Validation of the Danish translation of the illness intrusiveness rating scale in patients with rheumatic disease

Abstract

Background

Rheumatic diseases significantly affects quality of life, making self-management critical. The Illness Intrusiveness Rating Scale (IIRS) measures the extent to which illness interferes with various life domains, but until now it has not been translated into Danish.

Objective

This study aimed to translate the IIRS into Danish and assess its psychometric properties in Danish patients with a rheumatic disease.

Methods

Following COSMIN guidelines, the IIRS was translated and culturally adapted through a multi-step process, including forward and backward translations and cognitive interviews. Psychometric testing included assessments of internal construct validity by confirmatory factor analysis, internal consistency by Cronbach’s α, reliability by test–retest, standard error of measurement, and responsiveness by minimal detectable change.

Results

The final Danish version was well-understood, though minor issues arose, such as the relevance of “religious expression.” The scale demonstrated high internal consistency (Cronbach’s α = 0.92). Confirmatory factor analysis confirmed the original three-factor structure with an acceptable model fit (comparative fit index = 0.94, the Root Mean Square Error of Approximation = 0.10). Strong correlations were found within “Relationships and Personal Development” and “Instrumental” domains, but the “Diet” item did not meet factor assignment criteria. Test–retest reliability was acceptable (intraclass correlation coefficients ≥ 0.70 for most items).

Conclusion

The Danish IIRS showed strong psychometric properties, making it a reliable and valid tool for assessing illness impact and self-management interventions in Danish patients with rheumatic disease.

Key points

The scale was well-received by participants, with minimal missing data.

The Danish IIRS showed high internal consistency, acceptable test–retest reliability, and confirmed its three-factor structure.

The validated Danish IIRS is a reliable tool for assessing illness impact.

Background

Rheumatic diseases, in this study including rheumatoid arthritis, axial spondyloarthritis, psoriatic arthritis, fibromyalgia, and osteoarthritis, profoundly impact individuals’ lives. These conditions affect not only physical function but also psychological well-being and social dynamics [1, 2–3]. This comprehensive impact underscores the importance of effective self-management strategies to enhance the quality of life for those living with rheumatic disease [1, 2–3]. Self-management, as defined by frameworks from Corbin and Strauss and Lorig and Holman [3, 4], encompasses the individual’s ability to manage the symptoms, treatment, physical and psychosocial consequences and lifestyle changes inherent in living with a chronic condition. It encompasses medical responsibilities, such as medication adherence and managing medical appointments; role adjustments, including lifestyle and social changes; and emotional management, emphasizing coping strategies for the psychological challenges of chronic illness [2, 5, 6, 7, 8–9]. However, measuring the effectiveness of self-management interventions, especially in rheumatic diseases without objective indicators remains challenging. Reviews have highlighted small effects of self-management interventions, attributing this to a mismatch between the outcomes assessed and those targeted by the interventions [2, 5, 6, 7, 8–9].

To address the need for a scale that can capture the broad impact of rheumatic disease and the effectiveness of self-management interventions, we explored the Illness Intrusiveness Ratings Scale (IIRS) [10, 11]. The IIRS is designed to measure how illness and treatment interfere with lifestyles, activities, and interests, and offers a direct assessment of the disease’s disruptiveness, focusing on the extent to which it intrudes upon various life domains. Originally IIRS was developed in English for chronic, disabling, or life-threatening diseases, however it has later been employed across a wide range of conditions, showing promise in evaluating self-management interventions for rheumatic disease by qualifying the severity and disruptiveness of the disease [12, 13]. Previous findings indicate that the IIRS can be used to identify meaningful changes in illness intrusiveness attributable to therapeutic interventions [10].

The IIRS consists of 13 items [14] that prompt respondents to rate the extent to which they perceive their illness and/or treatment interferes with key aspects of life. Ratings are made using a seven-point visual analogue scale, with total score calculated as the sum of all item scores, resulting in a range from 13 to 91. This scale is specifically designed to measure the direct impact of illness but also facilitates the evaluation of therapeutic interventions, including self-management programs [13, 15, 16].

Based on confirmatory factor analysis of IIRS responses from 5,671 respondents, Devins et al. explored the construct validity of the IIRS [13]. The data was pooled from 15 separate studies examining quality of life across eight patient groups: rheumatoid arthritis, osteoarthritis, systemic lupus erythematosus, multiple sclerosis, end-stage renal disease on maintenance dialysis, renal transplantation, heart/liver/lung transplantation, and insomnia.

The analysis identified three distinct factors. The first factor, “Relationships and Personal Development,” included six items: 5. passive recreation, 9. family relations, 10. other social relations, 11. self-expression/self-improvement, 12. religious expression, and 13. community and civic involvement. The second factor, “Intimacy,” consisted of two items: 7. relationship with spouse and 8. sex life. The third factor, “Instrumental Life Domains,” encompassed four items: 1. health, 3. work, 4. active recreation, and 6. financial situation. The “Diet” item did not meet the criteria for factor assignment [13].

