BACKGROUND: Chronic low back pain (CLBP) is one of the most common musculoskeletal problems worldwide. Even though regular exercise is recommended as the primary conservative approach in treating this condition, significant part of patients lead sedentary lifestyle. Motivation to exercise is one of the variables that effects the adherence of exercise-based treatments. This study aimed to characterize the motives for exercise, as posited by self-determination theory, in persons with CLBP, and to identify subgroups (clusters) of motivational profiles in combination with socioeconomic and clinical characteristics using k-means cluster analysis. METHODS: Data were collected between September 2022 and September 2023. A total of 103 adults with CLBP completed the paper-pencil Exercise Self-Regulation Questionnaire (SRQ-E) and provided self-reported measures on anthropometric and socio-economic characteristics. Inclusion criteria were age (≥ 18 years) and non-specific CLBP (lasting longer than 12 weeks). Exclusion criteria included specific lumbar spine pathology (e.g., fracture, cancer), worsening neurological symptoms, recent injection therapy (within 3 months), and current alcohol or drug misuse. RESULTS: Three distinct motivational clusters were identified among the 103 participants: two clusters were characterized by predominantly autonomous motivation (moderately motivated cluster: 31.1%; highly motivated cluster: 54.4%), while one cluster (controlled convinced cluster: 14.6%) showed a higher level of controlled motivation. Associations were observed between the controlled cluster and factors such as higher disability scores, longer duration of pain, greater number of completed physiotherapy sessions, and elevated BMI. Notably, the controlled motivation cluster was linked with poorer clinical outcomes. CONCLUSIONS: This study provides insights into the exercise motivation of patients with CLBP, revealing that while most patients were primarily autonomously motivated, a notable subgroup exhibited lower, controlled motivation. The presence of controlled motivation was associated with worse functioning, longer pain duration, and increased utilization of physiotherapy services. Although these findings suggest a link between motivational profiles and clinical outcomes, the cross-sectional design limits causal inferences. Further research is needed to explore these relationships longitudinally. TRIAL REGISTRATION: ClinicalTrials.Gov Identifier: NCT05512338 (22.8.2022, NCT05512338).
- MeSH
- Chronic Pain * psychology therapy rehabilitation MeSH
- Exercise psychology MeSH
- Adult MeSH
- Middle Aged MeSH
- Humans MeSH
- Low Back Pain * psychology therapy rehabilitation MeSH
- Motivation * MeSH
- Surveys and Questionnaires MeSH
- Aged MeSH
- Exercise Therapy * methods MeSH
- Check Tag
- Adult MeSH
- Middle Aged MeSH
- Humans MeSH
- Male MeSH
- Aged MeSH
- Female MeSH
- Publication type
- Journal Article MeSH
OBJECTIVES: Acute hypoxemic respiratory failure in immunocompromised patients remains the leading cause of admission to the ICU, with high case fatality. The response to the initial oxygenation strategy may be predictive of outcome. This study aims to assess the response to the evolutionary profiles of oxygenation strategy and the association with survival. DESIGN: Post hoc analysis of EFRAIM study with a nonparametric longitudinal clustering technique (longitudinal K-mean). SETTING AND PATIENTS: Multinational, observational prospective cohort study performed in critically ill immunocompromised patients admitted for an acute respiratory failure. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: A total of 1547 patients who did not require invasive mechanical ventilation (iMV) at ICU admission were included. Change in ventilatory support was assessed and three clusters of change in oxygenation modality over time were identified. Cluster A: 12.3% iMV requirement and high survival rate, n = 717 patients (46.3%); cluster B: 32.9% need for iMV, 97% ICU mortality, n = 499 patients (32.3%); and cluster C: 37.5% need for iMV, 0.3% ICU mortality, n = 331 patients (21.4%). These clusters demonstrated a high discrimination. After adjustment for confounders, clusters B and C were independently associated with need for iMV (odds ratio [OR], 9.87; 95% CI, 7.26-13.50 and OR, 19.8; 95% CI, 13.7-29.1). CONCLUSIONS: This study identified three distinct highly performing clusters of response to initial oxygenation strategy, which reliably predicted the need for iMV requirement and hospital mortality.
