The WHO considers hearing loss to be a major global problem. A literature search was conducted to see whether high-frequency audiometry (HFA) could be used for the early detection of hearing loss. A further aim was to see whether any differences exist in the hearing threshold using conventional audiometry (CA) and HFA in workers of different age groups exposed to workplace noise. Our search of electronic databases yielded a total of 5938 scientific papers. The inclusion criteria were the keywords “high frequency” and “audiometry” appearing anywhere in the article and the participation of unexposed people or a group exposed to workplace noise. Fifteen studies met these conditions; the sample size varied (51–645 people), and the age range of the people studied was 5–90 years. Commercial high-frequency audiometers and high-frequency headphones were used. In populations unexposed to workplace noise, significantly higher thresholds of 14–16 kHz were found. In populations with exposure to workplace noise, significantly higher statistical thresholds were found for the exposed group (EG) compared with the control group (CG) at frequencies of 9–18 kHz, especially at 16 kHz. The studies also showed higher hearing thresholds of 10–16 kHz in respondents aged under 31 years following the use of personal listening devices (PLDs) for longer than 5 years. The effect of noise-induced hearing loss (NIHL) first became apparent for HFA rather than CA. However, normative data have not yet been collected. Therefore, it is necessary to establish a uniform evaluation protocol accounting for age, sex, comorbidities and exposures, as well as for younger respondents using PLDs. The monitoring of hearing functions in the context of the International Classification of Functioning, Disability, and Health has revealed that hearing loss is a major global problem . The WorConventional pure tone audiometry (CA) is currently one of the most widely used methods for the diagnosis of hearing impairment. CA operates over the 0.125–8 kHz frequency range. High-frequency audiometry (HFA) is used to examine the hearing threshold in the frequency range of 8–20 kHz. These are the frequencies at which damage to the hearing threshold can first be observed . HFA hasIn this study, a literature search was conducted to find out whether HFA can be used for early hearing loss detection. Furthermore, we set out to find out whether there are differences in the hearing threshold, as shown using CA and HFA, among different age groups between workers who are exposed to noise in their workplaces and unexposed people.The literature search was conducted from 15 November 2020 to 20 January 2021 in the PubMed, Scopus and Web of Science electronic databases. The search strategy combined key words related to HFA.The keywords “high frequency” and “audiometry” should appear somewhere in the text of the article;The date of publication is between 2000 and 2020, in line with the time frame determined by the standards IEC 60645-1; 2017, EN ISO 8253-1:2010 and EN ISO 266:1997 ,14,15;The study includes either an unexposed population or a group exposed to workplace noise;The results are evaluated with statistics;It is possible to compare the hearing thresholds for CA and at least 5 high frequencies in the range of 9–20 kHz;The study is written in either English, Portuguese or Spanish.The criteria for the selection of publications were as follows:All duplicate articles were excluded, as well as studies with unclear exposure conditions. Conference papers, letters and book chapter categories were excluded. Articles concerning hearing loss in association with ototoxic drugs, tinnitus or diabetes were also excluded.The search yielded a total of 5938 scientific publications. A total of 5855 publications that did not directly relate to the examined issues were excluded. Finally, 83 articles were selected, of which 30 were duplicated in three databases. The remaining 53 articles concerned HFA, but not all of them were relevant to our search, as 26 of them concerned hearing loss in association with ototoxic drugs, tinnitus or diabetes. Of the remaining 27 articles, 15 papers that met all six conditions for inclusion in our study were selected. n = 4), Iran (n = 2), Italy (n = 2), Spain (n = 1), Saudi Arabia (n = 1), India (n = 1), China (n = 1), Greece (n = 1) and the USA (n = 1), and there was also a systematical review comprising multiple countries around the world (n = 1).In this review, the studies were evaluated by the following parameters: the number of involved participants; the differences in hearing thresholds between an unexposed population and a population exposed to noise; the correlation between hearing thresholds, assessed by HFA and age, especially focused on particular age groups; the difference in hearing thresholds if the age groups defined by the studies had non-identical age ranges; and the similarity in hearing thresholds if the groups had identical age ranges.The number of participants in each study ranged from 51 to 645. For the years 2000–2020, 5 articles were published in 2000–2010, 10 in 2011–2020, 5 in 2015–2020 and 2 in 2017–2020. The articles were divided into two groups. The first group comprised studies on people who were not exposed to workplace noise . Two of As shown in As shown in Articles monitoring high-frequency hearing loss in unexposed individuals reported an increase in hearing thresholds with an increasing frequency as well as with increasing age ,18,21. IKumar et al. found that high frequency thresholds could be used for the early detection of noise-induced hearing loss (NIHL) in PLD users. It was found that those using PLDs at high volumes for less than 5 years showed no significant differences in hearing threshold values. In contrast, using a PLD for more than 5 years led to significantly increased hearing thresholds at 3, 10 and 13 kHz . Le PrelSpecific threshold analyses focused on risk stratification of permanent hearing loss according to clearly defined levels of exposure to music through PLDs are missing. This is due to many restrictions of conducting such research that are related to the long latency time from exposure to effect, the problems to correctly estimate the exposure and the lack of sensitive measures to detect early signs of hearing loss .Age was found to be the primary predictor and noise exposure the secondary predictor of an increase in high-frequency hearing thresholds. The results suggest that HFA may be useful for the early diagnosis of noise-induced hearing loss, especially for younger groups of workers (up to 30 years of age) ,28,29,30HFA is more sensitive for NIHL detection than CA; it can be used for early diagnosis of hearing loss, and thus it can contribute to the prevention of hearing loss, even at lower frequencies, especially at frequencies used for speech ,25,28. SIn another study comparing the EG and the CG, Mehrparvar et al. found that in the EG, the highest hearing thresholds were observed at 4 kHz in the left ear, 6 kHz in the right ear and 16 kHz when both ears were tested. The hearing threshold was significantly higher at 16 kHz than at 4 kHz. There was no statistically significant difference between the right and left ears in either group. Hearing loss was more common in males than females, but the difference was not statistically significant . Ma et aStatistically significantly higher hearing thresholds in the EG than the CG were found at all frequencies (9–18 kHz) for the 21–30-year age group and at frequencies of 9–14 kHz for those aged 31–40 years old. On the other hand, in the 41–50-year age group, statistically significantly higher hearing thresholds were found in the EG compared with the CG only at 12.5 and 14 kHz, while no significant differences were found for the oldest workers aged 51–60 years old .Antonioli et al. selected over 6000 articles for a retrospective and systematic review of the use of HFA for the monitoring of hearing loss of those exposed to workplace noise . Of thesOur review compared the use of HFA in populations exposed to workplace noise in addition to comparing the EG and CG with regard to age categories, taking