Skip to main content
SpringerLink
Log in

Digital Health Solutions Transforming Long-Term Care and Rehabilitation

  • Chapter
  • First Online:
Healthcare Information Management Systems

Part of the book series: Health Informatics ((HI))

Abstract

Emerging digital healthcare solutions (DHS) have opened wide range of opportunities for tele-monitoring and improvements in health behavior. These solutions not only help monitor health status, but also aid towards diagnosis, prevention and better management of health conditions. DHS have a broad scope in long-term care, disease management as well as addressing psychological and social needs of patients. In this chapter we discuss tele-monitoring solutions for long-term care and solutions for rehabilitation.

Long-term care includes a wide range of care services for patients of varied age groups with chronic conditions or functional disabilities. Their requirements can vary from minimal help for conducting daily activities to complete care. Tele-monitoring assistance can aid self-monitoring for such patients while also being digitally connected with their health care providers. The scope of these solutions for long-term care includes addressing issues such as fatigue and anxiety, quality of life, nutrition, sleep, physical activity, etc.

The advancements in rehabilitation technologies are increasingly enhancing the role of rehabilitation in building and maintaining the self-dependence and quality of life of patients. The field of rehabilitation often requires complex technologies, such as virtual reality, robotics and haptic devices. The healthcare application of these technologies revolves around providing solutions for efficient home rehabilitation, multimodal approaches for recovery, to support activities of daily living and to enhance clinical assessment. Thus, the use of emerging technologies can aid family members of apparently healthy older adults and also detect mild symptoms while relying on a user-friendly solution.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 59.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 79.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Agrawal R, Prabakaran S. Big data in digital healthcare: lessons learnt and recommendations for general practice. Heredity. 2020;124(4):525–34.

    Article  Google Scholar 

  2. Alliance DT. Transforming global healthcare by advancing digital therapeutics. [Cited 2021 Feb 8]. Available from: https://dtxalliance.org/.

  3. Food & Drug Administration. What is digital health? 2020 [cited 2021 Feb 8]. Available from: https://www.fda.gov/medical-devices/digital-health-center-excellence/what-digital-health.

  4. Le LB, et al. Patient satisfaction and healthcare utilization using telemedicine in liver transplant recipients. Dig Dis Sci. 2019;64(5):1150–7.

    Article  Google Scholar 

  5. Cheng AS, et al. Breast cancer application protocol: a randomised controlled trial to evaluate a self-management app for breast cancer survivors. BMJ Open. 2020;10(7):e034655.

    Article  Google Scholar 

  6. Lozano-Lozano M, et al. Mobile health and supervised rehabilitation versus mobile health alone in breast cancer survivors: randomized controlled trial. Ann Phys Rehabil Med. 2020;63(4):316–24.

    Article  Google Scholar 

  7. Silva EH, Lawler S, Langbecker D. The effectiveness of mHealth for self-management in improving pain, psychological distress, fatigue, and sleep in cancer survivors: a systematic review. J Cancer Surviv. 2019;13(1):97–107.

    Article  Google Scholar 

  8. Nasi G, Cucciniello M, Guerrazzi C. The role of mobile technologies in health care processes: the case of cancer supportive care. J Med Internet Res. 2015;17(2):e26.

    Article  Google Scholar 

  9. Kohler PO, Wunderlich GS. Improving the quality of long-term care. National Academies Press; 2001.

    Google Scholar 

  10. Walkden J-A, McCullagh PJ, Kernohan WG. Patient and carer survey of remote vital sign telemonitoring for self-management of long-term conditions. BMJ Health Care Inform. 2019;26(1):e100079.

    Article  Google Scholar 

  11. Eggermont LH, et al. Pain characteristics associated with the onset of disability in older adults: the maintenance of balance, independent living, intellect, and zest in the Elderly Boston Study. J Am Geriatr Soc. 2014;62(6):1007–16.

    Article  Google Scholar 

  12. Foster C, et al. A web-based intervention (RESTORE) to support self-management of cancer-related fatigue following primary cancer treatment: a multi-centre proof of concept randomised controlled trial. Support Care Cancer. 2016;24(6):2445–53.

