Dysphagia最新文献

Wearable sensors show promise for monitoring breathing and swallowing events in patients with dysphagia. However, little is known about factors that facilitate their use for swallowing rehabilitation, particularly in individuals with Parkinson's disease (PD). Examining these factors in patients with PD (PwPD) is critical given the prevalence of motor disorders in this population. The aims of this qualitative study were to explore PwPD's: 1) impressions of a novel wearable sensor and customized iOS application developed for monitoring swallowing and breathing; 2) recommendations and desired functions for future iterations; and 3) anticipated benefits and drawbacks of the sensor/app for managing swallowing difficulties. Semi-structured interviews were conducted with PwPD. Prior to the study session, a "sensor kit" was shipped to participants' homes. Study sessions were audio-recorded, transcribed verbatim, and inductively analyzed using a constant comparative approach. Study sessions were completed with 10 PwPD and 2 care partners. Five major themes were identified: 1) sensor/adhesive features; 2) wearing the sensor in public; 3) app features; 4) recommendations; and 5) anticipated benefits and drawbacks for managing swallowing disorders. PwPD highlighted the sensor's potential benefits for managing PD-related swallowing disorders, particularly the sensor's ability to track swallowing function over time. Participants' recommendations for additional features included adding reminder vibrations to prompt swallowing, however, they also expressed concerns about real-life use of the sensor including potential for sensory distractions and questioned the devices affordability. PwPD feedback revealed critical insights for the future adaptations of this innovative device for managing PD and related swallowing disorders.

Effect of interventions or diseases on the mechanisms of swallowing difficulties, and indications to change nipples, flows, or formula thickness in high-risk infants remains unclear. We evaluated and compared the effects of nipple flows and/or formula thickness on pharyngoesophageal timing and amplitude characteristics during oral milk challenge (OMC). A total of 48 OMC trials were undertaken in 12 infants [born at 36.4 ± 3.8 weeks gestation, studied at 48.0 ± 5.6 weeks postmenstrual age (PMA)] concurrent with high resolution pharyngoesophageal manometry (HRPM). We evaluated timing and amplitude of swallowing characteristics (pharyngeal and esophageal contractile strength, duration, activity, pharyngeal peak interval variability, bursts, and rhythm) and volume intakes. We compared within and between the entities: un-thickened formula vs. thickened formula and slower- vs. faster flow nipple. OMC durations averaged 71 ± 31 s and did not differ between interventions (p > 0.05). Pharyngeal swallow rhythms (timing) and contractile characteristics were dependent on the interaction between nipple flow rates and formula thickness (p < 0.05). Thickness decreased overall contractility and modified distal pharyngeal contractile activity (p < 0.05), which is critical to opening the upper esophageal sphincter. A structural equation model was developed that revealed the influence of PMA, respiratory status, volume extracted, nipple flow rate, and thickener presence on pharyngeal swallowing frequency with a comparative fit index of 0.83. Oropharyngeal stimulus-types trigger, integrate, and modify brainstem mediated oral and pharyngeal rhythms and modulate contractility and airway protection mechanisms. Effects of therapies, maturation and diagnoses are measurable using these novel approaches at crib-side.

White matter (WM) enables complex brain connectivity by linking several cortical and subcortical regions. Most studies investigating the association between WM tracts and swallowing function have predominantly used a disease (lesion) based approach, and there is currently a paucity of research investigating the associations between swallowing physiology and WM microstructure in healthy individuals. Moreover, studies in healthy individuals are essential to understanding typical WM architecture and identifying any deviations caused by diseases or adaptations resulting from specific interventions or training regimes. The current study addresses this critical gap by investigating the association between quantitative metrics of WM microstructure and kinematic and temporal measures of swallowing biomechanics in healthy young adults. Diffusion-weighted magnetic resonance imaging (DW-MRI) was obtained from 17 right-handed healthy adults (males = 9; females = 8) aged 20 to 35 (mean age = 27.11 years). DW-MRI was pre-processed and analyzed using a custom-developed analysis pipeline to generate diffusion tensor image (DTI) derived scalar measures. Furthermore, videofluoroscopic data were collected from these participants and quantified using computational analysis of swallowing mechanics (CASM) and traditional pixel-based temporal and kinematic measures. We performed partial correlations to explore the association between swallowing biomechanics and WM diffusion metrics, with participants' age and sex as covariates. Our study revealed that the corpus callosum, cerebellar peduncle, thalamic radiation, corticospinal tract, cingulum, stratum, corona radiata, fornix, internal capsule, external capsule, and the superior frontal-occipital fasciculus showed significant bidirectional associations with the kinematic and temporal measures of swallowing biomechanics investigated in the current study. These findings are interpreted in relation to lesion studies and well-established functions of WM tracts. Future directions and limitations of our study are also discussed.

