J Am Acad Audiol 25:187-198 (2014)
Patterns of Hearing Aid Usage Predict Hearing Aid Use Amount (Data Logged and Self-Reported) and Overreport DOI: 10.3766/jaaa.25.2.7 Ariane Laplante-Lévesque*t Claus Nielsen* Lisbeth Dons Jensen* Graham Naylor*
Abstract Background: Previous studies found that, on average, users overreport their daily amount of hearing aid use compared to objective measures such as data logging. However, the reasons for this are unclear. Purpose: This study assessed data-logged and self-reported amount of hearing aid use in a clinical sample of hearing aid users. It identified predictors of data-logged hearing aid use, self-reported hearing aid use, and hearing aid use overreport. Research Design: This observational study recruited adult hearing aid users from 22 private dispensers in the Netherlands and in Denmark. Study Sample: The sample consisted of 228 hearing aid users. Typical participants were over the age of 65 and retired, were fitted binauraily, and had financially contributed to the cost of their hearing aids. Participants had on average a mild-to-severe sloping bilateral hearing impairment. Data Collection and Analysis: Participants completed a purposefully designed questionnaire regarding hearing aid usage and the International Outcome Inventory—Hearing Aids. Dispensers collected audiometric results and data logging. Multiple linear regression identified predictors of data-logged hearing aid use, self-reported hearing aid use, and hearing aid use overreport when controlling for covariates. Results: Data logging showed on average 10.5 hr of hearing aid use (n = 184), while participants reported on average 11.8 hr of daily hearing aid use (n = 206). In participants for which both data-logged and self-reported hearing aid use data were available (n = 166), the average absolute overreport of daily hearing aid use was 1.2 (1 hr and 11 min). Relative overreport was expressed as a rate of absolute overreport divided by data-logged hearing aid use. A positive rate denotes hearing aid use overreport: the average overreport rate was .38. Cluster analysis identified two data-logged patterns: "Regular," where hearing aids are typically switched on for between 12 and 20 hr before their user powers them off (57% of the sample), and "On-off," where hearing aids are typically switched on for shorter periods of time before being powered off (43% of the sample). In terms of self-report, 77% of the sample described their hearing aid use to be the same every day, while 23% of the sample described their hearing aid use to be different from day to day. Participants for whom data logging showed an On-off pattern or who reported their hearing aid use to be different from day to day had significantly fewer data-logged and self-reported hours of hearing aid use. Having an On-off data-logging pattern or describing hearing aid use as the same every day was associated with a significantly greater hearing aid use overreport. Conclusions: Data-logged and self-reported usage patterns significantly predicted data-logged hearing aid use, self-reported hearing aid use, and overreport when controlling for covariates. The results point to patterns of hearing aid usage as being at least as important a concept as amount of hearing aid use.
*Eriksholm Research Centre, Oticon A/S, Denmark; tDepartment of Behavioural Sciences and Learning, Linköping University, Sweden Ariane Laplante-Lévesque, Eriksholm Research Centre, Oticon A/S, 20 R0rtangvej, DK-3070 Snekkersten, Denmark; Phone: +45 4829 8900; Fax: +45 4922 3629; E-mail: [email protected] Preliminary results were presented at the Academy of Rehabilitative Audiology Institute, September 9-11, 2012, Providence, Rl. This study was supported by the Oticon Foundation.
1S7
Journal of the American Academy of AudiologyA^olume 25, Number 2, 2014
Dispensers should discuss not only the "how much", but also the "how" of hearing aid usage with their clients. Key Words: Data logging, hearing aid outcomes, hearing aid use, self-report Abbreviation: lOI-HA = International Outcome Inventory—Hearing Aids
INTRODUCTION
A
mounts and patterns of hearing aid usage vary widely from one hearing aid owner to another. For example, a large survey of Swiss adult hearing aid owners found that 50% of the sample reported wearing their hearing aids more than 8 hr/day on average, 26% for 4-8 hr, 21% for 1-4 hr, and 3% for less than 1 hr (Staehelin et al, 2011). According to a systematic literature review, factors associated with greater selfreported hearing aid use include positive prefitting attitudes toward hearing impairment and hearing aids and, most importantly, greater self-reported hearing disability (Knudsen et al, 2010). Kochkin et al (2010) found significant relationships between user descriptions of the hearing aid fitting process (needing fewer visits to the dispenser and dispenser checking hearing aid fit, comfort, and sound quality) and hearing aid use in both new and experienced hearing aid users. Similarly, Solheim et al (2012) reported an association between older users' perceived follow-up support (organized checkups and accessibility to professionals) and hearing aid use. The audiologic community habitually regards amount of hearing aid use as an indicator of hearing aid success. However, a principal component analysis of 26 hearing aid outcome measures highhghted that amount of hearing aid use is a separate concept from subjective benefit and hearing aid satisfaction (Humes et al, 2001). The literature reports at least 15 different ways to measure hearing aid usage (Perez and Edmonds, 2012): the present study focused on daily average amount of hearing aid use as a continuous measure. Measuring Objective Hearing Aid Use Earlier efforts to determine objective hearing aid use focused on hearing aid battery weight (Brooks 1979, 1981) or internal clocks in experimental hearing aids (Haggard et al, 1981). Current technology allows data logging, in which the hearing aid records the mmiber of hours it is turned on in a given time period and divides it by the time period to provide a daily average (Humes et al, 1996). Data logging can also indicate battery life, program and volume control usage, typical input levels, and activation of digital signal processing features (Banerjee, 2011). Furthermore, it can allow data learning, where hearing aid settings adapt in response to hearing aid control alterations (Zakis et al, 2007).
188
Measuring Self-Reported Hearing Aid Use The hearing aid user or their significant others can also estimate average daily hearing aid use. The person is asked to report average amount of hearing aid use, usually expressed in hours per day. Hearing aid use diaries can also be employed to compute an average of selfreported hearing aid use over a time period (Humes, 1999; Mäki-Torkko et al, 2001). Most often, hearing aid use is estimated with a single item on a questionnaire (Knudsen et al, 2010). For example, the first item of the International Outcome Inventory—Hearing Aids (IOI-HA; Cox et al, 2000), a hearing aid outcome questionnaire available in 23 languages, asks: "Think about how much you used your present hearing aid(s) over the past two weeks. On an average day, how many hours did you use the hearing aid(s)?" The response options are none, less than 1 hr a day, 1 to 4 hr a day, 4 to 8 hr a day, and more than 8 hr a day. Responses are interpreted as the greater the number of hours a day hearing aids are used for, the better the hearing aid outcome (Cox et al, 2000). Some questionnaires also take an approach where hearing aid use is quantified for several listening situations. For example, the Glasgow Hearing Aid Benefit Profile (Gatehouse, 1999) asks hearing aid owners to describe their amount of hearing aid use in up to eight different listening situations, with five response options: never/not at all, about 1/4 of the time, about 1/2 of the time, about 3/4 of the time, and all the time. In their systematic review of the literature on hearing aid use, Perez and Edmonds (2012) observed the lack of a standard definition or categorization (division of response options) of hearing aid use. Several selfreport instruments assess hearing aid use but do so in different ways, which makes their results difficult to compare. Comparing Self-Reported and Objective Hearing Aid Use Previous studies have investigated the relationship between objective and self-reported amount of hearing aid use, with the objective measure as the benchmark. However, it should be acknowledged that objective measures of hearing aid use are not fiawless: they record the number of hours the hearing aid is turned on, which is not necessarily equal to the number of hours the hearing aid is worn. For example, failing to turn the hearing aid off when not wearing it will infiate
Hearing Aid Use Amount and Overreport/Laplante-Lévesque et al
objective hearing aid use. The data loggers Taubman and colleagues (1999) studied in the laboratory were generally effective and provided valuable information. But inaccuracies in hearing aid data loggers have also been reported, with a slightly lower hearing aid use log than the actual amount of time the hearing aids were on (Taubman et al, 1999). This cumulative error becomes greater the longer hearing aids are on for. Studies predating data logging (with weight of hearing aid battery or experimental hearing aids with internal clocks) found that an overreport of self-reported hearing aid use was frequent (Brooks, 1979, 1981; Haggard et al, 1981). Humes et al (1996) assessed data-logged and self-reported hearing aid use in 20 older people in the period from the hearing aid fitting to 90 days later. In the sample of both new and experienced hearing aid users, a significant (.76) correlation existed between both measures. However, while participants reported wearing their hearing aids for 10-11 hr per day on average, data logging measured 6-7 hr per day, with an absolute overreport of approximately 4 hr. During the period investigated, self-reported hearing aid use was more constant than data-logged hearing aid use, suggesting that people may formulate averages over relatively long periods of time when reporting their hearing aid use. This means that differences between data-logged and self-reported hearing aid use might be greater in people who vary their hearing aid usage greatly from day to day. Taubman et al (1999) assessed data-logged and selfreported hearing aid use in 24 experienced hearing aid users one week after having been fitted with experimental hearing aids. They divided the participants into two groups: the first group had been made aware that the hearing aids had data-logging while the second group had not. Participants in the first group were significantly more accurate in their hearing aid use report (over-report of 1.1 hr per day on average) than participants in the second group (overreport of 3.7 hr per day on average). These results show that knowledge of ohjective hearing aid use measurement yields more accurate hearing aid use self-report and suggests that people may formulate answers that they perceive as socially desirable when queried about their hearing aid use. IMäki-Torkko et al (2001) assessed data-logged and selfreported hearing aid use in 84 adults. Self-report was in the form of a daily diary entry or interview at the end of the study period, which varied from 21 to 73 days. Using hearing aids less than 8 hr per day on average was frequently reported (55% of participants in daily diary and 57% in interview) and data-logging showed an even greater frequency (64%). However, the authors did not report statistical analyses of these results. Gaffney (2008) assessed data-logged and self-reported hearing aid use in 39 new and experienced hearing aid users two weeks after hearing aid fitting. Again,
participants overreported their daily hearing aid use, here by approximately 2 hr on average. The two forms of hearing aid use measures were significantly correlated: .62 for new hearing aid users and .77 for experienced hearing aid users. This is the first study suggesting that experienced hearing aid users may be better at estimating hearing aid use than new hearing aid users. Hearing aid use overreport has also been demonstrated in a pédiatrie population: parents of 133 children with hearing aids overreported their children's daily hearing aid use by 2.6 hr (Walker et al, 2013). In this sample, parents of younger children overreported more than parents of older children. But reporting a behavior such as average daily hearing add use is not a simple task. Underlying Principles of Behavior Self-Report When reporting a behavior, respondents complete multiple tasks such as (1) understand the question; (2) recall the relevant behavior; (3) infer and estimate the behavior; (4) map the answer into the response format (if response options are provided); and (5) amend the answer according to social desirability (Schwarz and Oyserman, 2001). Social desirability results in people reporting behavior they believe others will perceive favorahly, for example, a high amount of hearing aid use. Differences in objective and self-reported measures have been observed in behaviors other than hearing aid use, for example television viewing, where, compatihle with social desirahility theory, objective viewing amount is greater than self-reported (Otten et al, 2010). In audiology, some discrepancies have also been found in how people quantify average daily use of hearing aid versus communication programs, pointing to the complexity of behavior self-report (Laplante-Lévesque et al, 2012). In summary, previous research has found that hearing aid users overestimate their hearing aid use. This could be due to irregular use patterns, social desirability bias, or lack of hearing aid experience. As hearing aid use is seen as an indicator of hearing aid success, how users estimate it needs to he better understood. AIMS
