Matching Person & Technology Model

The Matching Person & Technology Model organizes influences on the successful use of a variety of technologies: Assistive Technology, Educational Technology, and those used in the workplace, school, home; for healthcare, for mobility and performing daily activities. Specialized devices for hearing loss, speech, eyesight and cognition as well as general or everyday technologies are also included. Research shows that although a technology may appear perfect for a given need, it May Be used inappropriately or even go unused when critical personality preferences, psychosocial characteristics or needed environmental support are not considered. The use and non-use of technology as conceptualized in the Matching Person & Technology Model has been validated by many researchers and authors representing the fields of occupational therapy, physical therapy, speech language pathology, psychology, and others. The Matching Person and Technology Model is operationalized by a series of reliable and valid measures that provide a person-centered and individualized approach to matching individuals with the most appropriate technologies for their use. The Matching Person & Technology Model and measures were developed by Marcia J. Scherer, Ph.D. beginning in 1986.

MPT Process with Accompanying Assessment Measures

1. Initial Worksheet for the Matching Person and Technology (MPT) Model to determine initial goals, potential interventions, and technologies needed to support attainment of the goals.

2. History of Support Use identifies supports used in the past and satisfaction with those supports.

3. Specific technology matching:

'''

   General:  Survey of Technology Use
   Assistive:  Assistive Technology Device Predisposition Assessment.  
      Also specific types of AT:
      Cognitive Support Technology Predisposition Assessment and 
      Hearing Technology Predisposition Assessment. 
   Educational:  Educational Technology Device Predisposition Assessment    
   Workplace:  Workplace Technology Device Predisposition Assessment
   Healthcare:  Healthcare Technology Predisposition Assessment '''

4. Follow-up versions of the measures to determine degree of use, comparison of expected benefit and realization of benefit and change in functioning and subjective well-being.

External links

• Matching Person & Technology Homepage
• Online Bookstore, Rehabilitation Engineering and Assistive Technology Society of North America or RESNA

References

• Cook, A.M. and Hussey, S. (2001). Assistive Technologies: Principles and Practice (2nd Edition). Publisher: Mosby: ISBN 0-323-00643-4.

• de Jonge, D., Scherer, M. & Rodger, S. (2007). Assistive Technology in the Workplace. St. Louis, MO: Mosby: ISBN 0-323-04130-2.

• Judge, S.L. & Parette, H.P. (1998). Assistive Technology for Young Children with Disabilities: A Guide to Family-Centered Services. Cambridge, MA: Brookline Books: ISBN 1-571-29051-6.

• Lasker, J.P & Bedrosian, J.L. (2001). Promoting acceptance of augmentative and alternative communication by adults with acquired communication disorders. Augmentative and Alternative Communication, 17(3), 141 - 153.

• Scherer, M. J. (2005). Living in the State of Stuck: How Assistive Technology Impacts the Lives of People with Disabilities, Fourth Edition. Cambridge, MA: Brookline Books: ISBN 1-571-29098-2.

• Scherer, M.J. (2004). Connecting to Learn: Educational and Assistive Technology for People with Disabilities. Washington, DC: American Psychological Association (APA) Books: ISBN 1-557-98982-6.

• Scherer, M.J. (Ed.). (2002). Assistive Technology: Matching Device and Consumer for Successful Rehabilitation. Washington, DC: APA Books: ISBN 1-557-98840-4.

• Scherer, MJ & Sax, C. (2009). Measures of assistive technology predisposition and use. In E. Mpofu & T. Oakland (Eds.), Assessment in Rehabilitation and Health. Boston: Allyn & Bacon: ISBN 0-205-50174-5.

• Kirsch, N.L. & Scherer, M.J. (2009). Assistive technology for cognition and behavior. In R.G. Frank , M. Rosenthal & B. Caplan (eds.), Handbook of Rehabilitation Psychology, 2nd edition. Washington, DC: APA Books: ISBN 1-557-98644-4.

• Scherer, M.J., Sax, C., Vanbeirvliet, A. , Cushman, L.A. & Scherer, J.V. (2005). Predictors of assistive technology use: The importance of personal and psychosocial factors. Disability & Rehabilitation, 27(21), 1321-1331.

• Scherer, M. J. (1986). Values in the creation, prescription, and use of technological aids and assistive devices for people with physical disabilities. Doctoral dissertation, University of Rochester, and final report to the National Science Foundation. Dissertation Abstracts International, 48(01), 49. (University Microfilms No. ADG87-08247).

• Trish Wielandt, T., Mckenna, K., Tooth, L. & Strong, J. (2006). Factors that predict the post-discharge use of recommended assistive technology (AT). Disability and Rehabilitation: Assistive Technology, 1(1/2), 29 - 40.

See also

• technology adoption life cycle
• Technology acceptance model