughout the research cycle and that methods be combined for generation of new ideas. For e-cohorts, the use of digital tools is essential to scale up PPI. We encourage investigators to rely on our practical recommendations to extend PPI in future epidemiological studies. ",
doi="10.2196/25743",
url="https://www.jmir.org/2021/12/e25743",
url="http://www.ncbi.nlm.nih.gov/pubmed/34941554"
}


@Article{info:doi/10.2196/29467,
author="Anneveldt, J. Kimberley
and Nijholt, M. Ingrid
and Schutte, M. Joke
and Dijkstra, R. Jeroen
and Frederix, J. Geert W.
and Ista, Erwin
and Verpalen, M. Inez
and Veersema, Sebastiaan
and Huirne, F. Judith A.
and Hehenkamp, K. Wouter J.
and Boomsma, F. Martijn",
title="Comparison of (Cost-)Effectiveness of Magnetic Resonance Image--Guided High-Intensity--Focused Ultrasound With Standard (Minimally) Invasive Fibroid Treatments: Protocol for a Multicenter Randomized Controlled Trial (MYCHOICE)",
journal="JMIR Res Protoc",
year="2021",
month="Nov",
day="24",
volume="10",
number="11",
pages="e29467",
keywords="high-intensity--focused ultrasound ablation",
keywords="magnetic resonance imaging, interventional",
keywords="leiomyoma",
keywords="randomized controlled trial",
keywords="cost-effectiveness analysis",
keywords="clinical trial protocol",
abstract="Background: Magnetic resonance image--guided high-intensity--focused ultrasound (MR-HIFU) is a rather new, noninvasive option for the treatment of uterine fibroids. It is safe, effective, and has a very short recovery time. However, a lack of prospectively collected data on long-term (cost-)effectiveness of the MR-HIFU treatment compared with standard uterine fibroid care prevents the MR-HIFU treatment from being reimbursed for this indication. Therefore, at this point, when conservative treatment for uterine fibroid symptoms has failed or is not accepted by patients, standard care includes the more invasive treatments hysterectomy, myomectomy, and uterine artery embolization (UAE). Primary outcomes of currently available data on MR-HIFU treatment often consist of technical outcomes, instead of patient-centered outcomes such as quality of life (QoL), and do not include the use of the latest equipment or most up-to-date treatment strategies. Moreover, data on cost-effectiveness are rare and seldom include data on a societal level such as productivity loss or use of painkillers. Because of the lack of reimbursement, broad clinical implementation has not taken place, nor is the proper role of MR-HIFU in uterine fibroid care sufficiently clear. Objective: The objective of our study is to determine the long-term (cost-)effectiveness of MR-HIFU compared with standard (minimally) invasive fibroid treatments. Methods: The MYCHOICE study is a national, multicenter, open randomized controlled trial with randomization in a 2:1 ratio to MR-HIFU or standard care including hysterectomy, myomectomy, and UAE. The sample size is 240 patients in total. Women are included when they are 18 years or older, in premenopausal stage, diagnosed with symptomatic uterine fibroids, conservative treatment has failed or is not accepted, and eligible for MR-HIFU. Primary outcomes of the study are QoL 24 months after treatment and costs of treatment including direct health care costs, loss of productivity, and patient costs. Results: Inclusion for the MYCHOICE study started in November 2020 and enrollment will continue until 2024. Data collection is expected to be completed in 2026. Conclusions: By collecting data on the long-term (cost-)effectiveness of the MR-HIFU treatment in comparison to current standard fibroid care, we provide currently unavailable evidence about the proper place of MR-HIFU in the fibroid treatment spectrum. This will also facilitate reimbursement and inclusion of MR-HIFU in (inter)national uterine fibroid care guidelines. Trial Registration: Netherlands Trial Register NL8863; https://www.trialregister.nl/trial/8863 International Registered Report Identifier (IRRID): DERR1-10.2196/29467 ",
doi="10.2196/29467",
url="https://www.researchprotocols.org/2021/11/e29467",
url="http://www.ncbi.nlm.nih.gov/pubmed/34821569"
}


