Objective The Keele Assessment of Participation (KAP) questionnaire measures person-perceived participation in 11 aspects of life. Participation allows fulfilment of valued life activities and social roles, which are important to older adults. Since we aimed to use the KAP in a larger Dutch cohort, we examined the measurement properties of KAP in a Dutch sample of older adults with joint pain and comorbidity.
Design Cohort study.
Setting A community-based sample in Amsterdam, the Netherlands and North Staffordshire, UK.
Participants Participants were aged 65 years and over, had at least two chronic diseases (identified through general practice consultation) and reported joint pain on most days (questionnaire). The Dutch cohort provided baseline data (n=407), follow-up data at 6 months (n=364) and test–retest data 2 weeks after 6 months (n=122). The UK cohort provided comparable data (n=404).
Outcome measures The primary outcome was person-perceived participation, as measured with the KAP. The measurement properties examined were the following: structural validity (factor analysis), internal consistency (Cronbach's α), reliability (intraclass correlation coefficients; ICC), construct validity (hypothesis testing), responsiveness (hypothesis testing and area under the curve) and cross-cultural validity (differential item functioning; DIF).
Results Factor analysis revealed two domains: KAPd1: ‘participation in basic activities’ and KAPd2: ‘participation in complex activities’, with Cronbach's α of 0.74 and 0.57 and moderate test–retest reliability: ICC of 0.63 and 0.57, respectively. Further analyses of KAPd1 showed poor construct validity and responsiveness. Despite the uniform DIF in item ‘interpersonal relations’, the total KAPd1 score seemed comparable between the Dutch and UK sample.
Conclusions Only KAP domain ‘participation in basic activities’ showed good internal consistency and sufficient reliability. KAPd2 lacks sufficient measurement properties for application in studies, although items may be used as single items. Further development of the concept ‘participation’ may help the development and validation of instruments to measure participation.
- Quality of Life
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Social participation is a key component of healthy ageing, predicts morbidity and mortality and is increasingly targeted in intervention studies.
Using a structured approach, this study examined the ability of the Dutch Keele Assessment of Participation (KAP) to measure social participation in community-dwelling older adults with joint pain and comorbidity.
Measurement of person-perceived social participation has been considered as a single construct; however, this study has identified at least two underlying domains.
Only the first domain ‘participation in basic activities’ showed sufficient internal consistency and reliability. The construct validity and responsiveness of this domain require further testing, but without a clear definition of the concept participation this remains a challenge.
The results suggest that the KAP should embody a formative model (ie, the items together make up the construct) rather than a reflective model (ie, each item being an indicator of the underlying construct).
Strengths and limitations of this study
A structured approach was taken to examine all relevant measurement properties of the KAP.
The findings of this study highlight key issues for measuring social participation in older adults and contribute to this field of research.
Despite this extensive approach to examining the measurement properties of the KAP in this article, the construct validity and responsiveness still require further testing.
Identifying and preventing the disabling effects of highly prevalent diseases in older adults, such as joint pain, is a major health priority for clinicians.1 The WHO proposes the International Classification of Functioning, Disability and Health (ICF) to classify disability into biological (ie, impairments), individual (ie, activity limitation) and societal (ie, participation restriction) levels.2 Of the different levels of disability, participation has been considered least in research studies. Measuring participation (or restriction in participation) offers the potential to capture the impact of health conditions in the context in which people live. This goes beyond measuring the individual's capacity to fulfil basic tasks (ie, activity limitations), such as walking or gripping objects, and includes interaction between an individual's capabilities and his environment and needs. Participation allows fulfilment of valued life activities, aspects of identity and social roles (eg, being a worker, carer or community member),2–5 which are indicated as important by older adults.4 Furthermore, maintaining participation is linked with lower levels of morbidity and mortality,6 ,7 a key component of healthy ageing and well-being and therefore increasingly included as a target for intervention.1 ,8 Significantly, participation can be maintained in older adults despite the presence of impairments and physical limitations,9 which provides other opportunities for optimising healthcare.
