Health-Related Telemonitoring Parameters/Signals of Older Adults: An Umbrella Review

Aging is one of the greatest challenges in modern society. The development of wearable solutions for telemonitoring biological signals has been viewed as a strategy to enhance older adults’ healthcare sustainability. This study aims to review the biological signals remotely monitored by technologies in older adults. PubMed, the Cochrane Database of Systematic Reviews, the Web of Science, and the Joanna Briggs Institute Database of Systematic Reviews and Implementation Reports were systematically searched in December 2021. Only systematic reviews and meta-analyses of remote health-related biological and environmental monitoring signals in older adults were considered, with publication dates between 2016 and 2022, written in English, Portuguese, or Spanish. Studies referring to conference proceedings or articles with abstract access only were excluded. The data were extracted independently by two reviewers, using a predefined table form, consulting a third reviewer in case of doubts or concerns. Eighteen studies were included, fourteen systematic reviews and four meta-analyses. Nine of the reviews included older adults from the community, whereas the others also included institutionalized participants. Heart and respiratory rate, physical activity, electrocardiography, body temperature, blood pressure, glucose, and heart rate were the most frequently measured biological variables, with physical activity and heart rate foremost. These were obtained through wearables, with the waist, wrist, and ankle being the most mentioned body regions for the device’s placement. Six of the reviews presented the psychometric properties of the systems, most of which were valid and accurate. In relation to environmental signals, only two articles presented data on this topic. Luminosity, temperature, and movement were the most mentioned variables. The need for large-scale long-term health-related telemonitoring implementation of studies with larger sample sizes was pointed out by several reviews in order to define the feasibility levels of wearable devices.


Introduction
Digital technologies, such as smart wearable healthcare devices, are increasingly being used to support wellbeing, to encourage the independence of older adults, and to monitor health [1]. Telemonitoring, defined as the use of technologies for patient monitoring geographically separate from the health professional, at home, in healthcare units, and/or in hospitals [2], is currently viewed as a promising solution for older adults' healthcare. The data obtained by this kind of system not only inform caregivers and healthcare professionals about abnormal changes, helping therefore in the early detection and management of a health condition, but they can also be used in the self-management of older adults, promoting appropriate changes to their daily routines or behavior [3].
Several technologies, operating under different technical specifications and algorithms, have been developed in recent years, with different properties and levels of validity and reliability [4]. Depending on each system, devices can be placed in different body regions, for example, the wrist, the chest, the fingers, and the ankle, allowing the measurement and monitoring of several biological signals, such BT, HR, RR, BP, StO 2 , and BG [5]. Considering the role of the environment in biological signals as well as on older adults' health, the combination of biological signals' monitoring together with environment monitoring would better characterize health conditions or even the risk for older adults. It is known that environmental conditions have a significant impact on older adults, such as the house design, the sources of temperature and the temperature itself, gas density, air saturation, and luminosity. In this sense, the development of solutions as central stations that allow the daily analysis of the environment has also increased in recent years [6][7][8][9].
There have been several systematic reviews of health-related biological and environmental signals, measured in different age groups of healthy people or those with pathological conditions. These reviews identified vital signals such as HR, BP, BT, gas density, and humidity but have not always identified their normative values, information about the participants' related health status, the equipment and measurement method, or the validity or reliability values [10][11][12][13]. Technological advances have enabled the monitoring of several health-related biological signals in older adults, ranging from cardiovascular to movement-related signals. The specifications of the systems used vary in terms of size, portability, and normative values' characterization, dependent on health and environment conditions. Therefore, it is important to systematically gather information about the healthrelated biological signals measured in older adults, their measurement method, equipment and psychometric properties, the normative values of the health-related biological signals, as well as information about the health status of the elderly. These data are useful for the decision-making process, based on the biological signals, the significance of health-related parameters extracted, the system usability, and the psychometric properties.
Hence, an umbrella review of the type and usage of telemonitoring technologies in older adults is needed. Accordingly, this study developed an umbrella review to gather the health-related biological and environmental signals and instruments from the most recent telemonitoring technologies used in older adults.
Abbreviations part contains a table with all the notations in this article.

Materials and Methods
This umbrella review was conducted in accordance with the guidelines of PRISMA and the guidelines for developing and summarizing umbrella reviews [14,15]. No ethical approval was needed as we used data from published studies. This umbrella review was registered in PROSPERO under the number CRD42021282273. The search was carried out in December 2021, and the screening process occurred between January and May 2022.

Eligibility Criteria
Systematic reviews and meta-analyses, published between 2016 and 2022, written in English, Portuguese, or Spanish, aiming to review telemonitoring health-related biological and environmental signals in older adults, were included. Studies referring to conference proceedings, ineligible articles, or articles with abstract access only were excluded.