@Article{info:doi/10.2196/29554,
author="Hou, Xinyao
and Zhang, Yu
and Wang, Yanping
and Wang, Xinyi
and Zhao, Jiahao
and Zhu, Xiaobo
and Su, Jianbo",
title="A Markerless 2D Video, Facial Feature Recognition--Based, Artificial Intelligence Model to Assist With Screening for Parkinson Disease: Development and Usability Study",
journal="J Med Internet Res",
year="2021",
month="Nov",
day="19",
volume="23",
number="11",
pages="e29554",
keywords="Parkinson disease",
keywords="facial features",
keywords="artificial intelligence",
keywords="diagnosis",
abstract="Background: Masked face is a characteristic clinical manifestation of Parkinson disease (PD), but subjective evaluations from different clinicians often show low consistency owing to a lack of accurate detection technology. Hence, it is of great significance to develop methods to make monitoring easier and more accessible. Objective: The study aimed to develop a markerless 2D video, facial feature recognition--based, artificial intelligence (AI) model to assess facial features of PD patients and investigate how AI could help neurologists improve the performance of early PD diagnosis. Methods: We collected 140 videos of facial expressions from 70 PD patients and 70 matched controls from 3 hospitals using a single 2D video camera. We developed and tested an AI model that performs masked face recognition of PD patients based on the acquisition and evaluation of facial features including geometric and texture features. Random forest, support vector machines, and k-nearest neighbor were used to train the model. The diagnostic performance of the AI model was compared with that of 5 neurologists. Results: The experimental results showed that our AI models can achieve feasible and effective facial feature recognition ability to assist with PD diagnosis. The accuracy of PD diagnosis can reach 83\% using geometric features. And with the model trained by random forest, the accuracy of texture features is up to 86\%. When these 2 features are combined, an F1 value of 88\% can be reached, where the random forest algorithm is used. Further, the facial features of patients with PD were not associated with the motor and nonmotor symptoms of PD. Conclusions: PD patients commonly exhibit masked facial features. Videos of a facial feature recognition--based AI model can provide a valuable tool to assist with PD diagnosis and the potential of realizing remote monitoring of the patient's condition, especially during the COVID-19 pandemic. ",
doi="10.2196/29554",
url="https://www.jmir.org/2021/11/e29554",
url="http://www.ncbi.nlm.nih.gov/pubmed/34806994"
}


@Article{info:doi/10.2196/28857,
author="Beukenhorst, L. Anna
and Sergeant, C. Jamie
and Schultz, M. David
and McBeth, John
and Yimer, B. Belay
and Dixon, G. Will",
title="Understanding the Predictors of Missing Location Data to Inform Smartphone Study Design: Observational Study",
journal="JMIR Mhealth Uhealth",
year="2021",
month="Nov",
day="16",
volume="9",
number="11",
pages="e28857",
keywords="geolocation",
keywords="global positioning system",
keywords="smartphones",
keywords="mobile phone",
keywords="mobile health",
keywords="environmental exposures",
keywords="data analysis",
keywords="digital epidemiology",
keywords="missing data",
keywords="location data",
keywords="mobile application",
abstract="Background: Smartphone location data can be used for observational health studies (to determine participant exposure or behavior) or to deliver a location-based health intervention. However, missing location data are more common when using smartphones compared to when using research-grade location trackers. Missing location data can affect study validity and intervention safety. Objective: The objective of this study was to investigate the distribution of missing location data and its predictors to inform design, analysis, and interpretation of future smartphone (observational and interventional) studies. Methods: We analyzed hourly smartphone location data collected from 9665 research participants on 488,400 participant days in a national smartphone study investigating the association between weather conditions and chronic pain in the United Kingdom. We used a generalized mixed-effects linear model with logistic regression to identify whether a successfully recorded geolocation was associated with the time of day, participants' time in study, operating system, time since previous survey completion, participant age, sex, and weather sensitivity. Results: For most participants, the app collected a median of 2 out of a maximum of 24 locations (1760/9665, 18.2\% of participants), no location data (1664/9665, 17.2\%), or complete location data (1575/9665, 16.3\%). The median locations per day differed by the operating system: participants with an Android phone most often had complete data (a median of 24/24 locations) whereas iPhone users most often had a median of 2 out of 24 locations. The odds of a successfully recorded location for Android phones were 22.91 times higher than those for iPhones (95\% CI 19.53-26.87). The odds of a successfully recorded location were lower during weekends (odds ratio [OR] 0.94, 95\% CI 0.94-0.95) and nights (OR 0.37, 95\% CI 0.37-0.38), if time in study was longer (OR 0.99 per additional day in study, 95\% CI 0.99-1.00), and if a participant had not used the app recently (OR 0.96 per additional day since last survey entry, 95\% CI 0.96-0.96). Participant age and sex did not predict missing location data. Conclusions: The predictors of missing location data reported in our study could inform app settings and user instructions for future smartphone (observational and interventional) studies. These predictors have implications for analysis methods to deal with missing location data, such as imputation of missing values or case-only analysis. Health studies using smartphones for data collection should assess context-specific consequences of high missing data, especially among iPhone users, during the night and for disengaged participants. ",
doi="10.2196/28857",
url="https://mhealth.jmir.org/2021/11/e28857",
url="http://www.ncbi.nlm.nih.gov/pubmed/34783661"
}


