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Digital health interventions for women in frontline public service roles : A systematic review of effectiveness in reducing substance use
(2026-01) Williamson, Grace; Khatun, Toslima; King, Kate; Simms, Amos; Dymond, Simon; Goodwin, Laura; Carr, Ewan; Fear, Nicola T.; Murphy, Dominic; Leightley, Daniel; Department of Psychology
Frontline occupations, including military, healthcare, and first responders, often include frequent exposure to traumatic events, increasing the risk of substance use disorders (SUDs). Research has shown that those in high-intensity occupations are at higher risk of developing SUDs compared to the general population. Women face unique experiences related to substance use, including greater functional impairment and barriers to treatment access. Yet, understanding of the effectiveness of digital health technologies in addressing substance use among women in frontline occupations is limited. This systematic review evaluates the effectiveness of digital health interventions in reducing substance use among women in frontline roles. Four databases (PsycINFO, Ovid MEDLINE, Embase, PsycArticles) were searched for English language full-text articles (2007–2024) that (1) evaluated a digital intervention designed to reduce substance use, (2) reported changes in substance use outcomes such as frequency, intensity or duration, using validated tools (3) included current or former frontline public service workers, and (4) included women as the primary target population or as a subgroup within the sample. 13 papers met inclusion criteria, focusing on eight distinct web and mobile-based interventions for alcohol, tobacco and illicit substances. Most studies (n=11) reported substantial post-intervention reductions in alcohol and tobacco use, although results for PTSD symptoms, illicit drug use, and quality of life were mixed. This review highlights the potential of digital health interventions for reducing substance use but underscores significant gaps in research. The scarcity of studies focused on women, small and heterogeneous samples, and focus on veterans limits the generalisability to women in frontline roles. These gaps present a pressing challenge in understanding gender-specific digital intervention efficacy. Future research should prioritise larger, representative samples of women across diverse frontline occupations to drive the development of digital technologies tailored to the unique challenges faced by women in these roles.
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Development, validation and test-retest reliability of a load cell-based device for assessment of isometric forearm rotation torque
(2025-08-29) Köykkä, Miika; Laatikainen-Raussi, Iida; Vierola, Sami; Cronin, Neil J.; Waller, Benjamin; Vänttinen, Tomi
Objectives. This study aimed to develop and validate a load cell-based device for measuring isometric forearm rotation torque and to determine its test-retest reliability. Approach. The custom-built device was calibrated using known weights and validated against a high-precision torque transducer. For reliability assessment, 35 physically active participants (20 males, 15 females; age 30 ± 7 years) were tested for isometric forearm pronation and supination strength 5-7 d apart. Main results. The custom device demonstrated excellent validity (intraclass correlation coefficient (ICC), absolute agreement = 1.00; r2 = 1.00, p < 0.001; mean difference = −1.26-1.44%, p < 0.001). Test-retest reliability was excellent for absolute pronation and supination torque (ICC = 0.88-0.97; coefficient of variation percentage (CV%) = 4.1-5.6; minimal detectable change (MDC) at 90% confidence level = 13.1-19.9%), good to excellent for supination:pronation ratios (ICC = 0.60-0.88; CV% = 7.0-8.6; MDC = 0.10-0.13), and fair to good for dominant:non-dominant ratios (ICC = 0.42-0.66; CV% = 6.1-7.6; MDC = 0.07-0.10). Sex significantly influenced absolute torque values, with males demonstrating consistently higher torque, although reliability metrics were similar for both sexes. Significance. The device is valid, and the test is reliable. It is suitable for clinical assessments, rehabilitation monitoring, and performance evaluation, facilitating an improved understanding of factors affecting elbow overloading and injuries. Limb ratio metrics should be interpreted with caution due to their lower reliability.
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Development and Mechanical Testing of Synthetic 3D-Printed Models of Healthy and Metastatic Vertebrae
(2025-11) Bruno, Daniela; Forni, Riccardo; Palanca, Marco; Cristofolini, Luca; Gargiulo, Paolo; Department of Engineering
Experimental characterisation of ex vivo specimens is limited by specimen availability and high costs, whereas 3D printing provides a cost-effective alternative for producing multiple replicas. This study aimed to develop a methodology for evaluating the individual and combined effects of material composition and geometry on the biomechanical performance of 3D-printed vertebrae. CT scans of healthy human vertebrae and with lytic metastases were segmented to fabricate synthetic models through Digital Anatomy Printing. Three types of 3D-printed models were produced: Healthy vertebrae, Metastatic vertebrae, and Healed vertebrae (metastatic geometry filled with healthy material). All models were tested under axial compression to measure the strength, stiffness, and strain. Repeatability across replicas was assessed as well as comparison of mechanical properties among the different vertebral types. Results showed excellent repeatability, with coefficients of variation below 5% for strength and stiffness-related parameters. The Metastatic models exhibited significant reductions in strength compared to Healthy ones, while stiffness remained similar, consistent with ex vivo data trends. Healed models highlighted the role of material composition in driving mechanical behaviour, independently of geometry. This work provides the first quantitative assessment of 3D-printed vertebrae with metastatic lesions, supporting their future potential as standardised alternatives to cadaveric testing.
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Detecting arousals and sleep from respiratory inductance plethysmography
(2025-05) Finnsson, Eysteinn; Erlingsson, Ernir; Hlynsson, Hlynur D.; Valsdóttir, Vaka; Sigmarsdottir, Thora B.; Arnardóttir, Eydís; Sands, Scott A.; Jónsson, Sigurður; Islind, Anna S.; Ágústsson, Jón S.; Department of Computer Science; Department of Psychology
Purpose: Accurately identifying sleep states (REM, NREM, and Wake) and brief awakenings (arousals) is essential for diagnosing sleep disorders. Polysomnography (PSG) is the gold standard for such assessments but is costly and requires overnight monitoring in a lab. Home sleep testing (HST) offers a more accessible alternative, relying primarily on breathing measurements but lacks electroencephalography, limiting its ability to evaluate sleep and arousals directly. This study evaluates a deep learning algorithm which determines sleep states and arousals from breathing signals. Methods: A novel deep learning algorithm was developed to classify sleep states and detect arousals from respiratory inductance plethysmography signals. Sleep states were predicted for 30-s intervals (one sleep epoch), while arousal probabilities were calculated at 1-s resolution. Validation was conducted on a clinical dataset of 1,299 adults with suspected sleep disorders. Performance was assessed at the epoch level for sensitivity and specificity, with agreement analyses for arousal index (ArI) and total sleep time (TST). Results: The algorithm achieved sensitivity and specificity of 77.9% and 96.2% for Wake, 93.9% and 80.4% for NREM, 80.5% and 98.2% for REM, and 66.1% and 86.7% for arousals. Bland–Altman analysis showed ArI limits of agreement ranging from - 32 to 24 events/hour (bias: - 4.4) and TST limits from - 47 to 64 min (bias: 8.0). Intraclass correlation was 0.74 for ArI and 0.91 for TST. Conclusion: The algorithm identifies sleep states and arousals from breathing signals with agreement comparable to established variability in manual scoring. These results highlight its potential to advance HST capabilities, enabling more accessible, cost-effective and reliable sleep diagnostics.
Verk
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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