Lenka Lhotska

Introduction Musculoskeletal disorders associated with muscular strain on the neck and shoulders affect women in administration more often than men. Ergonomic guidelines have historically been derived from male models, often ignoring biomechanical differences between the sexes. This study aims to quantify the acute mechanical response of the neck muscles of men and women to a standardized writing task at university desks of the same height and 0° inclination. Methods: The research involved 57 healthy university students (34 women, 23 men; age 19–24 years). Muscle tone (F), stiffness (S), and viscoelastic decrement (D) in the sternocleidomastoideus muscle (SCM), trapezius muscle (UT), and semispinalis capitis muscle (SSC) were assessed by the non-invasive MyotonPRO device before and after 20 min of continuous writing. Results: In the entire cohort, a significant change before and after exercise was found only in the UT decrement (p = 0.021). Gender differences were found in the change in muscle tone (ΔF), SCM (p = 0.031), and SSC (p = 0.039). On average, SCM tone increased slightly in men (+0.120 Hz) and decreased in women (−0.294 Hz), while SSC tone decreased in men (−0.374 Hz) and increased in women (+0.450 Hz). Conclusions: A twenty-minute writing task at a university desk with a fixed height was associated with measurable acute changes in selected mechanical properties of the neck muscles and with differences between the sexes in the direction of change in the tone of SCM and SSC. The clinical relevance of these findings remains unclear and should be validated in larger studies with detailed anthropometric and ergonomic evaluation and longer follow-up.

Objectives: The aim of this article is to synthesize current evidence on childhood myopia and unify key findings and recommendations for its prevention or mitigation. Methods: A review of studies examining myopia prevalence and progression in school-age children was conducted, focusing on age, gender, family history, screen time, sleep duration, near-work demands, and ergonomic factors in school settings. Results: Myopia was more common in older children, in girls, and in those with a parental history of myopia. Increased near work—reading, writing, and digital device use—was strongly linked to progression, while poor posture and inadequate lighting further contributed. Interventions such as regular outdoor activity, reduced recreational screen use, and maintaining proper reading distance showed beneficial effects. Conclusion: Childhood myopia is shaped by both genetic and environmental influences. A multifactorial approach, including proper ergonomics, balanced academic load, outdoor time, and healthy sleep routines, appears most effective, though further research is needed to refine prevention strategies.

Backround: Musculoskeletal disorders associated with muscular strain on the neck and shoulders affect women in administration more often than men. Ergonomic guidelines have historically been derived from male models, often ignoring biomechanical differences between the sexes. The study aims to quantify the acute biomechanical response of the neck muscles of men and women to a standardized writing task. Methods: The research involved 57 healthy university students (34 women, 23 men; age 19–24 years). Muscle tone (F), stiffness (S), and viscoelastic decrement (D) in sternocleidomastoideus muscle (SCM), trapezius muscle (UT), and semispinalis capitis muscle (SSC) were assessed by the non-invasive MyotonPRO device before and after 20 minutes of continuous writing. Results: The results were statistically quantified and showed significant differences in muscle strategy between the sexes (p < 0.05). While women experienced a fatigue decrease in tone after exercise, the SCM as a superficial neck flexor and the deeper neck extensor SSC recorded a significant compensatory increase in tone, the opposite was true for men. Conslusion: These objective data show that identical office work causes a different physiological burden in women. Prevention of musculoskeletal disorders should therefore abandon a one-size-fits-all approach and move towards gender-specific solutions.

BACKGROUND Frailty affects more than 20% of older adults and increases falls, disability, and health care use. Traditional rehabilitation is limited by accessibility barriers. Interactive digital health interventions (DHIs), particularly mobile health (mHealth) programs integrating wearable activity trackers with behavioral coaching, may offer scalable alternatives. OBJECTIVE This systematic review and meta-analysis examined the effectiveness of interactive DHIs compared with conventional rehabilitation for physical function and physical activity in older adults with frailty or prefrailty, and whether intervention modality and behavioral coaching components influence outcomes. METHODS The authors searched MEDLINE, Embase, Cochrane Library, Airiti, CNKI, and WanFang through August 31, 2023, for randomized controlled trials (RCTs) involving adults aged ≥65 years with frailty or prefrailty. Primary outcomes included grip strength, Timed Up and Go (TUG), gait speed, and the Fried Frailty Index; secondary outcomes were moderate-to-vigorous physical activity (MVPA) and step count. Duplicate records were identified and removed automatically using PICO Portal (Evidence Partners). All subsequent screening of titles, abstracts, and full-text articles was performed manually by two independent reviewers without the use of automated eligibility tools; disagreements were resolved by a third reviewer. Two reviewers independently screened studies, extracted data, and assessed risk of bias (RoB 2); certainty of evidence was rated using GRADE. Random-effects meta-analyses were performed with prespecified subgroup and sensitivity analyses. RESULTS Nine RCTs (358 participants) were included. Pooling all trials, DHIs showed no clear superiority over conventional rehabilitation for primary functional outcomes, with low to very low certainty of evidence. DHIs produced a robust, clinically important increase in MVPA of 14.31 minutes per valid day (95% CI 12.45–16.16; 2 trials; I2 = 0%). Sensitivity analyses excluding high risk-of-bias trials revealed clinically meaningful improvements in gait speed (mean difference [MD] 0.11 m/s), Fried Frailty Index (MD −1.00 points), and daily step count (approximately 480 additional steps). Modality-based subgroup analyses showed that mHealth programs incorporating behavioral coaching achieved superior improvements in grip strength (MD 2.90 kgf) and TUG (MD −6.57 seconds, exceeding the minimal clinically important difference) compared with exergame-based interventions. Findings for the Fried Frailty Index, MVPA, and step count should be interpreted with caution, as each was informed by only two trials (73 participants total). CONCLUSIONS DHIs may increase MVPA and may improve selected functional outcomes, including gait speed and frailty status, when implemented with sufficient fidelity, but the certainty of evidence remains low to very low. mHealth interventions integrating wearable activity tracking and behavioral coaching appear more promising than stand- alone exergames, although confirmatory trials are needed before firm clinical prioritization. Future trials should evaluate long-term outcomes, economic impacts, and implementation fidelity. CLINICALTRIAL PROSPERO registration number CRD42023462594; https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42023462594

