Conceptual models provide a theoretical basis for advancing scientific knowledge and improving professional practice. Although numerous assistive technology-related models have appeared in the literature, there has been no systematic effort to assess them. Six conceptual models are reviewed here: Cook and Hussey's Human-Activity-Assistive Technology model; the World Health Organization's International Classification of Functioning, Disability, and Health; Scherer's Matching Person and Technology model; Gitlin's model of an AT user's "career"; social cognition decision-making theories; and Rogers' Perceived Attributes Theory. The models are reviewed in terms of six domains: background and goals; descriptive characteristics; indication of outcome measures; predictive characteristics; validation in the literature; and utility to assistive technology practitioners, developers, and consumers. The salient strengths and limitations are highlighted for each. Application of the models to advance theory, research, and practice is discussed.
Predictive conceptual models help us frame research questions, interpret results, and guide clinical practice. Although numerous models have appeared in the assistive technology (AT) literature, none has been shown to predict AT usage. The lack of a valid predictive model indicates the need for development of new approaches to modeling AT outcomes. This article proposes a user-centered conceptual model that predicts AT usage as a function of the perceived relative advantages of AT. Device usage is not modeled as a one-time, all-or-nothing proposition, but as a decision process recurring over time. The influence of parallel interventions working concurrently with, or as an alternative to, AT is a central consideration that ultimately drives AT usage. Usage is shown as a proximal influence on AT impact, and AT impact is shown to be a predictor of future use. Research is cited supporting various elements of the new model.
The objective of this research was to provide guidelines for the reliable assessment of ergonomics exposures in non-routinized work. Using a discrete-interval observational sampling approach, two or three observers collected a total of 5852 observations on tasks performed by three construction trades (iron workers, carpenters and labourers) for periods of several weeks. For each observation, nine exposure variables associated with awkward body postures, tool use and load handling were recorded. The frequency of exposure to each variable was calculated for each worker during each of the tasks on each of the days. ANOVA was used to assess the importance of task in explaining between-worker and within-worker variability in exposures across days. A statistical re-sampling method (bootstrap) was used to evaluate the reliability of exposure estimates for groups of workers performing the same task for different sampling periods. Most exposures were found to vary significantly across construction tasks within trade, and between-worker exposure variability was generally smaller than within-worker exposure variability within task. Bootstrapping showed that the reliability of the group estimates exposure for the most variable exposures within task tended to improve as the assessment periods approached 5-6 d, with marginal improvements for longer assessment periods. Reliable group estimates of exposure for the least variable exposures within task were obtained with 1 or 2 d of observation. The results of this study demonstrate that an initial estimate of the important environmental or task sources of exposure variability can be used to develop an efficient sampling strategy that provides reliable estimates of ergonomics exposures during non-routinized work.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.