@Article{info:doi/10.2196/28032,
author="Doumouchtsis, K. Stergios
and Nama, Vivek
and Falconi, Gabriele
and Rada, Patricia Maria
and Manonai, Jittima
and Iancu, George
and Haddad, Milhem Jorge
and Betschart, Cornelia",
title="Developing Core Outcome Sets (COS) and Core Outcome Measures Sets (COMS) in Cosmetic Gynecological Interventions: Protocol for a Development and Usability Study",
journal="JMIR Res Protoc",
year="2021",
month="Nov",
day="15",
volume="10",
number="11",
pages="e28032",
keywords="core outcome sets",
keywords="core outcome measures sets",
keywords="cosmetic gynecological surgery",
keywords="intervention",
keywords="labiaplasty",
keywords="vulva",
keywords="gynecology",
keywords="cosmetic surgery",
keywords="surgery",
keywords="framework",
keywords="outcome",
keywords="effective",
keywords="implementation",
abstract="Background: Studies evaluating cosmetic gynecological interventions have followed variable methodology and reported a diversity of outcomes. Such variations limit the comparability of studies and the value of research-based evidence. The development of core outcome sets (COS) and core outcome measures sets (COMS) would help address these issues, ensuring a minimum of outcomes important to all stakeholders, primarily women requesting or having experienced cosmetic gynecological interventions. Objective: This protocol describes the methods used in developing a COS and COMS for cosmetic gynecological interventions. Methods: An international steering group within CHORUS, including health care professionals, researchers, and women with experience in cosmetic gynecological interventions from 4 continents, will guide the development of COS and COMS. Potential outcome measures and outcomes will be identified through comprehensive literature reviews. These potential COS and COMS will be entered into an international, multi-perspective web-based Delphi survey where Delphi participants judge which domains will be core. A priori thresholds for consensus will get established before each Delphi round. The Delphi survey results will be evaluated quantitatively and qualitatively in subsequent stakeholder group consensus meetings in the process of establishing ``core'' outcomes. Results: Dissemination and implementation of the resulting COS and COMS within an international context will be promoted and reviewed. Conclusions: This protocol presents the steps in developing a COS and COMS for cosmetic gynecological interventions. Embedding the COS and COMS for cosmetic gynecological interventions within future clinical trials, systematic reviews, and practice guidelines could contribute to enhancing the value of research and improving overall patient care. Trial Registration: Core Outcome Measures in Effectiveness Trials (COMET) 1592; https://tinyurl.com/n8faysuh International Registered Report Identifier (IRRID): PRR1-10.2196/28032 ",
doi="10.2196/28032",
url="https://www.researchprotocols.org/2021/11/e28032",
url="http://www.ncbi.nlm.nih.gov/pubmed/34779787"
}


@Article{info:doi/10.2196/27896,
author="Noorbergen, J. Tyler
and Adam, P. Marc T.
and Teubner, Timm
and Collins, E. Clare",
title="Using Co-design in Mobile Health System Development: A Qualitative Study With Experts in Co-design and Mobile Health System Development",
journal="JMIR Mhealth Uhealth",
year="2021",
month="Nov",
day="10",
volume="9",
number="11",
pages="e27896",
keywords="co-design",
keywords="mHealth",
keywords="guidelines",
keywords="qualitative study",
keywords="mobile phone",
abstract="Background: The proliferation of mobile devices has enabled new ways of delivering health services through mobile health systems. Researchers and practitioners emphasize that the design of such systems is a complex endeavor with various pitfalls, including limited stakeholder involvement in design processes and the lack of integration into existing system landscapes. Co-design is an approach used to address these pitfalls. By recognizing users as experts of their own experience, co-design directly involves users in the design process and provides them an active role in knowledge development, idea generation, and concept development. Objective: Despite the existence of a rich body of literature on co-design methodologies, limited research exists to guide the co-design of mobile health (mHealth) systems. This study aims to contextualize an existing co-design framework for mHealth applications and construct guidelines to address common challenges of co-designing mHealth systems. Methods: Tapping into the knowledge and experience of experts in co-design and mHealth systems development, we conducted an exploratory qualitative study consisting of 16 semistructured interviews. Thereby, a constructivist ontological position was adopted while acknowledging the socially constructed nature of reality in mHealth system development. Purposive sampling across web-based platforms (eg, Google Scholar and ResearchGate) and publications by authors with co-design experience in mHealth were used to recruit co-design method experts (n=8) and mHealth system developers (n=8). Data were analyzed using thematic analysis along with our objectives of contextualizing the co-design framework and constructing guidelines for applying co-design to mHealth systems development. Results: The contextualized framework captures important considerations of the mHealth context, including dedicated prototyping and implementation phases, and an emphasis on immersion in real-world contexts. In addition, 7 guidelines were constructed that directly pertain to mHealth: understanding stakeholder vulnerabilities and diversity, health behavior change, co-design facilitators, immersion in the mHealth ecosystem, postdesign advocates, health-specific evaluation criteria, and usage data and contextual research to understand impact. Conclusions: System designers encounter unique challenges when engaging in mHealth systems development. The contextualized co-design framework and constructed guidelines have the potential to serve as a shared frame of reference to guide the co-design of mHealth systems and facilitate interdisciplinary collaboration at the nexus of information technology and health research. ",
doi="10.2196/27896",
url="https://mhealth.jmir.org/2021/11/e27896",
url="http://www.ncbi.nlm.nih.gov/pubmed/34757323"
}


