Dataset Information

Learning the full impact of migraine through patient voices: A qualitative study.

ABSTRACT:

Objective

To better characterize the ways that migraine affects multiple domains of life.

Background

Further understanding of migraine burden is needed.

Methods

Adults with migraine randomized to mindfulness-based stress reduction or headache education arms (n = 81) in two separate randomized clinical trials participated in semistructured in-person qualitative interviews conducted after the interventions. Interviews queried participants on migraine impact on life and were audio-recorded, transcribed, and summarized into a framework matrix. A master codebook was created until meaning saturation was reached and magnitude coding established code frequency. Themes and subthemes were identified using a constructivist grounded theory approach.

Results

Despite most participants being treated with acute and/or prophylactic medications, 90% (73/81) reported migraine had a negative impact on overall life, with 68% (55/81) endorsing specific domains of life impacted and 52% (42/81) describing impact on emotional health. Six main themes of migraine impact emerged: (1) global negative impact on overall life; (2) impact on emotional health; (3) impact on cognitive function; (4) impact on specific domains of life (work/career, family, social); (5) fear and avoidance (pain catastrophizing and anticipatory anxiety); and (6) internalized and externalized stigma. Participants reported how migraine (a) controls life, (b) makes life difficult, and (c) causes disability during attacks, with participants (d) experiencing a lack of control and/or (e) attempting to push through despite migraine. Emotional health was affected through (a) isolation, (b) anxiety, (c) frustration/anger, (d) guilt, (e) mood changes/irritability, and (f) depression/hopelessness. Cognitive function was affected through concentration and communication difficulties.

Conclusions

Migraine has a global negative impact on overall life, cognitive and emotional health, work, family, and social life. Migraine contributes to isolation, frustration, guilt, fear, avoidance behavior, and stigma. A greater understanding of the deep burden of this chronic neurological disease is needed to effectively target and treat what is most important to those living with migraine.

SUBMITTER: Estave PM

PROVIDER: S-EPMC8428538 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature

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Publications

Learning the full impact of migraine through patient voices: A qualitative study.

Estave Paige M PM Beeghly Summerlyn S Anderson Reid R Margol Caitlyn C Shakir Mariam M George Geena G Berger Anissa A O'Connell Nathaniel N Burch Rebecca R Haas Niina N Powers Scott W SW Seng Elizabeth E Buse Dawn C DC Lipton Richard B RB Wells Rebecca Erwin RE

Headache 20210603 7

<h4>Objective</h4>To better characterize the ways that migraine affects multiple domains of life.<h4>Background</h4>Further understanding of migraine burden is needed.<h4>Methods</h4>Adults with migraine randomized to mindfulness-based stress reduction or headache education arms (n = 81) in two separate randomized clinical trials participated in semistructured in-person qualitative interviews conducted after the interventions. Interviews queried participants on migraine impact on life and were a ...[more]

PMID: 34081779

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Project description:BackgroundConventional diagnostic methods for dysphagia have limitations such as long wait times, radiation risks, and restricted evaluation. Therefore, voice-based diagnostic and monitoring technologies are required to overcome these limitations. Based on our hypothesis regarding the impact of weakened muscle strength and the presence of aspiration on vocal characteristics, this single-center, prospective study aimed to develop a machine-learning algorithm for predicting dysphagia status (normal, and aspiration) by analyzing postprandial voice limiting intake to 3 cc.MethodsConducted from September 2021 to February 2023 at Seoul National University Bundang Hospital, this single center, prospective cohort study included 198 participants aged 40 or older, with 128 without suspected dysphagia and 70 with dysphagia-aspiration. Voice data from participants were collected and used to develop dysphagia prediction models using the Multi-Layer Perceptron (MLP) with MobileNet V3. Male-only, female-only, and combined models were constructed using 10-fold cross-validation. Through the inference process, we established a model capable of probabilistically categorizing a new patient's voice as either normal or indicating the possibility of aspiration.ResultsThe pre-trained models (mn40_as and mn30_as) exhibited superior performance compared to the non-pre-trained models (mn4.0 and mn3.0). Overall, the best-performing model, mn30_as, which is a pre-trained model, demonstrated an average AUC across 10 folds as follows: combined model 0.8361 (95% CI 0.7667-0.9056; max 0.9541), male model 0.8010 (95% CI 0.6589-0.9432; max 1.000), and female model 0.7572 (95% CI 0.6578-0.8567; max 0.9779). However, for the female model, a slightly higher result was observed with the mn4.0, which scored 0.7679 (95% CI 0.6426-0.8931; max 0.9722). Additionally, the other models (pre-trained; mn40_as, non-pre-trained; mn4.0 and mn3.0) also achieved performance above 0.7 in most cases, and the highest fold-level performance for most models was approximately around 0.9. The 'mn' in model names refers to MobileNet and the following number indicates the 'width_mult' parameter.ConclusionsIn this study, we used mel-spectrogram analysis and a MobileNetV3 model for predicting dysphagia aspiration. Our research highlights voice analysis potential in dysphagia screening, diagnosis, and monitoring, aiming for non-invasive safer, and more effective interventions.Trial registrationThis study was approved by the IRB (No. B-2109-707-303) and registered on clinicaltrials.gov (ID: NCT05149976).

Dataset Information

Learning the full impact of migraine through patient voices: A qualitative study.

Objective

Background

Methods

Results

Conclusions

Publications

Learning the full impact of migraine through patient voices: A qualitative study.

Similar Datasets

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