The IIRS has demonstrated high construct validity and reliability, with reliability coefficients ranging from 0.82 to 0.94 across different patient populations [13, 16]. Its ability to capture meaningful differences in illness intrusiveness across various conditions has been supported by both exploratory and confirmatory factor analyses, reinforcing its validity in both research and clinical settings [13, 15, 16].

IIRS has not yet been translated into Danish and has therefore not been validated for use in a Danish setting. Therefore, the primary aim of this study was to translate the illness intrusiveness rating scale into Danish. The secondary aim was to assess the translated versions’ psychometric properties in Danish patients with rheumatic disease.

Materials and methods

In our translation process, we were guided by standards of the COSMIN Study Design checklist [17] for translation of existing patient-reported outcome measures (PROMs). After the initial translation process, we tested the psychometric properties of the translation by exploring face validity. Psychometric testing included assessments of internal construct validity, test–retest reliability, and responsiveness of the Danish translation to ensure validity.

The translation and cultural adaption process

Preparation and approvals

An expert group was formed to oversee the translation process. The group consisted of a professor in self-management within rheumatology (BAE) and a post-doctoral nurse who had experience with translations and cross-cultural adaptions.

Permission to translate the IIRS was granted from the developer, Professor of Psychiatry Gerald Devins.

Forward translation and review

The IIRS was forward translated into Danish by two independent, professional translators, who had Danish as their mother tongue, and were fluent in English. They did not have a medical background, which was acceptable because the questionnaire does not contain medical language, health care terminology, or require any prior knowledge in the assessed topic. The focus was kept on the natural, spoken language with its cultural nuances addressing a common audience.

The project group consisted of three participants. They made comparisons between the independent translations regarding ambiguity and discrepancies of words, sentences, or meaning for each item in the questionnaire to create a consensus version.

Pilot testing

To explore the face validity [17], we conducted cognitive interviews with five patients diagnosed with either rheumatoid arthritis or psoriatic arthritis (aged 23–77 years; three men and two women). Participants were recruited through purposive sampling to ensure variation in age, gender, and diagnosis, thereby capturing diverse perspectives relevant to the instrument’s applicability. The sample size aligns with COSMIN guidelines [17], which consider five participants sufficient for evaluating face validity through cognitive interviewing.

The cognitive interviews were conducted by LHL, and a combination of the “think aloud” method and “probing” was applied [18] to ensure that the items, response options, and instructions were easy to understand and made sense to a Danish population.

Backward translation and review

The Danish consensus version was back-translated to English by two other independent bilingual professional translators blind to the original. The translators had Danish as their mother tongue and were fluent in English. The two translations were then compared by the project group and a new consensus version was made. This version (Supplementary Material 1) was approved by the developer of the IIRS, Dr Gerald Devins, to ensure that the translated versions reflected the same item content.

Participants and recruitment

To provide data for psychometric testing, patients accepted for a stay at the Danish Rheumatism Association’s rehabilitation center, Sano (Skælskør), were invited to take part in the study.

Referred patients to the rehabilitation center must be over 18 years old, diagnosed with a joint, back, or muscle conditions, have a rheumatic diagnosis, and experience significant pain or limitations in work capacity. Additionally, patients should be motivated for rehabilitation and capable of active participation. They should be on stable medication if needed, have previously attempted local training without success, have good understanding of Danish, and primarily be independent. Patients with severe cardiac, pulmonary, neurological, or cerebral conditions, untreated severe depression, current infections, or wounds are excluded from rehabilitation stay. Therefore, patients referred to the rehabilitation center were considered stable regarding the construct being measured and were deemed suitable inclusion in the study. Information about the study and a link to the questionnaire were circulated by e-mail by Sano.

Data collection for psychometric testing

Besides IIRS responses, we obtained demographic data, such as gender, age, and diagnosis when the participants filled in IIRS questionnaires for the first time.

To collect all questionnaire data, we used the database REDCap. REDCap provides full logging of all entries and changes [19, 20].

Statistical analysis

Sample size

No universal standards exist for determining sample size in evaluating internal consistency, and test–retest reliability. Nonetheless, the COSMIN guideline offers methodological criteria for assessing the quality of studies on measurement properties. Specifically, the COSMIN checklist suggests a minimum sample size of 100 respondents or seven times the number of items (7 × 13 = 91) [17]. We estimated that approximately 200 participants would be adequate for the evaluation of internal consistency, factor analysis, and test–retest reliability, when accounting for 50% missing responses in the re-test.

Internal construct validity

According to COSMIN guidelines [17], internal consistency and structural validity should be assessed for unidimensional (sub)scales. The initial step focused on examining the correlation matrix to assess how variables related. We aimed for correlation coefficients to exceed 0.30, identifying variables with lower correlations for potential exclusion [21, 22]. On the flip side, we were cautious of extreme multicollinearity or singularity, i.e., variables showing excessively high correlations (i.e., r > 0.90), as this complicates discerning unique variable contributions.

