Wearable Sensors and the Assessment of Frailty among Vulnerable Older Adults: An Observational Cohort Study

A comparative analysis of binary and multi-class classification machine learning algorithms to detect current frailty status using the English longitudinal study of ageing (ELSA)

Background

Physical frailty is a pressing public health issue that significantly increases the risk of disability, hospitalization, and mortality. Early and accurate detection of frailty is essential for timely intervention, reducing its widespread impact on healthcare systems, social support networks, and economic stability.

Objective

This study aimed to classify frailty status into binary (frail vs. non-frail) and multi-class (frail vs. pre-frail vs. non-frail) categories. The goal was to detect and classify frailty status at a specific point in time. Model development and internal validation were conducted using data from wave 8 of the English Longitudinal Study of Ageing (ELSA), with external validation using wave 6 data to assess model generalizability.

Methods

Nine classification algorithms, including Logistic Regression, Random Forest, K-nearest Neighbor, Gradient Boosting, AdaBoost, XGBoost, LightGBM, CatBoost, and Multi-Layer Perceptron, were evaluated and their performance compared.

Results

CatBoost demonstrated the best overall performance in binary classification, achieving high recall (0.951), balanced accuracy (0.928), and the lowest Brier score (0.049) on the internal validation set, and maintaining strong performance externally with a recall of 0.950, balanced accuracy of 0.913, and F1-score of 0.951. Multi-class classification was more challenging, with Gradient Boosting emerging as the top model, achieving the highest recall (0.666) and precision (0.663) on the external validation set, with a strong F1-score (0.664) and reasonable calibration (Brier Score = 0.223).

Conclusion

Machine learning algorithms show promise for the detection of current frailty status, particularly in binary classification. However, distinguishing between frailty subcategories remains challenging, highlighting the need for improved models and feature selection strategies to enhance multi-class classification accuracy.

Digital biomarkers for real-life, home-based monitoring of frailty: a systematic review and meta-analysis

BACKGROUND

Frailty, characterised by decreased physiological function and increased vulnerability to stressors, was associated with an increase in numerous adverse outcomes. Although the number of digital biomarkers for detecting frailty in older adults is increasing, there remains a lack of evidence regarding their effectiveness for early detection and follow-up in real-world, home-based settings.

METHODS

Five databases were searched from inception until 1 August 2024. Standardised forms were utilised for data extraction. The Quality Assessment of Diagnostic Accuracy Studies was used to assess the risk of bias and applicability of included studies. A meta-analysis was conducted to assess the overall sensitivity and specificity for frailty detection.

RESULTS

The systematic review included 16 studies, identifying digital biomarkers relevant for frailty detection, including gait, activity, sleep, heart rate, hand movements and room transition. Meta-analysis further revealed pooled sensitivity of 0.78 [95% confidence interval (CI): 0.70-0.86] and specificity of 0.79 (95% CI: 0.72-0.86) to classify robust and pre-frailty/frailty participants. The overall risk of bias indicated that all the included studies were characterised as having a high or unclear risk of bias.

CONCLUSION

This study offers a thorough characterisation of digital biomarkers for detecting frailty, underscoring their potential for early prediction in home settings. These findings are instrumental in bridging the gap between evidence and practice, enabling more proactive and personalised healthcare monitoring. Further longitudinal studies involving larger sample sizes are necessary to validate the effectiveness of these digital biomarkers as diagnostic tools or prognostic indicators.

A review of utility of wearable sensor technologies for older person frailty assessment

Frailty is one of the most concerning aspects of global population aging, and early identification is crucial to prevent or reverse its progression. Simple, universal, and efficient frailty assessment technologies are essential for the timely detection of frailty in older patients. Various multi-dimensional assessment instruments have been developed to quantify frailty phenotypes; we review the literature on wearable sensor technologies leveraged for older person frailty assessment. This review examines representative studies on older person frailty assessment published up to 2024, summarizing pertinent wearable sensor technologies utilized for frailty assessment. Our findings suggest that objective, simple, rapid, and affordable sensor-based frailty screening holds utility across diverse applications including diagnostic aid, prognostication, and endpoint ascertainment in research.

