Category: Statistics

Int J Occup Saf Ergon. 2026 May 27:1-10. doi: 10.1080/10803548.2026.2655008. Online ahead of print.

ABSTRACT

Objectives. Empirical data on how different workspace clearance dimensions affect manual task outcomes and worker posture remain scarce, despite the acknowledged importance of spatial design in ergonomics. This study investigates the effects of horizontal and vertical clearances on cycle time, error count and postural behaviors. Methods. A within-subject repeated-measures design was employed, where participants (n = 12, mean age 23 ± 3.4 years) performed a simulated assembly task under varying horizontal and vertical clearance conditions. Results. Horizontal clearance has a statistically significant effect on cycle time (F = 36.15, p < 0.001, η² = 0.475), with reduced horizontal clearance leading to longer cycle times. In contrast, vertical clearance did not significantly influence cycle time. Analysis of error counts using a Poisson generalized linear model showed no significant impact of either horizontal or vertical clearance on assembly errors (p = 0.628), suggesting that error rates were not strongly affected by experimental parameters. Postural observations revealed that reduced horizontal clearance resulted in adoption of compensatory postures such as increased shoulder elevation and arm adduction, especially under limited vertical clearance. Conclusion. These findings highlight the importance of optimizing horizontal clearance (minimum of 450 mm) to improve efficiency while managing vertical clearance to reduce ergonomic risk in constrained workspaces.

PMID:42202309 | DOI:10.1080/10803548.2026.2655008

JMIR Form Res. 2026 May 27;10:e80769. doi: 10.2196/80769.

ABSTRACT

BACKGROUND: A prevalent and serious complication of diabetes mellitus is the development of diabetic foot ulcer (DFU). There is a need for effective solutions that help prevent DFU to support our increasingly stressed health care systems. The use of mobile health (mHealth) tools has been shown to improve awareness and effective self-care management skills in people at risk of developing diabetic foot ulceration.

OBJECTIVE: In this study, we aimed to investigate the perceived usefulness, engagement, and overall user experience of the Feetchecker app, a self-monitoring mHealth app for people at risk of DFU.

METHODS: A total of 24 patients (mean age 71, SD 8.6 years) with type 2 diabetes mellitus at risk of developing diabetic foot ulceration completed a 3-month evaluation period (70 recruited, 36 included, 12 dropped out) of a self-monitoring mobile app called Feetchecker app. A mixed methods approach was used to combine insights from app data with qualitative data from a pre- and postsurvey as well as interviews with patients and involved podiatrists. Data were analyzed using descriptive statistics and thematic analysis. We evaluated overall use of the app, patient engagement, and user experiences.

RESULTS: Patients who fully completed the study conducted 393 feetchecks. In total, 7 patients sent in 9 pictures; all 7 were called for follow-up by a podiatrist. Overall, patients had a positive experience with the app and perceived the Feetchecker app as a valuable tool to monitor their feet for potential risk factors of DFU. Ease of use in performing a feetcheck and sending the podiatrist a picture was described as an important feature. Three main types of engagement with the Feetchecker app emerged: continuous, frequent, and no to little engagement. These patterns highlight enablers for self-monitoring such as ease-of-use, easy access to a podiatrist, and social support, as well as barriers such as digital skills and sustained engagement. Podiatrists highlighted the benefits of having patients report potential issues quicker and the ability to monitor their patients remotely. Challenges remain in integrating the promotion of the Feetchecker app into their consultations.

CONCLUSIONS: The Feetchecker app supported patients in self-monitoring risk factors associated with DFU through routine checks and quick contact with a health care professional in case of a potential issue. Overall, patients described a positive user experience and considered the app helpful. While mHealth tools are not for everyone, user engagement for many patients was high and shows that such apps can offer support for people able to use them. Future research should focus on improving usability and engagement with the app as well as extend the way patients can communicate with health care professionals beyond a picture.

PMID:42202301 | DOI:10.2196/80769

Int J Prosthodont. 2026 May 27;39(3):387-396. doi: 10.11607/ijp.9281.

ABSTRACT

PURPOSE: To investigate the effects of build angle and position in the build platform on the dimensional accuracy of 3D-printed molar crowns using digital light processing technology (DLP).