into account both CA and HFA. We found significant differences in the study methodologies used for HFA, mainly in terms of age group divisions. Some of the studies investigated did not consider particular age groups and evaluated hearing thresholds in HFA for one single group, regardless of the ages of the included individuals ,29,30,31Only a few published studies have used HFA to determine hearing thresholds in an unexposed population, and even fewer studies have compared cohorts exposed and unexposed to workplace noise;The different age groups considered in the existing studies have varying age ranges. Some studies have reported hearing thresholds independently of age, even though the hearing threshold worsens with age at each frequency;The standards for hearing loss classification and their corresponding audiometry values have not yet been defined for frequencies other than 0.125–8 kHz.The limitations of this review article are as follows:Due to these restrictions, it was not possible to perform a meta-analysis of the data. We could not compare the results of the individual studies with normative data. HFA needs to be studied further, especially with regard to dependence on age and other variables.Until recently, it was not straightforward to examine hearing thresholds with HFA. HFA has made it into clinical practice mainly due to recent developments in the technology and the construction of special headphones and specially adapted audiometers. Although it can be used for the early detection of hearing loss, normative data  have not yet been established. The effect of NIHL first became apparent through the use of HFA rather than CA. NIHL is a risk factor that is easily preventable in working environments, and moreover, it is highly underestimated as a risk factor in non-working environments in ordinary human life. Among people unexposed to workplace noise, significantly higher hearing thresholds are found with increasing age. The ability to detect sound is up to 18 kHz in those aged under 40 years, while it decreases to 14 kHz in the 40–49-year age group, and those above 50 years are able to detect sound only up to 11.2 kHz. Significantly higher hearing thresholds in EG compared with CG have been found in the 9–18 kHz range, especially at 16 kHz. Previous studies have also found higher hearing thresholds at 10–16 kHz in respondents aged under 31 years who have used PLDs for longer than 5 years. It is obvious that PLD users in younger age categories require more attention in terms of hearing loss detection, since these age categories are more familiar with modern technologies, including different types of PLDs.The hearing threshold increases with increasing age, frequency and noise exposure, and this is detectable earlier when HFA rather than CA is used.A number of studies on HFA have been conducted, but each had a different design, and so they cannot be reliably compared to other studies. A harmonized methodology needs to be established that takes into account variables such as age , sex, comorbidities and noise exposure, as well as other socioeconomic and psychosocial factors and the usage of PLDs. This is necessary given the broader objective of earlier detection of hearing loss and compensation for this impairment. According to this context and the development of modern technologies, HFA should be added into the standard examination protocol for the early detection of hearing loss.


Since the number of persons diagnosed with multi-morbidity is increasing, there is a need for generic instruments to be able to assess, measure and compare ADL ability across diagnoses. Accordingly, the ADL-Interview (ADL-I) was developed to be used in rehabilitation research and clinical practice. The aim of this study was to investigate if the ADL-I can be used to provide valid and reliable ADL ability measures across gender and diagnostic groups.ADL-I data were extracted from an existing research database on persons with chronic conditions including medical, rheumatological, oncological, neurological, geriatric and psychiatric diagnoses. Data were analysed based on Rasch Measurement methods to examine: the psychometric properties of the rating scale; ADL item and person fit to the Rasch model; if the difficulty of the ADL tasks differs across gender and diagnostic groups, and if the ADL-I provides precise and reliable measures of ADL ability.Data on n = 2098 persons were included in the final analysis. Initial evaluation of the 0–3 rating scale revealed threshold disordering between categories 1 and 2. After removal of 16 underfitting items, the variance explained by the Rasch dimension increased from 54.3 to 58.0%, thresholds were ordered, but the proportion of persons with misfitting ADL-I measures increased slightly from 8.7 to 9.1%. The person separation index improved slightly from 2.75 to 2.99 (reliability = 0.90). Differential test function analysis, however, supported that the 16 underfitting items did not represent a threat to the measurement system. Similarly, ADL items displaying differential item functioning across gender and diagnoses did not represent a threat to the measurement system. The ADL items and participants were well distributed along the scale, with item and person measures well targeted to each other, indicating a small ceiling effect and no floor effect.The study results overall suggest that the ADL-I is producing valid and reliable measures across gender and diagnostic groups among persons within a broad range of ADL ability, providing evidence to support generic use of the ADL-I.N/A. All human beings have a need to perform activities of daily living (ADL). A need perceived by the person and/or by society. ADL includes both Personal ADL (PADL) and Instrumental ADL (IADL). PADL tasks are typically performed on a daily basis, regardless of gender, housing conditions, culture and interests. PADL cover tasks such as toileting, bathing, dressing, and eating. IADL involves tasks that are necessary to live an independent life and cover more complex tasks including cleaning, shopping, and cooking . The abiWithin the field of rehabilitation, several instruments used to evaluate ADL ability have been developed. Some are diagnosis–specific, others are generic i.e., for use across gender and diagnostic groups. The Barthel Index  and the The Assessment of Motor and Process Skills (AMPS) , 6 is anThe ADL-I is an instrument developed to describe and measure the quality of ADL task performance based on self-report. When administering the ADL-I, persons are asked to evaluate the quality of their performance in 47 specific ADL tasks; 31 tasks related to PADL and 16 tasks related to IADL. The aspects of quality of performance evaluated in the ADL-I are similar to the aspects evaluated in the AMPS: physical effort, efficiency, safety and independence. ADL-I data can be used to generate an overall linear measure of self-reported quality of ADL task performance. Similar to the AMPS, Rasch measurement methods have been employed to develop the ADL-I . So far,Does a four-category rating scale used with the ADL tasks (items) of the ADL-I demonstrate sound psychometric properties?Do the ADL tasks (items) define a single unidimensional construct?Do the person responses demonstrate expected and valid response-pattern?Does the difficulty of the ADL tasks (items) differ across gender and diagnostic groups?Does the ADL-I provide precise and reliable measures of ADL ability?The overall aim was to examine the psychometric properties of the ADL-I applied among males and females living with various chronic conditions. More specifically, the following research questions were addressed.The study was a descriptive register-based study. All data was obtained from an existing research database at the ADL unit of the Parker Institute, Copenhagen University Hospital, Bispebjerg og Frederiksberg. The database contains data on self-reported ADL task performance using the ADL-I collected in a range of research studies since 2007 by occupational therapists, trained in administering the ADL-I. The database contains no client identifiers besides diagnosis, gender