We calculated Cronbach’s α [17] to explore internal consistency in our translation. Based on COSMIN guidelines, a score above 0.70 was taken as an indication of sufficient homogeneity of the items. Prior to conducting confirmatory factor analysis, we conducted a preliminary assessment of multicollinearity by examining the determinant value in the correlation matrix. A determinant value greater than 0.00001 was set as acceptable multicollinearity levels. Values below this threshold prompted a review to eliminate highly correlated variable pairs (r > 0.8) [23].

The PROC CALIS procedure in SAS Enterprise Guide version 8.3 [24] was used for Structural Equation Modeling and Confirmatory Factor Analysis to explore construct validity. A three-dimensional confirmatory factor analysis model was specified, where three latent factors (scale1, scale2, and scale3) were hypothesized to explain the relationships between the observed variables. The model fit statistics reported included the Goodness-of-Fit Index, Bentler-Bonett Normed Fit Index, Bentler Comparative Fit Index were 0.90 or above, indicate a well fitted model [25]. In addition, the Chi-Square statistic, degrees of freedom, and the p-value were tested. A non-significant result (p > 0.05) indicated a good fit between the model and the data. In addition to the Chi-Square test, the Root Mean Square Error of Approximation (RMSEA) was included, where values less than 0.06 generally indicated a good fit, while values between 0.08 and 0.10 suggested a fair fit. The Comparative Fit Index was also included with values close to or greater than 0.95 suggesting a good fit. Lastly, the Standardized Root Mean Square Residual was reported with values below 0.08 indicating a good fit. These indices provided a more comprehensive understanding of the model fit, complementing the results of the Chi-Square test [26].

Test–retest

To obtain test–retest data the questionnaire was completed twice with approximately 14 days between the two administrations [27]. Test–retest reliability is the extent to which scores for the same patients relate consistently among each other for repeated measurements over time. Test–retest reliability coefficients vary between 0 and 1, where 1 equal perfect reliability. Intraclass correlation coefficients were reported and test–retest intraclass correlation coefficients should be ≥ 0.70.

Measurement error

Measurement error was expressed as standard error of measurement (SEM), which was calculated as standard deviation × (√ 1 – intraclass correlation), where SD is the standard deviation of all scores from the participants [28, 29]. The SEM was used for calculating the MDC calculated as SEM × z-value × √2. The minimal detectable change (MDC) was estimated to reflect the smallest within-person change in scores which can be considered a true change above measurement error within a 5% significance level. Missing items were handled with multiple imputations using multivariate normal distribution.

For all analysis a significance level of p = 0.05 was chosen and all analyses were executed using SAS Enterprise Guide version 8.3.

Results

The translation and cultural adaption process

The translation process was characterized by a smooth consensus among the experts involved. Notably, none of the experts felt the need to compromise, although minor discrepancies arose, mainly regarding the interpretation of phrases such as ‘how much’ or ‘to what extent’.

A specific point of discussion revolved around item 7, where the choice between the words ‘spouse’ or ‘partner’ was deliberated, as many Danes live together without being married. The original version uses “How much does your illness and/or its treatment interfere your RELATIONSHIP WITH YOUR SPOUSE (girlfriend or boyfriend if not married)”. The consensus version in the backtranslation ended up as “How much does your illness and/or its treatment interfere with your RELATIONSHIP WITH YOUR SPOUSE (partner if not married)”.

Item 11 revealed a notable difference in wording between the two professional translators’ versions: “your opportunities for self-expression and self-improvement” versus “your possibilities of personal development.” However, the expert group quickly reached consensus, selecting the latter as it was considered to best capture the intended meaning of the original questionnaire.

Feedback from with participants indicated that the questionnaire was coherent, straightforward, and user-friendly. However, an interesting observation emerged regarding item 12, which pertains to religion (item 12: How much does your illness and/or its treatment interfere with your: religious expression). Participants expressed puzzlement over this question, noting a lack of relevance, particularly as they did not identify as religious.

All modifications made during the translation and validation process received approval from the original developer of the IIRS.

Results of psychometric testing

In total, 192 participants completed the questionnaire over an 8-month period in 2023. A total of 99 of them completed the questionnaire again after 14 days. No reminders were sent out. In total, 68% were female and the mean age was 62 years (Table 1).