MATERIALS AND METHODS: A mandibular right first molar crown was designed digitally and printed using DLP at nine standardized positions in the build platform at 90-, 120-, 135-, 150-, 180-, 210-, 225-, 240-, and 270-degree build angles. The experiment was repeated three times per build angle. Specimens were scanned with a TRIOS 4 scanner (3Shape). STL files of each specimen were compared to the original file using Geomagic Control X. Accuracy was evaluated by root mean square (RMS), 2D Compare, and simulated coordinate measuring machine (CMM) measurements. Results were analyzed using two-way and one-way ANOVA. Statistical significance was set at P < .05.

RESULTS: Build angle influenced the dimensional accuracy of DLP, with the lowest RMS values recorded at 210 degrees and C2 position. Crowns oriented toward 90 degrees (152 ± 46.6 μm) and 270 degrees (209 ± 25.7 μm) exhibited the greatest amount of deviation at mesial and distal internal axial surfaces and the greatest amount of deviation at external finish lines, which ranged from -81.9 to 79.9 μm.

CONCLUSIONS: Molar crowns can be placed in any position of the build platform of a DLP printer. However, crowns should be oriented at build angles that reduce the effects of resin pooling and minimize the number of layers at the finish line to maximize accuracy. A build angle of 210 degrees is recommended for optimal results.

PMID:42202298 | DOI:10.11607/ijp.9281

JMIR Med Inform. 2026 May 27;14:e80527. doi: 10.2196/80527.

ABSTRACT

BACKGROUND: The successful implementation of decision support systems promises to enhance high-quality care. However, the successful implementation of a clinical decision support system (CDSS) depends on user acceptance and adoption. A machine learning (ML)-based CDSS to assist primary care professionals treating urinary tract infections (UTIs) was implemented, and usability and usefulness were assessed through a questionnaire.

OBJECTIVE: This study aimed to assess the system’s usability by examining users’ experiences with the software. A secondary goal was to assess users’ attitudes toward evidence-based practice and innovation in health care.

METHODS: In collaboration with the Netherlands Institute for Health Services Research (NIVEL) and Leiden University Medical Center (LUMC), Pacmed Ltd developed the CDSS. The cohort was mostly recruited at the care group level; practices within participating care groups were required to participate. Health insurers partly funded the research. Practitioners participated in the implementation study for 4 months. A survey based on the Unified Theory of Acceptance and Use of Technology (UTAUT) was sent to 263 general practitioners and assistants shortly after the implementation period. Furthermore, usage data were analyzed.

RESULTS: Of the 34 participating practices that used the software, 30 (88%) submitted at least one survey response, with a mean of 2.23 responses per practice (SD 1.43). The CDSS was used throughout the pilot period, and 31 practices continued using the tool, with 9% dropping out during the first 8 weeks. Sixty-seven percent of respondents trusted the tool’s output, and 73% found it understandable how the algorithm came to predictions. Sixty-five percent of respondents indicated that the information provided was useful in addition to the available guidelines, and 52% agreed that it supported their decision-making. However, many respondents were uncertain whether the tool improved patient care (46%) or patient outcomes (66%). Forty-eight percent of respondents found the software easy to integrate into their clinical workflow.

CONCLUSIONS: The CDSS was perceived as trustworthy and easy to use. However, users were unable to determine whether the CDSS improved patient outcomes. In addition, the CDSS development could have benefited from including assistants as well as general practitioners more in the design phase of the software. Because assistants play an important role in UTI care, designing the software to better fit existing workflows may reduce the perceived time investment associated with using the tool. Finally, respondents reported strong motivation to contribute to further research in this field and indicated willingness to embrace change in health care delivery, which may also reflect selection bias in our sample.

PMID:42202295 | DOI:10.2196/80527

JMIR Med Inform. 2026 May 27;14:e84396. doi: 10.2196/84396.

ABSTRACT

BACKGROUND: Accurate preoperative prediction of renal tumor malignancy is critical for guiding decisions and reducing overtreatment, as a substantial proportion of renal masses prove benign. Although radiology assessments and structured electronic health record (EHR) data are routinely used, many tumor-specific descriptors remain embedded in free-text radiology reports and are underused due to extraction challenges.

