Individual Hearing Aid Benefit in Real Life Evaluated Using Ecological Momentary Assessment

Implementing Ecological Momentary Assessment in Audiological Research: Opportunities and Challenges

Ecological momentary assessment (EMA) is a way to evaluate experiences in everyday life. It is a powerful research tool but can be complex and challenging for beginners. Application of EMA in audiological research brings with it opportunities and challenges that differ from other research disciplines. This tutorial discusses important considerations when conducting EMA studies in hearing care. While more research is needed to develop specific guidelines for the various potential applications of EMA in hearing research, we hope this article can alert hearing researchers new to EMA to pitfalls when using EMA and help strengthen their study design. The current article elaborates study design details, such as choice of participants, representativeness of the study period for participants' lives, and balancing participant burden with data requirements. Mobile devices and sensors to collect objective data on the acoustic situation are reviewed alongside different possibilities for EMA setups ranging from online questionnaires paired with a timer to proprietary apps that also have access to parameters of a hearing device. In addition to considerations for survey design, a list of questionnaire items from previous studies is provided. For each item, an example and a list of references are given. EMA typically provides data sets that are rich but also challenging in that they are noisy, and there is often unequal amount of data between participants. After recommendations on how to check the data for compliance, reactivity, and careless responses, methods for statistical analysis on the individual level and on the group level are discussed including special methods for direct comparison of hearing device programs.

The feasibility of using ecological momentary assessment to understand urinary and fecal incontinence experiences in adults with spina bifida: A 30-day study

In this paper, we evaluate the feasibility of using ecological momentary assessment (EMA) to understand urinary (UI) and fecal (FI) incontinence in adults with spina bifida (SB). As part of a larger 30-day prospective study to understand the incontinence in adults with SB (N = 89), participants completed end-of-day EMA diaries assessing the frequency and context of UI and FI. We used these data to assess the method feasibility across six dimensions: (a) compliance, or data entry which is consistent with study protocol and substantially complete; (b) reactivity, or behavior change attributed to study participation; (c) participant acceptability, or convenience and ease of method beneficial to compliance; (d) data capture, or the volume of incontinence behaviors collected; (e) the accuracy of incontinence reports; and f) participant-provided feedback for future studies. Participants were highly compliant with diary entry protocol and schedule: submitting 95.7% (2576/2700) of the expected total daily entries. The average completion time was two minutes. Neither the total number of submissions nor the completion time varied by demographic characteristics or health history. A sufficient volume of incontinence and affective outcomes were captured, with small downtrends in reporting of UI and affect over time. Exit survey recall was highly correlated with diary reports. Participants found the methodology to be acceptable, reported their experiences honestly, enjoyed and felt comfortable participating in the study and would engage in similar study in the future. Accurate information about the daily context of UI and FI is a key factor in the success of intervention or education programs relying on this information. Our findings demonstrate that EMA is a feasible way to describe UI and FI in adults with SB.

Reasons for ceiling ratings in real-life evaluations of hearing aids: the relationship between SNR and hearing aid ratings

Introduction

In past Ecological Momentary Assessment (EMA) studies, hearing aid outcome ratings have often been close to ceiling.

Methods

To analyze the underlying reasons for the very positive ratings, we conducted a study with 17 experienced hearing aid wearers who were fitted with study hearing aids. The acceptable noise level and the noise level where participants were unable to follow speech were measured. The participants then rated hearing aid satisfaction, speech understanding and listening effort for pre-defined SNRs between -10 and +20 dB SPL in the laboratory. These ratings were compared to ratings of a two-week EMA trial. Additionally, estimates of SNRs were collected from hearing aids during the EMA trial and we assessed whether the participants experienced those SNRs rated poorly in the laboratory in real life.

Results

The results showed that for hearing aid satisfaction and speech understanding, the full rating scale was used in the laboratory, while the ratings in real life were strongly skewed towards the positive end of the scale. In the laboratory, SNRs where participants indicated they could not follow the narrator ("unable to follow" noise level) were rated clearly better than the lowest possible ratings. This indicates that very negative ratings may not be applicable in real-life testing. The lower part of the distribution of real-life SNR estimates was related to participants' individual "unable to follow" noise levels and the SNRs which were rated poorly in the laboratory made up less than 10% of the speech situations experienced in real life.

Discussion

This indicates that people do not seem to frequently experience listening situations at SNRs where they are dissatisfied with their hearing aids and this could be the reason for the overly positive hearing aid outcome ratings in EMA studies. It remains unclear to what extent the scarcity of such situations is due lack of encounters or intentional avoidance.

Bayesian analysis of Ecological Momentary Assessment (EMA) data collected in adults before and after hearing rehabilitation

This paper presents a new Bayesian method for analyzing Ecological Momentary Assessment (EMA) data and applies this method in a re-analysis of data from a previous EMA study. The analysis method has been implemented as a freely available Python package EmaCalc, RRID:SCR 022943. The analysis model can use EMA input data including nominal categories in one or more situation dimensions, and ordinal ratings of several perceptual attributes. The analysis uses a variant of ordinal regression to estimate the statistical relation between these variables. The Bayesian method has no requirements related to the number of participants or the number of assessments by each participant. Instead, the method automatically includes measures of the statistical credibility of all analysis results, for the given amount of data. For the previously collected EMA data, the analysis results demonstrate how the new tool can handle heavily skewed, scarce, and clustered data that were collected on ordinal scales, and present results on interval scales. The new method revealed results for the population mean that were similar to those obtained in the previous analysis by an advanced regression model. The Bayesian approach automatically estimated the inter-individual variability in the population, based on the study sample, and could show some statistically credible intervention results also for an unseen random individual in the population. Such results may be interesting, for example, if the EMA methodology is used by a hearing-aid manufacturer in a study to predict the success of a new signal-processing method among future potential customers.

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.