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Development and Validation of a Brief Version of the Vanderbilt Fatigue Scale for Adults: The VFS-A-10Hornsby, B.W.Y., Camarata, S., Cho, S.-J., Davis, H., McGarrigle, Ronan, Bess, F.H. 28 March 2023 (has links)
Yes / Objectives: Listening-related fatigue can be a significant problem for adults who struggle to
hear and understand, particularly adults with hearing loss. However, valid, sensitive, and
clinically useful measures for listening-related fatigue do not currently exist. The purpose of this
study was to develop and validate a brief clinical tool for measuring listening-related fatigue in
adults.
Design: The clinical scale was derived from the 40-item version of the Vanderbilt Fatigue Scale
for Adults (VFS-A-40), an existing, reliable, and valid research tool for measuring listening9 related fatigue. The study consisted of two phases. Phase 1 (N = 580) and Phase 2 (N = 607)
participants consisted of convenience samples of adults recruited via online advertisements,
clinical records review, and a pool of prior research participants. In Phase 1, results from item
response theory (IRT) analyses of VFS-A-40 items were used to identify high quality items for
the brief (10-item) clinical scale: the VFS-A-10. In Phase 2, the characteristics and quality of the
VFS-A-10 were evaluated in a separate sample of respondents. Dimensionality was evaluated
using exploratory factor analyses (EFA) and item quality and characteristics were evaluated
using IRT. VFS-A-10 reliability and validity were assessed multiple ways. IRT reliability
analysis was used to examine VFS-A-10 measurement fidelity. In addition, test-retest reliability
was assessed in a subset of Phase 2 participants (n = 145) who completed the VFS-A-10 a
second time approximately one month after their initial measure (range 5-90 days). IRT
differential item functioning (DIF) was used to assess item bias across different age, gender, and
hearing loss subgroups. Convergent construct validity was evaluated by comparing VFS-A-10
responses to two other generic fatigue scales and a measure of hearing disability. Known-groups validity was assessed by comparing VFS-A-10 scores between adults with and without self reported hearing loss
Results: EFA suggested a unidimensional structure for the VFS-A-10. IRT analyses confirmed
all test items were high quality. IRT reliability analysis revealed good measurement fidelity over
a wide range of fatigue severities. Test-retest reliability was excellent (rs = .88, collapsed across
participants). IRT DIF analyses confirmed the VFS-A-10 provided a valid measure of listening29 related fatigue regardless of respondent age, gender, or hearing status. An examination of
associations between VFS-A-10 scores and generic fatigue/vigor measures revealed only weak31 to-moderate correlations (Spearman’s correlation coefficient rs = -.36 to .57). Stronger
associations were seen between VFS-A-10 scores and a measure of perceived hearing difficulties
(rs = .79 to .81) providing evidence of convergent construct validity. In addition, the VFS-A-10
was more sensitive to fatigue associated with self-reported hearing difficulties than generic
measures. It was also more sensitive than generic measures to variations in fatigue as a function
of degree of hearing impairment.
Conclusions: These findings suggest that the VFS-A-10 is a reliable, valid, and sensitive tool for
measuring listening-related fatigue in adults. Its brevity, high sensitivity, and good reliability
make it appropriate for clinical use. The scale will be useful for identifying those most affected
by listening-related fatigue and for assessing benefits of interventions designed to reduce its
negative effects. / Starkey Inc, NIH National Institute on Deafness and Other Communication Disorders (NIDCD) Grant #R21DC012865, NICHD Grant P30HD15052 to the Vanderbilt Kennedy Center for Research on Human Development, Vanderbilt Institute for Clinical and Translational Research grant (UL1 TR000445 from NCATS/NIH) / The full-text of this article will be released for public view at the end of the publisher embargo on 1 Sep 2024.
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Escala cl?nica para prever a ades?o ao tratamento: transtorno bipolar do humor / Clinical rating scale to predict the compliance to treatment: bipolar disorderMarchi, Renato 12 February 2008 (has links)
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Previous issue date: 2008-02-12 / Bipolar disorder (BD) is associated with ps ychosocial and family relationships disturbing, mortality and economic burden high rates . The treatment aims the acute episodes and prevents new episodes. There are high rates of non-adherence in BD. The objective of this study was to develop and validate a clinical rating scale capable to predict the patient compliance to treatment in BD in both gend ers. The procedure involved the search in pertinent scientific literature for reports of factors of non-adherence of bipolar patients, analysis of patients during pilot studies and contact with professionals who deal with those patients in order to develop a comprehensive list of possible symptoms. This procedure was followed by items' selection and testing of the preliminary form of the clinical rating scale. The scale was analyzed statistically. Reliability study showed a high level of internal consistency. Factor analysis revealed five factors related to the clinical treatment: behaviors and beliefs of the patient, therapeutic alliance, therapeutic procedures, association of psychotherapy interventions and adverse effects of drug therapy. Predictive validation showed that items' factors were able to measure the non - adherence to treatment. It was concluded that the Clinical Rating Scale to Predict the Compliance to Treatment in Bipolar Disorder can be considered a valid instrument to predict the patient compliance to medical treatment . / O Transtorno Bipolar do Humor (TBH) est? associado a altas taxas de desajustes psicossociais e familiares, mortalidade e preju?zos econ?micos. O tratamento visa o controle de epis?dios agudos e preven??o de novos epis?dios. As taxas de n?o - ades?o ao tratamento s?o altas em TBH. Este trabalho teve por objetivo elaborar e validar uma escala de avalia??o clinica , capaz de indicar a probabilidade de ades?o ao tratamento medico clinico dos pacientes bipolares de ambos os g?neros. O procedimento foi constitu?do de levantamento dos fatores ligados a n?o-ades?o ao tratamento em TBH na literatura pertinente, analise de pacientes bipolares durante estudo piloto, analise por juizes, sele??o dos itens e aplica??o da escala em sua fase inicial, para subseq?ente an?lise psicom?trica. A analise de precis?o do instrumento evidenciou n?vel satisfat?rio de consist?ncia interna. Extra?ram-se cinco fatores de acordo com a analise fatorial explorat?ria, ligados ao tratamento medico clinico: atitudes e cren?as do paciente, alian?a terap?utica, procedimentos terap?uticos, associa??o de interven??es psicoter?picas e efeitos adversos dos psicof?rmacos. A valida??o preditiva revelou que os itens referentes a tais fatores medem a n?o-ades?o ao tratamento. Conclui -se que a ECPAT-TBH pode ser considerada um instrumento v?lido para prever a ades?o ao tratamento m?dico.