    Article  Google Scholar 

  13. Huberty J, et al. Experiences of using a consumer-based mobile meditation app to improve fatigue in myeloproliferative patients: qualitative study. JMIR Cancer. 2019;5(2):e14292.

    Article  Google Scholar 

  14. Rico TM, et al. Use of text messaging (SMS) for the management of side effects in cancer patients undergoing chemotherapy treatment: a randomized controlled trial. J Med Syst. 2020;44(11):1–12.

    Article  Google Scholar 

  15. Dorri S, et al. A Systematic Review of Electronic Health (eHealth) interventions to improve physical activity in patients with breast cancer. Breast Cancer. 2020;27(1):25–46.

    Article  Google Scholar 

  16. Delmastro F, Di Martino F, Dolciotti C. Cognitive training and stress detection in mci frail older people through wearable sensors and machine learning. IEEE Access. 2020;8:65573–90.

    Article  Google Scholar 

  17. Polanski J, et al. Quality of life of patients with lung cancer. Onco Targets Ther. 2016;9:1023.

    Google Scholar 

  18. Lozano-Lozano M, et al. A mobile system to improve quality of life via energy balance in breast cancer survivors (BENECA mHealth): prospective test-retest Quasiexperimental feasibility study. JMIR Mhealth Uhealth. 2019;7(6):e14136.

    Article  Google Scholar 

  19. Yang J, et al. Development and testing of a mobile app for pain management among cancer patients discharged from hospital treatment: randomized controlled trial. JMIR Mhealth Uhealth. 2019;7(5):e12542.

    Article  Google Scholar 

  20. Parks AC, et al. The effects of a digital well-being intervention on patients with chronic conditions: observational study. J Med Internet Res. 2020;22(1):e16211.

    Article  Google Scholar 

  21. Fox RS, et al. Sleep disturbance and cancer-related fatigue symptom cluster in breast cancer patients undergoing chemotherapy. Support Care Cancer. 2020;28(2):845–55.

    Article  Google Scholar 

  22. Randerath W, et al. Definition, discrimination, diagnosis and treatment of central breathing disturbances during sleep. Eur Respirat J. 2017;49(1):1600959.

    Article  Google Scholar 

  23. Martin MS, et al. Sleep perception in non-insomniac healthy elderly: a 3-year longitudinal study. Rejuvenation Res. 2014;17(1):11–8.

    Article  Google Scholar 

  24. Ong AA, Gillespie MB. Overview of smartphone applications for sleep analysis. World J Otorhinolaryngol Head Neck Surg. 2016;2(1):45–9.

    Article  Google Scholar 

  25. Lévi F, et al. Tele-monitoring of cancer patients’ rhythms during daily life identifies actionable determinants of circadian and sleep disruption. Cancers. 2020;12(7):1938.

    Article  Google Scholar 

  26. O’Connor-Reina C, et al. Myofunctional therapy app for severe apnea–hypopnea sleep obstructive syndrome: pilot randomized controlled trial. JMIR Mhealth Uhealth. 2020;8(11):e23123.

    Article  Google Scholar 

  27. Espie CA, et al. Effect of digital cognitive behavioral therapy for insomnia on health, psychological well-being, and sleep-related quality of life: a randomized clinical trial. JAMA Psychiat. 2019;76(1):21–30.

    Article  Google Scholar 

  28. Chumpangern W, Chirakalwasan N. Tele-monitoring system implementation in continuous positive airway pressure therapy in Asian obstructive sleep apnea. Eur Respirat Soc. 2020;56:1344.

    Google Scholar 

  29. McDonnell KK, et al. A prospective pilot study evaluating feasibility and preliminary effects of breathe easier: a mindfulness-based intervention for survivors of lung cancer and their family members (dyads). Integr Cancer Ther. 2020;19:1534735420969829.

    Article  Google Scholar 

  30. Asif-Ur-Rahman M, et al. Toward a heterogeneous mist, fog, and cloud-based framework for the internet of healthcare things. IEEE Internet Things J. 2018;6(3):4049–62.

    Article  Google Scholar 

  31. Martin B, et al. Economic benefits of the health-enhancing effects of physical activity: first estimates for Switzerland. Schweizerische Zeitschrift für Sportmedizin und Sporttraumatologie. 2001;49(3):131–3.

