Abstract

Objective

This scoping review aimed to describe and map the measures provided by smartwatches as a tool for identifying Frailty Syndrome in older adults.

Methods

Studies published in any language, without publication date restrictions, that described the use of measures provided by smartwatches in evaluating or identifying Frailty Syndrome and/or its criteria in older adults were included. English descriptors for smartwatches, smartbands, Frailty Syndrome and Older Adults were used to develop a comprehensive search strategy, which was then applied to search the following databases: COCHRANE LIBRARY, EMBASE, SCOPUS, PUBMED/MEDLINE, LILACS, WEB OF SCIENCE and PEDRO.

Results

The initial search identified a total of 156 articles and 2 articles were identified from the manual search in the references of eligible studies. Next, 4 studies that used daily step count measurements for descriptive synthesis were included, and three of the four also used sleep and heart rate data to assess frailty in older adults. The results obtained in this review indicate that parameters derived from smartwatches have been used to identify stages of frailty in different areas, with the majority of studies being associated with other clinical conditions.

Conclusion

Smartwatches are an excellent frailty monitoring tool through daily measurements of step count, sleep data and heart rate. The results obtained with the use of these devices may suggest a broader evaluation of older adults who face an increased risk of developing Frailty Syndrome.

Keywords
Aging; Frailty Syndrome; Wearable devices

Resumo

Objetivo

Esta Revisão de Escopo teve como objetivo descrever e mapear as medidas disponibilizadas pelos smartwatches como ferramenta para identificação da Síndrome de Fragilidade em idosos.

Métodos

Foram incluídos estudos publicados em qualquer idioma, sem restrição de data de publicação, que descrevessem o uso de medidas fornecidas por smartwatches na avaliação da Síndrome de Fragilidade e/ou seus critérios em idosos. Descritores em inglês para smartwatches, smartbands, Síndrome da Fragilidade e envelhecimento foram utilizados para desenvolver uma estratégia de busca abrangente, que foi então aplicada para pesquisar nas seguintes bases de dados: COCHRANE LIBRARY, EMBASE, SCOPUS, PUBMED/MEDLINE, LILACS, WEB OF SCIENCE e PEDRO.

Resultados

A busca inicial identificou um total de 156 artigos e foram identificados 2 artigos a partir da busca manual nas referências dos estudos elegíveis. Em seguida, foram incluídos 4 estudos que utilizaram medidas diárias de contagem de passos para síntese descritiva, e três dos quatro também utilizaram dados relacionados ao sono e FC para avaliar a fragilidade em idosos. Os resultados obtidos nesta revisão indicam que parâmetros derivados de smartwatches têm sido utilizados para identificar estágios de fragilidade em diferentes ambientes, sendo a maioria dos estudos associados a outras condições clínicas.

Conclusão

Os smartwatches são uma excelente ferramenta de monitoramento de fragilidade por meio de medições diárias de contagem de passos, dados de sono e frequência cardíaca. Os resultados obtidos com o uso desses dispositivos podem sugerir uma avaliação mais ampla dos idosos que enfrentam risco aumentado de desenvolver a Síndrome da Fragilidade.

Palavras-Chave:
Envelhecimento; Síndrome da Fragilidade; Dispositivos vestíveis

INTRODUCTION

Frailty Syndrome is closely related to the aging process, but it is not considered an inevitable condition for the older adult population, because a transience between the frailty stages may occur through appropriate interventions¹1 Kojima G, Taniguchi Y, Iliffe S, Jivraj S, Walters K. Transitions between frailty states among community-dwelling older people: A systematic review and meta-analysis. Ageing Res Rev 2019;50:81–88; https://doi.org/10.1016/j.arr.2019.01.010.
https://doi.org/10.1016/j.arr.2019.01.01... . In view of this, an adequate and early assessment of Frailty Syndrome is an important point to identify which older adults are at increased risk for this condition²2 Turner G, Clegg A, British Geriatrics Society, Age UK, Royal College of General Practioners. Best practice guidelines for the management of frailty: a British Geriatrics Society, Age UK and Royal College of General Practitioners report. Age Ageing 2014;43(6):744–747; http://doi.org/10.1093/ageing/afu138.
https://doi.org/10.1093/ageing/afu138... .

