Abstract
Measuring the behaviour and health status of informal caregivers of people with dementia can predict the personal well-being of the caregivers. Informal caregivers struggle to remain active during the daily life activities avoiding the care burden. For this reason, in this work, an analysis of both the environmental data coming from PIR sensors, installed in the home environment, and physiological parameters, directly measured by the user, is performed to highlight, unusual behaviors that can increase the stress level of the caregiver. In addition, daily survey and personal interview provide further information about the progression of the illness and the amount of the care burden. Coupling this information with the physiological quantities can provide an overall health status of the caregiver. Results show that the caregiver presents a decreasing trend of her daily self-reported health status associated with a change in the pattern of the domotic data. The questionnaire also exhibits a high correlation with body weight measurements (Pearson Coefficient of −86%) suggesting that the caregiver health status is limiting the normal daily activities, may be due to an increase of the care burden associated to a worsening of the illness.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Visser-Meily JMA, Post MWM, Riphagen II, Lindeman E (2004) Measures used to assess burden among caregivers of stroke patients: a review. Clin Rehabil 18:601–623. https://doi.org/10.1191/0269215504cr776oa
Mitabe N, Shinomiya N (2016) Support system for caregivers with sensor network and machine learning. In: 2016 IEEE 5th global conference on consumer electronics, pp 1–4
Black W, Almeida OP (2004) A systematic review of the association between the behavioral and psychological symptoms of dementia and burden of care. Int Psychogeriatr 16:295–315. https://doi.org/10.1017/S1041610204000468
Launer LJ (2019) Statistics on the burden of dementia: need for stronger data. Lancet Neurol 18:25–27. https://doi.org/10.1016/S1474-4422(18)30456-3
Hategan A, Bourgeois JA, Cheng T, Young J (2018) Caregiver burnout. In: Hategan A, Bourgeois JA, Cheng T, Young J (eds) Geriatric psychiatry study guide: mastering the competencies. Springer International Publishing, Cham, pp 433–442
Jütten LH, Mark RE, Sitskoorn MM (2019) Empathy in informal dementia caregivers and its relationship with depression, anxiety, and burden. Int J Clin Health Psychol 19:12–21. https://doi.org/10.1016/j.ijchp.2018.07.004
Quinn C, Nelis SM, Martyr A et al (2019) Influence of positive and negative dimensions of dementia caregiving on caregiver well-being and satisfaction with life: findings from the IDEAL study. Am J Geriatr Psychiatry. https://doi.org/10.1016/j.jagp.2019.02.005
Casaccia S, Pietroni F, Scalise L et al (2018) Health@Home: pilot cases and preliminary results: integrated residential sensor network to promote the active aging of real users. In: 2018 IEEE international symposium on medical measurements and applications (MeMeA), pp 1–6
Monteriù A, Prist MR, Frontoni E et al (2018) A smart sensing architecture for domestic monitoring: methodological approach and experimental validation. Sensors 18:2310. https://doi.org/10.3390/s18072310
Scalise L, Pietroni F, Casaccia S et al (2016) Implementation of an “at-home” e-Health system using heterogeneous devices. In: 2016 IEEE international smart cities conference (ISC2), pp 1–4
Pietroni F, Casaccia S, Revel GM et al (2019) Smart monitoring of user and home environment: the Health@Home acquisition framework. In: Casiddu N, Porfirione C, Monteriù A, Cavallo F (eds) Ambient assisted living. Springer International Publishing, pp 23–37
Hurst NP, Ruta DA, Kind P (1998) Comparison of the MOS short form-12 (SF12) health status questionnaire with the SF36 in patients with rheumatoid arthritis. Br J Rheumatol 37:862–869
Mallinson S (2002) Listening to respondents: a qualitative assessment of the short-form 36 health status questionnaire. Soc Sci Med 54:11–21
Elbayoudi A, Lotfi A, Langensiepen C (2019) The human behaviour indicator: a measure of behavioural evolution. Expert Syst Appl 118:493–505. https://doi.org/10.1016/j.eswa.2018.10.022
Lee H-A, Lee H-J, Moon J-H et al (2017) Comparison of wearable activity tracker with actigraphy for sleep evaluation and circadian rest-activity rhythm measurement in healthy young adults. Psychiatry Investig 14:179–185. https://doi.org/10.4306/pi.2017.14.2.179
Acknowledgements
The authors thank the project partners from Università La Sapienza and Università degli Studi di Genova and we gratefully acknowledge support from the Italian Ministry of Education, University and Research (under grant no. SCN_00558—Italian Smart-Cities Project “Health@Home”).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Casaccia, S. et al. (2021). Measuring Environmental Data and Physiological Parameters at Home to Assess the Caregiver Burden in Assistants of People with Dementia. In: Monteriù, A., Freddi, A., Longhi, S. (eds) Ambient Assisted Living. ForItAAL 2019. Lecture Notes in Electrical Engineering, vol 725. Springer, Cham. https://doi.org/10.1007/978-3-030-63107-9_1
Download citation
DOI: https://doi.org/10.1007/978-3-030-63107-9_1
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-63106-2
Online ISBN: 978-3-030-63107-9
eBook Packages: EngineeringEngineering (R0)