Mapping Movement: Applying Motion Measurement Technologies to the Psychiatric Care of Older Adults


Purpose of Review

Recent advances in technology have changed the landscape of treatment for adults with mental illness. This review highlights technological innovations that may improve care for older adults with mental illness and neurocognitive disorders through the measurement and assessment of physical motion. These technologies include wearable sensors (such as smart watches and Fitbits), passive motion sensors, and smart home models that incorporate both active and passive motion technologies.

Recent Findings

Clinicians have evaluated motion measurement technologies in older adults with depression, dementia, anxiety, and schizophrenia. Results from studies in dementia populations suggest that motion measurement technologies can assist clinicians in diagnosing dementia earlier through the evaluation of gait, balance, and postural kinematics. Motion detection technologies can also be used to identify mood episodes at an earlier stage by detecting subtle behavioral changes.


Clinicians may use the objective data provided by technologies such as accelerometers to identify illnesses earlier, which may inform treatment decisions. The data may be used as a suitable surrogate marker for detecting depression in older adults, predicting the likelihood of falls, or quantifying physical activity in older adults with chronic mental illnesses or anxiety. Motion-based technologies also have the potential to detect physical activity for older adults residing in nursing homes. Wearable technologies are generally well tolerated in older adults, although the use of new technology and electronic health data could involve privacy and security concerns among this vulnerable population.

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Papers of particular interest, published recently, have been highlighted as: • Of importance •• Of major importance

  1. 1.

    Torous J, Onnela JP, Keshavan M. New dimensions and new tools to realize the potential of RDoC: digital phenotyping via smartphones and connected devices. Transl Psychiatry. 2017;7(3):e1053.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  2. 2.

    Insel TR. Digital phenotyping: technology for a new science of behavior. JAMA. 2017;318(13):1215–6.

    Article  PubMed  Google Scholar 

  3. 3.

    Onnela JP, Rauch SL. Harnessing smartphone-based digital phenotyping to enhance behavioral and mental health. Neuropsychopharmacology. 2016;41(7):1691–6.

    Article  PubMed  PubMed Central  Google Scholar 

  4. 4.

    •• Association As. Alzheimer’s disease facts and figures. Alzheimer’s Dementia. 2017;2017(13):325–73. This article highlights that incorporating biomarkers into the diagnosis of AD could promote the diagnosis at an earlier stage of the disease and lead to a more accurate understanding of AD incidence and prevalence

    Google Scholar 

  5. 5.

    Kim KI, Gollamudi SS, Steinhubl S. Digital technology to enable aging in place. Exp Gerontol. 2017;88:25–31.

    Article  PubMed  Google Scholar 

  6. 6.

    • Peetoom KK, Lexis MA, Joore M, Dirksen CD, De Witte LP. Literature review on monitoring technologies and their outcomes in independently living elderly people. Disabil Rehabil Assist Technol. 2015;10(4):271–94. This article identifies five main types of monitoring technologies to monitor activity in-home and to prolong independent living

    Article  PubMed  Google Scholar 

  7. 7.

    Wang Z, Yang Z, Dong T. A review of wearable technologies for elderly care that can accurately track indoor position, recognize physical activities and monitor vital signs in real time. Sensors (Basel). 2017;17(2)

  8. 8.

    Kumari P, Mathew L, Syal P. Increasing trend of wearables and multimodal interface for human activity monitoring: a review. Biosens Bioelectron. 2017;90:298–307.

    Article  PubMed  CAS  Google Scholar 

  9. 9.

    Schrack JA, Cooper R, Koster A, Shiroma EJ, Murabito JM, Rejeski WJ, et al. Assessing daily physical activity in older adults: unraveling the complexity of monitors, measures, and methods. J Gerontol A Biol Sci Med Sci. 2016;71(8):1039–48.

    Article  PubMed  PubMed Central  Google Scholar 

  10. 10.

    Rapoport M, Moussaoui G, Whiteman KL. Smart and personalized geriatric psychiatry: how sensors, mobile devices and informatics may change the way we practice. Am J Geriatr Psychiatr. 2017;25(3):S36.

    Article  Google Scholar 

  11. 11.

    Godfrey A. Wearables for independent living in older adults: gait and falls. Maturitas. 2017;100:16–26.

    Article  PubMed  CAS  Google Scholar 

  12. 12.

    Li F, Al-Qaness MA, Zhang Y, Zhao B, Luan X. A robust and device-free system for the recognition and classification of elderly activities. Sensors (Basel). 2016;16(12)

  13. 13.

    Gong L, Yang W, Man D, Dong G, Yu M, Lv J. WiFi-based real-time calibration-free passive human motion detection. Sensors (Basel). 2015;15(12):32213–29.

