Tag: Medical device

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STAT+: Access granted: CMS greenlights more than 150 participants for chronic care experiment

More than 150 companies and providers have been provisionally approved to participate in an experimental Medicare program meant to expand access to technology-supported chronic care. They include popular mental health apps, wearable device makers, a life sciences company tied to Google, and startups that help large health systems manage heart failure patients.

Announced late last year by the Center for Medicare and Medicaid Innovation, the ACCESS model will pay participants set rates to treat chronic conditions like diabetes, hypertension, high cholesterol, musculoskeletal pain, anxiety, and depression. The payments are tied to measurable health outcomes; the model is meant as an alternative to paying for individual technology services. The initial deadline to participate in the first ACCESS cohort was April 1, but CMMI Monday announced it will extend the deadline to allow more to join.

CMS officials say the large number of applications to participate in ACCESS exceeded their expectations and that the enthusiasm suggests modest payment rates and restrictions did not discourage digital health companies from applying. According to officials, most of the participants had not previously served Medicare patients. 

Continue to STAT+ to read the full story…

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Design Implications of Comfort and Usability of Manual Stairclimbing Wheelchair: Ergonomic Assessment and Pilot Study Using Surface Electromyography Inputs

Background: Stairclimbing wheelchairs offer enhanced mobility for users navigating multilevel environments, yet limited research addresses the ergonomics of lever propulsion-based stair climbing mechanisms. Comprehensive ergonomic assessment integrating both subjective user feedback and objective biomechanical analysis is essential for optimizing assistive device design for comfort and usability. Objective: This pilot study aims to assess the ergonomic design of a transformable stair-climbing wheelchair through a dual-methodology approach, evaluating plane surface movement accessibility and quantifying muscle activation patterns during lever-propelled stair-climbing operations using surface electromyography (sEMG). Methods: This 2-part study involved anthropometric measurements from 20 male participants to establish design parameters using 5th and 95th percentile values. Part A assessed plane surface movement with 9 participants (7 healthy, 2 with paraplegia) navigating a simulated urban course featuring a 5° ramp, a 90° turn, and narrow passages across 3 trials. Task completion times and subjective ride easiness ratings were recorded. Part B used a Taguchi-based fractional factorial design to evaluate 3 ergonomic factors, including torso angle (λ), lever distance (L), and lever orientation (ψ), across 7 healthy participants. Maximum voluntary contraction (MVC) was measured for 4 muscles, including biceps brachii long head (BBL), triceps brachii long head (TBL), brachioradialis, and posterior deltoid (PDT). Results: In Part A, the ramp and 90° turn proved most challenging due to the wheelchair’s 65 kg weight and large turning radius (~1450 mm). Driving control scored highest (6/10), while comfort scored lowest due to the tilted seat design. In Part B, a straight torso (λ=0°) consistently reduced muscle strain, particularly for brachioradialis. A lever distance of approximately 50 mm and a neutral to slightly supinated orientation (ψ=0°-30°) optimized muscle effort. Interaction effects revealed high strain configurations (λ=45°; L=100 mm; ψ=−30°) exceeding 75 MVC, while optimal settings reduced strain to approximately 50 MVC. Conclusions: Optimal ergonomic parameters of λ=0°, L=37.5 mm, and ψ=15° are recommended to minimize fatigue and enhance user comfort. Design improvements should prioritize weight reduction, compact form factor for maneuverability, and adjustable seat tilt. The modular wheelchair design permits customization for diverse user populations. Future research should include larger, gender-diverse participant groups and real-world validation studies.
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A novel phase-difference transcranial alternating current stimulation system enables precise dual-site neuromodulation

Precise modulation of large-scale brain networks requires neuromodulation technologies capable of delivering frequency-locked stimulation with accurate and stable inter-regional phase control. However, conventional transcranial alternating current stimulation (tACS) systems generally lack robust dual-channel phase regulation and are rarely validated under realistic biological impedance conditions. Here, we present a novel phase-difference tACS system (PD-stim) designed to deliver programmable, high-precision phase offsets between stimulation targets. We performed a comprehensive engineering and in vivo validation of PD-stim, assessing biological impedance stability, waveform fidelity, amplitude stability, and phase-delivery accuracy. Impedance measurements obtained from the medial prefrontal cortex and hippocampus of rats demonstrated stable frequency-dependent profiles during stimulation. Benchmark comparisons against a clinically approved tACS device revealed comparable waveform fidelity and amplitude stability under both a standardised resistive load and in vivo recording conditions. Using simultaneous dual-channel oscilloscope recordings, PD-stim consistently generated stable sinusoidal waveforms with high phase-delivery accuracy across theta (8 Hz), beta (20 Hz), and gamma (40 Hz) frequency bands, under both biological and resistive conditions. Together, these results establish PD-stim as a precise, stable, and biologically robust dual-site neuromodulation platform that overcomes key technical limitations of existing tACS systems. This work provides a rigorously validated engineering framework for future mechanistic investigations of phase-specific modulation in distributed brain networks, while not addressing functional or therapeutic outcomes.
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Inside the challenging development of a low-friction micropump