Search Strategy
A protocol with the search strings for each scientific database, namely PubMed, the Cochrane Database of Systematic Reviews, the Web of Science, and the Joanna Brigs Institute Database of Systematic Reviews and Implementation Reports, was properly designed by the researchers prior to the search. The search included MeSH terms. The search string for each database is shown in the supplemental data, Appendix A. References of the systematic reviews were also analyzed to identify further possibly relevant articles.

Selection Process and Data Extraction
The articles' selection process involved two sequential phases, in which studies were independently reviewed by two reviewers (JF and JM). In case of doubt, another independent reviewer (ASP) was consulted; we excluded all studies that did not fit the criteria. In phase one, the selection was based on the analysis of the title and the abstract. In phase two, a full-text analysis was conducted.
Next, the data were extracted independently by two reviewers (JF and JM), using a predefined table form, consulting a third reviewer in case of doubts or concerns (ASP). The information extracted was organized into two domains. The first included the city and country of the review, as well as, when available, the city and country of the original studies; the number of the included studies; the number of participants included and their mean/range age; and the population (healthy or pathological condition) and the population context (community-dwelling/controlled lab/institutionalized). The second domain included information regarding the monitoring technology including the wearable type, sensor type, wearable/sensor location; the health-related biological signals; the health-related environmental signals; the psychometric properties of the outcome measures by body regions (validity and reliability); the cutoff of the biological and environmental signals; and the health status information of the measures of the biological signals and the environmental signals and the usability information.

Methodological Quality Assessment
The methodological quality assessment of the included reviews was performed independently by two reviewers (JF and JM). In case of disagreement, a third reviewer was consulted (ASP). AMSTAR 2.0 was used [16]. This tool contains 16 items, which can be answered with "yes", "partially yes", and "no". Depending on the score and how many critical and non-critical flaws an article had, it could be classified as "high quality", "moderate quality", "low quality", and "critically low quality". The critical domains of AMSTAR 2.0 are: "protocol registered before commencement of the review"; "adequacy of the literature search"; "justification for excluding individual studies"; "risk of bias from individual studies being included in the review"; "appropriateness of meta-analytical methods"; "consideration of risk of bias when interpreting the results of the review"; and "assessment of presence and likely impact of publication bias" [16].

Results
The database search retrieved 644 records, seven of them were duplicates, which were eliminated. After the analysis of titles and abstracts, 487 studies were excluded because they were not systematic reviews (n = 157) or they exclusively analyzed groups other than older adults (n = 330). Accordingly, 18 systematic reviews were included in this umbrella review (Figure 1). Nine of the reviews were developed in European countries [17][18][19][20][21][22][23][24][25], four in North American countries [26][27][28][29], three in Australia [30][31][32], one in Pakistan [33], and one in India [34]. The number of studies included in each review varied between 7 and 73, The database search retrieved 644 records, seven of them were duplicates, which were eliminated. After the analysis of titles and abstracts, 487 studies were excluded be cause they were not systematic reviews (n = 157) or they exclusively analyzed groups other than older adults (n = 330). Accordingly, 18 systematic reviews were included in this umbrella review (Figure 1). Nine of the reviews were developed in European countries [17][18][19][20][21][22][23][24][25], four in North American countries [26][27][28][29], three in Australia [30][31][32], one in Paki stan [33], and one in India [34]. The number of studies included in each review varied between 7 and 73, with an average of 32 studies, and a median of 25 studies. A detailed description of the studies is shown in Table 1.

General Characterization of the Studies: Country Origin, Included Types of Study, and Their Methodological Quality
Some reviews indicated the countries of the included studies, whereas others indicated only the continent; seven reviews did not report the origin of the included articles (Table 1).
The number of steps was the variable assessed on more body locations (lumbar spine, upper arm, bra, torso, chest, sternum, tight, wrist, waist, and ankle), while the fall risk was assessed only on the torso, the BP on the arm, and the ECG on the chest.
It is important to note that a movement category considered all free-living activities.

Environmental Signals
Only two reviews [24,34] reported unobtrusive in-home monitoring, allowing participants' quality of life to be assessed using environmental signals and a home base station.
Passive infrared motion, contact, pressure and electrical current sensors were the most frequently used to monitor the participants' behavior, measuring the presence in specific places or furniture or the time spent on activities [24].
Older adults' health-related biological signals were measured through equipment placed in the environment, such as temperature, ECG and HR; however, other signals were also identified such as presence, activity, gas concentration, and sound. A more detailed description of the results of the environmental signals is presented in Table 7. Several different types of sensors were identified in the review. The sensors included a contact sensor, a motion sensor, an electrical current sensor, a thermometer, a flowmeter, a camera, an infrared camera, a pressure sensor, a humidity sensor, a gas sensor (air quality and smoke), and other sensors, measuring the presence at home, the activity of daily living, the time on activity, the activity level, and several environmental data (temperature, humidity, gas, light, rain, and flame) [24,34]. A more detailed description of the results of the environmental signals is presented in Table 7.