Table 1. Participant characteristics

Baseline		Retest after 14 days
Demographics	Total N = 192	Demographics	N = 99
Gender		Gender
Females, n (%)	130 (68)	Females, n (%)	67 (68)
Males, n (%)	62 (32)	Males, n (%)	32 (32)
Age		Age
Mean (range)	59 (23–86)	Mean (range)	60 (27–86)
Diagnoses, n (%)		Diagnoses, n (%)
Rheumatoid arthritis	79 (41)	Rheumatoid arthritis	38 (38)
Axial spondyloarthritis	44 (23)	Axial spondyloarthritis	24 (24)
Psoriatic arthritis	33 (17)	Psoriatic arthritis	19 (19)
Fibromyalgia	17 (9)	Fibromyalgia	10 (10)
Osteoarthritis	19 (10)	Osteoarthritis	9 (9)

Missing data per participant amounted to 2%. The highest number of missing data for one item was in item 7, where we had 15 missing observations out of 192 (7%) (Table 2). The internal consistency was high since Cronbach’s α for the total scale was 0.92, i.e., above the 0.7 cutoff.

Table 2. Mean, standard deviation, missing data, correlation, and range of correlations

Illness Intrusiveness Items and subscales Total N = 192	Mean (SD)	N MISS	Correlation with total	Range of item correlations	Cronbach’s α
1. Health	4.7 (1.76)	0	0.72	0.16–0.67
2. Diet	3.2 (1.81)	2	0.44	0.15–0.44
3. Work	4.0 (2.39)	10	0.57	0.20–0.51
4. Active recreation	4.9 (1.83)	1	0.63	0.11–0.61
5. Passive recreation	3.3 (1.93)	0	0.69	0.30–0.65
6. Financial situation	3.3 (2.23)	1	0.60	0.26–0.55
7. Relationship with your spouse	3.4 (2.08)	15	0.68	0.26–0.69
8. Sex life	3.8 (2.28)	10	0.62	0.15–0.61
9. Family relations	3.4 (1.93)	2	0.82	0.26–0.87
10. Other social relations	3.8 (1.93)	6	0.86	0.24–0.77
11. Self-expression/self-improvement	3.8 (2.04)	2	0.76	0.20–0.71
12. Religious expression	1.3 (0.97)	10	0.30	0.11–0.26
13. Community and civic involvement	3.4 (2.01)	2	0.78	0.25–0.77
Factor 1. “Relationships and Personal Development.” (Item 5, 9, 10, 11, 12, and 13)					0.89
Factor 2 “Intimacy” (Item 7 and 8)					0.82
Factor 3 “Instrumental” (Item 1, 3,4, and 6)					0.78
Total Cronbach’s α for IIRS					0.92

The correlation with total IIRS score ranged from 0.30 to 0.86 (Table 2). The inter-item correlations ranged between 0.15 and 0.87 (Table 2), with items 2 and 12 showing the lowest correlation and items 9, 10 and 13 the highest (For full correlation matrix, see Supplementary Material 2). We found the determinant of the correlation matrix to be 0.00024, which is satisfactory as it is greater than the required 0.00001.

Based on the correlation matrix analysis we proceeded with a confirmatory factor analysis to see how well the pre-specified model previously identified by Devins et al. [13] was confirmed by the observed data to evaluate structural validity. The original 3-factor structure of the IIRS —comprising (1) Identity, (2) Intimacy, and (3) Relationships/Personal Development—demonstrated a better fit compared to a 1-factor model. Notably, fit statistics improved further in the 3-factor model after excluding item 2. As previously observed by Devins et al., the “diet” item did not meet the criteria for proper factor assignment [13]. Thus, no modifications to the original 3-factor model resulted in improved fit within our population.

Our results showed that the Goodness-of-Fit Index, Bentler-Bonett Normed Fit Index, Bentler Comparative Fit Index were all 0.90 or above, which indicate a well fitted model [25]. The Chi-square to degrees of freedom ratio was 140/51 = 2.8, indicating an acceptable model fit. However, the p-value was significant, which suggests that the model did not meet the ideal threshold for fit, as a non-significant p-value (p > 0.05) generally indicates a better fit between the model and the data.

The Comparative Fit Index, was 0.94, indicating a near-acceptable fit (values close to or greater than 0.95 suggest a good fit). Additionally, the Standardized Root Mean Square Residual, where values less than 0.08 indicate a good fit, was found to be 0.049 (Fig. 1).

[See PDF for image]

Fig. 1

The confirmatory factor analysis

The standardized RMSEA was 0.0957 (RMSEA Lower 90% Confidence Limit 0.0770 and RMSEA Upper 90% Confidence Limit 0.1148). In the case of RMSEA, values between 0.08 and 0.1 is indicative of fair fit [26].