Association between frailty and subjective and objective sleep indicators in Japanese community-dwelling older adults

Studies have linked frailty to sleep duration and/or quality using questionnaire-based subjective sleep assessments. This study clarified the relationship between frailty status and subjective and objective sleep indicators among community-dwelling older adults in a rural Japanese area. This cross-sectional cohort study analyzed the data of older adult participants in the FESTA Study, assessing subjective and objective sleep indicators using the Pittsburgh Sleep Quality Index (PSQI) and an actigraph, respectively. Frailty status was determined using the Japanese version of the Cardiovascular Health Study (J-CHS) and the Kihon Checklist (KCL). Its relationship was examined through multivariate logistic regression analysis. The data of 537 older adults (median age = 76 years; 177 men and 360 women) were analyzed. Aside from age, depression, and dietary variety score, the PSQI score and the number of awakening episodes after sleep onset were significantly associated with non-robustness when the J-CHS was utilized (OR 1.086 95%CI 1.017-1.159 P = .014 and OR .964 95%CI .934-.994 P = .019, respectively). When the KCL was utilized, non-robustness was significantly associated with the PSQI score (OR 1.100 95%CI 1.028-1.178 P = .006), along with age, gender, number of comorbidities, and depression. Among the seven items of the PSQI, non-robustness was significantly related to daytime dysfunction due to sleepiness. The associations between non-robustness and objective sleep indicators varied by the frailty assessment method, while non-robustness was significantly associated with subjective sleep quality, regardless of the diagnostic tool used for frailty status and age. Therefore, subjective sleep quality may be more reliable for preventing and/or managing frailty in older adults.

Frailty Modeling Using Machine Learning Methodologies: A Systematic Review With Discussions on Outstanding Questions

Studying frailty is crucial for enhancing the health and quality of life among older adults, refining healthcare delivery methods, and tackling the obstacles linked to an aging demographic. Approaches to frailty modeling often utilise simple analytic techniques rather than available advanced machine learning methods, which may be sub-optimal. There is no large-scale systematic review on applications of machine learning methods on frailty modeling. In this study we explore the use of machine learning methods to predict or classify frailty in older persons in routinely collected data. We reviewed 181 research articles, and categorised analytic methods into three categories: generalised linear models, survival models, and non-linear models. These methods have a moderate agreement with existing frailty scores and predictive validity for adverse outcomes. Limited evidence suggests that non-linear methods outperform generalised linear methods. The top-three predictor/input variables are specific diagnosis or groups of diagnoses, functional performance (e.g., ADLs), and impaired cognition. Mortality, hospital admissions and prolonged hospital stay are the mainly predicted outcomes. Most studies utilise classical machine learning methods with cross-sectional data. Longitudinal data collected by wearable sensors have been used for frailty modeling. We also discuss the opportunities to use more advanced machine learning methods with high dimensional longitudinal data for more personalised and accessible frailty tools.

Feasibility and acceptability of wrist-worn actigraphy to measure frailty in homebound older adults

Background/Objectives

Frailty assessments in older adults are an important prognostic indicator for predicting health-related outcomes. The purpose of this study was to determine the feasibility and acceptability of using a wrist-worn actigraphy device to measure frailty in homebound older adults.

Methods

In this US single-site cohort study, older adult participants were asked to continuously wear an actigraphy device on their wrist for three weeks. Caregivers were asked to complete short descriptive surveys. Surveys collected demographic and subjective frailty measures; sleep and activity data were collected via Actiwatch. Descriptive statistics were performed for quantitative data; qualitative analyses were conducted for open-ended survey questions.

Results

Twelve of the thirteen dyads completed all study activities, participants demonstrated feasibility and acceptability of device use, and dyads reported overall positive experiences.

Conclusion

Findings suggest that wearing a wrist-worn actigraphy device is feasible and acceptable in homebound older adults and can potentially measure objective frailty over time.

Improving Surgical Outcomes for Older Adults with Adoption of Technological Advances in Comprehensive Geriatric Assessment

Frailty is a well-recognized predictor of poor surgical outcomes for older adults, yet effective measurements and interventions remain limited. Technological advances offer an opportunity to address this gap and improve surgical care for older adults. This paper reviews the background of frailty and comprehensive geriatric assessments in surgery, and how technological innovations can advance frailty measurement and intervention in surgical settings. We review two broad areas of technological advancement as applied to frailty in surgery: 1) Innovation in the use of electronic health records (EHR) using Artificial Intelligence (AI) and Machine Learning (ML), and 2) Novel uses for wearable sensors and mobile health (mHealth) applications. We explore the integration of AI and ML with EHR systems, which can surpass traditional comorbidity indices by providing comprehensive health assessments and enhancing prediction models. Innovations like the electronic Frailty Index (eFI) show promise in expanding the reach of frailty assessments and facilitating real-time screening. Additionally, wearable devices and mobile health (mHealth) applications offer new ways to monitor and improve physical activity, nutrition, and psychological well-being, supporting perioperative rehabilitation. While these technologies present challenges, such as the need for infrastructure, training, and data interoperability, they offer promising strategies to facilitate the assessment and management of frailty among surgical patients. Continued research and tailored implementation strategies will be essential to fully realize the potential of these advancements in improving surgical outcomes for frail older adults.