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Prédiction d’états mentaux futurs à partir de données de phénotypage numériqueJean, Thierry 12 1900 (has links)
Le phénotypage numérique mobilise les nombreux capteurs du téléphone intelligent (p. ex. : accéléromètre, GPS, Bluetooth, métadonnées d’appels) pour mesurer le comportement humain au quotidien, sans interférence, et les relier à des symptômes psychiatriques ou des indicateurs de santé mentale. L’apprentissage automatique est une composante intégrale au processus de transformation de signaux bruts en information intelligible pour un clinicien. Cette approche émerge d’une volonté de caractériser le profil de symptômes et ses variations dans le temps au niveau individuel.
Ce projet consistait à prédire des variables de santé mentale (p. ex. : stress, humeur, sociabilité, hallucination) jusqu’à sept jours dans le futur à partir des données du téléphone intelligent pour des patients avec un diagnostic de schizophrénie. Le jeu de données CrossCheck, composé d’un échantillon de 62 participants, a été utilisé. Celui-ci inclut 23,551 jours de signaux du téléphone avec 29 attributs et 6364 autoévaluations de l’état mental à l’aide d’échelles ordinales à 4 ancrages.
Des modèles prédictifs ordinaux ont été employés pour générer des prédictions discrètes interprétables sur l’échelle de collecte de données. Au total, 240 modèles d’apprentissage automatique ont été entrainés, soit les combinaisons de 10 variables de santé mentale, 3 horizons temporels (même jour, prochain jour, prochaine semaine), 2 algorithmes (XGBoost, LSTM) et 4 tâches d’apprentissage (classification binaire, régression continue, classification multiclasse, régression ordinale). Les modèles ordinaux et binaires ont performé significativement au-dessus du niveau de base et des deux autres tâches avec une erreur moyenne absolue macro entre 1,436 et 0,767 et une exactitude balancée de 58% à 73%. Les résultats montrent l’effet prépondérant du débalancement des données sur la performance prédictive et soulignent que les mesures n’en tenant pas compte surestiment systématiquement la performance.
Cette analyse ancre une série de considérations plus générales quant à l’utilisation de l’intelligence artificielle en santé. En particulier, l’évaluation de la valeur clinique de solutions d’apprentissage automatique présente des défis distinctifs en comparaison aux traitements conventionnels. Le rôle grandissant des technologies numériques en santé mentale a des conséquences sur l’autonomie, l’interprétation et l’agentivité d’une personne sur son expérience. / Digital phenotyping leverages the numerous sensors of smartphones (e.g., accelerometer, GPS,
Bluetooth, call metadata) to measure daily human behavior without interference and link it to
psychiatric symptoms and mental health indicators. Machine learning is an integral component
of processing raw signals into intelligible information for clinicians. This approach emerges from
a will to characterize symptom profiles and their temporal variations at an individual level.
This project consisted in predicting mental health variables (e.g., stress, mood, sociability,
hallucination) up to seven days in the future from smartphone data for patients with a diagnosis
of schizophrenia. The CrossCheck dataset, which has a sample of 62 participants, was used. It
includes 23,551 days of phone sensor data with 29 features, and 6364 mental state self-reports
on 4-point ordinal scales.
Ordinal predictive models were used to generate discrete predictions that can be interpreted
using the guidelines from the clinical data collection scale. In total, 240 machine learning models
were trained, i.e., combinations of 10 mental health variables, 3 forecast horizons (same day, next
day, next week), 2 algorithms (XGBoost, LSTM), and 4 learning tasks (binary classification,
continuous regression, multiclass classification, ordinal regression). The ordinal and binary
models performed significantly better than the baseline and the two other tasks with a macroaveraged mean absolute error between 1.436 and 0.767 and a balanced accuracy between 58%
and 73%. Results showed a dominant effect of class imbalance on predictive performance and
highlighted that metrics not accounting for it lead to systematic overestimation of performance.
This analysis anchors a series of broader considerations about the use of artificial intelligence in
healthcare. In particular, assessing the clinical value of machine learning solutions present
distinctive challenges when compared to conventional treatments. The growing role of digital
technologies in mental health has implication for autonomy, sense-making, and agentivity over
one’s experience.
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