by Roison Andro R. Narvaez, MSN, RN, CMCS, LGBH

Citation

Narvaez, R. (2022). Uses and Effects of Internet of Things (IoT) On Quality of Life of Older Clients in Long Term Care Settings: An Integrative Review. Canadian Journal of Nursing Informatics, 17(1).  https://cjni.net/journal/?p=9762

Abstract

Background: There is no denying that the Internet of Things (IoT) represents a big technical advance. Nowadays, healthcare services are more expensive than ever, and often patients must remain in the hospital during their treatment. Using devices that can remotely monitor patients may help overcome these issues. As the population ages, the demand on healthcare and social services is likely to grow. As a result, demand for technology services will rise to meet the pressing demands of the aging population. When correctly applied, these technologies will not only enhance the lives of older adults, but also help caregivers provide acceptable services to the aged. Clearly, these elderly people require care and assistance in their daily lives. To the researcher’s knowledge, no comprehensive assessment of the literature on IoT and quality of life for older clients in long-term care exists. This review will give a modest survey on IoT in geriatric healthcare.

Objective: The purpose of this integrative review was to examine the uses and effects of Internet of Things (IoT) on quality of life for older clients in long term care.

Design: An integrative review of the literature.

Data sources: This review utilized electronic resources such as PubMED, Google Scholar, Taylor and Francis, SAGE, IEEE, Springer, ScienceDirect, Wiley, and ACM. The review covered publications published from January 1, 2016, to December 1, 2021, as well as articles published in English and translated into English. Nine (9) articles were included in this review.

Review methods: To gather data from the included articles, an assessment matrix was created. This review utilized the Mazurek Melnyk and Fineout-Overholt (2014) Rating System for the Hierarchy of Evidence for Intervention and Treatment Questions to establish the level of evidence (LOE) for each study. The researcher independently and manually assessed each article from the above-mentioned electronic databases.

Results: All nine (9) of the papers studied are classified as having a level of evidence VI. The total sample size for all papers analyzed was 144 respondents, while the sample size for each article reviewed varied between 9 and 49 respondents. The IoT systems or applications employed in investigations vary. There was no continuous usage of a single IoT platform. Each research study employed a different IoT platform or system, depending on the expertise and preference of the researchers involved.

Conclusion: With aging comes a greater need for medical and nursing care, which may lead to unexpected doctor visits. Recent advances in Internet-of-Things (IoT) technologies may help build aged healthcare systems. Additionally, gathered literature reviews were mostly derived from Western and European nations. As such in most Asian nations, substantial strain on the public healthcare system and lack of suitable facilities drives patient care. Although an Internet-of-Things-based system or software may enhance healthcare management among elderly people in improving their quality of life, it could be derived that user acceptance is poor. With this, there is a call for future research in the medical and nursing discipline to assess and evaluate frameworks, in the context of being theoretical or conceptual, to understand essential elements that could influence elder adoption of IoT-related healthcare services.

Background

There is no denying that the Internet of Things (IoT) represents a significant technical advancement (Cui, 2016). The research community has defined IoT from various angles. Paul & Jeyaraj (2019) defined IoT as a sophisticated network of individually identified “things” connected to a server that effectively offers appropriate services. Each of these ‘objects’ has distinct properties and participates actively in various circumstances. They can communicate with each other and the physical world by transferring data between the two. These items can also react independently to external events. All these processes can be activated by human or machine-to-machine communication (Cui, 2016).

IoT will soon change healthcare facilities. A primary function for this technology will be in patient telemonitoring in hospitals and at home (Yuehong et al., 2016). Remote patient monitoring may improve healthcare quality and save expenses by detecting and avoiding illnesses and dangerous complications (Lupiani et al., 2017). Nowadays, healthcare services are often more expensive than ever, and patients often remain in the hospital during their treatment. Using devices that can remotely monitor patients may help overcome these issues. With IoT technology, patients’ real-time health data is collected and sent to caregivers, allowing for early detection and treatment of health issues (Kim et al., 2017). Several authors and researchers have examined IoT applications, architecture, and associated technologies in healthcare.

As the population ages, the demand on healthcare and social services is likely to grow. As a result, demand for technology services will rise to meet the pressing demands of the aging population. When correctly applied, these technologies will not only enhance the lives of older adults, but also help caregivers provide acceptable services to the aged. Clearly, these elderly people require care and assistance in their daily lives. To the researcher’s knowledge, no comprehensive assessment of the literature on IoT and quality of life for older clients in long-term care exists. This review will give a modest survey on IoT in geriatric healthcare.

