Subtopic Deep Dive
Assistive Technology Abandonment
Research Guide
What is Assistive Technology Abandonment?
Assistive Technology Abandonment refers to the discontinuation of use of prescribed assistive devices such as wheelchairs, communication aids, and low vision magnifiers due to factors including poor device fit, inadequate training, and psychosocial barriers.
Longitudinal surveys identify predictors like device mismatch and training deficits in wheelchair and communication aid users (Lubarsky, 1993; 58 citations). Recent scoping reviews classify abandonment factors into personal, device-related, environmental, and interventional categories (Lorenzini & Wittich, 2019; 46 citations). Over 20 papers since 1993 quantify annual losses from abandonment, such as $46 million in Canada from low vision devices (Fok, 2011; 20 citations).
Why It Matters
Abandonment wastes healthcare investments, with $46 million annual losses in Canada from low vision devices alone (Fok, 2011). It reduces user participation in communication and mobility, as seen in wheelchair studies where device type influences community activity frequency (Ferretti, 2007). Interventions like matching algorithms and training improve outcomes, addressing ethical dilemmas in occupational therapy (Lubarsky, 1993; Scherer & Federici, 2015). Occupational therapists report virtual reality aids enhance patient collaboration to prevent abandonment (Atwal et al., 2014).
Key Research Challenges
Predicting User Abandonment
Multifactorial decision processes make abandonment hard to predict, involving personal, device, environmental, and interventional factors (Lorenzini & Wittich, 2019). Surveys show poor fit and training deficits as key predictors in wheelchair users (Ferretti, 2007). Over 50% of devices are abandoned due to these issues (Lubarsky, 1993).
Improving Device Matching
Matching person to technology during selection reduces abandonment, yet lacks standardized tools (Fok, 2011). Low vision product selection instruments like LV-PSI address this but require validation across devices (Fok, 2011). Virtual reality applications aid pre-discharge matching in occupational therapy (Atwal et al., 2014).
Addressing Psychosocial Barriers
Psychosocial impacts deter sustained use in deaf and low vision users, beyond technical factors (Jiménez Arberas & Díez Villoria, 2021). Sociocultural factors create ethical dilemmas in device adoption (Lubarsky, 1993). Training competencies remain low among specialists (Burgos, 2015).
Essential Papers
Why people use and don’t use technologies: Introduction to the special issue on assistive technologies for cognition/cognitive support technologies
Marcia J. Scherer, Stefano Federici · 2015 · Neurorehabilitation · 62 citations
This special issue focuses on assistive technologies for cognition/cognitive support technologies as well as the ways in which individuals are assessed and trained in their use. We provide eleven d...
Sociocultural Factors Shaping Technology Usage
Mark R. Lubarsky · 1993 · Technology and Disability · 58 citations
The widespread underuse and abandonment of adaptive devices is a critical issue for medicine, research, and technology development, as well as consumers and their families.It poses basic financial ...
Factors related to the use of magnifying low vision aids: a scoping review
Marie-Céline Lorenzini, Walter Wittich · 2019 · Disability and Rehabilitation · 46 citations
<b>Background:</b> The decision process around the (non-)use of assistive technologies is multifactorial. Its determinants have previously been classified into <i>personal</i>, <i>device-related</i...
Occupational Therapists’ Views on Using a Virtual Reality Interior Design Application Within the Pre-Discharge Home Visit Process
Anita Atwal, Arthur Money, Michele Harvey · 2014 · Journal of Medical Internet Research · 44 citations
Participants perceived the use of VRIDAs in practice would enhance levels of patient/practitioner collaboration and provide a much needed mechanism via which patients are empowered to become more e...
Factors related to the use of a head-mounted display for individuals with low vision
Marie-Céline Lorenzini, Anni Hämäläinen, Walter Wittich · 2019 · Disability and Rehabilitation · 27 citations
The decision-making process around the (non-)use of assistive technologies is multifactorial. The goal of the present study was to identify which factors predict or correlate with the use of a head...
Psychosocial Impact of Assistive Devices and Other Technologies on Deaf and Hard of Hearing People
Estíbaliz Jiménez Arberas, Emiliano Díez Villoria · 2021 · International Journal of Environmental Research and Public Health · 23 citations
Deaf and hard of hearing people use a variety of assistive devices and technologies as a strategy to mitigate, counter or compensate for life difficulties resulting from hearing loss. Although outc...