T
his study assessed data-logged and self-reported amount of hearing aid use in a clinical sample of hearing aid users. It identified predictors of data-logged hearing aid use, self-reported hearing aid use, and hearing aid use overreport. METHODS
T
he Eriksholm Research Centre in Denmark carried out this multicenter study. Staff from the Oticon
189
Journal of the American Academy of Audiology/Volume 25, Number 2, 2014
hearing aid company helped identify 22 private hearing aid dispensers (11 in the Netherlands and 11 in Denmark) willing to recruit eligible hearing aid users from their caseload. Participants The 22 dispensers were instructed to recruit consecutive eligible hearing aid users already scheduled for an appointment (typically for the final visit of a hearing aid fitting or for a routine hearing aid review). Inclusion criteria included the following: aged > 18 yr; have no more than a moderate hearing impairment (.5, 1, and 2 kHz pure-tone average 14 days and with data-logging period >14 days; and report no obstacle to hearing aid use (e.g., lost or defective hearing aids or health condition such as otitis externa). In total, 228 hearing aid users met the inclusion criteria and took part in this study. The study adhered to the Declaration of Helsinki ethical principles. Potential participants received a written description of the study, which stated that their hearing aids had data-logging capabilities and that their dispenser would retrieve their hearing aid data log if they consented to participate in the study. Voluntary participation and confidentiality was stressed. Each participant was assigned a study code, and the dispensers were instructed to only share de-identified data with the Eriksholm Research Centre. Measures Four measures were obtained for each participant. Questionnaire Regarding Hearing Aid Usage A 17-item questionnaire was purposefully designed to capture aspects relevant to hearing aid usage. The questionnaire focused on demographics, hearing aid provision, hearing aid usage (time and patterns), and participant/other people's views on hearing aid use. The questionnaire was initially formulated in English. Native speakers translated it to Dutch and Danish. Dutch and Danish speakers back translated the translations to English for validation purposes. The three (English, Dutch, and Danish) versions of the questionnaire are available from the authors. International Outcome Inventory—Hearing Aids (IOI-HA) Questionnaire The IOI-HA (Cox et al, 2000) is a composite questionnaire that measures seven dimensions of hearing aid outcomes: use, benefit, residual activity limitations, satisfaction, residual participation restrictions, impact on
ISO
others, and quality of life. The IOI-HA questionnaire contains seven items (one item for each of the seven dimensions listed above), with five response options scored from 1 to 5. The Dutch and Danish versions of the IOI-HA were used (Cox et al, 2002). Both the Dutch and the Danish IOI-HA have a two-factor structure (Kramer et al, 2002; Vestergaard, 2006). The two factors have previously been described as representing "Me and my hearing aids" (the use, benefit, satisfaction, and quality of life items make up IOI-HA Factor 1) and "Me and the rest of the world" (the residual activity limitations, residual participation restrictions, and impact on others items make up IOI-HA Factor 2; Cox and Alexander, 2002). Both the Dutch and the Danish IOI-HA also have good test-retest reliability (Kramer et al, 2002; Jespersen et al, 2006). Data Logging For each participant, dispensers connected the hearing aids to Oticon's fitting software, Genie, and saved the hearing aid "usage" screen. It reports the following eEir-specific information: date at which data logging was started, date at which data logging was completed, and average daily hearing aid use. Data-logged patterns were also obtained: they were the proportion of occurrences the hearing aid was on for (20 hr) before it was powered off Proportion of occurrences of each time interval was categorized into four options (0-25, 25-50, 50-75, and 75-100% of occurrences); see Figure 1 for example. Audiometric Data Hearing aid dispensers provided the latest audiometric results available on file for each participant. Air-conduction pure-tone audiometric results obtained under headphones were recorded for the frequencies .25-4 kHz for both ears. Procedure Dispensers gave the questionnaire regarding hearing aid usage and the IOI-HA to participants. Questionnaires were completed in a pen and paper format, typically in the cHnic waiting room. During the appointment, the dispenser made sure that audiometric results and data logs were available. Following the appointment, dispensers forwarded the data to the Eriksholm Research Centre. Data Cleaning and Analysis Data were cleaned and analyzed with Statistica version 10. IOI-HA factor scores were compiled according to the published IOI-HA factor structure (Cox and Alexander, 2002). Some missing data occurred in the data set. For the two IOI-HA factor scores, when only
Hearing Aid Use Amount and Overreport/Laplante-Lévesque et al
RUAilVt
: SBLA
Streamer Ui«
Figure 1. Example of Oticon's fitting software "usage" screen: Genie version 2013.1.
one item for a given factor was missing, the missing data were imputed with the average of the other items for that factor for that participant (16 occurrences). All other missing values were kept as missing. For each statistical analysis, the available dataset was used and the sample size is reported. According to Taubman et al (1999) data loggers are generally effective and provide reliable information. From our experience, defective data logs typically indicate average daily hearing aid use of exactly 0 or 24 hr. In our sample of 228 participants, eight defective data logs were identified in four participants fitted binaurally: they showed 24 hr of average daily hearing aid use, and the displayed dates at which the data log was started and ended were the same (data-logging period of 0 day). This represented 8 of the 355 hearing aids for which data-logging information was available (a 2.25% fail rate). These data points were deleted.
As mentioned before, for each of seven time intervals (20 hr), the proportion of occurrences of each time interval was categorized into four options (0-25, 25-50, 50-75, and 75-100% of occurrences). A cluster analysis was conducted on data-logged patterns. Cluster analysis assigns participants into subgroups called clusters (Everitt et al, 2001). These clusters are defined to minimize variability within clusters and maximize variability between clusters. Nonhierarchical cluster analysis (k-means) was used; k-means cluster analysis uses multiple iterations of Lloyd's algorithm to compute the optimal clustering. The Euclidean distance (geometric distance in the multidimensional space) was used as the dissimilarity metric. Visual inspection of the clustergram (Schonlau, 2004) for 2-10 cluster solutions was used to establish the final cluster solution.
131
Journal of the American Academy of AudiologyA'^olume 25, Number 2, 2014
Three mtjltiple linear regression models were built to identify predictors of (1) data-logged hearing aid use; (2) self-reported hearing aid use, and (3) overreport rate. For each of the multiple linear regression models, bivariate analyses (i-tests, analyses of variance [ANOVAs], and Pearson's correlations) were used to screen the predictor variables with a liberal alpha level of. 1. Potential predictors were age (in years); gender (male or female); country (the Netherlands or Denmark); living situation (alone or with others); occupational situation (work or other); education (primary school or less, secondary or technical school, or university); self-reported health (lower than average, average, or better than average); experience with hearing aids (in years); payment toward hearing aid cost (yes or no); hearing impairment (.5, 1, and 2 kHz average in better ear, in dB HL); self-reported hearing disability (no or mild disability, moderate disability, or severe or profoimd disability); reception of hearing aid use advice from dispenser, spouse/partner, other family members, and friends (yes or no for each); opinion regarding hearing aid use (own, dispenser's, spouse/partner's, other family members', and friends') (as much as possible or as I please for each); self-reported pattern from day to day (same every day or different from day to day); current hearing aid fitting configuration (monaural or binaural); self-reported monaural/binaural hearing aid use (hearing aid use sometimes monaural or hearing aid use always binaural); IOI-HA Factor 1 scores ("Me and my hearing aids": use, benefit, satisfaction, and quality of life items) and IOI-HA Factor 2 scoi-es ("Me and the rest ofthe world": residual activity limitations, residual participation restrictions, and impact on others items); data-logged monaural/binaural hearing aid use (absolute difference between right and left data-logged hearing aid use amount divided by maximum datalogged hearing aid use amount); data-logged pattern (cluster Regular or cluster On-off, see "Results" section below); and length of data-logging period (in days). Significant predictors at the bivariate level were entered into a backward stepwise multiple linear regression model with an alpha level of .05. Postestimation diagnostic tests were performed to evaluate each multiple linear regression model. Outliers were identified (participants for whom absolute values of standardized residuals were greater than 2) and removed from the models: 15 in the model explaining data-logged hearing aid use, 11 in the model explaining self-reported hearing aid use, and 7 in the model explaining overreport rate.
financially to the cost of their hearing aids. Participants reported good hearing aid outcomes, with an average score of 4.28 on the IOI-HA Factor 1 ("Me and my hearing aids") and 3.87 on the IOI-HA Factor 2 ("Me and the rest of the world"). Figure 2 reports the average audiogram of participants, which shows a mild-to-severe sloping bilateral hearing impairment. Hearing Aid Use Amount Table 2 summarizes the information regarding average daily hearing aid use. Data logging showed on average 10.5 hr of daily hearing aid use (n = 184), while participants reported on average 11.8 hr of daily hearing aid use (n = 206). In participants who were fitted binauraily, if there was a difference between the right and left data-logged hearing aid use amount, the greatest of the two values was kept, as per Walker et al (2013). The lower ofthe two values showed on average 9.8 hr of daily hearing aid use. In participants for whom both data-logged and selfreported hearing aid use data were available (n = 166), data logging showed on average 10.4 hr of daily hearing aid use, and participants reported on average 11.6 hr of daily hearing aid use. The average absolute overreport of daily hearing aid use was 1.2 hr per day (1 hr and 11 min). Figure 3 shows a scatterplot of average daily data-logged and self-reported hearing aid use. The correlation was highly significant (p < .001), with a correlation coefficient R of .74 and a coefficient of determination R^ of .54. Relative overreport can be expressed as a rate of absolute overreport divided by data-logged hearing aid use. A positive rate denotes overreport while a negative rate denotes underreport. Figure 4 shows a histogram ofthe overreport rate. The minimum value, -1.0, was obtained from a participant who reported not wearing his hearing aids at all (0 hr) but for whom data logging showed 5.3 hr of daily use. The maximum value, 12.3, was obtained from a participant who reported wearing his hearing aids for 12 hr per day but for whom data-logging showed .9 hr of daily use. Scores on the first IOI-HA item, targeting hearing aid use, were available for 225 participants: 1 participant (.4%) reported not wearing his hearing aids, and 1 participant (.4%) reported wearing his hearing aids less than 1 hr a day; 19 participants (8.4%) 1 to 4 hr a day; 27 participants (12.0%) 4 to 8 hr a day, and 177 participants (78.8%) more than 8 hr a day.