PICOT Question

The clinical question for this integrative review was, among older clients in long-term care (P), what are the uses and effects (O) of Internet of Things (IoT) (I) on quality of life (T)?

Methods

Design

This is a comprehensive review of the available literature. During this integrative review, the researcher did the following: identifying the clinical subject of interest and summarized findings by identifying medical and nursing implications.

Search strategy

This review utilized electronic resources such as PubMED, Google Scholar, Taylor and Francis, SAGE, IEEE, Springer, ScienceDirect, Wiley, and ACM. The review covered publications published from January 1, 2016, to December 1, 2021, as well as articles published in English and translated into English. Abstracts, protocols, and opinions were eliminated, as were studies that were not centered on the Internet of Things or IoT in relation to quality of life of older clients. Keywords used in the search were gerontology AND Internet of Things (IoT) AND quality of life; geriatrics AND Internet of Things (IoT) AND quality of life; elderly AND Internet of Things (IoT) AND quality of life; older person AND Internet of Things (IoT) AND quality of life; old people in long term care AND Internet of Things (IoT) AND quality of life; older clients in long term care AND Internet of Things (IoT) AND quality of life.

Quality assessment

The researcher independently and manually assessed each article from the above-mentioned electronic databases. All articles that fit the inclusion criteria were included in this integrative review. Each article had to be written in basic, comprehensible English and published between January 2016 and December 2021. Further, each article had to be on the Internet of Things (IoT) for the elderly and discuss entirely its uses and effects. For the PICOT question, each paper must present a way to using IoT technology for the elderly.

Figure 1. Search strategy and selection of papers used

Data Abstraction and Data Analysis

To gather data from the included articles, an assessment matrix was created. The publications were grouped and scored using an assessment matrix that included the following information: authors and publication date, design approach, sample settings, variables and measures, research results, and degree of evidence. This review utilized the Mazurek Melnyk and Fineout-Overholt (2014) Rating System for the Hierarchy of Evidence for Intervention and Treatment Questions to establish the level of evidence (LOE) for each study.

Results

Article/Sample characteristics

The first search for papers yielded 1297 results, of which nine (9) met the integrative review’s inclusion and exclusion criteria (See Table 1). The researcher demonstrated the search approach via manual identification and screening (see Fig. 1). All nine (9) of the papers studied are classified as having a level of evidence VI. The total sample size for all papers analyzed was 144 respondents, while the sample size for each article reviewed varied between 9 and 49 respondents. The IoT systems or applications employed in investigations varied.

Table 1: Included Articles Characteristics

Primary author (yr.)	Design	Data range collection	Sample, sample size and setting	Method/instruments used	Level of Evidence (LOE)
Woznowski et al. (2017)	Inclusive approach	Not clearly specified	Two-bedroom Victorian residential property in Bristol	Smart home platform of non-medical networked sensors	LOE VI
Mainetti et al. (2016)	Ambient Assisted Living (AAL) system	Not clearly specified	31 elderly people living in Lecce, Italy	Mobile middleware	LOE VI
Toh et al. (2016)	System design and care model	July-November 2015	10 elderly participants	Sensor-enabled medication boxes	LOE VI
Vaiyapuri et al. (2021)	IoT enabled elderly fall detection model using optimal deep convolutional neural network (IMEFD-ODCN)	2019-2021 (not clearly specified)	Not indicated	IMEFD-ODCN; SqueezeNet model	LOE VI
Hassen et al. (2019)	IoT and Fog computing	Not clearly specified	Not indicated	Mysignals HW V2 platform; Android app that plays the role of Fog server	LOE VI
Jo et al. (2021)	Technological trials of sensor-set	Not clearly specified	9 independently living elderly participants	Integrated smart home system (ISHS)	LOE VI
Kim et al. (2017)	Feature extraction module	Not clearly specified	20 elderly people suffering from depression	Unobtrusive sensing system using passive infra-red motion sensors	LOE VI
Lupiani et al. (2017)	Case-Based Reasoning (CBR)	Not clearly specified	25 normal elderly people with no specific disabilities or requirements	CBR system	LOE VI
Fischinger et al. (2017)	Controlled laboratory user studies	Not clearly specified	49 participants (aged 70 plus) in three EU countries (Austria, Greece, and Sweden)	Multimodal user interface	LOE VI

Table 2 summarizes the uses and effects of IoT as utilized in the literature review. There was no continuous usage of a single IoT platform. Each research study employed a different IoT platform or system, depending on the expertise and preference of the researchers involved.