Table 3. Test–retest analysis (N = 99)

Item	Difference between T1 and T2 (14 days)	SEMa	MDCb	Cronbach’s α	ICCc	Coefficients of stability
1. Health	0.11	1.08	1.07	0.90	0.99	0.82
2. Diet	0.12	1.39	3.84	0.85	0.63	0.74
3. Work	−0.26	1.62	4.49	0.87	0.70	0.78
4. Active recreation	−0.00	1.09	3.03	0.90	0.99	0.82
5. Passive recreation	0.15	1.69	4.68	0.78	0.70	0.63
6. Financial situation	0.08	1.29	3.57	0.91	0.62	0.84
7. Relationship with your spouse	−0.09	1.45	4.03	0.87	0.67	0.76
8. Sex life	−0.11	1.27	3.53	0.92	0.69	0.84
9. Family relations	0.02	1.45	4.02	0.83	0.66	0.71
10. Other social relations	0.17	1.14	3.15	0.89	0.46	0.80
11. Self-expression/self-improvement	−0.08	1.44	3.99	0.86	0.66	0.73
12. Religious expression	−0.13	0.80	2.23	0.82	0.40	0.74
13. Community and civic involvement	−0.24	1.70	4.71	0.78	0.70	0.64
Domains
Factor 1	0.15	1.69	4.68	0.93	0.46
Factor 2	−0.09	1.45	4.03	0.92	0.45
Factor 3	0.11	1.07	2.98	0.91	0.46

^aStandard error of measurement

^bMinimal detectable change

^cInter Class Correlation Coefficient

In the test–retest analysis (Table 3) we found that the minimal detectable change (MDC) ranged 1.07–4.71. The coefficients of stability ranged from 0.63 to 0.82, with to items below 0.7. The intraclass correlation coefficients ranged from 0.40 to 0.99. 5 out of 13 items were at or above the 0.7 threshold for acceptable intraclass correlation coefficients. domain scores for intraclass correlation coefficients ranged from 0.45 to 0.46, which indicate poor reliability. Cronbach’s α between timepoint1 and timepoint2 ranged between 0.78 and 0.92, showing acceptable α scores.

Discussion

This study confirmed the applicability of the IIRS in a Danish rheumatology context. The translation and adaptation of the IIRS into Danish were executed successfully and is therefore valid for use in a Danish setting. However, the translation process highlighted certain challenges. One of the main challenges was accurately conveying certain nuances, such as the interpretation of phrases like “how much” or “to what extent.” These nuances are significant because they can influence how respondents understand and respond to the questionnaire [30].

In the translation process we also came across specific cultural aspects. For instance, the original term “spouse” was translated to “partner” to better reflect Danish social and cultural norms. This choice was significant because it aligns with Denmark’s inclusive view of relationships, encompassing not only married couples but also cohabiting partners and same-sex relationships. By choosing terms that resonate more closely with the Danish culture, the translation enhances the relevance and utility of the scale in a Danish-speaking population [31]. Another interesting aspect emerged regarding the questionnaire item concerning religion (item 12). Participants in the cognitive interviews expressed confusion over its relevance, especially those who did not identify themselves as religious. The IIRS instructions specify that respondents should enter “1” to indicate that one’s illness and/or its treatment do not interfere very much when a person considers a life domain to be irrelevant, such as appears to be the case for people who describe themselves as non-religious [15]. Our results showed a very low mean score in the item 12 (1.3), indicating that the respondent’s illness either do not interfere with the practice of their religion or that they consider the item to be irrelevant. Thus, this finding highlights the cultural and personal factors that influence how our Danish respondents perceive the relevance of questionnaire items. This observation aligns with concerns discussed in the literature about the use of standardized psychometric instruments across different cultures. Schmitt and Allik [32] discuss the limitations of such instruments, particularly when constructs like social norms or religious beliefs do not translate directly across cultures. Other studies [31, 33] have emphasized the critical importance of cultural adaptation in the translation and validation of psychometric instruments. They point out that failing to account for cultural differences can lead to substantial biases, potentially distorting the results. To mitigate this risk, these studies [31, 33] recommend the integration of qualitative methods, such as interviews, to capture deeper cultural nuances and ensure the instrument’s contextual relevance and accuracy.

In our study, we conducted five cognitive interviews to assess and address cultural differences during the translation of the IIRS into Danish, and secure face validity. These interviews provided valuable feedback on how items were perceived, helping to ensure that the scale was culturally appropriate. However, we acknowledge if we had conducted additional interviews, they could have provided more nuanced feedback, potentially uncovering further cultural or contextual factors that influence how the scale is understood. This additional input might have enhanced the adaptation process, resulting in a more robust and culturally sensitive Danish scale. However, the approval of all modifications by the original developer ensured that the translated version remains a faithful and authentic tool for assessing the impact of illness on individuals’ lives across diverse linguistic and cultural settings.