The reliability and ethical use of artificial intelligence (AI) in medicine fundamentally depend on the integrity of underlying data. This peer-review-style report examines data integrity - covering data quality, noise, bias, and data collection design - as the foundation of trustworthy medical AI. Medical datasets are typically small, heterogeneous, high-dimensional, and shaped by human and technical variability, making them particularly vulnerable to quality deficits. We show how poor data quality, excessive noise, and systematic bias distort analyses and clinical decisions. We outline a ten-step framework for robust data collection that spans clear study objectives, population definition, standardized protocols, noise and bias mitigation, ethical oversight, and quality control. Beyond conceptual discussion, the report integrates practical examples, methodological considerations, and ethical implications to highlight data integrity as both a scientific and moral requirement for transparent, equitable, and clinically reliable AI. Ultimately, only well-curated, representative, and unbiased data allow medical AI to support safe patient care and advance precision medicine.

Long QT Syndrome (LQTS) is an inherited cardiac disorder characterized by dysfunctional cardiac ion channels, which result in prolonged QT intervals on electrocardiograms (ECGs). LQTS can lead to severe clinical manifestations, including syncope, ventricular arrhythmias, and sudden cardiac death. Effective genotype-specific management strategies are essential to mitigate the risk of life-threatening arrhythmias. This study aims to achieve automatic discrimination among the LQT1, LQT2, and LQT3 genotypes to enable targeted treatment and prevention strategies. Utilizing ECG data from the Telemetric and Holter ECG Warehouse’s LQTS database, our methodology involves an automated extraction process of short ECG signals, geometric parameterization techniques, and classification using a two-stage cascade of binary support vector machine classifiers. The input features for the classifiers are derived from Lead I ECG signals sampled at 200 Hz, highlighting the potential application in developing single-lead ECG monitoring devices and applications, such as widely used smartwatches, for which short recording duration, low sampling frequency, and arm-to-arm lead measurements are fundamental prerequisites for practical use. The proposed classifier achieved 71% weighted accuracy on out-of-sample data (LQT1: 65% recall, 58% precision; LQT2: 79% recall, 82% precision; and LQT3: 71% recall, 77% precision). Our findings demonstrate the feasibility of noninvasive genotype differentiation for LQTS based on the morphological analysis of ECG signals, providing an advancement in the field of personalized cardiology and the development of portable diagnostic tools.

Low back pain (LBP) remains one of the most prevalent and disabling musculoskeletal conditions worldwide, shaped by interacting mechanical, neurophysiological, inflammatory, vascular, and behavioral factors. Conventional care often relies on generalized exercise programs and episodic, predominantly subjective assessment, which can underrepresent inter-individual heterogeneity and longitudinal change. Recent bioengineering advances enable continuous, multimodal monitoring of objective correlates of function—neuromuscular activation and coordination (sEMG/polyEMG), movement patterns and activity exposure (IMU), and complementary physiological context (e.g., autonomic and perfusion-related signals). Rather than measuring pain directly, these signals can contextualize symptoms, support treatment stratification within non-surgical care, and enable trajectory monitoring with early non-response flags to guide timely rehabilitation adjustment under clinician oversight. When integrated with transparent, implementation-oriented analytics, biosensing can also support incremental closed-loop rehabilitation through patient-facing feedback and adaptive progression rules. This review synthesizes current and emerging biosensing approaches for LBP and highlights key translational requirements—outcome-linked validation, standardization, and workflow integration—to bridge engineering innovation with clinically usable, data-informed rehabilitation.