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Hearing loss (HL) is a chronic disease with high prevalence in older adults and with negative impact on quality of life. The primary intervention is hearing aids (HAs); however, HA acceptance and usage stay low and the psychological mechanisms of HL adaptation remain poorly understood. To address this knowledge gap, we developed a psychometric instrument to assess hearing-related adaptive strategies (AStra).
Material and methods
The study was administered via self-report in three different samples (online, paper-pencil, app-based). In total, 169 individuals with subjective HL (mean age 62.51 years; 49.11% female; 57.40% HA users) participated. We included coping measures, subjective HL and HA satisfaction to assess AStra’s construct validity, and HA use for criterion-related validity.
Results
The AStra demonstrated robust psychometric properties, yielding a three-factor solution with strong internal consistency. AStra showed good construct validity with coping measures and was associated with HA use independently of traditional factors such as age and education.
Discussion
The results highlight AStra’s utility in understanding hearing-related adaptive strategies and emphasize the proactive role of older adults when dealing with HL. Further research is needed on when, how, and why adaptive strategies are used.
Practical recommendations
Adaptive strategy use may be translated into behavioral interventions for people with HL.
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Theoretical background
Hearing loss (HL) is a chronic disease with high prevalence among older adults. In 2019, 65% of adults over the age of 60 years experienced HL [20]. The prevalence and severity of HL increase with age: 11% of 60–69-year-olds, 23% of 70–79-year-olds, 42% of 80–89-year-olds and 56% of those over 90 years old in Europe have mild to moderate HL [24]. Untreated HL has adverse effects on speech perception and communication and diverse quality of life (QoL) indicators [4, 5, 17].
Hearing aids (HAs) are the primary treatment for mild to severe sensory HL. A review by Ferguson et al. [8] revealed a strong beneficial effect of HAs on the ability to listen to other people, as well as on hearing-specific QoL. Despite these positive effects, individuals with HL delay HA uptake for years [16, 19]. While increasing severity of HL, positive attitudes towards HAs and own HL were consistently found to facilitate HA use, there is conflicting evidence for other personal resources, such as age, health and socioeconomic status [15].
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In summary, the existing literature suggests that traditional predictors are insufficient to fully explain HA use. This underscores the need for a theoretical framework that captures the psychological mechanisms driving HA use, with a particular focus on adaptive strategies and psychological resources. This perspective is provided by the model of selective optimization with compensation (SOC; [1]). It describes three distinctive dimensions of individual strategies namely, selection, optimization and compensation for the adaptation and mastery of health-related constraints across the life span.
Previous research applied the SOC model to HA use in older adults and proposed HA-related adaptive strategies, such as selection of listening situations and optimization of usage behavior [26]. Subsequent research has revealed empirical evidence for the proactive role of older adults in HA use: participants who used their HAs in more diverse listening situations (selection) were more satisfied with the devices and were using them more often [25]. These findings imply that people apply SOC strategies to master HA use.
As there is a huge proportion of people with HL who do not use HAs [14, 16], in the current work we generalized the application of the SOC model to individuals with HL whether they are using HAs. Building on the SOC model, we developed a comprehensive scale that captures three distinct dimensions of hearing-related adaptive strategies (Fig. 1): Selection encompasses goal-setting behavior and the proactive choice of listening environments. This dimension measures how individuals select and prioritize specific listening situations and activities that align with their hearing capabilities (i.e., withdrawing from situations where they have difficulties hearing). Optimization focuses on how individuals acquire, enhance and refine their hearing-related strategies. This includes proactive planning to improve listening situations and develop personal resources (i.e., requesting written information in advance). Compensation involves the utilization of alternative means when primary approaches are insufficient. This dimension assesses how individuals leverage external resources, social support, and environmental modifications to address their hearing difficulties (i.e., asking for repetition).
Fig. 1
Illustration of the SOC model and its application to hearing-related strategy use
These three dimensions operate within the context of personal resources, including sociodemographic variables and HL. Collectively, these factors contribute to various positive hearing-related outcomes, such as HA use or satisfaction.
