In 2001, the World Health Organization (WHO) approved the International Classification of Functioning, Disability and Health (ICF) to describe functioning in health and health-related contexts. The challenges of implementing the ICF  in various fields such as medicine, rehabilitation, long-term care, or social care include the operationalization and quantification of the ICF categories . Unlike the International Classification of Diseases (ICD) , also developed by the WHO, to which medical records can serve as an information resource, the ICF measures the problems in an individual’s functioning with respect to a health condition. The ICF provides alphanumeric codes that are arranged in a hierarchy for each ICF category or functioning domain. The number of digits in an ICF code represents an increasing level of precision in the categorization or definition for each function in that domain. However, the high number of codes (n = 1434) makes the use of the ICF by health care professionals particularly challenging. Therefore, to facilitate the use of the ICF codes, it is necessary to tailor them to the target population.
As the ICF was developed as a classification system, it requires an additional step for use as a measurement system, i.e., using a qualifier with the ICF code. A user must select an ICF code, followed by measurement using an ICF qualifier. Qualifiers are numeric codes that specify the extent or the magnitude of the disability in that category. The original ICF qualifier is used to record the severity of the problem: no problem; mild; moderate; severe; or complete problem (included in the codes are qualifier 8 (not specified) and qualifier 9 (not applicable)). However, this approach prohibited us from using the ICF for two reasons. First, it was difficult to select relevant ICF codes from the approximately 1434 ICF codes, and if we selected ICF codes for each person, we could not compare the specific function to other persons, because the ICF codes selected for various individuals may not be the same. Second, the reliability of a qualifier for quantification of severity of a disability was not always satisfactory [4, 5].
Therefore, for adaptation of the ICF codes, a priori selection of ICF codes specific to a target population can minimize the burden of selecting numerous ICF codes. In addition, the use of a simpler qualification tool makes the ICF easier to use as a basis for measurement
There have been several studies aimed at tailoring the number of ICF codes [4, 6–9]. ICF codes related to condition-specific ICF items, such as the codes for osteoarthritis and other chronic conditions, were selected in the development of ICF Core Sets [2, 10]. This developmental effort facilitated condition-specific selection of ICF codes, but the ICF codes selected for one chronic condition may not necessarily be adaptable to other chronic conditions. The WHO provided the ICF checklist as a simple version of the ICF; however, the broad and vague definitions of the ICF codes used in the checklist limit its use in a target population such as elderly patients, because some ICF codes do not have high reliability for the intended population [4, 5].
An alternative approach is to create linkage between the ICF and existing measures of activities of daily living (ADLs) and health-related quality of life (HRQOL) [2, 11]. This approach has allowed ICF users to tailor the number of ICF categories to fit specific clinical needs [12, 13]. This approach has qualitatively linked the ICF codes to existing ADL limitation-related scales such as the Functional Independence Measure (FIM) . However, in these cross-linking approaches, the absence of quantitative links limits the use of the ICF for measurement scales.
Some studies have tried to link the existing scale to the ICF codes quantitatively [15–17]. An example of such a linkage has been established between the Typology of the Aged with Illustrations (TAI) and the ICF [18, 19]. The TAI contains four Guttman-type scales for Mobility, Cognitive functioning, Eating, and Toileting. Each scale includes five thresholds that enable staging of the functioning of elderly persons. For example, the following five items are used as thresholds in the TAI mobility scale: threshold 5, “stair climbing”; threshold 4, “walking short distance”; threshold 3, “moving around on a flat floor”; threshold 2, “transferring, maintaining sitting position” and threshold 1, “rolling over on beds”. A Guttman scale is composed of a set of binary items with yes or no answers, with similar content, but differing in difficulty. In this case, items are arranged in order of difficulty so that an individual who performs a particular item also performs items of a lower difficulty rank-order. However, it has been shown that some items used in a TAI scale are not in the order of difficulty when they are assessed with the Rasch model .
Another approach is the proposed functional staging measurement. In this measurement, sets of items are used to construct scores; which are then converted into hierarchical stages using cut-off scores. Functional staging provides a detailed description of an individual’s expected ability within each identified stage, including the types of activities he or she can do. This is achieved by cross-linking Activity Measure for Post-Acute Care (AM-PAC) items to the ICF . However, the items used on the AM-PAC are numerous and are not always linkable to the ICF codes. For example, “Fastening a necklace (clasp) behind your neck” is difficult to code in terms of the ICF.
Therefore, in this study, the authors constructed a Guttman-type scale using the response pattern of the ICF items analyzed by the Rasch model. If we could successfully build such a scale starting from ICF codes, we could obtain a simple scale with a staging property.
The Rasch model assumes that the probability that a person will fit into a category within an item is a logistic function of the difference between the person’s ability (θ) and the difficulty of the item (b) [20
]. The probability of success (or failure) of an item or a task is a binary item (such as failure or success in transferring from a bed), and can be expressed as
where Pi (θ) is the probability that respondents with ability θ will answer item i correctly (or be able to do the task specified by that item i). From this formula, the expected pattern of responses to an item set is determined given that estimated θ and b.
If the items with a binary response pattern fit the Rasch model, they provide a Guttman-like response structure. For this purpose, we used a binary-type response for each ICF item in this study. In the Rasch model, the Guttman response pattern is the most probable response pattern for a person when items are ordered from least difficult to most difficult. Using these characteristics of the Rasch model, we used the item fitted to the Rasch model as a threshold item in the Guttman-type scale. Therefore, selected ICF items are used as the thresholds for the boundaries between categories. Using this property of the Rasch model, we constructed two Guttman-type scales that can be used as a staging tool.
The objective of our study was to construct Guttman-type scales with the ICF codes for use in geriatric care settings. The goal was to be able to use the scales to assign patients to one stage. Staging of the functional levels of patients enhances standardization of care, helps in the planning and development of health services, and allows for communication among health services professionals concerning patients’ functional capabilities. Therefore, we decided to construct a new ICF-based staging system, starting from ICF codes, rather than linkage from an extant measure, and to find a link to the ICF. This study departed from measurement of the ICF codes themselves. Using the results, we reconstructed a new measurement tool to stage the functioning of elderly persons.