Using Wearable Sensors to Measure Goal Achievement in Older Veterans with Dementia

Jennifer Freytag, Ram Kinker Mishra, Richard L Street Jr, Angela Catic, Lilian Dindo, Lea Kiefer, Bijan Najafi, Aanand D Naik, Jennifer Freytag, Ram Kinker Mishra, Richard L Street Jr, Angela Catic, Lilian Dindo, Lea Kiefer, Bijan Najafi, Aanand D Naik

Abstract

Aligning treatment with patients' self-determined goals and health priorities is challenging in dementia care. Wearable-based remote health monitoring may facilitate determining the active participation of individuals with dementia towards achieving the determined goals. The present study aimed to demonstrate the feasibility of using wearables to assess healthcare goals set by older adults with cognitive impairment. We present four specific cases that assess (1) the feasibility of using wearables to monitor healthcare goals, (2) differences in function after goal-setting visits, and (3) goal achievement. Older veterans (n = 17) with cognitive impairment completed self-report assessments of mobility, then had an audio-recorded encounter with a geriatrician and wore a pendant sensor for 48 h. Follow-up was conducted at 4-6 months. Data obtained by wearables augments self-reported data and assessed function over time. Four patient cases illustrate the utility of combining sensors, self-report, notes from electronic health records, and visit transcripts at baseline and follow-up to assess goal achievement. Using data from multiple sources, we showed that the use of wearable devices could support clinical communication, mainly when patients, clinicians, and caregivers work to align care with the patient's priorities.

Keywords: Alzheimer’s disease; aging; cognitive impairment; dementia; digital health; goal setting; patient goals; remote patient monitoring; telemedicine; wearable.

Conflict of interest statement

R.K.M. is currently employed with BioSensics LLC, the manufacturer of the wearable sensors used in this study. R.K.M. was employed by Baylor College of Medicine and not associated with BioSensics LLC during the recruitment, data collection and analysis portions of this study. B.N., Co-Author, is serving as a consultant for BioSensics LLC.

Figures

Figure 1
Figure 1
Intervention Design: Consort chart for the study.
Figure 2
Figure 2
Patient Priorities Care program to determine patient-centric health priorities and setting relevant goals. Wearable sensors were used to determine patient participation in achieving the goals and facilitate the goal-setting process.
Figure 3
Figure 3
PAMSysTM pendant.
Figure 4
Figure 4
Change in activity timelines from baseline to follow-up.