In a large cohort study in the Netherlands, we wanted to investigate the impact of joint pain and comorbidity on social participation, as joint pain and other chronic diseases often co-occur in older populations and the combination of diseases can increase levels of disability.10 There are instruments available that measure participation in accordance with the definition proposed by the ICF model.11 ,12 Four Dutch questionnaires have been designed to capture participation: the Impact on Participation and Autonomy (IPA),13 the Utrecht Scale for Evaluation of Rehabilitation-Participation,14 ,15 the Participation Scale16 and the Maastricht Social Participation Profile.17 However, these questionnaires were not suitable for our study because they were developed for specific populations, a different (rehabilitation) setting, were not to be self-administrated and/or too burdensome because of an extensive number of items. The same held for several commonly used instruments developed in English that is, the Participation Objective, Participation Subjective,18 the Rating of Perceived Participation19 and the Social Role Participation Questionnaire.20 In contrast, the Keele Assessment of Participation (KAP) was suitable for our study; it is brief (contains 11 items), designed to be self-administered and there is some evidence of sufficient measurement properties to support its potential application in epidemiological studies in older populations.21 However, it has not been tested in a Dutch older population and evidence is lacking for its dimensionality and responsiveness. Therefore, the aim of this study was to translate the KAP into Dutch and examine the measurement properties of the Dutch version of the KAP in older adults with joint pain and comorbidity.
Development of the Dutch version of the KAP
The KAP is a short self-report questionnaire designed to measure person-perceived participation restriction in 11 aspects of life.21 The conceptual model incorporates the influence of environmental factors (eg, use of devices or help from other people) and personal factors on participation. The items are phrased to capture performance (“I have”), individual judgement (“I wanted”) and the nature and timeliness of participation (“as and when I have wanted”). Responses are on a five-point ordinal scale (all, most, some, little, none of the time). To date, studies calculated a total score by dichotomising the categories into restricted (some, little, none) and not restricted (all, most) and simply counting the numbers of restrictions on the 11 items.22
Procedure of translation and adaptation
First, two persons (bilingual speakers with Dutch as first language; one with a medical background and the other without) independently translated the KAP into Dutch followed by a consensus meeting. Second, two other persons (bilingual speakers with English as first language, both unfamiliar with the KAP; one with some medical background and the other without), independently back-translated the first draft into English. Third, another consensus meeting, which consisted of the translators and researchers, resulted in revisions and a prefinal draft. We also contacted the developers of the original questionnaire for feedback on the translation.
Some amendments were made. First, we calculated a total score as the sum of individual ordinal item scores (0=all to 4=none of the time), to assess its use as continuous score in longitudinal studies. Second, in the original KAP, filter questions are included in four items, that is, looking after dependents, work, education and social activities. The filter questions first ask whether responders choose to participate in these activities (yes/no).21 If yes, they are asked to complete the relevant participation item (all, most, some, little, none of the time). If no, they are scored as not restricted. To improve the classification of participation restriction, we decided to add an extra question to the work, education and social activity items, asking why participants chose not to participate (not applicable, not able to, not willing to, other reason). Participants answering ‘not able to’ were considered to be restricted (score 4). If participants answered ‘other’, the reason was judged by the researcher and classified as (not) restricted depending on the answer. The categories ‘not applicable’ and ‘not willing to’ were scored as not restricted (score 0).
The prefinal version of the Dutch KAP was tested for face validity in patients with foot complaints (n=10). Most patients skipped the introduction, which provides important instructions in line with the conceptual model. Therefore, we added additional instructions to the items self-care, looking after the home, belongings, dependants and also managing money. For example: “During the past four weeks, I managed my self-care or someone else on my behalf managed my self-care, as and when I have wanted.”
The measurement properties of the KAP were tested using data from a prospective cohort study of Dutch older adults with joint pain and comorbidity.10 For the cross-cultural validity, additional data from the North Staffordshire Osteoarthritis Project (NorStOP) were used.23 Figure 1 provides an overview of the samples and tested measurement properties.
The baseline sample (T0) of the Dutch cohort consisted of 407 participants aged 65 years and over with self-reported joint pain on most days in the past month; pain in the neck, back, shoulder, elbow, hand, hip, knee and/or foot and comorbidity (≥2 chronic diseases apart from joint pain, ascertained from the electronic medical files of general practices).10 Follow-up data collected at 6 months (T1; n=364) were used to examine responsiveness and additional data (T1t; n=122) were collected 2 weeks after T1 to examine reliability.24 To test the cross-cultural validity, we used NorStOP data. NorStOP is a well-established population-based cohort study of the long-term prognosis of musculoskeletal pain in older people.23 The individuals in the sampling frame were registered with six general practices in North Staffordshire, UK which allowed survey data to be linked to medical record data. Joint pain was identified through self-reported joint pain in at least one of the eight joint pain sites, matching the method in the Dutch study. Comorbidity was identified using medical record data; primary care practitioners used the Read system to code all morbidity encounters in actual consultations. Morbidity data (ie, symptoms and diseases) in this system are grouped under 19 main Read chapters,25 which is comparable to the Dutch list of the 20 most relevant chronic diseases.10 We used the 3-year follow-up data of the NorStOP, as this data matched better to the Dutch sample, compared with the NorStOP baseline data. Using this information, we identified n=1785 older adults aged 65 and over, with joint pain and comorbidity (at least two chronic diseases). A local Medical Ethics Committee approved both studies and participants have given written informed consent.