RESULTS Hearing Aid Usage Patterns Study Sample Table 1 summarizes the sample of 228 hearing aid users. Typical participants were over the age of 65 and retired, were fitted binauraily, and had contributed
nss
Data-logged patterns (see Fig. 1) were collected in 171 participants. As most participants were fitted binaurally, this gave access to 313 unique data-logged patterns. Those were scrutinized with cluster analysis.
Hearing Aid Use Amount and Overreport/Laplante-Lévesque et ; Table 1. Characteristics of the Sampie of 228 Participants Age (n = 227) Mean (SD) 72.05 (10.50) Gender (n = 228) Male n (%) 124(54.39) Female n (%) 104(45.61) Country (n = 228) The Netherlands n (%) 93 (40.79) Denmark n (%) 135(59.21) Living situation (n = 226) Aione n (%) 61 (26.99) With other(s) n (%) 165(73.01) Occupationai situation (n = 227) Work n (%) • 42 (18.50) Retirement or unempioyment n (%) 185 (81.50) Education (n = 225) None or primary schooi n (%) 85 (37.78) Secondary or technioai schooi n (%) 85 (37.78) University n (%) 55 (24.44) Seif-reported heaith (n = 224) Lower than average n (%) 73 (32.59) Average n(%) 128(57.14) Better than average n (%) 23 (10.27) Experience with hearing aids, in years (n = 223) Mean (SD) 6.48 (7.34) Payment toward hearing aid cost (n = 227) No payment n (%) 85 (37.44) Partial or fuil payment n (%) 142 (62.56) Hearing impairment (.5, 1, and 2 kHz average in better ear, in dB HL) (n = 213) Mean (SD) 37.61 (11.51) Seif-reported hearing disability (n = 225) No or mild hearing disabiiity n (%) 34(15.11) Moderate hearing disabiiity n (%) 108 (48.00) Severe or profound hearing disabiiity n (%) 83 (36.89) Seif-reported pattern from day to day (n = 226) Same every day n (%) 174(76.99) Different from day to day n (%) 52 (23.01) Seif-reported current hearing aid fitting configuration (n = 228) Uniiaterai n (%) 20 (8.77) Biiaterai n (%) 208 (91.23) Seif-reported monaural/binaurai hearing aid use (n = 227) I wear both hearing aids the same amount of time. 203 (89.43) i sometimes wear oniy one hearing aid. 24 (10.57) iOI-HA Factor 1 score (n = 225) Mean (SD) 4.28 (.59) iOI-HA Factor 2 score (n = 225) Mean (SD) 3.87 (.75)
Visual inspection of the clustergram was used to establish that a two-cluster solution was most suitable. As can be seen in Figure 5, the first cluster of patterns, which we termed Regular, represents hearing aids that are most typically on for between 12 and 20 hr before their user powers them off. The second cluster of patterns.
which we termed On-off, represents hearing aids that are most typically on for shorter periods of time before their user powers them off. For 10 of the 134 participants fitted binaurally, the right and left hearing aids showed data-logged patterns that fell into different clusters: for these participants, the pattern arising from the hearing aid with the greatest data-logged hearing aid use was chosen. In the sample, 98 participants (57%) had a Regular data-logged pattern while 73 (43%) participants had an On-off data-logged pattern. These patterns are not measures of amount of hearing aid use, since hearing aids in the On-off cluster may be on for the same amount of time daily as hearing aids in the Regular cluster, only with breaks in between use. In participants with a Regular data-logged pattern, daily hearing aid use varied from .8 to 18.4 hr (data logged) and from 0 to 18 hr (self-reported). In participants with an On-off data-logged pattern, daily hearing aid use varied from .7 to 22.9 hr (data logged) and from 1 to 18 hr (self-reported). ANOVAs identified the following significant differences in average occurrences between the two clusters: 12-20 hr [F(l,312) = 84.88, p < .01] occurred significantly more frequently in the Regular cluster, while
o o. 4O)
O
^
0
I
T
4
8
12
16
20
24
Average data-logged hearing aid use (in hours/day) Figure 3. Average daily data-logged and self-reported hearing aid use (n = 166).
coefficient of determination R^ was .10. Participants for whom data logging showed an On-off pattern overreported significantly more than their peers for whom data logging showed a Regular pattern (.6 versus .5). By contrast, participants who stated different hearing aid use from day to day overreported significantly less than their peers who reported their hearing aid use is the same every day (.2 versus .4).
100 —i
-20
60 -
20
-15
3-
-10
a-
- 5
0 -J - 2 - 1 0 1
l~ 0
I 2
3
4
5
6
7
8
10 11 12 13
Overreport rate
Figure 4. Hearing aid use overreport rate (n = 166); absolute overreport divided by data-logged hearing aid use. A positive rate denotes overreport while a negative rate denotes underreport.
n a sample of 228 adult hearing aid users from the Netherlands and Denmark, participants reported on average 11.8 hr of daily hearing aid use, while data logging showed on average 10.5 hr of daily hearing aid use. Those values are similar (Humes et al, 1996; Purdy and Jerram, 1998; Arlinger and Billermark 1999; Taubman et al, 1999; Gaffney, 2008) or slightly higher (Humes et al, 2004; Waiden and Waiden, 2004; Colhns et al, 2007; Humes et al, 2009) than previous reports of average daily hearing aid use measured as a continuous value, either via data logging or self-report. The present study's inclusion criteria of having no obstacle for hearing aid use (e.g., lost or defective hearing aids or health condition such as otitis externa) could partially explain the greater hearing aid use uncovered. To the authors' knowledge, the present study is the first to have obtained data-logging information in both hearing aids in adults fitted binaurally. This study took the greatest of the right and left data-logged hearing aid use values if those were different, as per Walker et al (2013), also contributing to greater data-logged hearing aid use. Still, the strength of the correlation between datalogged and self-reported hearing aid use in the present study was comparable to previous reports (Humes et al, 1996; Taubman et al, 1999; Gaffney, 2008). The scores for the first IOI-HA item were negatively skewed, with 48 of the participants (21%) selecting one of the first four response options (0—8 hr per day) and 177 participants (79%) selecting the fifth response option (>8 hr a day). It is typical for instruments categorizing hearing aid use to offer more response options in the lower range of the 24 hr period (Perez and Edmonds, 2012). For example, as mentioned above, four of the five IOI-HA use categories cover the 0-8 hr period. For better differentiation, future instruments categorizing hearing aid use could offer more response options in the higher range of the 24 hr period (e.g.. Cox and Alexander, 1999; Kam et al, 2011). Response options like those of Kam et al (2011) (none, 16 hr) could help avoid ceiling effect when categorizing hearing aid use such as observed in the present study. The average absolute overreport of daily hearing aid use was of 1 hr and 11 min (n = 166). This is less than some of the previous reports of differences between selfreported and data-logged measures of hearing aid use, with Humes and colleagues (1996) reporting an absolute overreport of daily hearing aid use of 4 hr in their 20 participants. However, Humes' participants were not aware that their hearing aids had data-logging capabilities: in contrast, the present study's participants were informed of this prior to consent. The absolute overreport is comparable to Taubman et al (1999)'s
195
Journal of the American Academy of AudiologyA'^olume 25, Number 2, 2014
75% —, Legend > Cluster 1: "Regular* • Cluster 2:
50% —
Si
8 25%
-
0% - < 20 hr
Period for which hearing aid is turned on
Figure 5. Data-logged patterns according to cluster analysis (n = 213).
