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Development and assessment of a home environment checklist to evaluate mismatch between patients’ ability and home environment
Journal Pre-proof Development and assessment of a home environment checklist to evaluate mismatch between patients’ ability and home environment Masahiko Mukaino Birgit Prodinger Yuki Okouchi Kouji Mizutani Yuki Senju Megumi Suzuki Eiichi Saitoh
PII:
S1877-0657(19)30145-9
DOI:
https://doi.org/doi:10.1016/j.rehab.2019.09.004
Reference:
REHAB 1316
To appear in:
Annals of Physical and Rehabilitation Medicine
Received Date:
2 March 2019
Accepted Date:
12 September 2019
Please cite this article as: Mukaino M, Prodinger B, Okouchi Y, Mizutani K, Senju Y, Suzuki M, Saitoh E, Development and assessment of a home environment checklist to evaluate mismatch between patients’ ability and home environment, Annals of Physical and Rehabilitation Medicine (2019), doi: https://doi.org/10.1016/j.rehab.2019.09.004
This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier.
Development and assessment of a home environment checklist to evaluate mismatch between patients’ ability and home environment Masahiko Mukainoa, Birgit Prodingerb,c, Yuki Okouchid, Kouji Mizutanid, Yuki Senjue, Megumi Suzukif, Eiichi Saitoha a
Department of Rehabilitation Medicine I, School of Medicine, Fujita Health
University, Toyoake, Japan Faculty of Applied Health and Social Sciences, Technical University of Applied
Sciences, Rosenheim, Germany Swiss Paraplegic Research, Nottwil, Switzerland
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c
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b
Department of Rehabilitation, Fujita Health University Hospital, Toyoake, Japan
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Department of Rehabilitation, Fujita Health University Nanakuri Memorial Hospital,
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Tsu, Japan f
Faculty of Rehabilitation, School of Health Sciences, Fujita Health University,
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Toyoake, Japan
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Authors’ e-mail addresses
Masahiko Mukaino: [email protected] Birgit Prodinger: [email protected] Yuki Okouchi: [email protected] Kouji Mizutani: [email protected] Yuki Senju: [email protected] Megumi Suzuki: [email protected] Eiichi Saitoh: [email protected]
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Corresponding author: Masahiko Mukaino Department of Rehabilitation Medicine I, School of Medicine, Fujita Health University 1-98 Dengakugakubo, Kutsukake, Toyoake, Aichi 470-1192, Japan Tel: +81-562-93-2167
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Fax: +81-562-95-2906 E-mail: [email protected]
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between patients’ ability and home environment
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Development and assessment of a home environment checklist to evaluate mismatch
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Abstract
Background. Modification of the home environment, together with rehabilitative
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interventions, is important for maximizing the level of functioning after an individual with disability undergoes rehabilitation in the hospital.
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Objectives. We developed a simple screening scale — the home environment checklist (HEC) — to identify any mismatch between an individual’s abilities and their home environment to help clinicians monitor the appropriateness of the home environment to which individuals with disability will be discharged. We also examined the psychometric properties of the HEC. Methods. The HEC was developed by a multidisciplinary panel of rehabilitation experts using information routinely collected in rehabilitation clinics before discharge. The reliability of the checklist was assessed in 60 individuals undergoing rehabilitation. The inter-rater agreement and internal consistency of the scale were assessed by weighted kappa statistics and
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Cronbach’s alpha, respectively. Rasch analysis was performed with 244 rehabilitation individuals to evaluate the internal construct validity, and the known-groups validity was confirmed by a comparison of the daily activity levels of 30 individuals with disabilities under rehabilitation to the HEC score. Results. The HEC was developed as a simple, 10-item checklist. The weighted kappa statistics ranged from 0.73 to 0.93, indicating excellent inter-rater reliability. Cronbach’s alpha was 0.92,
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indicating high internal consistency. Rasch analysis with a testlet approach on 3 subscales demonstrated a good fit with the Rasch model (χ2 = 13.2, p = 0.153), and the demonstrated unidimensionality and absence of differential item functioning supported the internal
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construct validity of the HEC. HEC scores were significantly different (p < .01) among
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individuals with disability and 3 levels of restrictions in their activities (no limitation, home-
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bound, and bed-bound), which demonstrates the known-groups validity of the HEC. Conclusions. The HEC has good reliability and validity, which supports its utility in
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rehabilitation clinics.
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Keywords: ICF; environmental factors; activities of daily living Introduction
The environment experienced by individuals with disability has been recognised as
an important factor in improving the functioning level and disability status, along with rehabilitative interventions[1]. The International Classification of Functioning, Disability and Health (ICF) endorsed by the WHO in 2001 offers a conceptual framework to understand functioning and disability [1][2]. In this framework, the health condition (disease or disorder) might affect functioning at the 3 mutually interacting levels of body functions/structures, activities and participation, and these should be considered to closely interact with contextual
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factors, including environmental and personal factors. One of the important features of this framework is the inclusion of contextual factors comprising environmental and personal factors. Environmental factors can facilitate or hinder body functions, the execution of activities and societal participation[2]. The evaluation of environmental factors, in addition to the physical and mental abilities of the individual, is necessary to understand the level of functioning [2-5].
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With regard to the development of scales for environmental factors, the fundamental problem is that environmental factors differ depending on the level of physical activity[6]. There are great differences in physical activity levels of individuals and the required
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environments to conduct daily activities. For example, an ankle–foot orthosis would facilitate
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activity in an individual with foot drop due to peroneal nerve palsy. However, an orthosis would not facilitate activity in an individual with complete paraplegia after a spinal cord injury
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who uses a wheelchair.
One of the possible solutions is scoring the environment by investigating the
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perception of the individuals with disability about their environment. For example, the
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appropriateness of the home environment could be scored by asking whether the individuals feel any difficulty in their home environment. Numerous studies have developed scales to evaluate environmental factors using this model, evidencing excellent reliability and validity for quantifying environmental factors [7-13]. However, applying this self-reporting model to individuals with cognitive disorders associated with diseases, such as stroke, brain tumours and Alzheimer’s disease, is difficult. In many of these cases, environmental modifications are required for individuals with disability to safely return home from the hospital. For example, obtaining a prescribed orthosis, modifying the home environment or securing personal support should be considered. If these environmental factors are appropriately modified to
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match the abilities of individuals with disability, they will strongly support a discharge home and help in maintaining physical and mental abilities [14-16]. For individuals to whom the self-reporting model is not applicable, evaluating a mismatch between their abilities (as assessed by clinicians) and the home environment (which could be assessed by an interview with family members) could be another option. A previous report revealed that person–environment fit was related to activities of daily living (ADL) and
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dependence in community-dwelling older people [17-19]. This scenario might be important for individuals with severe movement disorders who can only perform daily activities in a specific environment. For example, an individual with severe hemiparesis may need an orthosis and a
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cane for walking, a handrail for toileting and family assistance for bathing. If all these
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environments are prepared at home, individuals with disability will be able to walk, go to the toilet and bathe at home. Such information is important for setting goals and planning
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rehabilitation, including the concrete targeting of rehabilitation and planning of environmental modification.
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In the development of scales, the clinical feasibility of the scale should be
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considered. The methodological complexity, length of administration and cost may render the scales infeasible for many clinicians to use in their daily work [20]. Although the evaluation of environmental factors is an extremely important part of daily rehabilitation practice, the modification of environmental factors is not the primary target of intervention for the clinicians; therefore, time-consuming scales for evaluating only environmental factors may not be appreciated in clinical practice. In addition to being reliable and valid, the simplicity of the scales may also be an important aspect that should be considered in developing a scale to measure environmental factors [21].