Feeding efficiency and safety are often driven by bolus volume, which is one of the most common clinical measures of assessing swallow performance. However, manual measurement of bolus area is time-consuming and suffers from high levels of inter-rater variability. This study proposes a machine learning (ML) pipeline using ilastik, an accessible bioimage analysis tool, to automate the measurement of bolus area during swallowing. The pipeline was tested on 336 swallows from videofluoroscopic recordings of 8 infant pigs during bottle feeding. Eight trained raters manually measured bolus area in ImageJ and also used ilastik's autocontext pixel-level labeling and object classification tools to train ML models for automated bolus segmentation and area calculation. The ML pipeline trained in 1h42min and processed the dataset in 2 min 48s, a 97% time saving compared to manual methods. The model exhibited strong performance, achieving a high Dice Similarity Coefficient (0.84), Intersection over Union (0.76), and inter-rater reliability (intraclass correlation coefficient = 0.79). The bolus areas from the two methods were highly correlated (R² = 0.74 overall, 0.78 without bubbles, 0.67 with bubbles), with no significant difference in measured bolus area between the methods. Our ML pipeline, requiring no ML expertise, offers a reliable and efficient method for automatically measuring bolus area. While human confirmation remains valuable, this pipeline accelerates analysis and improves reproducibility compared to manual methods. Future refinements can further enhance precision and broaden its application in dysphagia research.

Effortful swallowing (ES) is a promising exercise option for patients with dysphagia due to reduced tongue base retraction. However, studies confirming how exercise physiology changes when performing ES continuously over multiple sets in clinical practice are limited. This study aimed to determine the effect of visual biofeedback using the Iowa Oral Performance Instrument (IOPI) on the muscle activity of the floor-of-the-mouth (FOM) and oral swallowing pressure during ES exercise. Forty-five healthy older adults (mean age 76.1 ± 4.48y) participated in this randomized crossover study with two conditions. In the visual biofeedback condition using the IOPI, the participants were presented with a value corresponding to 80% of the maximum isometric pressure of the posterior oral tongue measured at baseline and asked to exceed the target value while performing ES. In the no-feedback condition, the IOPI screen was blocked to ensure that information regarding the swallowing pressure during ES was not provided. In the biofeedback condition, participants showed a significantly higher oral swallowing pressure from Sets 3-8 compared with the no-feedback condition (p < 0.05). In the no feedback condition, FOM muscle activity was significantly lower in Set 4 than in the Set 1 (p < 0.05). To improve the strength of the tongue and FOM muscles through ES exercise, providing biofeedback using the IOPI may be beneficial for maintaining a consistently high level of exercise intensity. Further research is required to enable the application of this study's finding to patients with dysphagia.

The aim of this study was to investigate the effects of game-based electromyography (EMG)-biofeedback therapy on swallowing functions and quality of life in patients with post-stroke dysphagia. This prospective, assessor-blind, randomized controlled trial included 33 patients with post-stroke dysphagia. The study group performed the Mendelsohn maneuver and effortful swallow using game-based EMG-biofeedback, while the control group performed the same tasks with only verbal feedback for 30 min across a total of 15 sessions. In addition, both groups received oral motor exercises and thermal-tactile stimulation for equal durations. Patients were evaluated immediately before and after the treatment using clinical swallowing assessments, including the Functional Oral Intake Scale (FOIS), Penetration-Aspiration Scale (PAS), Dysphagia Outcome and Severity Scale (DOSS), and Dysphagia Handicap Index (DHI). Statistically significant improvements were observed in FOIS scores (p = 0.038), PAS-Liquid scores (p = 0.026), and DOSS scores (p = 0.003) in the study group, while no significant changes were noted in the control group. PAS-Semisolid scores improved in both groups (study group, p = 0.002; control group, p = 0.023); however, post-treatment scores were significantly higher in the study group compared to the control group (p = 0.031). Although significant improvements in DHI total, physical, emotional, and functional scores were observed in both groups at the end of treatment (all p < 0.05), post-treatment DHI scores were significantly higher in the study group compared to the control group (all p < 0.05). The addition of game-based EMG-biofeedback to conventional therapy improved clinical and radiological outcomes, as evidenced by improvements in FOIS, PAS-Liquid, and DOSS scores, and led to statistically significant improvements in PAS-Semisolid and DHI scores. In conclusion, the inclusion of game-based EMG-biofeedback therapy in swallowing rehabilitation programs may have a positive impact on treatment outcomes.

Purpose: Dysphagia is a commonly occurring medical condition estimated to occur in between and 10% adults in the US. Despite this relatively high prevalence, the general population's understanding of this condition is currently unknown. Our aims were to (a) conduct a large-scale survey to determine the public's awareness and understanding of dysphagia in comparison to other three other health conditions and (b) compare this knowledge to relative prevalence rates of the conditions.