    Google Scholar 

  32. World Health Organization. Global recommendations on physical activity for health. 2010 [cited 2021 Feb 9]. Available from: https://www.who.int/publications/i/item/9789241599979.

  33. Beddhu S, et al. Physical activity and mortality in chronic kidney disease (NHANES III). Clin J Am Soc Nephrol. 2009;4(12):1901–6.

    Article  Google Scholar 

  34. Marley J, et al. The effectiveness of interventions aimed at increasing physical activity in adults with persistent musculoskeletal pain: a systematic review and meta-analysis. BMC Musculoskelet Disord. 2017;18(1):1–20.

    Article  Google Scholar 

  35. Clarkson P, et al. (Protocol) Maintaining physical activity through the use of digital tools for people with a long-term condition/s (LTCs): a scoping review. Protocols. io. 2020.

    Google Scholar 

  36. Vandelanotte C, et al. Past, present, and future of eHealth and mHealth research to improve physical activity and dietary behaviors. J Nutr Educ Behav. 2016;48(3):219–28.

    Article  Google Scholar 

  37. Daskalopoulou C, et al. Physical activity and healthy ageing: a systematic review and meta-analysis of longitudinal cohort studies. Ageing Res Rev. 2017;38:6–17.

    Article  CAS  Google Scholar 

  38. Torous J, Cerrato P, Halamka J. Targeting depressive symptoms with technology. Mhealth. 2019;5:19.

    Article  Google Scholar 

  39. Robertson MC, et al. Mobile health physical activity intervention preferences in cancer survivors: a qualitative study. JMIR Mhealth Uhealth. 2017;5(1):e3.

    Article  Google Scholar 

  40. Kanera IM, et al. Long-term effects of a web-based cancer aftercare intervention on moderate physical activity and vegetable consumption among early cancer survivors: a randomized controlled trial. Int J Behav Nutr Phys Act. 2017;14(1):1–13.

    Article  Google Scholar 

  41. Hartman SJ, Nelson SH, Weiner LS. Patterns of Fitbit use and activity levels throughout a physical activity intervention: exploratory analysis from a randomized controlled trial. JMIR Mhealth Uhealth. 2018;6(2):e29.

    Article  Google Scholar 

  42. Fujisawa D, et al. Actigraphy as an assessment of performance status in patients with advanced lung cancer. Palliat Support Care. 2019;17(5):574–8.

    Article  Google Scholar 

  43. Carpenter MJ, et al. Nicotine replacement therapy sampling for smoking cessation within primary care: results from a pragmatic cluster randomized clinical trial. Addiction. 2020;115(7):1358–67.

    Article  Google Scholar 

  44. Borrelli B, et al. Prevalence and frequency of mHealth and eHealth use among US and UK smokers and differences by motivation to quit. J Med Internet Res. 2015;17(7):e164.

    Article  Google Scholar 

  45. Regmi K, et al. Effectiveness of mobile apps for smoking cessation: a review. Tobac Prevent Cessat. 2017;3(12):1–11.

    Google Scholar 

  46. Ubhi HK, et al. A mobile app to aid smoking cessation: preliminary evaluation of SmokeFree28. J Med Internet Res. 2015;17(1):e17.

    Article  Google Scholar 

  47. Reck M, et al. Metastatic non-small-cell lung cancer (NSCLC): ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up. Ann Oncol. 2014;25:iii27–39.

    Article  Google Scholar 

  48. Garrison KA, et al. Craving to quit: a randomized controlled trial of smartphone app–based mindfulness training for smoking cessation. Nicot Tobacco Res. 2020;22(3):324–31.

    Article  Google Scholar 

  49. World Health Organization. Be healthy, be mobile: annual report 2018. 2019.

    Google Scholar 

  50. Hors-Fraile S, et al. A recommender system to quit smoking with mobile motivational messages: study protocol for a randomized controlled trial. Trials. 2018;19(1):1–12.

    Article  Google Scholar 

  51. Thomas DR. Nutrition assessment in long-term care. Nutr Clin Pract. 2008;23(4):383–7.

    Article  Google Scholar 

  52. Phillips MB, et al. Nutritional screening in community-dwelling older adults: a systematic literature review. Asia Pac J Clin Nutr. 2010;19(3):440.