    Article  Google Scholar 

  14. 14.

    Khan UM, Kabir Z, Hassan SA, editors. Wireless health monitoring using passive WiFi sensing. 13th International Wireless Communications and Mobile Computing Conference (IWCMC) 2017.

  15. 15.

    Vanleerberghe P, De Witte N, Claes C, Schalock RL, Verte D. The quality of life of older people aging in place: a literature review. Qual Life Res. 2017;26(11):2899–907.

    Article  PubMed  Google Scholar 

  16. 16.

    Cecil KM, DelBello MP, Morey R, Strakowski SM. Frontal lobe differences in bipolar disorder as determined by proton MR spectroscopy. Bipolar Disord. 2002;4(6):357–65.

    Article  PubMed  CAS  Google Scholar 

  17. 17.

    Beheydt LL, Schrijvers D, Docx L, Bouckaert F, Hulstijn W, Sabbe B. Psychomotor retardation in elderly untreated depressed patients. Front Psychiatry. 2014;5:196.

    PubMed  Article  Google Scholar 

  18. 18.

    Troxel WM, Kupfer DJ, Reynolds CF 3rd, Frank E, Thase ME, Miewald JM, et al. Insomnia and objectively measured sleep disturbances predict treatment outcome in depressed patients treated with psychotherapy or psychotherapy-pharmacotherapy combinations. J Clin Psychiatry. 2012;73(4):478–85.

    Article  PubMed  Google Scholar 

  19. 19.

    Van Den Berg JF, Van Rooij FJ, Vos H, Tulen JH, Hofman A, Miedema HM, et al. Disagreement between subjective and actigraphic measures of sleep duration in a population-based study of elderly persons. J Sleep Res. 2008;17(3):295–302.

    Article  Google Scholar 

  20. 20.

    •• Vahia IV, Sewell DD. Late-life depression: a role for accelerometer technology in diagnosis and management. Am J Psychiatry. 2016;173(8):763–8. This article describes a case that demonstrates the feasibility of utilizing motion-sensing technology in a clinical environment with older adults who have comorbid mood and cognitive symptoms

    Article  PubMed  Google Scholar 

  21. 21.

    O’Brien JT, Gallagher P, Stow D, Hammerla N, Ploetz T, Firbank M, et al. A study of wrist-worn activity measurement as a potential real-world biomarker for late-life depression. Psychol Med. 2017;47(1):93–102.

    Article  PubMed  Google Scholar 

  22. 22.

    Galambos C, Skubic M, Wang S, Rantz M. Management of dementia and depression utilizing in-home passive sensor data. Gerontechnology. 2013;11(3):457–68.

    Article  PubMed  Google Scholar 

  23. 23.

    Matthews JT, Campbell GB, Hunsaker AE, Klinger J, Mecca LP, Hu L, et al. Wearable technology to garner the perspective of dementia family caregivers. J Gerontol Nurs. 2016;42(4):16–22.

    Article  PubMed  Google Scholar 

  24. 24.

    Hsu YL, Chung PC, Wang WH, Pai MC, Wang CY, Lin CW, et al. Gait and balance analysis for patients with Alzheimer’s disease using an inertial-sensor-based wearable instrument. IEEE J Biomed Health Inform. 2014;18(6):1822–30.

    Article  PubMed  Google Scholar 

  25. 25.

    Costa L, Gago MF, Yelshyna D, Ferreira J, David Silva H, Rocha L, Sousa N, Bicho E Application of machine learning in postural control kinematics for the diagnosis of Alzheimer’s disease. Comput Intell Neurosci 2016;2016:3891253. doi:, 1, 15

  26. 26.

    Buchner DM, Larson EB. Falls and fractures in patients with Alzheimer-type dementia. JAMA. 1987;257(11):1492–5.

    Article  PubMed  CAS  Google Scholar 

  27. 27.

    Lord SR, Sherringotn C, Menz HB. Falls in older people. Cambridge University: Cambrudge University Press; 2001.

  28. 28.

    Schwenk M, Hauer K, Zieschang T, Englert S, Mohler J, Najafi B. Sensor-derived physical activity parameters can predict future falls in people with dementia. Gerontology. 2014;60(6):483–92.

    Article  PubMed  PubMed Central  Google Scholar 

  29. 29.

    Murphy J, Holmes J, Brooks C. Measurements of daily energy intake and total energy expenditure in people with dementia in care homes: the use of wearable technology. J Nutr Health Aging. 2017;21(8):927–32.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  30. 30.

