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The Effects of Digital eHealth Versus Onsite 2-Day Group-Based Education in 255 Patients With Irritable Bowel Syndrome: Cohort Study

The Effects of Digital eHealth Versus Onsite 2-Day Group-Based Education in 255 Patients With Irritable Bowel Syndrome: Cohort Study

CSQ-8: client satisfaction questionnaire; FODMAP: fermentable oligosaccharides, disaccharides, monosaccharides, and polyols; GI: gastrointestinal; HADS: Hospital Anxiety and Depression Scale; IBS: irritable bowel syndrome; IBS-QOL: irritable bowel syndrome quality of life; IBS-SSS: Irritable Bowel Syndrome Symptom Severity Scale; NICE: National Institute for Health and Care Excellence.

Birgitte Berentsen, Camilla Thuen, Eline Margrete Randulff Hillestad, Elisabeth Kjelsvik Steinsvik, Trygve Hausken, Jan Gunnar Hatlebakk

JMIR Hum Factors 2025;12:e43618

Cardiac Health Assessment Using a Wearable Device Before and After Transcatheter Aortic Valve Implantation: Prospective Study

Cardiac Health Assessment Using a Wearable Device Before and After Transcatheter Aortic Valve Implantation: Prospective Study

As transcatheter aortic valve implantation (TAVI) for severe aortic stenosis is increasingly used for older adults, including a high percentage of patients with substantial comorbidity, improvement in quality of life (Qo L) is as important as extending life expectancy [1,2]. Not all TAVI patients benefit from improved physical activity, as assessed by a 6-minute walking test (6 MWT) or the Qo L questionnaire [3,4], nor does physical activity change the incidence of aortic stenosis [5].

Rob Eerdekens, Jo Zelis, Herman ter Horst, Caia Crooijmans, Marcel van 't Veer, Danielle Keulards, Marcus Kelm, Gareth Archer, Titus Kuehne, Guus Brueren, Inge Wijnbergen, Nils Johnson, Pim Tonino

JMIR Mhealth Uhealth 2024;12:e53964

Prediction Model for Postoperative Quality of Life Among Breast Cancer Survivors Along the Survivorship Trajectory From Pretreatment to 5 Years: Machine Learning–Based Analysis

Prediction Model for Postoperative Quality of Life Among Breast Cancer Survivors Along the Survivorship Trajectory From Pretreatment to 5 Years: Machine Learning–Based Analysis

Although some predictors for Qo L have been identified in several studies [6], almost all focused on 1 specific predictor. Fewer models have made individual predictions on Qo L due to the complexity of clinical profiles and the inability to consider relevant interactions a priori. In addition, according to a recent cohort study, the Qo L and the Qo L-related factors change over time [7]. However, it is difficult to generate those models using traditional statistical methods.

Danbee Kang, Hyunsoo Kim, Juhee Cho, Zero Kim, Myungjin Chung, Jeong Eon Lee, Seok Jin Nam, Seok Won Kim, Jonghan Yu, Byung Joo Chae, Jai Min Ryu, Se Kyung Lee

JMIR Public Health Surveill 2023;9:e45212