and age. Hence, anonymized data from various client groups is included in the database, e.g., persons with rheumatologic diseases, cancer, COPD, schizophrenia, depression, mild stroke and persons with geriatric or orthopaedic problems.conditions that last a year or more and require ongoing medical attention and/or limit activities of daily living” [Self-reported ADL ability reflects the person’s perceived ability and is based on experiences of performance. Persons with decreased ADL ability due to acute illness will have very limited experience with their present ability to perform ADL task. Accordingly, only ADL-I data on diagnostic groups considered to be living with chronic conditions were included. Chronic conditions were defined by Goodman et al. as “ living” . Hence, Eating and drinking, Mobility, Going to the toilet, Dressing, Personal Hygiene, Grooming, Communication, Transportation, Cooking, Shopping, Cleaning and Washing, based on the ADL Taxonomy [The ADL-Interview (ADL–I)  is an ocTaxonomy , 20.When reporting the quality of ADL task performance, the person uses seven response categories: (a) I perform the task independently without the use of extra time or effort and without risk; (b) I perform the task independently without the use of extra time or effort and without risk, but I use helping aids; (c) I perform the task independently, but it takes me extra time; (d) I perform the task independently, but I use extra effort/get tired faster; (e) I perform the task independently, but there is a risk that I might hurt myself; (f) I need assistance from someone, but I do participate; and (g) The task is performed by others for me—I cannot participate actively. For clinical purposes, the persons can use more than one response category if several apply to their performance of the specific ADL task. If a person finds a task irrelevant to his or her daily life, the response category “Not relevant” is used.Competent (score = 3) covering response categories (a) and (b), Using extra time/effort (score = 2) covering response categories (c) and (d), Need for help/safety (score = 1) covering response categories (e) and (f), and Unable (score = 0) covering response category (g). The ordinal scores are then, based on the Rasch rating scale model, transformed into an overall linear  measure of self-reported quality of ADL task performance, adjusted for the difficulty of the ADL tasks.Evaluation of the quality of PADL task performance is based on the past 24 h, whereas quality of IADL task performance is based on the past week. ADL-I data can be used to describe task-specific self-reported quality of ADL task performance in a single person or a group of persons as well as to generate an overall measure of self-reported quality of ADL task performance. To generate overall linear measures, the mark given in the lowest response category on each task for each person is rated using a four-point ordinal rating scale: The ADL-I was initially developed and validated in persons with various types of rheumatic diseases . Later sThe Rasch computer program, WINSTEPS version 4.7.0  was usedp = 0.3548), thus, the RSM was applied. The RSM for ADL-I includes two facets (items and persons) and is based on two assertions: (a) a person experiencing more quality of ADL task performance is more likely to receive higher ratings on harder ADL-I items than a person experiencing lower quality of ADL task performance; and (b) any person is more likely to receive higher ratings on easier ADL-I items than on harder ADL-I items [Within Rasch measurement methods, the Partial Credit Model (PCM)  and the -I items . When daPrior to the RSM analysis, persons with maximum scores on ADL-I were removed, since they mathematically correspond to infinite or indefinite measures on the latent variable and so are not directly estimable . To addrThe PCA of the standardized residuals was performed to identify possible secondary dimensions within the data. The analyses of goodness-of-fit to the Rasch model included both infit and outfit statistics. While the infit statistics are more sensitive to unexpected patterns of observations on items that are roughly targeted to the people, outfit statistics are more sensitive to unexpected observations on items that are very easy or very hard . Also, bWhen items displaying misfit had been identified and removed, persons with maximum scores on this shorter version of ADL-I were removed, and analyses of the psychometric properties of the rating scale, PCA, person goodness-of-fit and DIF were repeated, to determine if scale properties and unidimensionality had improved.Afterwards, analyses of DTF were performed to determine if relevant, but removed, items would disrupt the measurement system if kept in the instrument. DTF occurs when person ability measures vary between two versions of a test. The evaluation of DTF related to inclusion or omission of misfitting ADL items was performed by comparing (a) measures of quality of ADL task performance based on a version of ADL-I containing only items displaying fit to the Rasch rating scale model to (b) measures of quality of ADL task performance based on a version of ADL-I containing all 47 items. Similarly, it was investigated whether retaining the items displaying DIF for gender or diagnosis would disrupt the measurement system by means of DTF. By comparing (a) quality of ADL task performance measures based on gender- or diagnosis specific item calibrations with (b) quality of ADL task performance measures based on common item calibrations, the variance of measures across a gender- or diagnosis-specific version and a common version could be explored, for male, females and the six diagnostic subgroups.Finally, the last research question concerned the precision and reliability of the ADL-I measures. First, it was evaluated whether the mean ADL item difficulty measure was appropriately targeted to the mean quality of ADL task performance measure of the participants. Second, the dispersion of the ADL item difficulty and quality of ADL task performance measures was evaluated for a reasonable match by examining the item-person map, a graphic display of the distribution of item and person measures, generated by the WINSTEPS program. Precision was evaluated by overall separation and reliability indices.Data on n = 2198 persons were extracted from the database, based on the inclusion criteria. After removing n = 60 (2.7%) persons with maximum scores on the ADL-I, the study sample used in the initial analyses was n = 2138 persons representing six diagnostic groups  misfit: on Table . During After removal of a total of 16 underfitting items, in the PCA the variance explained by the Rasch dimension increased to 58.0%, whereas the eigenvalue representing variance explained by first contrast slightly decreased to 3.5 on the 31-item measurement model. The disattenuated correlations between person measures generated based on three sets of item clusters remained above 0.7. The DTF analysis of the variance of ability estimates across the full (47-item) version of the ADL-I and the 31-item version is illustrated in Fig. When evaluating person-response validity based on the 47 items in the ADL-I, n = 186 (8.7%) persons did not have ADL-I measures with acceptable goodness-of-fit to the Rasch model. After removal of misfitting items, the proportion of persons with misfitting ADL-I measures increased slightly to 9.1%: psychiatric n = 22 (15.5%); rheumatologic n = 7 (3.6%); cancer n = 25 (14.0%); medical n = 4 (3.5%); neurologic n = 8 (12.3%) and geriatric n = 125 (8.9%).Combing one’s hair (p = 0.002) and Weekly shopping (p < 0.001) were relatively easier for males and Daily light cleaning (p < 0.001) was relatively easier for females  indicated that the participants had a higher mean level of ADL ability than the mean item difficulty estimate (expected to be zero logits). After removal of misfitting items and participants with maximum scores, the mean item difficulty measure SD increased to 1.22, and the mean ADL ability measure increased to 1.59 (SD decreased to 1.28), indicating slightly diminished targeting of the 31-item