group of 12 hearing aid users who had been made aware of their hearing aids' data logging and therefore support Taubman et al's findings that social desirability drives, at least in part, overreport of hearing aid use. Nevertheless, Gaffney (2008) also found relatively small overreport (1.9 hr), despite not having informed her 39 participants of their hearing aids' data logging. Together, data-logged and self-reported patterns significantly predicted data-logged hearing aid use amount, self-reported hearing aid use amount, and overreport rate when controlling for covariates. The length of the data-logging period could be hypothesized as contributing to under- or over-report of hearing aid use, as it could be more difficult to recall and average behavior occurrences over a long period of time. However, this proved not to be the case despite the data-logging period varying from 14 days to over four years in the sample. Hearing aid experience as measured in years in our sample also failed to predict hearing aid use amount and its overreport. This is contradictory to Gaffney (2008)'s findings, but the latter study measured hearing aid experience dichotomously (users with first or subsequent hearing aid fitting). The present study collected both data-logged and selfreported daily hearing aid use amount in 166 participants with the help of 22 private dispensers in two countries. Realistic and heterogeneous data collection contributes to the generalizability of the findings. However, the dispensers introduced the study questionnaires to the participants, therefore potentially introducing a social desirability bias, where responses could have been
1SB
influenced by participants wanting to please their dispensers. However, this study's procedure reflects chnical practice where the user and dispenser together discuss hearing aid use. Although the sampling instructions given to the dispensers stipulated that consecutive participants meeting the inclusion criteria should be invited to participate in the study, it is impossible to know if the dispensers displayed recruitment bias. It is also impossible to know which percentage of participants refused to participate in the study and if those were different from those who agreed to participate. This was the first study to investigate overreport as a rate rather than as an absolute value and to identify predictors of overreport. This is also by far the largest study of this type to date. Despite many potential predictors being identified with bivariate analyses, only data-logged and self-reported usage patterns remained significant predictors of data-logged hearing aid use amount, self-reported hearing aid use amount, and overreport rate after controlling for covariates. This highlights the importance of performing multivariate analyses when investigating complex interactions such as aspects of disability and intervention outcomes. We believe this is the first study to investigate patterns of hearing aid usage in such detail, using both data-logged and self-reported measures. It is intriguing that, in contradiction to a systematic review (Knudsen et al, 2010), greater self-reported hearing disability did not remain a significant predictor of hearing aid use amount after controlling for covariates in the present study. It should be noted that self-reported hearing disability was significantly associated with hearing aid use amount at the bivariate level but not at the multivariate level. In contrast, leaving the hearing aids turned on for long periods of time before turning-them off (data-logged Regular pattern) or reporting hearing aid use as the same every day significantly contributed to greater hearing aid use amount both when assessed with data-logging and with self-report and after controlling for covariates. It could be that the relationship between self-reported hearing disability and hearing aid use amount mentioned in previous studies was a confounding effect of patterns of hearing aid usage. A linear regression model identified two statistically significant predictors of overreport of hearing aid use. Despite reaching statistical significance, the coefficient of determination R^ was low (.10), and it is therefore reasonable to wonder, beyond the statistical significance, about the clinical significance of these results. Still, participants with a data-logged Regular pattern overestimated their hearing aid use amount to a lesser extent. People who reported their hearing aid usage pattern to be different from day to day also overestimated their hearing aid use amount to a lesser extent. The latter finding might appear surprising, but users who are conscious of their
Hearing Aid Use Amount and Overreport/Laplante-Lévesque et al
different patterns of hearing aid usage from day to day may also be more aware of their hearing aid use and therefore be more accurate reporters. As a whole, the results point to patterns of hearing aid usage as being at least as important a concept as amount of hearing aid use. When it comes to hearing aid usage, dispensers should discuss not only the "how much" but also the "how" with their users. Above and beyond how to best measure hearing aid use, previous qualitative studies (Lockey et al, 2010; Laplante-Lévesque et al, 2013) have highlighted the importance of hearing aid use that is purposeful and that relates to needs. In this context, more evidence regarding how audiologists and users do and should discuss hearing aid use is required. Furthermore, as Perez and Edmonds (2012) previously suggested, if aspects of amount of use are to remain within the battery of hearing aid outcome measures, then how much more time people spend performing the activities they enjoy as a result of wearing the hearing aids could be a more client-centered measure of hearing aid success. Acknowledgments. The authors sincerely thiink Oticon BV (the Netherlands) and Oticon A/S (Denmark) for assistance in identifying hearing aid dispensers, the dispensers and hearing aid users who participated in this study, and Maria Oxenb0ll, who assisted with manuscript preparation. REFERENCES Arlinger S, Billermark E. (1999) One year follow-up of users of a digital hearing aid. Br J Audiol 33{4):223-232. Banerjee S. (2011) Hearing aids in the real world: typical automatic behavior of expansion, directionality, and noise management. J Am Acad Audiol 22(l):34-48. Brooks DN. (1979) Counselling and its effect on hearing aid use. Scand Audiol 8(2):101-107.
Everitt BS, Landau S, Leese M. (2001) Cluster Analysis. 4th ed. London: Oxford University Press. Gaffney P. (2008) Reported hearing aid use versus datalogging in a VA population. Hear Rev 15(6):42, 44, 46-47. Gatehouse S. (1999) Glasgow Hearing Aid Benefit Profile: derivation and validation of a client-centered outcome measure for hearingaid services. JAm Acad Audiol 10(2):80-103. Haggard MP, Foster JR, Iredale FE. (1981) Use and benefit of postaural aid in sensory hearing loss. Scand Audiol 10(l):45-52. Humes LE. (1999) Dimensions of hearing aid outcome. JAm Acad Audiol 10(l):26-39. Humes L, Ahlstrom J, Bratt G, Peek B. (2009) Studies of hearingaid outcome measures in older adults: a comparison of technologies and an examination of individual differences. Semin Hear 30(2):112-128. Humes LE, Gamer CB, Wilson DL, et al. (2001) Hearing-aid outcome measures foUovráig one month of hearing aid use by the elderly. J Speech Lang Hear Res 44(3):469-486. Humes LE, Hailing D, Coughlin M. (1996) Reliability and stability of various hearing-aid outcome measures in a group of elderly hearing-aid wearers. J Speech Hear Res 39(5):923-935. Humes LE, Humes LE, Wilson DL. (2004) A comparison of singlechannel linear amplification and two-channel wide-dynamic-rangecompression amplification by means of an independent-group design. Am J Audiol 13(l):39-53. Jespersen CT, Bille M, Groth J, Hansen T. (2006) Danish reference data for the IOI-HA. In: Rasmussen AN, Poulsen T, Andersen T, Larsen CB, eds. Hearing Aid Fitting: Proceedings ofthe 21"' Danavox Symposium 2005. Ballerup, Danmark: Danavox Jubilee Foundation, 475-491. Kam AC, Tong MC, van Hasselt A. (2011) Cross-cultural adaptation and validation of the Chinese abbreviated profile of hearing aid benefit. Int J Audiol 50(5):334-339. Knudsen LV, Oberg M, Nielsen C, Naylor G, Kramer SE. (2010) Factors infiuencing help seeking, hearing aid uptake, hearing aid use and satisfaction with hearing aids: a review of the literature. Trends Amplif 14(3):127-154.
Brooks DN. (1981) Use of post-aural aids by National Health Service patients. Br J Audiol 15(2):79-86.
Kochkin S, Beck DL, Christensen LA, et al. (2010) MarkeTrak VIII: the impact ofthe hearing healthcare professional on hearing aid user success. Hear Rev 17(4): 12, 14, 16, 18, 23, 26-28, 30, 32, 34.
Collins MP, Souza PE, O'Neill S, Yueh B. (2007) Effectiveness of group versus individual hearing aid visits. J Rehabil Res Deu 44 (5):739-749.
Kramer SE, Goverts ST, Dreschler WA, Boymans M, Festen JM. (2002) International Outcome Inventory for Hearing Aids (IOIHA): results from The Netherlands. Int J Audiol 41(1):3&-41.
Cox RM, Alexander GC. (1999) Measuring satisfaction with amplification in daily life: the SADL scale. Ear Hear 20(4): 306-320.
Laplante-Lévesque A, Hickson L, Worrall L. (2012) Comparing response options for the International Outcome Inventory for Hearing Aids (IOI-HA) and for Alternative Interventions (IOIAI) daily-use items. Int J Audiol 51(10):788-791.
Cox RM, Alexander GC. (2002) The International Outcome Inventory for Hearing Aids (IOI-HA): psychometric properties of the English version. Int J Audiol 41(l):30-35. Cox R, Hyde M, Gatehouse S, et al. (2000) Optimal outcome measures, research priorities, and international cooperation. Ear Hear 21(Suppl. 4):106S-115S. Cox RM, Stephens D, Kramer SE. (2002) Translations ofthe International Outcome Inventory for Hearing Aids (IOI-HA). Int J Audiol 41(l):3-26.
Laplante-Lévesque A, Jensen LD, Dawes P, Nielsen C. (2013) Optimal hearing aid use: focus groups with hearing aid clients and audiologists. Ear Hear 34(2): 193-202. Lockey K, Jennings MB, Shaw L. (2010) Exploring hearing aid use in older women through narratives. Int J Audiol 49(8): 542-549. Mäki-Torkko EM, Sorri MJ, Laukli E. (2001) Objective assessment of hearing aid use. Scand Audiol 30(Suppl. 52):81-82.
137
Journal of the American Academy of Audiology/Volume 25, Number 2, 2014
Otten JJ, Littenberg B, Harvey-Berino JR. (2010) Relationsbip between self-report and an objective measure of television-viewing time in adults. Obesity (Silver Spring) 18(6): 1273-1275.
Staebelin K, Bertoli S, Probst R, Schindler C, Dratva J, Stutz EZ. (2011) Gender and bearing aids: patterns of use and determinants of nonregular use. Ear Hear 32(6):e26-e37.
Perez E, Edmonds BA. (2012) A systematic review of studies measuring and reporting bearing aid usage in older adults since 1999: a descriptive summary of measurement tools. PLoS One 7(3):e31831.
Taubman LB, Palmer CV, Durrant JD, Pratt S. (1999) Accuracy of bearing aid use time as reported by experienced bearing aid wearers. Ear Hear 20(4):299-305.
Purdy SC, Jerram JC. ( 1998) Investigation of tbe profile of bearing aid performance in experienced hearing aid users. Ear Hear 19(6): 473-480.
Vestergaard MD. (2006) Self-report outcome in new bearing-aid users: longitudinal trends and relationsbips between subjective measures of benefit and satisfaction. Int J Audiol 45(7):382-392.
Scbonlau M. (2004) Visualizing bierarcbical and non-bierarcbical cluster analyses witb clustergrams. Comput Stat 19(1):95-111.
Waiden TC, Waiden BE. (2004) Predicting success with bearing aids in everyday living. J Am Acad Audiol 15(5):342-352.
Scbwarz N, Oyserman D. (2001) Asking questions about bebavior: cognition, communication, and questionnaire construction. Am J Eval 22(2):127-160.
Walker EA, Spratford M, Moeller MP, et al. (2013) Predictors of bearing aid use time in children witb mild-to-severe bearing loss. Lang Speech Hear Serv Sch 44(l):73-88.
Solbeim J, Kvaerner KJ, Sandvik L, et al. (2012) Factors affecting older adults' use of bearing aids. Scand J Disabil Res 14(4): 300-312.
Zakis JA, Dillon H, McDermott HJ. (2007) Tbe design and evaluation of a bearing aid witb trainable amplification parameters. Ear Hear 28(6):812-830.
1S8
Copyright of Journal of the American Academy of Audiology is the property of American Academy of Audiology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use.