@Article{info:doi/10.2196/23898,
author="Hao, Tianyong
and Huang, Zhengxing
and Liang, Likeng
and Weng, Heng
and Tang, Buzhou",
title="Health Natural Language Processing: Methodology Development and Applications",
journal="JMIR Med Inform",
year="2021",
month="Oct",
day="21",
volume="9",
number="10",
pages="e23898",
keywords="health care",
keywords="unstructured text",
keywords="natural language processing",
keywords="methodology",
keywords="application",
doi="10.2196/23898",
url="https://medinform.jmir.org/2021/10/e23898",
url="http://www.ncbi.nlm.nih.gov/pubmed/34673533"
}


@Article{info:doi/10.2196/29174,
author="Gaudet-Blavignac, Christophe
and Rudaz, Andrea
and Lovis, Christian",
title="Building a Shared, Scalable, and Sustainable Source for the Problem-Oriented Medical Record: Developmental Study",
journal="JMIR Med Inform",
year="2021",
month="Oct",
day="13",
volume="9",
number="10",
pages="e29174",
keywords="medical records",
keywords="problem-oriented",
keywords="electronic health records",
keywords="semantics",
abstract="Background: Since the creation of the problem-oriented medical record, the building of problem lists has been the focus of many studies. To date, this issue is not well resolved, and building an appropriate contextualized problem list is still a challenge. Objective: This paper aims to present the process of building a shared multipurpose common problem list at the Geneva University Hospitals. This list aims to bridge the gap between clinicians' language expressed in free text and secondary uses requiring structured information. Methods: We focused on the needs of clinicians by building a list of uniquely identified expressions to support their daily activities. In the second stage, these expressions were connected to additional information to build a complex graph of information. A list of 45,946 expressions manually extracted from clinical documents was manually curated and encoded in multiple semantic dimensions, such as International Classification of Diseases, 10th revision; International Classification of Primary Care 2nd edition; Systematized Nomenclature of Medicine Clinical Terms; or dimensions dictated by specific usages, such as identifying expressions specific to a domain, a gender, or an intervention. The list was progressively deployed for clinicians with an iterative process of quality control, maintenance, and improvements, including the addition of new expressions or dimensions for specific needs. The problem management of the electronic health record allowed the measurement and correction of encoding based on real-world use. Results: The list was deployed in production in January 2017 and was regularly updated and deployed in new divisions of the hospital. Over 4 years, 684,102 problems were created using the list. The proportion of free-text entries decreased progressively from 37.47\% (8321/22,206) in December 2017 to 18.38\% (4547/24,738) in December 2020. In the last version of the list, over 14 dimensions were mapped to expressions, among which 5 were international classifications and 8 were other classifications for specific uses. The list became a central axis in the electronic health record, being used for many different purposes linked to care, such as surgical planning or emergency wards, or in research, for various predictions using machine learning techniques. Conclusions: This study breaks with common approaches primarily by focusing on real clinicians' language when expressing patients' problems and secondarily by mapping whatever is required, including controlled vocabularies to answer specific needs. This approach improves the quality of the expression of patients' problems while allowing the building of as many structured dimensions as needed to convey semantics according to specific contexts. The method is shown to be scalable, sustainable, and efficient at hiding the complexity of semantics or the burden of constraint-structured problem list entry for clinicians. Ongoing work is analyzing the impact of this approach on how clinicians express patients' problems. ",
doi="10.2196/29174",
url="https://medinform.jmir.org/2021/10/e29174",
url="http://www.ncbi.nlm.nih.gov/pubmed/34643542"
}


@Article{info:doi/10.2196/32365,
author="Szinay, Dorothy
and Cameron, Rory
and Naughton, Felix
and Whitty, A. Jennifer
and Brown, Jamie
and Jones, Andy",
title="Understanding Uptake of Digital Health Products: Methodology Tutorial for a Discrete Choice Experiment Using the Bayesian Efficient Design",
journal="J Med Internet Res",
year="2021",
month="Oct",
day="11",
volume="23",
number="10",
pages="e32365",
keywords="discrete choice experiment",
keywords="stated preference methods",
keywords="mHealth",
keywords="digital health",
keywords="quantitative methodology",
keywords="uptake",
keywords="engagement",
keywords="methodology",
keywords="preference",
keywords="Bayesian",
keywords="design",
keywords="tutorial",
keywords="qualitative",
keywords="user preference",
doi="10.2196/32365",
url="https://www.jmir.org/2021/10/e32365",
url="http://www.ncbi.nlm.nih.gov/pubmed/34633290"
}