Descriptive statistics were used to describe the baseline characteristics.
Exploratory factor analysis (EFA) was performed in M-plus 6.12, to examine the factor structure of the 11-item KAP, using oblique GEOMIN rotation. To evaluate model fits, one absolute close-fit indexes, that is, root mean squared error of approximation (RMSEA) and two incremental close-fit indexes, that is, comparative fit index (CFI) and Tucker-Lewis index (TLI) were used, according to Hu and Bentler.26 The following cut-off points were considered as indicative of an adequate model fit: RMSEA <0.06, CFI and TLI >0.95.26 Items with factor loadings <0.5 were considered as not important to the domain.27 As EFA revealed two relevant KAP domains, further testing of the measurement properties of the KAP was performed separately for both KAP domains (KAPd1 and KAPd2).
Cronbach's α were calculated per domain and considered to be adequate when between 0.70 and 0.95.24
To examine test–retest reliability, we calculated the intraclass correlation coefficients (ICCs) for the KAP domains, based on a two-way random effects model. The ICCs were valuated as good when above 0.70.27 Furthermore, we quantified the measurement error using the Bland and Altman method. A paired t-test provided the mean change (meanchange) in score between the two time points and the SD of this change (SDchange). Then, the 95% limits of agreement were calculated: meanchange ±1.96×SDchange. Also, the SE of measurement, type agreement (SEMagreement) was calculated, to check how far apart the scores of the two measurements were.28 Finally, we calculated the smallest detectable change (SDC) as 1.96×√2×SEM,28 which is the smallest change that can be considered a ‘real’ change, beyond measurement error.
Spearman correlations were calculated between the KAP domain scores and other similar and dissimilar questionnaire scores. Similar questionnaires were the IPA (five domains)29 and the domains Role-Physical and Role-Emotional of the RAND-36 Item Health Survey (RAND-36) that all intend to measure participation.11 Dissimilar questionnaires were the domain Physical Functioning of the RAND-36,30 the KATZ index of Independence in Activities of Daily Living index (ADL)31 and the Lawton Instrumental Activities of Daily Living index (IADL)31 that intend to measure related constructs (activity limitations), but not participation. Hypotheses were formulated based on three general assumptions. First, correlations between KAP domains and similar questionnaires should be >0.50. Second, correlations between KAP domains and dissimilar questionnaires should be lower (ie, 0.30–0.50). Third, as KAPd1 and KAPd2 are supposed to measure different aspects of participation, correlations including the KAPd1 and KAPd2 should differ by a minimum of 0.10. Based on these assumptions, we formulated the following hypotheses: (i–xiv) correlations between each KAP domain and the five IPA domains and the Role-Physical and Role-Emotional domains of the RAND-36 should be >0.50; (xv–xx) correlations between each KAP domain and ADL, IADL and the domain Physical Functioning of the RAND-36 should be between 0.30 and 0.50; (xxi-xxvi) KAPd1 should show higher correlations with ADL, IADL, the IPA domains ‘autonomy indoors’, ‘family role’ and ‘social life and relationships’ and the domain Physical Functioning of the RAND-36, compared with KAPd2 (≥0.10 difference); and (xxvii–xxx) KAPd2 should show higher correlations with the IPA domains ‘autonomy outdoors’ and ‘work and education’ and the domains Role-Physical and Role-Emotional of the RAND-36, compared with KAPd1 (≥0.10 difference). Construct validity was considered high when 75% of the formulated hypotheses are in agreement, moderate when 50–75% are in agreement and low when less then 50% are in agreement with the results.32
Floor and ceiling effect
Floor and ceiling effects were assessed and considered present when more than 15% of the sample reported the worst or best possible score for each KAP domain.32
On the basis of the results of the measurement properties of KAPd2 (presented below), we decided to exclude this domain from further analysis. So only for KAPd1, we tested the same hypotheses as used for the construct validity, but now focused on the change scores between baseline and follow-up after 6 months. Since we had no baseline data on IPA domain scores, these hypotheses were omitted from responsiveness testing. Again, at least 75% of the hypotheses should be in agreement with the results.32 To have an external criterion to evaluate changes over time, we included a Global Rating Scale (GRS), which asked participants to score their change in participation in the past 6 months on a five-point scale (much better, better, stable, worse or much worse). The mean change score for KAPd1 was calculated for those who remained stable or deteriorated (primary focus of the cohort is on deterioration in participation). The receiver operating characteristic curve was plotted and the area under the curve (AUC) was calculated to test the ability of the KAPd1 to discriminate between stable and deteriorated participants according to the external criterion (GRS). The AUC was considered acceptable if >0.70.27 Except for the factor analysis, all above statistical analyses were performed in IBM SPSS, V.20.
