Contemporary Review of Smartphone Apps for Tinnitus Management and Treatment

Mehdi, Muntazir; Dode, Albi; Pryss, Rüdiger; Schlee, Winfried; Reichert, Manfred; Hauck, Franz J.

doi:10.3390/brainsci10110867

Cited by 22 publications

(19 citation statements)

References 68 publications

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“…Therefore, the number of tinnitus-related apps available at any time is difficult to report but recent reviews have suggested over 200 ( 116 , 118 ). However, a common observation was that very few apps had been scientifically validated or tested for efficacy, and there was a high risk of bias in many of the studies that were available ( 83 , 117 ). Furthermore, Sereda et al ( 113 ) found that most of the apps that they reviewed were self-help apps that did involve clinicians.…”

Section: Resultsmentioning

confidence: 99%

“…Several recent reviews have previously identified tinnitusrelated apps, although scope, inclusion/exclusion criteria, and definitions differed between them (83,(113)(114)(115)(116)(117)(118). Tinnitus patients have been surveyed about their preferences in apps ( 113), but aside from a small trials (119) evidence of benefit from apps for tinnitus is absent.…”

Section: Appsmentioning

confidence: 99%

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A State-of-Art Review of Digital Technologies for the Next Generation of Tinnitus Therapeutics

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Sanders

Doborjeh

et al. 2021

Front. Digit. Health

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Background: Digital processing has enabled the development of several generations of technology for tinnitus therapy. The first digital generation was comprised of digital Hearing Aids (HAs) and personal digital music players implementing already established sound-based therapies, as well as text based information on the internet. In the second generation Smart-phone applications (apps) alone or in conjunction with HAs resulted in more therapy options for users to select from. The 3rd generation of digital tinnitus technologies began with the emergence of many novel, largely neurophysiologically-inspired, treatment theories that drove development of processing; enabled through HAs, apps, the internet and stand-alone devices. We are now of the cusp of a 4th generation that will incorporate physiological sensors, multiple transducers and AI to personalize therapies.Aim: To review technologies that will enable the next generations of digital therapies for tinnitus.Methods: A “state-of-the-art” review was undertaken to answer the question: what digital technology could be applied to tinnitus therapy in the next 10 years? Google Scholar and PubMed were searched for the 10-year period 2011–2021. The search strategy used the following key words: “tinnitus” and [“HA,” “personalized therapy,” “AI” (and “methods” or “applications”), “Virtual reality,” “Games,” “Sensors” and “Transducers”], and “Hearables.” Snowballing was used to expand the search from the identified papers. The results of the review were cataloged and organized into themes.Results: This paper identified digital technologies and research on the development of smart therapies for tinnitus. AI methods that could have tinnitus applications are identified and discussed. The potential of personalized treatments and the benefits of being able to gather data in ecologically valid settings are outlined.Conclusions: There is a huge scope for the application of digital technology to tinnitus therapy, but the uncertain mechanisms underpinning tinnitus present a challenge and many posited therapeutic approaches may not be successful. Personalized AI modeling based on biometric measures obtained through various sensor types, and assessments of individual psychology and lifestyles should result in the development of smart therapy platforms for tinnitus.

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Section: Resultsmentioning

confidence: 99%

Section: Appsmentioning

confidence: 99%

A State-of-Art Review of Digital Technologies for the Next Generation of Tinnitus Therapeutics

Searchfield

Sanders

Doborjeh

et al. 2021

Front. Digit. Health

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“…It has already been suggested that mobile applications offer a promising way for tinnitus assessment and treatment [ 76 – 78 ]. Hence, several specific mobile applications will be developed for the UNITI-RCT creating the opportunity to significantly reduce the number of tinnitus-related clinical visits and as such the necessary health care resources.…”

Section: Discussionmentioning

confidence: 99%

Unification of Treatments and Interventions for Tinnitus Patients (UNITI): a study protocol for a multi-center randomized clinical trial

