Evaluating the Efficacy of a Self-administered Speech-Language App for People With Chronic, Nonfluent Aphasia: A Pilot Study
Notice bibliographique
Résumé
Background Aphasia is a language deficit that is most often caused by stroke. Speech-language therapy is effective in helping people recover lost language, and it should lead to generalization to untrained tasks and gains in functional language. However, not everyone is able to receive therapy due to a lack of finances; a lack of insurance coverage; or, most recently, COVID-19. Objective This study has 3 aims. For aim 1, we investigated whether a person with moderate to severe aphasia could manage the setup of an app-based treatment independently. Our second aim was to evaluate whether conducting an intensive treatment without speech-language pathologist (SLP) involvement was feasible. Participants with aphasia were asked to use the app 2 hours per day for 10 days, and we were interested to see if they could maintain this regimen or if frustration or boredom would promote dropout. For the third aim, we determined whether participants with aphasia in our digital treatment would make the same kinds of language gains that they would if they were working with an SLP and whether treatment gains would generalize to other language modalities, indicating that neuroplastic changes occurred due to treatment. Methods Our pilot study used a single-subject design, with 3 participants experiencing nonfluent aphasia at least 1 year poststroke. Participants were trained to use a comprehension and production app, with instructions to use the app 2 hours per day for 10 days (total treatment time=20 hours). Multiple standardized assessments were taken at the following three time points: pretreatment, 1 week posttreatment, and 10 weeks posttreatment. A recording device was used to capture pretreatment and 10-week posttreatment at-home conversations between the participants with aphasia and the conversational partner. Results Data were variable among our 3 participants (P1, P2, and P3). P1 and P3 showed clinically significant improvements on several measurements of language; P2 did not. Aphasia severity also decreased in P1 and P3. The analysis of the discourse recorded in the home environments showed that P1 and P3 each made use of the app-trained words in spontaneous conversation (increase of >63%). All 3 participants with aphasia reported positive increases in quality of life, and all continued to use the app even after the treatment period ended. Conclusions Independently administered, intensive treatment had salubrious effects on 3 participants with aphasia. P2’s lack of improvement on language measures was attributed to not feeling challenged enough by the app. In general, the participants in this study were able to guide themselves in an independent manner to complete an intensive study, without using any SLP support. Though this study was only piloted on 3 individuals, it lays the groundwork for future studies assessing the independence of participants with aphasia in managing their own treatment. Conflicts of Interest None declared.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,005 | 0,008 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,002 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,001 |
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 source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».