    Google Scholar 

  53. Michel M, Burbidge A. Nutrition in the digital age-How digital tools can help to solve the personalized nutrition conundrum. Trends Food Sci Technol. 2019;90:194–200.

    Article  CAS  Google Scholar 

  54. Orlemann T, et al. A novel mobile phone app (OncoFood) to record and optimize the dietary behavior of oncologic patients: pilot study. JMIR cancer. 2018;4(2):e10703.

    Article  Google Scholar 

  55. Halse RE, et al. Correction: improving nutrition and activity behaviors using digital technology and tailored feedback: protocol for the tailored diet and activity (ToDAy) randomized controlled trial. JMIR Res Protocol. 2020;9(12):e25940.

    Article  Google Scholar 

  56. Agapito G, et al. DIETOS: a recommender system for health profiling and diet management in chronic diseases. In: HealthRecSys@ RecSys. 2017.

    Google Scholar 

  57. Norouzi S, et al. A mobile application for managing diabetic patients’ nutrition: a food recommender system. Arch Iran Med. 2018;21(10):466–72.

    Google Scholar 

  58. De Cola MC, et al. Tele-health services for the elderly: a novel southern Italy family needs-oriented model. J Telemed Telecare. 2016;22(6):356–62.

    Article  Google Scholar 

  59. Silva de Lima AL, et al. Home-based monitoring of falls using wearable sensors in Parkinson’s disease. Mov Disord. 2020;35(1):109–15.

    Article  Google Scholar 

  60. Endicott KM, et al. A modified activity protocol for claudication. J Cardiovasc Surg. 2018;60(3):382–7.

    Google Scholar 

  61. Humphreys J, et al. Rapid implementation of inpatient telepalliative medicine consultations during COVID-19 pandemic. J Pain Symptom Manag. 2020;60(1):e54–9.

    Article  Google Scholar 

  62. Stoddart A, et al. Telemonitoring for chronic obstructive pulmonary disease: a cost and cost-utility analysis of a randomised controlled trial. J Telemed Telecare. 2015;21(2):108–18.

    Article  Google Scholar 

  63. María Cavanillas J, Curry E, Wahlster W. New horizons for a data-driven economy: a roadmap for usage and exploitation of big data in Europe. Springer; 2016.

    Google Scholar 

  64. Jensen RE, et al. Review of electronic patient-reported outcomes systems used in cancer clinical care. J Oncol Pract. 2014;10(4):e215–22.

    Article  Google Scholar 

  65. Jensen RE, Gummerson SP, Chung AE. Overview of patient-facing systems in patient-reported outcomes collection: focus and design in cancer care. J Oncol Pract. 2016;12(10):873.

    Article  Google Scholar 

  66. De La Torre-Díez I, et al. Cost-utility and cost-effectiveness studies of telemedicine, electronic, and mobile health systems in the literature: a systematic review. Telemed e-Health. 2015;21(2):81–5.

    Article  Google Scholar 

  67. Sullivan AN, Lachman ME. Behavior change with fitness technology in sedentary adults: a review of the evidence for increasing physical activity. Front Public Health. 2016;4:289.

    Google Scholar 

  68. Cheatham SW, et al. The efficacy of wearable activity tracking technology as part of a weight loss program: a systematic review. J Sports Med Phys Fitness. 2018;58(4):534–48.

    Article  Google Scholar 

  69. Demos JN, Getting started with neurofeedback. N.Y.W.W.N. Company; 2005.

    Google Scholar 

  70. Burdea GC. Virtual rehabilitation--benefits and challenges. Methods Inf Med. 2003;42(5):519–23.

    Article  CAS  Google Scholar 

  71. Pollock A, et al. Interventions for improving upper limb function after stroke. Cochrane Database Syst Rev. 2014;11:CD010820.

    Google Scholar 

  72. Muratori LM, et al. Applying principles of motor learning and control to upper extremity rehabilitation. J Hand Ther. 2013;26(2):94–102. quiz 103

    Article  Google Scholar 

  73. Cirstea MC, Levin MF. Improvement of arm movement patterns and endpoint control depends on type of feedback during practice in stroke survivors. Neurorehabil Neural Repair. 2007;21(5):398–411.

