ATOMS Project Technical Report:
Methods to Identify Assistive Technology Device Use
Sally Fennema-Jansen, PhD., OTR, Fiona Whyte, MS, OT & Roger O. Smith, Ph.D., OT
The purpose of this report is to present a summary of the issues and methods used to identify the quantity and quality of assistive technology devices (AT) used by an individual. To measure the impact of AT devices, one must know which devices were used and how they were used. Successfully identifying device use is highly relevant to AT outcomes measurement and research. For example, even if we perfectly measured an outcome, without knowing “outcome of what,” the outcomes result is meaningless. The scope of this fieldscan focuses on exploring methods for documenting the use of AT devices. (By extension, similar issues pertain to AT services.) Superficially, identifying device interventions would seem to be an easy task. As the following discussion will report, the task is daunting in spite of its appearance.
One of the challenges of measuring outcomes, as identified by Smith (2000), is isolating the impact of assistive technology from the many other interventions an individual receives. Additionally, teasing out the impact of one particular AT device from the others is important in order to produce an unambiguous representation of a specific device's effects. However, it is difficult for the researcher and/or the individual to properly identify which AT device(s) the individual is using, especially considering the large and growing number of AT devices. If all of the devices have not been identified when researching AT outcomes, a significant impact may be attributed to the wrong device. It is imperative to clearly identify the device so the effects can be determined using an accurate and consistent method.
Reasons why it is difficult for individuals to exhaustively identify assistive technology devices they use vary. First, an individual may simply forget they are using a specific device or may not realize the importance of a device. This particularly occurs with regards to small or low tech devices. Another reason may be due to the nature of an item or the level at which it has been integrated into daily function. For example, consider an individual who has worn glasses on a daily basis throughout most of their life. If the individual was asked to detail which AT devices they use, they may not include their glasses. This may occur simply because the individual's glasses have become a part of their daily wardrobe. Therefore, use of some devices becomes so automatic that, at times, the individual may not realize they are using them. Improper identification of the devices used does not seem to be intentional act. In most cases the device slips the individual's mind.
Additionally, a single device might have many features, configurations, adjustments or settings. A device may work or fail, not because of a device, but because a response rate was set too fast, or the vocabulary was too large, or an accessory was mis-aligned or missing. So, not only do we need to know about the device itself, but how the device was set-up is essential.
Another reason why it is important to examine current methods to identify AT devices is to facilitate large scale data collection. A standard approach to device identification would assist in the ability to learn from data that was collected across the large and diverse population of individuals who use AT. This information could be used to promote a system of device identification that would meet a variety of needs in a variety of settings. Properly indicating the device(s) used and establishing a consistent way to do so should be prerequisites to future outcomes research.
Of course, the fact that there are more than 20,000 devices, many varieties of models within device types and combinations of using these devices and models only exacerbates the problem. Fortunately, some research strategies have successfully been applied to clarify and document device use. This scan of the field identifies, classifies and describes historical methods for determining device use. Based on this review we hope to propose some general ideas for standardizing a process.
For this fieldscan, researchers reviewed both published research articles and assistive technology outcomes instruments. The focus of the literature search was on articles that documented AT device use, with the publication dates ranging from1991 through 2003. Tools that were developed to measure AT outcomes were also reviewed in order to determine and document the AT identification methods that were used. Techniques used to identify AT services were not included, as they lie beyond the scope of this report. In all, a review of 84 AT outcomes articles and six AT outcome assessments were incorporated in the fieldscan.
After obtaining the literature, researchers listed the methods used to identify the AT devices. During this process, researchers developed a table to assist in coding characteristics and methods of device identification and use. This table led to the recognition of a few categories. A summary of the findings in the table are provided in this report.
Although many of the references were carefully written research articles and well documented published instruments that focus on AT outcomes, many of these sources do not provide sufficient information about AT device use and set-up. Some of the articles do not provide sufficient information to allow for classification about how the AT device information was gathered (e.g., Pell, Gillies, and Carss, 1999; Riemer-Reiss and Wacker, 2000; Shone Stickel, Ryan, Rigby, and Jutai, 2002). An analysis of the remaining articles and instruments revealed that while no standard and consistent method was used to identify the AT devices used, the methods could be grouped into four main categories: 1) recall, 2) recognition, 3) a priori defined, and 4) observation.
Recall: The first method used to identify devices involves the use of recall. This method requires an individual to bring forth or retrieve information from memory (Best, 1989). For this fieldscan, recall methods included studies in which individuals were asked to list which assistive technology devices they use. Researchers asked questions ranging from open-ended (e.g., Lupton &Seymour, 2000) to specific (e.g., Laki, 2002).
Recognition: The second method used to identify devices involves recognition, which is a process where items are identified from a group of presented items (Best, 1989). Whereas recall requires the participants to list from memory, recognition systems provide a checklist that helps the participants recognize the AT they use. These checklists usually provide a system of categories to classify devices. Some studies categorize by disability area (e.g., Forbes, Hayward, and Agwani, 1993), while others categorize devices by the type of activity they are used for (e.g., Gitlin, Levine, and Geiger, 1993; Agree and Freedman, 2000). Occasionally these checklists leave out specific devices within the categories, which unfortunately may result in the failure by an individual to identify some devices. Therefore, information about some of the specific device(s) used may not be provided when using this approach.