    Sergi G, De Rui M, Coin A, Inelmen EM, Manzato E. Weight loss and Alzheimer’s disease: temporal and aetiologic connections. Proc Nutr Soc. 2013;72(1):160–5.

    Article  PubMed  Google Scholar 

  31. 31.

    Valembois L, Oasi C, Pariel S, Jarzebowski W, Lafuente-Lafuente C, Belmin J. Wrist actigraphy: a simple way to record motor activity in elderly patients with dementia and apathy or aberrant motor behavior. J Nutr Health Aging. 2015;19(7):759–64.

    Article  PubMed  CAS  Google Scholar 

  32. 32.

    David R, Mulin E, Friedman L, Le Duff F, Cygankiewicz E, Deschaux O, et al. Decreased daytime motor activity associated with apathy in Alzheimer disease: an actigraphic study. Am J Geriatr Psychiatry. 2012;20(9):806–14.

    Article  PubMed  Google Scholar 

  33. 33.

    Teipel S, Heine C, Hein A, Kruger F, Kutschke A, Kernebeck S, et al. Multidimensional assessment of challenging behaviors in advanced stages of dementia in nursing homes—the insideDEM framework. Alzheimers Dement (Amst). 2017;8:36–44.

    Article  Google Scholar 

  34. 34.

    Matthews JT, Lingler JH, Campbell GB, Hunsaker AE, Hu L, Pires BR, et al. Usability of a wearable camera system for dementia family caregivers. J Healthc Eng. 2015;6(2):213–38.

    Article  PubMed  PubMed Central  Google Scholar 

  35. 35.

    Vahia IV, Kabelac Z, Hsu CY, Hristov R, Monette P, Harper D et al. Using radio signal-based sensing and machine learning for continuous longitudinal monitoring of behavioral symptoms in dementia: a pilot case study. Clinical Trials on Alzheimer’s Disease; Boston: Journal of Prevention of Alzheimer’s Disease; 2017. p. 422–3.

  36. 36.

    Hsu CY, Liu, Y., Kabelac, Z., et al., editor. Extracting gait velocity and stride length from surrounding radio signals. CHI Conference on Human Factors in Computing Systems; 2017.

  37. 37.

    Hsu CY, Ahuja A, Yue S, Hristov R, Kabelac Z, Katabi D. Zero-effort in-home sleep and insomnia monitoring using radio signals. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies September 2017: Association for Computing Machinery; 2017 p. 18.

  38. 38.

    Lazarou I, Karakostas A, Stavropoulos TG, Tsompanidis T, Meditskos G, Kompatsiaris I, et al. A novel and intelligent home monitoring system for care support of elders with cognitive impairment. J Alzheimers Dis. 2016;54(4):1561–91.

    Article  PubMed  Google Scholar 

  39. 39.

    Lauderdale DS, Philip Schumm L, Kurina LM, McClintock M, Thisted RA, Chen JH, et al. Assessment of sleep in the National Social Life, Health, and Aging Project. J Gerontol B Psychol Sci Soc Sci. 2014;69(Suppl 2):S125–33.

    Article  PubMed  PubMed Central  Google Scholar 

  40. 40.

    Ibanez-Del Valle V, Silva J, Castello-Domenech AB, Martinez-Martinez M, Verdejo Y, Sanantonio-Camps L, et al. Subjective and objective sleep quality in elderly individuals: the role of psychogeriatric evaluation. Arch Gerontol Geriatr. 2018;76:221–6.

    Article  PubMed  Google Scholar 

  41. 41.

    Roberts RE, Shema SJ, Kaplan GA, Strawbridge WJ. Sleep complaints and depression in an aging cohort: a prospective perspective. Am J Psychiatry. 2000;157(1):81–8.

    Article  PubMed  CAS  Google Scholar 

  42. 42.

    Buysse DJ. Insomnia, depression and aging. Assessing sleep and mood interactions in older adults. Geriatrics 2004;59(2):47–51; quiz 2.

  43. 43.

    Gould CE, Beaudreau SA, O’Hara R, Edelstein BA. Perceived anxiety control is associated with sleep disturbance in young and older adults. Aging Ment Health. 2016;20(8):856–60.

    Article  PubMed  Google Scholar 

  44. 44.

    Brostrom A, Wahlin A, Alehagen U, Ulander M, Johansson P. Sex-specific associations between self-reported sleep duration, depression, anxiety, fatigue and daytime sleepiness in an older community-dwelling population. Scand J Caring Sci. 2018;32(1):290–8.

    Article  PubMed  Google Scholar 

  45. 45.

    Jeste DV, Meeks TW, Kim DS, Zubenko GS. Research agenda for DSM-V: diagnostic categories and criteria for neuropsychiatric syndromes in dementia. J Geriatr Psychiatry Neurol. 2006;19(3):160–71.

