Investigating Bimodal Fitting Solutions in Children
Publication: American Journal of Audiology
Volume 34, Number 2
Pages 409-4220
Abstract
Purpose:
The effectiveness of a dedicated bimodal hearing aid and fitting formula was investigated in pediatric cochlear implant recipients who were experienced Desired Sensation Level (DSL) v5.0 users.
Method:
In Experiment 1, five children assessed four hearing aid fittings based on combinations of fitting formula (Adaptive Phonak Digital Bimodal [APDB], DSL v5.0, APDB fit to DSL v5.0 targets), hearing aid type (dedicated hearing aid for bimodal listeners, independent hearing aid), and/or prescriptive targets. Speech recognition scores were obtained in a sound booth in quiet and in noise. In Experiment 2, two participants from Experiment 1 were retested with an updated bimodal system.
Results:
Bimodal speech recognition in quiet in both experiments was similar across all fitting formulas. In Experiment 1, bimodal speech recognition in noise was similar across fitting formulas; however, both in noise and in quiet, individual differences were observed. In Experiment 2, optimized APDB improved speech recognition in noise performance compared to APDB and similar performance compared to DSL v5.0 in a small set of bimodal listeners.
Conclusion:
Preliminary results point to the value of individualized selection of hearing aid fitting formula settings for pediatric bimodal recipients.
Individuals who use a cochlear implant (CI) in one ear and a hearing aid (HA) in the other, termed bimodal hearing, process two distinct types of signals simultaneously. Despite the challenges associated with mismatched inputs (e.g., Blamey et al., 2000; Liu et al., 2019; Reiss et al., 2014), most bimodal listeners derive multidimensional benefits from the combined input compared to unilateral electric-only hearing. In adults, bimodal hearing yields significant speech recognition improvements in noise (Dorman et al., 2008; Firszt et al., 2012; Gifford & Dorman, 2019; Potts et al., 2009; Zhang et al., 2014), improves localization ability (e.g., Firszt et al., 2012; Potts et al., 2009), reduces listening effort (Devocht et al., 2017; Gifford et al., 2017), improves sound quality (Ching et al., 2007; Devocht et al., 2017; Warren et al., 2020), and enhances voice pitch perception (Kong et al., 2005) and music perception (Ching, 2005; D'Onofrio & Gifford, 2021; Duret et al., 2021; Gifford et al., 2007).
Bimodal benefits have also been documented in children, including improved speech recognition in noise (Cheng et al., 2018; Choi et al., 2017; Gifford et al., 2021), greater ability to benefit from talker and masker gender differences (Liu et al., 2019), better sound localization (Choi et al., 2017), and music perception (Driscoll et al., 2016; Yüksel et al., 2019). In children, bimodal hearing can lead to improved preverbal and verbal auditory perceptual skills (Çolak et al., 2019; Marsella et al., 2015), higher phonological awareness (Moberly et al., 2016; Nittrouer et al., 2012), increased receptive language (Davidson et al., 2019; Nittrouer et al., 2012), greater generative language (mean length of utterance and number of pronouns used; Nittrouer & Chapman, 2009), and better reading abilities (Guerzoni et al., 2020; Moberly et al., 2016) compared to unilateral CI use, pointing to the value and need for optimal HA programming in this population.
The current pediatric recommendations for programming HAs (American Academy of Audiology, 2013; Holder et al., 2022) suggest that a good starting point for pediatric amplification is to use prescriptive algorithms paired with thorough verification using real-ear measurements with individual measured real-ear-to-coupler differences (RECDs). Desired Sensation Level (DSL v5.0; Scollie, 2007; Scollie et al., 2005) is a commonly used prescriptive formula for pediatric fittings, which recommends more amplification for children than adults based on adult–child differences in listening preferences, performance ceilings, and loudness ratings.
However, conventional HA prescriptions do not specifically consider bimodal use and differences in sound processing in HAs and CIs. For example, CI sound processors can have higher compression thresholds and slow-acting attack and release times (RTs) for automatic gain control (AGC; Boyle et al., 2009; Vaerenberg et al., 2014). CI sound processors from Advanced Bionics (AB) use a wideband dual loop AGC comprising a slow loop (63 dB SPL kneepoint, 240-ms attack time [AT], 1500-ms RT) and a fast loop (71 dB SPL knee point, 3-ms AT, 80-ms RT). HAs may use multichannel syllabic compression with a low kneepoint (e.g., < 50 dB SPL in Phonak HAs) and a fast AT (< 10 ms) and RT (10–50 ms). Compression ratios (CRs) can also vary widely between HA and CI. For example, the CR in Phonak's proprietary fitting formula is 2:1 compared to 12:1 in AB CIs (AB, 2016). Veugen et al. (2016) studied the impact of matching AGC characteristics in the HA to the CI and reported a 1.9-dB improvement in speech recognition when noise was presented toward the HA side.
Conventional HA prescriptions also do not consider the improved audibility at higher frequencies with CIs and reduced reliance on HA input at these frequencies, especially if cochlear dead regions (CDRs) are present in the HA ear, as they often are in individuals with steeply sloping hearing losses (Moore, 2007; Preminger et al., 2005). While the consensus is not definitive, a body of work has reported that amplifying frequencies well within a high-frequency dead region (DR) typically does not improve speech recognition and may even impair it (Baer et al., 2002; Cox et al., 2012; Gordo & Martinelli Iório, 2007; Hornsby & Ricketts, 2006; Turner & Cummings, 1999; Vickers et al., 2001; Yanz, 2002). Specific to bimodal listeners, Zhang et al. (2014) reported better speech recognition performance in bimodal users when amplification was reduced at frequencies where a CDR was presumed by the threshold equalizing noise (TEN) test (Moore et al., 2000, 2004).