Quantitative biomechanical analysis in validating a video-based model to remotely assess physical frailty: a potential solution to telehealth and globalized remote-patient monitoring

Assessing physical frailty (PF) is vital for early risk detection, tailored interventions, preventive care, and efficient healthcare planning. However, traditional PF assessments are often impractical, requiring clinic visits and significant resources. We introduce a video-based frailty meter (vFM) that utilizes machine learning (ML) to assess PF indicators from a 20 s exercise, facilitating remote and efficient healthcare planning. This study validates the vFM against a sensor-based frailty meter (sFM) through elbow flexion and extension exercises recorded via webcam and video conferencing app. We developed the vFM using Google's MediaPipe ML model to track elbow motion during a 20 s elbow flexion and extension exercise, recorded via a standard webcam. To validate vFM, 65 participants aged 20-85 performed the exercise under single-task and dual-task conditions, the latter including counting backward from a random two-digit number. We analyzed elbow angular velocity to extract frailty indicators-slowness, weakness, rigidity, exhaustion, and unsteadiness-and compared these with sFM results using intraclass correlation coefficient analysis and Bland-Altman plots. The vFM results demonstrated high precision (0.00-7.14%) and low bias (0.00-0.09%), showing excellent agreement with sFM outcomes (ICC(2,1): 0.973-0.999), unaffected by clothing color or environmental factors. The vFM offers a quick, accurate method for remote PF assessment, surpassing previous video-based frailty assessments in accuracy and environmental robustness, particularly in estimating elbow motion as a surrogate for the 'rigidity' phenotype. This innovation simplifies PF assessments for telehealth applications, promising advancements in preventive care and healthcare planning without the need for sensors or specialized infrastructure.

A Wrist-Worn Wearable Device Can Identify Frailty in Middle-Aged and Older Adults: The UK Biobank Study

OBJECTIVES

Digital gait biomarkers collected from body-worn devices can remotely and continuously collect movement types, quantity, and quality in real life. This study assessed whether digital gait biomarkers from a wrist-worn device could identify people with frailty in a large sample of middle-aged and older adults.

DESIGN

Cross-sectional study.

SETTING AND PARTICIPANTS

A total of 5822 middle-aged (43-64 years) and 4344 older adults (65-81 years) who participated in the UK Biobank study.

MEASURES

Frailty was assessed using a modified Fried's frailty assessment and was defined as having ≥3 of the 5 frailty criteria (weakness, low activity levels, slowness, exhaustion, and weight loss). Fourteen digital gait biomarkers were extracted from accelerometry data collected from wrist-worn sensors worn continuously by participants for up to 7 days.

RESULTS

A total of 238 (4.1%) of the middle-aged group and 196 (4.5%) of the older group were categorized as frail. Multivariable logistic regression analysis revealed that less daily walking (as assessed by step counts), slower maximum walking speed, and increased step time variability best-identified people with frailty in the middle-aged group [area under the curve (95% CI): 0.70 (0.66-0.73)]. Less daily walking, slower maximum walking speed, increased step time variability, and a lower proportion of walks undertaken with a manual task best-identified people with frailty in the older group [0.73 (0.69-0.76)].

CONCLUSIONS AND IMPLICATIONS

Our findings indicate that measures obtained from wrist-worn wearable devices worn in everyday life can identify individuals with frailty in both middle-aged and older people. These digital gait biomarkers may facilitate screening programs and the timely implementation of frailty-prevention interventions.

Systematic design of health monitoring systems centered on older adults and ADLs

BACKGROUND

Older adults face unique health challenges as they age, including physical and mental health issues and mood disorders. Negative emotions and social isolation significantly impact mental and physical health. To support older adults and address these challenges, healthcare professionals can use Information and Communication Technologies (ICTs) such as health monitoring systems with multiple sensors. These systems include digital biomarkers and data analytics that can streamline the diagnosis process and help older adults to maintain their independence and quality of life.