Table 2: Internet of Things (IoT) uses and effects

Study	Uses	Effects
Woznowski et al. (2017)	Sensor Platform for Healthcare in a Residential Environment (SPHERE)	Can detect environmental, vision, and wearable sensors.
Mainetti et al. (2016)	Ambient Assisted Living (AAL) system	Improved living circumstances for the elderly or disabled. AAL system can continually monitor the health of the elderly using data from various sensors.
Toh et al. (2016)	Sensor-enabled medication boxes	Can efficiently track medicine consumption and detect non-compliant seniors.
Vaiyapuri et al. (2021)	Deep Learning Enabled Elderly Fall Detection Model for Smart Homecare	The IMEFD-ODCNN model outperformed current techniques by 99.76% and 99.57% on the multiple cameras fall and UR fall detection datasets, respectively.
Hassen et al. (2019)	E-health system for monitoring elderly health based on Internet of Things and Fog computing	Most users find the system useful and easy to learn, suggesting it can improve elderly health care.
Jo et al. (2021)	Integrated smart home system (ISHS)	Negative reactions to usability complexity and everyday tasks.
Kim et al. (2017)	Unobtrusive sensing system using passive infrared motion sensors with three layers-states, events, and activities-and algorithms	The non-intrusive sensor technology may be used for long-term depression monitoring and early diagnosis of mental diseases. This allows caregivers to intervene quickly with older people at risk of depression.
Lupiani et al. (2017)	Case-Based Reasoning (CBR) system	Long-term CBR systems are beneficial. The temporal CBM algorithms tested can effectively decrease case-bases to identify aberrant events.
Fischinger et al. (2017)	Hobbit project	Its intended user group has received the robot well based on user perceptions of use, acceptability, and affordability.

Discussion

This integrative review showed a considerable amount of literature focused on the uses and effects of the Internet of Things (IoT) in improving the quality of life of elderly clients in the long-term run. As a cited example, SPHERE (Sensor Platform for Healthcare in a Residential Environment) was an EPSRC-funded multidisciplinary research initiative coordinated by the University of Bristol, as reiterated in the research study of Woznowski et al. (2017). The goal was to create a smart home platform of non-medical networked sensors that could collect and integrate data about the home environment and people’s behaviours to understand healthcare requirements better. They wanted to implement this technology in up to 100 households in Bristol for long-term and ‘in the wild’ investigations. A prototype of this technology was placed in a two-bedroom Victorian home in Bristol for elderly people, to be used for user research. This has enabled researchers to learn about the obstacles of installing this technology into actual dwellings.

Similarly, Mainetti et al. (2016) described an AAL (Ambient Assisted Living) architecture for elderly monitoring that can gather diverse sensor data and identify critical events like older adult falls. A remote reasoning system was used to generate events. To show the suggested architecture’s practicality, a proof of concept was employed, along with functional validation tests. Toh et al. (2016) leveraged the Internet of Things (IoT) to monitor medication adherence and identify changes in geriatric medicine consumption habits, allowing caregivers to intervene immediately. Sensor-enabled medicine boxes were installed in the homes of 10 elderly participants for over four months. Initial tests showed that their approach can efficiently monitor consumption trends and detect senior non-adherence.

An IoT-enabled elderly fall detection model employing an optimum deep convolutional neural network (IMEFD-ODCNN) was presented in the study by Vaiyapuri et al. (2021). The IMEFD-ODCNN model enabled smartphones and deep learning algorithms to detect falls in the smart home. Initial pre-processing included scaling, augmentation, and min-max normalization of IoT device collected video. The SqueezeNet model was also used to extract feature vectors for fall detection. Finally, a SSOA-VAE based classifier is used to classify fall and non-fall events. The smartphone notified caregivers and hospital administration if a fall was detected. The IMEFD-ODCNN model was tested on the UR fall detection and multiple camera fall datasets. The IMEFD-ODCNN model outperformed current techniques in the multiple camera fall and UR fall detection experiments, respectively, with maximum accuracy of 99.76% and 99.57%.

Hassen et al. (2019) presented an e-health monitoring system for the elderly based on IoT and fog computing. The system was built on the Mysignals HW V2 platform and used an Android app as a Fog server to gather older people’s physiological and general health data. The setup allowed them to contact health care professionals (administrators and physicians) and get advice, notifications and alerts using this Android app. Hassen et al.’s (2019) work may increase the quality of geriatric health care by analyzing this approach.