The potential for a misalignment in the frequency response, loudness growth functions, and AGC characteristics between the CI and HA informed the development of a dedicated prescriptive fitting formula (Adaptive Phonak Digital Bimodal [APDB]). APDB builds upon the Adaptive Phonak Digital (APD) fitting formula commonly used in clinical practice for severe and profound hearing losses with several modifications (Scherf & Arnold, 2014). First, the frequency response is adjusted by optimizing low-frequency gain and bandwidth. Less gain is provided at frequencies where hearing is most impaired to allow more gain at frequencies where audibility is most useful (i.e., the model of effective audibility; Ching et al., 2001). Low-frequency gain is increased (in reference to APD) to ensure the audibility of cues that contribute to speech recognition in relatively quiet environments (i.e., 55 dB SPL). Note that this typically increases gain below 1 kHz; however, gain increases will not be applied for certain hearing loss configurations (e.g., reversed, mild-to-moderate sloping, and flat losses).
APDB optimized bandwidth (a) ensures that bandwidth is set to as wide as possible as increases in bimodal hearing performance have been observed with increasing bandwidth (Neuman & Svirsky, 2013); (b) ensures that < 250 Hz to < 750 Hz is audible given that Sheffield and Gifford (2014) reported that the minimum acoustic low-pass bandwidth that produced a significant bimodal benefit was 250 Hz and that the bimodal benefit increased with increasing bandwidth up to 750 Hz; and (c) implements a CDR estimate according to the work of Summers et al. (2003) and Preminger et al. (2005). Regarding this last point, a simple 80-dB “rule of thumb,” suggested by Moore et al. (2000, 2004), or a 90–dB HL rule, as proposed by Summers, did not prescribe enough gain for flat 90–dB HL losses. Therefore, a sloping audiogram was added as a prerequisite for estimating a CDR. The literature on this topic is inconclusive: Steeply sloping hearing loss is usually defined as ~40 dB/octave, while Preminger found CDRs for slopes > 19 dB/octave. In Zhang et al. (2014), the group with CDRs had a slope of ~40 dB between 750 and 1.5 kHz, while the group without had a ~30-dB/octave slope. Consequently, gain in APDB is reduced if the hearing loss slope exceeds 35 dB/octave to prevent amplification into a suspected CDR, and if high-frequency loss exceeds 85 dB HL to avoid triggering broadband maximum power output, which can reduce low-frequency audibility. Data from Chalupper et al. (2013) showed that reducing gain based on this audiogram rule could provide benefit and aligned with data reported in Zhang (i.e., reducing gain in a suspected DR benefits bimodal CI recipients).
Second, the input/output curves and dynamic compression characteristics between devices are matched (AB, 2016). For additional details on aligning the compression characteristics, the authors direct the reader to Veugen et al. (2016). APDB is accessible via the Phonak Naída Link HA on the Quest platform and via the Naída Link M HA and the Sky Link M HA on the Marvel chip platform.
A growing body of work has mostly shown the benefit of APDB in adults. Auletta et al. (2021) compared the performance of bimodal CI users using their own HAs (fitted with the National Acoustics Labs, Non-Linear, Version 1 [NAL-NL1] formula) with Phonak Naìda Link HAs, fitted using both the NAL-NL1 formula and APDB. Results in 11 users showed that the APDB fitting provided significantly better noise performance than the users' previous HAs and the standard NAL-NL1 formula. Warren et al. (2020) evaluated the benefits of the APDB compared to the traditional NAL-NL2 formula in 10 bimodal listeners. The outcome measures included speech recognition, sound quality, and preference using both fitting methods. While there were no statistically significant group differences between APDB and NAL-NL2, improved individual outcomes were observed with APDB, and participants reported subjective preference and sound quality benefits for APDB. The study concluded that clinicians could consider using APDB for CI recipients due to its individual benefits and user preference. Vroegop et al. (2019) also compared APDB compared to NAL-NL2 in 19 experienced bimodal adult listeners. Outcomes were assessed across three visits, each spaced 3 weeks apart, and included speech recognition tests in quiet and noise. Despite variations in fitting outputs, the study found no notable differences in auditory performance between the two formulas; however, like Warren and colleagues, individual differences and preferences did exist, thus concluding that both fitting prescriptions are effective for bimodal users. In another fitting formula comparison study, Cuda et al. (2019) investigated the efficacy of APDB compared to DSL v5.0 in nine bimodal CI adult users with moderate to severe hearing loss. While there were no significant differences between the two on the Italian matrix sentence test in fluctuating noise, the APDB fitting required minimal adjustments, and 78% of participants preferred it for clarity over the DSL v5.0 fitting. Lastly, Holtmann et al. (2020) examined the effects of APDB in 12 bimodal participants who were fit with a conventional HA and an HA fitted with APDB. Their results showed significant improvement in speech recognition in noise, and participants also reported positive subjective experiences with linked devices and APDB settings.
Currently, there are few, if any, studies on how children perform with the APDB fitting formula, and no known work has compared APDB to DSL v5.0 in pediatric bimodal recipients. The present exploratory study investigated the effectiveness of a dedicated bimodal HA and fitting formula. Due to the prescription of less gain, particularly for high frequencies, and prior experience with DSL v5.0, we hypothesized that participants would prefer settings that were more like DSL v5.0, but that APDB would provide similar benefits as reported in adults.
Method
The effectiveness of a dedicated bimodal HA and fitting formula on speech perception in quiet and noise was examined in two experiments. Four HA fittings were explored based on combinations of fitting formulas, HA type, and prescriptive targets. In Experiment 1, participants were fit with a Naída Link UP HA. In Experiment 2, participants were fit with an updated bimodal system (Sky Link M HA). The test conditions were randomized for each participant to control for order effects, and participants were blinded to which fitting they received. For an overview of the HA fitting conditions in Experiments 1 and 2, see Table 1.
| Experiment | Hearing aid fitted | Unimplanted ear fitting conditions |
|---|---|---|
| Experiment 1 | Naída Link UP HA | 1. APDB with default gain settings (APDB) |
| 2. APDB fit to DSL v5.0 prescriptive targets (APDB to DSL) | ||
| 3. DSL v5.0 set to DSL v5.0 prescriptive targets (DSL) | ||
| 4. Independent Phonak HA using DSL v5.0 prescriptive targets | ||
| Experiment 2 | Sky Link M HA | 1. APDB with default gain settings (APDB) |
| 2. APDB fit to DSL v5.0 prescriptive targets (APDB to DSL) | ||
| 3. DSL v5.0 set to DSL v5.0 prescriptive targets (DSL) | ||
| 4. Optimized APDB with gain set at default manufacturer settings |
Note. Italics represent conditions that differed across the experiments. HA = hearing aid; APDB = Adaptive Phonak Digital Bimodal; DSL = Desired Sensation Level.