METHOD

A design research methodology is followed to define a conceptual model as the main artifact and basis for the systematic design of successful systems centered on older adults monitoring within the health domain.

RESULTS

The results include a conceptual model focused on older adults' Activities of Daily Living (ADLs) and Health Status, considering various health dimensions, including social, emotional, physical, and cognitive dimensions. We also provide a detailed instantiation of the model in real use cases to validate the usefulness and feasibility of the proposal. In particular, the model has been used to develop two health systems intended to measure the degree of the elders' frailty and dependence with biomarkers and machine learning.

CONCLUSIONS

The defined conceptual model can be the basis to develop health monitoring systems with multiple sensors and intelligence based on data analytics. This model offers a holistic approach to caring for and supporting older adults as they age, considering ADLs and various health dimensions. We have performed an experimental and qualitative validation of the proposal in the field of study. The conceptual model has been instantiated in two specific case uses, showing the provided abstraction level and the feasibility of the proposal to build reusable, extensible and adaptable health systems. The proposal can evolve by exploiting other scenarios and contexts.

Harnessing physical activity monitoring and digital biomarkers of frailty from pendant based wearables to predict chemotherapy resilience in veterans with cancer

This study evaluated the use of pendant-based wearables for monitoring digital biomarkers of frailty in predicting chemotherapy resilience among 27 veteran cancer patients (average age: 64.6 ± 13.4 years), undergoing bi-weekly chemotherapy. Immediately following their first day of chemotherapy cycle, participants wore a water-resistant pendant sensor for 14 days. This device tracked frailty markers like cadence (slowness), daily steps (inactivity), postural transitions (weakness), and metrics such as longest walk duration and energy expenditure (exhaustion). Participants were divided into resilient and non-resilient groups based on adverse events within 6 months post-chemotherapy, including dose reduction, treatment discontinuation, unplanned hospitalization, or death. A Chemotherapy-Resilience-Index (CRI) ranging from 0 to 1, where higher values indicate poorer resilience, was developed using regression analysis. It combined physical activity data with baseline Eastern Cooperative Oncology Group (ECOG) assessments. The protocol showed a 97% feasibility rate, with sensor metrics effectively differentiating between groups as early as day 6 post-therapy. The CRI, calculated using data up to day 6 and baseline ECOG, significantly distinguished resilient (CRI = 0.2 ± 0.27) from non-resilient (CRI = 0.7 ± 0.26) groups (p < 0.001, Cohen's d = 1.67). This confirms the potential of remote monitoring systems in tracking post-chemotherapy functional capacity changes and aiding early non-resilience detection, subject to validation in larger studies.

Kinect-based objective assessment for early frailty identification in patients with Parkinson's disease

BACKGROUND

Frailty is common in Parkinson's disease (PD) and increases vulnerability to adverse outcomes. Early detection of this syndrome aids in early intervention.

AIMS

To objectively identify frailty at an early stage during routine motor tasks in PD patients using a Kinect-based system.

METHODS

PD patients were recruited and assessed with the Fried criteria to determine their frailty status. Each participant was recorded performing the Movement Disorder Society-Sponsored Revision of the Unified Parkinson's Disease Rating Scale part III (MDS-UPDRS III) extremity tasks with a Kinect-based system. Statistically significant kinematic parameters were selected to discriminate the pre-frail from the non-frail group.

RESULTS

Of the fifty-two participants, twenty were non-frail and thirty-two were pre-frail. Decreased frequency in finger tapping (P = 0.005), hand grasping (P = 0.002), toe tapping (P = 0.002), and leg agility (P = 0.019) alongside reduced hand grasping speed (P = 0.030), lifting (P < 0.001) and falling speed (P < 0.001) in leg agility were observed in the pre-frail group. Amplitude in leg agility (P = 0.048) and amplitude decrement rate (P = 0.046) in hand grasping showed marginally significant differences between two groups. Moderate discriminative values were found in frequency and speed of the extremity tasks to identify pre-frailty with sensitivity, specificity, and area under the curve (AUC) in the range of 45.00-85.00%, 68.75-100%, and 0.701-0.836, respectively. The combination of frequency and speed in extremity tasks showed moderate to high discriminatory ability, with AUC of 0.775 (95% CI 0.637-0.913, P < 0.001) for upper limb tasks and 0.909 (95% CI 0.832-0.987, P < 0.001) for lower limb tasks. When combining these features in both upper and lower limb tasks, the AUC increased to 0.942 (95% CI 0.886-0.999, P < 0.001).

