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BMJ Open Jun 2024The main aim of this study was to demonstrate how ordered network analysis of video-recorded interactions combined with verbal response mode (VRM) coding (eg,... (Observational Study)
Observational Study
Applying ordered network analysis to video-recorded physician-nurse interactions to examine communication patterns associated with shared understanding in inpatient oncology care settings.
OBJECTIVES
The main aim of this study was to demonstrate how ordered network analysis of video-recorded interactions combined with verbal response mode (VRM) coding (eg, edification, disclosure, reflection and interpretation) can uncover specific communication patterns that contribute to the development of shared understanding between physicians and nurses. The major hypothesis was that dyads that reached shared understanding would exhibit different sequential relationships between VRM codes compared with dyads that did not reach shared understanding.
DESIGN
Observational study design with the secondary analysis of video-recorded interactions.
SETTING
The study was conducted on two oncology units at a large Midwestern academic health care system in the USA.
PARTICIPANTS
A total of 33 unique physician-nurse dyadic interactions were included in the analysis. Participants were the physicians and nurses involved in these interactions during patient care rounds.
PRIMARY AND SECONDARY OUTCOME MEASURES
The primary outcome measure was the development of shared understanding between physicians and nurses, as determined by prior qualitative analysis. Secondary measures included the frequencies, orders and co-occurrences of VRM codes in the interactions.
RESULTS
A Mann-Whitney U test showed that dyads that reached shared understanding (N=6) were statistically significantly different (U=148, p=0.00, r=0.93) from dyads that did not reach shared understanding (N=25) in terms of the sequential relationships between edification and disclosure, edification and advisement, as well as edification and questioning. Dyads that reached shared understanding engaged in more edification followed by disclosure, suggesting the importance of this communication pattern for reaching shared understanding.
CONCLUSIONS
This novel methodology demonstrates a robust approach to inform interventions that enhance physician-nurse communication. Further research could explore applying this approach in other healthcare settings and contexts.
Topics: Humans; Communication; Video Recording; Physician-Nurse Relations; Female; Male; Adult; Middle Aged; Inpatients
PubMed: 38889940
DOI: 10.1136/bmjopen-2024-084653 -
Frontiers in Psychology 2024There is preliminary evidence that children after traumatic brain injury (TBI) have accelerated long-term forgetting (ALF), i.e., an adequate learning and memory...
Impaired episodic verbal memory recall after 1 week and elevated forgetting in children after mild traumatic brain injury - results from a short-term longitudinal study.
OBJECTIVE
There is preliminary evidence that children after traumatic brain injury (TBI) have accelerated long-term forgetting (ALF), i.e., an adequate learning and memory performance in standardized memory tests, but an excessive rate of forgetting over delays of days or weeks. The main aim of this study was to investigate episodic memory performance, including delayed retrieval 1 week after learning, in children after mild TBI (mTBI).
METHODS
This prospective study with two time-points (T1: 1 week after injury and T2: 3-6 months after injury), included data of 64 children after mTBI and 57 healthy control children aged between 8 and 16 years. We assessed episodic learning and memory using an auditory word learning test and compared executive functions (interference control, working memory, semantic fluency and flexibility) and divided attention between groups. We explored correlations between memory performance and executive functions. Furthermore, we examined predictive factors for delayed memory retrieval 1 week after learning as well as for forgetting over time.
RESULTS
Compared to healthy controls, patients showed an impaired delayed recall and recognition performance 3-6 months after injury. Executive functions, but not divided attention, were reduced in children after mTBI. Furthermore, parents rated episodic memory as impaired 3-6 months after injury. Additionally, verbal learning and group, but not executive functions, were predictive for delayed recall performance at both time-points, whereas forgetting was predicted by group.
DISCUSSION
Delayed recall and forgetting over time were significantly different between groups, both post-acutely and in the chronic phase after pediatric mTBI, even in a very mildly injured patient sample. Delayed memory performance should be included in clinical evaluations of episodic memory and further research is needed to understand the mechanisms of ALF.
PubMed: 38887630
DOI: 10.3389/fpsyg.2024.1359566 -
Frontiers in Psychology 2024In clinical neuropsychology, the phenomenon of accelerated long-term forgetting (ALF) has advanced to be a marker for subtle but clinically relevant memory problems...
INTRODUCTION
In clinical neuropsychology, the phenomenon of accelerated long-term forgetting (ALF) has advanced to be a marker for subtle but clinically relevant memory problems associated with a range of neurological conditions. The normal developmental trajectory of long-term memory, in this case, memory recall after 1 week, and the influence of cognitive variables such as intelligence have not extensively been described, which is a drawback for the use of accelerated long-term forgetting measures in pediatric neuropsychology.