Fried’s Frailty Phenotype³3 Fried LP, Tangen CM, Walston J, Newman AB, Hirsch C, Gottdiener J, et al. Frailty in older adults: evidence for a phenotype. J Gerontol A Biol Sci Med Sci 2001;56(3):M146-156; http://doi.org/10.1093/gerona/56.3.m146.
https://doi.org/10.1093/gerona/56.3.m146... is one of the most widely-used frailty assessment tools which classifies older adults as frail, pre-frail and non-frail according to the presence of the following criteria: muscle weakness, slow gait, exhaustion, unintentional weight loss and low physical activity level³3 Fried LP, Tangen CM, Walston J, Newman AB, Hirsch C, Gottdiener J, et al. Frailty in older adults: evidence for a phenotype. J Gerontol A Biol Sci Med Sci 2001;56(3):M146-156; http://doi.org/10.1093/gerona/56.3.m146.
https://doi.org/10.1093/gerona/56.3.m146... . Assessment based on the Frailty index which includes a variety of factors is also widely used for screening this condition resulting in a continuous scale with higher frailty scores for a greater number of conditions present⁴4 Mitnitski AB, Mogilner AJ, Rockwood K. Accumulation of Deficits as a Proxy Measure of Aging. Sci World J 2001;1:323–336; http://doi.org/10.1100/tsw.2001.58.
https://doi.org/10.1100/tsw.2001.58... .

Frailty assessment instruments can be classified as: objective when they are not based on direct performance measures; subjective when they are based on self-assessments and/or self-reports; and mixed when they include the two previous types⁵5 Bouillon K, Kivimaki M, Hamer M, Sabia S, Fransson EI, Singh-Manoux A, et al. Measures of frailty in population-based studies: an overview. BMC Geriatr 2013;13:64; http://doi.org/10.1186/1471-2318-13-64.
https://doi.org/10.1186/1471-2318-13-64... . Although the use of questionnaires is considered a low-cost way to reach larger groups, self-reports can be prone to a variety of biases, such as perception and memory bias⁶6 Choi BCK, Pak AWP. A catalog of biases in questionnaires. Prev Chronic Dis 2005;2(1):A13.PubMed; PMID: 15670466..

New technologies have been proposed for early screening of frailty, including the use of wearable sensors that can help monitor the risk of developing frailty in the older adult population⁷7 Kańtoch E, Kańtoch A. What Features and Functions Are Desired in Telemedical Services Targeted at Polish Older Adults Delivered by Wearable Medical Devices?—Pre-COVID-19 Flashback. Sensors 2020;20(18):5181; http://doi.org/10.3390/s20185181.
https://doi.org/10.3390/s20185181...

8 Zanotto T, Mercer TH, van der Linden ML, Raynor JP, & Koufaki, P. Use of a wearable accelerometer to evaluate physical frailty in people receiving haemodialysis. BMC Nephrol 2023;24(1):82; http://doi.org/10.1186/s12882-023-03143-z.
https://doi.org/10.1186/s12882-023-03143...

9 Kraus M, Saller MM, Baumbach SF, Neuerburg C, Stumpf UC, Böcker W, Keppler AM. Prediction of Physical Frailty in Orthogeriatric Patients Using Sensor Insole–Based Gait Analysis and Machine Learning Algorithms: Cross-sectional Study. JMIR Med Inform 2022;10(1):e32724; http://doi.org/10.2196/32724.
https://doi.org/10.2196/32724... -¹⁰10 Cobo A, Villalba-Mora E, Pérez-Rodríguez R, Ferre X, & Rodríguez-Mañas L. Unobtrusive Sensors for the Assessment of Older Adult’s Frailty: A Scoping Review. Sensors 2021;21(9):2983; http://doi.org/10.3390/s21092983.
https://doi.org/10.3390/s21092983... . A systematic review¹¹11 Vavasour G, Giggins OM, Doyle J, Kelly D. How wearable sensors have been utilised to evaluate frailty in older adults: a systematic review. J NeuroEngineering Rehabil 2021;18(1):112; http://doi.org/10.1186/s12984-021-00909-0.
https://doi.org/10.1186/s12984-021-00909... which included 29 observational studies involving older adults who used wearable sensors to identify the presence of frailty and pre-frailty highlighted the heterogeneity of the parameters examined in relation to identifying frailty and the body locations used. Postural transitions, number of steps, percentage of time and intensity of physical activity together were the most frequently measured parameters, closely followed by gait speed. Also, one study demonstrated an association between physical activity and frailty level¹¹11 Vavasour G, Giggins OM, Doyle J, Kelly D. How wearable sensors have been utilised to evaluate frailty in older adults: a systematic review. J NeuroEngineering Rehabil 2021;18(1):112; http://doi.org/10.1186/s12984-021-00909-0.
https://doi.org/10.1186/s12984-021-00909... .