Patterns of Hearing Aid Usage Predict Hearing Aid Use Amount (Data Logged and Self-Reported) and Overreport DOI: 10.3766/jaaa.25.2.7 Ariane Laplante-Lévesque*t Claus Nielsen* Lisbeth Dons Jensen* Graham Naylor*
Abstract Background: Previous studies found that, on average, users overreport their daily amount of hearing aid use compared to objective measures such as data logging. However, the reasons for this are unclear. Purpose: This study assessed data-logged and self-reported amount of hearing aid use in a clinical sample of hearing aid users. It identified predictors of data-logged hearing aid use, self-reported hearing aid use, and hearing aid use overreport. Research Design: This observational study recruited adult hearing aid users from 22 private dispensers in the Netherlands and in Denmark. Study Sample: The sample consisted of 228 hearing aid users. Typical participants were over the age of 65 and retired, were fitted binauraily, and had financially contributed to the cost of their hearing aids. Participants had on average a mild-to-severe sloping bilateral hearing impairment. Data Collection and Analysis: Participants completed a purposefully designed questionnaire regarding hearing aid usage and the International Outcome Inventory—Hearing Aids. Dispensers collected audiometric results and data logging. Multiple linear regression identified predictors of data-logged hearing aid use, self-reported hearing aid use, and hearing aid use overreport when controlling for covariates. Results: Data logging showed on average 10.5 hr of hearing aid use (n = 184), while participants reported on average 11.8 hr of daily hearing aid use (n = 206). In participants for which both data-logged and self-reported hearing aid use data were available (n = 166), the average absolute overreport of daily hearing aid use was 1.2 (1 hr and 11 min). Relative overreport was expressed as a rate of absolute overreport divided by data-logged hearing aid use. A positive rate denotes hearing aid use overreport: the average overreport rate was .38. Cluster analysis identified two data-logged patterns: "Regular," where hearing aids are typically switched on for between 12 and 20 hr before their user powers them off (57% of the sample), and "On-off," where hearing aids are typically switched on for shorter periods of time before being powered off (43% of the sample). In terms of self-report, 77% of the sample described their hearing aid use to be the same every day, while 23% of the sample described their hearing aid use to be different from day to day. Participants for whom data logging showed an On-off pattern or who reported their hearing aid use to be different from day to day had significantly fewer data-logged and self-reported hours of hearing aid use. Having an On-off data-logging pattern or describing hearing aid use as the same every day was associated with a significantly greater hearing aid use overreport. Conclusions: Data-logged and self-reported usage patterns significantly predicted data-logged hearing aid use, self-reported hearing aid use, and overreport when controlling for covariates. The results point to patterns of hearing aid usage as being at least as important a concept as amount of hearing aid use.
*Eriksholm Research Centre, Oticon A/S, Denmark; tDepartment of Behavioural Sciences and Learning, Linköping University, Sweden Ariane Laplante-Lévesque, Eriksholm Research Centre, Oticon A/S, 20 R0rtangvej, DK-3070 Snekkersten, Denmark; Phone: +45 4829 8900; Fax: +45 4922 3629; E-mail: [email protected] Preliminary results were presented at the Academy of Rehabilitative Audiology Institute, September 9-11, 2012, Providence, Rl. This study was supported by the Oticon Foundation.
1S7
Journal of the American Academy of AudiologyA^olume 25, Number 2, 2014
Dispensers should discuss not only the "how much", but also the "how" of hearing aid usage with their clients. Key Words: Data logging, hearing aid outcomes, hearing aid use, self-report Abbreviation: lOI-HA = International Outcome Inventory—Hearing Aids
INTRODUCTION
A
mounts and patterns of hearing aid usage vary widely from one hearing aid owner to another. For example, a large survey of Swiss adult hearing aid owners found that 50% of the sample reported wearing their hearing aids more than 8 hr/day on average, 26% for 4-8 hr, 21% for 1-4 hr, and 3% for less than 1 hr (Staehelin et al, 2011). According to a systematic literature review, factors associated with greater selfreported hearing aid use include positive prefitting attitudes toward hearing impairment and hearing aids and, most importantly, greater self-reported hearing disability (Knudsen et al, 2010). Kochkin et al (2010) found significant relationships between user descriptions of the hearing aid fitting process (needing fewer visits to the dispenser and dispenser checking hearing aid fit, comfort, and sound quality) and hearing aid use in both new and experienced hearing aid users. Similarly, Solheim et al (2012) reported an association between older users' perceived follow-up support (organized checkups and accessibility to professionals) and hearing aid use. The audiologic community habitually regards amount of hearing aid use as an indicator of hearing aid success. However, a principal component analysis of 26 hearing aid outcome measures highhghted that amount of hearing aid use is a separate concept from subjective benefit and hearing aid satisfaction (Humes et al, 2001). The literature reports at least 15 different ways to measure hearing aid usage (Perez and Edmonds, 2012): the present study focused on daily average amount of hearing aid use as a continuous measure. Measuring Objective Hearing Aid Use Earlier efforts to determine objective hearing aid use focused on hearing aid battery weight (Brooks 1979, 1981) or internal clocks in experimental hearing aids (Haggard et al, 1981). Current technology allows data logging, in which the hearing aid records the mmiber of hours it is turned on in a given time period and divides it by the time period to provide a daily average (Humes et al, 1996). Data logging can also indicate battery life, program and volume control usage, typical input levels, and activation of digital signal processing features (Banerjee, 2011). Furthermore, it can allow data learning, where hearing aid settings adapt in response to hearing aid control alterations (Zakis et al, 2007).
188
Measuring Self-Reported Hearing Aid Use The hearing aid user or their significant others can also estimate average daily hearing aid use. The person is asked to report average amount of hearing aid use, usually expressed in hours per day. Hearing aid use diaries can also be employed to compute an average of selfreported hearing aid use over a time period (Humes, 1999; Mäki-Torkko et al, 2001). Most often, hearing aid use is estimated with a single item on a questionnaire (Knudsen et al, 2010). For example, the first item of the International Outcome Inventory—Hearing Aids (IOI-HA; Cox et al, 2000), a hearing aid outcome questionnaire available in 23 languages, asks: "Think about how much you used your present hearing aid(s) over the past two weeks. On an average day, how many hours did you use the hearing aid(s)?" The response options are none, less than 1 hr a day, 1 to 4 hr a day, 4 to 8 hr a day, and more than 8 hr a day. Responses are interpreted as the greater the number of hours a day hearing aids are used for, the better the hearing aid outcome (Cox et al, 2000). Some questionnaires also take an approach where hearing aid use is quantified for several listening situations. For example, the Glasgow Hearing Aid Benefit Profile (Gatehouse, 1999) asks hearing aid owners to describe their amount of hearing aid use in up to eight different listening situations, with five response options: never/not at all, about 1/4 of the time, about 1/2 of the time, about 3/4 of the time, and all the time. In their systematic review of the literature on hearing aid use, Perez and Edmonds (2012) observed the lack of a standard definition or categorization (division of response options) of hearing aid use. Several selfreport instruments assess hearing aid use but do so in different ways, which makes their results difficult to compare. Comparing Self-Reported and Objective Hearing Aid Use Previous studies have investigated the relationship between objective and self-reported amount of hearing aid use, with the objective measure as the benchmark. However, it should be acknowledged that objective measures of hearing aid use are not fiawless: they record the number of hours the hearing aid is turned on, which is not necessarily equal to the number of hours the hearing aid is worn. For example, failing to turn the hearing aid off when not wearing it will infiate
Hearing Aid Use Amount and Overreport/Laplante-Lévesque et al
objective hearing aid use. The data loggers Taubman and colleagues (1999) studied in the laboratory were generally effective and provided valuable information. But inaccuracies in hearing aid data loggers have also been reported, with a slightly lower hearing aid use log than the actual amount of time the hearing aids were on (Taubman et al, 1999). This cumulative error becomes greater the longer hearing aids are on for. Studies predating data logging (with weight of hearing aid battery or experimental hearing aids with internal clocks) found that an overreport of self-reported hearing aid use was frequent (Brooks, 1979, 1981; Haggard et al, 1981). Humes et al (1996) assessed data-logged and self-reported hearing aid use in 20 older people in the period from the hearing aid fitting to 90 days later. In the sample of both new and experienced hearing aid users, a significant (.76) correlation existed between both measures. However, while participants reported wearing their hearing aids for 10-11 hr per day on average, data logging measured 6-7 hr per day, with an absolute overreport of approximately 4 hr. During the period investigated, self-reported hearing aid use was more constant than data-logged hearing aid use, suggesting that people may formulate averages over relatively long periods of time when reporting their hearing aid use. This means that differences between data-logged and self-reported hearing aid use might be greater in people who vary their hearing aid usage greatly from day to day. Taubman et al (1999) assessed data-logged and selfreported hearing aid use in 24 experienced hearing aid users one week after having been fitted with experimental hearing aids. They divided the participants into two groups: the first group had been made aware that the hearing aids had data-logging while the second group had not. Participants in the first group were significantly more accurate in their hearing aid use report (over-report of 1.1 hr per day on average) than participants in the second group (overreport of 3.7 hr per day on average). These results show that knowledge of ohjective hearing aid use measurement yields more accurate hearing aid use self-report and suggests that people may formulate answers that they perceive as socially desirable when queried about their hearing aid use. IMäki-Torkko et al (2001) assessed data-logged and selfreported hearing aid use in 84 adults. Self-report was in the form of a daily diary entry or interview at the end of the study period, which varied from 21 to 73 days. Using hearing aids less than 8 hr per day on average was frequently reported (55% of participants in daily diary and 57% in interview) and data-logging showed an even greater frequency (64%). However, the authors did not report statistical analyses of these results. Gaffney (2008) assessed data-logged and self-reported hearing aid use in 39 new and experienced hearing aid users two weeks after hearing aid fitting. Again,
participants overreported their daily hearing aid use, here by approximately 2 hr on average. The two forms of hearing aid use measures were significantly correlated: .62 for new hearing aid users and .77 for experienced hearing aid users. This is the first study suggesting that experienced hearing aid users may be better at estimating hearing aid use than new hearing aid users. Hearing aid use overreport has also been demonstrated in a pédiatrie population: parents of 133 children with hearing aids overreported their children's daily hearing aid use by 2.6 hr (Walker et al, 2013). In this sample, parents of younger children overreported more than parents of older children. But reporting a behavior such as average daily hearing add use is not a simple task. Underlying Principles of Behavior Self-Report When reporting a behavior, respondents complete multiple tasks such as (1) understand the question; (2) recall the relevant behavior; (3) infer and estimate the behavior; (4) map the answer into the response format (if response options are provided); and (5) amend the answer according to social desirability (Schwarz and Oyserman, 2001). Social desirability results in people reporting behavior they believe others will perceive favorahly, for example, a high amount of hearing aid use. Differences in objective and self-reported measures have been observed in behaviors other than hearing aid use, for example television viewing, where, compatihle with social desirahility theory, objective viewing amount is greater than self-reported (Otten et al, 2010). In audiology, some discrepancies have also been found in how people quantify average daily use of hearing aid versus communication programs, pointing to the complexity of behavior self-report (Laplante-Lévesque et al, 2012). In summary, previous research has found that hearing aid users overestimate their hearing aid use. This could be due to irregular use patterns, social desirability bias, or lack of hearing aid experience. As hearing aid use is seen as an indicator of hearing aid success, how users estimate it needs to he better understood. AIMS