For optimal comparison, gender and age matched complete data (n=404) were randomly selected from the larger UK cohort (n=1785), to test the cross-cultural validity. The R-software package lordif was used to assess Differential Item Functioning (DIF) with Ordinal Logistic Regression (OLR).33 This approach incorporates Item Response Theory (IRT) derived trait scores (level of participation restriction), rather than the observed KAP domain score (as in usual OLR), as the estimator of the ability to score an item. It uses iterative cycles of DIF detection and trait score estimation, in which trait scores are purified during the analysis, that is, items with DIF are excluded one by one from the trait score and analyses are reiterated, to correct for pseudo DIF (item does not really contain DIF) or concealed DIF (hidden DIF in an item). In our study, the dependent variable was the item response (0–4) and the independent variable was the IRT derived trait score, that is, level of participation (restriction) on KAPd1. Then, DIF was detected by comparing three OLR models. In a base model (model 1), only the trait score was used to predict the item response. In a second model (model 2), both trait score and country (the Netherland (NL) vs UK) predicted the item response (DIF). In a third model (model 3), an interaction term was included between the trait score and country. This model tested whether DIF had either consistent impact across levels of participation (uniform DIF) or DIF that varied by levels of participation (non-uniform DIF). If the difference between models 1 and 3 showed an R2 difference >0.02, then the item was considered to show DIF.33 If the difference between models 2 and 3 showed an R2 difference >0.02, then the item was considered to show non-uniform DIF. We produced the item characteristic curve of the DIF items for both countries to examine the direction of the difference. Finally, we plotted the test characteristic curve (TCC) to provide insight into the actual impact of DIF items on the total score.
The baseline characteristics of the Dutch and UK samples are presented in table 1. In the Dutch sample, there was no non-response at baseline, because we visited all participants at home and were able to check the questionnaires. During the 6 months follow-up period, 37 participants dropped out (9.1%), because of physical/mental deterioration (40.5%), death (2.7%), motivational problems (43.2%) or other reasons (13.5%). The baseline characteristics were not significantly different between the baseline sample (n=407) and the follow-up sample (n=364). The same was the case for the test–retest sample (n=122; table 1).
EFA identified two underlying domains in the 11 KAP items. A two-factor model showed substantially better model fits (CFI 0.977, TLI 0.963, RMSEA 0.054), compared with a one-factor model (CFI 0.902, TLI 0.878, RMSEA 0.098). Items 1, 2, 3, 4, 5 and 7 had high factor loadings on KAPd1, which we labelled ‘participation in basic activities’. Items 9, 10 and 11 had high factor loadings on KAPd2, which we labelled ‘participation in complex activities’ (table 2). Items 6 and 8 had low factor loadings on both domains and were excluded from further analyses.
Cronbach's α for KAPd1 ‘participation in basic activities’ and KAPd2 ‘participation in complex activities’ were 0.74 and 0.57, respectively.
Within a time period of 2 weeks, the mean scores on KAPd1 indicated some deterioration, whereas the scores on KAPd2 suggested a small improvement in participation. The KAP domains showed a ICCagreement of 0.63 (95% CI 0.49 to 0.73), limits of agreement from –4.09 to 5.59, a SEM of 1.75 and SDC of 4.8 for KAPd1 (scale score of 0–24) and a ICCagreement of 0.57 (95% CI 0.44 to 0.68), limits of agreement from –5.75 to 5.40, a SEM of 2.02 and SDC of 5.6 for KAPd2 (scale score of 0–12).
The correlations between the two KAP domains and the three RAND-36 domains, five IPA domains and (I)ADL index are presented in table 3. In total, 43% of the hypotheses were confirmed. The highest correlation was found between KAPd1 and IPA domain ‘autonomy outdoors’ (0.63).