Smartwatches are wearable devices worn on the wrist, which, depending on the model and manufacturer, provide various measurements about the number of daily steps, heart rate (HR), sleep quality, and physical activity level, among others¹²12 Henriksen A, Haugen Mikalsen M, Woldaregay AZ, Muzny M, Hartvigsen G, Hopstock LA, et al. Using Fitness Trackers and Smartwatches to Measure Physical Activity in Research: Analysis of Consumer Wrist-Worn Wearables. J Med Internet Res 2018;20(3):e110; http://doi.org/10.2196/jmir.9157.
https://doi.org/10.2196/jmir.9157... . Based on knowledge about these devices and considering that Frailty Syndrome is closely related to the aging process, this review was based on the following guiding question: “Can the measurements provided by smartwatches be used to identify Frailty Syndrome in older adults?”. Faced with this question, the objective of this scoping review was to describe and map the measures provided by smartwatches as a tool to identify Frailty Syndrome in older adults.

METHODS

Studies published in any language with no restriction on the publication date, that described the use of measures provided by smartwatches in evaluating or identifying Frailty Syndrome and/or its criteria in older adults were included. The following study designs were considered: prospective and retrospective observational cohort studies, case reports, and cross-sectional studies. Studies that described the evaluation of Frailty Syndrome through measurements provided by wearable devices other than smartwatches/smartbands, used on the wrist, were excluded.

Based on PCC elements¹³13 Peters M, Godfrey C, Khalil H, Mcinerney P, Soares C, Parker D. 2017 Guidance for the Conduct of JBI Scoping Reviews. Int J Evid Based Healthc. 2015 Sep;13(3):141-6. http://doi.org/10.1097/XEB.0000000000000050
https://doi.org/10.1097/XEB.000000000000... , which advocates the mnemonic as fundamental elements: P - Population, C - Concept and C - Context, the keywords in English about smartwatches, smartbands, Frailty Syndrome and older adults were used to develop a complete search strategy applied to search in the following databases: COCHRANE LIBRARY, EMBASE, SCOPUS, PUBMED/MEDLINE, LILACS, WEB OF SCIENCE and PEDRO. The database search was carried out between July and September 2023.

The search strategy, including all identified keywords and indexing terms, was adapted for each database and/or information source included. The reference list of all included sources of evidence will be reviewed for further study. The combinations of search strategies used on the platforms are described in Chart 1.

The research protocol was registered and made publicly available on the Open Science Framework (OSF) platform (DOI 10.17605/OSF.IO/42VFJ).

After the search, all identified citations were grouped and uploaded to Rayyan, a free web application developed by QCRI (Qatar Computing Research Institute). After a pilot test, titles and abstracts were selected by two independent reviewers, in which they were assessed against the eligibility criteria for the review. The reasons for excluding full-text evidence sources which did not meet the inclusion criteria were recorded and reported in this scoping review. Any disagreements that arose between reviewers at each step of the selection process were resolved through discussion with a third reviewer.

Data were manually extracted by two independent reviewers using an “extraction form” developed by the reviewers. Differences were resolved by consensus, and when this was not possible, a third reviewer was called and had the final decision.