T
his study assessed data-logged and self-reported amount of hearing aid use in a clinical sample of hearing aid users. It identified predictors of data-logged hearing aid use, self-reported hearing aid use, and hearing aid use overreport. METHODS
T
he Eriksholm Research Centre in Denmark carried out this multicenter study. Staff from the Oticon
189
Journal of the American Academy of Audiology/Volume 25, Number 2, 2014
hearing aid company helped identify 22 private hearing aid dispensers (11 in the Netherlands and 11 in Denmark) willing to recruit eligible hearing aid users from their caseload. Participants The 22 dispensers were instructed to recruit consecutive eligible hearing aid users already scheduled for an appointment (typically for the final visit of a hearing aid fitting or for a routine hearing aid review). Inclusion criteria included the following: aged > 18 yr; have no more than a moderate hearing impairment (.5, 1, and 2 kHz pure-tone average 14 days and with data-logging period >14 days; and report no obstacle to hearing aid use (e.g., lost or defective hearing aids or health condition such as otitis externa). In total, 228 hearing aid users met the inclusion criteria and took part in this study. The study adhered to the Declaration of Helsinki ethical principles. Potential participants received a written description of the study, which stated that their hearing aids had data-logging capabilities and that their dispenser would retrieve their hearing aid data log if they consented to participate in the study. Voluntary participation and confidentiality was stressed. Each participant was assigned a study code, and the dispensers were instructed to only share de-identified data with the Eriksholm Research Centre. Measures Four measures were obtained for each participant. Questionnaire Regarding Hearing Aid Usage A 17-item questionnaire was purposefully designed to capture aspects relevant to hearing aid usage. The questionnaire focused on demographics, hearing aid provision, hearing aid usage (time and patterns), and participant/other people's views on hearing aid use. The questionnaire was initially formulated in English. Native speakers translated it to Dutch and Danish. Dutch and Danish speakers back translated the translations to English for validation purposes. The three (English, Dutch, and Danish) versions of the questionnaire are available from the authors. International Outcome Inventory—Hearing Aids (IOI-HA) Questionnaire The IOI-HA (Cox et al, 2000) is a composite questionnaire that measures seven dimensions of hearing aid outcomes: use, benefit, residual activity limitations, satisfaction, residual participation restrictions, impact on
ISO
others, and quality of life. The IOI-HA questionnaire contains seven items (one item for each of the seven dimensions listed above), with five response options scored from 1 to 5. The Dutch and Danish versions of the IOI-HA were used (Cox et al, 2002). Both the Dutch and the Danish IOI-HA have a two-factor structure (Kramer et al, 2002; Vestergaard, 2006). The two factors have previously been described as representing "Me and my hearing aids" (the use, benefit, satisfaction, and quality of life items make up IOI-HA Factor 1) and "Me and the rest of the world" (the residual activity limitations, residual participation restrictions, and impact on others items make up IOI-HA Factor 2; Cox and Alexander, 2002). Both the Dutch and the Danish IOI-HA also have good test-retest reliability (Kramer et al, 2002; Jespersen et al, 2006). Data Logging For each participant, dispensers connected the hearing aids to Oticon's fitting software, Genie, and saved the hearing aid "usage" screen. It reports the following eEir-specific information: date at which data logging was started, date at which data logging was completed, and average daily hearing aid use. Data-logged patterns were also obtained: they were the proportion of occurrences the hearing aid was on for (20 hr) before it was powered off Proportion of occurrences of each time interval was categorized into four options (0-25, 25-50, 50-75, and 75-100% of occurrences); see Figure 1 for example. Audiometric Data Hearing aid dispensers provided the latest audiometric results available on file for each participant. Air-conduction pure-tone audiometric results obtained under headphones were recorded for the frequencies .25-4 kHz for both ears. Procedure Dispensers gave the questionnaire regarding hearing aid usage and the IOI-HA to participants. Questionnaires were completed in a pen and paper format, typically in the cHnic waiting room. During the appointment, the dispenser made sure that audiometric results and data logs were available. Following the appointment, dispensers forwarded the data to the Eriksholm Research Centre. Data Cleaning and Analysis Data were cleaned and analyzed with Statistica version 10. IOI-HA factor scores were compiled according to the published IOI-HA factor structure (Cox and Alexander, 2002). Some missing data occurred in the data set. For the two IOI-HA factor scores, when only
Hearing Aid Use Amount and Overreport/Laplante-Lévesque et al
RUAilVt
: SBLA
Streamer Ui«
Figure 1. Example of Oticon's fitting software "usage" screen: Genie version 2013.1.
one item for a given factor was missing, the missing data were imputed with the average of the other items for that factor for that participant (16 occurrences). All other missing values were kept as missing. For each statistical analysis, the available dataset was used and the sample size is reported. According to Taubman et al (1999) data loggers are generally effective and provide reliable information. From our experience, defective data logs typically indicate average daily hearing aid use of exactly 0 or 24 hr. In our sample of 228 participants, eight defective data logs were identified in four participants fitted binaurally: they showed 24 hr of average daily hearing aid use, and the displayed dates at which the data log was started and ended were the same (data-logging period of 0 day). This represented 8 of the 355 hearing aids for which data-logging information was available (a 2.25% fail rate). These data points were deleted.
As mentioned before, for each of seven time intervals (20 hr), the proportion of occurrences of each time interval was categorized into four options (0-25, 25-50, 50-75, and 75-100% of occurrences). A cluster analysis was conducted on data-logged patterns. Cluster analysis assigns participants into subgroups called clusters (Everitt et al, 2001). These clusters are defined to minimize variability within clusters and maximize variability between clusters. Nonhierarchical cluster analysis (k-means) was used; k-means cluster analysis uses multiple iterations of Lloyd's algorithm to compute the optimal clustering. The Euclidean distance (geometric distance in the multidimensional space) was used as the dissimilarity metric. Visual inspection of the clustergram (Schonlau, 2004) for 2-10 cluster solutions was used to establish the final cluster solution.
131
Journal of the American Academy of AudiologyA'^olume 25, Number 2, 2014
Three mtjltiple linear regression models were built to identify predictors of (1) data-logged hearing aid use; (2) self-reported hearing aid use, and (3) overreport rate. For each of the multiple linear regression models, bivariate analyses (i-tests, analyses of variance [ANOVAs], and Pearson's correlations) were used to screen the predictor variables with a liberal alpha level of. 1. Potential predictors were age (in years); gender (male or female); country (the Netherlands or Denmark); living situation (alone or with others); occupational situation (work or other); education (primary school or less, secondary or technical school, or university); self-reported health (lower than average, average, or better than average); experience with hearing aids (in years); payment toward hearing aid cost (yes or no); hearing impairment (.5, 1, and 2 kHz average in better ear, in dB HL); self-reported hearing disability (no or mild disability, moderate disability, or severe or profoimd disability); reception of hearing aid use advice from dispenser, spouse/partner, other family members, and friends (yes or no for each); opinion regarding hearing aid use (own, dispenser's, spouse/partner's, other family members', and friends') (as much as possible or as I please for each); self-reported pattern from day to day (same every day or different from day to day); current hearing aid fitting configuration (monaural or binaural); self-reported monaural/binaural hearing aid use (hearing aid use sometimes monaural or hearing aid use always binaural); IOI-HA Factor 1 scores ("Me and my hearing aids": use, benefit, satisfaction, and quality of life items) and IOI-HA Factor 2 scoi-es ("Me and the rest ofthe world": residual activity limitations, residual participation restrictions, and impact on others items); data-logged monaural/binaural hearing aid use (absolute difference between right and left data-logged hearing aid use amount divided by maximum datalogged hearing aid use amount); data-logged pattern (cluster Regular or cluster On-off, see "Results" section below); and length of data-logging period (in days). Significant predictors at the bivariate level were entered into a backward stepwise multiple linear regression model with an alpha level of .05. Postestimation diagnostic tests were performed to evaluate each multiple linear regression model. Outliers were identified (participants for whom absolute values of standardized residuals were greater than 2) and removed from the models: 15 in the model explaining data-logged hearing aid use, 11 in the model explaining self-reported hearing aid use, and 7 in the model explaining overreport rate.
financially to the cost of their hearing aids. Participants reported good hearing aid outcomes, with an average score of 4.28 on the IOI-HA Factor 1 ("Me and my hearing aids") and 3.87 on the IOI-HA Factor 2 ("Me and the rest of the world"). Figure 2 reports the average audiogram of participants, which shows a mild-to-severe sloping bilateral hearing impairment. Hearing Aid Use Amount Table 2 summarizes the information regarding average daily hearing aid use. Data logging showed on average 10.5 hr of daily hearing aid use (n = 184), while participants reported on average 11.8 hr of daily hearing aid use (n = 206). In participants who were fitted binauraily, if there was a difference between the right and left data-logged hearing aid use amount, the greatest of the two values was kept, as per Walker et al (2013). The lower ofthe two values showed on average 9.8 hr of daily hearing aid use. In participants for whom both data-logged and selfreported hearing aid use data were available (n = 166), data logging showed on average 10.4 hr of daily hearing aid use, and participants reported on average 11.6 hr of daily hearing aid use. The average absolute overreport of daily hearing aid use was 1.2 hr per day (1 hr and 11 min). Figure 3 shows a scatterplot of average daily data-logged and self-reported hearing aid use. The correlation was highly significant (p < .001), with a correlation coefficient R of .74 and a coefficient of determination R^ of .54. Relative overreport can be expressed as a rate of absolute overreport divided by data-logged hearing aid use. A positive rate denotes overreport while a negative rate denotes underreport. Figure 4 shows a histogram ofthe overreport rate. The minimum value, -1.0, was obtained from a participant who reported not wearing his hearing aids at all (0 hr) but for whom data logging showed 5.3 hr of daily use. The maximum value, 12.3, was obtained from a participant who reported wearing his hearing aids for 12 hr per day but for whom data-logging showed .9 hr of daily use. Scores on the first IOI-HA item, targeting hearing aid use, were available for 225 participants: 1 participant (.4%) reported not wearing his hearing aids, and 1 participant (.4%) reported wearing his hearing aids less than 1 hr a day; 19 participants (8.4%) 1 to 4 hr a day; 27 participants (12.0%) 4 to 8 hr a day, and 177 participants (78.8%) more than 8 hr a day.