References

    1. Adams T., Gardiner P. Communication and interaction within dementia care triads: Developing a theory for relationship-centred care. Dementia. 2005;4:185–205. doi: 10.1177/1471301205051092.
    1. Lee M., Mishra R.K., Momin A., El-Refaei N., Bagheri A.B., York M.K., Kunik M.E., Derhammer M., Fatehi B., Lim J. Smart-Home Concept for Remote Monitoring of Instrumental Activities of Daily Living (IADL) in Older Adults with Cognitive Impairment: A Proof of Concept and Feasibility Study. Sensors. 2022;22:6745. doi: 10.3390/s22186745.
    1. Mishra R.K., Park C., Momin A.S., El Rafaei N., Kunik M., York M.K., Najafi B. Care4AD: A Technology-Driven Platform for Care Coordination and Management: Acceptability Study in Dementia. Gerontology. 2022:1–12. doi: 10.1159/000526219.
    1. Blaum C.S., Rosen J., Naik A.D., Smith C.D., Dindo L., Vo L., Hernandez-Bigos K., Esterson J., Geda M., Ferris R. Feasibility of implementing patient priorities care for older adults with multiple chronic conditions. J. Am. Geriatr. Soc. 2018;66:2009–2016. doi: 10.1111/jgs.15465.
    1. Freytag J., Dindo L., Catic A., Johnson A.L., Bush Amspoker A., Gravier A., Dawson D.B., Tinetti M.E., Naik A.D. Feasibility of clinicians aligning health care with patient priorities in geriatrics ambulatory care. J. Am. Geriatr. Soc. 2020;68:2112–2116. doi: 10.1111/jgs.16662.
    1. Tinetti M.E., Naik A.D., Dindo L., Costello D.M., Esterson J., Geda M., Rosen J., Hernandez-Bigos K., Smith C.D., Ouellet G.M. Association of patient priorities–aligned decision-making with patient outcomes and ambulatory health care burden among older adults with multiple chronic conditions: A nonrandomized clinical trial. JAMA Intern. Med. 2019;179:1688–1697. doi: 10.1001/jamainternmed.2019.4235.
    1. Mishra R., Park C., York M.K., Kunik M.E., Wung S.-F., Naik A.D., Najafi B. Decrease in mobility during the COVID-19 pandemic and its association with increase in depression among older adults: A longitudinal remote mobility monitoring using a wearable sensor. Sensors. 2021;21:3090. doi: 10.3390/s21093090.
    1. Najafi B., Mishra R. Harnessing Digital Health Technologies to Remotely Manage Diabetic Foot Syndrome: A Narrative Review. Medicina. 2021;57:377. doi: 10.3390/medicina57040377.
    1. Park C., Mishra R., Sharafkhaneh A., Bryant M.S., Nguyen C., Torres I., Naik A.D., Najafi B. Digital biomarker representing frailty phenotypes: The use of machine learning and sensor-based sit-to-stand test. Sensors. 2021;21:3258. doi: 10.3390/s21093258.
    1. Park C., Mishra R., Golledge J., Najafi B. Digital Biomarkers of Physical Frailty and Frailty Phenotypes Using Sensor-Based Physical Activity and Machine Learning. Sensors. 2021;21:5289. doi: 10.3390/s21165289.
    1. Kinker Mishra R., Park C., Zhou H., Najafi B., Thrasher T.A. Evaluation of Motor and Cognitive Performance in People with Parkinson’s Disease Using Instrumented Trail-Making Test. Gerontology. 2022;68:234–240. doi: 10.1159/000515940.
    1. Mishra R.K., Bara R.O., Zulbaran-Rojas A., Park C., Fernando M.E., Ross J., Lepow B., Najafi B. The Application of Digital Frailty Screening to Triage Nonhealing and Complex Wounds. J. Diabetes Sci. Technol. 2022:19322968221111194. doi: 10.1177/19322968221111194.
    1. Park C., Mishra R., Vigano D., Macagno M., Rossotti S., D’Huyvetter K., Garcia J., Armstrong D.G., Najafi B. Smart offloading boot system for remote patient monitoring: Toward adherence reinforcement and proper physical activity prescription for diabetic foot ulcer patients. J. Diabetes Sci. Technol. 2022:19322968211070850. doi: 10.1177/19322968211070850.
    1. Park C., Atique M.M.U., Mishra R., Najafi B. Association between fall history and gait, balance, physical activity, depression, fear of falling, and motor capacity: A 6-month follow-up study. Int. J. Environ. Res. Public Health. 2022;19:10785. doi: 10.3390/ijerph191710785.
    1. Vaiyapuri T., Lydia E.L., Sikkandar M.Y., Diaz V.G., Pustokhina I.V., Pustokhin D.A. Internet of Things and Deep Learning Enabled Elderly Fall Detection Model for Smart Homecare. IEEE Access. 2021;9:113879–113888. doi: 10.1109/ACCESS.2021.3094243.
    1. Krichen M. Anomalies Detection Through Smartphone Sensors: A Review. IEEE Sens. J. 2021;21:7207–7217. doi: 10.1109/JSEN.2021.3051931.
    1. Muangprathub J., Sriwichian A., Wanichsombat A., Kajornkasirat S., Nillaor P., Boonjing V. A Novel Elderly Tracking System Using Machine Learning to Classify Signals from Mobile and Wearable Sensors. Int. J. Environ. Res. Public Health. 2021;18:12652. doi: 10.3390/ijerph182312652.
    1. Qaisar S., Mihoub A., Krichen M., Nisar H. Multirate Processing with Selective Subbands and Machine Learning for Efficient Arrhythmia Classification. Sensors. 2021;21:1511. doi: 10.3390/s21041511.
    1. Qaisar S.M., Khan S.I., Dallet D., Tadeusiewicz R., Pławiak P. Signal-piloted processing metaheuristic optimization and wavelet decomposition based elucidation of arrhythmia for mobile healthcare. Biocybern. Biomed. Eng. 2022;42:681–694. doi: 10.1016/j.bbe.2022.05.006.
    1. Takahashi S., Nakazawa E., Ichinohe S., Akabayashi A., Akabayashi A. Wearable Technology for Monitoring Respiratory Rate and SpO2 of COVID-19 Patients: A Systematic Review. Diagnostics. 2022;12:2563. doi: 10.3390/diagnostics12102563.