Speech Recognition Testing
Our clinic follows the Pediatric Minimum Speech Test Battery (Uhler et al., 2017). Because each participant had previously exhibited ceiling-level performance on pediatric materials, speech recognition in quiet was measured using one recorded consonant–nucleus–consonant (CNC) 50-word list (Peterson & Lehiste, 1962). Speech recognition in noise was assessed using one list pair from the Bamford–Kowal–Bench Speech-in-Noise test (BKB-SIN; Bench et al., 1979; Etymotic Research, 2005; Niquette et al., 2003). Speech and noise were presented from a co-located source 1 m in front of the participants (S0N0) with speech presented at 60 dBA. Speech-in-quiet results are reported as percent correct of the total number of words presented. Speech-in-noise results are presented as the signal-to-noise ratio (SNR) loss or the additional SNR needed compared to the average normal-hearing peer for equivalent performance on the task.
Device Programming
HAs were programmed using measured RECDs, which were entered into the Phonak Target software and used during HA verification with the Audioscan Verifit or Verifit 2. For programming conditions that used DSL v5.0 prescriptive targets, output was adjusted to within 5 dB of the prescribed target. In the “APDB to DSL” condition, overall output was adjusted equally for soft, medium, and loud inputs to maintain the loudness growth characteristics of APDB, while the aim was to match DSL v5.0 targets for medium-level inputs (i.e., 65 dB SPL). In the “DSL” and “Independent HA fit to DSL” conditions, overall output was adjusted with medium-level inputs and then with soft and loud inputs if the output was not within 3–5 dB SPL of the prescriptive target. In the APDB conditions, the gain was kept at default manufacturer settings. Additional HA settings (e.g., frequency lowering and automatic sound management) were matched across all test conditions.
For the CI ear, participants used their baseline everyday program for testing, and no adjustments were made during study enrollment. CI-only aided detection thresholds were ≤ 30 dB HL across the frequency range (250 Hz to 6 kHz), measured in the soundfield from 0° at 1 m. The baseline program was created using standard clinic practices, including electrically evoked stapedial reflex thresholds and loudness scaling for M levels, behavioral T-level measurements, and clinical judgment. All participants used an extended low frequency filter, ClearVoice set to medium, and a HiRes Optima sound coding strategy. Participants 1–3 used Optima-P, and Participants 4–5 used Optima-S. Of note, due to the time between experiments and research indicating better speech recognition with Optima-S, Participant s03 switched from Optima-P to Optima-S after participating in Experiment 1 (Holcomb et al., 2021; Reynolds & Gifford, 2019). Participant s03 consistently used Optima-S for 3 years before participating in Experiment 2.
After each fitting, participants were given an adjustment period before returning for speech recognition testing. The duration of each chronic take-home period was 1 week, except for one participant whose adaptation period to two conditions was less due to imminent cochlear implantation of the study ear during the study timeline. This participant was an experienced DSL listener but had just 5 hr of experience with the independent Phonak HA fit with DSL v5.0 before evaluation and 6.5 hr of experience before testing with APDB.
Data Analysis
Due to the constraints imposed by the small sample size, analyses will focus on individual differences within these data. CNC word scores in quiet that fell outside the critical differences first established by Thornton and Raffin (1978) and updated by Carney and Schlauch (2007) for 50-word lists were considered to be statistically different for a given individual. Statistically significant differences for speech in noise for a given individual were based on the BKB-SIN 95% critical difference tables published in the BKB-SIN manual (Etymotic Research, 2005).
Experiment 1
Participants
All procedures were reviewed and approved by the Committee to Review Studies Involving Human Subjects, acting as the institutional review board (IRB) at The River School, an independent school specializing in children with hearing loss, before participant recruitment (IRB Approval No. IRB-22-AUD-01). All participants were served by the same nonprofit audiology clinic, were fluent in English, and were enrolled in the study after obtaining written informed assent and parental consent. Five experienced bimodal users of an AB HiRes 90 K CI and a Phonak HA participated. Figure 1 shows each participant's unaided air-conduction thresholds in the nonimplanted ear. Further demographic, device, and fitting formula details are presented in Table 2.
| Participant ID | Etiology | Implant age (years) | Electrode | CI duration (years) | Study age (years) | CI processor | Baseline HA | Baseline fitting formula |
|---|---|---|---|---|---|---|---|---|
| s01 | Connexin 26 | 1 | HiRes 90 K | 3 | 5 | Naída Q70 | Sky Q50-SP | DSL |
| s02 | Connexin 26 | 5 | HiRes 90 K | 2 | 7 | Naída Q90 | Sky Q90-SP | DSL |
| s03 | Enlarged vestibular aqueduct | 2 | HiRes 90 K | 4 | 7 | Naída Q70 | Naída Link UP | APDB to DSL |
| s04 | Enlarged vestibular aqueduct | 4 | HiRes 90 K | 4 | 8 | Naída Q70 | Naída Link UP | APDB to DSL |
| s05 | Unknown | 6 | HiRes 90 K | 2 | 8 | Naída Q90 | Sky Q50-SP | DSL |
Note. All participants were fit with some form of DSL settings prior to enrollment in the study. CI = cochlear implant; HA = hearing aid; DSL = Desired Sensation Level; APDB = Adaptive Phonak Digital Bimodal.
In Experiment 1, a fitting with an Independent Phonak HA fit to DSL v5.0 prescriptive targets (referred to as independent HA fit to DSL) was completed to investigate whether programming the Naída Link HA to DSL v5.0 can achieve outcomes comparable to those of an independent HA if APDB is not appropriate for a particular patient, but bimodal streaming and access to binaural features are desired. The independent HA chosen was based on the severity of the child's hearing loss and the most current technology available at the time of the evaluation. See Table 1 for a summary of the unimplanted ear fitting conditions.