version of the ADL-I. The item/person distribution map  indicating that the items separated the persons into three significantly different levels of ADL ability . After rAs the number of persons diagnosed with multi-morbidity is increasing, there is a need for generic instruments to be able to assess, measure and compare ADL ability across gender and diagnoses. Since the ADL-I was developed as a generic instrument to be used across gender and diagnostic groups in rehabilitation research and clinical practice, the aim of this study was to examine the psychometric properties of the ADL-I applied among males and females living with various chronic conditions. While the ADL items displaying misfit overall did not represent a threat to the measurement system, removal of the misfitting items improved rating scale functioning and increased the sensitivity of the ADL-I ability measures. When the misfitting ADL items were removed, the results overall suggested that the ADL-I is producing valid and reliable measures across gender and diagnostic groups among persons within a broad range of ADL ability, providing evidence to support generic use of the ADL-I.Comprehension and Expression, were found to misfit [Comprehension and Expression, were reported to misfit [There might be several explanations to the misfitting items revealed in the present study. The majority of items misfitting belonged to two domains: communication and transportation. Items related to communication have been found to misfit in other ADL instruments , 44, 45.o misfit . Similaro misfit . Like inDriving did display misfit [Public Transport was found to misfit [Items related to transportation have also been found to misfit in other instruments , 45. In y misfit  and in tbowel and urine elimination in the ADL-I should be contained but reformulated to more clearly reflect the intended content.It is well established that items concerning bowl and bladder functioning often display misfit in traditional ADL scales like the Barthel Index , 48 and Shaving/make-up, Manicuring, Pedicuring, Making plans for shopping, and Light washing by hand, all of which seem relevant in an ADL instrument. The items in the ADL-I was originally adopted from the ADL Taxonomy—a classification of ADL tasks in twelve ADL domains, carefully developed in several steps [Shaving/make-up covers two separate ADL tasks in one and, along with the items Making plans for shopping, and Light washing by hand, it represents items not all people do. In terms of the two items concerning nail care, one reason for misfit could be that answers were based on what the person did  rather than what he or she was able to perform himself/herself. Since ADL-I is designed to assess the ability to perform ADL tasks in a safe, efficient, effortless and independent manner, it is necessary that the interviewers ask what the person is able to, not what he or she actually does.The remaining five items displaying misfit involve al steps , 20. StiCombing one’s hair and Weekly shopping) relatively easier for males and one item (Daily light cleaning) relatively easier for females. Similar examples of DIF have previously been observed in a study by Fleishman et al. where elderly females were more likely to receive help with shopping than elderly males, whereas elderly males potentially needed more help doing light housework [The analysis for DIF by gender identified two items , communication and transportation. Still, a few misfitting items do represent the ADL dimension. Thus, other reasons for misfit e.g., unclear definitions of the items should be explored and if possible, resolved. Also, since up to 5% of items on a scale are expected to misfit by chance , 1–2 iteSince the 47 ADL items of the ADL-I, based on the ADL Taxonomy, previously have been reported as relevant for clinical use, all ADL items can be retained in the ADL-I for the purposes of assessing and describing single client’s quality of ADL task performance, and identifying targets in rehabilitation processes. For measuring self-reported quality of ADL task performance in clinical and research only items displaying fit to the Rasch measurement model should be used. For this purpose, conversion tables can be made available based on the 31-item version. Future studies are needed to evaluate the clinical utility of the ADL-I, including the ease to use in clinical research and practice .The inclusion of a large and diverse study sample representing variation across age, gender, diagnoses and self-reported quality of ADL task performance was a great strength of the Rasch analysis. Moreover, the data was extracted from a research database containing ADL-I data from a range of research studies. Hence, all data were collected for research purposes by trained occupational therapists, supporting data quality. As the database is constructed based on anonymized datasets only including age, gender, diagnosis and raw ADL-I ratings, the study was limited in terms of describing the study sample in more detail. Moreover, the sample sizes for persons with medical, rheumatological, oncological, neurological, and psychiatric chronic conditions may be considered small and potentially result in unstable measures. Still, according to Linacre, a sample size of 64 to 144 persons will provide stable item calibrations and person ability measures within 0.5 logits (CI 95%) .The 31-item version of the ADL-I is producing valid and reliable measures across gender and diagnostic groups among persons within a broad range of ADL ability.


To examine whether adolescents exceed recommended noise exposure limits when using personal listening devices (PLDs) and to investigate the relationship between objectively measured PLD use and hearing thresholdsCross-sectional study.This study was embedded within an ongoing prospective birth cohort study in Rotterdam, the Netherlands. Data were collected from May 2017 to September 2019.A smartphone application was developed to measure daily noise exposure from PLDs. Listening habits were monitored among 314 adolescents with a mean age of 13 years 7 months , of whom 51.6% were male. Hearing acuity was measured by pure tone audiometry, and tympanometry was performed in both ears.Within the study group, 2.2% adolescents exceeded the recommended daily noise dose (85 dBA as an 8-hour time-weighted average) among all days when the application was active and 9.9% when among only the listening days. No significant correlation was found between the daily noise dose from PLDs and pure tone thresholds.The majority of adolescents exhibited listening habits that could be considered safe. As noise-induced hearing loss develops slowly over time, it could be that the effects of PLD use on hearing are not evident yet in this young population with a relatively short duration of PLD use. Although researchers agree that PLDs are capable of producing hazardous output levels,57 the actual risk of hearing loss due to PLD use remains a subject of debate. Whereas some studies demonstrated that hearing thresholds are significantly elevated in teenagers and young adults who use PLDs,812 others did not observe an association between PLD use and hearing at the conventional frequencies.1315 Since hearing loss can lead to a number of disabilities and can reduce the quality of life, prevention is of great importance.Personal listening devices (PLDs) such as smartphones and MP3 players with audio playback function have become increasingly popular. More than 90% of adolescents and young adults use PLDs on a regular basis, with smartphones the most commonly used.The results of previous described studies are based on self-reports of PLD use, sometimes in combination with physical measurements of preferred listening levels. However, little is known about the validity of these measures. Self-reports are prone to recall bias, and single measurements assume that listeners do not change their exposure levels during the day or week. To date, no studies have evaluated the association between objectively measured PLD use and hearing thresholds. In search of ways to objectively monitor noise exposure, we developed a