CONCLUSIONS

Our findings demonstrated the promise of utilizing Kinect-based kinematic data from MDS-UPDRS III tasks as early indicators of frailty in PD patients.

Machine learning approaches for frailty detection, prediction and classification in elderly people: A systematic review

BACKGROUND

Frailty in older people is a syndrome related to aging that is becoming increasingly common and problematic as the average age of the world population increases. Detecting frailty in its early stages or, even better, predicting its appearance can greatly benefit health in later years of life and save the healthcare system from high costs. Machine Learning models fit the need to develop a tool for supporting medical decision-making in detecting or predicting frailty.

METHODS

In this review, we followed the PRISMA methodology to conduct a systematic search of the most relevant Machine Learning models that have been developed so far in the context of frailty. We selected 41 publications and compared them according to their purpose, the type of dataset used, the target variables, and the results they obtained, highlighting their shortcomings and strengths.

RESULTS

The variety of frailty definitions allows many problems to fall into this field, and it is often challenging to compare results due to the differences in target variables. The data types can be divided into gait data, usually collected with sensors, and medical records, often in the context of aging studies. The most common algorithms are well-known models available from every Machine Learning library. Only one study developed a new framework for frailty classification, and only two considered Explainability.

CONCLUSIONS

This review highlights some gaps in the field of Machine Learning applied to the assessment and prediction of frailty, such as the need for a universal quantitative definition. It emphasizes the need for close collaboration between medical professionals and data scientists to unlock the potential of data collected in hospital and clinical settings. As a suggestion for future work, the area of Explainability, which is crucial for models in medicine and health care, was considered in very few studies.

Objective assessment of the association between frailty and sedentary behavior in older adults: a cross-sectional study

BACKGROUND

Given the inconsistent findings of the association between frailty and sedentary behavior in older adults, this cross-sectional study investigated the aforementioned association using four different frailty criteria and two sedentary behavior indices in older adults.

METHODS

Data from older adults (age ≥ 65 y) who participated in health examinations or attended outpatient integrated clinics at a medical center in Taipei, Taiwan, were collected. Frailty was measured using the modified Fried Frailty Phenotype (mFFP), Clinical Frailty Scale in Chinese Translation (CFS-C), Study of Osteoporotic Fractures (SOF) index, and Clinical Frailty-Deficit Count (CF-DC) index; sedentary behavior was assessed with a waist-worn accelerometer. Adjusted linear regression ascertained the association between frailty and both sedentary behavior outcomes.

RESULTS

Among the 214 participants (mean age 80.82 ± 7.14 y), 116 were women. The average total sedentary time and number of sedentary bouts were 609.74 ± 79.29 min and 5.51 ± 2.09 times per day, respectively. Frail participants had a longer total sedentary time (odds ratio [OR]: 30.13, P = .01 and 39.43, P < .001) and more sedentary bouts (OR: 3.50 and 5.86, both P < .001) on mFFP and CFS-C assessments, respectively. The SOF index revealed more sedentary bouts among frail than in robust participants (OR: 2.06, P = .009), without a significant difference in the total sedentary time. Frail participants defined by the CF-DC index were more likely to have frequent sedentary bouts (OR: 2.03, P = .016), but did not have a longer total sedentary time.

CONCLUSIONS

Regardless of the frailty criteria adopted, frailty was positively associated with the number of sedentary bouts per day in older adults. A significant correlation between frailty and total sedentary time was detected only with mFFP and CFS-C indices. Further research may target decreasing the sedentary bouts in older adults as a strategy to improve frailty.

Healthcare Application of In-Shoe Motion Sensor for Older Adults: Frailty Assessment Using Foot Motion during Gait