@Article{info:doi/10.2196/31421,
author="Markowski, L. Kelly
and Smith, A. Jeffrey
and Gauthier, Robin G.
and Harcey, R. Sela",
title="Patterns of Missing Data With Ecological Momentary Assessment Among People Who Use Drugs: Feasibility Study Using Pilot Study Data",
journal="JMIR Form Res",
year="2021",
month="Sep",
day="24",
volume="5",
number="9",
pages="e31421",
keywords="EMA",
keywords="ecological momentary assessment",
keywords="PWUD",
keywords="people who use drugs",
keywords="noncompliance",
keywords="missing data",
keywords="mobile phone",
abstract="Background: Ecological momentary assessment (EMA) is a set of research methods that capture events, feelings, and behaviors as they unfold in their real-world setting. Capturing data in the moment reduces important sources of measurement error but also generates challenges for noncompliance (ie, missing data). To date, EMA research has only examined the overall rates of noncompliance. Objective: In this study, we identify four types of noncompliance among people who use drugs and aim to examine the factors associated with the most common types. Methods: Data were obtained from a recent pilot study of 28 Nebraskan people who use drugs who answered EMA questions for 2 weeks. We examined questions that were not answered because they were skipped, they expired, the phone was switched off, or the phone died after receiving them. Results: We found that the phone being switched off and questions expiring comprised 93.34\% (1739/1863 missing question-instances) of our missing data. Generalized structural equation model results show that participant-level factors, including age (relative risk ratio [RRR]=0.93; P=.005), gender (RRR=0.08; P=.006), homelessness (RRR=3.80; P=.04), personal device ownership (RRR=0.14; P=.008), and network size (RRR=0.57; P=.001), are important for predicting off missingness, whereas only question-level factors, including time of day (ie, morning compared with afternoon, RRR=0.55; P<.001) and day of week (ie, Tuesday-Saturday compared with Sunday, RRR=0.70, P=.02; RRR=0.64, P=.005; RRR=0.58, P=.001; RRR=0.55, P<.001; and RRR=0.66, P=.008, respectively) are important for predicting expired missingness. The week of study is important for both (ie, week 2 compared with week 1, RRR=1.21, P=.03, for off missingness and RRR=1.98, P<.001, for expired missingness). Conclusions: We suggest a three-pronged strategy to preempt missing EMA data with high-risk populations: first, provide additional resources for participants likely to experience phone charging problems (eg, people experiencing homelessness); second, ask questions when participants are not likely to experience competing demands (eg, morning); and third, incentivize continued compliance as the study progresses. Attending to these issues can help researchers ensure maximal data quality. ",
doi="10.2196/31421",
url="https://formative.jmir.org/2021/9/e31421",
url="http://www.ncbi.nlm.nih.gov/pubmed/34464327"
}