This drug delivery wearable’s micropump needed an engineered solution for both adhesion and slip. By Philipp Begert, Trelleborg Medical Solutions I want to take you straight to the heart of a project that challenged not only my team’s technical skills, but the fundamentals of medical device engineering. It’s a classic example of requirements in conflict.…

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FUCHS highlights NYEMED 7477 grease for medical devices

NEWS RELEASE: FUCHS Lubricants Co. spotlights NYEMED 7477 high-performance medical grease Engineered for extreme conditions and broad material compatibility FUCHS Lubricants Co., the world’s largest independent lubricant manufacturer, highlights NYEMED 7477, a high-performance, general-purpose grease engineered for medical device environments where durability and patient safety are critical. Designed for applications exposed to thermal stress, mechanical load,…

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A digital audio workstation approach for matching the sound quality of speech and music for single-sided deaf patients fit with cochlear implants

IntroductionCochlear implant (CI) patients who are single-sided deaf can match the sound quality of speech presented to their CI ear and speech presented to their normal hearing ear. Previous work using this patient population has generated acoustic approximations of CI sound quality for speech, achieving high similarity ratings through interactive manipulation of sound parameters such as filtering, pitch shifting, and spectral smearing. The present study aimed to extend this approach to music.MethodsA digital audio workstation (DAW) methodology was developed for generating sound quality matches to both speech and music in 11 adults with unilateral MED-EL CIs and contralateral acoustic hearing. Participants compared the sound quality created by acoustically manipulated signals presented to their better hearing ear with the sound quality of unprocessed signals presented to the CI ear. The similarity of the two signals was rated on a scale of 1 to 10 with 10 indicating a perfect match.ResultsOn average, speech matches achieved higher similarity ratings (9.3) than music matches (6.7). Speech matches were typically achieved using bandpass filtering, pitch shifts, and distortion. Similarity ratings for speech using the digital audio workstation (9.3) were not different from those (8.7) using the custom, speech-specific software of previous studies. Music matches frequently required additional manipulations, including frequency equalization and modulation. The specific manipulations required varied across participants, and several individuals could not complete music matches despite extensive attempts.DiscussionThese findings suggest that music introduces perceptual dimensions not fully addressed by speech-based matching procedures. The DAW methodology provides an accessible framework for investigating CI sound quality and may guide future efforts to characterize and optimize sound quality for signals beyond speech.
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Predicting Momentary Suicidal Ideation From Smartphone Screenshots Using Vision-Language Models: Prospective Machine Learning Study

Background: Passive smartphone sensing shows promise for suicide prevention, but behavioral metadata (GPS, screen time, and accelerometry) often lacks the contextual information needed to detect acute psychological distress. Analyzing what people actually see, read, and type on their phones—rather than just usage patterns—may provide more proximal signals of risk. Objective: This study aimed to test whether vision-language models (VLMs) applied to passively captured smartphone screenshots can predict momentary suicidal ideation (SI). Methods: Seventy-nine adults with past month suicidal thoughts or behaviors completed ecological momentary assessments (EMA) over 28 days while screenshots were captured every 5 seconds during active phone use. We fine-tuned open-source VLMs (Qwen2.5-VL [Alibaba Cloud], LFM2-VL [Liquid AI]), and text-only models (Qwen3 [Alibaba Cloud]) to predict SI from screenshots captured in the 2 hours preceding each EMA. We evaluated performance with temporal and subject holdouts. Results: The analytic sample comprised 2.5 million screenshots from 70 participants. Temporal holdout models achieved strong discrimination at the EMA level (AUC=0.83; AUPRC=0.77), with image-based models outperforming text-only models (AUC=0.83 vs 0.79; 95% CI 0.003-0.07). Subject holdout generalization was near chance (AUC≈0.50), though a simple lexical screening method retained modest discrimination (AUC=0.62). Smaller models performed comparably to larger models, supporting feasible on-device deployment. Conclusions: Screen content predicts short-term SI with clinically meaningful accuracy when models are personalized but does not generalize across individuals. These findings support a 2-stage clinical architecture, coarse lexical screening for new patients, with personalized VLM-based monitoring after a calibration period. On-device inference may enable privacy-preserving deployment.
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