METHODS
In this clinical observation study, we analyzed the normal developmental trajectory of verbal memory recall after 1 week in healthy children and adolescents. We hypothesized that 1-week recall and 1-week forgetting would be age-dependent and correlate with other cognitive functions such as intelligence and working memory. Sixty-three healthy participants between the ages of 8 and 16 years completed a newly developed auditory verbal learning test (WoMBAT) and the WISC-V intelligence test (General Ability Index, GAI). Using these tests, 1 week recall and 1 week forgetting have been studied in relation to GAI, verbal learning performance, and verbal working memory.
RESULTS
Neither 1-week recall nor 1-week forgetting seems to be age-dependent. They are also not significantly predicted by other cognitive functions such as GAI or working memory. Instead, the ability to recall a previously memorized word list after 7 days seems to depend solely on the initial learning capacity.
CONCLUSION
In the clinical setting, this finding can help interpret difficulties in free recall after 7 days or more since they can probably not be attributed to young age or low intelligence.
PubMed: 38887625
DOI: 10.3389/fpsyg.2024.1338826 -
Multiple Sclerosis and Related Disorders Jun 2024Several studies have shown the different relationships between cognitive functions and structural magnetic resonance imaging (MRI) measurements in people with multiple... (Review)
Review
BACKGROUND
Several studies have shown the different relationships between cognitive functions and structural magnetic resonance imaging (MRI) measurements in people with multiple sclerosis (pwMS). However, there is an ongoing debate regarding the magnitude of correlation between MRI measurements and specific cognitive function tests. This systematic review and meta-analysis aimed to synthesize the most consistent correlations between MRI measurements and cognitive function in pwMS.
METHODS
PubMed/MEDLINE, Embase, Scopus, and Web of Science databases were systematically searched up to February 2023, to find relevant data. The search utilized syntax and medical subject headings (MeSH) relevant to cognitive performance tests and MRI measurements in pwMS. The R software version 4.3.3 with random effect models was used to estimate the pooled effect sizes.
RESULTS
13,559 studies were reviewed, of which 136 were included. The meta-analyses showed that thalamic volume had the most significant correlations with Symbol Digit Modalities Test (SDMT) r = 0.47 (95 % CI: 0.39 to 0.56, p < 0.001, I = 88 %), Brief Visual Memory Test-Revised-Total Recall (BVMT-TR) r = 0.51 (95 % CI: 0.36 to 0.66, p < 0.001, I = 81 %), California Verbal Learning Test-II-Total Recall (CVLT-TR) r = 0.47 (95 % CI: 0.34 to 0.59, p < 0.001, I = 69 %,), and Delis-Kaplan Executive Function System (DKEFS) r = 0.48 (95 % CI: 0.34 to 0.63, p < 0.001, I = 22 %,).
CONCLUSION
We conclude that thalamic volume exhibits highest relationships with information processing speed (IPS), visuospatial learning-memory, verbal learning-memory, and executive function in pwMS. A comprehensive understanding of the intricacies of the mechanisms underpinning this association requires additional research.
PubMed: 38885600
DOI: 10.1016/j.msard.2024.105705 -
JMIR MHealth and UHealth Jun 2024Despite the increasing need for digital services to support geriatric mental health, the development and implementation of digital mental health care systems for older...
Digital Phenotyping of Geriatric Depression Using a Community-Based Digital Mental Health Monitoring Platform for Socially Vulnerable Older Adults and Their Community Caregivers: 6-Week Living Lab Single-Arm Pilot Study.
BACKGROUND
Despite the increasing need for digital services to support geriatric mental health, the development and implementation of digital mental health care systems for older adults have been hindered by a lack of studies involving socially vulnerable older adult users and their caregivers in natural living environments.
OBJECTIVE
This study aims to determine whether digital sensing data on heart rate variability, sleep quality, and physical activity can predict same-day or next-day depressive symptoms among socially vulnerable older adults in their everyday living environments. In addition, this study tested the feasibility of a digital mental health monitoring platform designed to inform older adult users and their community caregivers about day-to-day changes in the health status of older adults.
METHODS
A single-arm, nonrandomized living lab pilot study was conducted with socially vulnerable older adults (n=25), their community caregivers (n=16), and a managerial social worker over a 6-week period during and after the COVID-19 pandemic. Depressive symptoms were assessed daily using the 9-item Patient Health Questionnaire via scripted verbal conversations with a mobile chatbot. Digital biomarkers for depression, including heart rate variability, sleep, and physical activity, were measured using a wearable sensor (Fitbit Sense) that was worn continuously, except during charging times. Daily individualized feedback, using traffic signal signs, on the health status of older adult users regarding stress, sleep, physical activity, and health emergency status was displayed on a mobile app for the users and on a web application for their community caregivers. Multilevel modeling was used to examine whether the digital biomarkers predicted same-day or next-day depressive symptoms. Study staff conducted pre- and postsurveys in person at the homes of older adult users to monitor changes in depressive symptoms, sleep quality, and system usability.