@Article{info:doi/10.2196/19023,
author="Memiah, Peter
and Kamau, Anne
and Opanga, Yvonne
and Muhula, Samuel
and Nyakeriga, Emmanuel
and Humwa, Felix
and Cook, Courtney
and Kingori, Caroline
and Muriithi, Job",
title="Using Friendship Ties to Understand the Prevalence of, and Factors Associated With, Intimate Partner Violence Among Adolescents and Young Adults in Kenya: Cross-Sectional, Respondent-Driven Survey Study",
journal="Interact J Med Res",
year="2020",
month="Dec",
day="31",
volume="9",
number="4",
pages="e19023",
keywords="intimate partner violence",
keywords="adolescents",
keywords="young adults",
keywords="bullying",
keywords="physical abuse",
keywords="abuse",
keywords="Africa",
keywords="prevalence",
keywords="risk",
abstract="Background: Optimization of innovative approaches is required for estimating the intimate partner violence (IPV) burden among adolescents and young adults (AYA). Further investigation is required to identify risk and protective factors associated with IPV among AYA. There remain significant gaps in understanding these factors among this vulnerable population. Objective: The goal of our study was to determine the prevalence of IPV among an urban population of AYA and to identify factors associated with IPV among AYA. Methods: A cross-sectional study design utilizing respondent-driven sampling was adopted. The study was conducted among 887 AYA, aged 15 to 24 years, residing in Nairobi, Kenya.  Data were collected through a phone-based survey using the REACH (Reaching, Engaging Adolescents and Young Adults for Care Continuum in Health)-AYA app. Questions on behavioral and psychosocial factors were adopted from different standardized questionnaires. Descriptive, bivariate, and multivariable statistics were used to describe the characteristics of the study sample. Results: Of the 887 participants, a higher proportion were male (540/887, 60.9\%) compared to female (347/887, 39.1\%). The prevalence of IPV was 22.3\% (124/556). IPV was associated with being unsure if it was okay for a boy to hit his girlfriend, living in a home with physical violence or abuse, and being bullied (P=.005). The likelihood of experiencing IPV was higher among respondents whose friends and family members used alcohol (odds ratio [OR] 1.80, 95\% CI 1.09-2.98) and among those who had repeated a class at school in the past two years (OR 1.90, 95\% CI 1.11-3.23). Respondents who visited a health facility or doctor for reproductive health services were 2 times more likely to experience IPV (OR 2.23, 95\% CI 1.40-3.70).  Respondents who had used illicit drugs were 2 times more likely to experience IPV  (OR 4.31, 95\% CI 2.64-7.04). The probability of experiencing IPV decreased by 63\% (OR 0.37, 95\% CI 0.16-0.85) among respondents who refused to have sex with someone who was not prepared to use a condom. Conclusions: IPV remains a significant public health priority because of its impact to society. Our results are in congruence with other similar studies. Efforts toward incorporating appropriate IPV core measures into the comprehensive care package for every AYA seeking health services should be explored. Programs need to address constellations of risk and protective factors linked to IPV in an effort to prevent its occurrence. ",
doi="10.2196/19023",
url="http://www.i-jmr.org/2020/4/e19023/",
url="http://www.ncbi.nlm.nih.gov/pubmed/33382380"
}


@Article{info:doi/10.2196/17182,
author="Appalasamy, Rani Jamuna
and Joseph, Pauline Joyce
and Seeta Ramaiah, Siva
and Md Zain, Zaini Anuar
and Quek, Fatt Kia
and Tha, Kyi Kyi",
title="Video Narratives Intervention Among Stroke Survivors: Feasibility and Acceptability Study of a Randomized Controlled Trial",
journal="JMIR Aging",
year="2020",
month="Jul",
day="10",
volume="3",
number="2",
pages="e17182",
keywords="feasibility and acceptability",
keywords="medication understanding",
keywords="use self-efficacy",
keywords="stroke",
keywords="video narratives",
abstract="Background: A large number of stroke survivors worldwide suffer from moderate to severe disability. In Malaysia, long-term uncontrolled stroke risk factors lead to unforeseen rates of recurrent stroke and a growing incidence of stroke occurrence across ages, predominantly among the elderly population. This situation has motivated research efforts focused on tapping into patient education, especially related to patient self-efficacy of understanding and taking medication appropriately. Video narratives integrated with health belief model constructs have demonstrated potential impacts as an aide to patient education efforts. Objective: The aim of this study was to investigate the feasibility and acceptability of study procedures based on a randomized controlled trial protocol of a video narratives intervention among poststroke patients. We also aimed to obtain preliminary findings of video narratives related to medication understanding and use self-efficacy (MUSE) and blood pressure control. Methods: A parallel group randomized controlled trial including a control group (without video viewing) and an intervention group (with video viewing) was conducted by researchers at a neurology outpatient clinic on poststroke patients (N=54). Baseline data included patients' sociodemographic characteristics, medical information, and all outcome measures. Measurements of MUSE and blood pressure following the trial were taken during a 3-month follow-up period. Feasibility of the trial was assessed based on recruitment and study completion rates along with patients' feedback on the burden of the study procedures and outcome measures. Acceptability of the trial was analyzed qualitatively. Statistical analysis was applied to ascertain the preliminary results of video narratives. Results: The recruitment rate was 60 out of 117 patients (51.3\%). Nevertheless, the dropout rate of 10\% was within the acceptable range. Patients were aged between 21 and 74 years. Nearly 50 of the patients (>85\%) had adequate health literacy and exposure to stroke education. Most of the patients (>80\%) were diagnosed with ischemic stroke, whereby the majority had primary hypertension. The technicalities of randomization and patient approach were carried out with minimal challenge and adequate patient satisfaction. The video contents received good responses with respect to comprehension and simplicity. Moreover, an in-depth phone interview with 8 patients indicated that the video narratives were considered to be useful and inspiring. These findings paralleled the preliminary findings of significant improvement within groups in MUSE (P=.001) and systolic blood pressure control (P=.04). Conclusions: The queries and feedback from each phase in this study have been acknowledged and will be taken forward in the full trial. Trial Registration: Australian New Zealand Clinical Trials Registry ACTRN 12618000174280; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=373554 ",
doi="10.2196/17182",
url="https://aging.jmir.org/2020/2/e17182",
url="http://www.ncbi.nlm.nih.gov/pubmed/32469839"
}