smartphone application that was able to collect data on listening habits in a large population-based cohort of adolescents. The first aim was to determine daily noise exposure from PLDs and examine whether adolescents exceed recommended exposure limits. The second aim was to investigate the dose-response relationship between daily noise exposure from PLDs and pure tone thresholds. Adolescents aged 12 to 17 years who visited the research center at the Erasmus Medical Center between May 2017 and September 2019 were invited to participate in this substudy (n = 3237). To be eligible to participate, adolescents needed to have a smartphone. The first version of the application was designed only for smartphones running the Android operation. The updated version, available from April 2019 onward, ran on iOS smartphones as well. The study was approved by the Medical Ethical Committee at the Erasmus Medical Center. Written informed consent was obtained from all participants and their parents.This study was embedded within Generation R, a population-based prospective cohort study from fetal life onward in Rotterdam, the Netherlands. The design and population have been described. Although the application period was intended to be 35 days, participants could delete the application at any time. After 35 days, participants received a notification that the study ended and the application could be deleted. Yet, some participants did not remove the application, resulting in a monitoring period >35 days. Participants were included in the analyses if the time of installation or the time between first and last measurement was between 7 and 40 days.The application was overseen by the departments of communication, security, legal affairs, and data protection of the Erasmus Medical Center. Several rounds of technical development and field testing were conducted before the application was implemented within the Generation R Study. The application was tested 3 times by 10 individuals, after which adjustments to the design and software were made. The application was aimed to record listening habits . Participants were able to use their own headphones. To log in, a unique username and password were required, provided during participants’ visit at the research center. After logging in, participants received a questionnaire regarding the brand and type of smartphone, volume limit settings, the use of earphones or headphones for listening, and whether they tend to listen to music with 1 or 2 ears. When participants completed the questionnaire, the application started to run constantly in the background to collect data on listening habits. For the specific methodology on how listening habits were recorded, see Paping et al. For every 3-dB increase in noise level, the allowable exposure time is reduced by half. Exposures from individual activities in a given day are added to calculate the daily noise dose. The recommended daily noise dose should not be exceeded, as it places an individual at a higher risk of acquired hearing loss.Consistent with previous studies, the daily noise dose was calculated on the basis of occupational safety standards. The National Institute for Occupational Safety and Health (NIOSH) defines a time-weighted average of 85 A- weighted decibels (dBA) for an 8-hour period as the maximum recommended noise dose per day.21 Therefore, we performed our own measurement with a modified probe test cavity of Otodynamics. A hole was drilled at the bottom of the cavity to tightly fit a Brüel & Kjaer 4189 microphone, and intra-aural earphones were inserted into the cavity . For 3 devices, output levels were measured at every intensity control level available with a sample of white noise . As the output levels measured were most compatible with the study of Williams et al , this regression equation was used to convert listening levels to sound levels. The equation is presented byThe data collected by the application were used to calculate participants’ daily noise dose in several stages. First, we converted the listening levels into estimated output levels in dBA with a regression equation from literature. Several studies have evaluated output levels of smartphones, reporting different results.Hereafter, the daily noise doses were calculated with the following formula:D = daily noise dose (%), nC = total time of exposure at a specified noise level (min), and nT = exposure time at which the noise for this specified level becomes hazardous (min). The exposure time at which the noise for a specified level becomes hazardous was calculated bynT = exposure time at which the noise for this level becomes hazardous (min) and L = exposure level (dBA). Last, the mean daily noise dose was calculated for each participant by adding all daily noise doses and dividing by the number of days that the application was active  or the number of listening days (daily noise dose–listening days).Participants underwent pure tone audiometry and tympanometry in a sound-treated booth meeting the maximum permissible ambient sound pressure levels of ISO standard 8253-1 . Hearing assessment was performed before participants installed the application. Air conduction thresholds were obtained from 0.5 to 8 kHz via a clinical audiometer (Decos audiology workstation version 210.2.6 with AudioNigma interface) and TDH-39P headphones with MX-41/AR ear cushions. Due to time constraints, no bone conduction thresholds were measured. All thresholds were measured according to the shortened ascending method based on ISO standard 8253-1, which means that thresholds were defined by the intensity level at which the tone was heard in 2 of 3 ascents. The right and left ears were alternately tested first. Tympanometry (Interacoustics AT235h tympanometer with a 226-Hz probe tone) was used to assess middle ear function. Ear canal volume >0.3 mL, compliance >0.25 mL, and middle ear pressure between −100 and 100 daPa were considered normal.24 Therefore, we calculated the pure tone average at 3, 4, and 6 kHz (notch) and 6 and 8 kHz (high frequency). When participants reported listening to the PLD with both ears, we averaged the pure tone average of the right and left ears. Otherwise, the pure tone average of the right or left ear was selected, depending on which the participant predominantly listened with. Participants who had missing or abnormal tympanometry with a pure tone average >15 dB HL (notch or high frequency) were excluded to avoid possible inference of external or middle ear pathology when the relationship between noise exposure from PLDs and hearing thresholds was examined.Noise-induced hearing loss is often presented as a notch between 3 and 6 kHz or as high-frequency hearing loss.Data on demographics were obtained by parental questionnaires. Ethnicity was categorized as Western  or non-Western.P value <.05 was considered statistically significant.Statistical analyses were performed in IBM SPSS statistics version 24 and R version 3.6.1. Descriptive statistics were used to evaluate participants’ demographic characteristics and listening habits. Spearman correlation coefficients were calculated to evaluate the correlation between daily noise dose and pure tone thresholds, which did not show a normal distribution. A Supplemental Table S1 (available online).Of the 3237 adolescents visiting the research center between May 2016 and September 2019, 978 (30.2%) agreed to participate in this substudy. Of these participants, 484 (49.5%) actually installed and started the application. We excluded participants with a OnePlus smartphone, as listening levels were incorrectly converted (n = 2), and participants with an installation time or a time between first and last measurement of <7 days or >40 days (n = 69). After the exclusion of participants who did not listen to music or watch a video with headphones at least once while the application was active (n = 99), 314 participants were included Figure 1Most participants had a Samsung smartphone (63.7%), followed by Huawei (11.1%) and Motorola (9.2%). Apple was less common (4.1%), since the first version of the application was designed for smartphones running the Android