The psychometric testing indicated that the Danish version of the IIRS is a reliable and valid tool for assessing illness intrusiveness within the Danish-speaking populations of patients with rheumatic disease. The scale’s administration was effective, evidenced by the completion of the questionnaire by 192 participants with minimal missing data. The Danish IIRS demonstrated high structural validity and internal consistency, with a Cronbach’s α of 0.92, suggesting that the items consistently measure the intended construct. Confirmatory factor analysis supported the original three-factor structure—comprising following items Identity, Intimacy, and Relationships/Personal Development—over a one-factor model. The fit indices, all surpassed the acceptable threshold of 0.90, and the RMSEA was 0.0957, indicating a fair fit. While the chi-square test yielded a significant p-value, this result should be interpreted with caution. Firstly, the chi-square test assumes multivariate normality, and severe deviations from this assumption may lead to model rejection even when the model is correctly specified. Secondly, the chi-square statistic is essentially a test of statistical significance and is highly sensitive to sample size. In large samples, even minor discrepancies between the model and the data can result in the rejection of the model, even if it is otherwise well-specified [25].

Moreover, the scale exhibited generally acceptable test–retest reliability. Test–retest reliability showed moderate to good stability, with 5 out of 13 items having intraclass correlation coefficients ≥ 0.70. Cronbach’s alpha between two timepoints remained high (0.78 to 0.92), though items like “diet” and “religious expression” showed lower intraclass correlation coefficients.

Responsiveness was tested by analyzing MDC values, which suggested that the questionnaire can detect changes. These results are consistent with previous findings and adaptations of the IIRS into other languages. Devins et al. [13] found that the original English version of the IIRS demonstrated high internal consistency and construct validity across various chronic illnesses, supporting the reliability findings of the Danish version. Also, similar to our results Chae et al. [34] observed that the Korean adaptation maintained good psychometric properties, including a high Cronbach’s alpha and satisfactory factor structure, further validating the cross-linguistic consistency of the scale’s reliability.

However, some limitations of this study need acknowledgment. De Vet et al. [35] provide guidelines emphasizing the importance of sample size and diversity in validating psychometric tools, and highlight that demographic diversity is crucial for generalizability. While our sample size was adequate for initial validation, future research should aim for a larger and more diverse sample to enhance the generalizability of the findings. Our study involved patients with rheumatic disease, suggesting a need to explore the scale’s applicability across different conditions, but also including participants from various age groups and socio-economic backgrounds. This would provide a more comprehensive view of the scale’s applicability.

The Danish IIRS has thus been supported as both valid and consistent in measuring illness intrusiveness, making it a reliable tool for assessing the impact of illness on various aspects of interpersonal relationships. The availability of a culturally adapted version of the IIRS is significant for both clinical and research settings, as it enables a more nuanced understanding of how illness affects interpersonal dynamics among Danish-speaking people.

Conclusion

In conclusion, the Danish translation of the IIRS has demonstrated acceptable psychometric properties, making it a reliable and valid tool for assessing illness intrusiveness in Danish-speaking populations with rheumatic disease. However, addressing the noted limitations in future research will be crucial to further validate and refine the scale’s application across diverse conditions, to enhance generalizability. Additional validation will not only strengthen the tool’s utility and applicability but also ensure that it accurately captures the complexities of illness intrusiveness in a culturally sensitive manner. This study establishes the foundation for using the IIRS in Denmark, offering a valuable tool for both clinicians and researchers.

Acknowledgements

The authors express their gratitude to the patients who participated in the cognitive interviews and to all participants who completed the IIRS questionnaires for their valuable contributions to this study. Special thanks are extended to Sano Skælskør for facilitating contact with the participants.

Author contributions

Design of the study: LHL, BAE, GD. Data collection and assembly of data: LHL and KH. Data analysis and interpretation: All authors. Manuscript writing: All authors. Final approval: All authors.

Funding

The study was supported by the Novo Nordic Foundation, Nursing Programme provided a grant (NNF19OC0056658) to the research program TASEMA (A Research Programme on TArgeted SElf-MAnagement In Patients with Inflammatory Arthritis), and the Danish Rheumatism Association (R212-A7721). None of the organizations had any role in designing the study, collecting, analyzing, and interpreting of data or writing the manuscript.

Data availability

Questionnaires, notes, and reports are stored at the Department of Rheumatology and Spine diseases Rigshospitalet Glostrup. Anonymized data and coding can be provided at reasonable request. Please contact corresponding author for more information. Permission for this translation was obtained from the original developer and adaptor Dr. Gerald Devins.

Declarations

Ethics approval and consent to participate

Informed consent to participate was obtained electronically when the patients completed the questionnaire for the first time. Here they were also asked for consent for the follow-up questionnaire. Participants were informed in writing about the purpose and the content of the study and that they could withdraw at any time without any consequences for their treatment. All methods were carried out in accordance with the principles of the Declaration of Helsinki [36]. According to Danish law, approval from the ethics committee was not required. Permission for storage of data was obtained from the Danish Data Protection Agency. The study was registered with ref. P-2022-565.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

References

1. Nikiphorou, E; Santos, EJF; Marques, A; Böhm, P; Bijlsma, JW; Daien, CI et al. 2021 EULAR recommendations for the implementation of self-management strategies in patients with inflammatory arthritis. Ann Rheum Dis; 2021; 80, pp. 1278-85. [DOI: https://dx.doi.org/10.1136/annrheumdis-2021-220249] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/33962964]