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Scale construction
To research the effect of hearing-related adaptive strategies, we aimed to develop a scale that provides information on their utilization. Based on a literature review on related audiological, psychological and gerontological research (e.g., [3, 11, 21]) and questionnaires (i.e., SOC questionnaire [6], communication profile for the hearing impaired [2]), we generated 45 items on behavioral hearing-related adaptive strategies. As proposed by the SOC model [2], the items were assigned to the three dimensions selection, optimization and compensation. Responses were collected on a 7-point Likert scale ranging from 0 (never) to 6 (always) regarding strategy use within the past month. The questionnaire was presented to individuals with subjective HL in an online study (n = 51). Based on statistical data analyses, 36 items were preselected and used in subsequent research.
Objective of the current work
In the present research, we evaluated the hearing-related adaptive strategies (AStra) scale with respect to its psychometric quality (reliability, factorial, construct and criterion-related validity).
In a first step, we aimed to provide a short and robust scale. For this purpose, we analyzed data collected in three studies (online, paper-pencil, app-based) and assessed the item structure and factorial validity of the AStra scale.
Secondly, we investigated construct validity in subsamples. We used the Ways of Coping Questionnaire [18] as validation criteria and assumed that the AStra scale moderately relates to coping strategies. Subjective HL was used as second validation criteria. In line with research on the relation between severeness of chronic diseases and use of adaptive strategies [10, 27], we presumed that the use of hearing-related adaptive strategies increases with more severe HL. As revealed in previous research [25], we further presumed that the application of hearing-related adaptive strategies is positively associated with HA use and satisfaction.
Finally, we investigated the criterion-related validity of AStra for successful adaptation to HL. In accordance with research in the context of orthopedic rehabilitation [28], we presumed that the application of hearing-related adaptive strategy predicts HA use.
Methods
Participants and procedure
In total, 169 subjects were included with ages ranging between 40 and 84 years (M = 62.51 years, SD = 10.91 years). Gender distribution was almost balanced (49.11% female). The HAs were used by 57.40% of the participants. Individuals with lower educational attainment were underrepresented with 16.61% compared to intermediate (29.59%) and higher (53.85%) level education. Appendix A gives an overview of the sample characteristics of the three studies (online, paper-pencil, app-based).
The online study was promoted through relevant social media channels for people with HL and information flyers were shared with hearing care professionals.
Paper-pencil data were collected prior to a home trial study with 23 experienced HA users who were recruited from the participant database of Jade University Oldenburg.
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The app-based study was promoted to people with HL through social media channels and with support from senior citizen networks, hearing care professionals and in relevant journals.
Measures
Ways of coping.
German translation of the revised Ways of Coping Questionnaire [20] assessed three subscales (seeking social support: 6 items, e.g. I got professional help., α = 0.824; escape-avoidance: 8 items, α = 0.754; Planful problem solving: 6 items, α = 0.854) using 4‑point Likert scales (0 = I do not use; 3 = I use to a great extent).
Subjective HL.
4‑item Amsterdam Inventory [17] measured hearing abilities without HAs (e.g., Do you have difficulties in conversations via phone? on a 4-point scale: 0 = no difficulties; 3 = not possible; α = 0.881). Previous validation showed moderate correlation with pure tone average across 0.5,1,2 and 4kHz (n = 54, r = 0.46, p < 0.001; [26]).
HA use and satisfaction.
HA use was measured dichotomously (0 = no; 1 = yes). An adapted 12-item Satisfaction with amplification in daily life (SADL) questionnaire [6] assessed HA satisfaction across three subscales (positive effects: 6 items, e.g. How natural is the sound from your hearing aids?, α = 0.925; negative features: 3 items, α = 0.440; personal image: 3 items, α = 0.656) on 5‑point scales (0 = not at all; 4 = completely).
Covariates.
Analysis controlled for age (years), sex (0 = female; 1 = male), education (ISCED 2011: 0 (levels 0–2) = low, 1 (levels 3–4) = intermediate, 2 (levels 5–8) = high) and health satisfaction (German Socioeconomic Panel: How satisfied are you with your health today? rated on a 0–10 scale).