operation only. Earphones were used by 80.3% of the participants and headphones by 19.7%. The majority reported listening to music with both ears (80.6%); 12.1% listened predominantly with the right ear and 7.3% with the left. Listening habits were monitored over a median course of 33 days . The median number of days listening a week was 2.4 . The median listening time was 20.2 minutes a day  when averaged over all days that the application was active and 81.5 minutes  when calculated over only the listening days. The mean listening level was 55.0% . When the listening habits of Android (n = 301) and Apple (n = 13) users were compared, we observed that Apple users listened less frequently  and of shorter duration . The listening levels were comparable .Supplemental Figure S4, available online). Listening levels changed to a lesser extent.The majority of participants varied their listening habits during the month . No specific pattern was observed. The mean listening time did not differ among days of the week  for all days and 2.7%  for the listening days. Seven participants (2.2%) exceeded the 100% daily noise dose for all days and 31 (9.9%) for the listening days. The 100% daily noise dose was exceeded at least once during the monitoring period by 28.8% of the participants. P > .8).Of the 314 participants whose listening habits were monitored, 5 (1.6%) were excluded as no pure tone thresholds were obtained and 10 (3.2%) because of missing or abnormal tympanometry in combination with a pure tone average >15 dB HL (notch or high frequency). For the remaining 299 participants, scatterplots were created and Spearman correlation coefficients calculated. No relationship was observed between the daily noise dose and pure tone thresholds Figure 4 solely by listening to PLDs. An increase to 9.9% was observed when only listening days were taken into account. To our knowledge, just 1 study has measured PLD use with a smartphone application. The 100% daily noise dose was exceeded by 2.7% of the participants, which is consistent with our findings. Studies based on self-reports to assess daily noise exposure found that 16% to 26% of young people exceed the safety standard when using PLDs.27 This finding may indicate that participants tend to overestimate their listening habits and that self-reports cannot predict the actual daily noise dose sufficiently. In the present study, we observed that listening habits are highly variable. Self-reports measuring listening habits at a single point in time may not account for this variability. Our research group recently examined the accuracy of self-reported listening habits. The results showed that the majority of adolescents were not able to estimate their PLD use in a reliable manner. Listening habits were overestimated by a quarter to a half of the participants. We therefore consider an application of added value when assessing PLD use.About 2% of the adolescents in this population-based study were estimated to exceed the NIOSH safety standard for occupational noise As a result, the number of young people exceeding safety standards when listening to PLDs may be higher in the general population than observed in our study.Another possible explanation for the lower proportion of adolescents exceeding the recommended daily noise dose in present study is that adolescents who frequently expose themselves to harmful listening levels through PLDs decided not to participate. There was a selection toward a relatively Western and highly educated population. Vogel et al found that adolescents with a lower educational level report relatively more exposure to hazardous music levels.No significant association was found between daily noise exposure from PLDs and hearing thresholds. However, the majority of participants had a relatively low daily noise dose, which made it difficult to study the association. Second, as noise-induced hearing loss develops slowly over years of exposure, it could be that the effects of overexposure to PLDs are not yet evident in this population with a relatively short duration of PLD use. Kim et al found no association between daily PLD use and hearing thresholds, but students who used their PLDs for >5 years had significantly elevated hearing thresholds.An application was developed for this study to collect real-time data on PLD listening habits. The application was easy to use and required minimal storage capacity and battery. A limitation was that the application could be installed on only a single PLD. If participants used multiple PLDs, the daily noise dose is an underestimation of true value. Meanwhile, it is also possible that the smartphone was used by someone other than the participant or that the earphones or headphones were not worn during a listening session, resulting in an overestimation of the listening habits. According to the literature, output levels vary by the type of headphones used and style of music.20 We did not take these factors into account, which could lead to an over- or underestimation of participants’ true daily noise dose. However, we expect this possible bias to be nondifferential. Furthermore, when daily noise dose is calculated, other sources of recreational and occupational noise exposure should be taken into account. We would therefore recommend a lower cutoff for the maximum daily noise dose due to PLDs alone.To calculate participants’ daily noise dose, the listening levels registered by the smartphone were converted into estimated output levels via a regression equation. the results should be interpreted with caution.In line with previous research, the daily noise dose was calculated on the basis of occupational safety standards. To date, there are hardly any guidelines on recreational noise exposure. As there are authors suggesting that it is not appropriate to simply adopt current occupational exposure limits to recreation settings,The current study was conducted as part of the Generation R Study. Hearing acuity was assessed by dedicated research assistants with a small variance, resulting in a relatively homogenous setting. A limitation of this study is that otoscopic examination and bone conduction audiometry were not performed due to time constraints. Since our main interest was sensorineural hearing loss, we decided to include only participants with normal tympanometry in case of elevated thresholds.Unfortunately, the participation rate was lower than expected. As mentioned previously, there was a selection toward a relatively Western and highly educated population, which may affect the generalizability of our findings. A lack of consent and not having an Android smartphone were the main reasons for exclusion. The lower response rate due to no consent may be caused by the limited time at the research center allocated to inform about the application. We observed that the willingness to participate increased when more information was provided. It is possible that we did not have adequate power to detect an association between PLD use and hearing acuity in present study, especially considering the small sample exceeding the recommended daily noise dose.The findings of this study revealed several important knowledge gaps, including the impact of long-term PLD use and other sources of recreational noise exposure on young people’s hearing. It would also be of interest to examine which sociodemographic and psychosocial factors are related to unsafe listening habits, for the purpose of prevention.The application is currently being developed. An important adjustment is that adolescents using Bluetooth headphones can also participate in the new version of the application. Hearing acuity and listening habits will be repeatedly measured in the future. We aim to examine the change in listening habits with age and the long-term impact of PLD use on hearing.The majority of adolescents exhibited safe listening habits according to NIOSH criteria. The degree of noise exposure to PLDs was not associated with hearing acuity. As noise-induced hearing loss develops slowly over time, it could be that the effect of PLD is not evident yet in this young population with a relatively