Floor and ceiling effect
Both KAP domains showed ceiling effects: the best possible score was obtained by 28% and 33% of the participants on KAPd1 and KAPd2, respectively. There were no floor effects (indicating worse possible score) present.
The results were not in agreement with the hypotheses (table 3, right column). Based on the scores on the GRS of perceived change in participation after 6 months (n=363), 274 participants remained stable (75%) and 58 participants reported deterioration in participation (worse/much worse) (16%). The results indicate that the deteriorated group also had higher mean change scores on KAPd1: mean change score 1.41 (SD 4.25). The AUC for discrimination between stable and deteriorated participants was 0.62 (95% CI 0.53 to 0.72).
In the Dutch sample, there were only three participants with incomplete KAP data at baseline. However, the UK sample had incomplete data in 124 participants (6.9%). Compared with the group with complete UK data (n=1661), the group with incomplete data (n=124) was significantly younger and more likely to live alone, but were no different for gender, number of chronic diseases, number of joint pain sites and level of physical functioning (data not shown). For optimal comparison, we matched the Dutch and UK sample based on age (four categories) and gender, which provided a final sample of 404 Dutch and 404 UK participants. The proportion of women was 62%. The proportion of participants in the four age categories was as follow: 15.6% between 65 and 70 years, 25.5% between 70 and 75 years, 26.5% between 75 and 80 years and 32.4% ≥80 years. The distribution of the trait scores of both countries showed that, in general, the Dutch participants scored better on KAPd1 (less restrictions), compared with the UK participants. The lordif method detected one item with DIF, that is, item 7 ‘interpersonal relations’. The R2 difference between models 1 and 3 was 0.0381, which indicated overall DIF. As the R2 difference between models 2 and 3 was 0.0001, item 7 showed no non-uniform DIF, which indicated only the presence of uniform DIF (figure 2). The item characteristic curve showed that the Dutch participants scored easier on this item, thus higher values, especially between the categories 0–1 and 1–2 (figure 2). This indicates that the Dutch participants had a higher chance to score restrictions on this item, given a similar trait level. Although item 7 showed uniform DIF, this DIF only had limited impact on the total domain score. Figure 3 displays the TCC and indicates no substantial impact of DIF in item 7 on the total domain score, because of the similar slopes and overlap in scorings between the two samples.
In this study, we explored the measurement properties of the Dutch version of the KAP in a sample of older adults with joint pain and comorbidity. We found evidence for two underlying constructs in the 11-item KAP questionnaire, that is, KAPd1 ‘participation in basic activities’ (based on six items) and KAPd2 ‘participation in complex activities’ (based on three items). KAPd1 showed good internal consistency and sufficient reliability. However, the construct validity of this domain was less than expected and suggests that further research is required to better understand what the domain actually measures. To make accurate recommendations about its use in longitudinal studies, KAPd1 also needs more testing on responsiveness, because of some limitations in our study sample (see below). Overall, KAPd1 seemed comparable between the Dutch and UK sample. KAPd2 lacks sufficient measurement properties for further use. Each item not included in KAPd1 should be considered as a distinct form of participation.
Although previous studies have treated the KAP as a unidimensional construct, the current study revealed two underlying constructs in the 11-item KAP. The first domain is considered as measuring ‘participation in basic activities’, as it includes mobility inside/outside the home, self-care, looking after the home/belongings and interpersonal relations. These are activities that take place on a more personal level, within someone's own environment. In contrast, the second domain is interpreted as corresponding to ‘participation in complex activities’, as these activities have a more complex nature, can involve greater interaction with contextual (environmental and personal) factors and so link more with one's ability to participate in the community.
For our prospective cohort study, we wanted to measure participation in line with the construct proposed in the ICF. This construct is slightly ambiguous and lacks the clarity required to facilitate the development of instruments that can be considered to measure the construct participation. As a result, there are several instruments which intend to measure participation but have used different operationalisations, resulting in dissimilar items and different qualifiers, such as capacity, participation accomplishment, participation problems and satisfaction with participation.11 While all KAP items measure person-perceived performance (satisfaction with participation), only 33% of the IPA items measure satisfaction with participation.11 The Role-Physical and Role-Emotional domains of the RAND-36 also measured various aspects of participation.11 This presents a challenge when examining construct validity, as no two instruments are completely identical. This diversity may explain to some extent why the construct validity in this study was poorer than expected. It also hampers an assessment of content validity of participation instruments. Without a clear definition of participation it is hard to evaluate whether the KAP items comprehensively measure the construct of participation. Further conceptual development of the construct ‘participation’ may help the development and validation of instruments to measure participation.
