Martin Jacobsson

Can Muse's Latest Brain-Sensing Headband Transform Sleep Monitoring? Muse S Athena headband combines EEG and fNIRS for brain monitoring at home. Dive into the world of portable neurotech and its potential for sleep science.

Over a billion minutes of #brain #data from #Muse’s brain-sensing headbands have led to an AI model of the brain on their new Muse S Athena. Muse’s new headband is a cost-effective brain monitor. “We’re focused on bringing neurotechnology to the home.”
spectrum.ieee.org/muse-headband

Effects of an Exercise Intervention Based on mHealth Technology on the Physical Health of Male University Students With Overweight and Obesity: Randomized Controlled Trial Background: Obesity has become one of today's global health challenges. According to the World Health Organisation, in 2022, 2.5 billion adults aged 18 years and older will be overweight, including more than 890 million adults with obesity. Objective: Exercise interventions based on mobile health technology are widely available, but the effectiveness and feasibility of interventions using mobile health apps and exercise watches to improve the physical health of overweight and obese male college students are unknown, and this study compares the effects of online interventions carried out by mobile health technology and offline interventions guided by physical trainers on the physical health of overweight and obese male college students. Methods: This study used a randomised controlled trial with a pre-test post-test design, and participants were randomly divided into an online group, an offline group and a control group. The online group exercised online through the fitness APP, and the offline group was instructed by a professional trainer to exercise offline, and both groups wore sports watches to monitor their activities, and the training content was the same. The control group did not carry out any intervention. Results: At the end of the intervention, the BMI of the online and offline groups decreased by 1.5 kg/m² and 1.6 kg/m², respectively (P

Effects of an Exercise Intervention Based on mHealth Technology on the Physical Health of Male University Students With Overweight and Obesity: Randomized Controlled Trial

Low-quality papers based on public health data are flooding the scientific literature The appearance of thousands of formulaic biomedical studies has been linked to the rise of text-generating AI tools.

#Medsky🧪 #academicsky The appearance of thousands of formulaic biomedical studies has been linked to the rise of text-generating AI tools.
www.nature.com/articles/d41...
@nature.com

Unplanned transfers from wards to intensive care units: how well does NEWS identify patients in need of urgent escalation of care? - Scandinavian Journal of Trauma, Resuscitation and Emergency Medicine Background The National Early Warning Score (NEWS) is implemented internationally for in-hospital monitoring. It has been superior to other predictive scores, but its preventive abilities are still…

Unplanned transfers from wards to intensive care units: how well does NEWS identify patients in need of urgent escalation of care?

'NEWS did *not* predictably identify patients who were urgently transferred to an ICU from a ward. '

Read more: sjtrem.biomedcentral.com/articles/10....

Effectiveness of diaphragmatic ultrasound as a predictor of successful weaning from mechanical ventilation - Journal of Clinical Monitoring and Computing Journal of Clinical Monitoring and Computing - Purpose: Weaning from mechanical ventilation (MV) is the transition from ventilator dependence to independent breathing. Optimal timing reduces...

🫁🛏️ Weaning from mechanical ventilation:
📊 DE-RSBI (≥1.685) had best accuracy (AUC=0.851)

⚠️ Odds of failure:
🔺 DE-RSBI >1.56 ➡ OR=12
🔺 DTF-RSBI >62.3 ➡ OR=8.04
📉 RSBI alone ➡ OR=4.84

✅ Ultrasound-derived indices improve weaning prediction!

link.springer.com/article/10.1...

Episource is notifying millions of people that their health data was stolen | TechCrunch The UnitedHealth-owned medical coding service was hacked earlier this year by a ransomware gang.

Episource is one of those giant medical billing and adjustment companies (owned by UnitedHealth's Optum, no less) that you've probably never heard of, but was hit by ransomware.

It's one of the biggest breaches of the year so far, affecting millions. If you got a data breach notice, this is why.