Data analyses
Data analysis proceeded in three stages. Initially, exploratory factor analysis (EFA) was performed on the full sample (online, app-based, paper-pencil) using minimum residual factoring with oblimin rotation to assess AStra scale factorial validity. Factor determination combined parallel analysis, scree plot examination, and theoretical considerations, with items loading below 0.40 considered for removal. Multiple imputation (mice package, R, R Core Team (2024). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria.) generated five datasets using predictive mean matching, excluding participants with > 35% missing items (n = 18). Factor loadings were pooled across imputations. Subsequent analyses were conducted in respective subsamples where necessary data were available. Construct validity was evaluated through correlations between AStra factors and established measures, e.g., Ways of Coping (app-based), subjective HL (online, app-based), HA use (online, app-based, paper-pencil) and HA satisfaction (app-based, HA users), using pairwise correlations. Finally, in the online and app-based subsample, criterion-related validity was assessed via logistic regression, with HA use as dichotomous outcome (0 = no; 1 = yes) and AStra composite factor as primary predictor, controlling for subjective HL, age, sex, education, and health satisfaction. Cases with > 35% missing data were excluded (n = 2), with remaining missing data handled through multiple imputation by Chained equations (MICE).
Results
Exploratory factor analysis
The EFA yielded a three-factor solution of 18 items, 6 items per factor, which emerged as the most appropriate structure for the data. Table 1 presents the factor loadings, ranging from 0.45 to 0.87 across the 3 factors, indicating strong item-factor relationships. Additional information and German items can be found in supplementary Table B1, inter-item correlations in Table B2. The intercorrelations between the three factors were low to moderate (0.04–0.52), suggesting related, but distinct constructs (Table B3). Cronbach’s alpha coefficients were 0.81 for optimization, 0.82 for compensation and 0.87 for selection, demonstrating good internal consistency for all three subscales. The overall Cronbach’s alpha for the 18-item scale was 0.86, indicating high reliability of the entire instrument. Supplementary confirmatory factor analysis (CFA) results are provided in Appendix E.
Table 1
Factor loadings for the AStra scale items (N = 151)
Item No.
Selection
Optimization
Compensation
1
If I have a hard time hearing in a situation or during an activity, I withdraw from the situations or activities where I struggle
0.87
–
–
2
If I have a hard time hearing in a situation or during an activity, I tend to spend less time in such situations or activities
0.81
–
–
3
If I have a hard time hearing in a situation or during an activity, I give up on such situations or activities
0.77
–
–
4
If I have a hard time hearing in a situation or during an activity, I tend to “zone out”
0.67
–
–
5
When I haven’t understood something, I pretend that I did
0.62
–
–
6
I ignore people when I cannot understand them
0.59
–
–
10
I try to plan the settings of listening situations in advance (e.g., by choosing quiet rooms for work meetings or social gatherings)
–
0.81
–
8
I plan sufficient rest time before or after tiring listening situations
–
0.67
–
9
In social situations, I try to stay in well-lit areas
–
0.67
–
7
When I arrange a date and time to meet someone, I ask them for the information in writing
–
0.57
–
12
I ask others to support me in dealing with my hearing difficulties
–
0.55
–
11
I keep myself informed about technology and tools to improve my hearing
–
0.45
–
13
If I do not understand, I specifically ask about what I did not understand
–
–
0.84
14
If I do not understand, I ask for repetition
–
–
0.80
18
I ask others (e.g. my partner, family or friends) to help me by repeating what was said
–
–
0.68
16
If I do not understand, I ask my communication partner to speak up
–
–
0.57
15
If I have a hard time hearing, I try to position myself so that I can hear as well as possible (e.g., when in crowded areas)
–
–
0.53
17
If I have a hard time hearing, I increase the volume of any devices I am listening to (e.g., the TV or radio)
–
–
0.51
Note. Factor extraction method: minimum residual (minres). Rotation method: oblimin. Factor loadings < 0.30 are suppressed. Participants with more than 35% missing items (n = 18) were excluded from the analysis. Items appear in order of their factor loading and are indexed according to the order of the AStra questionnaire
Construct validity
Construct validity analyses examined correlations between AStra factors and established measures. The AStra composite score showed significant correlations with Revised Ways of Coping subscales (0.38–0.51), indicating that adaptive strategies extend beyond coping constructs. A positive correlation with subjective HL (r = 0.59, p < 0.01) supported construct validity, confirming the hypothesized relationship between adaptive behavior and HL severity. Full correlation matrix and descriptive statistics are presented in supplementary Table B4.