Results
HA Formula Output Characteristics and Speech Intelligibility Index
HA output varied across the HA test programs (see Figure 2). APDB prescribed less amplification across the frequency range, particularly in the low and high frequencies. All conditions manipulated to fit DSL targets using the Naída Link HA met targets through 4000 Hz across participants, except for s03 whose high-frequency hearing thresholds were in the mild hearing loss range. The output characteristics from the independent Phonak HA fit with DSL met DSL targets through 6000–8000 Hz, depending on the participant; the independent HA provided more amplification at 6000 Hz than the Naída Link across participants. The Speech Intelligibility Index (SII) for each participant and each fitting formula is shown in Figure 2.
Speech Recognition
Figure 3 shows the individual speech recognition scores in quiet and noise as a function of the fitting formula. No statistically significant individual differences were observed in speech recognition scores in quiet or noise across the tested HA fitting formulas for Participants s01, s02, and s04. However, for Participant s03, speech recognition in quiet was significantly higher (better) with the Naída Link HA fit with APDB to DSL versus APDB (80% vs. 62%) and with the independent HA fit with DSL versus Naída Link HA fit with APDB (84% vs. 62%). For Participant 5, speech recognition in quiet was significantly higher (better) with APDB to DSL versus DSL (80% vs. 60%). For s03 and s05, no statistically significant differences in speech recognition scores in noise across the tested fitting formulas were observed.
Experiment 2
Experiment 2 investigated bimodal fittings in an updated bimodal system (Sky CI M90 sound processor, Sky Link M HA) and an updated version of the APDB fitting formula (see Table 1 for a summary of the HA fitting conditions tested). In this updated bimodal fitting formula, referred to as “optimized APDB” going forward, APDB behaves like scientific fitting formulas such as NAL-NL1 and DSL in that loss of gain due to leakage through the vent is fully compensated through 1 kHz, improving low-frequency audibility. We hypothesized that optimized APDB would yield better speech recognition outcomes than APDB due to the increased audibility through 1 kHz and that participants would prefer settings that were more like DSL v5.0 because of their familiarity with this fitting formula.
Participants
Two bimodal listeners who participated in Experiment 1, s03 and s04, were recruited to participate in Experiment 2. It is important to note that significant time passed since s03 (now 11 years old) and s04 (now 13 years old) participated in the first experiment. Participant s03 exhibited a moderately severe rising to mild mixed hearing loss in a “W” configuration, indicating a 20 dB HL improvement in hearing thresholds at 2000 Hz compared to Experiment 1, while Participant s04 had a mild-to-severe mixed hearing loss. Low-frequency air–bone gaps were noted in both participants, which was consistent with their etiology of enlarged vestibular aqueduct (EVA).
Results
HA Formula Output Characteristics and SII
As in Experiment 1, output varied across the HA test programs. As designed, APDB and optimized APDB prescribed less amplification across the frequency range compared to DSL v5.0, particularly in the low and high frequencies (see Figure 4). Settings for s03 met prescriptive targets through 6000 Hz for APDB to DSL and DSL. For s04, DSL matched targets through 3000 Hz, while APDB to DSL only matched targets through 2000 Hz. SII is shown in Figure 4.
Speech Recognition
Individual speech recognition in quiet and in noise are shown in Figure 5. No statistically significant individual differences in speech recognition scores in quiet across the fitting formulas were observed for either participant. For s04, speech recognition in noise was significantly better with optimized APDB versus APDB (1.9 dB vs. 5.4 dB SNR loss).
Discussion
Bimodal Hearing
The main goal of these exploratory studies was to compare the results of fitting HAs using the APDB and DSL formulas and a combination of both (APDB fit to DSL v5.0 targets) with the Link HA and an independent HA. As cochlear implantation becomes more widely accepted as a treatment for asymmetric hearing loss, more bimodal listeners with substantial hearing in their nonimplanted ear will be encountered. Determining an appropriate fitting for this group of patients is important and can provide additional advantages over conventional HA prescriptions that do not specifically consider the bimodal user. While the benefits of bimodal fitting are well established, work has been done to investigate how to optimize bimodal benefit.
Currently, the evidence on the value of a broad frequency bandwidth is mixed. Gifford et al. (2021) examined the effects of acoustic bandwidth on speech recognition in noise in pediatric bimodal listeners. They found that low pass filtering 250 Hz to the HA ear provided significant bimodal benefit in noise, but there was no significant additional benefit with increasing bandwidth. Likewise, Davidson et al. (2015) found no significant differences in bimodal benefit in pediatric bimodal listeners for speech in quiet, noise, or talker discrimination when the HA side was programmed with a wideband frequency response or a high-frequency restricted bandwidth. Both of these works differ from the literature from adult bimodal listeners—wherein wider acoustic bandwidths maximize bimodal benefit in adult listeners (Neuman & Svirsky, 2013; Sheffield & Gifford, 2014; Zhang et al., 2010).
In Experiment 1, we found no significant individual differences (per the BKB-SIN manual for an individual) in bimodal speech recognition in noise between APDB, which provides a narrower amplification bandwidth, functionally resembling a restricted bandwidth, and DSL, which provides a wider amplification bandwidth. Interestingly, significant differences for bimodal speech recognition in quiet were observed in two participants; these results were unexpected as speech-in-noise testing is generally considered more sensitive to differences in auditory performance than speech in quiet (Fitzgerald et al., 2023). Participant s03 performed best when output was set using APDB to DSL or at DSL targets. Participant s05 performed worse with DSL compared to APDB to DSL. Looking at their individual audiograms may shed some light on these results. Participant s03 likely could have benefitted from two different mechanisms. One would be the additional audibility provided by APDB set to DSL targets, and the second being the additional acoustic bandwidth provided by DSL. Both are reasonable explanations due to their moderate rising to mild sensorineural hearing loss. In contrast, s05 has a precipitously downward-sloping hearing loss starting at 2 kHz, and it is possible that the DSL program provided amplification into high-frequency CDRs.
A combination of APDB and DSL settings provided greater benefit than APDB alone. As shown by prior studies in adults, performance generally improves with increasing bandwidth in the absence of DRs, especially in noise. Likewise, in individuals with CDRs, amplifying past the edge of the CDR with increasing bandwidth does not typically provide added benefit (Baer et al., 2002; Zhang et al., 2014). Thus, the bimodal data in this study are consistent with the findings from bimodal adults but differ from more recent work in children.
