Stephen Uzor
ABSTRACT
Although gait/balance analysis methods have proven effective for assessing falls risk (FR), they are mostly confined to the laboratory and rely on expensive specialist equipment. Recent sensor technologies have made it possible to capture FR data accurately; however, no exploration has been done on how to effectively communicate these data to seniors in both healthcare and free-living settings. We describe IDA (Insole Device for Assessment of Falls Risk), comprising a relatively inexpensive insole and prototype application that provides feedback to stakeholders. To explore what level of FR data should best be communicated to different stakeholders, we conducted workshops with 26 seniors and interviewed 7 healthcare workers in the UK. We highlight stakeholder preferences on viewing FR data to foster greater understanding of outcomes and enhance communication between stakeholders. Finally, we identify opportunities for design on enhancing understanding of gait/balance outcomes; these have potential applications in other areas of physical rehabilitation.
- Mobolaji Ayoade and Lynne Baillie. 2014. A Novel Knee Rehabilitation System for the Home. In Proceedings of the International Conference on Human Factors in Computing Systems (CHI `14). ACM, 2521--30. Google Scholar
Digital Library
- Lynne Baillie. 2002. The Home Workshop: A method for investigating the home (Doctoral dissertation, Edinburgh Napier University)Google Scholar
- Emma Barry, Rose Galvin, Claire Keogh, F. Horgan and T. Fahey. 2014. Is the Timed Up and Go test a useful predictor of risk of falls in community dwelling older adults: a systematic review and meta- analysis. BMC Geriatr. 14.Google Scholar
- Benedikt Braun, Nils Veith, Rebecca Hell, Stefan Döbele, Michael Roland et al. 2015. Validation and reliability testing of a new, fully integrated gait analysis insole. J. Foot Ankle Res. 8.Google Scholar
- Archibald J. Campbell. 1999. Falls Prevention Over 2 Years: A Randomized Controlled Trial in Women 80 Years and Older. Age and Ageing 28, pp. 513--18Google ScholarCross Ref
- Marcelo P. de Castro, Marco Meucci, Denise P. Soares, Pedro Fonseca, Márcio Borgonovo-Santos et al. 2014. Accuracy and Repeatability of the Gait Analysis by the WalkinSense System. BioMed Res. Int. 2014, 1--11.Google ScholarCross Ref
- Ross A. Clark, Stephanie Vernon, Benjamin F. Mentiplay, Kimberly J. Miller, Jennifer L. McGinley et al. 2015. Instrumenting gait assessment using the Kinect in people living with stroke: reliability and association with balance tests. J. NeuroEngineering Rehabil. 12, 15.Google ScholarCross Ref
- J. C. Close, and Stephen R. Lord. 2011. Fall assessment in older people. BMJ 343, d5153--d5153.Google ScholarCross Ref
- GAITRite, CIR Systems Inc. http://www.gaitrite.com/GAITRite.htm. Accessed: 15<sup>th</sup> Sept 2017.Google Scholar
- Alejandro Galán-Mercant, and Antonio I. Cuesta-Vargas. 2014. Mobile Romberg test assessment (mRomberg). BMC Res. Notes 7, 640.Google ScholarCross Ref
- Barney G. Glaser, Anselm L. Strauss, and Elizabeth Strutzel. 1968. The discovery of grounded theory; strategies for qualitative research. Nursing research, 17(4), p.364.Google Scholar
- Richard Harte, Liam Glynn, Alejandro Rodríguez-Molinero, Paul MA Baker, Thomas Scharf et al. 2017. A Human-Centered Design Methodology to Enhance the Usability, Human Factors, and User Experience of Connected Health Systems: A Three-Phase Methodology. JMIR Hum. Factors 4, e8.Google ScholarCross Ref
- Jeffrey M. Hausdorff, Dean A. Rios, and Helen K. Edelberg. 2001. Gait variability and fall risk in community-living older adults: a 1-year prospective study. Arch. Phys. Med. Rehabil. 82, 1050--1056.Google ScholarCross Ref
- J. Howcroft, J. Kofman, and E. D. Lemaire. 2013. Review of fall risk assessment in geriatric populations using inertial sensors. J. NeuroEng. Rehabil. 10, 91.Google ScholarCross Ref
- A. Khasnis and R. M. Gokula. 2003. Romberg's test. J. Postgrad. Med., 49:169Google Scholar
