Author(s):

  • Julia Thorpe
  • Birgitte Hysse Forchhammer
  • Anja M Maier

Abstract:

Background: Mobile and wearable devices are increasingly being used to support our everyday lives and track our behavior. Since daily support and behavior tracking are two core components of cognitive rehabilitation, such personal devices could be employed in rehabilitation approaches aimed at improving independence and engagement among people with dementia.

Objective: The aim of this work was to investigate the feasibility of using smartphones and smartwatches to augment rehabilitation by providing adaptable, personalized support and objective, continuous measures of mobility and activity behavior.

Methods: A feasibility study comprising 6 in-depth case studies was carried out among people with early-stage dementia and their caregivers. Participants used a smartphone and smartwatch for 8 weeks for personalized support and followed goals for quality of life. Data were collected from device sensors and logs, mobile self-reports, assessments, weekly phone calls, and interviews. This data were analyzed to evaluate the utility of sensor data generated by devices used by people with dementia in an everyday life context; this was done to compare objective measures with subjective reports of mobility and activity and to examine technology acceptance focusing on usefulness and health efficacy.

Results: Adequate sensor data was generated to reveal behavioral patterns, even for minimal device use. Objective mobility and activity measures reflecting fluctuations in participants’ self-reported behavior, especially when combined, may be advantageous in revealing gradual trends and could provide detailed insights regarding goal attainment ratings. Personalized support benefited all participants to varying degrees by addressing functional, memory, safety, and psychosocial needs. A total of 4 of 6 (67%) participants felt motivated to be active by tracking their step count. One participant described a highly positive impact on mobility, anxiety, mood, and caregiver burden, mainly as a result of navigation support and location-tracking tools.

Conclusions: Smartphones and wearables could provide beneficial and pervasive support and monitoring for rehabilitation among people with dementia. These results substantiate the need for further investigation on a larger scale, especially considering the inevitable presence of mobile and wearable technology in our everyday lives for years to come.

Documentation:

https://doi.org/10.2196/12346

References:
  1. The Lancet Neurology. Response to the growing dementia burden must be faster. Lancet Neurol 2018 Aug;17(8):651. [CrossRef] [Medline]
  2. World Health Organization. Global Action Plan on the Public Health Response to Dementia: 2017-2025. Geneva, Switzerland: World Health Organization; 2017.   URL: https://apps.who.int/iris/bitstream/handle/10665/259615/9789241513487-eng.pdf?sequence=1 [accessed 2019-10-08]
  3. Wade DT, de Jong BA. Recent advances in rehabilitation. BMJ 2000 May 20;320(7246):1385-1388 [FREE Full text] [CrossRef] [Medline]
  4. Wade D. Describing rehabilitation interventions. Clin Rehabil 2005 Dec;19(8):811-818. [CrossRef] [Medline]
  5. Bahar-Fuchs A, Clare L, Woods B. Cognitive training and cognitive rehabilitation for mild to moderate Alzheimer’s disease and vascular dementia. Cochrane Database Syst Rev 2013 Jun 05(6):CD003260. [CrossRef] [Medline]
  6. Martínez-Alcalá CI, Pliego-Pastrana P, Rosales-Lagarde A, Lopez-Noguerola JS, Molina-Trinidad EM. Information and communication technologies in the care of the elderly: Systematic review of applications aimed at patients with dementia and caregivers. JMIR Rehabil Assist Technol 2016 May 02;3(1):e6 [FREE Full text] [CrossRef] [Medline]
  7. Lauriks S, Reinersmann A, Van der Roest HG, Meiland FJ, Davies RJ, Moelaert F, et al. Review of ICT-based services for identified unmet needs in people with dementia. Ageing Res Rev 2007 Oct;6(3):223-246. [CrossRef] [Medline]
  8. LaMonica HM, English A, Hickie IB, Ip J, Ireland C, West S, et al. Examining Internet and eHealth practices and preferences: Survey study of Australian older adults with subjective memory complaints, mild cognitive impairment, or dementia. J Med Internet Res 2017 Oct 25;19(10):e358 [FREE Full text] [CrossRef] [Medline]
  9. Maier A, Özkil A, Bang M, Hysse FB, Forchhammer B. Remember to remember: A feasibility study adapting wearable technology to the needs of people aged 65 and older with mild cognitive impairment (MCI) and Alzheimer’s dementia. In: Proceedings of the 20th International Conference on Engineering Design (ICED15). 2015 Presented at: 20th International Conference on Engineering Design (ICED15); July 27-30, 2015; Milan, Italy p. 331-340   URL: https://orbit.dtu.dk/files/107110242/Remember_to_remember.pdf
  10. Stucki RA, Urwyler P, Rampa L, Müri R, Mosimann UP, Nef T. A Web-based nonintrusive ambient system to measure and classify activities of daily living. J Med Internet Res 2014 Jul 21;16(7):e175 [FREE Full text] [CrossRef] [Medline]
  11. Evald L. Prospective memory rehabilitation using smartphones in patients with TBI. Disabil Rehabil 2018 Sep;40(19):2250-2259. [CrossRef] [Medline]
  12. Liddle J, Ireland D, McBride SJ, Brauer SG, Hall LM, Ding H, et al. Measuring the lifespace of people with Parkinson’s disease using smartphones: Proof of principle. JMIR Mhealth Uhealth 2014 Mar 12;2(1):e13 [FREE Full text] [CrossRef] [Medline]
  13. Zylstra B, Netscher G, Jacquemot J, Schaffer M, Shen G, Bowhay AD, et al. Extended, continuous measures of functional status in community dwelling persons with Alzheimer’s and related dementia: Infrastructure, performance, tradeoffs, preliminary data, and promise. J Neurosci Methods 2018 Apr 15;300:59-67 [FREE Full text] [CrossRef] [Medline]
  14. Dicianno BE, Henderson G, Parmanto B. Design of mobile health tools to promote goal achievement in self-management tasks. JMIR Mhealth Uhealth 2017 Jul 24;5(7):e103 [FREE Full text] [CrossRef] [Medline]
  15. Hood L, Balling R, Auffray C. Revolutionizing medicine in the 21st century through systems approaches. Biotechnol J 2012 Aug;7(8):992-1001 [FREE Full text] [CrossRef] [Medline]
  16. Sagner M, McNeil A, Puska P, Auffray C, Price ND, Hood L, et al. The P4 health spectrum: A predictive, preventive, personalized and participatory continuum for promoting healthspan. Prog Cardiovasc Dis 2017;59(5):506-521. [CrossRef] [Medline]
  17. Thorpe J, Forchhammer B, Maier A. Development of a sensor-based behavioral monitoring solution to support dementia care. JMIR Mhealth Uhealth 2019 May 30;7(6):e12013 [FREE Full text] [CrossRef] [Medline]
  18. Thorpe JR, Rønn-Andersen KV, Bień P, Özkil AG, Forchhammer BH, Maier AM. Pervasive assistive technology for people with dementia: A UCD case. Healthc Technol Lett 2016 Dec;3(4):297-302 [FREE Full text] [CrossRef] [Medline]
  19. Baker P, Bodner EV, Allman RM. Measuring life-space mobility in community-dwelling older adults. J Am Geriatr Soc 2003 Nov;51(11):1610-1614. [CrossRef] [Medline]
  20. Bull FC, Maslin TS, Armstrong T. Global physical activity questionnaire (GPAQ): Nine country reliability and validity study. J Phys Act Health 2009 Nov;6(6):790-804. [Medline]
  21. Allet L, Knols RH, Shirato K, de Bruin ED. Wearable systems for monitoring mobility-related activities in chronic disease: A systematic review. Sensors (Basel) 2010;10(10):9026-9052 [FREE Full text] [CrossRef] [Medline]
  22. Canzian L, Musolesi M. Trajectories of depression: Unobtrusive monitoring of depressive states by means of smartphone mobility traces analysis. In: Proceedings of the ACM International Joint Conference on Pervasive and Ubiquitous Computing. New York, NY: ACM; 2015 Presented at: ACM International Joint Conference on Pervasive and Ubiquitous Computing; September 7-11, 2015; Osaka, Japan p. 1293-1304. [CrossRef]
  23. Boissy P, Brière S, Hamel M, Jog M, Speechley M, Karelis A, EMAP Group. Wireless inertial measurement unit with GPS (WIMU-GPS): Wearable monitoring platform for ecological assessment of lifespace and mobility in aging and disease. Conf Proc IEEE Eng Med Biol Soc 2011;2011:5815-5819. [CrossRef] [Medline]
  24. Hirsch J, Winters M, Clarke P, McKay H. Generating GPS activity spaces that shed light upon the mobility habits of older adults: A descriptive analysis. Int J Health Geogr 2014 Dec 12;13:51 [FREE Full text] [CrossRef] [Medline]
  25. Turner-Stokes L. Goal attainment scaling (GAS) in rehabilitation: A practical guide. Clin Rehabil 2009 Apr;23(4):362-370. [CrossRef] [Medline]
  26. Davis FD. Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q 1989 Sep;13(3):319. [CrossRef]
  27. Bardram JE. CACHET Unified Methodology for Assessment of Clinical Feasibility. Copenhagen, Denmark: Copenhagen Center for Health Technology; 2018.   URL: http:/​/www.​cachet.dk/​-/​media/​Sites/​Cachet/​Layout-og-arkiv/​Arkiv/​cumacf/​cumacf.​ashx?la=da&hash=097004CD61CE0EBA12AFABAF46D79AA17290483D [accessed 2019-10-09]
  28. Watermeyer TJ, Hindle JV, Roberts J, Lawrence CL, Martyr A, Lloyd-Williams H, et al. Goal setting for cognitive rehabilitation in mild to moderate Parkinson’s disease dementia and dementia with Lewy bodies. Parkinsons Dis 2016:8285041-8285048 [FREE Full text] [CrossRef] [Medline]