@Article{info:doi/10.2196/16606,
author="Schoeb, Dominik
and Suarez-Ibarrola, Rodrigo
and Hein, Simon
and Dressler, Friedrich Franz
and Adams, Fabian
and Schlager, Daniel
and Miernik, Arkadiusz",
title="Use of Artificial Intelligence for Medical Literature Search: Randomized Controlled Trial Using the Hackathon Format",
journal="Interact J Med Res",
year="2020",
month="Mar",
day="30",
volume="9",
number="1",
pages="e16606",
keywords="artificial intelligence",
keywords="literature review",
keywords="medical information technology",
abstract="Background: Mapping out the research landscape around a project is often time consuming and difficult. Objective: This study evaluates a commercial artificial intelligence (AI) search engine (IRIS.AI) for its applicability in an automated literature search on a specific medical topic. Methods: To evaluate the AI search engine in a standardized manner, the concept of a science hackathon was applied. Three groups of researchers were tasked with performing a literature search on a clearly defined scientific project. All participants had a high level of expertise for this specific field of research. Two groups were given access to the AI search engine IRIS.AI. All groups were given the same amount of time for their search and were instructed to document their results. Search results were summarized and ranked according to a predetermined scoring system. Results: The final scoring awarded 49 and 39 points out of 60 to AI groups 1 and 2, respectively, and the control group received 46 points. A total of 20 scientific studies with high relevance were identified, and 5 highly relevant studies (``spot on'') were reported by each group. Conclusions: AI technology is a promising approach to facilitate literature searches and the management of medical libraries. In this study, however, the application of AI technology lead to a more focused literature search without a significant improvement in the number of results. ",
doi="10.2196/16606",
url="http://www.i-jmr.org/2020/1/e16606/",
url="http://www.ncbi.nlm.nih.gov/pubmed/32224481"
}


@Article{info:doi/10.2196/11012,
author="Gower, D. Aubrey
and Moreno, A. Megan",
title="A Novel Approach to Evaluating Mobile Smartphone Screen Time for iPhones: Feasibility and Preliminary Findings",
journal="JMIR Mhealth Uhealth",
year="2018",
month="Nov",
day="19",
volume="6",
number="11",
pages="e11012",
keywords="smartphone",
keywords="youth",
keywords="mobile apps",
keywords="mobile phone",
keywords="screenshot",
abstract="Background: Increasingly high levels of smartphone ownership and use pose the potential risk for addictive behaviors and negative health outcomes, particularly among younger populations. Previous methodologies to understand mobile screen time have relied on self-report surveys or ecological momentary assessments (EMAs). Self-report is subject to bias and unreliability, while EMA can be burdensome to participants. Thus, a new methodology is needed to advance the understanding of mobile screen time. Objective: The objective of this study was to test the feasibility of a novel methodology to record and evaluate mobile smartphone screen time and use: battery use screenshot (BUS). Methods: The BUS approach, defined for this study as uploading a mobile phone screenshot of a specific page within a smartphone, was utilized within a Web-based cross-sectional survey of adolescents aged 12-15 years through the survey platform Qualtrics. Participants were asked to provide a screenshot of their battery use page, a feature within an iPhone, to upload within the Web-based survey. Feasibility was assessed by smartphone ownership and response rate to the BUS upload request. Data availability was evaluated as apps per BUS, completeness of data within the screenshot, and five most used apps based on battery use percentage. Results: Among those surveyed, 26.73\% (309/1156) indicated ownership of a smartphone. A total of 105 screenshots were evaluated. For data availability, screenshots contained an average of 10.2 (SD 2.0) apps per screenshot and over half (58/105, 55.2\%) had complete data available. The most common apps or functions included Safari and Home and Lock Screen. Conclusions: Study findings describe the BUS as a novel approach for real-time data collection focused on iPhone screen time and use among young adolescents. Although feasibility showed some challenges in the upload capacity of young teens, data availability was generally strong across this large dataset. These data from screenshots have the potential to provide key insights into precise mobile smartphone screen use and time spent per mobile app. Future studies could explore the use of the BUS methodology on other mobile smartphones such as Android phones to correlate mobile smartphone screen time with health outcomes. ",
doi="10.2196/11012",
url="http://mhealth.jmir.org/2018/11/e11012/",
url="http://www.ncbi.nlm.nih.gov/pubmed/30455163"
}