Optimized APDB
The objective of Experiment 2 was to reexamine the fitting formulas in an updated bimodal system and an updated version of APDB. While we observed no statistically significant individual differences in speech recognition scores in quiet or noise across the tested fitting formulas in Participant s03, Participant s04 demonstrated improved bimodal speech recognition in noise with optimized APDB over the original APDB. However, the output in Figure 4 showed a difference in gain of less than 5 dB between the two formulas and no difference in SII. Interestingly, this participant reported that the original APDB fitting was too quiet. One possible explanation could be the etiology of this individual's hearing loss. One of the hallmark presentations of EVA is fluctuating hearing loss. However, in the current investigation, audiograms were only measured at the start of the two experimental phases, further warranting larger scale study.
Clinically Relevant Differences
The measured differences in speech recognition across the different test conditions tended to be clinically meaningful, that is, potentially impactful on the participants' quality of life and real-world hearing abilities. This was also true for some of the differences that were not significant statistically. For example, clinically meaningful differences in bimodal hearing in noise between DSL and APDB (−0.3 dB vs. 2.7 dB) and DSL and APDB to DSL (−0.3 dB vs. 2.2 dB) in Participant s04's data were observed. While not statistically significant, these differences can be appreciable considering that every 1-dB improvement in SNR can translate to an 8- to 15-percentage-point improvement in speech recognition in normal-hearing adults and the association between quality of life and speech recognition (Dorismond et al., 2023; McRackan et al., 2018; Nilsson et al., 1994). Thus, while statistical significance provides valuable insights, the authors urge the importance of clinical and practical implications to be carefully considered, especially when dealing with individual outcomes.
Limitations and Future Directions
Several limitations in our study hinder the application of these findings to the general population. This exploratory study included a small sample of pediatric bimodal recipients, using devices exclusively from one manufacturer, with a variety of audiometric configurations in the nonimplanted ear and high across-participant performance abilities. In addition to looking at within-participant differences, a larger group of participants would allow us to investigate statistical differences that could be used to generalize study findings.
In the context of improved speech recognition, the evidence for HA auditory acclimatization in adults has been mixed. While some literature reports positive effects of an acclimatization period (e.g., Doherty & Desjardins, 2015; Yund et al., 2006), others have not (e.g., Dawes et al., 2014; Humes et al., 2002). Unlike the cohort in the current work, most of the work in this area has been completed to investigate acclimatization periods in adults with no exposure to amplification. The literature is limited on the impact of auditory acclimatization in children who are experienced HA users. Pinkl et al. (2021) did not observe any significant improvement in speech recognition in quiet or noise after 2 months of using a novel signal processing system, while Glista et al. (2012) reported that a period of acclimatization was necessary for some listeners. Future work in this area should consider modified study methods to control for these factors via larger study sample sizes and possibly a longer study duration that uses datalogging to track program adherence during the take-home period to address any effect of acclimatization.
The current study did not consider any diagnostic tools, such as the TEN test or psychophysical tuning curves, to inform us of the functional status of the acoustic hearing ear. The results could have been used to identify regions of the cochlea that may have been impaired and to determine the appropriateness of the APDB formula's reduction in high-frequency gain for each patient. This could have provided insight into who may benefit more from one fitting formula or another. Specifically, bimodal patients with more intact high-frequency regions might benefit more from HA fitting formulas that provide greater amplification bandwidth, while those with CDRs may perform better with fitting formulas that emphasize low-frequency gain or reduced amplification bandwidth. Future work should include this consideration.
Lastly, the current clinical speaker arrangement may not have been optimal for investigating bimodal benefit. Specifically, King et al. (2021) recently reported that traditional clinical measures with speech and noise collocated, as in this study, may not fully evaluate the benefit of listening in a bimodal configuration. Instead, the group points out that clinical assessment of bimodal listening with spatially separated maskers may be a more sensitive measure of the benefit to determine when an individual might consider a second CI. Future work in this area could consider more realistic and complex speaker arrangements to assess bimodal benefit.
Conclusions
This study aimed to investigate the effectiveness of a dedicated bimodal HA and fitting formula for pediatric CI recipients who were experienced DSL v5.0 users. While some pediatric audiologists may be hesitant to fit APDB in children based on the high CRs and lack of verification ability, our study findings support its utility in individualized, evidence-based clinical practice. For example, the results from Experiment 1 indicate that APDB fit to DSL may be beneficial for individuals with lesser degrees of hearing loss in the unimplanted ear. Additionally, in Experiment 2, there were no significant individual differences between optimized APDB and DSL fittings for bimodal performance in quiet or in noise. This suggests that optimized APDB could be a viable option for pediatric listeners in challenging listening situations; however, additional studies are required to confirm these results and to determine their generalizability.
Data Availability Statement
The data sets generated and/or analyzed during the current study are available from the corresponding author on reasonable request.
Acknowledgments
This research was supported by a research grant from Advanced Bionics. The authors extend their sincere gratitude to Josef Chalupper for his technical expertise and to the participants who contributed their time and effort to this work.
References
Advanced Bionics. (2016). Optimizing hearing for listeners with a cochlear implant and contralateral hearing aid.
American Academy of Audiology. (2013). Clinical practice guidelines: Pediatric amplification. https://www.audiology.org/practice-guideline/clinical-practice-guidelines-pediatric-amplification/
Auletta, G., Franzè, A., Laria, C., Piccolo, C., Papa, C., Riccardi, P., Pisani, D., Sarnelli, A., Del Vecchio, V., Malesci, R., & Marciano, E. (2021). Integrated bimodal fitting for unilateral CI users with residual contralateral hearing. Audiology Research, 11(2), 200–206.
Baer, T., Moore, B. C. J., & Kluk, K. (2002). Effects of low pass filtering on the intelligibility of speech in noise for people with and without dead regions at high frequencies. The Journal of the Acoustical Society of America, 112(3), 1133–1144.
Bench, J., Kowal, Å., & Bamford, J. (1979). The BKB (Bamford–Kowal–Bench) sentence lists for partially-hearing children. British Journal of Audiology, 13(3), 108–112.