OBJECTIVE: This study aimed to develop and evaluate a multimodal pipeline that integrates structured EHR variables with natural language processing features from computed tomography (CT) radiology reports, including large language model (LLM)-extracted abnormality characteristics and transformer-based report embeddings, to improve malignancy prediction.

METHODS: We conducted a retrospective cohort study using University of Florida Health Integrated Data Repository Observational Medical Outcomes Partnership-mapped EHR data from December 2011 to August 2024. Adults with renal tumors were included if they had longitudinal diagnostic documentation consistent with a renal mass and at least 1 preoperative renal CT report; final benign or malignant status served as the outcome. Structured features included demographics, comorbidities, medications, vital signs, and laboratory measurements. From the recent preindex CT report, an on-premises LLM isolated kidney-specific findings and extracted abnormality characteristics. Four locally deployed LLMs were evaluated against manual annotations of 500 reports. Kidney-specific text was encoded using pretrained biomedical transformer models, including radiology Bidirectional Encoder Representations from Transformers (BERT) variants. We evaluated unimodal baselines and multimodal early, middle, and late fusion strategies. Model development used 5-fold cross-validation within the 80% training partition; each fold-specific model was evaluated on the same independent 20% held-out test set, with performance reported as mean and SD across the 5 held-out test evaluations. The primary metric was area under the receiver operating characteristic curve (AUC).

RESULTS: The final cohort included 967 patients (n=712, 73.6% malignant). In extraction evaluation, Qwen2.5-32B achieved 88.3% overall accuracy with a 100% extraction success rate and was selected for downstream feature generation. Among unimodal models, the structured clinical variable model achieved an AUC of 0.758 (SD 0.012), kidney-specific text with radiology BERT achieved an AUC of 0.746 (SD 0.058), and abnormality characteristics alone achieved an AUC of 0.716 (SD 0.015). Multimodal fusion models achieved higher descriptive performance than unimodal models. Early fusion achieved the highest AUC (mean 0.813, SD 0.008), and F₁-score (mean 0.809, SD 0.030), while late fusion achieved an AUC of 0.805 (SD 0.016). Ablation and interpretability analyses suggested complementary predictive information from structured clinical variables and kidney-specific text embeddings.

CONCLUSIONS: Integrating unstructured radiology report text with structured EHR variables achieved higher mean predictive performance than unimodal approaches in descriptive comparisons. Multimodal fusion, particularly early fusion incorporating radiology BERT-derived kidney-specific text embeddings, achieved the strongest discrimination, suggesting potential value of natural language processing-enabled multimodal EHR pipelines for informing preoperative risk stratification.

PMID:42202288 | DOI:10.2196/84396

JMIR Aging. 2026 May 27;9:e82867. doi: 10.2196/82867.

ABSTRACT

BACKGROUND: Integrated Care for Older People (ICOPE), focused on monitoring and optimizing the intrinsic capacity (IC) of older adults, is a new model of geriatric care that is currently being accelerated globally. Digital health technologies are recommended for longitudinal IC monitoring to provide precise and timely interventions. However, little is known about the psychological intentions of engaging in digital monitoring of IC according to the profile heterogeneity of IC among older adults.

OBJECTIVE: This study aims to map a set of customized personas to capture the profiles of IC and match psychological intentions that support personalized digital IC monitoring.

METHODS: An explanatory sequential mixed methods study was conducted at 16 sites in Beijing, China. Older adults aged ≥60 years (n=481) were selected to complete the quantitative survey. Latent profile analysis, descriptive statistics, and logistic regression analyses were performed to cluster subgroups using Mplus (Muthén & Muthén) and SPSS (IBM Corp). A subsample of participants from each profile (n=25) was purposively sampled for semistructured interviews. An inductive-deductive content analysis was used to identify similar attributes and to affirm the personas gradually. A joint statistical and thematic visualization method was used to integrate the customized personas.