- MeSH
- Hypoxia * therapy mortality MeSH
- Immunocompromised Host * MeSH
- Intensive Care Units statistics & numerical data MeSH
- Critical Illness mortality therapy MeSH
- Middle Aged MeSH
- Humans MeSH
- Hospital Mortality MeSH
- Oxygen Inhalation Therapy * methods MeSH
- Prospective Studies MeSH
- Respiratory Insufficiency * therapy mortality MeSH
- Aged MeSH
- Cluster Analysis MeSH
- Respiration, Artificial * methods statistics & numerical data MeSH
- Check Tag
- Middle Aged MeSH
- Humans MeSH
- Male MeSH
- Aged MeSH
- Female MeSH
- Publication type
- Journal Article MeSH
- Multicenter Study MeSH
- Observational Study MeSH
BACKGROUND: This study investigated the subthalamic nucleus (STN) function and deep brain stimulation (DBS) effects on single-unit activity (SUA) in Parkinson's disease (PD) patients with dysarthria. METHODS: After presurgical speech analysis, we recorded STN neuronal activities while PD patients (n = 16) articulated Chinese Pinyin consonants. The Pinyin consonants were categorized by the manner and place of articulation for SUA cluster analysis. The cohort was then divided into normal articulation and dysarthria groups based on diadochokinetic (DDK) assessments. The STN SUA patterns, represented by the mean firing rate (FR), peak time, and response intensity during articulation, were analyzed and compared between the two groups. Finally, a stimulation cohort of 7 PD patients was included to test articulation and SUA pattern changes following intraoperative DBS. RESULTS: Clustering analysis of STN neuronal firing patterns demonstrated that neurons encode articulation by grouping consonants with the same manner of articulation into distinct clusters. Using k-means clustering, we further classified SUAs into two waveform types: negative spikes (type 1) and positive spikes (type 2). Dysarthria patients exhibited an increased mean FR of type 1 spikes and a reduced response intensity of type 2 spikes. During intraoperative stimulation, PD patients showed accelerated DDK, accompanied by a decrease in type 1 mean FR and an increase in type 2 mean FR. CONCLUSION: Our findings indicate the crucial role of the STN in consonant encoding and dysarthria at the single-unit level. Both SUA firing patterns in the STN and DDK performance can be modulated by DBS.
- MeSH
- Action Potentials physiology MeSH
- Dysarthria * etiology physiopathology MeSH
- Deep Brain Stimulation * methods MeSH
- Middle Aged MeSH
- Humans MeSH
- Neurons * physiology MeSH
- Subthalamic Nucleus * physiopathology MeSH
- Parkinson Disease * physiopathology complications therapy MeSH
- Aged MeSH
- Check Tag
- Middle Aged MeSH
- Humans MeSH
- Male MeSH
- Aged MeSH
- Female MeSH
- Publication type
- Journal Article MeSH
BACKGROUND: Antineutrophil cytoplasmic antibody (ANCA)-associated vasculitis is a heterogenous autoimmune disease. While traditionally stratified into two conditions, granulomatosis with polyangiitis (GPA) and microscopic polyangiitis (MPA), the subclassification of ANCA-associated vasculitis is subject to continued debate. Here we aim to identify phenotypically distinct subgroups and develop a data-driven subclassification of ANCA-associated vasculitis, using a large real-world dataset. METHODS: In the collaborative data reuse project FAIRVASC (Findable, Accessible, Interoperable, Reusable, Vasculitis), registry records of patients with ANCA-associated vasculitis were retrieved from six European vasculitis registries: the Czech Registry of ANCA-associated vasculitis (Czech Republic), the French Vasculitis Study Group Registry (FVSG; France), the Joint Vasculitis Registry in German-speaking Countries (GeVas; Germany), the Polish Vasculitis Registry (POLVAS; Poland), the Irish Rare Kidney Disease Registry (RKD; Ireland), and the Skåne Vasculitis Cohort (Sweden). We performed model-based clustering of 17 mixed-type clinical variables using a parsimonious mixture of two latent Gaussian variable models. Clinical validation of the optimal cluster solution was made through summary statistics of the clusters' demography, phenotypic and serological characteristics, and outcome. The predictive value of models featuring the cluster affiliations were compared with classifications based on clinical diagnosis and ANCA specificity. People with lived experience were involved throughout the FAIRVASVC project. FINDINGS: A total of 3868 patients diagnosed with ANCA-associated vasculitis between Nov 1, 1966, and March 1, 2023, were included in the study across the six registries (Czech Registry n=371, FVSG n=1780, GeVas n=135, POLVAS n=792, RKD n=439, and Skåne Vasculitis Cohort n=351). There were 2434 (62·9%) patients with GPA and 1434 (37·1%) with MPA. Mean age at diagnosis was 57·2 years (SD 16·4); 2006 (51·9%) of 3867 patients were men and 1861 (48·1%) were women. We identified five clusters, with distinct phenotype, biochemical presentation, and disease outcome. Three clusters were characterised by kidney involvement: one severe kidney cluster (555 [14·3%] of 3868 patients) with high C-reactive protein (CRP) and serum creatinine concentrations, and variable ANCA specificity (SK cluster); one myeloperoxidase (MPO)-ANCA-positive kidney involvement cluster (782 [20·2%]) with limited extrarenal disease (MPO-K cluster); and one proteinase 3 (PR3)-ANCA-positive kidney involvement cluster (683 [17·7%]) with widespread extrarenal disease (PR3-K cluster). Two clusters were characterised by relative absence of kidney involvement: one was a predominantly PR3-ANCA-positive cluster (1202 [31·1%]) with inflammatory multisystem disease (IMS cluster), and one was a cluster (646 [16·7%]) with predominantly ear-nose-throat involvement and low CRP, with mainly younger patients (YR cluster). Compared with models fitted with clinical diagnosis or ANCA status, cluster-assigned models demonstrated improved predictive power with respect to both patient and kidney survival. INTERPRETATION: Our study reinforces the view that ANCA-associated vasculitis is not merely a binary construct. Data-driven subclassification of ANCA-associated vasculitis exhibits higher predictive value than current approaches for key outcomes. FUNDING: European Union's Horizon 2020 research and innovation programme under the European Joint Programme on Rare Diseases.
- MeSH
- Anti-Neutrophil Cytoplasmic Antibody-Associated Vasculitis * classification diagnosis epidemiology blood immunology MeSH
- Adult MeSH
- Cohort Studies MeSH
- Middle Aged MeSH
- Humans MeSH
- Microscopic Polyangiitis classification epidemiology blood diagnosis immunology MeSH
- Registries * statistics & numerical data MeSH
- Aged MeSH
- Cluster Analysis MeSH
- Check Tag
- Adult MeSH
- Middle Aged MeSH
- Humans MeSH
- Male MeSH
- Aged MeSH
- Female MeSH
- Publication type
- Journal Article MeSH
- Geographicals
- Europe MeSH
INTRODUCTION: Data from mHealth apps can provide valuable information on rhinitis control and treatment patterns. However, in MASK-air®, these data have only been analyzed cross-sectionally, without considering the changes of symptoms over time. We analyzed data from MASK-air® longitudinally, clustering weeks according to reported rhinitis symptoms. METHODS: We analyzed MASK-air® data, assessing the weeks for which patients had answered a rhinitis daily questionnaire on all 7 days. We firstly used k-means clustering algorithms for longitudinal data to define clusters of weeks according to the trajectories of reported daily rhinitis symptoms. Clustering was applied separately for weeks when medication was reported or not. We compared obtained clusters on symptoms and rhinitis medication patterns. We then used the latent class mixture model to assess the robustness of results. RESULTS: We analyzed 113,239 days (16,177 complete weeks) from 2590 patients (mean age ± SD = 39.1 ± 13.7 years). The first clustering algorithm identified ten clusters among weeks with medication use: seven with low variability in rhinitis control during the week and three with highly-variable control. Clusters with poorly-controlled rhinitis displayed a higher frequency of rhinitis co-medication, a more frequent change of medication schemes and more pronounced seasonal patterns. Six clusters were identified in weeks when no rhinitis medication was used, displaying similar control patterns. The second clustering method provided similar results. Moreover, patients displayed consistent levels of rhinitis control, reporting several weeks with similar levels of control. CONCLUSIONS: We identified 16 patterns of weekly rhinitis control. Co-medication and medication change schemes were common in uncontrolled weeks, reinforcing the hypothesis that patients treat themselves according to their symptoms.