Location of the Measurement of the Environmental Signals
In the analyzed studies, different locations were identified for the placement of the sensors. The locations ranged from household appliances, such as kitchen equipment, to audiovisual equipment. Infrastructures such as walls and floors or even furniture were also mentioned. Finally, divisions in general were also mentioned, such as the kitchen, living room, or bedroom [24,34]. A more detailed description of the results of the environmental signals are present in Table 7.

Psychometric Properties of Sensors Used to Measure the Environmental Signals
The psychometric properties were not reported.

Discussion
Aging and increased longevity are two of the greatest developmental difficulties in modern society. In the next 40 years, in Europe, it is projected that people over 65 years will be the fastest growing age group, leading to a doubling of the older adult population compared to the younger population [35,36]. This growth will imply an increase in care to maintain the quality of life of this population, considering the three strongest aspects of aging, namely the loss of autonomy, the increase in loneliness, and the management of acute or chronic health conditions [37]. Altogether, this represents an increase in total cost expenditures, as well as an intensification of healthcare or social care. The development of efficient methods and strategies to collaborate in the monitoring of the older adult population has been stated as essential to reduce accidents and traumatic events, manage chronic conditions, and increase older adults' control over their health and quality of life, thus meeting the third objective of the 2030 agenda developed by the United Nations, which aims for good health and wellbeing [38][39][40]. This challenge motivated the development of the present review to understand the progress in monitoring older adults, namely which health-related biological and environmental signals are being used, as well as which instruments are being used to access them.
Most of the reviews were performed in developed countries. This is in line with what is known in the scientific community; developed countries conduct more research for the maturation and development of scientific knowledge and are at the forefront of technological innovations and their applications [41]. However, it is relevant to note that two reviews were conducted in developing countries, and those countries have a strong presence in the release of scientific material to the international community [24,34]. The same trend was observed in the countries of the original studies included in each of the reviews, where European countries and North America were the most reported. Again, in these developed countries, the economic factor and the gross domestic product available for research are important.
All reviews indicated the databases searched varied between MEDLINE, PubMed, and EMBASE. These were the most inclusive databases that could assist in finding all available articles [42]. However, some recent articles have shown that it is advisable to conduct a review at least in EMBASE, MEDLINE, Web of Science, and Google Scholar to be inclusive, a fact that was not always fulfilled by the included reviews [42].
As would be expected, the health-related biological signals that appeared to be the most frequently measured in older adults corresponded to the vital signals [43][44][45]. However, other signals related to movement variables were frequently considered, the steps being most the frequent, followed by energy expenditure. Body temperature, peripheral oxygen saturation, fall risk detection, glucose levels, and weight were also assessed. The signals monitored were used to assess the daily activity time, PA level, posture, sleep, stress, energy expenditure, fall risk, and movement quality. Naturally, movement is one of the most studied biological signals due to its ease of acquisition through a wide range of accelerometers and movement sensors, thus making it the most frequent variable measured in older adults. On the other hand, its measurement is extremely important, since a sedentary lifestyle increases the risk of heart and metabolic diseases, which already have a high incidence rate in this population. In this way, the measurement of this variable is extremely important, since the diagnosis of movement and activity allows an early intervention in the sense of promoting health in older adults [19,23,30,31,46,47].
Biological signals related to the cardiac and respiratory systems, such as HR, RR, and oxygen saturation, also presented a high frequency of measurement indication in the age group under study. This factor is again due to the need to monitor the health status of older adults, assessing vital signals, which are essential for understanding the proper functioning of the cardiorespiratory system. In this population, the cardiovascular system and respiratory system are more fragile and probably experiencing pathological changes; better monitoring of older adults allows early diagnosis and intervention [21,[26][27][28]31,48,49].
Other variables, which are not new, but appeared less often, such as sleep, glucose levels, and fall risk, are also extremely important for the population under study. All variables report the health status of older adults; so, their monitoring is also relevant and gives health professionals information about the health status of the older adult [22,28,29,50,51].
Different biological and environmental signals were measured through different types of wearable/sensors. Through this review, we saw that a wearable group included different types of sensors, making it possible to measure several signals with a single device. For example, a waist-worn device can be used to monitor the HR, ECG, RR, and PA, measuring the state of the cardiac, respiratory, and movement systems.
In relation to body areas for the wearables, most of the studies pointed to the thigh, wrist, and waist as the most suitable places to measure biological signals. Evidently, the systems tend to be increasingly simple, user-friendly, and less intrusive; so the individual can carry out their normal tasks throughout the day, without the system interfering. In this sense, the places identified through different devices, allowed the user to quickly forget their use, enabling monitoring and evaluation in a real context without feeling the pressure of being evaluated, avoiding the modification of values [21,[26][27][28]31,48,49,52].
Reliability and validity are considered two of the main measurement properties of instruments [53]. In this review, the steps variable was the only one with values presented for reliability and validity, and the values found agreed with Evenson et al. (2015) [54]. In relation to the other variables, such as posture, daily activity time, and sleep, in which it was possible to identify the reliability and validity values, these appeared to be acceptable [53][54][55]. The PA level and cardiac rhythm also seemed to have acceptable validity values. These are the oldest variables in terms of health investigation, which has meant more development time and thus better psychometric characteristics, due to their development and continuous improvement.
Only two reviews [24,34] assessed environmental signals. These reviews demonstrated a lack of information in scientific data about how environmental signals modify biological signals or the quality of life/health status of the users. The most common measures used were related to physiological monitoring, functional monitoring, emergency detection, and safety/security monitoring. These are very important measures to improve the quality of life/health status of the user, especially in cases where safety is very important to guarantee their health status [56].
Understand the methodological quality of the studies is important. In umbrella reviews, the quality of the original studies included in the reviews, as well as the quality of the reviews themselves, should be assessed. The risk of bias and the quality of the studies were not always defined, which means that there was no knowledge about the relevance/quality of the studies included in the reviews, which made interpretation of the results difficult; however, in relation to the reviews' methodological quality, they were average to good quality. The quality of the studies was dispersed, ranging from very low to high quality, making comparisons difficult. In line with these limitations, some factors should be considered in the interpretation of the results of the present study. Although wearables are for everyone, some studies did not mention, did not characterize, or included different populations (different ages, different health conditions, and community-dwelling or institutionalized older adults). These factors can cause variations in the health-related biological signals. Moreover, different health conditions and different environments cause variations in the functioning and performance of the different wearables, making the comparation between studies very difficult [4,[57][58][59][60][61][62][63][64]. Differences between the studies in terms of the aims and sample sizes (number of studies included in the original reviews) made it difficult to compare them. For example, checking the reliability and validity of different systems versus finding the usability of system and studies with a large sample (73) versus a small sample (7) indicated high heterogeneity between the studies and between their methodologies. Future studies comparing various uses of wearables, their advantages, and disadvantages in different age groups, living conditions, and specific pathologies are required. Moreover, more studies assessing the systems' effectiveness for older adults' health are required.