2. Nuñez, DE; Keller, C; Ananian, CD. A review of the efficacy of the self-management model on health outcomes in community-residing older adults with arthritis. Worldviews Evid Based Nurs; 2009; 6, pp. 130-48. [DOI: https://dx.doi.org/10.1111/j.1741-6787.2009.00157.x] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/19656354]

3. Lorig, KR; Holman, H. Self-management education: history, definition, outcomes, and mechanisms. Ann Behav Med Publ Soc Behav Med; 2003; 26, pp. 1-7. [DOI: https://dx.doi.org/10.1207/S15324796ABM2601_01]

4. Corbin, JM; Strauss, A. Unending work and care: managing chronic illness at home; 1988; San Francisco, CA, US, Jossey-Bass:

5. Lorig, K; Ritter, PL; Plant, K. A disease-specific self-help program compared with a generalized chronic disease self-help program for arthritis patients. Arthritis Rheum; 2005; 53, pp. 950-7. [DOI: https://dx.doi.org/10.1002/art.21604] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/16342084]

6. Nolte, S; Osborne, RH. A systematic review of outcomes of chronic disease self-management interventions. Qual Life Res; 2013; 22, pp. 1805-16. [DOI: https://dx.doi.org/10.1007/s11136-012-0302-8] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/23111571]

7. Warsi, A; LaValley, MP; Wang, PS; Avorn, J; Solomon, DH. Arthritis self-management education programs: a meta-analysis of the effect on pain and disability. Arthritis Rheum; 2003; 48, pp. 2207-13. [DOI: https://dx.doi.org/10.1002/art.11210] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/12905474]

8. Betteridge N. Self-management of rheumatic diseases: State of the art and future perspectives.

9. Brady M, Beauchesne B. Sorting through the evidence of the arthritis self-management program and the chronic disease self-management program: executive summary of the ASMP/CDSMP meta-analyses. Atlanta, GA: Centers for Disease Control and Prevention (US) 2011. Available from: http://www.cdc.gov/arthritis/docs/ASMP-executive-summary.pdf. Centers for Disease Control and Prevention n.d.

10. Devins, GM. Using the illness intrusiveness ratings scale to understand health-related quality of life in chronic disease. J Psychosom Res; 2010; 68, pp. 591-602. [DOI: https://dx.doi.org/10.1016/j.jpsychores.2009.05.006] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/20488277]

11. Devins, GM; Deckert, A. Illness intrusiveness and self-management of medical conditions. Promot. Self-Manag. Chronic health cond. Theor. Pract; 2018; New York, NY, US, Oxford University Press: pp. 80-125.

12. Cinà, CS; Clase, CM. The illness intrusiveness rating scale: a measure of severity in individuals with hyperhidrosis. Qual Life Res; 1999; 8, pp. 693-8. [DOI: https://dx.doi.org/10.1023/A:1008968401068] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/10855343]

13. Devins, GM; Dion, R; Pelletier, LG; Shapiro, CM; Abbey, S; Raiz, LR et al. Structure of lifestyle disruptions in chronic disease: a confirmatory factor analysis of the illness intrusiveness ratings scale. Med Care; 2001; 39, pp. 1097-104. [DOI: https://dx.doi.org/10.1097/00005650-200110000-00007] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/11567172]

14. Kennedy, CA; Beaton, DE; Warmington, K; Shupak, R; Jones, C; Hogg-Johnson, S. Prescription for education: development, evaluation, and implementation of a successful interprofessional education program for adults with inflammatory arthritis. J Rheumatol; 2011; 38, pp. 2247-57. [DOI: https://dx.doi.org/10.3899/jrheum.101307] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/21765108]

15. Devins, G. Using the illness intrusiveness ratings scale to understand health-related quality of life in chronic disease. J Psychosom Res; 2010; 68, pp. 591-602. [DOI: https://dx.doi.org/10.1016/j.jpsychores.2009.05.006] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/20488277]

16. Novak, M; Mah, K; Molnar, MZ; Ambrus, C; Csepanyi, G; Kovacs, A et al. Factor structure and reliability of the Hungarian version of the illness intrusiveness scale: invariance across North American and Hungarian Dialysis patients. J Psychosom Res; 2005; 58, pp. 103-10. [DOI: https://dx.doi.org/10.1016/j.jpsychores.2004.05.008] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/15771877]

17. Mokkink LB, Prinsen CA, Patrick DL, Alonso J, Bouter LM, de Vet HC, et al. COSMIN study design checklist for patient-reported outcome measurement instruments. 32.