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With these considerations, we developed a home environment checklist (HEC) that can be used to identify the mismatch between the functional ability of individuals with disability and the home environment. Considering the clinical feasibility of the checklist, the HEC was developed as an experience-based simple screening tool. The present study aimed to describe the development of our checklist and examine its psychometric properties. Materials and Methods
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Development of HEC For identifying information on the home environment that is empirically considered
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important in rehabilitation clinics, the content of a document form called the “Comprehensive Rehabilitation Implementation Plan”, which is commonly used in rehabilitation clinics in Japan,
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was used as a base [22]. This form was developed according to the ICF concepts and clinical
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perspectives by the Japanese Ministry of Health, Labour and Welfare as an official rehabilitation planning form and is mainly used for planning rehabilitation in the hospital to
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return home and providing relevant information to individuals with disability and their families. The form requires that health professionals provide detailed descriptions of body
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functions/structures, activities, participation and environmental factors of the individuals and to mention concrete plans for the problems raised. Two independent researchers have linked the items in this form to the ICF according to previously reported linking rules [23-25]. A multidisciplinary panel comprising 2 medical doctors, 2 physical therapists and 2 occupational therapists developed the checklist according to the identified categories. The process was as follows. Initially, among the linked categories, those directly related to the home environment (necessary for ADL) were identified. Subsequently, the appropriateness of the identified categories for the checklist and the need for additions were discussed. Finally, after identifying the categories, questions were developed to collect data related to each of the categories.
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The checklist was originally developed in Japanese and was translated in English as follows: first, 3 researchers independently translated each item of the checklist. Second, the researchers voted on the translation that seemed the most accurate, and the translation with ≥2 votes was used. This process was done for each item. The translation of the items selected in the session was checked by the researchers in terms of consistency. Finally, the whole checklist was checked by a native English speaker.
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Participants Non-probability sampling procedures were used to recruit a convenience sample of
hospital participated in the reliability study.
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participants. A total of 60 individuals undergoing a rehabilitation programme at the university
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The inclusion criteria were as follows: 1) intention to return home after treatment,
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2) family members in contact with clinicians to provide data regarding the home environment and 3) age >20 years. Of the 60 participants, 36 (60%) were males; the mean (SD) age was 70.2
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(15.3) years. Additionally, 22 (37%) had neurological diseases, 21 (35%) had respiratory diseases, 8 (13%) had orthopaedic diseases and 9 (15%) had other diseases (renal, gastric and
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collagen diseases).
Non-probability sampling procedures were used to recruit a convenience sample of
250 individuals with disability undergoing a rehabilitation programme at 9 hospitals for the internal construct validity study involving Rasch analysis. The data for the reliability study were also included in the validity study. Among 2 scores for each participant by 2 raters in the reliability study, the first score related to timing was used. The inclusion criteria were the same as for the reliability study. Six individuals were excluded because of missing data. Of the remaining 244 participants, 124 (51%) were males and the mean age was 68.7 (15.4) years.
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Additionally, 171 (70%) had neurological diseases, 51 (21%) had orthopaedic diseases, 12 (5%) had respiratory diseases and 10 (4%) had other diseases (renal, gastric, cardiovascular and collagen diseases). A total of 30 individuals with disability who lived at home and underwent a rehabilitation programme in the university hospital participated in a study to test knowngroups validity[26]. The inclusion criteria were 1) living at home and 2) the individual or family
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members in contact with clinicians to provide data regarding the home environment. Measurements
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The HEC comprised 10 questions asking about the mismatch between the abilities of individuals with disability and the home environment. The response options were 2
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(complete mismatch between the individual’s ability and home environment), 1 (partial
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mismatch between the individual’s ability and home environment) and 0 (no mismatch between the individual’s ability and home environment/no need for a specific environment).
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The following instructions were appended to the questions for rating: 1) rate the basis of environmental requirements according to the individual’s present ability; 2) note that “going
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out” implies an outdoor activity required for daily living (e.g., shopping and visiting an ambulatory care facility) and 3) note that the difference between “partial mismatch” and “complete mismatch” is based on whether the home environment matches the individual’s needs to perform activities at the desired time. Two kinds of scores were defined to integrate the score: a Mismatch score and a
Total score. The scores were defined as follows: 1. Total score = Sum of all the items 2. Mismatch score = Indoor sub-score + Outdoor sub-score
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a) Indoor sub-score = The sum of the scores in items 1, 2 and 3 + the smaller of the score in items 4 or 5 b) Outdoor sub-score = The sum of the scores in items 6, 7 and 8 + the smaller of the score in items 9 or 10 A Mismatch score of 0 means that the home environment is ready for the individual’s discharge. The Total score could be used to measure the extent of mismatch in the
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abilities of individuals with disability and the environment. The complete questionnaire is shown in the Appendix.
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In the reliability study, ratings on the HEC were provided by 3 independent raters who were not familiar with the HEC (a medical doctor, a physical therapist and an occupational
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therapist), with each participant rated by 2 of the 3 raters. In the validity study, ratings were
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provided by a medical doctor, physical therapist or occupational therapist who was in charge of the participant.
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Calculation of sample sizes
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The sample size for the reliability study was determined according to the number of response options (3), the minimum value for the kappa coefficient expected (0.4 for every item), power (90.0%) and alpha value (0.05). The calculated required sample size was a minimum of 39 participants [27]. The total sample size was determined such that the data for each pair of raters would be > 39. Each of the raters assessed 40 participants, and accordingly, evaluation pairs for 60 participants were obtained. For Rasch analysis, a larger sample is needed because a small sample size might result in a false signal misfit [28]. According to parameters shown by Linacre, a minimal sample of 108 to 243 individuals is needed for 99%
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confidence that the person estimates are within ±0.5 logits [29]. Accordingly, the sample size of 244 individuals we obtained in this study can be considered sufficient for Rasch analysis. Data analysis Reliability Kappa statistical analysis was used to determine inter-rater agreement between 2 raters. Weighted kappa statistics were calculated for each item of the HEC. The standards for
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interpreting kappa statistics were as follows: <0.2, poor; 0.2–0.4, fair; 0.4–0.6, moderate; 0.6– 0.8, substantial and >0.8, excellent [30]. The internal consistency of the HEC was assessed by
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Cronbach’s alpha and item–total correlations. According to Bland and Altman, a Cronbach’s alpha ≥0.7 can be considered sufficient for group comparisons and >0.9 sufficient for clinical
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application [31]. Item–total correlations were also computed.
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Validity
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Rasch analysis
Rasch analysis was performed to examine the internal construct validity of the HEC.
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It was analysed by using a partial credit model, which is considered a Rasch model for items for which the rating scale structure may vary across items, because in the present study, the testlet approach was used, aggregating the items into super-items. Rasch analysis is a probabilistic approach for estimating the difficulty of items and different levels of personal ability. Additionally, Rasch analysis can be used to assess the extent to which data satisfy psychometric assumptions, including scale unidimensionality and differential item functioning (DIF) across relevant group characteristics.
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The overall fit of the data in the Rasch model was examined by chi-square statistics. A non-significant χ2 (>0.05) was considered overall good fit. A testlet approach was used to accommodate high local dependency of the HEC items, which could cause a problem in the fit of the scale to the model [32]. The testlets were constructed by aggregating the items with high residual correlations into super-items, and the same iterative process of scale adjustment as a single-item design was applied. Principal component analysis (PCA) of standardised Rasch residuals was used to examine the unidimensionality of the scale [33]. Items were grouped
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according to the loading with the first principal component, and t-tests were used for analysis of each individual. Significant t-test values (i.e., the lower bound of the 95% confidence
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interval) <5% indicate unidimensionality.