    1. Javed A.R., Fahad L.G., Farhan A.A., Abbas S., Srivastava G., Parizi R.M., Khan M.S. Automated cognitive health assessment in smart homes using machine learning. Sustain. Cities Soc. 2021;65:102572. doi: 10.1016/j.scs.2020.102572.
    1. Javed A.R., Sarwar M.U., ur Rehman S., Khan H.U., Al-Otaibi Y.D., Alnumay W.S. PP-SPA: Privacy Preserved Smartphone-Based Personal Assistant to Improve Routine Life Functioning of Cognitive Impaired Individuals. Neural Process. Lett. 2021 doi: 10.1007/s11063-020-10414-5.
    1. Razjouyan J., Najafi B., Horstman M., Sharafkhaneh A., Amirmazaheri M., Zhou H., Kunik M.E., Naik A. Toward using wearables to remotely monitor cognitive frailty in community-living older adults: An observational study. Sensors. 2020;20:2218. doi: 10.3390/s20082218.
    1. Tinetti M.E., Naik A.D., Dodson J.A. Moving from disease-centered to patient goals–directed care for patients with multiple chronic conditions: Patient value-based care. JAMA Cardiol. 2016;1:9–10. doi: 10.1001/jamacardio.2015.0248.
    1. Naik A.D., Dindo L.N., Van Liew J.R., Hundt N.E., Vo L., Hernandez-Bigos K., Esterson J., Geda M., Rosen J., Blaum C.S. Development of a clinically feasible process for identifying individual health priorities. J. Am. Geriatr. Soc. 2018;66:1872–1879. doi: 10.1111/jgs.15437.
    1. Najafi B., Aminian K., Loew F., Blanc Y., Robert P.A. Measurement of stand-sit and sit-stand transitions using a miniature gyroscope and its application in fall risk evaluation in the elderly. IEEE Trans. Biomed. Eng. 2002;49:843–851. doi: 10.1109/TBME.2002.800763.
    1. Najafi B., Aminian K., Paraschiv-Ionescu A., Loew F., Bula C.J., Robert P. Ambulatory system for human motion analysis using a kinematic sensor: Monitoring of daily physical activity in the elderly. IEEE Trans. Biomed. Eng. 2003;50:711–723. doi: 10.1109/TBME.2003.812189.
    1. Nasreddine Z.S., Phillips N.A., Bédirian V., Charbonneau S., Whitehead V., Collin I., Cummings J.L., Chertkow H. The Montreal Cognitive Assessment, MoCA: A brief screening tool for mild cognitive impairment. J. Am. Geriatr. Soc. 2005;53:695–699. doi: 10.1111/j.1532-5415.2005.53221.x.
    1. Mitchell A.J. Cognitive Screening Instruments. Springer; Berlin/Heidelberg, Germany: 2013. The Mini-Mental State Examination (MMSE): An update on its diagnostic validity for cognitive disorders; pp. 15–46.
    1. Wallace M., Shelkey M. Katz index of independence in activities of daily living (ADL) Urol Nurs. 2007;27:93–94.
    1. Group W. Quality of Life Assessment: International Perspectives. Springer; Berlin/Heidelberg, Germany: 1994. The development of the World Health Organization quality of life assessment instrument (the WHOQOL) pp. 41–57.
    1. Willer B., Ottenbacher K.J., Coad M.L. The community integration questionnaire. A comparative examination. Am. J. Phys. Med. Rehabil. 1994;73:103–111. doi: 10.1097/00002060-199404000-00006.
    1. Duncan P., Murphy M., Man M.-S., Chaplin K., Gaunt D., Salisbury C. Development and validation of the multimorbidity treatment burden questionnaire (MTBQ) BMJ Open. 2020;8:e019413.
    1. Ullrich P., Werner C., Bongartz M., Kiss R., Bauer J., Hauer K. Validation of a Modified Life-Space Assessment in Multimorbid Older Persons With Cognitive Impairment. Gerontol. 2019;59:e66–e75. doi: 10.1093/geront/gnx214.
    1. Najafi B., Armstrong D.G., Mohler J. Novel Wearable Technology for Assessing Spontaneous Daily Physical Activity and Risk of Falling in Older Adults with Diabetes. SAGE Publications Sage CA; Los Angeles, CA, USA: 2013.
    1. Razjouyan J., Lee H., Parthasarathy S., Mohler J., Sharafkhaneh A., Najafi B. Improving Sleep Quality Assessment Using Wearable Sensors by Including Information From Postural/Sleep Position Changes and Body Acceleration: A Comparison of Chest-Worn Sensors, Wrist Actigraphy, and Polysomnography. J. Clin. Sleep Med. 2017;13:1301–1310. doi: 10.5664/jcsm.6802.
    1. Schulman-Green D.J., Naik A.D., Bradley E.H., McCorkle R., Bogardus S.T. Goal setting as a shared decision making strategy among clinicians and their older patients. Patient Educ. Couns. 2006;63:145–151. doi: 10.1016/j.pec.2005.09.010.
    1. Sanders J.J., Curtis J.R., Tulsky J.A. Achieving goal-concordant care: A conceptual model and approach to measuring serious illness communication and its impact. J. Palliat. Med. 2018;21:S-17–S-27. doi: 10.1089/jpm.2017.0459.
    1. Bhattacherjee A., Hikmet N. Physicians’ resistance toward healthcare information technology: A theoretical model and empirical test. Eur. J. Inf. Syst. 2007;16:725–737. doi: 10.1057/palgrave.ejis.3000717.
    1. Piau A., Wild K., Mattek N., Kaye J. Current state of digital biomarker technologies for real-life, home-based monitoring of cognitive function for mild cognitive impairment to mild Alzheimer disease and implications for clinical care: Systematic review. J. Med. Internet Res. 2019;21:e12785. doi: 10.2196/12785.
    1. Taylor J.K., Buchan I.E., van der Veer S.N. Assessing life-space mobility for a more holistic view on wellbeing in geriatric research and clinical practice. Aging Clin. Exp. Res. 2019;31:439–445. doi: 10.1007/s40520-018-0999-5.

Source: PubMed

3
Se inscrever