short duration of PLD use. Moreover, it is possible that an association between PLD use and hearing acuity would emerge with an increase in sample size.Click here for additional data file.Supplemental material, sj-docx-5-oto-10.1177_01945998211012274 for Objective Measurement of Listening Device Use and Its Relation to Hearing Acuity by Danique E. Paping, Jantien L. Vroegop, Geert Geleijnse, MSc, Carlijn M.P. le Clercq, Simone P.C. Koenraads and Marc P. van der Schroeff in Otolaryngology–Head and Neck SurgeryClick here for additional data file.Supplemental material, sj-jpg-1-oto-10.1177_01945998211012274 for Objective Measurement of Listening Device Use and Its Relation to Hearing Acuity by Danique E. Paping, Jantien L. Vroegop, Geert Geleijnse, MSc, Carlijn M.P. le Clercq, Simone P.C. Koenraads and Marc P. van der Schroeff in Otolaryngology–Head and Neck SurgeryClick here for additional data file.Supplemental material, sj-jpg-2-oto-10.1177_01945998211012274 for Objective Measurement of Listening Device Use and Its Relation to Hearing Acuity by Danique E. Paping, Jantien L. Vroegop, Geert Geleijnse, MSc, Carlijn M.P. le Clercq, Simone P.C. Koenraads and Marc P. van der Schroeff in Otolaryngology–Head and Neck SurgeryClick here for additional data file.Supplemental material, sj-jpg-3-oto-10.1177_01945998211012274 for Objective Measurement of Listening Device Use and Its Relation to Hearing Acuity by Danique E. Paping, Jantien L. Vroegop, Geert Geleijnse, MSc, Carlijn M.P. le Clercq, Simone P.C. Koenraads and Marc P. van der Schroeff in Otolaryngology–Head and Neck SurgeryClick here for additional data file.Supplemental material, sj-jpg-4-oto-10.1177_01945998211012274 for Objective Measurement of Listening Device Use and Its Relation to Hearing Acuity by Danique E. Paping, Jantien L. Vroegop, Geert Geleijnse, MSc, Carlijn M.P. le Clercq, Simone P.C. Koenraads and Marc P. van der Schroeff in Otolaryngology–Head and Neck Surgery


Bilateral cochlear implantation is increasing worldwide. In adults, bilateral cochlear implants (BICI) are often performed sequentially with a time delay between the first (CI1) and the second (CI2) implant. The benefits of BICI have been reported for well over a decade. This study aimed at investigating these benefits for a consecutive sample of adult patients. Improvements in speech-in-noise recognition after CI2 were followed up longitudinally for 12 months with the internationally comparable Finnish matrix sentence test. The test scores were statistically significantly better for BICI than for either CI alone in all assessments during the 12-month period. At the end of the follow-up period, the bilateral benefit for co-located speech and noise was 1.4 dB over CI1 and 1.7 dB over CI2, and when the noise was moved from the front to 90 degrees on the side, spatial release from masking amounted to an improvement of 2.5 dB in signal-to-noise ratio. To assess subjective improvements in hearing and in quality of life, two questionnaires were used. Both questionnaires revealed statistically significant improvements due to CI2 and BICI. The association between speech recognition in noise and background factors  or subjective improvements was markedly smaller than what has been previously reported on sequential BICI in adults. Despite the relatively heterogeneous sample, BICI improved hearing and quality of life. Bilateral cochlear implantation is becoming a standard of care in the treatment of bilateral severe-to-profound hearing loss, not only for pediatric patients but also for adult patients. Over more than a decade, several studies have investigated the audiological benefits, self-reported improvements, and cost-effectiveness of bilateral cochlear implants (BICI) when compared to unilateral implantation. These findings are summarized in systematic literature reviews ,2,3 and In addition to bilateral benefits, potential detrimental effects of CI surgery need to also be considered in patient counseling. While the overall clinical effect of unilateral CI surgery on balance and vestibular function is generally small , there iIn the clinical routine, adult patients are often implanted sequentially with BICI while simultaneous implantation is more common in children born with severe-to-profound hearing loss. Early simultaneous BICI in pediatric cases aims to promote hearing development in both ears, to provide benefits of binaural hearing, and to prevent abnormal asymmetric strengthening and preference for the first-implanted ear associated with a long delay between the first (CI1) and the second (CI2) implant . In adulIn Finland, bilateral cochlear implantation commenced first in pediatrics around 2010, and, subsequently, children have received simultaneous implants in a single surgery. Based on an individual candidacy assessment, sequential BICI in working-age adults followed some years later and the first outcomes were published soon thereafter . Since tIn addition to inter-individual differences in outcomes, another source of variability may stem from the physical configuration of the speech and noise signals around the test participant. While the speech signal is typically presented from the front, the noise may be co-located with the speech, it may be emanating from a different direction or hemifield than the speech, or it may be diffuse, reaching the listener with a seemingly random phase from multiple locations. These differences make direct comparisons of speech-in-noise data difficult . To compThis prospective study assessed the benefits of sequential BICI for a consecutive sample of Finnish working-age adult CI recipients. The objective benefit was evaluated using the FMST in a relatively simple and clinically feasible spatial configuration of the speech and the noise signals to assess hearing within the first 12 months after receiving CI2. Improvement in SRT in noise was also assessed in the CI1 ear and compared with the observed improvement in normally hearing adults over the same period. Questionnaires were used to assess the subjective benefits of sequential BICI in this population. All adult patients eligible for CI2 were included in the study, and despite large differences in background factors, significant benefits due to BICI were identified.Prior to the commencement of sequential bilateral implantation in adults in our clinics, BICI were limited to the pediatric population and special cases in adults, such as deafblindness or meningitis. We originally included the first 33 working-age adult patients eligible for BICI in this study. Four patients were pre-lingually deafened exhibiting poor development of speech recognition in their CI2 ear and they were excluded from this study. In addition, data collection was not completed for two other patients. Thus, 27 patients in total were included, and at the time of the CI2 surgery, their mean age was 44.2 (range 19.6–64.8) years and the mean duration of hearing loss (DHL) and duration of deafness (DD) in the CI2 ear were 35.1 (range 18.2–56.2) and 21.0 (range 1.8–47.9) years, respectively. DHL and DD were computed from the patient records for each ear and defined as the time between the CI surgery and when the pure-tone average  in that ear had been worse than 25 and 80 dB hearing level (HL), respectively. The mean time between the implantation of CI1 and CI2 was 5.4 (range 1.0–16) years. The mean  preoperative PTA in the CI2 ear was 99.1 ± 12.4 dB HL. Of the 27 patients, eight used a hearing aid (HA) up until the CI2 surgery.A soft surgical technique was utilized in the CI2 surgery . The surThe ethical committee of Helsinki University Hospital approved the study protocol . All patients gave their voluntary informed consent to participate in the study.Speech recognition in noise was measured in a soundproof listening room over two loudspeakers . The loudspeakers were located at ear level and at a distance of 1 m from the listening position at a 90° angle in between them. Post-operative sound-field hearing thresholds were measured separately for CI1 and CI2 according to the provisions of ISO 8253-2 and ISO 389-7 standards ,29 with The SRTs in noise for the