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The absence of DIF is an important assumption in scale evaluation with the Rasch model. It indicates that an individual can achieve comparable levels of ability regardless of
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group characteristics, such as age and disease. DIF was investigated by ANOVA for sex (male and female), age groups (<65, 65–70, 70–75 and ≥75 years) and disease groups (neurological,
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musculoskeletal, respiratory and others).
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The ceiling and floor effects were analysed to measure the proportion of
participants with the best and worst possible score obtained, respectively. Ceiling or floor effects were assigned when >15 % of the responses attained the best or worst possible score, respectively.
Known-groups validity
Because of no similar scale that can be used to evaluate concurrent validity, knowngroups validity[26] was assessed by determining whether the HEC reflects the level of restriction in daily activities at home. Individuals with disability living at home were divided
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into the 3 groups according to the extent of restriction in performing daily activities: bedbound (restriction for indoor daily activities), home-bound (no restriction for indoor activities, but restriction for outdoor activities) and no limitation (no restriction for both indoor and outdoor activities). The known-groups validity was evaluated by comparing the HEC scores for each group. One-way ANOVA was used for analysis of Mismatch scores and Total scores of the HEC. When a significant main effect was found, post-hoc Tukey tests were performed.
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Analyses involved using JMP 11 (SAS Inst. Inc., Cary, NC, USA) and RUMM2030 (RUMM Laboratory, Perth, Australia). This study was approved by our institutional medical
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ethics committee (Fujita Health University, protocol #HM18-020). Results
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Development of the HEC
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On linking the Comprehensive Rehabilitation Implementation Plan with the ICF categories, we identified 56 categories, including 5 environmental factors (e115, products and
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technology for personal use in daily living; e120, products and technology for personal indoor
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and outdoor mobility and transportation; e155, design construction and building products and technology of buildings for private use; e310, immediate family; and e575, general social support services, systems and policies). The Comprehensive Rehabilitation Implementation Plan is mainly used for planning rehabilitation in the hospital to return home, and the categories are generally considered important at discharge. According to multidisciplinary panel discussions, all 5 categories were used as target categories, with no category added. For each of the categories, 2 questions were developed. The questions asked about environmental factors for in-home activities (e.g., toileting, eating and bathing) and for access to outside
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activities, such as shopping and visiting ambulatory care facility. Accordingly, the final version of the HEC comprised 10 questions and 3 response options (0–2) for each question (Appendix). Questions 1–3 and 6–8 refer to materials and questions 4, 5 and 9, 10 to human support. For discharge from the hospital, the entire material environment should match the ability of individuals with disability; conversely, human support could be provided from families or public/private services. The Mismatch score was defined to describe whether 1) all
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materials match the individual’s ability and 2) any human support (that matches the individual’s ability) is to be provided. Accordingly, the Mismatch score was set so that score 0
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represents the ready state of the home environment for discharge.
The Total score includes questions on both family support and the use of
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public/private services, thus reflecting the information on whether the requirements for
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discharge are satisfied with only family support or whether additional public/private service support is needed. Therefore, the Total score was considered to cover a wider level of individuals than the Mismatch score. The instructions for scoring and the formulae for
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Reliability
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calculating the Total and Mismatch scores are in the Methods section.
Table 1 shows weighted kappa statistics for inter-rater agreement for individual
items of the HEC. The weighted kappa statistics for each of the 10 items ranged from 0.73 to 0.93. The results of the internal consistency of the scale are in Table 2. The Cronbach’s alpha was 0.92. The item–total correlation ranged from 0.61 to 0.87. Removal of any one item did not significantly improve the internal consistency of the total scale as compared with the Cronbach’s alpha for the complete HEC.
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Validity We found no significant ceiling effects for both the Mismatch and Total scores. However, we found a significant floor effect for the Mismatch score (20.5%). The floor effect for the Total score was approximately borderline (14.8%). Thus, the investigation of validity by Rasch analysis was performed with the Total score. The initial Rasch analysis findings did not fit the assumptions of the Rasch model. A
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disordered threshold was observed for items 1 and 6 for both indoor and outdoor activities (Is there any mismatch between the personal assistive devices of the individual [orthosis,
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prosthesis, self-help devices etc.] and his/her ability?) (Supplemental table 1). For these 2 items, we observed a relatively low frequency for score 1. The average person ability was
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consistent with the rating structure. The residual correlations indicated a robust local
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dependency between items within the same ICF chapter (e1, products and technology; e3, support and relationships; and e5, services, systems and policies). Therefore, we attempted a testlet approach by grouping items into 3 groups by ICF chapter (subscales for e1, e3 and e5).
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With this strategy, we revealed good model fit for the HEC, which was supported by a non-
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significant χ2 (13.2, p = 0.153). The proportion of significant pairwise comparisons in the PCA of Rasch residuals was <5%, which supports the unidimensionality of the scale (Table 3). Figure 1 shows the distributions of person (top half of the graph) and item
thresholds (bottom half of the graph) for the total HEC score. We did not observe any DIF related to sex, disease or age.
The HEC scores for home-living individuals who received rehabilitation are shown in Figures 2 and 3. HEC differentiated the no-limitation, home-bound, and bed-bound groups. The distribution of scores in these groups were as follows (Figure 2): no-limitation group, 0 (n =
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11, mean 0); home-bound group, 2-4 (n = 13, mean [SD] 2.9 [0.9]) and bed-bound group, 5–9 (n = 6, mean 6.3 [1.5]). The mismatch scores for individuals in these groups were as follows (Figure 3): no-limitation group, 0 (n = 11, mean 0); home-bound group, 1–3 (n = 13, mean 1.6 [0.8]) and bed-bound group, 2–5 (n = 6, mean 3.8 [1.2]). The indoor sub-scores of mismatch scores were as follows: no-limitation and home-bound groups, 0 (mean 0) and bed-bound group, 1–2 (mean 1.7 [0.5]). We found a significant difference among groups in total and mismatch scores (P < 0.01). On Tukey post-hoc tests, the differences in Total scores and
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Mismatch scores between the 3 groups were all significant (Figures 2 and 3).
