Performance of Hearing Test Software Applications to Detect Hearing Loss
Notice bibliographique
Résumé
Importance: Hearing loss is common and may impact health and quality of life if not properly managed. It is diagnosed following formal audiological assessment, which may not be available or practical. Hearing test software applications (apps) may help identify people who might benefit from audiological assessment, but their diagnostic accuracy has been incompletely studied. Objective: To measure and compare the validity and reliability of 2 commonly recommended apps (hearWHO and SHOEBOX) to detect moderately severe or greater hearing loss. Secondary objectives were to evaluate the apps' ability to detect less severe hearing loss and the diagnostic performance of 2 questionnaires for detecting both severities of hearing loss. Design, Setting, and Participants: This prospective diagnostic accuracy study compared the hearWHO and SHOEBOX apps with a 4-frequency pure-tone average audiological assessment reference standard. All consenting English-speaking patients aged 18 years or older and referred for routine audiological assessment at a publicly funded health center in Calgary, Canada, were included between May 17, 2023, and March 12, 2024. Main Outcome and Measures: The main outcome was the validity and reliability of 4 index tests, including the hearWHO app, SHOEBOX app, Revised Hearing Handicap Inventory-Screening (RHHI-S) questionnaire, and the Single-Item Self-Assessment (SISA) questionnaire, to detect moderate to severe hearing loss. All index test results were compared with an audiological assessment reference standard (hearing loss defined by a better ear hearing threshold of ≥50 dB [more severe denoted as HL50] or ≥20 dB [less severe denoted as HL20]). Test-retest reliability of the 2 apps and C statistics, sensitivity, specificity, and positive and negative predicted values of all index tests were measured. Results: A total of 130 participants were recruited (median [IQR] age, 58 [47-67] years; 82 female [63.1%]). Complete data for each comparison ranged from 123 to 129 participants. The prevalence of HL50 was 16.3% (21 or 130 participants). Neither the hearWHO nor the SHOEBOX app had high test-retest reliability (all κ-values <0.80), with the SHOEBOX having a κ of 0.64 (95% CI, 0.48-0.79) and hearWHO having a κ of 0.32 (95% CI, 0.18-0.46). All C statistics for HL50 were less than 0.80. When testing for HL50, diagnostic performance for both apps was better for the second measurement than the first measurement or the mean. Sensitivity and specificity for the second measurement of SHOEBOX were 0.26 (95% CI, 0.09-0.51) and 1.00 (95% CI, 0.97-1.00), respectively, and for the second measurement of hearWHO, 0.67 (95% CI, 0.43-0.85) and 0.71 (95% CI, 0.62-0.79), respectively. Sensitivity and specificity for the RHHI-S were 0.76 (95% CI, 0.53-0.92) and 0.42 (95% CI, 0.32-0.52), respectively, and for SISA, 0.10 (95% CI, 0.01-0.30) and 0.90 (95% CI, 0.83-0.95), respectively. Using a less stringent diagnostic threshold with SHOEBOX increased sensitivity for HL50 to at least 95% while retaining a specificity of 47% to 54%. Sensitivity and specificity for both apps were higher for HL20. Conclusions and Relevance: These findings suggest that both hearWHO and SHOEBOX have limited test-retest reliability, perhaps because of a learning effect. Both apps may be suitable if a sensitive strategy is desired for identifying people who may benefit from diagnostic audiological assessment, whereas the SHOEBOX app may be preferable if a specific strategy is desired. If neither app is available, the RHHI-S or the SISA could be used depending on whether sensitivity or specificity is desired.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
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