The data listed below were extracted from the text, tables or figures of the articles included in the review:

• Study design;

• Study setting (including country where it was conducted);

• Demographics of the population (mean age and gender distribution);

• Frailty assessment criteria and tools;

• Biological signals derived from the smartwatch used in the evaluation and identification of Frailty Syndrome in older adults.

A descriptive synthesis of the results of the studies was written in a structured way, describing the content of the scoping review.

DATA AVAILABILITY

The entire dataset supporting the results of this study is available upon request to the corresponding author, Juliana Fernandes de Souza Barbosa.

RESULTS

The initial search identified a total of 158 articles, and an additional 2 were identified from the manual search in the references of eligible studies (Figure 1).

Figure 1
Flowchart of the selection process of articles included in the scoping review according to PRISMA-ScR (Preferred Reporting Items for Systematic Review and Meta-Analyses extension for Scoping Reviews)¹⁴14 Tricco AC, Lillie E, Zarin W, O'Brien KK, Colquhoun H, Levac D, et al. PRISMA Extension for Scoping Reviews (PRISMA-ScR): Checklist and Explanation. Ann Intern Med 2018;169(7):467–473; http://doi.org/10.7326/M18-0850.
https://doi.org/10.7326/M18-0850... .

After excluding duplicates, 154 articles remained. The articles were analyzed by reading the titles and abstracts, with another 144 being excluded after this process. After reading the full papers, 6 articles were excluded for not meeting the eligibility criteria.

Chart 2 contains the main characteristics of the articles included, such as the study design and location, sample characterization, type of smartwatch used in the study and the main measures derived from the smartwatch, which were used to assess the frailty criteria. The articles are organized in chronological order.

All studies included in this review are prospective observational and used smartwatch or smartband monitoring bracelets to assess frailty criteria, with or without association with other health conditions. The studies were carried out in the following areas: home (2), long-stay institution (1) and hospital and/or outpatient setting (1).

The number of participants ranged from 12 to 88, the average age was 76.76 years and most were female.

A study by Kim and Lee¹⁵15 Kim B, McKay SM, Lee J. Consumer-Grade Wearable Device for Predicting Frailty in Canadian Home Care Service Clients: Prospective Observational Proof-of-Concept Study. J Med Internet Res 2020;22(9):e19732; http://doi.org/10.2196/19732.
https://doi.org/10.2196/19732... demonstrated that participants classified as frail were significantly older (p < 0.01). Data from these participants reported significantly lower daily step counts than non-frail (mean steps per day: 367.11 vs. 1,023.95, respectively; p = 0.04). The five sleep measures evaluated (total sleep time, deep sleep time, light sleep time, sleep quality and awake time) were moderately correlated with frailty. In this study, no relationship was found between HR measurements and the state of frailty. Regarding the prediction of frailty, the logistic regression model that used the variables derived only from the wearable device data (step count, deep sleep time, light sleep time, HR standard deviation) demonstrated that the time of deep sleep was a predictor of frailty (p < 0.01), and increased sleep time was significantly associated with increased odds of frailty (adjusted odds ratio [OR] 1.02, 95%CI 1.01-1.05, p <0.01).

A study developed by Mach et al.¹⁶16 Mach M, Watzal V, Hasan W, Andreas M, Winkler B, Weiss G, et al. Fitness-Tracker Assisted Frailty-Assessment Before Transcatheter Aortic Valve Implantation: Proof-of-Concept Study. JMIR MHealth UHealth 2020;8(10):e19227; http://doi.org/10.2196/19227.
https://doi.org/10.2196/19227... used a non-randomized open-concept test, which evaluated the pre-procedure activity of transcatheter aortic valve implantation, in which the prevalence of frailty was also evaluated through the Fitness-tracker assisted Frailty-Assessment Score (FIFA) and compared to the assessment data using the Edmonton Frail Scale (EFS-C) and the 6-minute walk test (6MWT). The production of daily data through the measures provided by the smartwatch were used to calculate the weekly average values, excluding the incomplete activity data available from the first and last day of monitoring. From this data compilation, threshold levels in three predefined categories (HR, pre-procedure stress and walking) were calculated. Patients were assigned one point per category in the FIFA when exceeding (in categories with positive correlation) or falling back (in categories with negative correlation) threshold levels, and then grouped into four categories (0, no frailty; 1, mild frailty; 2, moderate frailty; 3, severe frailty). The study demonstrated a strong predictive performance of a smartwatch-based frailty assessment in which the FIFA score correctly identified frail patients, as demonstrated from the strong correlation with baseline serum albumin levels (p=0.005) – a well-established biomarker for frailty.