    Article  PubMed  Google Scholar 

  46. 46.

    Moyle W, Jones C, Murfield J, Thalib L, Beattie E, Shum D, et al. Effect of a robotic seal on the motor activity and sleep patterns of older people with dementia, as measured by wearable technology: a cluster-randomised controlled trial. Maturitas. 2018;110:10–7.

    Article  PubMed  Google Scholar 

  47. 47.

    Bartels SJ, Pratt SI. Psychosocial rehabilitation and quality of life for older adults with serious mental illness: recent findings and future research directions. Curr Opin Psychiatry. 2009;22(4):381–5.

    Article  PubMed  PubMed Central  Google Scholar 

  48. 48.

    Stubbs B, Williams J, Gaughran F, Craig T. How sedentary are people with psychosis? A systematic review and meta-analysis. Schizophr Res. 2016;171(1–3):103–9.

    Article  PubMed  Google Scholar 

  49. 49.

    • Stubbs B, Chen LJ, Chung MS, Ku PW. Physical activity ameliorates the association between sedentary behavior and cardiometabolic risk among inpatients with schizophrenia: a comparison versus controls using accelerometry. Compr Psychiatry. 2017;74:144–50. This systemic review and meta-analysis demonstrates that people with psychosis engage in very high levels of sedentary behavior

    Article  PubMed  Google Scholar 

  50. 50.

    Naslund JA, Aschbrenner KA, Scherer EA, McHugo GJ, Marsch LA, Bartels SJ. Wearable devices and mobile technologies for supporting behavioral weight loss among people with serious mental illness. Psychiatry Res. 2016;244:139–44.

    Article  PubMed  PubMed Central  Google Scholar 

  51. 51.

    Parker SJ, Strath SJ, Swartz AM. Physical activity measurement in older adults: relationships with mental health. J Aging Phys Act. 2008;16(4):369–80.

    Article  PubMed  PubMed Central  Google Scholar 

  52. 52.

    Arrieta H, Rezola-Pardo C, Echeverria I, Iturburu M, Gil SM, Yanguas JJ, et al. Physical activity and fitness are associated with verbal memory, quality of life and depression among nursing home residents: preliminary data of a randomized controlled trial. BMC Geriatr. 2018;18(1):80.

    Article  PubMed  PubMed Central  Google Scholar 

  53. 53.

    Kerr J, Marshall S, Godbole S, Neukam S, Crist K, Wasilenko K, et al. The relationship between outdoor activity and health in older adults using GPS. Int J Environ Res Public Health. 2012;9(12):4615–25.

    Article  PubMed  PubMed Central  Google Scholar 

  54. 54.

    Webber SC, Porter MM. Monitoring mobility in older adults using global positioning system (GPS) watches and accelerometers: a feasibility study. J Aging Phys Act. 2009;17(4):455–67.

    Article  PubMed  Google Scholar 

  55. 55.

    Merilahti J, Korhonen I. Association between continuous wearable activity monitoring and self-reported functioning in assisted living facility and nursing home residents. J Frailty Aging. 2016;5(4):225–32.

    PubMed  CAS  Article  Google Scholar 

  56. 56.

    Beach S, Schulz R, Downs J, Matthews J, Seelman K, Barron B et al., editors. End-user perspectives in privacy and other trade-offs in acceptance of quality of life technologies. 1st International Symposium on Quality of Life Technology; 2009; Pittsburgh, PA.

  57. 57.

    Hassan L, Swarbrick C, Sanders C, Parker A, Machin M, Tully MP, et al. Tea, talk and technology: patient and public involvement to improve connected health ‘wearables’ research in dementia. Res Involv Engagem. 2017;3:12.

    Article  PubMed  PubMed Central  Google Scholar 

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Brent P. Forester reports grants from Eli Lilly, Biogen, Assurex, Roche, Rogers Family Foundation, and National Institute of Aging.

Ipsit V. Vahia reports a grant from Once Upon a Time Foundation and honoraria from the American Journal of Geriatric Psychiatry.

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Correspondence to Ipsit V. Vahia.

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Stephanie Collier, Patrick Monette, Katherine Hobbs, and Edward Tabasky each declares no potential conflicts of interest.

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This article is part of the Topical Collection on Geriatric Disorders

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Collier, S., Monette, P., Hobbs, K. et al. Mapping Movement: Applying Motion Measurement Technologies to the Psychiatric Care of Older Adults. Curr Psychiatry Rep 20, 64 (2018).

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  • Technology
  • Data
  • Dementia
  • Older adults
  • Motion