@Article{info:doi/10.2196/jmir.7406,
author="Amann, Julia
and Rubinelli, Sara",
title="Views of Community Managers on Knowledge Co-creation in Online Communities for People With Disabilities: Qualitative Study",
journal="J Med Internet Res",
year="2017",
month="Oct",
day="10",
volume="19",
number="10",
pages="e320",
keywords="community networks",
keywords="internet",
keywords="patient-centered care",
keywords="telemedicine",
keywords="community participation",
keywords="co-creation",
abstract="Background: The use of online communities to promote end user involvement and co-creation in the product and service innovation process is well documented in the marketing and management literature. Whereas online communities are widely used for health care service provision and peer-to-peer support, only little is known about how they could be integrated into the health care innovation process. Objective: The overall objective of this qualitative study was to explore community managers' views on and experiences with knowledge co-creation in online communities for people with disabilities. Methods: A descriptive qualitative research design was used. Data were collected through semi-structured interviews with nine community managers. To complement the interview data, additional information was retrieved from the communities in the form of structural information (number of registered users, number and names of topic areas covered by the forum) and administrative information (terms and conditions and privacy statements, forum rules). Data were analyzed using thematic analysis. Results: Our results highlight two main aspects: peer-to-peer knowledge co-creation and types of collaboration with external actors. Although community managers strongly encouraged peer-to-peer knowledge co-creation, our findings indicated that these activities were not common practice in the communities under investigation. In fact, much of what related to co-creation, prototyping, and product development was still perceived to be directed by professionals and experts. Community managers described the role of their respective communities as informing this process rather than a driving force. The role of community members as advisors to researchers, health care professionals, and businesses was discussed in the context of types of collaboration with external actors. According to the community managers, most of the external inquiries related to research projects of students or health care professionals in training, who often joined a community for the sole purpose of recruiting participants for their research. Despite this unilateral form of knowledge co-creation, community managers acknowledged the mere interest of these user groups as beneficial, as long as their interest was not purely financially motivated. Being able to contribute to advancing research, improving products, and informing the planning and design of health care services were described as some of the key motivations to engage with external stakeholders. Conclusions: This paper draws attention to the currently under-investigated role of online communities as platforms for collaboration and co-creation between patients, health care professionals, researchers, and businesses. It describes community managers' views on and experiences with knowledge co-creation and provides recommendations on how these activities can be leveraged to foster knowledge co-creation in health care. Engaging in knowledge co-creation with online health communities may ultimately help to inform the planning and design of products, services, and research activities that better meet the actual needs of those living with a disability. ",
doi="10.2196/jmir.7406",
url="https://www.jmir.org/2017/10/e320/",
url="http://www.ncbi.nlm.nih.gov/pubmed/29017993"
}


@Article{info:doi/10.2196/ijmr.3712,
author="Legleye, Stephane
and Pennec, Sophie
and Monnier, Alain
and Stephan, Amandine
and Brouard, Nicolas
and Bilsen, Johan
and Cohen, Joachim",
title="Surveying End-of-Life Medical Decisions in France: Evaluation of an Innovative Mixed-Mode Data Collection Strategy",
journal="Interact J Med Res",
year="2016",
month="Feb",
day="18",
volume="5",
number="1",
pages="e8",
keywords="end-of-life decisions",
keywords="France",
keywords="methodology",
keywords="mixed-mode survey",
abstract="Background: Monitoring medical decisions at the end of life has become an important issue in many societies. Built on previous European experiences, the survey and project Fin de Vie en France (``End of Life in France,'' or EOLF) was conducted in 2010 to provide an overview of medical end-of-life decisions in France. Objective: To describe the methodology of EOLF and evaluate the effects of design innovations on data quality. Methods: EOLF used a mixed-mode data collection strategy (paper and Internet) along with follow-up campaigns that employed various contact modes (paper and telephone), all of which were gathered from various institutions (research team, hospital, and medical authorities at the regional level). A telephone nonresponse survey was also used. Through descriptive statistics and multivariate logistic regressions, these innovations were assessed in terms of their effects on the response rate, quality of the sample, and differences between Web-based and paper questionnaires. Results: The participation rate was 40.0\% (n=5217). The respondent sample was very close to the sampling frame. The Web-based questionnaires represented only 26.8\% of the questionnaires, and the Web-based secured procedure led to limitations in data management. The follow-up campaigns had a strong effect on participation, especially for paper questionnaires. With higher participation rates (63.21\% and 63.74\%), the telephone follow-up and nonresponse surveys showed that only a very low proportion of physicians refused to participate because of the topic or the absence of financial incentive. A multivariate analysis showed that physicians who answered on the Internet reported less medication to hasten death, and that they more often took no medical decisions in the end-of-life process. Conclusions: Varying contact modes is a useful strategy. Using a mixed-mode design is interesting, but selection and measurement effects must be studied further in this sensitive field. ",
doi="10.2196/ijmr.3712",
url="http://www.i-jmr.org/2016/1/e8/",
url="http://www.ncbi.nlm.nih.gov/pubmed/26892632"
}