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

Manual switching between programs intended for specific real-life listening environments by adult cochlear implant users: do they use the intended program?

OBJECTIVE

The aim of the current study was to investigate the use of manually and automatically switching programs in everyday day life by adult cochlear implant (CI) users.

DESIGN

Participants were fitted with an automatically switching sound processor setting and 2 manual programs for 3-week study periods. They received an extensive counselling session. Datalog information was used to analyse the listening environments identified by the sound processor, the program used and the number of program switches.

STUDY SAMPLES

Fifteen adult Cochlear CI users. Average age 69 years (range: 57-85 years).

RESULTS

Speech recognition in noise was significantly better with the "noise" program than with the "quiet" program. On average, participants correctly classified 4 out of 5 listening environments in a laboratory setting. Participants switched, on average, less than once a day between the 2 manual programs and the sound processor was in the intended program 60% of the time.

CONCLUSION

Adult CI users switch rarely between two manual programs and leave the sound processor often in a program not intended for the specific listening environment. A program that switches automatically between settings, therefore, seems to be a more appropriate option to optimise speech recognition performance in daily listening environments.

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

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.

Real-World Hearing Aid Usage Patterns and Smartphone Connectivity

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.