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Discussion
In the present study, we describe the development of the HEC and assessed its
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reliability and validity. The HEC was established as a 10-item checklist that considered 5 ICF
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categories. The findings largely supported the reliability and validity of HEC. HEC was developed as a simple screening tool; thus, the number of questions was
low as compared with existing scales for environmental factors [7-11]. In developing clinical tools, there is a conflict between comprehensiveness and clinical feasibility[34]. The scope of the evaluation of environmental factors can be extensively broad if an attempt is made to evaluate all aspects of the environment to which an individual potentially has access [10]. One of the major approaches previous studies used was to comprehensively evaluate the environment by asking about individuals’ perception of the environment [7-13]. This approach would make sense in assessing the environment of the individuals living in the community,
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such as individuals with spinal cord injury who use a wheelchair and are working. However, the self-report model or interview model used in most previous studies may not fit well in assessing individuals with cognitive disorders who are among the individuals who require an assessment of the environment. To realistically develop a screening tool for use in clinical settings, we used a pragmatic approach involving discussion among clinicians who are directly engaged in the
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environmental setting of individuals who receive rehabilitation with regard to routinely collected information in an empirically established system for bridging the gap between the individual’s situation and the home environment. All 5 categories selected have been included
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in a minimal set of environmental factors developed with regression methods and
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international expert consultation [35], which might support their appropriateness. Our simple screening tool could be used in rehabilitation to indicate areas of mismatch between the
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individual’s ability and home environment, thus indicating requirements for discharge and to aid in goal-setting for the individual with the disability. For example, if the desire of an
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individual and the family is to return home, this screening tool can indicate a gap between the
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present situation and the minimal attributes required for discharge. The HEC is scored by the gap between the individuals’ situation that the clinician
evaluates and the information about the home environment that could be indirectly collected from families; thus, the HEC could be used for individuals with cognitive disorders. Clinicians can then make a pragmatic plan to bridge the gap by improving the ADL ability of the individuals or providing advice on preparing an environment that is friendly to individuals with disability. The HEC allows for easily monitoring the problems and provides insights into the issues that need to be resolved before discharge to home. In addition, the HEC could be used to monitor the home environment of individuals who receive long-term care at home. Here,
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the HEC could be used to determine whether the environment supports indoor daily activities and access to outdoor activities, and if the environment does not provide such support, the tool can indicate the missing aspects. The reliability of HEC appears to be acceptable for clinical use. The weighted kappa statistics for all items were > 0.6, considered good inter-rater reliability, and for 8 of the 10 items, the kappa was > 0.8, considered excellent reliability [30]. The internal consistency of the
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HEC was confirmed by Cronbach’s alpha, so the tool is acceptable for clinical use [31]. We investigated the construct validity of the HEC. Rasch analysis supports the
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evaluation of the internal construct validity of a scale by assessing construct-irrelevant variance and construct underrepresentation [36]. The existence of construct-irrelevant
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variance indicates the presence of sub-dimensions irrelevant to the focal construct of the
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scale, which can be shown as item misfit and DIF [36]. In the present study, the Rasch analysis initially showed that the scale did not meet the criteria required by the Rasch model. We observed a disordered threshold for items 1 and 6 and strong local dependency between
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chapter groups. Thus, we used a testlet approach according to the chapter structure of the ICF,
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which revealed excellent fit to the Rasch model and unidimensionality of the scale. Although the common solution for a disordered threshold is to collapse the response options, this solution did not significantly improve the fit statistics but lowered the reliability indices in our analysis (data not shown). The disordered threshold may reflect the inconsistency between the order of the response options and the order of the person’s ability, but it was also affected by other factors such as strong local dependency and the low frequency of the category [37, 38], both of which were also observed in the present study. Thus, the disordered threshold we found may not necessarily indicate a significant problem in the rating structure. In fact, the mean person ability in each response option was properly ordered for both items 1 and 6. In
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addition, the response option “1” aims to show partial mismatch, which may be clinically important for individuals who need multiple devices in a home environment. On the basis of these considerations, we kept the original structure and used the testlet approach without collapsing the response options to improve the fit to the Rasch model and found no DIF with regard to age, sex or disease. These results negate the existence of construct-irrelevant variance in HEC.
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However, the scale might have some issues in terms of construct underrepresentation. Construct underrepresentation indicates the imperfectness of tests for assessing all features of the construct. The distribution of the item threshold was well
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balanced along the continuum; however, the range of distribution appeared slightly narrower
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than that for the person location (Figure 1), which indicates some degree of construct underrepresentation of the scale. The floor effect was borderline (14.8%), which may also
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indicate the limitation of the HEC as an assessment scale perhaps because it is structured as a screening scale with limited target of evaluation. One common solution to solve this kind of
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targeting problem is to add items of various difficulties. Thus, adding more difficult items may
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be a solution. However, the score of 0 in the HEC total score indicates that the indoor and outdoor environment completely matches the individual’s ability without the assistance of public/private health care services. Developing more difficult items may not be realistic because the original purpose of HEC was to assess the extent of preparation of the home environment.
Considering the aim of the HEC, whether this scale is consistent with the actual activity level of individuals in their home could be investigated. As an additional validity study, we evaluated 30 individuals with disability who were living at home to confirm whether the HEC could differentiate individuals who have restrictions in performing indoor daily activities
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or limited access to outdoor activities from those who do not have such restrictions in daily activities. We found that the HEC scores identified the 3 levels of restriction in activities (no limitation, home-bound and bed-bound), thereby evidencing the known-groups validity. These results support the feasibility of the scale for evaluating the appropriateness of the environment according to the abilities of individuals with disability.
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Study limitations The scope evaluating the HEC is limited to 5 ICF categories. The HEC has fewer
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categories than other comprehensive environmental factor scales [8-12]. The evaluation covered only the home environment. The HEC has a clear purpose of evaluating the mismatch
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between the disabled individual’s ability and the home environment to determine the actual
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problem in considering discharge of individuals with disability and to facilitate goal-oriented interventions. Therefore, the HEC should be limited to this purpose. Comprehensive
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understanding of environmental barriers/facilitators and their measurements, as mentioned in
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previous studies [39, 40], are also necessary. The HEC was not developed according to an established scale development process
[41]. However, considering the extensive clinical utility of such a scale, this pragmatic approach to developing a clinical scale by investigating an empirically developed and accepted system could be another option, at least for developing simple screening tools that can be used in real clinical settings.
The HEC is designed to be rated by clinicians based on their assessment of abilities of individuals with disability and information about the home environment. Thus, this scale could be used for individuals with cognitive disorders by collecting information on the home
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environment from family members. However, the HEC does not have detailed instructions for assessment, and the information from the family may be limited because of insufficient understanding of the disability by family members or the clinicians’ way of asking. This tool should be used to identify areas in the home environment to facilitate further detailed evaluation of the home environment. We used non-probability sampling procedures. Although data collection from
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multiple facilities (9 hospitals) might have reduced the bias, the results of Rasch analysis could still be biased because of the distribution of the samples. In addition, the sample size might not be large enough to accurately investigate DIF [42]. Another study with larger samples with
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random sampling is encouraged for more accurate evaluation of indices such as item difficulty
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and DIF.
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According to the COnsensus-based Standards for the selection of health Measurement INstruments (COSMIN) Risk of Bias checklist, the methodological quality of presenting the structural validity, construct validity and reliability of the HEC would be
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classified as adequate to very good [43]. However, we still lack data supporting several other
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psychometric properties of the scale, such as cross-cultural validity and responsiveness. Further research to investigate more detailed psychometric properties of the HEC is encouraged.
Conclusions
We found that the HEC had substantial-to-excellent inter-rater reliability and excellent internal consistency, and we confirmed the validity of the HEC. Our findings support the use of HEC as an easy and quick screening tool that could be used in a clinical setting to identify the areas in
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the home environment that individuals with disability potentially have problems with after discharge. It gives an overview of the mismatch between the individual’s ability and home environment for further planning the rehabilitation and discharge. In addition, this instrument can be useful in clinical work when prioritizing healthcare resources. Further investigating the clinical utility of the HEC in combination with functioning and environment assessment tools is
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encouraged.
Acknowledgements. The authors thank Dr. Carolina Saskia Fellinghauer for her kind advice on
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statistical procedures.
Funding. This work was supported by a Health Labour Sciences Research frant (H28-statistics-
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general-004) from The Japanese Ministry of Health, Labour and Welfare.
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Conflict of interest: None declared.
Figure legends
Figure 1. Person–item thresholds for home environment checklist (HEC). Shows the distribution of person ability estimates (red bars) and item thresholds (blue bars) based on Rasch analysis. The X-axis shows the location of the person and items, reflecting the person’s ability and item difficulty.