Blamey, P. J., Dooley, G. J., James, C. J., & Parisi, E. S. (2000). Monaural and binaural loudness measures in cochlear implant users with contralateral residual hearing. Ear and Hearing, 21(1), 6–17.
Boyle, P. J., Büchner, A., Stone, M. A., Lenarz, T., & Moore, B. C. J. (2009). Comparison of dual-time-constant and fast-acting automatic gain control (AGC) systems in cochlear implants. International Journal of Audiology, 48(4), 211–221.
Carney, E., & Schlauch, R. S. (2007). Critical difference table for word recognition testing derived using computer simulation. Journal of Speech, Language, and Hearing Research, 50(5), 1203–1209.
Chalupper, J., Agrawal, S., Fredelake, S., & Spahr, T. (2013, January). Rationale and implementation of a bimodal fitting formula [Paper presentation]. Conference on Implantable Auditory Prostheses, Lake Tahoe, CA.
Cheng, X., Liu, Y., Wang, B., Yuan, Y., Galvin, J. J., Fu, Q.-J., Shu, Y., & Chen, B. (2018). The benefits of residual hair cell function for speech and music perception in pediatric bimodal cochlear implant listeners. Neural Plasticity, 2018, Article 4610592.
Ching, T. Y. C. (2005). The evidence calls for making binaural-bimodal fittings routine. The Hearing Journal, 58(11), 32–41.
Ching, T. Y. C., Dillon, H., Katsch, R., & Byrne, D. (2001). Maximizing effective audibility in hearing aid fitting. Ear and Hearing, 22(3), 212–224.
Ching, T. Y. C., van Wanrooy, E., & Dillon, H. (2007). Binaural-bimodal fitting or bilateral implantation for managing severe to profound deafness: A review. Trends in Amplification, 11(3), 161–192.
Choi, J. E., Moon, I. J., Kim, E. Y., Park, H.-S., Kim, B. K., Chung, W.-H., Cho, Y.-S., Brown, C. J., & Hong, S. H. (2017). Sound localization and speech perception in noise of pediatric cochlear implant recipients: Bimodal fitting versus bilateral cochlear implants. Ear and Hearing, 38(4), 426–440.
Çolak, M., Bayramoğlu, İ., Tutar, H., & Altinyay, Ş. (2019). Benefits of using a contralateral hearing aid in cochlear implanted children with bilateral pre-lingual profound sensorineural hearing loss on language development and auditory perception performance. ENT Updates, 9(3), Article 3. https://dergipark.org.tr/tr/pub/entupdates/issue/50773/601175
Cox, R. M., Johnson, J. A., & Alexander, G. C. (2012). Implications of high-frequency cochlear dead regions for fitting hearing aids to adults with mild to moderately severe hearing loss. Ear and Hearing, 33(5), 573–587.
Cuda, D., Murri, A., Mainardi, A., & Chalupper, J. (2019). Effectiveness and efficiency of a dedicated bimodal fitting formula. Audiology Research, 9(1), Article 219.
Davidson, L. S., Firszt, J. B., Brenner, C., & Cadieux, J. H. (2015). Evaluation of hearing aid frequency response fittings in pediatric and young adult bimodal recipients. Journal of the American Academy of Audiology, 26(4), 393–407.
Davidson, L. S., Geers, A. E., Uchanski, R. M., & Firszt, J. B. (2019). Effects of early acoustic hearing on speech perception and language for pediatric cochlear implant recipients. Journal of Speech, Language, and Hearing Research, 62(9), 3620–3637.
Dawes, P., Munro, K. J., Kalluri, S., & Edwards, B. (2014). Acclimatization to hearing aids. Ear and Hearing, 35(2), 203–212.
Devocht, E. M. J., Janssen, A. M. L., Chalupper, J., Stokroos, R. J., & George, E. L. J. (2017). The benefits of bimodal aiding on extended dimensions of speech perception: Intelligibility, listening effort, and sound quality. Trends in Hearing, 21.
Doherty, K. A., & Desjardins, J. L. (2015). The benefit of amplification on auditory working memory function in middle-aged and young-older hearing impaired adults. Frontiers in Psychology, 6, Article 721.
D'Onofrio, K. L., & Gifford, R. H. (2021). Bimodal benefit for music perception: Effect of acoustic bandwidth. Journal of Speech, Language, and Hearing Research, 64(4), 1341–1353.
Dorismond, C., Patro, A., Holder, J. T., & Perkins, E. L. (2023). Correlation between quality of life and speech recognition outcomes following cochlear implementation. Otology & Neurotology, 44(10), 1015–1020.
Dorman, M. F., Gifford, R. H., Spahr, A. J., & McKarns, S. A. (2008). The benefits of combining acoustic and electric stimulation for the recognition of speech, voice and melodies. Audiology & Neurotology, 13(2), 105–112.
Driscoll, V. D., Welhaven, A. E., Gfeller, K., Oleson, J., & Olszewski, C. P. (2016). Music perception of adolescents using electroacoustic hearing. Otology & Neurotology, 37(2), e141–e147.
Duret, S., Bigand, E., Guigou, C., Marty, N., Lalitte, P., & Bozorg Grayeli, A. (2021). Participation of acoustic and electric hearing in perceiving musical sounds. Frontiers in Neuroscience, 15, Article 558421.
Etymotic Research. (2005). Bamford–Kowal–Bench Speech-in-Noise Test (Version 1.03) [CD].
Firszt, J. B., Holden, L. K., Reeder, R. M., Cowdrey, L., & King, S. (2012). Cochlear implantation in adults with asymmetric hearing loss. Ear and Hearing, 33(4), 521–533.
Fitzgerald, M. B., Gianakas, S. P., Qian, Z. J., Losorelli, S., & Swanson, A. C. (2023). Preliminary guidelines for replacing word-recognition in quiet with speech in noise assessment in the routine audiologic test battery. Ear and Hearing, 44(6), 1548–1561.
Gifford, R. H., Davis, T. J., Sunderhaus, L. W., Menapace, C., Buck, B., Crosson, J., O'Neill, L., Beiter, A., & Segel, P. (2017). Combined electric and acoustic stimulation with hearing preservation: Effect of cochlear implant low-frequency cutoff on speech understanding and perceived listening difficulty. Ear and Hearing, 38(5), 539–553.