- MeSH
- Humans MeSH
- Longitudinal Studies MeSH
- Surveys and Questionnaires MeSH
- Rhinitis * epidemiology MeSH
- Telemedicine * MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
With the alarming increase in dying trees and massive logging in the Czech forests due to bark beetle infestation, the collection of non-wood forest products, a beneficial recreational activity in the Czech Republic, is now being promoted as an alternative to wood provisioning services. This paper aims to present findings on the non-wood forest product preferences in the country as part of a baseline assessment for promoting the usage. This study relied on the 2019 national survey data of public preferences in collecting forest berries, mushrooms, honey, and medicinal herbs. K-means cluster analysis was employed to classify the respondents. A binary logistic regression with a conditional forward approach was employed to identify the potential predictors of the high preference for each non-wood forest product. Data from 1,050 online respondents were included, and two groups of respondents were clustered based on their preferences for the entire non-wood forest, i.e., higher and lower utilization. The regression analysis revealed that frequent forest visitors were the primary predictor of high utilization of all non-wood forest products (between 1.437 to 4.579 odd ratios), in addition to age, gender, and location of the forest property. By clustering the respondents based on the high and low preferences in utilizing non-wood forest products, the promotion of this service, from recreational to potential livelihood activities and economic benefits, can be better targeted, e.g., target customer, infrastructure development in the location with high preferences, scenarios based on the type of owners (municipal or private forest owners), which in accordance to the national forest policy and laws, and, at the same time, maintain the ecological stability.
- Publication type
- Journal Article MeSH
AIMS: Rates of obesity have reached epidemic proportions, especially among people with psychiatric disorders. While the effects of obesity on the brain are of major interest in medicine, they remain markedly under-researched in psychiatry. METHODS: We obtained body mass index (BMI) and magnetic resonance imaging-derived regional cortical thickness, surface area from 836 bipolar disorders (BD) and 1600 control individuals from 14 sites within the ENIGMA-BD Working Group. We identified regionally specific profiles of cortical thickness using K-means clustering and studied clinical characteristics associated with individual cortical profiles. RESULTS: We detected two clusters based on similarities among participants in cortical thickness. The lower thickness cluster (46.8% of the sample) showed thinner cortex, especially in the frontal and temporal lobes and was associated with diagnosis of BD, higher BMI, and older age. BD individuals in the low thickness cluster were more likely to have the diagnosis of bipolar disorder I and less likely to be treated with lithium. In contrast, clustering based on similarities in the cortical surface area was unrelated to BD or BMI and only tracked age and sex. CONCLUSIONS: We provide evidence that both BD and obesity are associated with similar alterations in cortical thickness, but not surface area. The fact that obesity increased the chance of having low cortical thickness could explain differences in cortical measures among people with BD. The thinner cortex in individuals with higher BMI, which was additive and similar to the BD-associated alterations, may suggest that treating obesity could lower the extent of cortical thinning in BD.