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Figure 2. Total scores for the home environment checklist (HEC) for 30 individuals living at home. Individuals 1–6 were classified as the bed-bound group (restriction for indoor daily activities), 7–19 the home-bound group (no restriction for indoor activities but restriction for outdoor activities) and 20–30 the no-limitation group (no restriction for both indoor and outdoor activities). *P < 0.01
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Figure 3. Mismatch scores for the HEC for 30 individuals living at home. Individuals 1–6 were the bed-bound group, 7–19 the home-bound group, and 20–30 the no-limitation group. *P <
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0.01
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PII:
S1877-0657(19)30145-9
DOI:
https://doi.org/doi:10.1016/j.rehab.2019.09.004
Reference:
REHAB 1316
To appear in:
Annals of Physical and Rehabilitation Medicine
Received Date:
2 March 2019
Accepted Date:
12 September 2019
Please cite this article as: Mukaino M, Prodinger B, Okouchi Y, Mizutani K, Senju Y, Suzuki M, Saitoh E, Development and assessment of a home environment checklist to evaluate mismatch between patients’ ability and home environment, Annals of Physical and Rehabilitation Medicine (2019), doi: https://doi.org/10.1016/j.rehab.2019.09.004
This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier.
Development and assessment of a home environment checklist to evaluate mismatch between patients’ ability and home environment Masahiko Mukainoa, Birgit Prodingerb,c, Yuki Okouchid, Kouji Mizutanid, Yuki Senjue, Megumi Suzukif, Eiichi Saitoha a
Department of Rehabilitation Medicine I, School of Medicine, Fujita Health
University, Toyoake, Japan Faculty of Applied Health and Social Sciences, Technical University of Applied
Sciences, Rosenheim, Germany Swiss Paraplegic Research, Nottwil, Switzerland
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c
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b
Department of Rehabilitation, Fujita Health University Hospital, Toyoake, Japan
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Department of Rehabilitation, Fujita Health University Nanakuri Memorial Hospital,
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Tsu, Japan f
Faculty of Rehabilitation, School of Health Sciences, Fujita Health University,
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Toyoake, Japan
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Authors’ e-mail addresses
Masahiko Mukaino: [email protected] Birgit Prodinger: [email protected] Yuki Okouchi: [email protected] Kouji Mizutani: [email protected] Yuki Senju: [email protected] Megumi Suzuki: [email protected] Eiichi Saitoh: [email protected]
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Corresponding author: Masahiko Mukaino Department of Rehabilitation Medicine I, School of Medicine, Fujita Health University 1-98 Dengakugakubo, Kutsukake, Toyoake, Aichi 470-1192, Japan Tel: +81-562-93-2167
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Fax: +81-562-95-2906 E-mail: [email protected]
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between patients’ ability and home environment
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Development and assessment of a home environment checklist to evaluate mismatch
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Abstract
Background. Modification of the home environment, together with rehabilitative
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interventions, is important for maximizing the level of functioning after an individual with disability undergoes rehabilitation in the hospital.
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Objectives. We developed a simple screening scale — the home environment checklist (HEC) — to identify any mismatch between an individual’s abilities and their home environment to help clinicians monitor the appropriateness of the home environment to which individuals with disability will be discharged. We also examined the psychometric properties of the HEC. Methods. The HEC was developed by a multidisciplinary panel of rehabilitation experts using information routinely collected in rehabilitation clinics before discharge. The reliability of the checklist was assessed in 60 individuals undergoing rehabilitation. The inter-rater agreement and internal consistency of the scale were assessed by weighted kappa statistics and
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Cronbach’s alpha, respectively. Rasch analysis was performed with 244 rehabilitation individuals to evaluate the internal construct validity, and the known-groups validity was confirmed by a comparison of the daily activity levels of 30 individuals with disabilities under rehabilitation to the HEC score. Results. The HEC was developed as a simple, 10-item checklist. The weighted kappa statistics ranged from 0.73 to 0.93, indicating excellent inter-rater reliability. Cronbach’s alpha was 0.92,
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indicating high internal consistency. Rasch analysis with a testlet approach on 3 subscales demonstrated a good fit with the Rasch model (χ2 = 13.2, p = 0.153), and the demonstrated unidimensionality and absence of differential item functioning supported the internal
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construct validity of the HEC. HEC scores were significantly different (p < .01) among
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individuals with disability and 3 levels of restrictions in their activities (no limitation, home-
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bound, and bed-bound), which demonstrates the known-groups validity of the HEC. Conclusions. The HEC has good reliability and validity, which supports its utility in
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rehabilitation clinics.
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Keywords: ICF; environmental factors; activities of daily living Introduction
The environment experienced by individuals with disability has been recognised as
an important factor in improving the functioning level and disability status, along with rehabilitative interventions[1]. The International Classification of Functioning, Disability and Health (ICF) endorsed by the WHO in 2001 offers a conceptual framework to understand functioning and disability [1][2]. In this framework, the health condition (disease or disorder) might affect functioning at the 3 mutually interacting levels of body functions/structures, activities and participation, and these should be considered to closely interact with contextual
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factors, including environmental and personal factors. One of the important features of this framework is the inclusion of contextual factors comprising environmental and personal factors. Environmental factors can facilitate or hinder body functions, the execution of activities and societal participation[2]. The evaluation of environmental factors, in addition to the physical and mental abilities of the individual, is necessary to understand the level of functioning [2-5].
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With regard to the development of scales for environmental factors, the fundamental problem is that environmental factors differ depending on the level of physical activity[6]. There are great differences in physical activity levels of individuals and the required
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environments to conduct daily activities. For example, an ankle–foot orthosis would facilitate
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activity in an individual with foot drop due to peroneal nerve palsy. However, an orthosis would not facilitate activity in an individual with complete paraplegia after a spinal cord injury
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who uses a wheelchair.
One of the possible solutions is scoring the environment by investigating the
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perception of the individuals with disability about their environment. For example, the
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appropriateness of the home environment could be scored by asking whether the individuals feel any difficulty in their home environment. Numerous studies have developed scales to evaluate environmental factors using this model, evidencing excellent reliability and validity for quantifying environmental factors [7-13]. However, applying this self-reporting model to individuals with cognitive disorders associated with diseases, such as stroke, brain tumours and Alzheimer’s disease, is difficult. In many of these cases, environmental modifications are required for individuals with disability to safely return home from the hospital. For example, obtaining a prescribed orthosis, modifying the home environment or securing personal support should be considered. If these environmental factors are appropriately modified to
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match the abilities of individuals with disability, they will strongly support a discharge home and help in maintaining physical and mental abilities [14-16]. For individuals to whom the self-reporting model is not applicable, evaluating a mismatch between their abilities (as assessed by clinicians) and the home environment (which could be assessed by an interview with family members) could be another option. A previous report revealed that person–environment fit was related to activities of daily living (ADL) and
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dependence in community-dwelling older people [17-19]. This scenario might be important for individuals with severe movement disorders who can only perform daily activities in a specific environment. For example, an individual with severe hemiparesis may need an orthosis and a
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cane for walking, a handrail for toileting and family assistance for bathing. If all these
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environments are prepared at home, individuals with disability will be able to walk, go to the toilet and bathe at home. Such information is important for setting goals and planning
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rehabilitation, including the concrete targeting of rehabilitation and planning of environmental modification.
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In the development of scales, the clinical feasibility of the scale should be
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considered. The methodological complexity, length of administration and cost may render the scales infeasible for many clinicians to use in their daily work [20]. Although the evaluation of environmental factors is an extremely important part of daily rehabilitation practice, the modification of environmental factors is not the primary target of intervention for the clinicians; therefore, time-consuming scales for evaluating only environmental factors may not be appreciated in clinical practice. In addition to being reliable and valid, the simplicity of the scales may also be an important aspect that should be considered in developing a scale to measure environmental factors [21].