RESULTS
Among the 31 older adult participants, 25 provided data for the living lab and 24 provided data for the pre-post test analysis. The multilevel modeling results showed that increases in daily sleep fragmentation (P=.003) and sleep efficiency (P=.001) compared with one's average were associated with an increased risk of daily depressive symptoms in older adults. The pre-post test results indicated improvements in depressive symptoms (P=.048) and sleep quality (P=.02), but not in the system usability (P=.18).
CONCLUSIONS
The findings suggest that wearable sensors assessing sleep quality may be utilized to predict daily fluctuations in depressive symptoms among socially vulnerable older adults. The results also imply that receiving individualized health feedback and sharing it with community caregivers may help improve the mental health of older adults. However, additional in-person training may be necessary to enhance usability.
TRIAL REGISTRATION
ClinicalTrials.gov NCT06270121; https://clinicaltrials.gov/study/NCT06270121.
Topics: Humans; Pilot Projects; Aged; Male; Female; Depression; Caregivers; COVID-19; Aged, 80 and over; Middle Aged; Vulnerable Populations; Heart Rate; Telemedicine
PubMed: 38885033
DOI: 10.2196/55842 -
Zhurnal Nevrologii I Psikhiatrii Imeni... 2024Post-traumatic stress disorder (PTSD) is a common mental health disorder, with an incidence of up to 12.5% among primary care patients. Most often, PTSD is detected in... (Review)
Review
Post-traumatic stress disorder (PTSD) is a common mental health disorder, with an incidence of up to 12.5% among primary care patients. Most often, PTSD is detected in combat veterans, victims of terrorist attacks and terror, but it can also be a consequence of traumatic brain injury and medical interventions. Impaired cognitive functioning is a key feature of PTSD, including attention deficits and reduced processing speed, executive dysfunction, and impairments in verbal learning and memory. Cognitive impairments in PTSD are significantly persistent and are largely similar in nature to neuropsychological impairments in neurodegenerative pathology. Possible pathogenetic mechanisms underlying PTSD are the development of neuroinflammation, oxidative stress and decreased production of neurotrophic factors. One of the promising areas of treatment is the use of Cerebrolysin, which has powerful neurotrophic and anti-inflammatory activity.
Topics: Humans; Stress Disorders, Post-Traumatic; Cognitive Dysfunction; Amino Acids; Oxidative Stress
PubMed: 38884432
DOI: 10.17116/jnevro202412405169 -
Indian Journal of Otolaryngology and... Jun 2024Attention is a fundamental aspect of human cognitive function and is crucial for essential activities such as learning, social interaction, and routine tasks. Notably,...
Attention is a fundamental aspect of human cognitive function and is crucial for essential activities such as learning, social interaction, and routine tasks. Notably, Auditory attention involves complex interactions and collaboration among multiple brain networks. Recognizing the impairment of auditory attention, comprehending its underlying mechanisms, and identifying the activated brain regions essential for the development of treatments and interventions for individuals facing auditory attention deficits, emphasizes the significance of investigating these matters. In the current study, we conducted a review by searching for the full text of 53 articles published related to auditory attention, mechanisms, and networks in databases like Science Direct, Google Scholar, ProQuest, and PubMed using the keywords Attention, Auditory Attention, Auditory Attention Impairment, theories of attention were investigated in the years 2000 to 2023 And focused on articles that provided discussions within this research domain. The studies have demonstrated that auditory attention exceeds being an acoustic attribute and assumes a fundamental role in complex acoustic environments, information processing, and even speech comprehension. In the context of this study, we have conducted a review and summary of the proposed theories related to attention and the brain networks involved in different forms of auditory attention. In conclusion, the integration of auditory attention assessments, behavioral observations, and an understanding of the neural mechanisms and brain regions implicated in auditory attention proves to be an effective approach for the diagnosis and treatment of attention-related disorders.