RESULTS Hearing Aid Usage Patterns Study Sample Table 1 summarizes the sample of 228 hearing aid users. Typical participants were over the age of 65 and retired, were fitted binauraily, and had contributed
nss
Data-logged patterns (see Fig. 1) were collected in 171 participants. As most participants were fitted binaurally, this gave access to 313 unique data-logged patterns. Those were scrutinized with cluster analysis.
Hearing Aid Use Amount and Overreport/Laplante-Lévesque et ; Table 1. Characteristics of the Sampie of 228 Participants Age (n = 227) Mean (SD) 72.05 (10.50) Gender (n = 228) Male n (%) 124(54.39) Female n (%) 104(45.61) Country (n = 228) The Netherlands n (%) 93 (40.79) Denmark n (%) 135(59.21) Living situation (n = 226) Aione n (%) 61 (26.99) With other(s) n (%) 165(73.01) Occupationai situation (n = 227) Work n (%) • 42 (18.50) Retirement or unempioyment n (%) 185 (81.50) Education (n = 225) None or primary schooi n (%) 85 (37.78) Secondary or technioai schooi n (%) 85 (37.78) University n (%) 55 (24.44) Seif-reported heaith (n = 224) Lower than average n (%) 73 (32.59) Average n(%) 128(57.14) Better than average n (%) 23 (10.27) Experience with hearing aids, in years (n = 223) Mean (SD) 6.48 (7.34) Payment toward hearing aid cost (n = 227) No payment n (%) 85 (37.44) Partial or fuil payment n (%) 142 (62.56) Hearing impairment (.5, 1, and 2 kHz average in better ear, in dB HL) (n = 213) Mean (SD) 37.61 (11.51) Seif-reported hearing disability (n = 225) No or mild hearing disabiiity n (%) 34(15.11) Moderate hearing disabiiity n (%) 108 (48.00) Severe or profound hearing disabiiity n (%) 83 (36.89) Seif-reported pattern from day to day (n = 226) Same every day n (%) 174(76.99) Different from day to day n (%) 52 (23.01) Seif-reported current hearing aid fitting configuration (n = 228) Uniiaterai n (%) 20 (8.77) Biiaterai n (%) 208 (91.23) Seif-reported monaural/binaurai hearing aid use (n = 227) I wear both hearing aids the same amount of time. 203 (89.43) i sometimes wear oniy one hearing aid. 24 (10.57) iOI-HA Factor 1 score (n = 225) Mean (SD) 4.28 (.59) iOI-HA Factor 2 score (n = 225) Mean (SD) 3.87 (.75)
Visual inspection of the clustergram was used to establish that a two-cluster solution was most suitable. As can be seen in Figure 5, the first cluster of patterns, which we termed Regular, represents hearing aids that are most typically on for between 12 and 20 hr before their user powers them off. The second cluster of patterns.
which we termed On-off, represents hearing aids that are most typically on for shorter periods of time before their user powers them off. For 10 of the 134 participants fitted binaurally, the right and left hearing aids showed data-logged patterns that fell into different clusters: for these participants, the pattern arising from the hearing aid with the greatest data-logged hearing aid use was chosen. In the sample, 98 participants (57%) had a Regular data-logged pattern while 73 (43%) participants had an On-off data-logged pattern. These patterns are not measures of amount of hearing aid use, since hearing aids in the On-off cluster may be on for the same amount of time daily as hearing aids in the Regular cluster, only with breaks in between use. In participants with a Regular data-logged pattern, daily hearing aid use varied from .8 to 18.4 hr (data logged) and from 0 to 18 hr (self-reported). In participants with an On-off data-logged pattern, daily hearing aid use varied from .7 to 22.9 hr (data logged) and from 1 to 18 hr (self-reported). ANOVAs identified the following significant differences in average occurrences between the two clusters: 12-20 hr [F(l,312) = 84.88, p < .01] occurred significantly more frequently in the Regular cluster, while
o o. 4O)
O
^
0
I
T
4
8
12
16
20
24
Average data-logged hearing aid use (in hours/day) Figure 3. Average daily data-logged and self-reported hearing aid use (n = 166).
coefficient of determination R^ was .10. Participants for whom data logging showed an On-off pattern overreported significantly more than their peers for whom data logging showed a Regular pattern (.6 versus .5). By contrast, participants who stated different hearing aid use from day to day overreported significantly less than their peers who reported their hearing aid use is the same every day (.2 versus .4).
100 —i
-20
60 -
20
-15
3-
-10
a-
- 5
0 -J - 2 - 1 0 1
l~ 0
I 2
3
4
5
6
7
8
10 11 12 13
Overreport rate
Figure 4. Hearing aid use overreport rate (n = 166); absolute overreport divided by data-logged hearing aid use. A positive rate denotes overreport while a negative rate denotes underreport.
n a sample of 228 adult hearing aid users from the Netherlands and Denmark, participants reported on average 11.8 hr of daily hearing aid use, while data logging showed on average 10.5 hr of daily hearing aid use. Those values are similar (Humes et al, 1996; Purdy and Jerram, 1998; Arlinger and Billermark 1999; Taubman et al, 1999; Gaffney, 2008) or slightly higher (Humes et al, 2004; Waiden and Waiden, 2004; Colhns et al, 2007; Humes et al, 2009) than previous reports of average daily hearing aid use measured as a continuous value, either via data logging or self-report. The present study's inclusion criteria of having no obstacle for hearing aid use (e.g., lost or defective hearing aids or health condition such as otitis externa) could partially explain the greater hearing aid use uncovered. To the authors' knowledge, the present study is the first to have obtained data-logging information in both hearing aids in adults fitted binaurally. This study took the greatest of the right and left data-logged hearing aid use values if those were different, as per Walker et al (2013), also contributing to greater data-logged hearing aid use. Still, the strength of the correlation between datalogged and self-reported hearing aid use in the present study was comparable to previous reports (Humes et al, 1996; Taubman et al, 1999; Gaffney, 2008). The scores for the first IOI-HA item were negatively skewed, with 48 of the participants (21%) selecting one of the first four response options (0—8 hr per day) and 177 participants (79%) selecting the fifth response option (>8 hr a day). It is typical for instruments categorizing hearing aid use to offer more response options in the lower range of the 24 hr period (Perez and Edmonds, 2012). For example, as mentioned above, four of the five IOI-HA use categories cover the 0-8 hr period. For better differentiation, future instruments categorizing hearing aid use could offer more response options in the higher range of the 24 hr period (e.g.. Cox and Alexander, 1999; Kam et al, 2011). Response options like those of Kam et al (2011) (none, 16 hr) could help avoid ceiling effect when categorizing hearing aid use such as observed in the present study. The average absolute overreport of daily hearing aid use was of 1 hr and 11 min (n = 166). This is less than some of the previous reports of differences between selfreported and data-logged measures of hearing aid use, with Humes and colleagues (1996) reporting an absolute overreport of daily hearing aid use of 4 hr in their 20 participants. However, Humes' participants were not aware that their hearing aids had data-logging capabilities: in contrast, the present study's participants were informed of this prior to consent. The absolute overreport is comparable to Taubman et al (1999)'s
195
Journal of the American Academy of AudiologyA'^olume 25, Number 2, 2014
75% —, Legend > Cluster 1: "Regular* • Cluster 2:
50% —
Si
8 25%
-
0% - < 20 hr
Period for which hearing aid is turned on
Figure 5. Data-logged patterns according to cluster analysis (n = 213).