Schoisswohl

Langguth

Schecklmann

et al. 2021

Trials

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Background Tinnitus represents a relatively common condition in the global population accompanied by various comorbidities and severe burden in many cases. Nevertheless, there is currently no general treatment or cure, presumable due to the heterogeneity of tinnitus with its wide variety of etiologies and tinnitus phenotypes. Hence, most treatment studies merely demonstrated improvement in a subgroup of tinnitus patients. The majority of studies are characterized by small sample sizes, unstandardized treatments and assessments, or applications of interventions targeting only a single organ level. Combinatory treatment approaches, potentially targeting multiple systems as well as treatment personalization, might provide remedy and enhance treatment responses. The aim of the present study is to systematically examine established tinnitus therapies both alone and in combination in a large sample of tinnitus patients. Further, it wants to provide the basis for personalized treatment approaches by evaluating a specific decision support system developed as part of an EU-funded collaborative project (Unification of treatments and interventions for tinnitus patients; UNITI project). Methods/study design This is a multi-center parallel-arm randomized clinical trial conducted at five different clinical sites over the EU. The effect of four different tinnitus therapy approaches (sound therapy, structured counseling, hearing aids, cognitive behavioral therapy) applied over a time period of 12 weeks as a single or rather a combinatory treatment in a total number of 500 chronic tinnitus patients will be investigated. Assessments and interventions are harmonized over the involved clinical sites. The primary outcome measure focuses on the domain tinnitus distress assessed via the Tinnitus Handicap Inventory. Discussion Results and conclusions from the current study might not only provide an essential contribution to combinatory and personalized treatment approaches in tinnitus but could also provide more profound insights in the heterogeneity of tinnitus, representing an important step towards a cure for tinnitus. Trial registration ClinicalTrials.gov NCT04663828. Registered on 11 December 2020.

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“…According to De Ridder et al ( 2021 ), “The capacity to measure the incidence, prevalence, and impact will help in identification of human, financial, and educational needs required to address acute tinnitus as a symptom but chronic tinnitus as a disorder.” Mobile health apps for tinnitus have the potential of assisting patients in self-assessment of their condition and of delivering insights on tinnitus heterogeneity to the medical experts, as reported by Probst et al ( 2017 ); Cederroth et al ( 2019 ), and Pryss et al ( 2019 , 2021 ) among others. This is particularly the case for mHealth apps that collect Ecological Momentary Assessments (EMA): several studies on mHealth tinnitus apps have demonstrated that EMA recordings deliver insights on tinnitus stages during the day and on the interplay of personal traits and severity of tinnitus symptoms (see, e.g., Probst et al, 2017 ; Mehdi et al, 2020 ; Unnikrishnan et al, 2020a ; Jamaludeen et al, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

Predicting Ecological Momentary Assessments in an App for Tinnitus by Learning From Each User's Stream With a Contextual Multi-Armed Bandit

et al. 2022

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Ecological Momentary Assessments (EMA) deliver insights on how patients perceive tinnitus at different times and how they are affected by it. Moving to the next level, an mHealth app can support users more directly by predicting a user's next EMA and recommending personalized services based on these predictions. In this study, we analyzed the data of 21 users who were exposed to an mHealth app with non-personalized recommendations, and we investigate ways of predicting the next vector of EMA answers. We studied the potential of entity-centric predictors that learn for each user separately and neighborhood-based predictors that learn for each user separately but take also similar users into account, and we compared them to a predictor that learns from all past EMA indiscriminately, without considering which user delivered which data, i.e., to a “global model.” Since users were exposed to two versions of the non-personalized recommendations app, we employed a Contextual Multi-Armed Bandit (CMAB), which chooses the best predictor for each user at each time point, taking each user's group into account. Our analysis showed that the combination of predictors into a CMAB achieves good performance throughout, since the global model was chosen at early time points and for users with few data, while the entity-centric, i.e., user-specific, predictors were used whenever the user had delivered enough data—the CMAB chose itself when the data were “enough.” This flexible setting delivered insights on how user behavior can be predicted for personalization, as well as insights on the specific mHealth data. Our main findings are that for EMA prediction the entity-centric predictors should be preferred over a user-insensitive global model and that the choice of EMA items should be further investigated because some items are answered more rarely than others. Albeit our CMAB-based prediction workflow is robust to differences in exposition and interaction intensity, experimentators that design studies with mHealth apps should be prepared to quantify and closely monitor differences in the intensity of user-app interaction, since users with many interactions may have a disproportionate influence on global models.

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Contemporary Review of Smartphone Apps for Tinnitus Management and Treatment

Cited by 22 publications

References 68 publications

A State-of-Art Review of Digital Technologies for the Next Generation of Tinnitus Therapeutics

A State-of-Art Review of Digital Technologies for the Next Generation of Tinnitus Therapeutics

Unification of Treatments and Interventions for Tinnitus Patients (UNITI): a study protocol for a multi-center randomized clinical trial

Predicting Ecological Momentary Assessments in an App for Tinnitus by Learning From Each User's Stream With a Contextual Multi-Armed Bandit

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