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Record W2889208250 · doi:10.2196/11814

Mobile Behavioral Therapy for Headache: Pilot Study

2018· article· en· W2889208250 on OpenAlexvenueno aff
Adam Kirell, Rebecca M. Shingleton

Bibliographic record

VenueIproceedings · 2018
Typearticle
Languageen
FieldMedicine
TopicMigraine and Headache Studies
Canadian institutionsnot available
Fundersnot available
KeywordsHeadachesMigrainePsychological interventionMedicinemHealthMobile appsPopulationMindfulnessBehavioral therapyAffect (linguistics)Physical therapyPsychologyPsychiatryClinical psychologyComputer scienceWorld Wide Web

Abstract

fetched live from OpenAlex

Background: Frequently recurring tension headaches, migraines, and orofacial pain affect over 2.4 billion people worldwide, representing the second most prevalent medical condition in the world. In-office behavioral therapy (ie, relaxation and mindfulness exercises) has been shown to be one of the most effective treatment options for these patients. However, it has been underutilized due to stigma, cost, and accessibility. Mobile health apps may be one way to provide accessible, lower cost care in a nonstigmatized environment. As a result, apps are becoming an increasingly popular method for delivering behavioral therapy interventions and may provide an effective means to reach this population. Objective: The aim of this study was to assess the feasibility and acceptability of delivering a clinically validated behavioral therapy for migraine/headaches through a mobile app. Methods: Twenty-one migraine or headache sufferers met the criteria for severe migraine disability determined by the MIDAS test. Participants completed a two-week baseline period in which they kept a daily headache diary through the Halo app. They then began a four-week intervention period. During this period they were instructed to complete ten minutes of relaxation training exercises provided by the app each day, as well as monitor headaches via the daily headache diary. Relaxation training exercises consisted of various breathing techniques, progressive muscle relaxation, and mindfulness. Headache frequency was tracked throughout the intervention period, and further assessments, such as an in-person interview and posttreatment MIDAS test, were completed at the end of the four-week intervention period. Results: Analysis of data showed that there were significant differences between baseline scores (Headache Frequency Average=11.47/mo, MIDAS average=25.4) and postintervention follow-up (Headache Frequency Average=6.63/mo, MIDAS average=11.7, Frequency P<.01, MIDAS P<.01). Postintervention interviews revealed that more than 90% of the participants felt that the Halo beta app was easy to use and provided a benefit to the user. More than 85% of participants enjoyed using a daily headache diary and felt it gave them greater insights into their condition. More than 90% of the patients felt that the relaxation training had helped reduce the frequency of their headaches or headache related disability. Conclusions: We found that mindfulness and relaxation-based interventions administered through the Halo beta app was a feasible and acceptable treatment for people suffering from frequently recurring headaches and migraines or disability associated with headaches and migraines. The pilot demonstrated the potential feasibility and usability of a mobile health app in delivering behavioral therapy for headache and migraine as an adjunct or alternative to in-office behavioral therapy for headache and migraine.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.331
Threshold uncertainty score0.522

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.105
GPT teacher head0.397
Teacher spread0.292 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations1
Published2018
Admission routes1
Has abstractyes

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