Frailty poses a threat to the daily lives of healthy older adults, highlighting the urgent need for technologies that can monitor and prevent its progression. Our objective is to demonstrate a method for providing long-term daily frailty monitoring using an in-shoe motion sensor (IMS). We undertook two steps to achieve this goal. Firstly, we used our previously established SPM-LOSO-LASSO (SPM: statistical parametric mapping; LOSO: leave-one-subject-out; LASSO: least absolute shrinkage and selection operator) algorithm to construct a lightweight and interpretable hand grip strength (HGS) estimation model for an IMS. This algorithm automatically identified novel and significant gait predictors from foot motion data and selected optimal features to construct the model. We also tested the robustness and effectiveness of the model by recruiting other groups of subjects. Secondly, we designed an analog frailty risk score that combined the performance of the HGS and gait speed with the aid of the distribution of HGS and gait speed of the older Asian population. We then compared the effectiveness of our designed score with the clinical expert-rated score. We discovered new gait predictors for HGS estimation via IMSs and successfully constructed a model with an "excellent" intraclass correlation coefficient and high precision. Moreover, we tested the model on separately recruited subjects, which confirmed the robustness of our model for other older individuals. The designed frailty risk score also had a large effect size correlation with clinical expert-rated scores. In conclusion, IMS technology shows promise for long-term daily frailty monitoring, which can help prevent or manage frailty for older adults.

Waist-What? Can a single sensor positioned at the waist detect parameters of gait at a speed and distance reflective of older adults' activity?

One of the problems facing an ageing population is functional decline associated with reduced levels of physical activity (PA). Traditionally researcher or clinician input is necessary to capture parameters of gait or PA. Enabling older adults to monitor their activity independently could raise their awareness of their activitiy levels, promote self-care and potentially mitigate the risks associated with ageing. The ankle is accepted as the optimum position for sensor placement to capture parameters of gait however, the waist is proposed as a more accessible body-location for older adults. This study aimed to compare step-count measurements obtained from a single inertial sensor positioned at the ankle and at the waist to that of a criterion measure of step-count, and to compare gait parameters obtained from the sensors positioned at the two different body-locations. Step-count from the waist-mounted inertial sensor was compared with that from the ankle-mounted sensor, and with a criterion measure of direct observation in healthy young and healthy older adults during a three-minute treadmill walk test. Parameters of gait obtained from the sensors at both body-locations were also compared. Results indicated there was a strong positive correlation between step-count measured by both the ankle and waist sensors and the criterion measure, and between ankle and waist sensor step-count, mean step time and mean stride time (r = .802-1.0). There was a moderate correlation between the step time variability measures at the waist and ankle (r = .405). This study demonstrates that a single sensor positioned at the waist is an appropriate method for the capture of important measures of gait and physical activity among older adults.

Sensor-Based Assessment of Variability in Daily Physical Activity and Frailty

INTRODUCTION

Frailty is a common geriatric syndrome associated with decline in physiological reserve. While several digital biomarkers of daily physical activity (DPA) have been used in frailty assessment, the association between DPA variability and frailty is still not clear. The goal of this study was to determine the association between frailty and DPA variability.

METHODS

This is an observational cross-sectional study conducted between September 2012 and November 2013. Older adults (≥65 years), without any severe mobility disorder, and the ability to walk 10 m (with or without an assistive device) were eligible for the study. DPA including sitting, standing, walking, lying, and postural transition were recorded for 48 h continuously. DPA variability was analyzed from two perspectives: (i) DPA duration variability in terms of coefficient of variation (CoV) of sitting, standing, walking, and lying down durations; and (ii) DPA performance variability in terms of CoV of sit-to-stand (SiSt) and stand-to-sit (StSi) durations and stride time (i.e., slope of power spectral density - PSD).

RESULTS

Data was analyzed from 126 participants (44 non-frail, 60 pre-frail, and 22 frail). For DPA duration variability, CoV of lying and walking duration was significantly larger among non-frail compared to pre-frail and frail groups (p < 0.03, d = 0.89 ± 0.40). For DPA performance variability, StSi CoV and PSD slope were significantly smaller for non-frail compared to pre-frail and frail groups (p < 0.05, d = 0.78 ± 0.19).

CONCLUSION

Lower DPA duration variability in pre-frail and frail groups may be attributed to the set daily routines frail older adults tend to follow, compared to variable physical activity routines of non-frail older adults. Higher DPA performance variability in the frail group may be attributed to reduced physiological capabilities toward walking for longer durations and the reduced muscle strength in the lower extremities, leading to incosistency in performing postural transitions.

Benefits and Barriers of Technology for Home Function and Mobility Assessment: Perspectives of Older Patients With Blood Cancers, Caregivers, and Clinicians

PURPOSE

Advances in digital health technology can overcome barriers to measurement of function and mobility for older adults with blood cancers, but little is known about how older adults perceive such technology for use in their homes.