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With these considerations, we developed a home environment checklist (HEC) that can be used to identify the mismatch between the functional ability of individuals with disability and the home environment. Considering the clinical feasibility of the checklist, the HEC was developed as an experience-based simple screening tool. The present study aimed to describe the development of our checklist and examine its psychometric properties. Materials and Methods
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Development of HEC For identifying information on the home environment that is empirically considered
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important in rehabilitation clinics, the content of a document form called the “Comprehensive Rehabilitation Implementation Plan”, which is commonly used in rehabilitation clinics in Japan,
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was used as a base [22]. This form was developed according to the ICF concepts and clinical
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perspectives by the Japanese Ministry of Health, Labour and Welfare as an official rehabilitation planning form and is mainly used for planning rehabilitation in the hospital to
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return home and providing relevant information to individuals with disability and their families. The form requires that health professionals provide detailed descriptions of body
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functions/structures, activities, participation and environmental factors of the individuals and to mention concrete plans for the problems raised. Two independent researchers have linked the items in this form to the ICF according to previously reported linking rules [23-25]. A multidisciplinary panel comprising 2 medical doctors, 2 physical therapists and 2 occupational therapists developed the checklist according to the identified categories. The process was as follows. Initially, among the linked categories, those directly related to the home environment (necessary for ADL) were identified. Subsequently, the appropriateness of the identified categories for the checklist and the need for additions were discussed. Finally, after identifying the categories, questions were developed to collect data related to each of the categories.
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The checklist was originally developed in Japanese and was translated in English as follows: first, 3 researchers independently translated each item of the checklist. Second, the researchers voted on the translation that seemed the most accurate, and the translation with ≥2 votes was used. This process was done for each item. The translation of the items selected in the session was checked by the researchers in terms of consistency. Finally, the whole checklist was checked by a native English speaker.
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Participants Non-probability sampling procedures were used to recruit a convenience sample of
hospital participated in the reliability study.
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participants. A total of 60 individuals undergoing a rehabilitation programme at the university
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The inclusion criteria were as follows: 1) intention to return home after treatment,
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2) family members in contact with clinicians to provide data regarding the home environment and 3) age >20 years. Of the 60 participants, 36 (60%) were males; the mean (SD) age was 70.2
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(15.3) years. Additionally, 22 (37%) had neurological diseases, 21 (35%) had respiratory diseases, 8 (13%) had orthopaedic diseases and 9 (15%) had other diseases (renal, gastric and
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collagen diseases).
Non-probability sampling procedures were used to recruit a convenience sample of
250 individuals with disability undergoing a rehabilitation programme at 9 hospitals for the internal construct validity study involving Rasch analysis. The data for the reliability study were also included in the validity study. Among 2 scores for each participant by 2 raters in the reliability study, the first score related to timing was used. The inclusion criteria were the same as for the reliability study. Six individuals were excluded because of missing data. Of the remaining 244 participants, 124 (51%) were males and the mean age was 68.7 (15.4) years.
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Additionally, 171 (70%) had neurological diseases, 51 (21%) had orthopaedic diseases, 12 (5%) had respiratory diseases and 10 (4%) had other diseases (renal, gastric, cardiovascular and collagen diseases). A total of 30 individuals with disability who lived at home and underwent a rehabilitation programme in the university hospital participated in a study to test knowngroups validity[26]. The inclusion criteria were 1) living at home and 2) the individual or family
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members in contact with clinicians to provide data regarding the home environment. Measurements
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The HEC comprised 10 questions asking about the mismatch between the abilities of individuals with disability and the home environment. The response options were 2
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(complete mismatch between the individual’s ability and home environment), 1 (partial
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mismatch between the individual’s ability and home environment) and 0 (no mismatch between the individual’s ability and home environment/no need for a specific environment).
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The following instructions were appended to the questions for rating: 1) rate the basis of environmental requirements according to the individual’s present ability; 2) note that “going
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out” implies an outdoor activity required for daily living (e.g., shopping and visiting an ambulatory care facility) and 3) note that the difference between “partial mismatch” and “complete mismatch” is based on whether the home environment matches the individual’s needs to perform activities at the desired time. Two kinds of scores were defined to integrate the score: a Mismatch score and a
Total score. The scores were defined as follows: 1. Total score = Sum of all the items 2. Mismatch score = Indoor sub-score + Outdoor sub-score
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a) Indoor sub-score = The sum of the scores in items 1, 2 and 3 + the smaller of the score in items 4 or 5 b) Outdoor sub-score = The sum of the scores in items 6, 7 and 8 + the smaller of the score in items 9 or 10 A Mismatch score of 0 means that the home environment is ready for the individual’s discharge. The Total score could be used to measure the extent of mismatch in the
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abilities of individuals with disability and the environment. The complete questionnaire is shown in the Appendix.
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In the reliability study, ratings on the HEC were provided by 3 independent raters who were not familiar with the HEC (a medical doctor, a physical therapist and an occupational
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therapist), with each participant rated by 2 of the 3 raters. In the validity study, ratings were
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provided by a medical doctor, physical therapist or occupational therapist who was in charge of the participant.
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Calculation of sample sizes
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The sample size for the reliability study was determined according to the number of response options (3), the minimum value for the kappa coefficient expected (0.4 for every item), power (90.0%) and alpha value (0.05). The calculated required sample size was a minimum of 39 participants [27]. The total sample size was determined such that the data for each pair of raters would be > 39. Each of the raters assessed 40 participants, and accordingly, evaluation pairs for 60 participants were obtained. For Rasch analysis, a larger sample is needed because a small sample size might result in a false signal misfit [28]. According to parameters shown by Linacre, a minimal sample of 108 to 243 individuals is needed for 99%
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confidence that the person estimates are within ±0.5 logits [29]. Accordingly, the sample size of 244 individuals we obtained in this study can be considered sufficient for Rasch analysis. Data analysis Reliability Kappa statistical analysis was used to determine inter-rater agreement between 2 raters. Weighted kappa statistics were calculated for each item of the HEC. The standards for
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interpreting kappa statistics were as follows: <0.2, poor; 0.2–0.4, fair; 0.4–0.6, moderate; 0.6– 0.8, substantial and >0.8, excellent [30]. The internal consistency of the HEC was assessed by
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Cronbach’s alpha and item–total correlations. According to Bland and Altman, a Cronbach’s alpha ≥0.7 can be considered sufficient for group comparisons and >0.9 sufficient for clinical
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application [31]. Item–total correlations were also computed.
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Validity
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Rasch analysis
Rasch analysis was performed to examine the internal construct validity of the HEC.
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It was analysed by using a partial credit model, which is considered a Rasch model for items for which the rating scale structure may vary across items, because in the present study, the testlet approach was used, aggregating the items into super-items. Rasch analysis is a probabilistic approach for estimating the difficulty of items and different levels of personal ability. Additionally, Rasch analysis can be used to assess the extent to which data satisfy psychometric assumptions, including scale unidimensionality and differential item functioning (DIF) across relevant group characteristics.
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The overall fit of the data in the Rasch model was examined by chi-square statistics. A non-significant χ2 (>0.05) was considered overall good fit. A testlet approach was used to accommodate high local dependency of the HEC items, which could cause a problem in the fit of the scale to the model [32]. The testlets were constructed by aggregating the items with high residual correlations into super-items, and the same iterative process of scale adjustment as a single-item design was applied. Principal component analysis (PCA) of standardised Rasch residuals was used to examine the unidimensionality of the scale [33]. Items were grouped
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according to the loading with the first principal component, and t-tests were used for analysis of each individual. Significant t-test values (i.e., the lower bound of the 95% confidence
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interval) <5% indicate unidimensionality.