Development and Testing of a Low Vision Product Selection Instrument (LV-PSI): A Mixed-Methods Approach
Daniel Fok · 2011 · 20 citations
In Canada, it is conservatively estimated that $46 million is lost per annum from low vision (LV) assistive technology device (ATD) abandonment alone. The proper matching of the person and the tech...
Reading Guide
Foundational Papers
Start with Lubarsky (1993; 58 citations) for sociocultural framing; Fok (2011; 20 citations) for economic impacts and LV-PSI; Atwal et al. (2014; 44 citations) for occupational therapy matching.
Recent Advances
Lorenzini & Wittich (2019; 46 citations) on factor classification; Jiménez Arberas & Díez Villoria (2021; 23 citations) on psychosocial effects; Scherer & Federici (2015; 62 citations) on cognitive aids.
Core Methods
Scoping reviews for factor categorization (Lorenzini & Wittich, 2019); mixed-methods for selection instruments (Fok, 2011); surveys on wheelchair participation (Ferretti, 2007).
How PapersFlow Helps You Research Assistive Technology Abandonment
Discover & Search
Research Agent uses searchPapers and citationGraph on 'assistive technology abandonment predictors' to map 250M+ papers, centering Lubarsky (1993; 58 citations) as foundational. exaSearch uncovers hidden surveys on wheelchair abandonment; findSimilarPapers expands to Lorenzini & Wittich (2019).
Analyze & Verify
Analysis Agent applies readPaperContent to extract abandonment rates from Fok (2011), then verifyResponse with CoVe chain-of-verification flags inconsistencies across surveys. runPythonAnalysis with pandas computes meta-analysis of predictor correlations (e.g., training deficits vs. abandonment) from 10+ papers; GRADE grading scores evidence quality on device matching interventions.
Synthesize & Write
Synthesis Agent detects gaps in psychosocial interventions via contradiction flagging between Lubarsky (1993) and recent reviews, exporting Mermaid diagrams of factor models. Writing Agent uses latexEditText, latexSyncCitations for Scherer & Federici (2015), and latexCompile to generate review manuscripts with intervention tables.
Use Cases
"Run meta-analysis on abandonment rates in low vision devices from Canadian studies"
Research Agent → searchPapers → Analysis Agent → runPythonAnalysis (pandas aggregation of rates from Fok 2011 + Lorenzini 2019) → matplotlib abandonment trend plot.
"Draft LaTeX review on wheelchair abandonment predictors with citations"
Research Agent → citationGraph (Ferretti 2007 cluster) → Synthesis Agent → gap detection → Writing Agent → latexEditText + latexSyncCitations + latexCompile → PDF with factor diagram.
"Find code for assistive device matching algorithms in abandonment papers"
Research Agent → paperExtractUrls → Code Discovery → paperFindGithubRepo → githubRepoInspect → Python sandbox test of LV-PSI matching script from Fok (2011)-linked repos.
Automated Workflows
Deep Research workflow conducts systematic review of 50+ abandonment papers, chaining searchPapers → citationGraph → GRADE grading for structured report on predictors. DeepScan applies 7-step analysis with CoVe checkpoints to verify Fok (2011) cost estimates across datasets. Theorizer generates hypotheses on VR interventions from Atwal et al. (2014) + Scherer & Federici (2015).
Frequently Asked Questions
What is Assistive Technology Abandonment?
It is the discontinuation of prescribed devices like wheelchairs and magnifiers due to poor fit, training gaps, and psychosocial issues (Lubarsky, 1993).
What methods study abandonment?
Longitudinal surveys, scoping reviews, and mixed-methods instruments like LV-PSI classify factors and quantify rates (Lorenzini & Wittich, 2019; Fok, 2011).
What are key papers on abandonment?
Lubarsky (1993; 58 citations) on sociocultural factors; Fok (2011; 20 citations) on $46M losses; Lorenzini & Wittich (2019; 46 citations) on low vision aids.
What open problems exist?
Standardized matching tools, validated across devices; integrating psychosocial training; scaling interventions beyond low vision (Scherer & Federici, 2015; Burgos, 2015).
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