Implementing Large Language Models in Health Care: Clinician-Focused Review With Interactive Guideline Background: Large language models (LLMs) can generate outputs understandable by humans, such as answers to medical questions and radiology reports. With the rapid development of LLMs, clinicians face a growing challenge in determining the most suitable algorithms to support their work. Objective: We aimed to provide clinicians and other health care practitioners with systematic guidance in selecting an LLM that is relevant and appropriate to their needs and facilitate the integration process of LLMs in health care. Methods: We conducted a literature search of full-text publications in English on clinical applications of LLMs published between January 1, 2022, and March 31, 2025, on PubMed, ScienceDirect, Scopus, and IEEE Xplore. We excluded papers from journals below a set citation threshold, as well as papers that did not focus on LLMs, were not research based, or did not involve clinical applications. We also conducted a literature search on arXiv within the same investigated period and included papers on the clinical applications of innovative multimodal LLMs. This led to a total of 270 studies. Results: We collected 330 LLMs and recorded their application frequency in clinical tasks and frequency of best performance in their context. On the basis of a 5-stage clinical workflow, we found that stages 2, 3, and 4 are key stages in the clinical workflow, involving numerous clinical subtasks and LLMs. However, the diversity of LLMs that may perform optimally in each context remains limited. GPT-3.5 and GPT-4 were the most versatile models in the 5-stage clinical workflow, applied to 52% (29/56) and 71% (40/56) of the clinical subtasks, respectively, and they performed best in 29% (16/56) and 54% (30/56) of the clinical subtasks, respectively. General-purpose LLMs may not perform well in specialized areas as they often require lightweight prompt engineering methods or fine-tuning techniques based on specific datasets to improve model performance. Most LLMs with multimodal abilities are closed-source models and, therefore, lack of transparency, model customization, and fine-tuning for specific clinical tasks and may also pose challenges regarding data protection and privacy, which are common requirements in clinical settings. Conclusions: In this review, we found that LLMs may help clinicians in a variety of clinical tasks. However, we did not find evidence of generalist clinical LLMs successfully applicable to a wide range of clinical tasks. Therefore, their clinical deployment remains challenging. On the basis of this review, we propose an interactive online guideline for clinicians to select suitable LLMs by clinical task. With a clinical perspective and free of unnecessary technical jargon, this guideline may be used as a reference to successfully apply LLMs in clinical settings.

Implementing Large Language Models in Health Care: Clinician-Focused Review With Interactive Guideline

The main take away from this paper is that all heart rate monitors used in sports have a builtin delay due to filtering. If you do high intensive interval training (HIIT) or similar exercise styles, HR readings are not correct.

Accepted paper: Dynamic response of Bluetooth wearable heart rate monitors during rapid changes in heart rate doi.org/10.1088/1361... via @ioppublishing.bsky.social

Paper published: Predicting Opportunities for Improvement in Trauma Care using Machine Learning: A retrospective registry-based study at a major trauma centre #MedSky bmjopen.bmj.com/content/15/6...

WebAssembly - ACM Queue Our free ~monthly newsletter showcases all of ACM Queue's latest articles and columns.

The May/June 2025 issue of ACM Queue dives into WebAssembly: from DOM support to end-user programmable AI, there’s something for everyone! Grab an e-copy here:

VR-based or traditional physical activity does not matter much; both works (with some differences). However, one important difference can be spotted in Figure 4 in the supplement. After 6 months, VR-based adolescents seems more motivated. I would love to see the 24-months follow up! #MedSky

PNG has been updated for the first time in 22 years — new spec supports HDR and animation The demand for subtitles in HDR content led to this update.

The file format PNG has been updated for the first time in 22 years, now finally supports animations and HDR! www.tomshardware.com/software/png...

Incidence of opportunities for improvement in trauma patient care: a retrospective registry-based study Introduction Trauma is a leading cause of death in individuals aged 45 and younger, contributing significantly to the global disease burden. Local trauma quality improvement programs have been impleme...

Paper published: Incidence of opportunities for improvement in trauma patient care: a retrospective registry-based study. #MedSky tsaco.bmj.com/content/10/2...

Reminder>> Investigating how clinicians form trust in an AI-based #MentalHealth model: A qualitative case #Study (preprint) #openscience #PeerReviewMe #PlanP

Preoperative risk prediction tools that predict morbidity risk in adults undergoing surgery: An Evidence Review https://www.medrxiv.org/content/10.1101/2025.06.27.25330118v1

Case-Control Matching Erodes Feature Discriminability for AI-driven Sepsis Prediction in ICUs: A Retrospective Cohort Study https://www.medrxiv.org/content/10.1101/2025.06.26.25330281v1

AI slashes time to produce gold-standard medical reviews — but sceptics urge caution Although language models can help to accelerate systematic reviews, a fully automated system is still some way off.

Although language models can help to accelerate systematic reviews, a fully automated system is still some way off