A priori: The third category, a priori defined, refers to situations where research is being conducted or information is being gathered on a device the researcher is already aware the individual is using. Research involving follow-up regarding the outcomes of a particular device provided to an individual falls within this category and was common within the reviewed literature. Within this category, the specificity regarding the type of devices being researched varies. In some studies, specific device information is provided (e.g., Bentur, Barnea, and Mizrahi, 1996). However, most often research articles identify the category of the device, rather than detail the specific device (e.g., Garber and Gregorio, 1990;Angelo, 2000; Angelo and Trefler, 1998; Bell and Hinojosa, 1995; Benedict, Lee, Marrujo, and Farel, 1999).
As evident by the literature review, more often than not the tools used to measure assistive technology outcomes are employed to determine the impact of a given, known device (a priori). AT outcome tools such as the Individually Prioritised Problem Assessment (Persson et al., 2000), Psychosocial Impact of Assistive Devices Scale (Jutai and Day, 2002), and the Quebec User Evaluation of Satisfaction with Assistive Technology (Demers, Weiss-Lambrou, & Ska, 2002) appear to most often be used to examine the outcome of an a priori defined devices.
Direct Observation: The fourth method used to identify AT devices is direct observation of the devices while they are being used by the individual (e.g., Parker and Thorslund, 1991; Scherer, 1990). This method could but could also involve locating the assistive technology devices within the individual’s environment (e.g., Laki, 2002).
Although the methods listed above are presented as discrete categories, it should be noted that some studies used a combination of methods. For example, an interview (recall method) might be followed by observation of the devices within the environment (Tomita, Mann, Fraas and Burns, 1997; Tomita, Mann, and Welch, 2001; Mann, Hurren, Charvat, and Tomita, 1995; Mann, Hurren, and Tomita, 1995). On the other hand, instead of using an interview, a checklist may offer another option which would enact the use of recognition.
In addition to the methods identified in the literature, two projects were completed that directly addressed the issue of identifying the AT used by an individual. In the first, Fennema-Jansen and Whyte (2002) designed a study to find out whether there is a need for a cuing system. This would allow professionals to detail all the AT devices that their consumers use or find out if the desired information could be gained by having the individuals list the devices from memory (free recall method). All respondents identified more assistive technology devices when using the taxonomy as a cue than they did without cuing. Even with the taxonomy, there were instances in which all of the AT that the student used was not identified. Use of a taxonomy helped clarify what was and was not included under the definition of “assistive technology.” The taxonomy was particularly helpful in detailing the AT device(s) use by those with complex needs. Although further testing must be done to validate the results of this study, the results highlight the need for a cuing system to help professionals document AT device use. This is particularly important when consumers use multiple AT devices. This study suggests that recognition is more accurate than free recall.
In a related project, Whyte (2002) explored the development of an Assistive Technology Device Use Inventory. Following a review of available taxonomies, Whyte proposed a branching methodology to facilitate identification of devices based on questions related to completion of functional tasks. Whyte suggested branching from a broad category (e.g., personal care) to increasingly specific categories of devices (e.g., sock aid) with trichotomous response options at each level (yes, no, and don’t know). Examples of devices are provided to cue the individual. For example, one screen might ask “Do you use any assistive devices or equipment for personal care? (Examples: adapted cup, shower chair, long handled sponge, a transfer board, sock aid, etc.)” (p. 22). Whyte proposed an electronic format used on a tablet computer that allows dynamic branching. The proposed device inventory was generic in that it was applicable to all clinical settings, to all ages and all disabilities, and included all AT devices. The proposed inventory would be available in two formats, one for completion by the consumer and one for completion by someone else.
No single tool is consistently used to describe the AT devices that are being studied and currently no published tool is available to investigate AT device use. The results of the fieldscan indicate that current approaches do not necessarily ensure that all devices used by a client are identified. Furthermore, it is evident from the literature that there is no standard method of attempting to determine or report the devices used. In order for outcome research to be reliable and valid, researchers need to ensure that all devices by a client are identified, and that each device is identified with adequate specificity. Current methods do not provide this assurance. This missing information is important in order to draw valid conclusions from the outcomes measures.
A priori defined methodology offers the potential advantage of knowing exactly which devices are being used. This advantage suggests the usefulness of a system that allows a consumer to track the AT devices as they are used or provided. A systematic and consistent methodology is imperative to ensure that all of the necessary features are properly stored and therefore retrievable as needed. The potential benefits of this are apparent not only for outcomes researchers, but also consumers and clinicians.
Until a system is developed that proactively ensures that all AT device information is collected and documented in an easily retrievable format, there will be a need for a system to gather information retrospectively. Cuing efficiency is a term used to describe the likelihood of retrieving information as a function of the type of prompt that is presented. Research into the efficiency of various types of prompts could assist the field of AT outcomes research when developing retrospective procedures for device identification.
This project is supported in part by the National Institute on Disability and Rehabilitation Research, grant number H133A010403. The opinions contained in this paper are those of the grantee and do not necessarily reflect those of the NIDRR and the U.S. Department of Education.
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