METHODS

To characterize potential benefits and barriers associated with using technology for home functional assessment, we conducted three semistructured focus groups (FGs) in January 2022. Eligible patients came from the Older Adult Hematologic Malignancies Program at Dana-Farber Cancer Institute (DFCI), which includes adults 73 years and older enrolled during their initial consult with their oncologist. Eligible caregivers were 18 years and older and identified by enrolled patients as their primary caregiver. Eligible clinicians were practicing DFCI hematologic oncologists, nurse practitioners, or physician assistants with ≥2 years of clinical experience. A qualitative researcher led thematic analysis of FG transcripts to identify key themes.

RESULTS

Twenty-three participants attended the three FGs: eight patients, seven caregivers, and eight oncology clinicians. All participants valued function and mobility assessments and felt that technology could overcome barriers to their measurement. We identified three themes related to potential benefits: making it easier for oncology teams to consider function and mobility; providing standardized, objective data; and facilitating longitudinal data. We also identified four themes related to barriers to home functional assessment: concerns related to privacy and confidentiality, burden of measuring additional patient data, challenges in operating new technology, and concerns related to data improving care.

CONCLUSION

These data suggest that specific concerns raised by older patients, caregivers, and oncology clinicians must be addressed to improve acceptability and uptake of technology used to measure function and mobility in the home.

Use of a wearable accelerometer to evaluate physical frailty in people receiving haemodialysis

BACKGROUND

Physical frailty is a major health concern among people receiving haemodialysis (HD) for stage-5 chronic kidney disease (CKD-5). Wearable accelerometers are increasingly being recommended to objectively monitor activity levels in CKD-5 and recent research suggests they may also represent an innovative strategy to evaluate physical frailty in vulnerable populations. However, no study has yet explored whether wearable accelerometers may be utilised to assess frailty in the context of CKD-5-HD. Therefore, we aimed to examine the diagnostic performance of a research-grade wearable accelerometer in evaluating physical frailty in people receiving HD.

METHODS

Fifty-nine people receiving maintenance HD [age = 62.3 years (SD = 14.9), 40.7% female] participated in this cross-sectional study. Participants wore a uniaxial accelerometer (ActivPAL) for seven consecutive days and the following measures were recorded: total number of daily steps and sit-to-stand transitions, number of daily steps walked with cadence < 60 steps/min, 60-79 steps/min, 80-99 steps/min, 100-119 steps/min, and ≥ 120 steps/min. The Fried phenotype was used to evaluate physical frailty. Receiver operating characteristics (ROC) analyses were performed to examine the diagnostic accuracy of the accelerometer-derived measures in detecting physical frailty status.

RESULTS

Participants classified as frail (n = 22, 37.3%) had a lower number of daily steps (2363 ± 1525 vs 3585 ± 1765, p = 0.009), daily sit-to-stand transitions (31.8 ± 10.3 vs 40.6 ± 12.1, p = 0.006), and lower number of steps walked with cadence of 100-119 steps/min (336 ± 486 vs 983 ± 797, p < 0.001) compared to their non-frail counterparts. In ROC analysis, the number of daily steps walked with cadence ≥ 100 steps/min exhibited the highest diagnostic performance (AUC = 0.80, 95% CI: 0.68-0.92, p < 0.001, cut-off ≤ 288 steps, sensitivity = 73%, specificity = 76%, PPV = 0.64, NPV = 0.82, accuracy = 75%) in detecting physical frailty.

CONCLUSIONS

This study provided initial evidence that a wearable accelerometer may be a useful tool in evaluating physical frailty in people receiving HD. While the total number of daily steps and sit-to-stand transitions could significantly discriminate frailty status, the number of daily steps walked with cadences reflecting moderate to vigorous intensity of walking may be more useful in monitoring physical frailty in people receiving HD.

The relationship between self-reported physical frailty and sensor-based physical activity measures in older adults - a multicentric cross-sectional study

BACKGROUND

The decline in everyday life physical activity reflects and contributes to the frailty syndrome. While especially self-reported frailty assessments have the advantage of reaching large groups at low costs, little is known about the relationship between the self-report and objective measured daily physical activity behavior. The main objective was to evaluate whether and to what extent a self-reported assessment of frailty is associated with daily physical activity patterns.