RESULTS: Three profiles of IC patterns emerged: “multisubdomain decline-IC imbalance group,” “multisubdomain moderate-sensory deficit group,” and “multisubdomain robust-whole balance group.” The distribution of latent profiles was influenced by age, education, monthly per capita household income, self-rated health, and number of chronic diseases, while positively impacting older adults’ functional ability. The following customized personas were captured regarding established themes: “affects my mood-anxious evader,” characterized by avoidance and anxiety, low digital interest, and perceived social isolation; “capitalize on what comes-accommodative adopter,” pragmatically oriented toward disease detection, with moderate digital openness but limited self-efficacy; and “more autonomy-active improver,” who exhibited proactive engagement, high digital literacy, and motivation rooted in self-management and social participation.

CONCLUSIONS: This study is the first to integrate latent profile analysis with customized qualitative personas to link the heterogeneity of IC with the psychological intentions underlying digital monitoring. The resulting personas model provides an actionable framework for tailoring digital IC monitoring strategies in community-based integrated care. The findings emphasize the need to align monitoring approaches with older adults’ IC characteristics, psychological readiness, digital literacy, and social support to enhance engagement in digital IC monitoring.

PMID:42202287 | DOI:10.2196/82867

Brief Bioinform. 2026 May 4;27(3):bbag275. doi: 10.1093/bib/bbag275.

ABSTRACT

Nonlinear genetic architectures, including epistasis and threshold effects, are increasingly recognized as contributors to complex disease risk, yet most existing SNP-set association tests rely on linear modeling assumptions, resulting in reduced power and unstable inference when genetic effects are nonlinear or heterogeneously distributed across variants. To address this limitation, we propose a nonlinear high-dimensional inference framework for set-based genetic association analysis that integrates scalable kernel representations with valid statistical inference. The framework combines distance correlation-based sure independence screening to reduce ultra-high dimensional predictors, kernel principal component analysis with Nyström approximation for nonlinear feature extraction, and de-sparsified LASSO to enable asymptotically valid hypothesis testing in high dimensions, together with a two-stage omnibus testing strategy that adaptively aggregates evidence across complementary signal models. Extensive simulation studies demonstrate that the proposed method maintains well-calibrated Type I error and consistently achieves higher power than established set-based approaches, including Sequence Kernel Association Test and adaptive Sum of Powered Score test, particularly under nonlinear and heterogeneous genetic effect scenarios, while remaining competitive in linear settings. Application to Alzheimer’s Disease Neuroimaging Initiative data identifies gene-level associations with brain regional volumes that converge on neuronal excitability, calcium signaling, and cytoskeletal regulation, biological processes centrally implicated in neurodegeneration. Together, this work provides a robust and scalable framework for nonlinear set-based inference in genome-wide studies, expanding the analytical toolbox for dissecting complex genetic contributions to disease.

PMID:42202283 | DOI:10.1093/bib/bbag275

Brief Bioinform. 2026 May 4;27(3):bbag258. doi: 10.1093/bib/bbag258.

ABSTRACT

Expression quantitative trait locus (eQTL) mapping is used to identify the functional link between a genomic variant and a gene’s expression. A significant eQTL association does not mean a causal relationship or mechanism, and further investigation is needed to understand how a single-nucleotide polymorphism (SNP) impacts gene expression. One of the most plausible explanations for eQTL is that a genomic variant affects transcription factor (TF) binding and thus impacts its regulation on target genes (TGs). However, the current eQTL model does not provide information on the TF and how its regulation is mediated by the SNP’s genotypes. Here, we propose a new method called differential co-expression QTL (ceQTL) among different alleles using Chow statistics to specifically detect eQTLs that are bound by a particular TF. We start with building a trio of TF, its TG, and related SNP, and then test the significant coefficient difference among different genotypes of the SNP. We applied this ceQTL model to simulated data and the lung tissue datasets from the genotype-tissue expression project. The simulated data results showed that the model was robust to detect true ceQTLs at variable sample sizes and different minor allele frequencies as measured by area under the curve. Our tool also performed a TF binding affinity analysis to add another layer of evidence for functional interpretation. In summary, ceQTL analysis provides a more interpretable and biological insight into the mechanism of eQTL and transcriptomic regulation, which would help us better understand how genomic variants affect phenotypes and diseases.