https://go.nature.com/43RZ4oK

Development of a Machine Learning–Based Predictive Model for Postoperative Delirium in Older Adult Intensive Care Unit Patients: Retrospective Study Background: Delirium is a prevalent phenomenon among patients admitted to the geriatric intensive care unit (ICU) and can adversely impact prognosis and augment the risk of complications. Objective: We aimed to construct and validate a predictive model for postoperative delirium state in older ICU patients, providing timely and effective early identification of high-risk individuals and assisting clinicians in decision-making. Methods: The data from patients admitted to the ICU for over 24 hours were extracted from the Medical Information Marketplace for Intensive Care IV (MIMIC-IV) database and the eICU Collaborative Research Database (eICU-CRD). The MIMIC-IV data were split (7:3) into training and internal validation sets, while the eICU-CRD data served as an external validation set. Delirium predictions were conducted for the subsequent prediction windows (12 h, 24 h, 48 h, and whole stay time) using data from the first 24 hours post admission. The corresponding feature variables were subjected to Boruta feature selection, and the prediction models were constructed using logistic regression, support vector classifier, random forest classifier, and extreme gradient boosting (XGB). Subsequently, model performance was evaluated using areas under the receiver operating characteristic curves (AUCs), Brier scores, and decision curve analysis, and external validation. Results: The MIMIC-IV and eICU-CRD datasets comprised 6129 and 709 patients, respectively, who were included in the analysis. Fifty-four features were selected to construct the predictive model. Regarding internal validation, the XGB model demonstrated the most effective prediction of delirium across different prediction windows. The AUCs for the 4 prediction windows (12 h, 24 h, 48 h, and whole stay time) were 0.848 (95% CI 0.826-0.869), 0.852 (95% CI 0.831-0.872), 0.851 (95% CI 0.831-0.871), and 0.844 (95% CI 0.823-0.863), respectively, and those of the external validation set were 0.777 (95% CI 0.726-0.825), 0.761 (95% CI 0.710-0.808), 0.753 (95% CI 0.704-0.798), and 0.737 (95% CI 0.695-0.777), respectively. Furthermore, the XGB model demonstrated the most accurate calibration across all prediction windows, with values of 0.129, 0.136, 0.144, and 0.148, respectively. Additionally, decision curve analysis revealed that the XGB model outperformed the other models in terms of net gain for the majority of threshold probability values. The 6 most significant predictive features identified were the first day’s delirium assessment results, type of first care unit, minimum Glasgow Coma Scale (GCS) score, Acute Physiology Score III, acetaminophen, and nonsteroidal anti-inflammatory drugs. Conclusions: The high-performance XGB model for predicting postoperative delirium state in older adult ICU patients has been successfully developed and validated. The model predicts the delirium state at 12 h, 24 h, 48 h, and whole stay time after the first day of hospitalization within the ICU. This enables physicians to identify high-risk patients early, thus facilitating the optimization of personalized management strategies and care plans.

Development of a Machine Learning–Based Predictive Model for Postoperative Delirium in Older Adult Intensive Care Unit Patients: Retrospective Study

The ECSS Congress App for the 30th Annual Congress in #Rimini is LIVE!

Registered Congress participants can explore sessions, plan schedule and stay updated in real time.

For more info, visit 👉 bit.ly/43Y42yO

📲Download NOW!

#WeAreSportScience #ECSS2025

Structured Exercise after Adjuvant Chemotherapy for Colon Cancer | NEJM Preclinical and observational studies suggest that exercise may improve cancer outcomes. However, definitive level 1 evidence is lacking. In this phase 3, randomized trial conducted at 55 centers, ...

Exercise significantly reduces risk after colon cancer and chemotherapy. #MedSky www.nejm.org/doi/full/10....

AI Takes Center Court, Infosys and FFT Unveil Innovative Fan Features for Roland-Garros 2025 /PRNewswire/ -- Infosys (NSE: INFY), (BSE: INFY), (NYSE: INFY), a global leader in next-generation digital services and consulting, in partnership with the...

AI takes center court, as Infosys and FFT unveil innovative fan features for Roland-Garros 2025. #SportTech www.prnewswire.com/in/news-rele...

In the last weeks, I have worked on a major overhaul of website for the KTH Center for Sports Engineering. Don't forget to subscribe to our newsletter. www.kth.se/sports-engin...

DIGIfor1healthSE highlights need for coordinated health data infrastructure in Sweden. Coordination between national and regional efforts is required for excellent research, improved lifestyle, healthcare and precision medicine. www.scilifelab.se/news/digifor...

EU/EEA startups and SMEs can apply for price reductions on services offered by TEF-Health, such as technical and scientific support for Health AI providers. tefhealth.eu/call/call-2

Effectiveness of hypotension prediction index software in reducing intraoperative hypotension in prolonged prone-position spine surgery: a single-center clinical trial - Journal of Clinical Monitoring... Intraoperative hypotension (IOH) is associated with morbidity and mortality. The Hypotension Prediction Index (HPI), a machine learning-based tool, offers the opportunity for a proactive approach by p...

A new single center study on the effectiveness of using HPI to guide intraoperative hypotension IOH. Post-operative complications similar/inconclusive. link.springer.com/article/10.1...

Link to the games: liopep.com/en/index.html

Spela spel på jobbet – bli frisk och glad This is "Spela spel på jobbet – bli frisk och glad" by Linköping University on Vimeo, the home for high quality videos and the people…

We need more physical computer games! (subtitles in English available) vimeo.com/1074562115

Off to teach my first years that health is more than hospitals and people in white coats! Health is architecture and urban planning and industrial design and economics and education and engineering and, and, and... #PublicHealth #AcademicSky