- MeSH
- Bipolar Disorder * diagnosis MeSH
- Body Mass Index MeSH
- Humans MeSH
- Magnetic Resonance Imaging MeSH
- Obesity complications diagnostic imaging MeSH
- Cluster Analysis MeSH
- Temporal Lobe pathology MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
- Research Support, N.I.H., Extramural MeSH
V práci poukazujeme na skupiny tréningových ukazovateľov determinujúcich športovú výkonnosť v chôdzi na 35 km. Na redukciu ukazovateľov do množím faktorov sme použili exploračnú faktorovú analýzu. K posúdeniu jej vhodnosti bola využitá KMO štatistika a Bartlettov test sféričnosti (BTS). Na minimalizáciu počtu faktorov sme použili ortogonálnu rotáciu Quartimax.Odhad založený na metóde hlavných komponentov vysvetľujú 3 zhluky tréningových ukazovateľov na 65,38 % variability. Zhluk prvej množiny faktorov (33,21 %) tvorili ukazovatele chodeckej intenzívnej tempovej rýchlosti (STU 101), extenzívnej špeciálnej vytrvalosti (STU 104), intenzívnej a extenzívnej tempovej vytrvalosti (STU 105 a STU 106) i aeróbnej vytrvalosti (STU 107). Druhú množinu faktorov (18,67 %) tvorili chodecká extenzívna tempová rýchlosť (STU 102) a intenzívna špeciálna vytrvalosť (STU 103). Z ukazovateľov chodeckého aeróbneho zaťaženia definujeme STU 108 ako tretí faktor (13,50 %). Všetky faktory sú pozitívne sýtené jednotlivými ukazovateľmi.Výsledky poukázali na intraindividuálnu štruktúru tréningových ukazovateľov determinujúcich športovú výkonnosť chodca v novej atletickej disciplíne. Predkladaný faktorový model môžeme orientačne použiť pri plánovaní periodizácie objemu a intenzity tréningového zaťaženia, pričom vychádzame z cieľových kategórií tréningových prostriedkov determinujúcich športovú výkonnosť chodcov v chôdzi na 35 km pri športovej výkonnosti 2:34:00.
The work refers to the groups of training indicators which determine a sport performance at 35 kilometer walking race. We used an exploration factor analysis to reduce the indicators into the sets of some factors. As we wanted to judge its suitability KMO statistics and Bartlet test of sphericity (BTS) were adopted for the test. We also employed ortogonal rotation Quartimax to minimize the number of the factors.The estimation based on the method of the main components is explained with help of three clusters of training indicators with the variability of 65,38 \%. The cluster of the first set of factors (33,21 \%) was made by indicators of intensive tempo walking speed (STI 101), extensive specific endurance (STI 104), intensive and extensive tempo endurance (STI 105 a STI 106) and also aerobic endurance (STI 107). The second set of factors (18,67 \%) was created by extensive tempo walking speed (STI 102) and intensive specific endurance (STI 103). It was possible to define the third factor (13,50 \%) STI 108 from the indicators of aerobic walking limit. All the factors are saturated positively by individual indexes.The results refer to an intraindividual structure of training indicators which determine a sport performance of a race walker at a new athletic discipline. The suggested model of factors can be used while planning the periodization of an volume and intensity of a training loud. We will start from the objective categories of training means which determine a sport performance of walkers at a walking race for 35 kilometers at the sport limit 2:34:00.