Our results provided evidence for two domains within the 11-item KAP questionnaire, with adequate internal consistency for KAPd1, but not for KAPd2. This suggests that there may be more constructs of participation captured within the three items of KAPd2 and that a number of participation areas may contribute to a more general paradigm. Based on this, high correlations between items may not be possible and explains why the consistency for KAPd2 was low. It is questionable whether participation in work, social activities and education address similar areas of participation; participation will involve different individual capabilities and environmental factors. Previous psychometric work suggests that these items can be used to measure individual domains of participation at single time points.21 One may even question whether ‘participation’ can be considered a real construct in the strict psychometric sense. Maybe the items better reflect a formative model, that is, the items together make up the construct, rather than a reflective model, that is, each item being an indicator of the underlying construct.34
The level of responsiveness of both KAP domains was low, which raises questions about its application in longitudinal studies. Earlier studies cited ceiling effects in other participation instruments35 and clarified that the poor responsiveness was due to the small proportion of participants who actually changed in level of participation over time. Even in the presence of poor health and activity limitations, participation can still be unaffected because of other ways of compensating.9 Therefore, greater knowledge about the natural history and course of participation would aid the assessment of responsiveness. In our sample, almost 75% showed no change in participation on the anchor question for change in participation. The interval period was short (6 months) and a longer period may be necessary to find significant changes.
DIF analysis revealed DIF in only item 7 ‘interpersonal relations’. This means that, assuming similar levels of participation, the Dutch sample scored worse on interpersonal relations, compared with the UK sample. We can speculate about possible explanations for this DIF. Despite the similar selection criteria applied within the two studies (based on age, joint pain and chronic diseases) the UK participants were more often men, younger and scored worse on various physical outcomes, such as number of chronic conditions, physical functioning, anxiety and depression. We tried to minimise the most important differences, by matching the samples on age and gender. The higher level of multimorbidity in the UK sample was most probably due to different registration systems in the general practices between the two countries. Whereas the Dutch study selected participants from the electronic medical files of general practices based on International Classification of Primary Care codes that present only chronic diseases, the NorStOP study selected participants based on Read codes, which contain not only chronic conditions, but also more symptomatic complaints. While the UK participants had more diseases, these diseases were most likely not all chronic. We indicate that this normalised some of the differences in multimorbidity between the two samples. Unfortunately, we were not able to correct for other physical differences. Probably, more interesting is the diversity in research areas between the two studies. Many studies showed the devastating effects of poor health, low socioeconomic status, losing a partner and living in urbanised areas on social isolation (loneliness), the scope of social networks and subsequently the level of interpersonal interaction.36 ,37 As the Dutch participants lived in more urbanised areas, this may provide a better explanation for the found DIF. Unfortunately, we were not able to disentangle other factors that could influence social interaction, such as size of social network, housing condition, financial status and population composition in the community.37 There is scope for further research around the cross-cultural differences in interpersonal relations (social integration) in specific, and levels of participation in general between the Netherland and UK.
In conclusion, the KAP domain ‘participation in basic activities’ showed good internal consistency and sufficient reliability and was comparable between the Dutch and UK sample of older adults with joint pain and comorbidity. Further testing is needed on the construct validity and responsiveness, but without a clearer definition of the construct participation, this remains challenging. The KAPd2 domain lacks sufficient measurement properties for application in studies, although items may be used as single items.
Review history and Supplementary material
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Contributors LAHH participated in study design, translation, data collection, data analysis, interpretation and manuscript preparation. CBT, SSL and RW participated in the study design, interpretation, manuscript preparations and discussed all versions of the manuscript. BvdZ participated in the translation, pilot study and was involved in the data analysis. HEvdH, JD, BvdZ and MS revised and approved two manuscript versions. All authors revised and approved the final version of the manuscript.
Funding Netherlands Organisation for Health Research and Development ZonMw; grant number: 313080301; The NorStOP study was funded by the Medical Research Council and by the North Staffordshire Primary Care R&D Consortium.
Competing interests None.
Patient consent Obtained.
Ethics approval A local Medical Ethics Committee approved both studies and participants have given written informed consent.
Provenance and peer review Not commissioned; externally peer reviewed.
Data sharing statement No additional data are available.
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