Jo et al. (2021) sought to assess the perceived advantages and drawbacks of an integrated smart home system (ISHS) among the elderly. The ISHS sensor set was selected, and interviews were constructed around four factors: perceived comfort, use, privacy, and benefit. The sensor-set was then tested on nine older people living independently in a senior welfare facility in South Korea, followed by two focus group interviews. In line with earlier research, senior participants expressed displeasure with usability difficulty and everyday tasks. This shows that the establishment of IoT among elderly users is not always positively preceived.

According to Kim et al. (2017), mental health issues are prevalent among the elderly and one possible danger to ageing-in-place is depression. To monitor the everyday activities of elderly people living alone, they presented a simple unobtrusive sensing system that employed passive infrared motion sensors, a feature extraction module with three layers: states, events, and activities, with associated algorithms. This was followed by four common categorization models: neural network, C4.5 decision tree, Bayesian network, and support vector machine. Using a neural network as a classification technique, the suggested algorithms could identify both normal and moderate depression with up to 96% accuracy. The non-intrusive sensor technology may be used for long-term depression monitoring and early diagnosis of mental diseases. This allows caregivers to intervene quickly with older people at risk of depression.

Lupiani et al. (2017) investigated why and how Case-Based Reasoning (CBR) might aid elderly individuals living alone in Smart Homes. An integrated CBR system with sensors, data connection, and data integration was proposed. The CBR system analyzed everyday household activities as temporal event sequences and compared them using temporal edit distance. These methods were used to lower the number of cases in the Case Base to contribute to its long-term upkeep. A variety of temporal CBM algorithms were tested in a number of risk situations (such as waking up during the night, falls and falls with loss of consciousness). The data studies were based on real-time behavior patterns of 12 and 24 hours. Experiments showed that temporal CBM algorithms may effectively decrease case-bases to identify aberrant events. However, maintaining a case-base comparable to the original relies on the quantity of cases.

Fischinger et al. (2017) identified that falling was the most common reason for moving into a care facility. The Hobbit project integrated robotics, gerontology, and human-robot interaction research to create a care robot capable of preventing falls and detecting emergencies. Other features like delivering items, reminding, and entertaining were added to allow everyday engagement with the robot. The user interface consisted of automated voice recognition, text-to-speech, gesture recognition, and a graphical touch-based user interface. Fischinger et al. (2017) studied 49 elderly people (aged 70+) in three EU nations (Austria, Greece, and Sweden). Its intended user group received the robot well based on user perceptions of use, acceptability, and affordability.

With the increased efficiency and innovation brought by the Internet of Things (IoT) to the healthcare system, improved accessibility, greater patient comfort and enhanced delivery of care will become more evident, especially in nursing. Furthermore, the significant role of IoT during the COVID-19 pandemic and its state-of-the-art architectures, applications, platforms, and industrial solutions have shown encouraging results in fighting against the disease and progressing the overall performance of healthcare (Nasajpour et al., 2020). The uses of IoT in nursing interventions play an important aspect in patient care and monitoring (Feng & Hou, 2021; Hou et al., 2021; Feng et al., 2021; Ou et al., 2021). The growing theory and applications of IoT in nursing are still a work in progress, but will likely contribute to the future of healthcare and nursing.

Conclusion

With aging comes a greater need for medical and nursing care. Recent advances in Internet of things (IoT) technologies may help build healthcare systems that support older adults and their quality of life. Additionally, it could be noticed that the reviewed articles were mostly derived from Western and European nations. As such, it can be stated that in most Asian countries, the substantial strain on the public healthcare system and lack of suitable facilities drives patient care. A shift from a physician-centered to a patient-centered healthcare system is underway.

The elderly are a primary target for IoT related healthcare services, but their conservatism poses a severe problem. Although an Internet-of-Things-based system or software may enhance healthcare management among elderly people by improving their quality of life, it could be derived that user acceptance is poor. With this, there is a call for future research in the medical and nursing disciplines to look at and evaluate frameworks to understand essential elements that could influence elder adoption of IoT-related healthcare services.

References

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Author Biography

Roison Andro R. Narvaez, MSN, RN, CMCS, LGBH

Rois is currently practicing telehealth as a Clinical Case Manager at Ace Home Health and Hospice, a part-time Professor and PhD student in Nursing Science at St. Paul University of the Philippines (MSN), a part-time Faculty of Pamantasan ng Lungsod  (BSN) and University of Makati Center of Nursing (BSN).

Correspondence concerning this article should be addressed to Roison Andro Narvaez, St. Paul University Philippines, Mabini Street, Tuguegarao, Cagayan, 3500 Philippines, rnarvaez@spup.edu.ph