Conclusions
The results of the present review demonstrated that the most frequent body regions used to assess older adults' biological signals were the wrist, waist, and chest. The signals collected at these regions were mostly used to assess PA (through various variables such as energy expenditure, posture, and METs) and cardiovascular variables (through signals such as HR and cardiac rhythm). The environmental systems were used to assess environmental features; however, the health-related biological signals of older adults were also measured. This monitoring strategy had the advantage of monitoring the elderly person in the place/house, where the older adult was. Among all biological signals, the most frequent were ECG, temperature, HR, and body mass. These systems used a wide variety of sensors (mechanical, acoustic, optical, and air-related), and among the most frequent environment signals assessed, we highlight gas (density and saturation) and sound.
Despite providing a global overview of the monitoring of older adults' biological signals, the divergence observed between the studies included in the present review limited the comparison between different systems. Therefore, future studies with more specific criteria regarding study methodology are required. Moreover, while the psychometric properties of some systems were presented, the study of these properties needs to be extended to the other systems. This information will help the decision-making process regarding the selection of the system to be used.  Keywords Plus ® OLDER ADULTS TS=("aged" OR "elder*" OR "older adult*" OR "older person*" OR "centenarian*" OR "nonagenarian*" OR "octogenarian*") BIOLOGICAL AND ENVIRONMENTAL SIGNALS TS=("vital" OR "vital sign*" OR "vital function*" OR "vital parameter*" OR "biological sign*" OR "physical activity" OR "sedentary behavior" OR "cardiorespiratory fitness" OR "electrocardiography" OR "blood glucose" OR "galvanic skin response" OR "oximetry" OR "humidity" OR "temperature" OR "lighting") TELEMONITORING TS=("wearable electronic devices" OR "wearable devices" OR "wearable technology" OR "sensor" OR "device*" OR "wearable" OR "Internet of Things" OR "Remote continuous monitoring" OR "wireless device" OR "patch" OR "appliance" OR "portable" OR "sensor" OR "Monitoring, Physiologic" OR "tracker*" OR "Environmental Monitoring" OR "Environmental Quality") STUDY DESIGN