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The use of HA positively correlated with all AStra measures (0.19–0.38, p < 0.05), while SADL’s positive effects subscale correlated with AStra composite score (r = 0.40, p < 0.05), suggesting that individuals using more hearing-related adaptive strategies report higher HA use and satisfaction (Table 2).
Table 2
Descriptive statistics and intercorrelations for AStra subscales with hearing-related outcomes
Correlations
n
M
SD
Selection
Optimization
Compensation
Overall
Hearing aid Use
168
0.58
0.50
0.19*
0.38**
0.19*
0.33**
Satisfaction with amplification in daily life (SADL)
Positive effects
33
2.93
0.95
0.29
0.29
0.34
0.40*
Negative features
33
2.10
0.92
−0.28
−0.09
0.01
−0.19
Personal image
33
2.85
0.83
−0.22
−0.11
−0.02
−0.17
SADL composite score
33
2.70
0.64
0.04
0.15
0.25
0.17
Note. M and SD represent mean and standard deviation, respectively. The SADL questionnaire was only collected for HA users in the app-based study. Due to longitudinal dropouts, the number of test subjects for the SADL is limited to n = 33. These correlations should be interpreted as preliminary findings requiring replication in larger samples. * indicates p < 0.05. ** indicates p < 0.01
Criterion-related validity
We conducted a logistic regression analysis to assess the criterion-related validity of the AStra scale. The outcome variable was HA use, with AStra composite score and subjective HL as predictors. Age, sex, education and health satisfaction were included as covariates.
In the logistic regression model (Appendix C), subjective HL was positively associated with HA use (odds ratio, OR = 4.90, p < 0.001), indicating that individuals with higher levels of HL were more likely to use HAs. Similarly, the AStra composite score showed a positive relationship with HA use (OR = 2.29, p < 0.05), suggesting that those who employed more hearing-related adaptive strategies were also more likely to use HAs. Age, sex, education and health satisfaction were not significantly associated with HA use.
To examine whether hearing-related adaptive strategies provide unique variance beyond general coping strategies, we conducted supplementary analyses including ways of coping subscales as covariates. These analyses were restricted to the app-based subsample with complete Ways of Coping data (n = 64). While subjective HL remained significantly associated with HA use (OR = 5.45, p = 0.010), the AStra composite score showed a positive but nonsignificant association (OR = 2.47, p = 0.150). None of the Ways of Coping subscales demonstrated significant associations with HA use (all p > 0.20). The substantially reduced sample size limits the interpretability of these findings, as the loss of significance for AStra may reflect either shared variance with general coping strategies or insufficient statistical power (see Appendix D).
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Discussion
The present study introduces and provides preliminary validation of the AStra scale, a novel psychometric instrument designed to measure the use of hearing-related adaptive strategies in people with HL. The results provide strong evidence for the scale’s reliability and validity while offering insights into the psychological mechanisms underlying HL adaptation.
The EFA revealed a robust three-factor structure aligning with the theoretical framework of selective optimization with compensation [1]. The factors demonstrated good internal consistency, comparable to other established measures in the field. Highly related items in the selection factor were retained due to distinct withdrawal facets. Moderate intercorrelations between factors suggest that these strategies represent distinct adaptive approaches, supporting the multidimensional nature of HL adaptation. The relatively low correlation between selection and compensation is consistent with previous research on the SOC model [2] and suggests that individuals may preferentially employ either selective or compensatory approaches rather than utilizing both simultaneously.
The significant correlations between AStra scores and established coping measures support the scale’s construct validity while suggesting that hearing-related adaptive strategies represent a distinct construct from general coping mechanisms. This finding aligns with recent theoretical work emphasizing the need for domain-specific measures in understanding sensory loss adaptation [23].
The strong positive correlation between AStra scores and subjective HL supports the theoretical assumption that increasing hearing difficulties relate to greater strategy deployment. This relationship parallels findings in other chronic condition domains where symptom severity correlates with adaptive strategy use [10, 27].