18. Willis, GB; Artino, AR. What do our respondents think we’re asking?? Using cognitive interviewing to improve medical education surveys. J Grad Med Educ; 2013; 5, pp. 353-6. [DOI: https://dx.doi.org/10.4300/JGME-D-13-00154.1] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/24404294][PubMedCentral: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3771159]

19. Harris, PA; Taylor, R; Minor, BL; Elliott, V; Fernandez, M; O’Neal, L et al. The REDCap consortium: Building an international community of software platform partners. J Biomed Inf; 2019; 95, 103208. [DOI: https://dx.doi.org/10.1016/j.jbi.2019.103208]

20. Harris, PA; Taylor, R; Thielke, R; Payne, J; Gonzalez, N; Conde, JG. Research electronic data capture (REDCap)—a metadata-driven methodology and workflow process for providing translational research informatics support. J Biomed Inform; 2009; 42, pp. 377-81. [DOI: https://dx.doi.org/10.1016/j.jbi.2008.08.010] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/18929686]

21. Schober, P; Boer, C; Schwarte, LA. Correlation coefficients: appropriate use and interpretation. Anesth Analg; 2018; 126, 1763. [DOI: https://dx.doi.org/10.1213/ANE.0000000000002864] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/29481436]

22. Turney S. Pearson Correlation Coefficient (r), Scribbr. 2022. https://www.scribbr.com/statistics/pearson-correlation-coefficient/. Accessed 26 Sept 2024.

23. Samuels P. Advice on exploratory factor analysis. 2016. https://doi.org/10.13140/RG.2.1.5013.9766

24. SAS Help Center. PROC CALIS Statement n.d. https://documentation.sas.com/doc/en/pgmsascdc/9.4_3.4/statug/statug_calis_syntax13.htm. Accessed 17 Jun 2025.

25. Hooper D, Coughlan J, Mullen M. Structural equation modelling: guidelines for determining model fit 2008;6.

26. Boateng, GO; Neilands, TB; Frongillo, EA; Melgar-Quiñonez, HR; Young, SL. Best practices for developing and validating scales for health, social, and behavioral research: a primer. Front Public Health; 2018; [DOI: https://dx.doi.org/10.3389/fpubh.2018.00149] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/29942800][PubMedCentral: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6004510]

27. Dutil, É; Bottari, C; Auger, C. Test-Retest reliability of a measure of independence in everyday activities: the ADL profile. Occup Ther Int; 2017; 2017, 3014579. [DOI: https://dx.doi.org/10.1155/2017/3014579] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/29097964][PubMedCentral: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5612758]

28. Norman G, Cairney J. Health measurement scales: a practical guide to their development and use. 2015;117. https://doi.org/10.1093/acprof:oso/9780199231881.003.0006

29. Weir, JP. Quantifying test-retest reliability using the intraclass correlation coefficient and the SEM. J Strength Cond Res; 2005; 19, pp. 231-40. [DOI: https://dx.doi.org/10.1519/15184.1] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/15705040]

30. Rahman, A; Iqbal, Z; Waheed, W; Hussain, N. Translation and cultural adaptation of health questionnaires. JPMA J Pak Med Assoc; 2003; 53, pp. 142-7. [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/12776898]

31. Cruchinho, P; López-Franco, MD; Capelas, ML; Almeida, S; Bennett, PM; Silva, MM et al. Translation, cross-cultural adaptation, and validation of measurement instruments: a practical guideline for novice researchers. J Multidiscip Healthc; 2024; 17, pp. 2701-2728. [DOI: https://dx.doi.org/10.2147/JMDH.S419714] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/38840704][PubMedCentral: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11151507]

32. Schmitt, DP; Allik, J. Simultaneous administration of the Rosenberg Self-Esteem scale in 53 nations: exploring the universal and culture-specific features of global self-esteem. J Pers Soc Psychol; 2005; 89, 4 pp. 623-42. [DOI: https://dx.doi.org/10.1037/0022-3514.89.4.623] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/16287423]

33. Borsa, J; Damasio, B; Bandeira, D. Cross-cultural adaptation and validation of psychological instruments: some considerations. Paid Ribeirão Preto; 2012; 22, 10.

34. Chae, S-M; Kim, C-J; Yoo, H. Psychometric evaluation of the Korean version of the adapted illness intrusiveness rating scale. Asian Nurs Res; 2010; 4, pp. 194-204. [DOI: https://dx.doi.org/10.1016/S1976-1317(11)60004-2]

35. de Vet, HCW; Adèr, HJ; Terwee, CB; Pouwer, F. Are factor analytical techniques used appropriately in the validation of health status questionnaires? A systematic review on the quality of factor analysis of the SF-36. Qual Life Res; 2005; 14, pp. 1203-18. [DOI: https://dx.doi.org/10.1007/s11136-004-5742-3] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/16047498]

36. WMA-The World Medical Association-Declaration of Helsinki. 1975 n.d. https://www.wma.net/what-we-do/medical-ethics/declaration-of-helsinki/doh-oct1975/. Accessed 8 Apr 2025.