Gifford, R. H., & Dorman, M. F. (2019). Bimodal hearing or bilateral cochlear implants? Ask the patient. Ear and Hearing, 40(3), 501–516.
Gifford, R. H., Dorman, M. F., McKarns, S. A., & Spahr, A. J. (2007). Combined electric and contralateral acoustic hearing: Word and sentence recognition with bimodal hearing. Journal of Speech, Language, and Hearing Research, 50(4), 835–843.
Gifford, R. H., Sunderhaus, L., & Sheffield, S. (2021). Bimodal hearing with pediatric cochlear implant recipients: Effect of acoustic bandwidth. Otology & Neurotology, 42(10S), S19–S25.
Glista, D., Scollie, S., & Sulkers, J. (2012). Perceptual acclimatization post nonlinear frequency compression hearing aid fitting in older children. Journal of Speech, Language, and Hearing Research, 55(6), 1765–1787.
Gordo, A., & Martinelli Iório, M. C. (2007). Dead regions in the cochlea at high frequencies: Implications for the adaptation to hearing aids. Brazilian Journal of Otorhinolaryngology, 73(3), 299–307.
Guerzoni, L., Mancini, P., Nicastri, M., Fabrizi, E., Giallini, I., & Cuda, D. (2020). Does early cochlear implantation promote better reading comprehension skills? International Journal of Pediatric Otorhinolaryngology, 133, Article 109976.
Holcomb, M. A., Dornhoffer, J. R., & McRackan, T. R. (2021). The effects of paired versus sequential stimulation on speech recognition outcomes of adult cochlear implant recipients. Audiology & Neurotology, 26(3), 188–194.
Holder, J. T., Holcomb, M. A., Snapp, H., Labadie, R. F., Vroegop, J., Rocca, C., Elgandy, M. S., Dunn, C., & Gifford, R. H. (2022). Guidelines for best practice in the audiological management of adults using bimodal hearing configurations. Otology & Neurotology Open, 2(2), Article e011.
Holtmann, L. C., Janosi, A., Bagus, H., Scholz, T., Lang, S., Arweiler-Harbeck, D., & Hans, S. (2020). Aligning hearing aid and cochlear implant improves hearing outcome in bimodal cochlear implant users. Otology & Neurotology, 41(10), 1350–1356.
Hornsby, B. W. Y., & Ricketts, T. A. (2006). The effects of hearing loss on the contribution of high- and low-frequency speech information to speech understanding. II. Sloping hearing loss. The Journal of the Acoustical Society of America, 119(3), 1752–1763.
Humes, L. E., Wilson, D. L., Barlow, N. N., & Garner, C. (2002). Changes in hearing-aid benefit following 1 or 2 years of hearing-aid use by older adults. Journal of Speech, Language, and Hearing Research, 45(4), 772–782.
King, K., Dillon, M. T., O'Connell, B. P., Brown, K. D., & Park, L. R. (2021). Spatial release from masking in bimodal and bilateral pediatric cochlear implant recipients. American Journal of Audiology, 30(1), 67–75.
Kong, Y.-Y., Stickney, G. S., & Zeng, F.-G. (2005). Speech and melody recognition in binaurally combined acoustic and electric hearing. The Journal of the Acoustical Society of America, 117(3), 1351–1361.
Liu, Y.-W., Tao, D.-D., Chen, B., Cheng, X., Shu, Y., Galvin, J. J., & Fu, Q.-J. (2019). Factors affecting bimodal benefit in pediatric Mandarin-speaking Chinese cochlear implant users. Ear and Hearing, 40(6), 1316–1327.
Marsella, P., Giannantonio, S., Scorpecci, A., Pianesi, F., Micardi, M., & Resca, A. (2015). Ruolo della stimolazione bimodale nello sviluppo delle abilità percettivo-uditive nei bambini con impianto cocleare monolaterale [Role of bimodal stimulation for auditory-perceptual skills development in children with a unilateral cochlear implant]. Acta Otorhinolaryngologica Italica, 35(6), 442–448.
McRackan, T. R., Bauschard, M., Hatch, J. L., Franko-Tobin, E., Droghini, H. R., Nguyen, S. A., & Dubno, J. R. (2018). Meta-analysis of quality-of-life improvement after cochlear implantation and associations with speech recognition abilities. The Laryngoscope, 128(4), 982–990.
Moberly, A. C., Lowenstein, J. H., & Nittrouer, S. (2016). Early bimodal stimulation benefits language acquisition for children with cochlear implants. Otology & Neurotology, 37(1), 24–30.
Moore, B. C. J. (2007). Prevalence of dead regions in subjects with sensorineural hearing loss. Ear and Hearing, 28(2), 231–241.
Moore, B. C. J., Glasberg, B. R., & Stone, M. A. (2004). New version of the TEN test with calibrations in dB HL. Ear and Hearing, 25(5), 478–487.
Moore, B. C. J., Huss, M., Vickers, D. A., Glasberg, B. R., & Alcántara, J. I. (2000). A test for the diagnosis of dead regions in the cochlea. British Journal of Audiology, 34(4), 205–224.
Neuman, A. C., & Svirsky, M. A. (2013). Effect of hearing aid bandwidth on speech recognition performance of listeners using a cochlear implant and contralateral hearing aid (bimodal hearing). Ear and Hearing, 34(5), 553–561.
Nilsson, M., Soli, S. D., & Sullivan, J. A. (1994). Development of the Hearing In Noise Test for the measurement of speech reception thresholds in quiet and in noise. The Journal of the Acoustical Society of America, 95(2), 1085–1099.
Niquette, P., Arcaroli, J., Revit, L., Parkinson, A., Staller, S., Skinner, M., & Killion, M. (2003, March). Development of the BKB-SIN Test [Paper presentation]. Annual meeting of the American Auditory Society, Scottsdale, AZ.
Nittrouer, S., Caldwell, A., Lowenstein, J. H., Tarr, E., & Holloman, C. (2012). Emergent literacy in kindergartners with cochlear implants. Ear and Hearing, 33(6), 683–697.
