Listening to Stakeholders Involved in Speech-Language Therapy for Children With Communication Disorders: Content Analysis of Apple App Store Reviews
Bibliographic record
Abstract
BACKGROUND: With the plethora of mobile apps available on the Apple App Store, more speech-language pathologists (SLPs) have adopted apps for speech-language therapy services, especially for pediatric clients. App Store reviews are publicly available data sources that can not only create avenues for communication between technology developers and consumers but also enable stakeholders such as parents and clinicians to share their opinions and view opinions about the app content and quality based on user experiences. OBJECTIVE: This study examines the Apple App Store reviews from multiple key stakeholders (eg, parents, educators, and SLPs) to identify and understand user needs and challenges of using speech-language therapy apps (including augmentative and alternative communication [AAC] apps) for pediatric clients who receive speech-language therapy services. METHODS: We selected 16 apps from a prior interview study with SLPs that covered multiple American Speech-Language-Hearing Association Big Nine competencies, including articulation, receptive and expressive language, fluency, voice, social communication, and communication modalities. Using an automatic Python (Python Software Foundation) crawler developed by our research team and a Really Simple Syndication feed generator provided by Apple, we extracted a total of 721 app reviews from 2009 to 2020. Using qualitative coding to identify emerging themes, we conducted a content analysis of 57.9% (418/721) reviews and synthesized user feedback related to app features and content, usability issues, recommendations for improvement, and multiple influential factors related to app design and use. RESULTS: Our analyses revealed that key stakeholders such as family members, educators, and individuals with communication disorders have used App Store reviews as a platform to share their experiences with AAC and speech-language apps. User reviews for AAC apps were primarily written by parents who indicated that AAC apps consistently exhibited more usability issues owing to violations of design guidelines in areas of aesthetics, user errors, controls, and customization. Reviews for speech-language apps were primarily written by SLPs and educators who requested and recommended specific app features (eg, customization of visuals, recorded feedback within the app, and culturally diverse character roles) based on their experiences working with a diverse group of pediatric clients with a variety of communication disorders. CONCLUSIONS: To our knowledge, this is the first study to compile and analyze publicly available App Store reviews to identify areas for improvement within mobile apps for pediatric speech-language therapy apps from children with communication disorders and different stakeholders (eg, clinicians, parents, and educators). The findings contribute to the understanding of apps for children with communication disorders regarding content and features, app usability and accessibility issues, and influential factors that impact both AAC apps and speech-language apps for children with communication disorders who need speech therapy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".