Smart homes for the older population: particularly important during the COVID-19 outbreak

[Interventions for functionally dependent adults and their caregivers during the COVID-19 pandemic]

The COVID-19 pandemic has affected the world population, especially people from social groups in a situation of greater vulnerability among people with some functional dependency. Therefore, the aim of this review was to describe interventions during the pandemic to preserve general health, maintain function and independence, and prevent COVID-19 infection for functionally dependent adults (FDA). A systematic search in databases was carried out. Titles and abstracts of each publication were reviewed for relevance. Full-text articles were accessed by two independent reviewers. The information found was classified into three categories: 1) FDA during the COVID-19 pandemic, 2) FDA during the COVID-19 pandemic according to a specific condition (neurological conditions, sensory disabilities/impairments, and cognitive impairment), and 3) Older adults with functional dependence. The FDAs have faced difficulties and barriers during the COVID-19 pandemic. Strengthening strategies such as telerehabilitation is suggested to avoid deterioration or aggravation of the functionality of dependent people.

Application of Artificial Intelligence in Geriatric Care: Bibliometric Analysis

BACKGROUND

Artificial intelligence (AI) can improve the health and well-being of older adults and has the potential to assist and improve nursing care. In recent years, research in this area has been increasing. Therefore, it is necessary to understand the status of development and main research hotspots and identify the main contributors and their relationships in the application of AI in geriatric care via bibliometric analysis.

OBJECTIVE

Using bibliometric analysis, this study aims to examine the current research hotspots and collaborative networks in the application of AI in geriatric care over the past 23 years.

METHODS

The Web of Science Core Collection database was used as a source. All publications from inception to August 2022 were downloaded. The external characteristics of the publications were summarized through HistCite and the Web of Science. Keywords and collaborative networks were analyzed using VOSviewers and Citespace.

RESULTS

We obtained a total of 230 publications. The works originated in 499 institutions in 39 countries, were published in 124 journals, and were written by 1216 authors. Publications increased sharply from 2014 to 2022, accounting for 90.87% (209/230) of all publications. The United States and the International Journal of Social Robotics had the highest number of publications on this topic. The 1216 authors were divided into 5 main clusters. Among the 230 publications, 4 clusters were modeled, including Alzheimer disease, aged care, acceptance, and the surveillance and treatment of diseases. Machine learning, deep learning, and rehabilitation had also become recent research hotspots.

CONCLUSIONS

Research on the application of AI in geriatric care has developed rapidly. The development of research and cooperation among countries/regions and institutions are limited. In the future, strengthening the cooperation and communication between different countries/regions and institutions may further drive this field's development. This study provides researchers with the information necessary to understand the current state, collaborative networks, and main research hotspots of the field. In addition, our results suggest a series of recommendations for future research.

Health Monitoring Using Smart Home Technologies: A Scoping Review (Preprint)

BACKGROUND

The Internet of Things (IoT) has become integrated into everyday life, with devices becoming permanent fixtures in many homes. As countries face increasing pressure on their health care systems, smart home technologies have the potential to support population health through continuous behavioral monitoring.

OBJECTIVE

This scoping review aims to provide insight into this evolving field of research by surveying the current technologies and applications for in-home health monitoring.

METHODS

Peer-reviewed papers from 2008 to 2021 related to smart home technologies for health care were extracted from 4 databases (PubMed, Scopus, ScienceDirect, and CINAHL); 49 papers met the inclusion criteria and were analyzed.

RESULTS

Most of the studies were from Europe and North America. The largest proportion of the studies were proof of concept or pilot studies. Approximately 78% (38/49) of the studies used real human participants, most of whom were older females. Demographic data were often missing. Nearly 60% (29/49) of the studies reported on the health status of the participants. Results were primarily reported in engineering and technology journals. Almost 62% (30/49) of the studies used passive infrared sensors to report on motion detection where data were primarily binary. There were numerous data analysis, management, and machine learning techniques employed. The primary challenges reported by authors were differentiating between multiple participants in a single space, technology interoperability, and data security and privacy.

CONCLUSIONS

This scoping review synthesizes the current state of research on smart home technologies for health care. We were able to identify multiple trends and knowledge gaps-in particular, the lack of collaboration across disciplines. Technological development dominates over the human-centric part of the equation. During the preparation of this scoping review, we noted that the health care research papers lacked a concrete definition of a smart home, and based on the available evidence and the identified gaps, we propose a new definition for a smart home for health care. Smart home technology is growing rapidly, and interdisciplinary approaches will be needed to ensure integration into the health sector.