The analysis and segmentation of articular cartilage magnetic resonance (MR) images belongs to one of the most commonly routine tasks in diagnostics of the musculoskeletal system of the knee area. Conventional regional segmentation methods, which are based either on the histogram partitioning (e.g., Otsu method) or clustering methods (e.g., K-means), have been frequently used for the task of regional segmentation. Such methods are well known as fast and well working in the environment, where cartilage image features are reliably recognizable. The well-known fact is that the performance of these methods is prone to the image noise and artefacts. In this context, regional segmentation strategies, driven by either genetic algorithms or selected evolutionary computing strategies, have the potential to overcome these traditional methods such as Otsu thresholding or K-means in the context of their performance. These optimization strategies consecutively generate a pyramid of a possible set of histogram thresholds, of which the quality is evaluated by using the fitness function based on Kapur's entropy maximization to find the most optimal combination of thresholds for articular cartilage segmentation. On the other hand, such optimization strategies are often computationally demanding, which is a limitation of using such methods for a stack of MR images. In this study, we publish a comprehensive analysis of the optimization methods based on fuzzy soft segmentation, driven by artificial bee colony (ABC), particle swarm optimization (PSO), Darwinian particle swarm optimization (DPSO), and a genetic algorithm for an optimal thresholding selection against the routine segmentations Otsu and K-means for analysis and the features extraction of articular cartilage from MR images. This study objectively analyzes the performance of the segmentation strategies upon variable noise with dynamic intensities to report a segmentation's robustness in various image conditions for a various number of segmentation classes (4, 7, and 10), cartilage features (area, perimeter, and skeleton) extraction preciseness against the routine segmentation strategies, and lastly the computing time, which represents an important factor of segmentation performance. We use the same settings on individual optimization strategies: 100 iterations and 50 population. This study suggests that the combination of fuzzy thresholding with an ABC algorithm gives the best performance in the comparison with other methods as from the view of the segmentation influence of additive dynamic noise influence, also for cartilage features extraction. On the other hand, using genetic algorithms for cartilage segmentation in some cases does not give a good performance. In most cases, the analyzed optimization strategies significantly overcome the routine segmentation methods except for the computing time, which is normally lower for the routine algorithms. We also publish statistical tests of significance, showing differences in the performance of individual optimization strategies against Otsu and K-means method. Lastly, as a part of this study, we publish a software environment, integrating all the methods from this study.
BACKGROUND AND OBJECTIVES: Patterns of speech disorder in Parkinson disease (PD), which are highly variable across individual patients, have not been systematically studied. Our aim was to identify speech subtypes in treatment-naive patients with PD and to examine their response to long-term dopaminergic therapy. METHODS: We recorded speech data from a total of 111 participants with de novo PD; 83 of the participants completed the 12-month follow-up (69 patients with PD on stable dopaminergic medication and 14 untreated controls with PD). Unsupervised k-means cluster analysis was performed on 8 distinctive parameters of hypokinetic dysarthria examined with quantitative acoustic analysis. RESULTS: Three distinct speech subtypes with similar prevalence, symptom duration, and motor severity were detected: prosodic, phonatory-prosodic, and articulatory-prosodic. Besides monopitch and monoloudness, which were common in each subtype, speech impairment was more severe in the phonatory-prosodic subtype with predominant dysphonia and the articulatory-prosodic subtype with predominant imprecise consonant articulation than in the prosodic subtype. Clinically, the prosodic subtype was characterized by a prevalence of women and younger age, while articulatory-prosodic subtype was characterized by the prevalence of men, older age, greater severity of axial gait symptoms, and poorer cognitive performance. The phonatory-prosodic subtype clinically represented intermediate status in age with mostly men and preserved cognitive performance. While speech of untreated controls with PD deteriorated over 1 year (p = 0.02), long-term dopaminergic medication maintained stable speech impairment severity in the prosodic and articulatory-prosodic subtypes and improved speech performance in patients with the phonatory-prosodic subtype (p = 0.002). DISCUSSION: Distinct speech phenotypes in de novo PD reflect divergent underlying mechanisms and allow prediction of response of speech impairment to levodopa therapy. CLASSIFICATION OF EVIDENCE: This study provides Class II evidence that, in patients with newly diagnosed PD with speech impairment, speech phenotype is associated with levodopa responsiveness.
- MeSH
- Dysarthria complications MeSH
- Levodopa * therapeutic use MeSH
- Humans MeSH
- Parkinson Disease * complications diagnosis drug therapy MeSH
- Speech Disorders etiology MeSH
- Speech physiology MeSH
- Check Tag
- Humans MeSH
- Female MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