PMID:42202282 | DOI:10.1093/bib/bbag258

Brief Bioinform. 2026 May 4;27(3):bbag267. doi: 10.1093/bib/bbag267.

ABSTRACT

Transcriptome assembly and quantification are crucial steps in the differential expression analysis of RNA-seq data. As transcriptome assembly precedes quantification, its results inevitably influence the outcomes of quantification. This study investigates the impact of transcriptome assembly algorithms on quantification outcomes in next-generation RNA-seq data analysis. From the perspective of quantification results, we evaluate the performance of transcriptome assembly algorithms. We assess the assembly quality and stability of three commonly used transcriptome assemblers-StringTie2, Scallop, and Cufflinks-on both simulated and real datasets. Our evaluation provides references for downstream analyses and identifies the most effective and stable pipeline, which is specifically the pipeline combining HISAT2 (for transcriptome alignment) and StringTie2 (for assembly). Furthermore, we compare simulated data generated by RNA-seq data simulation tools with real RNA-seq data and reveal that simulated data fails to fully capture the complexity of real data. Through this analysis, we identify transcript features associated with poor assembly and quantification performance, specifically highlighting two extreme cases: long, low-expression transcripts that are often overlooked and short transcripts that are prone to quantification errors. These findings offer valuable insights into future software development directions.

PMID:42202281 | DOI:10.1093/bib/bbag267

J Med Internet Res. 2026 May 27;28:e87049. doi: 10.2196/87049.

ABSTRACT

BACKGROUND: Health care workers (HCWs) face sustained psychological demands that place them at heightened risk for burnout and posttraumatic stress disorder (PTSD). However, assessing psychological distress in this population remains challenging because of stigma, underreporting, and the limitations of self-report tools. Although nonverbal behaviors such as facial expressions and gaze hold diagnostic promise, most approaches overlook the fine-grained, temporal fluctuations in these signals. In this study, we focused on microbehavior intervals-brief, involuntary changes in multimodal nonverbal signals-that emerge during emotion-eliciting interviews.

OBJECTIVE: This study aimed to determine whether microbehavior intervals improve the discrimination of psychological distress profiles among HCWs with symptoms of burnout and PTSD.

METHODS: HCWs participated in a semistructured interview that included 5 work-related, emotionally charged questions and that was recorded via Webex (online video platform). Participants also completed validated questionnaires for burnout (Maslach Burnout Inventory General Survey 9-item) and PTSD (PTSD checklist for Diagnostic and Statistical Manual, 5th edition). Recordings were analyzed with computer vision models to generate time-series data of facial expressions, head movement, gaze, body posture, and hand gestures. An unsupervised anomaly detection model (MOMENT [a Family of Open Time-Series Foundation Models]) isolated microbehavior intervals without requiring manual labels. Features derived from these intervals were used to train a deep learning classifier that predicted 4 symptom classes of psychological distress: “moderate-severe burnout,” “subthreshold-provisional PTSD,” “burnout+PTSD,” and “resilient.” We conducted an ablation study by systematically removing one behavioral data stream at a time. Finally, we conducted an explainability analysis to characterize the features driving model predictions.

RESULTS: We analyzed 258 interview recordings from 151 HCWs. Per interview, an average of 19.65 (SD 6.01) microbehavior intervals were detected, each lasting an average of 1.31 (SD 1.10) seconds. The classifier demonstrated robust performance across classes, achieving a macro- F₁-score of 0.75 and a macro area under the receiver operating characteristic curve of 0.80 on held-out data. Ablation analysis showed that excluding gaze or arousal-valence signals caused the largest performance declines, particularly in recall and F₁-score. The explainability analysis revealed distinct temporal patterns across symptom classes, with irregularity and variability in microbehaviors emerging as key predictors.

CONCLUSIONS: Focusing on microbehavior intervals yields a scalable, interpretable, and annotation-free framework for detecting psychological distress from nonverbal signals. By moving from whole-video features to fine-grained multimodal temporal modeling, we successfully captured subtle, involuntary fluctuations in nonverbal responses to emotion-eliciting questions. This multimodal approach enables an objective, robust, and explainable assessment of psychological distress and offers a promising complement to conventional psychometric assessments.

PMID:42202278 | DOI:10.2196/87049