CoChat
Sign Up
Cited by 52Hearing, Cochlea, Tinnitus, GeneticsMobile Health and mHealth ApplicationsHearing Loss and Rehabilitation

Mobile Apps for Management of Tinnitus: Users’ Survey, Quality Assessment, and Content Analysis

Open access
Sereda, Magdalena, Smith, Sandra, Newton, Kiri, Stockdale, David·2019·JMIR mHealth and uHealth
DOI 10.2196/10353PMC PMC6364200

Published version of a preprint

Open this paper in CoChat — free accountOpen PDF

Abstract

BACKGROUND: Tinnitus is the perception of a sound without any outside source. It affects 6 million people in the United Kingdom. Sound therapy is a core component of many tinnitus management programs. Potential mechanisms of benefit include making tinnitus less noticeable, habituation, distracting attention from tinnitus, relaxation, and promoting neuroplastic changes within the brain. In recent years, there has been a substantial increase in the use of mobile technology. This provided an additional medium through which people with tinnitus can access different tinnitus management options, including sound therapy. OBJECTIVE: The aim of this study was to (1) generate the list of apps that people use for management of their tinnitus, (2) explore reasons for app use and nonuse, (3) perform quality assessment of the most cited apps, and (4) perform content analysis to explore and describe options and management techniques available in the most cited apps. METHODS: A Web-based survey consisting of 33 open and closed questions captured (1) demographic information, information about tinnitus, and hearing loss and (2) mobile app-specific information about the motivation to use an app, the apps which respondents used for tinnitus, important factors when choosing an app, devices used to access apps, and reasons for not using apps. The quality of the most cited apps was assessed using the Mobile Apps Rating Scale (MARS). Content and features of the most cited apps were analyzed. RESULTS: Data from 643 respondents were analyzed. The majority of respondents (482/643, 75.0%) had never used an app for management of tinnitus mainly because of lack of awareness (381/643, 59.3%). The list of the 55 apps that people use for their tinnitus was generated. These included apps that were developed specifically for the management of tinnitus; however, the majority of cited apps were developed for other problems (eg, sleep, depression or anxiety, and relaxation). Quality assessment of the 18 most popular apps using MARS resulted in a range of mean scores from 1.6 to 4.2 (out of 5). In line with the current model of tinnitus management, sound was the main focus of the majority of the apps. Other components included relaxation exercises, elements of cognitive behavioral therapy, information and education, and hypnosis. CONCLUSIONS: People used apps for the management of their tinnitus; however, this was done mostly as a self-help option, without conjunction with management provided by hearing health care professionals. Further research should consider the place for apps in tinnitus management (stand-alone self-management intervention vs part of the management by a hearing professional). As the content of the apps varies with respect to sound options, information, and management strategies, it seems that the choice of the best management app should be guided by individual patient's needs and preferences.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.2196/10353

Cites

14 of 14 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • This work is the first to systematically identify and evaluate smartphone apps within the context of tinnitus.

    “To the best of our knowledge, this work is the first to systematically identify and evaluate smartphone apps within the context of tinnitus.”

    From Smartphone and mobile health apps for tinnitus: Systematic identification, analysis, and assessment (2020) · cites this paper as [38]

    In Strengths and Limitations, the paper calls itself the first review of tinnitus-management apps and the first to assess their quality using the MARS scale. It also explicitly says it used a bottom-up approach rather than a systematic search of app stores, so the claim that it systematically identified apps overstates the source. (LLM, conf=0.88)

    Experimental — accuracy not yet measured

  • The MARS scores in [38] are for general tinnitus apps such as Beltone Tinnitus Calmer, Relax Noise 3, ReSound Relief, myNoise, Tinnitus Therapy (Lite), and White Noise (Lite), while MARS scores in [52] are for mindfulness and cognitive behavior therapy apps such as Relax Melodies and MindShift CBT.

    “The MARS scores in [38] are for general tinnitus apps such as Beltone Tinnitus Calmer, Relax Noise 3, ReSound Relief, myNoise, Tinnitus Therapy (Lite), and White Noise (Lite), while MARS scores in [52] are given for mindfulness and cognitive behavior therapy apps such as Relax Melodies and MindShift CBT.”

    From Smartphone and mobile health apps for tinnitus: Systematic identification, analysis, and assessment (2020) · cites this paper as [38]

    Split into 2 atoms — 0 confirmed, 2 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.

    Experimental — accuracy not yet measured

  • Sereda and colleagues gathered tinnitus management apps based on patient opinion via a web-based survey and analyzed features of the most cited apps with MARS.

    “In the field of tinnitus, Sereda and colleagues [38] gathered tinnitus management apps based on patient opinion via a web-based survey. Features of the patients’ most cited apps were then analyzed with MARS.”

    From Smartphone and mobile health apps for tinnitus: Systematic identification, analysis, and assessment (2020) · cites this paper as [38]

    The Abstract (Methods) says a web-based survey identified apps used by respondents and that the most cited apps were assessed with MARS. But the Methods (Content and Features Analysis; no page given) says their features were analyzed using a separate coding manual, not MARS. The claim conflates the quality assessment with the feature analysis. (LLM, conf=0.88)

    Experimental — accuracy not yet measured

  • The reviews by Sereda et al [38] and Smith et al [63] are the only closely related works to this work in terms of assessing tinnitus-related smartphone apps using MARS.

    “Specifically, in terms of the assessment of tinnitus-related smartphone apps using MARS, the reviews by Sereda et al [38], which were further extended and repeated by Smith et al [63], are the only closely related works in relation to this work.”

    From Smartphone and mobile health apps for tinnitus: Systematic identification, analysis, and assessment (2020) · cites this paper as [38]

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Cited by

Sorted by citations

Related

Authors
Sereda, M., Smith, S., Newton, K., & Stockdale, D.
Affiliations
  • University of Nottingham (GB)
  • De Montfort University (GB)
  • British Tinnitus Association (GB)
Corresponding author
Sereda, Magdalena
ORCID
Venue
JMIR mHealth and uHealth
Publisher
JMIR Publications Inc.
Published
2019-01-23
Volume
7
Issue
1
Pages
e10353
ISSN
2291-5222 (ISSN-L)
DOI
10.2196/10353
PMC
PMC6364200
OpenAlex
W2913162758
Cited by
52
References
14
Citations by year
2026: 3 · 2025: 7 · 2024: 7 · 2023: 3 · 2022: 13 · 2021: 11 · 2020: 7 · 2019: 1
FWCI
3.07
Type
journal article
Language
en
Open access
Licence
cc-by
Locations
  • JMIR mhealth and uhealth (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Repository@Nottingham (University of Nottingham) (publishedVersion, cc-by)
  • Repository@Nottingham (University of Nottingham) (submittedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • National Institute for Health and Care Research
  • Department of Health and Social Care
Topics
  • Life Sciences › Neuroscience › Sensory Systems › Hearing, Cochlea, Tinnitus, Genetics
  • Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Hearing Loss and Rehabilitation
Keywords
tinnitus, mobile application, sound therapy, tinnitus management, app quality assessment, self-help, relaxation techniques, cognitive behavioral therapy, hypnosis, user motivation, hearing loss, neuroplasticity, habituation, attentional distraction, hearing aids, hearing protection, hearing loss prevention
MeSH terms
Adolescent, Adult, Aged, Female, United Kingdom, Humans, Male, Middle Aged, Quality Assurance, Health Care, Surveys and Questionnaires, Tinnitus, Disease Management, Qualitative Research, Mobile Applications

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review