In another study by Kim et al.¹⁷17 Kim B, Hunt M, Muscedere J, Maslove DM, Lee J. Using Consumer-Grade Physical Activity Trackers to Measure Frailty Transitions in Older Critical Care Survivors: Exploratory Observational Study. JMIR Aging 2021;4(1):e19859; http://doi.org/10.2196/19859.
https://doi.org/10.2196/19859... , three of the 12 study participants used smartwatches for at least five days during the post-hospital discharge monitoring period. The patients wore the smartwatches for an average of 26.33 days. Frail patients had significantly lower daily step counts than non-frail patients (1,336.40 vs 3,781.04 steps; p = 0.02; d = 1.81). They performed less daily physical activity than non-frail participants (2.02 vs 16.34 minutes per day; p = 0.04; d = 0.94). There was no difference in sleep and HR measures between frail and non-frail groups. However, there was a strong correlation between mean HR and the Clinical Frailty Scale (CFS) and (r =−0.72; p= 0.046) with the CFS score at hospital discharge.

In a study by Schmidle et al.¹⁸18 Schmidle S, Gulde P, Koster R, Maslove DM, Lee J. The relationship between self-reported physical frailty and sensor-based physical activity measures in older adults – a multicentric cross-sectional study. BMC Geriatr 2023;23(1):43; http://doi.org/10.1186/s12877-022-03711-2.
https://doi.org/10.1186/s12877-022-03711... , the older adults used the smartwatch for an average of 17.5(± 5.1) days, for a period of at least eight hours a day. The parameters derived from the smartwatch used to assess frailty were the measure of physical activity intensity based on changes in acceleration intensity, taking into account the median of all values ​​(MAD median); and the daily step count, based on the cadence percentile parameter in steps per minute (STEP95). Correlations between clock measurements were made with two frailty scores, the classic 0 to 5 scale including all five physical frailty criteria (weight loss, exhaustion, muscle strength, physical activity, and weakness) and a shortened version omitting the two parameters (muscle strength and weight loss) which could not be assessed by a wrist-worn sensor.

Moderate negative correlations were found between the ‘STEP95’ gait parameter and both frailty scores (R²2 Turner G, Clegg A, British Geriatrics Society, Age UK, Royal College of General Practioners. Best practice guidelines for the management of frailty: a British Geriatrics Society, Age UK and Royal College of General Practitioners report. Age Ageing 2014;43(6):744–747; http://doi.org/10.1093/ageing/afu138.
https://doi.org/10.1093/ageing/afu138... = 0.25 and 0.26). Furthermore, weak to moderate negative correlations were also found between the ‘MADmedian’ activity parameter and both scores (R²2 Turner G, Clegg A, British Geriatrics Society, Age UK, Royal College of General Practioners. Best practice guidelines for the management of frailty: a British Geriatrics Society, Age UK and Royal College of General Practitioners report. Age Ageing 2014;43(6):744–747; http://doi.org/10.1093/ageing/afu138.
https://doi.org/10.1093/ageing/afu138... = 0.07 & 0.14). There were three different types of cluster behavior based on the behavioral data: (1) participants with high activity and low extent of ambulation; (2) participants with high activity and high extent of ambulation; and (3) participants with low activity and low extent of ambulation. The cluster analyzes showed statistically significant differences for the variables of: activity, gait, age, gender, number of chronic diseases, current health status, and use of walking aids; the chance of being female and frail increased significantly for cluster 1. A significant difference for gait-related parameters was found for almost all frailty criteria, suggesting that mobility may be the driving parameter related to frailty¹⁸18 Schmidle S, Gulde P, Koster R, Maslove DM, Lee J. The relationship between self-reported physical frailty and sensor-based physical activity measures in older adults – a multicentric cross-sectional study. BMC Geriatr 2023;23(1):43; http://doi.org/10.1186/s12877-022-03711-2.
https://doi.org/10.1186/s12877-022-03711... .