    Article  CAS  Google Scholar 

  74. Fasoli SE, Krebs HI, Hogan N. Robotic technology and stroke rehabilitation: translating research into practice. Top Stroke Rehabil. 2004;11(4):11–9.

    Article  Google Scholar 

  75. Comani S, et al. Monitoring neuro-motor recovery from stroke with high-resolution EEG, robotics and virtual reality: a proof of concept. IEEE Trans Neural Syst Rehabil Eng. 2015;23(6):1106–16.

    Article  Google Scholar 

  76. Béjot Y, et al. Epidemiology of stroke in Europe and trends for the 21st century. Presse Med. 2016;45(12 Pt 2):e391–8.

    Article  Google Scholar 

  77. Cao S. Virtual reality applications in rehabilitation. Cham: Springer; 2016.

    Book  Google Scholar 

  78. Laver KE, et al. Virtual reality for stroke rehabilitation. Cochrane Database Syst Rev. 2017;11(11):Cd008349.

    Google Scholar 

  79. Tieri G, et al. Virtual reality in cognitive and motor rehabilitation: facts, fiction and fallacies. Expert Rev Med Devices. 2018;15(2):107–17.

    Article  CAS  Google Scholar 

  80. Munroe C, et al. Augmented reality eyeglasses for promoting home-based rehabilitation for children with cerebral palsy. In: The eleventh ACM/IEEE international conference on human robot interaction. Christchurch, NZ: New York, NY: IEEE Press; 7–10 March 2016.

    Google Scholar 

  81. Clark WE, Sivan M, O’Connor RJ. Evaluating the use of robotic and virtual reality rehabilitation technologies to improve function in stroke survivors: a narrative review. J Rehabil Assist Technol Eng. 2019;6:2055668319863557.

    Google Scholar 

  82. Alexandre R, Postolache O, Girão PS. Physical rehabilitation based on smart wearable and virtual reality serious game. In: 2019 IEEE international instrumentation and measurement technology conference (I2MTC); 2019.

    Google Scholar 

  83. Parker J, Powell L, Mawson S. Effectiveness of upper limb wearable technology for improving activity and participation in adult stroke survivors: systematic review. J Med Internet Res. 2020;22(1):e15981.

    Article  Google Scholar 

  84. Nelson ME, et al. Physical activity and public health in older adults: recommendation from the American College of Sports Medicine and the American Heart Association. Med Sci Sports Exerc. 2007;39(8):1435–45.

    Article  Google Scholar 

  85. Zak M, Swine C, Grodzicki T. Combined effects of functionally-oriented exercise regimens and nutritional supplementation on both the institutionalised and free-living frail elderly (double-blind, randomised clinical trial). BMC Public Health. 2009;9:39.

    Article  Google Scholar 

  86. Kaminska MS, et al. The effectiveness of virtual reality training in reducing the risk of falls among elderly people. Clin Interv Aging. 2018;13:2329–38.

    Article  Google Scholar 

  87. Berwick DM, Nolan TW, Whittington J. The triple aim: care, health, and cost. Health Aff (Millwood). 2008;27(3):759–69.

    Article  Google Scholar 

  88. Noel K, et al. Tele-transitions of care. A 12-month, parallel-group, superiority randomized controlled trial protocol, evaluating the use of telehealth versus standard transitions of care in the prevention of avoidable hospital readmissions. Contemp Clin Trials Commun. 2018;12:9–16.

    Article  Google Scholar 

  89. Aisen ML, et al. The effect of robot-assisted therapy and rehabilitative training on motor recovery following stroke. Arch Neurol. 1997;54(4):443–6.

    Article  CAS  Google Scholar 

  90. Brokaw EB, et al. Robotic therapy provides a stimulus for upper limb motor recovery after stroke that is complementary to and distinct from conventional therapy. Neurorehabil Neural Repair. 2014;28(4):367–76.

    Article  Google Scholar 

  91. Brunner I, et al. Virtual reality training for upper extremity in subacute stroke (VIRTUES): a multicenter RCT. Neurology. 2017;89(24):2413–21.

    Article  Google Scholar 

  92. Carabeo CGG, et al. Stroke Patient Rehabilitation. Simulat Gam. 2014;45(2):151–66.

    Article  Google Scholar 

  93. Hijmans JM, et al. Bilateral upper-limb rehabilitation after stroke using a movement-based game controller. J Rehabil Res Dev. 2011;48(8):1005–13.