group of 12 hearing aid users who had been made aware of their hearing aids' data logging and therefore support Taubman et al's findings that social desirability drives, at least in part, overreport of hearing aid use. Nevertheless, Gaffney (2008) also found relatively small overreport (1.9 hr), despite not having informed her 39 participants of their hearing aids' data logging. Together, data-logged and self-reported patterns significantly predicted data-logged hearing aid use amount, self-reported hearing aid use amount, and overreport rate when controlling for covariates. The length of the data-logging period could be hypothesized as contributing to under- or over-report of hearing aid use, as it could be more difficult to recall and average behavior occurrences over a long period of time. However, this proved not to be the case despite the data-logging period varying from 14 days to over four years in the sample. Hearing aid experience as measured in years in our sample also failed to predict hearing aid use amount and its overreport. This is contradictory to Gaffney (2008)'s findings, but the latter study measured hearing aid experience dichotomously (users with first or subsequent hearing aid fitting). The present study collected both data-logged and selfreported daily hearing aid use amount in 166 participants with the help of 22 private dispensers in two countries. Realistic and heterogeneous data collection contributes to the generalizability of the findings. However, the dispensers introduced the study questionnaires to the participants, therefore potentially introducing a social desirability bias, where responses could have been
1SB
influenced by participants wanting to please their dispensers. However, this study's procedure reflects chnical practice where the user and dispenser together discuss hearing aid use. Although the sampling instructions given to the dispensers stipulated that consecutive participants meeting the inclusion criteria should be invited to participate in the study, it is impossible to know if the dispensers displayed recruitment bias. It is also impossible to know which percentage of participants refused to participate in the study and if those were different from those who agreed to participate. This was the first study to investigate overreport as a rate rather than as an absolute value and to identify predictors of overreport. This is also by far the largest study of this type to date. Despite many potential predictors being identified with bivariate analyses, only data-logged and self-reported usage patterns remained significant predictors of data-logged hearing aid use amount, self-reported hearing aid use amount, and overreport rate after controlling for covariates. This highlights the importance of performing multivariate analyses when investigating complex interactions such as aspects of disability and intervention outcomes. We believe this is the first study to investigate patterns of hearing aid usage in such detail, using both data-logged and self-reported measures. It is intriguing that, in contradiction to a systematic review (Knudsen et al, 2010), greater self-reported hearing disability did not remain a significant predictor of hearing aid use amount after controlling for covariates in the present study. It should be noted that self-reported hearing disability was significantly associated with hearing aid use amount at the bivariate level but not at the multivariate level. In contrast, leaving the hearing aids turned on for long periods of time before turning-them off (data-logged Regular pattern) or reporting hearing aid use as the same every day significantly contributed to greater hearing aid use amount both when assessed with data-logging and with self-report and after controlling for covariates. It could be that the relationship between self-reported hearing disability and hearing aid use amount mentioned in previous studies was a confounding effect of patterns of hearing aid usage. A linear regression model identified two statistically significant predictors of overreport of hearing aid use. Despite reaching statistical significance, the coefficient of determination R^ was low (.10), and it is therefore reasonable to wonder, beyond the statistical significance, about the clinical significance of these results. Still, participants with a data-logged Regular pattern overestimated their hearing aid use amount to a lesser extent. People who reported their hearing aid usage pattern to be different from day to day also overestimated their hearing aid use amount to a lesser extent. The latter finding might appear surprising, but users who are conscious of their
Hearing Aid Use Amount and Overreport/Laplante-Lévesque et al
different patterns of hearing aid usage from day to day may also be more aware of their hearing aid use and therefore be more accurate reporters. As a whole, the results point to patterns of hearing aid usage as being at least as important a concept as amount of hearing aid use. When it comes to hearing aid usage, dispensers should discuss not only the "how much" but also the "how" with their users. Above and beyond how to best measure hearing aid use, previous qualitative studies (Lockey et al, 2010; Laplante-Lévesque et al, 2013) have highlighted the importance of hearing aid use that is purposeful and that relates to needs. In this context, more evidence regarding how audiologists and users do and should discuss hearing aid use is required. Furthermore, as Perez and Edmonds (2012) previously suggested, if aspects of amount of use are to remain within the battery of hearing aid outcome measures, then how much more time people spend performing the activities they enjoy as a result of wearing the hearing aids could be a more client-centered measure of hearing aid success. Acknowledgments. The authors sincerely thiink Oticon BV (the Netherlands) and Oticon A/S (Denmark) for assistance in identifying hearing aid dispensers, the dispensers and hearing aid users who participated in this study, and Maria Oxenb0ll, who assisted with manuscript preparation. REFERENCES Arlinger S, Billermark E. (1999) One year follow-up of users of a digital hearing aid. Br J Audiol 33{4):223-232. Banerjee S. (2011) Hearing aids in the real world: typical automatic behavior of expansion, directionality, and noise management. J Am Acad Audiol 22(l):34-48. Brooks DN. (1979) Counselling and its effect on hearing aid use. Scand Audiol 8(2):101-107.
Everitt BS, Landau S, Leese M. (2001) Cluster Analysis. 4th ed. London: Oxford University Press. Gaffney P. (2008) Reported hearing aid use versus datalogging in a VA population. Hear Rev 15(6):42, 44, 46-47. Gatehouse S. (1999) Glasgow Hearing Aid Benefit Profile: derivation and validation of a client-centered outcome measure for hearingaid services. JAm Acad Audiol 10(2):80-103. Haggard MP, Foster JR, Iredale FE. (1981) Use and benefit of postaural aid in sensory hearing loss. Scand Audiol 10(l):45-52. Humes LE. (1999) Dimensions of hearing aid outcome. JAm Acad Audiol 10(l):26-39. Humes L, Ahlstrom J, Bratt G, Peek B. (2009) Studies of hearingaid outcome measures in older adults: a comparison of technologies and an examination of individual differences. Semin Hear 30(2):112-128. Humes LE, Gamer CB, Wilson DL, et al. (2001) Hearing-aid outcome measures foUovráig one month of hearing aid use by the elderly. J Speech Lang Hear Res 44(3):469-486. Humes LE, Hailing D, Coughlin M. (1996) Reliability and stability of various hearing-aid outcome measures in a group of elderly hearing-aid wearers. J Speech Hear Res 39(5):923-935. Humes LE, Humes LE, Wilson DL. (2004) A comparison of singlechannel linear amplification and two-channel wide-dynamic-rangecompression amplification by means of an independent-group design. Am J Audiol 13(l):39-53. Jespersen CT, Bille M, Groth J, Hansen T. (2006) Danish reference data for the IOI-HA. In: Rasmussen AN, Poulsen T, Andersen T, Larsen CB, eds. Hearing Aid Fitting: Proceedings ofthe 21"' Danavox Symposium 2005. Ballerup, Danmark: Danavox Jubilee Foundation, 475-491. Kam AC, Tong MC, van Hasselt A. (2011) Cross-cultural adaptation and validation of the Chinese abbreviated profile of hearing aid benefit. Int J Audiol 50(5):334-339. Knudsen LV, Oberg M, Nielsen C, Naylor G, Kramer SE. (2010) Factors infiuencing help seeking, hearing aid uptake, hearing aid use and satisfaction with hearing aids: a review of the literature. Trends Amplif 14(3):127-154.
Brooks DN. (1981) Use of post-aural aids by National Health Service patients. Br J Audiol 15(2):79-86.
Kochkin S, Beck DL, Christensen LA, et al. (2010) MarkeTrak VIII: the impact ofthe hearing healthcare professional on hearing aid user success. Hear Rev 17(4): 12, 14, 16, 18, 23, 26-28, 30, 32, 34.
Collins MP, Souza PE, O'Neill S, Yueh B. (2007) Effectiveness of group versus individual hearing aid visits. J Rehabil Res Deu 44 (5):739-749.
Kramer SE, Goverts ST, Dreschler WA, Boymans M, Festen JM. (2002) International Outcome Inventory for Hearing Aids (IOIHA): results from The Netherlands. Int J Audiol 41(1):3&-41.
Cox RM, Alexander GC. (1999) Measuring satisfaction with amplification in daily life: the SADL scale. Ear Hear 20(4): 306-320.
Laplante-Lévesque A, Hickson L, Worrall L. (2012) Comparing response options for the International Outcome Inventory for Hearing Aids (IOI-HA) and for Alternative Interventions (IOIAI) daily-use items. Int J Audiol 51(10):788-791.
Cox RM, Alexander GC. (2002) The International Outcome Inventory for Hearing Aids (IOI-HA): psychometric properties of the English version. Int J Audiol 41(l):30-35. Cox R, Hyde M, Gatehouse S, et al. (2000) Optimal outcome measures, research priorities, and international cooperation. Ear Hear 21(Suppl. 4):106S-115S. Cox RM, Stephens D, Kramer SE. (2002) Translations ofthe International Outcome Inventory for Hearing Aids (IOI-HA). Int J Audiol 41(l):3-26.
Laplante-Lévesque A, Jensen LD, Dawes P, Nielsen C. (2013) Optimal hearing aid use: focus groups with hearing aid clients and audiologists. Ear Hear 34(2): 193-202. Lockey K, Jennings MB, Shaw L. (2010) Exploring hearing aid use in older women through narratives. Int J Audiol 49(8): 542-549. Mäki-Torkko EM, Sorri MJ, Laukli E. (2001) Objective assessment of hearing aid use. Scand Audiol 30(Suppl. 52):81-82.
137
Journal of the American Academy of Audiology/Volume 25, Number 2, 2014
Otten JJ, Littenberg B, Harvey-Berino JR. (2010) Relationsbip between self-report and an objective measure of television-viewing time in adults. Obesity (Silver Spring) 18(6): 1273-1275.
Staebelin K, Bertoli S, Probst R, Schindler C, Dratva J, Stutz EZ. (2011) Gender and bearing aids: patterns of use and determinants of nonregular use. Ear Hear 32(6):e26-e37.
Perez E, Edmonds BA. (2012) A systematic review of studies measuring and reporting bearing aid usage in older adults since 1999: a descriptive summary of measurement tools. PLoS One 7(3):e31831.
Taubman LB, Palmer CV, Durrant JD, Pratt S. (1999) Accuracy of bearing aid use time as reported by experienced bearing aid wearers. Ear Hear 20(4):299-305.
Purdy SC, Jerram JC. ( 1998) Investigation of tbe profile of bearing aid performance in experienced hearing aid users. Ear Hear 19(6): 473-480.
Vestergaard MD. (2006) Self-report outcome in new bearing-aid users: longitudinal trends and relationsbips between subjective measures of benefit and satisfaction. Int J Audiol 45(7):382-392.
Scbonlau M. (2004) Visualizing bierarcbical and non-bierarcbical cluster analyses witb clustergrams. Comput Stat 19(1):95-111.
Waiden TC, Waiden BE. (2004) Predicting success with bearing aids in everyday living. J Am Acad Audiol 15(5):342-352.
Scbwarz N, Oyserman D. (2001) Asking questions about bebavior: cognition, communication, and questionnaire construction. Am J Eval 22(2):127-160.
Walker EA, Spratford M, Moeller MP, et al. (2013) Predictors of bearing aid use time in children witb mild-to-severe bearing loss. Lang Speech Hear Serv Sch 44(l):73-88.
Solbeim J, Kvaerner KJ, Sandvik L, et al. (2012) Factors affecting older adults' use of bearing aids. Scand J Disabil Res 14(4): 300-312.
Zakis JA, Dillon H, McDermott HJ. (2007) Tbe design and evaluation of a bearing aid witb trainable amplification parameters. Ear Hear 28(6):812-830.
1S8
Copyright of Journal of the American Academy of Audiology is the property of American Academy of Audiology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use.