DISCUSSION

This scoping review aimed to describe and map the measures provided by smartwatches as a tool to identify Frailty Syndrome in older adults. The use of wearable sensors, such as the smartwatch, can be useful in combating the challenges in measuring frailty, as it is a viable, practical, accessible, reproducible and reliable instrument, without jeopardizing daily activity in the various areas in which the older adult is inserted¹⁹19 Toosizadeh N, Joseph B, Heusser MR, Orouji Jokar T, Mohler J, et al. Assessing Upper-Extremity Motion: An Innovative, Objective Method to Identify Frailty in Older Bed-Bound Trauma Patients. J Am Coll Surg 2016;223(2):240–248; http://doi.org/10.1016/j.jamcollsurg.2016.03.030.
https://doi.org/10.1016/j.jamcollsurg.20... . The four studies included¹⁵15 Kim B, McKay SM, Lee J. Consumer-Grade Wearable Device for Predicting Frailty in Canadian Home Care Service Clients: Prospective Observational Proof-of-Concept Study. J Med Internet Res 2020;22(9):e19732; http://doi.org/10.2196/19732.
https://doi.org/10.2196/19732... –¹⁸18 Schmidle S, Gulde P, Koster R, Maslove DM, Lee J. The relationship between self-reported physical frailty and sensor-based physical activity measures in older adults – a multicentric cross-sectional study. BMC Geriatr 2023;23(1):43; http://doi.org/10.1186/s12877-022-03711-2.
https://doi.org/10.1186/s12877-022-03711... used daily step count measures, and three of the four also used sleep and HR data to assess frailty in older adults¹⁵15 Kim B, McKay SM, Lee J. Consumer-Grade Wearable Device for Predicting Frailty in Canadian Home Care Service Clients: Prospective Observational Proof-of-Concept Study. J Med Internet Res 2020;22(9):e19732; http://doi.org/10.2196/19732.
https://doi.org/10.2196/19732... –¹⁷17 Kim B, Hunt M, Muscedere J, Maslove DM, Lee J. Using Consumer-Grade Physical Activity Trackers to Measure Frailty Transitions in Older Critical Care Survivors: Exploratory Observational Study. JMIR Aging 2021;4(1):e19859; http://doi.org/10.2196/19859.
https://doi.org/10.2196/19859... .

The studies carried out by Kim and Lee¹⁵15 Kim B, McKay SM, Lee J. Consumer-Grade Wearable Device for Predicting Frailty in Canadian Home Care Service Clients: Prospective Observational Proof-of-Concept Study. J Med Internet Res 2020;22(9):e19732; http://doi.org/10.2196/19732.
https://doi.org/10.2196/19732... and Kim et al.¹⁷17 Kim B, Hunt M, Muscedere J, Maslove DM, Lee J. Using Consumer-Grade Physical Activity Trackers to Measure Frailty Transitions in Older Critical Care Survivors: Exploratory Observational Study. JMIR Aging 2021;4(1):e19859; http://doi.org/10.2196/19859.
https://doi.org/10.2196/19859... reported that daily step counts were significantly lower in frail than non-frail individuals. In a study by Lefferts et al.²⁰20 Lefferts EC, Bakker EA, Carbone S, Lavie CJ, Lee DC. Associations of total and aerobic steps with the prevalence and incidence of frailty in older adults with hypertension. Prog Cardiovasc Dis 2021;67:18–25; http://doi.org/10.1016/j.pcad.2021.02.011.
https://doi.org/10.1016/j.pcad.2021.02.0... , older adult individuals with a greater number of daily steps had lower BMI, greater grip strength, greater walking speed, greater energy expenditure, less exhaustion, less frailty and fewer comorbidities (p < 0.05). In addition, daily step count is strongly correlated with moderate to vigorous daily physical activity, as this type of activity is positively associated with health-related quality of life components²¹21 Watanabe D, Yoshida T, Watanabe Y, Yamada Y, Kimura M, Group KS. Objectively Measured Daily Step Counts and Prevalence of Frailty in 3,616 Older Adults. J Am Geriatr Soc 2020;68(10):2310–2318; http://doi.org/10.1111/jgs.16655.
https://doi.org/10.1111/jgs.16655... .