PubMed: 38883545
DOI: 10.1007/s12070-023-04373-1 -
Journal of Graduate Medical Education Jun 2024The integration of entrustable professional activities (EPAs) within objective structured clinical examinations (OSCEs) has yielded a valuable avenue for delivering... (Randomized Controlled Trial)
Randomized Controlled Trial
The integration of entrustable professional activities (EPAs) within objective structured clinical examinations (OSCEs) has yielded a valuable avenue for delivering timely feedback to residents. However, concerns about feedback quality persist. This study aimed to assess the quality and content alignment of verbal feedback provided by examiners during an entrustment-based OSCE. We conducted a progress test OSCE for internal medicine residents in 2022, assessing 7 EPAs. The immediate 2-minute feedback provided by examiners was recorded and analyzed using the Quality of Assessment of Learning (QuAL) score. We also analyzed the degree of alignment with EPA learning objectives: competency milestones and task-specific abilities. In a randomized crossover experiment, we compared the impact of 2 scoring methods used to assess residents' clinical performance (3-point entrustability scales vs task-specific checklists) on feedback quality and alignment. Twenty-one examiners provided feedback to 67 residents. The feedback demonstrated high quality (mean QuAL score 4.3 of 5) and significant alignment with the learning objectives of the EPAs. On average, examiners addressed in their feedback 2.5 milestones (61%) and 1.2 task-specific abilities (46%). The scoring methods used had no significant impact on QuAL scores (95% CI -0.3, 0.1, =.28), alignment with competency milestones (95% CI -0.4, 0.1, =.13), or alignment with task-specific abilities (95% CI -0.3, 0.1, =.29). In our entrustment-based OSCE, examiners consistently offered valuable feedback aligned with intended learning outcomes. Notably, we explored high-quality feedback and alignment as separate dimensions, finding no significant impact from our 2 scoring methods on either aspect.
Topics: Internship and Residency; Humans; Clinical Competence; Educational Measurement; Internal Medicine; Competency-Based Education; Feedback; Education, Medical, Graduate; Formative Feedback; Cross-Over Studies; Checklist
PubMed: 38882423
DOI: 10.4300/JGME-D-23-00569.1 -
Speech Communication Nov 2023To compare verbal fluency scores derived from manual transcriptions to those obtained using automatic speech recognition enhanced with machine learning classifiers.
OBJECTIVE
To compare verbal fluency scores derived from manual transcriptions to those obtained using automatic speech recognition enhanced with machine learning classifiers.
METHODS
Using Amazon Web Services, we automatically transcribed verbal fluency recordings from 1400 individuals who performed both animal and letter F verbal fluency tasks. We manually adjusted timings and contents of the automatic transcriptions to obtain "gold standard" transcriptions. To make automatic scoring possible, we trained machine learning classifiers to discern between valid and invalid utterances. We then calculated and compared verbal fluency scores from the manual and automatic transcriptions.
RESULTS
For both animal and letter fluency tasks, we achieved good separation of valid versus invalid utterances. Verbal fluency scores calculated based on automatic transcriptions showed high correlation with those calculated after manual correction.
CONCLUSION
Many techniques for scoring verbal fluency word lists require accurate transcriptions with word timings. We show that machine learning methods can be applied to improve off-the-shelf ASR for this purpose. These automatically derived scores may be satisfactory for some applications. Low correlations among some of the scores indicate the need for improvement in automatic speech recognition before a fully automatic approach can be reliably implemented.
PubMed: 38881790
DOI: 10.1016/j.specom.2023.102990 -
Scientific Reports Jun 2024Dementia is a progressive neurological disorder that affects the daily lives of older adults, impacting their verbal communication and cognitive function. Early...
Dementia is a progressive neurological disorder that affects the daily lives of older adults, impacting their verbal communication and cognitive function. Early diagnosis is important to enhance the lifespan and quality of life for affected individuals. Despite its importance, diagnosing dementia is a complex process. Automated machine learning solutions involving multiple types of data have the potential to improve the process of automated dementia screening. In this study, we build deep learning models to classify dementia cases from controls using the Pitt Cookie Theft dataset from DementiaBank, a database of short participant responses to the structured task of describing a picture of a cookie theft. We fine-tune Wav2vec and Word2vec baseline models to make binary predictions of dementia from audio recordings and text transcripts, respectively. We conduct experiments with four versions of the dataset: (1) the original data, (2) the data with short sentences removed, (3) text-based augmentation of the original data, and (4) text-based augmentation of the data with short sentences removed. Our results indicate that synonym-based text data augmentation generally enhances the performance of models that incorporate the text modality. Without data augmentation, models using the text modality achieve around 60% accuracy and 70% AUROC scores, and with data augmentation, the models achieve around 80% accuracy and 90% AUROC scores. We do not observe significant improvements in performance with the addition of audio or timestamp information into the model. We include a qualitative error analysis of the sentences that are misclassified under each study condition. This study provides preliminary insights into the effects of both text-based data augmentation and multimodal deep learning for automated dementia classification.
Topics: Humans; Deep Learning; Dementia; Aged; Female; Male; Aged, 80 and over; Databases, Factual
PubMed: 38880810
DOI: 10.1038/s41598-024-64438-1