Is there evidence for the added value and correct use of manual and automatically switching multimemory hearing devices? A scoping review

Graaff, Feike de; Huysmans, Elke; Ket, Hans; Merkus, Paul; Goverts, S. Theo; Leemans, C. René; Smits, Cas

doi:10.1080/14992027.2017.1385864

Cited by 6 publications

(10 citation statements)

References 25 publications

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“…Because smartphones usually have ample space for data storage, fine-grained temporal data can be collected for numerous acoustic parameters. Remote data logging has the potential to provide new insights into ways in which hearing aids are being used in real life ( 11 , 28 ), and provides new opportunities for research and for development. For example, it has been used to establish evidence regarding daily hearing aid usage for public health decision-making ( 29 , 30 ), to augment EMA with hearing devices with acoustic information ( 21 , 25 ) and to support development of advanced hearing aid technology ( 31 – 34 ).…”

Section: Introductionmentioning

confidence: 99%

Real-World Hearing Aid Usage Patterns and Smartphone Connectivity

Christensen

Saunders

Havtorn

et al. 2021

Front. Digit. Health

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Data for monitoring individual hearing aid usage has historically been limited to retrospective questionnaires or data logged intrinsically in the hearing aid cumulatively over time (e. g., days or more). This limits the investigation of longitudinal interactions between hearing aid use and environmental or behavioral factors. Recently it has become possible to analyze remotely logged hearing aid data from in-market and smartphone compatible hearing aids. This can provide access to novel insights about individual hearing aid usage patterns and their association to environmental factors. Here, we use remotely logged longitudinal data from 64 hearing aid users to establish basic norms regarding smartphone connectivity (i.e., comparing remotely logged data with cumulative true hearing aid on-time) and to assess whether such data can provide representative information about ecological usage patterns. The remotely logged data consists of minute-by-minute timestamped logs of cumulative hearing aid on-time and characteristics of the momentary acoustic environment. Using K-means clustering, we demonstrate that hourly hearing aid usage patterns (i.e., usage as minutes/hour) across participants are separated by four clusters that account for almost 50% of the day-to-day variation. The clusters indicate that hearing aids are worn either sparsely throughout the day; early morning to afternoon; from noon to late evening; or across the day from morning to late evening. Using linear mixed-effects regression modeling, we document significant associations between daily signal-to-noise, sound intensity, and sound diversity with hearing aid usage. Participants encounter louder, noisier, and more diverse sound environments the longer the hearing aids are worn. Finally, we find that remote logging via smartphones underestimates the daily hearing aid usage with a pooled median of 1.25 h, suggesting an overall connectivity of 85%. The 1.25 h difference is constant across days varying in total hearing aid on-time, and across participants varying in average daily hearing aid-on-time, and it does not depend on the identified patterns of daily hearing aid usage. In sum, remote data logging with hearing aids has high representativeness and face-validity, and can offer ecologically true information about individual usage patterns and the interaction between usage and everyday contexts.

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Section: Introductionmentioning

confidence: 99%

Real-World Hearing Aid Usage Patterns and Smartphone Connectivity

Christensen

Saunders

Havtorn

et al. 2021

Front. Digit. Health

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“…The failure to match the multimemory HA settings to the communication and environmental needs of the individual may lead to delays in fully realizing its benefits [31]. None of the studies included in the systematic review examined whether a certain program was used in the correct listening environment (eg, whether users selected a Speech in Noise program in noisy environments) during everyday life [22]. Furthermore, most of these studies relied on self-reported measures collected over a short period.…”

Section: Related Workmentioning

confidence: 99%

“…Programs can be manually selected via the HA buttons, a remote control, or a smartphone app. Users are usually advised to use a program in a specific listening situation [ 22 ]. This is reflected by the name of the program, which often conveys the situation where it is meant to be used (eg, Speech in Noise and Music ) [ 22 ].…”

Section: Introductionmentioning

confidence: 99%

“…Users are usually advised to use a program in a specific listening situation [ 22 ]. This is reflected by the name of the program, which often conveys the situation where it is meant to be used (eg, Speech in Noise and Music ) [ 22 ]. Thus, programs are a way for users to contextually adapt the device settings in specific listening situations and thereby personalize their listening experience.…”

Section: Introductionmentioning

confidence: 99%

“…To benefit from listening programs, HA users need to be able to characterize the listening environment adequately and actively select the appropriate program [ 22 ]. Previous research conducted on 11 experienced HA users has shown that the percentage of users who selected identical programs in the same situation (repeatability) surpassed the level corresponding to pure guess under almost all listening conditions [ 23 ].…”

Section: Introductionmentioning

confidence: 99%

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Investigating the Provision and Context of Use of Hearing Aid Listening Programs From Real-world Data: Observational Study

Pasta¹,

Szatmari²,

Christensen³

et al. 2022

J Med Internet Res

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Background Listening programs enable hearing aid (HA) users to change device settings for specific listening situations and thereby personalize their listening experience. However, investigations into real-world use of such listening programs to support clinical decisions and evaluate the success of HA treatment are lacking. Objective We aimed to investigate the provision of listening programs among a large group of in-market HA users and the context in which the programs are typically used. Methods First, we analyzed how many and which programs were provided to 32,336 in-market HA users. Second, we explored 332,271 program selections from 1312 selected users to investigate the sound environments in which specific programs were used and whether such environments reflect the listening intent conveyed by the name of the used program. Our analysis was based on real-world longitudinal data logged by smartphone-connected HAs. Results In our sample, 57.71% (18,663/32,336) of the HA users had programs for specific listening situations, which is a higher proportion than previously reported, most likely because of the inclusion criteria. On the basis of association rule mining, we identified a primary additional listening program, Speech in Noise, which is frequent among users and often provided when other additional programs are also provided. We also identified 2 secondary additional programs (Comfort and Music), which are frequent among users who get ≥3 programs and usually provided in combination with Speech in Noise. In addition, 2 programs (TV and Remote Mic) were related to the use of external accessories and not found to be associated with other programs. On average, users selected Speech in Noise, Comfort, and Music in louder, noisier, and less-modulated (all P<.01) environments compared with the environment in which they selected the default program, General. The difference from the sound environment in which they selected General was significantly larger in the minutes following program selection than in the minutes preceding it. Conclusions This study provides a deeper insight into the provision of listening programs on a large scale and demonstrates that additional listening programs are used as intended and according to the sound environment conveyed by the program name.

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