    Article  Google Scholar 

  94. Fluet GG, et al. Robotic/virtual reality intervention program individualized to meet the specific sensorimotor impairments of an individual patient: a case study. Int J Disabil Hum Dev. 2014;13(3):401–7.

    Article  Google Scholar 

  95. Abdullah HA, et al. Results of clinicians using a therapeutic robotic system in an inpatient stroke rehabilitation unit. J Neuroeng Rehabil. 2011;8:50.

    Article  Google Scholar 

  96. Askin A, et al. Effects of Kinect-based virtual reality game training on upper extremity motor recovery in chronic stroke. Somatosens Mot Res. 2018;35(1):25–32.

    Article  Google Scholar 

  97. King M, et al. An affordable, computerised, table-based exercise system for stroke survivors. Disabil Rehabil Assist Technol. 2010;5(4):288–93.

    Article  Google Scholar 

  98. Deutsch JE, et al. Development and application of virtual reality technology to improve hand use and gait of individuals post-stroke. Restor Neurol Neurosci. 2004;22(3–5):371–86.

    Google Scholar 

  99. Yeh SC, et al. The efficacy of a haptic-enhanced virtual reality system for precision grasp acquisition in stroke rehabilitation. J Healthc Eng. 2017;2017:9840273.

    Article  Google Scholar 

  100. Byl NN, et al. Chronic stroke survivors achieve comparable outcomes following virtual task specific repetitive training guided by a wearable robotic orthosis (UL-EXO7) and actual task specific repetitive training guided by a physical therapist. J Hand Ther. 2013;26(4):343–52. Quiz 352.

    Article  Google Scholar 

  101. Huang X, et al. The combined effects of adaptive control and virtual reality on robot-assisted fine hand motion rehabilitation in chronic stroke patients: a case study. J Stroke Cerebrovasc Dis. 2018;27(1):221–8.

    Article  Google Scholar 

  102. Adamovich SV, et al. Design of a complex virtual reality simulation to train finger motion for persons with hemiparesis: a proof of concept study. J Neuroeng Rehabil. 2009;6:28.

    Article  Google Scholar 

  103. Adamovich SV, et al. Incorporating haptic effects into three-dimensional virtual environments to train the hemiparetic upper extremity. IEEE Trans Neural Syst Rehabil Eng. 2009;17(5):512–20.

    Article  Google Scholar 

  104. Rowe JB, et al. Robotic assistance for training finger movement using a hebbian model: a randomized controlled trial. Neurorehabil Neural Repair. 2017;31(8):769–80.

    Article  Google Scholar 

  105. Broeren J, et al. Assessment and training in a 3-dimensional virtual environment with haptics: a report on 5 cases of motor rehabilitation in the chronic stage after stroke. Neurorehabil Neural Repair. 2007;21(2):180–9.

    Article  Google Scholar 

  106. Mapping connections: an understanding of neurological conditions in Canada—Protecting Canadians from illness. Public Health Agency of Canada. 2014 [cited 2020 Mar 20]. Available from: https://www.phac-aspc.gc.ca/publicat/cd-mc/mc-ec/assets/pdf/mc-ec-eng.pdf.

  107. Making the case for investing in mental health in Canada. Calgary: Mental Health Commission of Canada. 2011 [cited 2020 Mar 20]. Available from: https://www.mentalhealthcommission.ca/sites/default/files/2016-06/Investing_in_Mental_Health_FINAL_Version_ENG.pdf.

  108. García-Betances RI, et al. A succinct overview of virtual reality technology use in Alzheimer’s disease. Front Aging Neurosci. 2015;7:80.

    Google Scholar 

  109. Mitchell AJ, Malladi S. Screening and case finding tools for the detection of dementia. Part I: evidence-based meta-analysis of multidomain tests. Am J Geriatr Psychiatry. 2010;18(9):759–82.

    Article  Google Scholar 

  110. Ashford JW, et al. Should older adults be screened for dementia? It is important to screen for evidence of dementia! Alzheimers Dement. 2007;3(2):75–80.