Participants classified as frail also performed less daily physical activity than non-frail participants¹⁵15 Kim B, McKay SM, Lee J. Consumer-Grade Wearable Device for Predicting Frailty in Canadian Home Care Service Clients: Prospective Observational Proof-of-Concept Study. J Med Internet Res 2020;22(9):e19732; http://doi.org/10.2196/19732.
https://doi.org/10.2196/19732... ,¹⁷17 Kim B, Hunt M, Muscedere J, Maslove DM, Lee J. Using Consumer-Grade Physical Activity Trackers to Measure Frailty Transitions in Older Critical Care Survivors: Exploratory Observational Study. JMIR Aging 2021;4(1):e19859; http://doi.org/10.2196/19859.
https://doi.org/10.2196/19859... . This corroborates what was found by Razjouyan et al.²²22 Razjouyan J, Naik AD, Horstman MJ, Kunik ME, Amirmazaheri M, Zhou H, et al. Wearable Sensors and the Assessment of Frailty among Vulnerable Older Adults: An Observational Cohort Study. Sensors 2018;18(5):1336; http://doi.org/10.3390/s18051336.
https://doi.org/10.3390/s18051336... , as their results suggest that the total number of steps, amount of sedentary behavior and moderate to vigorous physical activity were associated with the progression of the frailty stages. The more physically active a person is, the better their physical capacity, which may contribute to the transience between the different frailty stages, as they can go from pre-frail to robust, and from frail to robust, although to a lesser extent¹1 Kojima G, Taniguchi Y, Iliffe S, Jivraj S, Walters K. Transitions between frailty states among community-dwelling older people: A systematic review and meta-analysis. Ageing Res Rev 2019;50:81–88; https://doi.org/10.1016/j.arr.2019.01.010.
https://doi.org/10.1016/j.arr.2019.01.01... ,²³23 Tak E, Kuiper R, Chorus A, Hopman-Rock M. Prevention of onset and progression of basic ADL disability by physical activity in community dwelling older adults: a meta-analysis. Ageing Res Rev 2013;12(1):329–338; http://doi.org/10.1016/j.arr.2012.10.001.
https://doi.org/10.1016/j.arr.2012.10.00... .

With regard to sleep parameters, the results obtained by Kim and Lee¹⁵15 Kim B, McKay SM, Lee J. Consumer-Grade Wearable Device for Predicting Frailty in Canadian Home Care Service Clients: Prospective Observational Proof-of-Concept Study. J Med Internet Res 2020;22(9):e19732; http://doi.org/10.2196/19732.
https://doi.org/10.2196/19732... suggest that increased deep sleep time was significantly associated with increased odds of frailty. Studies indicate that poor sleep is a significant risk factor for increasing the likelihood of frailty²⁴24 Çavuşoğlu Ç, Deniz O, Tuna Doğrul R, Çöteli S, Öncül A, Kızılarslanoğlu M C, & Gçker B. Frailty is associated with poor sleep quality in the oldest old. Turk J Med Sci 2021;51(2):540–546; http://doi.org/10.3906/sag-2001-168.
https://doi.org/10.3906/sag-2001-168... ,²⁵25 Wang X, Hu J, Wu D. Risk factors for frailty in older adults. Medicine (Baltimore) 2022;101(34):e30169; http://doi.org/10.1097/MD.0000000000030169.
https://doi.org/10.1097/MD.0000000000030... . Among the findings in a study carried out by Ensrud et al.²⁶26 Ensrud KE, Blackwell TL, Redline S, Ancoli-Israel S, Paudel ML, Cawthon PM, et al. Sleep disturbances and frailty status in older community-dwelling men. J Am Geriatr Soc 2009;57(11):2085–2093; http://doi.org/10.1111/j.1532-5415.2009.02490.x.
https://doi.org/10.1111/j.1532-5415.2009... , it was found that sleep disturbances, such as poor sleep quality, excessive daytime sleepiness and prolonged sleep latency, are associated with greater evidence of frailty status, as sleep disturbances can be considered an indicator of health problems, comorbidities, depressive symptoms, cognitive dysfunction and functional impairments, which not only affect sleep quality, but also increase the likelihood of a greater state of frailty. The study by Razjouyan et al.²²22 Razjouyan J, Naik AD, Horstman MJ, Kunik ME, Amirmazaheri M, Zhou H, et al. Wearable Sensors and the Assessment of Frailty among Vulnerable Older Adults: An Observational Cohort Study. Sensors 2018;18(5):1336; http://doi.org/10.3390/s18051336.
https://doi.org/10.3390/s18051336... demonstrated that although the non-frail group had significantly less sleep disturbances, there was no significant difference between pre-frail and frail.