Methods: The survey was designed to measure four constructs of interest comparing dysphagia with insomnia, vertigo and ataxia. Constructs included: (1) Knowledge of the Condition, (2) Source of Knowledge, (3) Health Impact, (4) Treating Medical Professionals. The survey was launched via Qualtrics™ software and participants were recruited and paid using Prolific™. Descriptive statistics were used to compare participants knowledge of dysphagia with the other conditions. To obtain relative prevalence rates, Cosmos was used to quantify the number of Epic-based patient encounters with any ICD-10 code for each condition in 2023 and expressed as a percent of all patient encounters in the US in 2023.

Results: 2000 adults (n = 1030 female) aged 18-95 (median 49, IQR = 33-62) completed our survey. When asked, 'Do you know what ____ is'? participants expressed higher familiarity with insomnia (99%) and vertigo (87%) in comparison to dysphagia (25%) and ataxia (18%). From a list of 8 options, 99% and 94% participants selected the correct impairment for insomnia and vertigo respectively, compared with 44% correct for dysphagia and 22% for ataxia. Participants selected an appropriate healthcare provider for dysphagia 47% of the time compared with 74% for insomnia, 56% for vertigo and 36% for ataxia. When asked to identify up to 3 sequelae (from a list of 9), only 4% of participants were able to correctly identify all three for dysphagia, in comparison to 16% for ataxia, 27% for vertigo and 60% for insomnia. The Cosmos analysis revealed that while insomnia had the highest prevalence in 2023 (5.5% of patient encounters), dysphagia occurred much more frequently (2.4%) than vertigo (0.68%) and ataxia (0.24%).

Conclusions: These discrepancies highlight a notable gap in public awareness between dysphagia and more recognized conditions of insomnia and vertigo, even though the prevalence of dysphagia is higher than vertigo. Increasing public awareness of dysphagia is vital for early intervention, increasing quality of life, and advocating for equitable access to healthcare resources.

Digital cervical auscultation (CA) has high diagnostic test accuracy in the detection of aspiration in children. However, the clinical application of digital CA is limited because swallow sound recordings require manual segmentation by trained experts, which is time consuming and not feasible in clinical practice. The automated detection of swallowing sounds in adults from sound recordings have reported accuracies between 76 and 95%. No equivalent literature exists for the automated detection of swallowing sounds in children. This study aimed to establish whether automated machine learning using a transfer learning approach can accurately detect and segment swallows from digital CA recordings in children. Swallow sounds were collected from 16 typically developing children, median age 18 months (range 4-35 months, 50% males); and 19 videofluoroscopic swallow studies of children with pediatric feeding disorders, median age 9 months (range 3-71 months, males 36.8% males). All swallowing sounds were on thin fluids. A deep convolutional neural network (DCNN) that was pre-trained for the task of audio event classification was used as the base machine learning model. Using the raw swallow audio data as input, embedding vectors from the base DCNN were computed and used to train a feedforward neural network to identify whether an audio segment was a swallow or not. A high overall accuracy of 91% was achieved using our model, with a sensitivity (or recall) and positive predictability (or precision) of 81% and 79%, respectively. Interestingly, the model was also able to detect saliva swallows in the clinical feeding evaluation test set, even though these non-nutritive swallows were not part of the training set. This indicates a level of generalizability of the model, where it was able to recognize swallowing events that it had not "seen" before. Our study provides the highest accuracy reported to date on the automatic segmentation and detection of swallowing sounds in children.

Swallowing disorder or dysphagia is one of the common functional disorders in stroke survivors, and its early screening is important for reducing patient dependence, pneumonia incidence, mortality, and shortening the hospital stay. However, the commonly used methods to examine dysphagia, which include Toronto bedside swallowing screening test, volume viscosity swallowing test, and swallowing angiography screening/examination, are all invasive with the risk of aspiration. Here we have undertaken a detailed voice analysis on stroke patients with dysphagia by monitoring a series of acoustic parameters including maximum volume, maximum pitch, glottal noise excitation ratio, fundamental frequency perturbation, amplitude perturbation (Shimmer), maximum pronunciation time, irregularity, breath sound, overall severity, and voice disorder severity index. We show that all these acoustic parameters change significantly for the stroke patients with dysphagia compared with the healthy group as well as stroke group with no dysphagia, and the changes are correlated with the severity of pharyngeal residue especially for Shimmer and overall severity of the voice. Our findings suggest that voice analysis, which is quick and non-invasive, may give an important initial indication on the severity of dysphagia and cross-validate results from swallowing tests that clinicians may further pursue for a thorough diagnosis of dysphagia.