FMST were measured with the Oldenburg Measurement Applications test software , a laptop computer, and an Auritec ear 3.0 sound card in three different configurations: speech and noise co-located in the front (S0N0), speech in the front and noise either to the left or right of the patient . The speech material consisted of equally intelligible test lists, and each list comprised 20 sentences. The noise was stationary with the same long-term spectrum as the speech signal.After switching CI2 on, sound-field hearing thresholds were measured when needed, to ensure that the audiograms for CI1 and CI2 were equal or better than 30 dB HL in the audiometric frequencies from 250 Hz to 6 kHz. Adjustments to CI fitting were done accordingly to achieve this.The SRTs in noise were measured preoperatively with CI1 and a potential HA at 1, 3, 6, and 12 months after switching CI2 on. Five test configurations were used: three in the S0N0 condition , and bilaterally in the S0N −90 and S0N + 90 conditions, where the noise signal was either on the side of the first or the second implanted ear .The overall level of the noise was fixed at 65 dB SPL. Each session started with two practice lists; the first list of sentences was administered with a fixed + 10 dB signal-to-noise ratio (SNR) and the second starting from 0 dB SNR and varying the SNR in an adaptive procedure to converge to 50% correct speech recognition, i.e., to SRT in noise. After the two practice lists, the five configurations were administered in a counterbalanced order across sessions and patients to minimize the effect of within-session fatigue. The test list for each SRT-in-noise measurement was selected at random, and the word matrix was presented in writing to the patients at the beginning of each test session to minimize the effect of content learning of the matrix sentence material.The impact of CI2 on quality of life (QoL) was measured using the Glasgow Benefit Inventory (GBI) and the Glasgow Health Status Inventory (GHSI). GBI is a validated, generic patient-recorded outcome measure widely used in otolaryngology to report changes in QoL after an intervention . Both quA nonparametric Friedman’s test of differences among repeated measures was performed on the SRT-in-noise data separately for the CI1, CI2, and BICI in the S0N0 condition, and for the bilateral S0N0, S0NCI1, and S0NCI2 conditions using the IBM SPSS Statistics Version 22 software . The GBI and GHSI scores were analyzed using Student’s t-test in Graphpad Prism 8 . Pearson’s correlation coefficients were computed for the SRT-in-noise data, the background factors of Post-operative PTA (mean ± SD) in the CI2 ear was 35.8 ± 10.5 dB HL after the switch on and 31.5 ± 5.4 dB HL at 1–2 months from switching CI2 on. For two patients, the audiogram after the switch on was not available. The patients were instructed to wear both the CI1 and CI2 sound processors in their everyday life and occasionally to practice their listening abilities with CI2 only.p < 0.05) and, therefore, the nonparametric Friedman’s test was performed on the data. For CI1, CI2, and BICI in the co-located S0N0 condition, Friedman’s test produced an χ2 value of 166.83, and for the bilateral S0N0, S0NC1, and S0NCI2 conditions an χ2 value of 106.73, which were both significant (p < 0.001).Based on the Shapiro–Wilk test, the preoperative and some of the 1-, 3-, and 6-month SRT-in-noise data were not normally distributed (N = 34). SRTs in noise were statistically significantly better for BICI than for CI1 (p < 0.00016) and for CI2 (p < 0.00002) at each post-operative assessment point. One month after switching CI2 on, the SRT in noise was better for CI1 than for CI2 (p = 0.00076). These differences are marked with asterisks on the corresponding significance level in p = 0.00013), 1 and 6 months , 1 and 12 months , and 3 and 12 months . For BICI, the improvement was significant between 1 and 6 months  and 1 and 12 months . Finally, the SRT in noise between the preoperative CI with a potential HA and the post-operative CI1 improved significantly 6 and 12 months after switching CI2 on .As is evident in p < 0.00050; marked with asterisks in p < 0.05). There is a moderate association . For the rest of the data in p < 0.05). This further bolsters the association between the duration of hearing loss and poorer hearing outcomes in the CI1 ear. However, no such association was found for CI2 or BICI in our consecutive patient sample.In To measure the impact of CI2 on the patients’ QoL, the GBI was self-completed by the patients (N = 27) 12 months after switching CI2 on A. One pap < 0.05; p < 0.05). There were no differences in the social support or physical health subscales between the time points.In comparison to the GBI, GHSI is more focused on hearing difficulties and their impact on daily life. It allows comparison between two time points. In this study, the GHSI was self-completed by the patients (N = 23) prior to receiving CI2 and 12 months after switching CI2 on. Five patients failed to return the questionnaire either once or twice. When the GHSI scores were compared between prior to CI2 and 12 months after the CI2 switch on, the total score was higher 12 months after the CI2 switch on than prior to CI2 surgery (66 ± 2 vs. 61 ± 2, respectively; Finally, the SRT-in-noise improvement from the preoperative CI and a potential HA to BICI 12 months after switching CI2 on is plotted against the total GBI CI2 and the three subscale scores in Improvements in SRT in noise, in hearing, and in QoL were investigated in this study for a consecutive sample of sequentially implanted deafened adult patients over a follow-up period of 12 months from switching CI2 on. In the co-located S0N0 condition see , there wWhen looking at the longitudinal improvement in SRT in noise, the median CI2 performance reached that of CI1 3 months after switching CI2 on. Again, this is in line with earlier research on CI2 improvement over time in sequential BICI in adults . In addiIn clinical practice, it is often assumed that speech audiometric results are stable unless there are changes in hearing. However, learning effects are acknowledged for most sentence-based speech in noise tests. While within-session learning in SRT-in-noise measurements is also well documented for the multilingual matrix test  and for To explore the inter-session improvement in more detail, we compared the median SRT in noise for CI1 of the present study with the mean of normally hearing listeners from , both asIn normally hearing listeners, SRM has been reported to be up to and above 10 dB when SRT is compared between co-located and spatially separated speech and noise signals ,31,32. IThe background factors, however, correlated fairly little with the SRT in noise data in the present study. Patient age or etiology did not seem to influence the audiometric outcome in our study sample: SRTs in noise were close to the median results for the oldest patients, as well as for patients with genetic hearing loss. For 14 out of 27 patients, however, the etiology of hearing loss was unknown. In an earlier longitudinal study of sequential bilateral CIs in adults . In our Generic QoL instruments typically underestimate the impact of BICI. Meta-analysis with hearing or CI-specific QoL instruments shows large improvements in patients with BICI and medium improvements when comparing bilateral to unilateral CI . Our resStatistically significant improvements in hearing and in QoL were obtained over a follow-up period of 12 months for the consecutive patient sample of this study. The benefit of BICI was consistent with the literature. The background factors were only moderately associated with speech recognition in noise and this was the case only for the CI1 ear. For CI2 or BICI, the association was insignificant, which is inconsistent with recent research on a similar study sample. A significant improvement over the follow-up period was also noted for CI1, to the same extent as has been reported in repeated testing for normally hearing listeners. Finally, the association between a generic QoL questionnaire and speech recognition in noise was moderate.