Nittrouer, S., & Chapman, C. (2009). The effects of bilateral electric and bimodal electric—acoustic stimulation on language development. Trends in Amplification, 13(3), 190–205.
Peterson, G. E., & Lehiste, I. (1962). Revised CNC lists for auditory tests. Journal of Speech and Hearing Disorders, 27(1), 62–70.
Pinkl, J., Cash, E. K., Evans, T. C., Neijman, T., Hamilton, J. W., Ferguson, S. D., Martinez, J. L., Rumley, J., Hunter, L. L., Moore, D. R., & Stewart, H. J. (2021). Short-term pediatric acclimatization to adaptive hearing aid technology. American Journal of Audiology, 30(1), 76–92.
Potts, L. G., Skinner, M. W., Litovsky, R. A., Strube, M. J., & Kuk, F. (2009). Recognition and localization of speech by adult cochlear implant recipients wearing a digital hearing aid in the nonimplanted ear (bimodal hearing). Journal of the American Academy of Audiology, 20(6), 353–373.
Preminger, J., Carpenter, R., & Ziegler, C. (2005). A clinical perspective on cochlear dead regions: Intelligibility of speech and subjective hearing aid benefit. Journal of the American Academy of Audiology, 16(8), 600–613.
Reiss, L. A. J., Ito, R. A., Eggleston, J. L., & Wozny, D. R. (2014). Abnormal binaural spectral integration in cochlear implant users. Journal of the Association for Research in Otolaryngology, 15(2), 235–248.
Reynolds, S. M., & Gifford, R. H. (2019). Effect of signal processing strategy and stimulation type on speech and auditory perception in adult cochlear implant users. International Journal of Audiology, 58(6), 363–372.
Scherf, F. W. A. C., & Arnold, L. P. (2014). Exploring the clinical approach to the bimodal fitting of hearing aids and cochlear implants: Results of an international survey. Acta Oto-Laryngologica, 134(11), 1151–1157.
Scollie, S. (2007). DSL version v5.0: Description and early results in children. AudiologyOnline. https://www.audiologyonline.com/articles/dsl-version-v5-0-description-959
Scollie, S., Seewald, R., Cornelisse, L., Moodie, S., Bagatto, M., Laurnagaray, D., Beaulac, S., & Pumford, J. (2005). The Desired Sensation Level multistage input/output algorithm. Trends in Amplification, 9(4), 159–197.
Sheffield, S. W., & Gifford, R. H. (2014). The benefits of bimodal hearing: Effect of frequency region and acoustic bandwidth. Audiology & Neurotology, 19(3), 151–163.
Summers, V., Molis, M. R., Müsch, H., Walden, B. E., Surr, R. K., & Cord, M. T. (2003). Identifying dead regions in the cochlea: Psychophysical tuning curves and tone detection in threshold-equalizing noise. Ear and Hearing, 24(2), 133–142.
Thornton, A. R., & Raffin, M. J. (1978). Speech-discrimination scores modeled as a binomial variable. Journal of Speech and Hearing Research, 21(3), 507–518.
Turner, C. W., & Cummings, K. J. (1999). Speech audibility for listeners with high-frequency hearing loss. American Journal of Audiology, 8(1), 47–56.
Uhler, K., Warner-Czyz, A., Gifford, R., & Working Group PMSTB. (2017). Pediatric Minimum Speech Test Battery. Journal of the American Academy of Audiology, 28(3), 232–247.
Vaerenberg, B., Govaerts, P. J., Stainsby, T., Nopp, P., Gault, A., & Gnansia, D. (2014). A uniform graphical representation of intensity coding in current-generation cochlear implant systems. Ear and Hearing, 35(5), 533–543.
Veugen, L. C. E., Chalupper, J., Snik, A. F. M., van Opstal, A. J., & Mens, L. H. M. (2016). Matching automatic gain control across devices in bimodal cochlear implant users. Ear and Hearing, 37(3), 260–270.
Vickers, D. A., Moore, B. C. J., & Baer, T. (2001). Effects of low-pass filtering on the intelligibility of speech in quiet for people with and without dead regions at high frequencies. The Journal of the Acoustical Society of America, 110(2), 1164–1175.
Vroegop, J. L., Homans, N. C., van der Schroeff, M. P., & Goedegebure, A. (2019). Comparing two hearing aid fitting algorithms for bimodal cochlear implant users. Ear and Hearing, 40(1), 98–106.
Warren, S. E., Noelle Dunbar, M., Bosworth, C., & Agrawal, S. (2020). Evaluation of a novel bimodal fitting formula in Advanced Bionics cochlear implant recipients. Cochlear Implants International, 21(6), 323–337.
Yanz, J. L. (2002). Cochlear dead regions and practical implications for high frequency amplification. The Hearing Review, 9(4), 58–63. https://hearingreview.com/hearing-products/amplification/cochlear-dead-regions-and-practical-implications-for-high-frequency-amplification
Yüksel, M., Meredith, M. A., & Rubinstein, J. T. (2019). Effects of low frequency residual hearing on music perception and psychoacoustic abilities in pediatric cochlear implant recipients. Frontiers in Neuroscience, 13, Article 924.
Yund, E. W., Roup, C. M., Simon, H. J., & Bowman, G. A. (2006). Acclimatization in wide dynamic range multichannel compression and linear amplification hearing aids. Journal of Rehabilitation Research and Development, 43(4), 517–536.
Zhang, T., Dorman, M. F., Gifford, R., & Moore, B. C. J. (2014). Cochlear dead regions constrain the benefit of combining acoustic stimulation with electric stimulation. Ear and Hearing, 35(4), 410–417.
Zhang, T., Dorman, M. F., & Spahr, A. J. (2010). Information from the voice fundamental frequency (F0) region accounts for the majority of the benefit when acoustic stimulation is added to electric stimulation. Ear and Hearing, 31(1), 63–69.
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This work is licensed under a Creative Commons Attribution 4.0 International License.History
- Received: Jul 30, 2024
- Revised: Nov 12, 2024
- Accepted: Feb 19, 2025
- Published online: May 5, 2025
- Published in issue: Jun 3, 2025
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