    Article  Google Scholar 

  111. Solomon PR, Murphy CA. Should we screen for Alzheimer’s disease? A review of the evidence for and against screening Alzheimer’s disease in primary care practice. Geriatrics. 2005;60(11):26–31.

    Google Scholar 

  112. Connell CM, et al. Attitudes toward the diagnosis and disclosure of dementia among family caregivers and primary care physicians. The Gerontologist. 2004;44(4):500–7.

    Article  Google Scholar 

  113. Boise L, et al. Delays in the diagnosis of dementia: perspectives of family caregivers. Am J Alzheimers Dis. 1999;14(1):20–6.

    Article  Google Scholar 

  114. Maguire CP, et al. Family members’ attitudes toward telling the patient with Alzheimer’s disease their diagnosis. BMJ (Clin Res Ed). 1996;313(7056):529–30.

    Article  CAS  Google Scholar 

  115. Fortinsky RH. How linked are physicians to community support services for their patients with dementia? J Appl Gerontol. 1998;17(4):480–98.

    Article  Google Scholar 

  116. Glasser M, Miller B. Caregiver and physician perspectives of medical encounters involving dementia patients. Am J Alzheimers Dis. 1998;13(2):70–80.

    Article  Google Scholar 

  117. Halbach T, et al. A mobile application for supporting dementia relatives: a case study. Stud Health Technol Inform. 2018;256:839–46.

    Google Scholar 

  118. Lucero A, et al, Mobile collocated interactions interactions, 2013;20(2).

    Google Scholar 

  119. Morone G, et al. Wearable devices and virtual reality for neurorehabilitation: an opportunity for home rehabilitation, in converging clinical and engineering research on. NeuroRehabilitation. 2019;III:601–5.

    Google Scholar 

  120. Pérez C, et al, A novel approach to integrate VR exer-games for stroke rehabilitation: evaluating the implementation of a ‘games room’. 2017. p. 1–7.

    Google Scholar 

  121. Meiland F, et al. Technologies to support community-dwelling persons with dementia: a position paper on issues regarding development, usability, effectiveness and cost-effectiveness, deployment, and ethics. JMIR Rehabil Assist Technol. 2017;4(1):e1.

    Article  Google Scholar 

  122. Syed-Abdul S, et al. Virtual reality among the elderly: a usefulness and acceptance study from Taiwan. BMC Geriatr. 2019;19(1):223.

    Article  Google Scholar 

  123. Seo J, et al. Effect of health literacy on decision-making preferences among medically underserved patients. Med Decis Mak. 2016;36(4):550–6.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Occupational Therapy, College of Rehabilitation Sciences, University of Manitoba, Winnipeg, MB, Canada

    Mohamed-Amine Choukou

  2. Center for Biomedical Data Science, School of Medicine, Yale University, New Haven, CT, USA

    XinXin (Katie) Zhu

  3. Taipei Medical University, ICHIT, Taipei, Taiwan

    Shwetambara Malwade

  4. Graduate Institute of Biomedical Informatics, Taipei Medical University, Taipei, Taiwan

    Eshita Dhar

  5. Artificial Intelligence and Digital Health, Taipei Medical University, Taipei, Taiwan

    Shabbir Syed Abdul

Authors
  1. Mohamed-Amine Choukou
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. XinXin (Katie) Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Shwetambara Malwade
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Eshita Dhar
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Shabbir Syed Abdul
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Shabbir Syed Abdul .

Editor information

Editors and Affiliations

  1. Duquesne University, Pittsburgh, PA, USA

    Joan M. Kiel

  2. The Johns Hopkins University, Baltimore, MD, USA

    George R. Kim

  3. The University of Texas at Arlington, Arlington, TX, USA

    Marion J. Ball

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Choukou, MA., (Katie) Zhu, X., Malwade, S., Dhar, E., Abdul, S.S. (2022). Digital Health Solutions Transforming Long-Term Care and Rehabilitation. In: Kiel, J.M., Kim, G.R., Ball, M.J. (eds) Healthcare Information Management Systems. Health Informatics. Springer, Cham. https://doi.org/10.1007/978-3-031-07912-2_19

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-07912-2_19

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-07911-5

  • Online ISBN: 978-3-031-07912-2

  • eBook Packages: MedicineMedicine (R0)

Publish with us

Policies and ethics

Search

Navigation