- David Loudon, Bruce Carse and Alastair Macdonald. 2011. Investigating the Use of Visualizations of Biomechanics in Physical Rehabilitation, CENTERIS 2011: ENTERprise Information Systems, pp. 30--39Google Scholar
- Vipul Lugade, Victor Lin, and Li-Shan Chou. 2011. Center of mass and base of support interaction during gait. Gait Posture 33, 406--411.Google ScholarCross Ref
- Hannah R. Marston, Ashley Woodbury, Yves J. Gschwind, Michael Kroll, Denis Fink, Sabine Eichberg, Karl Kreiner et al. 2015. The design of a purpose-built exergame for fall prediction and prevention for older people. Eur. Rev. Aging Phys. Act. 12.Google ScholarCross Ref
- Paramita Mitra, Tahseen Chaudhury and S. A. Ali. 2012. Fragility fractures in the elderly: Evolving approaches in the NHS. Trauma 14, 39--46Google ScholarCross Ref
- Yaejin Moon, Douglas A. Wajda, Robert W. Motl, and Jacob J. Sosnoff. 2015. Stride-Time Variability and Fall Risk in Persons with Multiple Sclerosis. Mult. Scler. Int. 2015, 1--7.Google ScholarCross Ref
- David Oliver. 2008. Falls risk-prediction tools for hospital inpatients. Time to put them to bed? Age Ageing 37, 248--250.Google ScholarCross Ref
- Martin J-D. Otis, Johannes C. Ayena, et al. 2016. Use of an Enactive Insole for Reducing the Risk of Falling on Different Types of Soil Using Vibrotactile Cueing for the Elderly. PLOS ONE 11, e0162107.Google ScholarCross Ref
- Karen L. Perell, Audrey Nelson, Ronald L. Goldman, Stephen L. Luther, Nicole Prieto-Lewis, and Laurence Z. Rubenstein. 2001. Fall Risk Assessment Measures: An Analytic Review. J. Gerontol. A. Biol. Sci. Med. Sci. 56, M761--M766.Google ScholarCross Ref
- Diane Podsiadlo, and Sandra Richardson. 1991. The timed "Up & Go": a test of basic functional mobility for frail elderly persons. J.Am.Geriatr.Soc.39, 142--148Google ScholarCross Ref
- Marilyn Rantz, Marjorie Skubic, Carmen Abbott, Colleen Galambos, Mihail Popescu et al. 2015. Automated In-Home Fall Risk Assessment and Detection Sensor System for Elders. The Gerontologist 55, S78--S87.Google ScholarCross Ref
- Samuel Schülein, Jens Barth, Alexander Rampp, Roland Rupprecht, Björn M. Eskofier et al. 2017. Instrumented gait analysis: a measure of gait improvement by a wheeled walker in hospitalized geriatric patients. J. NeuroEngineering Rehabil. 14.Google Scholar
- Christina Seimetz, Danica Tan, Riemann Katayama, and T. Lockhart. 2012. A comparison between methods of measuring postrual stability: force plates versus accelerometers. Biomed. Sci. Instrum. 48, 386--392Google Scholar
- Pete B. Shull, Wisit Jirattigalachote, Michael A. Hunt, Mark R. Cutkosky and Scott L. Delp. 2014. Quantified self and human movement: A review on the clinical impact of wearable sensing and feedback for gait analysis and intervention. Gait Posture 40, 11--19.Google ScholarCross Ref
- Dawn Skelton and Chris Todd. 2004. What are the main risk factors for falls amongst older people and what are the most effective interventions to prevent these falls? How should interventions to prevent falls be implemented. World Health OrganizationGoogle Scholar
- Weijun Tao, Tao Liu, Rencheng Zheng and Hutian Feng. 2012. Gait Analysis Using Wearable Sensors. Sensors 12, 2255--2283.Google ScholarCross Ref
- Anne Tiedemann, Hiroyuki Shimada, Catherine Sherrington, Susan Murray, and Stephen Lord. 2008. The comparative ability of eight functional mobility tests for predicting falls in community-dwelling older people. Age Ageing 37, 430--435.Google ScholarCross Ref
- Mary E. Tinetti, Mark Speechley, and Sandra F. Ginter. 1988. Risk Factors for Falls among Elderly Persons Living in the Community. N. Engl. J. Med. 319, 1701--1707.Google ScholarCross Ref
- Stephen Uzor and Lynne Baillie. 2014. Investigating the Long-Term Use of Exergames in the Home with Elderly Fallers. In Proceedings of the International Conference on Human Factors in Computing Systems (CHI `14). ACM, New York, NY, 2813--22. Google ScholarDigital Library
- Vicon Motion Systems (https://www.vicon.com/). Accessed: 15<sup>th</sup> Sept 2017.Google Scholar
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