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The absence of DIF is an important assumption in scale evaluation with the Rasch model. It indicates that an individual can achieve comparable levels of ability regardless of
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group characteristics, such as age and disease. DIF was investigated by ANOVA for sex (male and female), age groups (<65, 65–70, 70–75 and ≥75 years) and disease groups (neurological,
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musculoskeletal, respiratory and others).
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The ceiling and floor effects were analysed to measure the proportion of
participants with the best and worst possible score obtained, respectively. Ceiling or floor effects were assigned when >15 % of the responses attained the best or worst possible score, respectively.
Known-groups validity
Because of no similar scale that can be used to evaluate concurrent validity, knowngroups validity[26] was assessed by determining whether the HEC reflects the level of restriction in daily activities at home. Individuals with disability living at home were divided
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into the 3 groups according to the extent of restriction in performing daily activities: bedbound (restriction for indoor daily activities), home-bound (no restriction for indoor activities, but restriction for outdoor activities) and no limitation (no restriction for both indoor and outdoor activities). The known-groups validity was evaluated by comparing the HEC scores for each group. One-way ANOVA was used for analysis of Mismatch scores and Total scores of the HEC. When a significant main effect was found, post-hoc Tukey tests were performed.
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Analyses involved using JMP 11 (SAS Inst. Inc., Cary, NC, USA) and RUMM2030 (RUMM Laboratory, Perth, Australia). This study was approved by our institutional medical
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ethics committee (Fujita Health University, protocol #HM18-020). Results
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Development of the HEC
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On linking the Comprehensive Rehabilitation Implementation Plan with the ICF categories, we identified 56 categories, including 5 environmental factors (e115, products and
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technology for personal use in daily living; e120, products and technology for personal indoor
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and outdoor mobility and transportation; e155, design construction and building products and technology of buildings for private use; e310, immediate family; and e575, general social support services, systems and policies). The Comprehensive Rehabilitation Implementation Plan is mainly used for planning rehabilitation in the hospital to return home, and the categories are generally considered important at discharge. According to multidisciplinary panel discussions, all 5 categories were used as target categories, with no category added. For each of the categories, 2 questions were developed. The questions asked about environmental factors for in-home activities (e.g., toileting, eating and bathing) and for access to outside
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activities, such as shopping and visiting ambulatory care facility. Accordingly, the final version of the HEC comprised 10 questions and 3 response options (0–2) for each question (Appendix). Questions 1–3 and 6–8 refer to materials and questions 4, 5 and 9, 10 to human support. For discharge from the hospital, the entire material environment should match the ability of individuals with disability; conversely, human support could be provided from families or public/private services. The Mismatch score was defined to describe whether 1) all
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materials match the individual’s ability and 2) any human support (that matches the individual’s ability) is to be provided. Accordingly, the Mismatch score was set so that score 0
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represents the ready state of the home environment for discharge.
The Total score includes questions on both family support and the use of
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public/private services, thus reflecting the information on whether the requirements for
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discharge are satisfied with only family support or whether additional public/private service support is needed. Therefore, the Total score was considered to cover a wider level of individuals than the Mismatch score. The instructions for scoring and the formulae for
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Reliability
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calculating the Total and Mismatch scores are in the Methods section.
Table 1 shows weighted kappa statistics for inter-rater agreement for individual
items of the HEC. The weighted kappa statistics for each of the 10 items ranged from 0.73 to 0.93. The results of the internal consistency of the scale are in Table 2. The Cronbach’s alpha was 0.92. The item–total correlation ranged from 0.61 to 0.87. Removal of any one item did not significantly improve the internal consistency of the total scale as compared with the Cronbach’s alpha for the complete HEC.
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Validity We found no significant ceiling effects for both the Mismatch and Total scores. However, we found a significant floor effect for the Mismatch score (20.5%). The floor effect for the Total score was approximately borderline (14.8%). Thus, the investigation of validity by Rasch analysis was performed with the Total score. The initial Rasch analysis findings did not fit the assumptions of the Rasch model. A
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disordered threshold was observed for items 1 and 6 for both indoor and outdoor activities (Is there any mismatch between the personal assistive devices of the individual [orthosis,
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prosthesis, self-help devices etc.] and his/her ability?) (Supplemental table 1). For these 2 items, we observed a relatively low frequency for score 1. The average person ability was
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consistent with the rating structure. The residual correlations indicated a robust local
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dependency between items within the same ICF chapter (e1, products and technology; e3, support and relationships; and e5, services, systems and policies). Therefore, we attempted a testlet approach by grouping items into 3 groups by ICF chapter (subscales for e1, e3 and e5).
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With this strategy, we revealed good model fit for the HEC, which was supported by a non-
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significant χ2 (13.2, p = 0.153). The proportion of significant pairwise comparisons in the PCA of Rasch residuals was <5%, which supports the unidimensionality of the scale (Table 3). Figure 1 shows the distributions of person (top half of the graph) and item
thresholds (bottom half of the graph) for the total HEC score. We did not observe any DIF related to sex, disease or age.
The HEC scores for home-living individuals who received rehabilitation are shown in Figures 2 and 3. HEC differentiated the no-limitation, home-bound, and bed-bound groups. The distribution of scores in these groups were as follows (Figure 2): no-limitation group, 0 (n =
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11, mean 0); home-bound group, 2-4 (n = 13, mean [SD] 2.9 [0.9]) and bed-bound group, 5–9 (n = 6, mean 6.3 [1.5]). The mismatch scores for individuals in these groups were as follows (Figure 3): no-limitation group, 0 (n = 11, mean 0); home-bound group, 1–3 (n = 13, mean 1.6 [0.8]) and bed-bound group, 2–5 (n = 6, mean 3.8 [1.2]). The indoor sub-scores of mismatch scores were as follows: no-limitation and home-bound groups, 0 (mean 0) and bed-bound group, 1–2 (mean 1.7 [0.5]). We found a significant difference among groups in total and mismatch scores (P < 0.01). On Tukey post-hoc tests, the differences in Total scores and
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Mismatch scores between the 3 groups were all significant (Figures 2 and 3).
