Evidence map›Paper›PMID 33720025›Full record

ReviewJMIR mHealth and uHealth2021

An mHealth App (Speech Banana) for Auditory Training: App Design and Development Study.

J Tilak Ratnanather, Rohit Bhattacharya, Margo B Heston, Joanne Song, Lindsey R Fernandez, Hong Seo Lim, Seung-Wook Lee, Edric Tam, Sungho Yoo, Seung-Ho Bae and 4 more

Open access · goldAbstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in JMIR mHealth and uHealth, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
1.1field-weighted citation impact, top 26% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed, 13 citations in OpenAlex.

  1. Article
  2. Article
  3. Review
  4. Visualization of Speech Perception AnalysisFrontiers in neurology · 2021
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

14 authors at 2 institutions in 2 countries.

J Tilak RatnanatherCenter for Imaging Science and Institute for Computational Medicine, Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States.ORCID 0000-0002-8631-489X
Rohit Bhattacharya *Center for Imaging Science and Institute for Computational Medicine, Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States.ORCID 0000-0002-0555-9714
Margo B Heston *Center for Imaging Science and Institute for Computational Medicine, Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States.ORCID 0000-0002-0980-2275
Joanne Song *Center for Imaging Science and Institute for Computational Medicine, Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States.ORCID 0000-0003-3727-0437
Lindsey R Fernandez *Center for Imaging Science and Institute for Computational Medicine, Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States.ORCID 0000-0002-7928-3175
Hong Seo Lim *Center for Imaging Science and Institute for Computational Medicine, Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States.ORCID 0000-0002-3641-231X
Seung-Wook Lee *Center for Imaging Science and Institute for Computational Medicine, Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States.ORCID 0000-0003-4371-5703
Edric Tam *Center for Imaging Science and Institute for Computational Medicine, Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States.ORCID 0000-0003-4469-1168
Sungho Yoo *Center for Imaging Science and Institute for Computational Medicine, Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States.ORCID 0000-0003-4037-4835
Seung-Ho Bae *Center for Imaging Science and Institute for Computational Medicine, Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States.ORCID 0000-0002-6139-7617
Inez Lam *Center for Imaging Science and Institute for Computational Medicine, Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States.ORCID 0000-0003-4254-7381
Hyoung Won JeonDepartment of Otorhinolaryngology, Seoul National University College of Medicine, Seoul National University Bundang Hospital, Seongnam-si, Republic of Korea.ORCID 0000-0002-8235-7649
Son A ChangSoree Ear Clinic Rehabilitation Center, Seoul, Republic of Korea.ORCID 0000-0002-1476-6643
Ja-Won KooDepartment of Otorhinolaryngology, Seoul National University College of Medicine, Seoul National University Bundang Hospital, Seongnam-si, Republic of Korea.ORCID 0000-0002-5538-2785
Johns Hopkins University · USSeoul National University Bundang Hospital · KR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundWith the growing adult population using electronic hearing devices such as cochlear implants or hearing aids, there is an increasing worldwide need for auditory training (AT) to promote optimal device use. However, financial resources and scheduling conflicts make clinical AT infeasible.

objectiveTo address this gap between need and accessibility, we primarily aimed to develop a mobile health (mHealth) app called Speech Banana for AT. The app would be substantially more affordable and portable than clinical AT; would deliver a validated training model that is reflective of modern techniques; and would track users' progress in speech comprehension, providing greater continuity between periodic in-person visits. To improve international availability, our secondary aim was to implement the English language training model into Korean as a proof of concept for worldwide usability.

methodsA problem- and objective-centered Design Science Research Methodology approach was adopted to develop the Speech Banana app. A review of previous literature and computer-based learning programs outlined current AT gaps, whereas interviews with speech pathologists and users clarified the features that were addressed in the app. Past and present users were invited to evaluate the app via community forums and the System Usability Scale.

resultsSpeech Banana has been implemented in English and Korean languages for iPad and web use. The app comprises 38 lessons, which include analytic exercises pairing visual and auditory stimuli, and synthetic quizzes presenting auditory stimuli only. During quizzes, users type the sentence heard, and the app provides visual feedback on performance. Users may select a male or female speaker and the volume of background noise, allowing for training with a range of frequencies and signal-to-noise ratios. There were more than 3200 downloads of the English iPad app and almost 100 downloads of the Korean app; more than 100 users registered for the web apps. The English app received a System Usability Scale rating of "good" from 6 users, and the Korean app received a rating of "OK" from 16 users.

conclusionsSpeech Banana offers AT accessibility with a validated curriculum, allowing users to develop speech comprehension skills with the aid of a mobile device. This mHealth app holds potential as a supplement to clinical AT, particularly in this era of global telemedicine.

Indexed as

Mobile ApplicationsMusaTelemedicineAdultFemaleHumansMaleSpeechcochlear implantscomputers, handheldhearing aidsmobile phonespeech therapy

Identifiers

PMID33720025
PMCPMC8088859
OpenAlexW3138965520

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.