HR correlated with the presence of frailty in participants during the hospital discharge period in the study by Kim and Lee¹⁵15 Kim B, McKay SM, Lee J. Consumer-Grade Wearable Device for Predicting Frailty in Canadian Home Care Service Clients: Prospective Observational Proof-of-Concept Study. J Med Internet Res 2020;22(9):e19732; http://doi.org/10.2196/19732.
https://doi.org/10.2196/19732... , but no associations were found between this parameter and frailty in the other studies included in this review. This may be due to the fact that the participants in the aforementioned study had recently experienced a critical illness. Literature data²⁷27 Varadhan R, Chaves PHM, Lipsitz LA, Stein PK, Tian J, Windham BG, et al. Frailty and impaired cardiac autonomic control: new insights from principal components aggregation of traditional heart rate variability indices. J Gerontol A Biol Sci Med Sci 2009;64(6):682–687; http://doi.org/10.1093/gerona/glp013.
https://doi.org/10.1093/gerona/glp013.... show that changes in HR patterns may be caused by the inability to evoke dynamic physiological processes to restore balance.

The results obtained from this review generally indicate that the parameters derived from the smartwatch have been used to identify the frailty stages in different areas, with most of the studies associated with other clinical conditions (cardiovascular risk, pre-procedure of transcatheter aortic valve implantation and post-hospital discharge periods of critically ill patients)¹⁵15 Kim B, McKay SM, Lee J. Consumer-Grade Wearable Device for Predicting Frailty in Canadian Home Care Service Clients: Prospective Observational Proof-of-Concept Study. J Med Internet Res 2020;22(9):e19732; http://doi.org/10.2196/19732.
https://doi.org/10.2196/19732... –¹⁸18 Schmidle S, Gulde P, Koster R, Maslove DM, Lee J. The relationship between self-reported physical frailty and sensor-based physical activity measures in older adults – a multicentric cross-sectional study. BMC Geriatr 2023;23(1):43; http://doi.org/10.1186/s12877-022-03711-2.
https://doi.org/10.1186/s12877-022-03711... .

Despite the efforts to carry out a complete search in the databases most frequently used for reviews in general, it is possible that some references were missed. Studies that correlate the use of smartwatches and the assessment of frailty are still scarce. Some studies had small numbers of participants and some frail and pre-frail cohorts were combined for statistical analysis. It was also observed that most studies were not restricted to the isolated assessment of Frailty Syndrome, but this factor did not invalidate the findings. It is worth mentioning that this is the first scoping review that we are aware of which addresses the use of smartwatches as having technological potential to be used to help in screening Frailty Syndrome criteria in older adults.

CONCLUSION

Smartwatches make an excellent frailty tracking tool through daily step count measurements, sleep data and heart rate. It consists of an accessible and practical evaluation method which can be used in different areas (home, outpatient and hospital). The results obtained from the use of these devices may suggest a broader assessment of older adults when faced with an increased risk for developing Frailty Syndrome. In view of the wide variety of smartwatch models available on the market and the results offered by each device, the discussion of this topic is an open field for further research aimed at establishing parameters in order to define how to more accurately track people at greater risk of frailty.

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