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Design principles for enhancing a digital health platform for patients with atrial fibrillation
(2025-10-01) Jóhannsdóttir, Lilja Guðrún; Erlingsdóttir, Helga Ýr; Gizurarson, Sigfús Örvar; Guðmundsson, Kristján; Kristjánsdóttir, Herdís; Jónsson, Björn; Óskarsdóttir, María; Islind, Anna Sigríður; Department of Computer Science
Objective: Atrial fibrillation (AF) is the most common sustained arrhythmia in clinical practice and is associated with an elevated risk of stroke, heart failure, dementia, and mortality. As its clinical consequences are strongly influenced by modifiable risk factors, this study aims to design a patient journey for individuals undergoing AF treatment, with the goal of improving patient safety and healthcare delivery. Methods: An empirical study was conducted using an action design research approach. The research focused on identifying and implementing design principles to enhance digital health platforms and support AF management. Results: The study resulted in five key design principles: (i) comprehensive requests for medical interventions, (ii) visualization of patient trajectories, (iii) prioritization of waiting lists informed by real-time data, (iv) equality and inclusion throughout the patient journey, and (v) rapid access to and visualization of quality indicators. These principles collectively address current challenges in AF care by optimizing data use, strengthening patient involvement, and improving decision-making. Conclusion: We propose adjustments to the design of digital health platforms for AF management based on the identified principles. Such adaptations have the potential to enhance patient safety, improve healthcare delivery, and create more efficient, inclusive, and data-driven processes in AF management.
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Why it is important to conduct gambling research that is fair and free from conflicts of interest
(2025-04) Roberts, Amanda; Rogers, Jim; Sharman, Steve; Stark, Sasha; Dymond, Simon; Ludvig, Elliot A.; Tunney, Richard J.; O’Reilly, Matthew; Young, Matthew M.; Department of Psychology
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Correction to : Biological and psychological predictors of cognitive function in breast cancer patients before surgery (Supportive Care in Cancer, (2024), 32, 1, (88), 10.1007/s00520-023-08282-5)
(2025-05) Aspelund, Snaefridur Gudmundsdottir; Halldorsdottir, Thorhildur; Agustsson, Gudjon; Tobin, Hannah Ros Sigurdardottir; Wu, Lisa M.; Amidi, Ali; Johannsdottir, Kamilla R.; Lutgendorf, Susan K.; Telles, Rachel; Daly, Huldis Franksdottir; Sigurdardottir, Kristin; Valdimarsdottir, Heiddis B.; Baldursdottir, Birna; Department of Psychology
Correction to: Supportive Care in Cancer (2024) 32:88. https://doi.org/10.1007/s00520-023-08282-5. During the writing of a PhD thesis, an error was identified in the published article “Biological and psychological predictors of cognitive function in breast cancer patients before surgery”. Due to an R coding error, participants with stage 0 breast cancer were initially removed but were inadvertently reintroduced during later data merging. Consequently, these cases were not excluded from the final analyses as intended. Demographic and clinical characteristics of breast cancer patients compared with the healthy control group (Corrected) Women with breast cancer (N = 112) Healthy controls (N = 67) Age in years (mean, SD) 61.8 (10.7) 60.9 (9.5) 0.57 Currently partnered, N(%) 1 Yes 74 (66.1%) 44 (65.7%) No 33 (29.5%) 19 (28.4%) Education level, N(%) 0.55 Primary 18 (16.1%) 10 (14.9%) Secondary 36 (32.1%) 17 (25.4%) University 53 (47.3%) 37 (55.2%) BMI (mean, SD) 27.7 (5.0) 28.1 (4.8) 0.61 Physical activity, N(%) 0.48 None 17 (15.2%) 12 (17.9%) Once a week 10 (8.9%) 2 (3.0%) Twice a week 16 (14.3%) 10 (14.9%) ≥ 3 times a week 64 (57.1%) 40 (59.7%) Menopause, yes % 88 (78.6%) 56 (83.6%) 0.62 Cortisol (mean, SD) 5.1 (2.1) - - α-amylase (mean, SD) 140.5 (94.8) - - Depressive symptoms (mean, SD) 11.0 (8.5) 8.8 (7.3) 0.08 Anxiety symptoms (mean, SD) 4.1 (3.8) 2.8 (3.3) 0.02* Overall cancer-related stress (mean, SD) 25.5 (14.6) - - Average time since diagnosis (weeks) 3.2 - - Cancer stage, N(%) 0 4 (3.6%) - - I 57 (50.9%) - - II 41 (36.6%) - - III 10 (8.9%) - - HER- 2 positive, N(%) 8 (7.1%) - - Estrogen positive, N(%) 101 (90.2%) - - Progesterone positive, N(%) 80 (71.4%) - - BMI = Body Mass Index; HER- 2 = human epidermal growth factor receptor 2 * = p < 0.05 (two-sided). Two-sample t-tests were performed for continuous variables to compare means between groups, and chi-squared tests were used for categorical variables to test for group differences The original article has been corrected.
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Correction : Screening and biosensor-based approaches for lung cancer detection (sensors, (2017), 17)
(2019-10-02) Wang, Lulu; Department of Engineering
The authors wish to make the following corrections to this paper [1]: In the Introduction section of the paper [1], up to 96.4% was mistakenly used in the sentences “To solve this limitation, LDCT was applied for lung imaging and it reduced 20% of lung cancer mortality [18]. However, LDCT continues to have a high false positive rate (up to 96.4%) [19].” So, the correct sentence is given below: “To solve this limitation, LDCT was applied for lung imaging and it reduced 20% of lung cancer mortality [18,19].” In the References section of the paper [1], the References 19 and 41 were mistakenly used, so the correct reference is given below: 19. National Lung Screening Trial Research Team. Reduced lung-cancer mortality with low-dose computed tomographic screening. N. Engl. J. Med. 2011, 365, 395–409. 41. Kaneko, M. Peripheral lung cancer: Screening and detection with low-dose spiral CT versus radiography. Radiology 1996, 201, 789–802.

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