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The AStra scale demonstrated significant criterion-related validity for HA use, even after controlling for HL and traditional predictors such as age, sex, education and health satisfaction. This finding has important clinical implications, suggesting that assessment of adaptive strategies could enhance current models of HA uptake and use. The positive correlation between AStra scores and HA-related positive effects further suggests a link between adaptive strategy use and favorable HA evaluations.
Limitations
Several limitations should be considered when interpreting these results. The cross-sectional nature of the study precludes causal inferences about the relationship between strategy use and outcomes. Future longitudinal research should examine how adaptive strategies evolve over time and their long-term impact on hearing-related outcomes. Data collection across three modalities (online, app-based, paper-pencil) was necessitated by recruitment constraints. While this approach introduced potential response bias, it also enhanced sample diversity: paper-pencil administration enabled the inclusion of older participants with limited digital literacy, whereas online modalities reached lower-educated individuals typically absent from traditional recruitment channels. Despite these complementary strategies, individuals with lower educational attainment remain underrepresented, highlighting persistent sampling challenges in hearing loss research. The sample size, while adequate for initial validation, was relatively modest for some analyses, particularly regarding HA satisfaction. Given the insufficient internal consistency of some SADL subscales, the HA satisfaction measure should also be reconsidered. Future studies with larger samples could provide more robust estimates of these relationships and enable more sophisticated analyses of potential moderating factors. Such studies should assess both general SOC strategies and hearing-specific adaptive strategies to examine the incremental validity of domain-specific vs. general adaptation measures. Although factorial analysis via EFA was appropriate given the sample size, it was not suitable for the theory-driven item development. Nevertheless, it established a three-factor structure, and the supplementary CFA provided further evidence for this solution; however, a robust, formal validation of the proposed three-factor structure via CFA requires larger datasets in future studies. Finally, the absence of audiometric data represents another limitation. Future studies should include pure-tone audiometry to examine whether adaptive strategies moderate the relationship between measured and subjective HL.
Implications for research and practice
The AStra scale represents a significant advancement in our understanding of HL adaptation, providing a reliable and valid measure of hearing-related adaptive strategies. The findings support the utility of the SOC model in understanding HL adaptation while highlighting the complex interplay between different adaptive strategies. Future research should focus on the investigation of age-normative effects on strategy use to provide insights into how HL adaptation varies across the lifespan [23]. Further, the impact of hearing-related adaptive strategies on the age-specific relation between hearing and awareness of age-related changes might be of research interest [22].
As the application of SOC strategies was found to positively relate to subjective indicators of successful aging [9, 12, 13], QoL indicators should be included in future studies to further explore the relevance of hearing-related adaptive strategies for successful aging with HL.
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For clinicians, the AStra scale offers a valuable tool for assessing patients’ adaptive strategies, potentially informing intervention approaches for individuals with HL. Strategy-based knowledge sharing or interventions could also benefit the significant share of people who perceive hearing difficulties, but do not get a HA recommendation (29% in Germany) or who despite HAs experience issues in noisy situations or large groups [7].
Future research should investigate strategy-based behavioral interventions, especially optimization techniques that showed positive associations with both HA use and satisfaction.
Acknowledgements
We thank Inga Holube, Petra von Gablenz and Nadja Schinkel-Bielefeld for their support in data collection and for valuable comments on earlier versions of the AStra scale. Further, we appreciate the work of the people who were involved in the development and conduction of the whole research project: Stefanie Schmitt-Rüth, Susanne Sczogiel, Leonie Manzke, Anja Winkelmann and Lukas Graber.
Declarations
Conflict of interest
R.-L. Fischer is and L. Incerti was employed by WS Audiology. B. Williger and S.T. Kamin declare that they have no competing interests.
All studies on humans described in the present manuscript were carried out with the approval of the responsible ethics committee (Friedrich-Alexander-Universität Erlangen-Nürnberg, 22-299‑S; Carl von Ossietzky Universität Oldenburg, Drs.EK/2022/065) and in accordance with national law and the Helsinki Declaration of 1975 (in its current, revised form). Informed consent was obtained from all participants included in studies.
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