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Discussion
In the present study, we describe the development of the HEC and assessed its
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reliability and validity. The HEC was established as a 10-item checklist that considered 5 ICF
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categories. The findings largely supported the reliability and validity of HEC. HEC was developed as a simple screening tool; thus, the number of questions was
low as compared with existing scales for environmental factors [7-11]. In developing clinical tools, there is a conflict between comprehensiveness and clinical feasibility[34]. The scope of the evaluation of environmental factors can be extensively broad if an attempt is made to evaluate all aspects of the environment to which an individual potentially has access [10]. One of the major approaches previous studies used was to comprehensively evaluate the environment by asking about individuals’ perception of the environment [7-13]. This approach would make sense in assessing the environment of the individuals living in the community,
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such as individuals with spinal cord injury who use a wheelchair and are working. However, the self-report model or interview model used in most previous studies may not fit well in assessing individuals with cognitive disorders who are among the individuals who require an assessment of the environment. To realistically develop a screening tool for use in clinical settings, we used a pragmatic approach involving discussion among clinicians who are directly engaged in the
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environmental setting of individuals who receive rehabilitation with regard to routinely collected information in an empirically established system for bridging the gap between the individual’s situation and the home environment. All 5 categories selected have been included
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in a minimal set of environmental factors developed with regression methods and
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international expert consultation [35], which might support their appropriateness. Our simple screening tool could be used in rehabilitation to indicate areas of mismatch between the
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individual’s ability and home environment, thus indicating requirements for discharge and to aid in goal-setting for the individual with the disability. For example, if the desire of an
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individual and the family is to return home, this screening tool can indicate a gap between the
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present situation and the minimal attributes required for discharge. The HEC is scored by the gap between the individuals’ situation that the clinician
evaluates and the information about the home environment that could be indirectly collected from families; thus, the HEC could be used for individuals with cognitive disorders. Clinicians can then make a pragmatic plan to bridge the gap by improving the ADL ability of the individuals or providing advice on preparing an environment that is friendly to individuals with disability. The HEC allows for easily monitoring the problems and provides insights into the issues that need to be resolved before discharge to home. In addition, the HEC could be used to monitor the home environment of individuals who receive long-term care at home. Here,
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the HEC could be used to determine whether the environment supports indoor daily activities and access to outdoor activities, and if the environment does not provide such support, the tool can indicate the missing aspects. The reliability of HEC appears to be acceptable for clinical use. The weighted kappa statistics for all items were > 0.6, considered good inter-rater reliability, and for 8 of the 10 items, the kappa was > 0.8, considered excellent reliability [30]. The internal consistency of the
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HEC was confirmed by Cronbach’s alpha, so the tool is acceptable for clinical use [31]. We investigated the construct validity of the HEC. Rasch analysis supports the
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evaluation of the internal construct validity of a scale by assessing construct-irrelevant variance and construct underrepresentation [36]. The existence of construct-irrelevant
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variance indicates the presence of sub-dimensions irrelevant to the focal construct of the
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scale, which can be shown as item misfit and DIF [36]. In the present study, the Rasch analysis initially showed that the scale did not meet the criteria required by the Rasch model. We observed a disordered threshold for items 1 and 6 and strong local dependency between
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chapter groups. Thus, we used a testlet approach according to the chapter structure of the ICF,
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which revealed excellent fit to the Rasch model and unidimensionality of the scale. Although the common solution for a disordered threshold is to collapse the response options, this solution did not significantly improve the fit statistics but lowered the reliability indices in our analysis (data not shown). The disordered threshold may reflect the inconsistency between the order of the response options and the order of the person’s ability, but it was also affected by other factors such as strong local dependency and the low frequency of the category [37, 38], both of which were also observed in the present study. Thus, the disordered threshold we found may not necessarily indicate a significant problem in the rating structure. In fact, the mean person ability in each response option was properly ordered for both items 1 and 6. In
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addition, the response option “1” aims to show partial mismatch, which may be clinically important for individuals who need multiple devices in a home environment. On the basis of these considerations, we kept the original structure and used the testlet approach without collapsing the response options to improve the fit to the Rasch model and found no DIF with regard to age, sex or disease. These results negate the existence of construct-irrelevant variance in HEC.
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However, the scale might have some issues in terms of construct underrepresentation. Construct underrepresentation indicates the imperfectness of tests for assessing all features of the construct. The distribution of the item threshold was well
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balanced along the continuum; however, the range of distribution appeared slightly narrower
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than that for the person location (Figure 1), which indicates some degree of construct underrepresentation of the scale. The floor effect was borderline (14.8%), which may also
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indicate the limitation of the HEC as an assessment scale perhaps because it is structured as a screening scale with limited target of evaluation. One common solution to solve this kind of
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targeting problem is to add items of various difficulties. Thus, adding more difficult items may
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be a solution. However, the score of 0 in the HEC total score indicates that the indoor and outdoor environment completely matches the individual’s ability without the assistance of public/private health care services. Developing more difficult items may not be realistic because the original purpose of HEC was to assess the extent of preparation of the home environment.
Considering the aim of the HEC, whether this scale is consistent with the actual activity level of individuals in their home could be investigated. As an additional validity study, we evaluated 30 individuals with disability who were living at home to confirm whether the HEC could differentiate individuals who have restrictions in performing indoor daily activities
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or limited access to outdoor activities from those who do not have such restrictions in daily activities. We found that the HEC scores identified the 3 levels of restriction in activities (no limitation, home-bound and bed-bound), thereby evidencing the known-groups validity. These results support the feasibility of the scale for evaluating the appropriateness of the environment according to the abilities of individuals with disability.
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Study limitations The scope evaluating the HEC is limited to 5 ICF categories. The HEC has fewer
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categories than other comprehensive environmental factor scales [8-12]. The evaluation covered only the home environment. The HEC has a clear purpose of evaluating the mismatch
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between the disabled individual’s ability and the home environment to determine the actual
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problem in considering discharge of individuals with disability and to facilitate goal-oriented interventions. Therefore, the HEC should be limited to this purpose. Comprehensive
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understanding of environmental barriers/facilitators and their measurements, as mentioned in
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previous studies [39, 40], are also necessary. The HEC was not developed according to an established scale development process
[41]. However, considering the extensive clinical utility of such a scale, this pragmatic approach to developing a clinical scale by investigating an empirically developed and accepted system could be another option, at least for developing simple screening tools that can be used in real clinical settings.
The HEC is designed to be rated by clinicians based on their assessment of abilities of individuals with disability and information about the home environment. Thus, this scale could be used for individuals with cognitive disorders by collecting information on the home
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environment from family members. However, the HEC does not have detailed instructions for assessment, and the information from the family may be limited because of insufficient understanding of the disability by family members or the clinicians’ way of asking. This tool should be used to identify areas in the home environment to facilitate further detailed evaluation of the home environment. We used non-probability sampling procedures. Although data collection from
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multiple facilities (9 hospitals) might have reduced the bias, the results of Rasch analysis could still be biased because of the distribution of the samples. In addition, the sample size might not be large enough to accurately investigate DIF [42]. Another study with larger samples with
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random sampling is encouraged for more accurate evaluation of indices such as item difficulty
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and DIF.
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According to the COnsensus-based Standards for the selection of health Measurement INstruments (COSMIN) Risk of Bias checklist, the methodological quality of presenting the structural validity, construct validity and reliability of the HEC would be
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classified as adequate to very good [43]. However, we still lack data supporting several other
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psychometric properties of the scale, such as cross-cultural validity and responsiveness. Further research to investigate more detailed psychometric properties of the HEC is encouraged.
Conclusions
We found that the HEC had substantial-to-excellent inter-rater reliability and excellent internal consistency, and we confirmed the validity of the HEC. Our findings support the use of HEC as an easy and quick screening tool that could be used in a clinical setting to identify the areas in
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the home environment that individuals with disability potentially have problems with after discharge. It gives an overview of the mismatch between the individual’s ability and home environment for further planning the rehabilitation and discharge. In addition, this instrument can be useful in clinical work when prioritizing healthcare resources. Further investigating the clinical utility of the HEC in combination with functioning and environment assessment tools is
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encouraged.
Acknowledgements. The authors thank Dr. Carolina Saskia Fellinghauer for her kind advice on
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statistical procedures.
Funding. This work was supported by a Health Labour Sciences Research frant (H28-statistics-
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general-004) from The Japanese Ministry of Health, Labour and Welfare.
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Conflict of interest: None declared.
Figure legends
Figure 1. Person–item thresholds for home environment checklist (HEC). Shows the distribution of person ability estimates (red bars) and item thresholds (blue bars) based on Rasch analysis. The X-axis shows the location of the person and items, reflecting the person’s ability and item difficulty.
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Figure 2. Total scores for the home environment checklist (HEC) for 30 individuals living at home. Individuals 1–6 were classified as the bed-bound group (restriction for indoor daily activities), 7–19 the home-bound group (no restriction for indoor activities but restriction for outdoor activities) and 20–30 the no-limitation group (no restriction for both indoor and outdoor activities). *P < 0.01
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Figure 3. Mismatch scores for the HEC for 30 individuals living at home. Individuals 1–6 were the bed-bound group, 7–19 the home-bound group, and 20–30 the no-limitation group. *P <
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0.01
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