METHODS

Daily activity data were obtained from 88 elderly participants (mean 80.6 ± 9.1 years) over up to 21 days. Acceleration data were collected via smartwatch. According to the results of a self-report frailty questionnaire, participants were retrospectively split up into three groups, F (frail, n = 43), P (pre-frail, n = 33), and R (robust, n = 12). Gait- and activity-related measures were derived from the built-in step detector and acceleration sensor and comprised, i.a., standard deviation of 5-s-mean amplitude deviation (MADstd), median MAD (MADmedian), and the 95th percentile of cadence (STEP95). Parameters were fed into a PCA and component scores were used to derive behavioral clusters.

RESULTS

The PCA suggested two components, one describing gait and one upper limb activity. Mainly gait related parameters showed meaningful associations with the self-reported frailty score (STEP95: R² = 0.25), while measures of upper limb activity had lower coefficients (MADmedian: R² = 0.07). Cluster analysis revealed two clusters with low and relatively high activity in both dimensions (cluster 2 and 3). Interestingly, a third cluster (cluster 1) was characterized by high activity and low extent of ambulation. Comparisons between the clusters showed significant differences between activity, gait, age, sex, number of chronic diseases, health status, and walking aid. Particularly, cluster 1 contained a higher number of female participants, whose self-reports tended towards a low health status, the frequent use of a walking aid, and a higher score related to frailty questions.

CONCLUSIONS

The results demonstrate that subjective frailty assessments may be a simple first screening approach. However, especially older women using walking aids may classify themselves as frail despite still being active. Therefore, the results of self-reports may be particularly biased in older women.

Discovery of associative patterns between workplace sound level and physiological wellbeing using wearable devices and empirical Bayes modeling

We conducted a field study using multiple wearable devices on 231 federal office workers to assess the impact of the indoor environment on individual wellbeing. Past research has established that the workplace environment is closely tied to an individual's wellbeing. Since sound is the most-reported environmental factor causing stress and discomfort, we focus on quantifying its association with physiological wellbeing. Physiological wellbeing is represented as a latent variable in an empirical Bayes model with heart rate variability measures-SDNN and normalized-HF as the observed outcomes and with exogenous factors including sound level as inputs. We find that an individual's physiological wellbeing is optimal when sound level in the workplace is at 50 dBA. At lower (<50dBA) and higher (>50dBA) amplitude ranges, a 10 dBA increase in sound level is related to a 5.4% increase and 1.9% decrease in physiological wellbeing respectively. Age, body-mass-index, high blood pressure, anxiety, and computer use intensive work are person-level factors contributing to heterogeneity in the sound-wellbeing association.

Data-driven systems to detect physical weakening from daily routine: A pilot study on elderly over 80 years old

The use of telemonitoring solutions via wearable sensors is believed to play a major role in the prevention and therapy of physical weakening in older adults. Despite the various studies found in the literature, some elements are still not well addressed, such as the study cohort, the experimental protocol, the type of research design, as well as the relevant features in this context. To this end, the objective of this pilot study was to investigate the efficacy of data-driven systems to characterize older individuals over 80 years of age with impaired physical function, during their daily routine and under unsupervised conditions. We propose a fully automated process which extracts a set of heterogeneous time-domain features from 24-hour files of acceleration and barometric data. After being statistically tested, the most discriminant features fed a group of machine learning classifiers to distinguish frail from non-frail subjects, achieving an accuracy up to 93.51%. Our analysis, conducted over 570 days of recordings, shows that a longitudinal study is important while using the proposed features, in order to ensure a highly specific diagnosis. This work may serve as a basis for the paradigm of future monitoring systems.

Association between Fall History and Gait, Balance, Physical Activity, Depression, Fear of Falling, and Motor Capacity: A 6-Month Follow-Up Study

Maintaining function in older adults is key to the quality of life and longevity. This study examined the potential impact of falls on accelerating further deterioration over time in gait, balance, physical activity, depression, fear of falling, and motor capacity in older adults. 163 ambulatory older adults (age = 76.5 ± 7.7 years) participated and were followed for 6 months. They were classified into fallers or non-fallers based on a history of falling within the past year. At baseline and 6 months, all participants were objectively assessed for gait, balance, and physical activity using wearable sensors. Additional assessments included psychosocial concerns (depression and fear of falling) and motor capacity (Timed Up and Go test). The fallers showed lower gait performance, less physical activity, lower depression level, higher fear of falling, and less motor capacity than non-fallers at baseline and 6-month follow-up. Results also revealed acceleration in physical activity and motor capacity decline compared to non-fallers at a 6-month follow-up. Our findings suggest that falls would accelerate deterioration in both physical activity and motor performance and highlight the need for effective therapy to reduce the consequences of falls in older adults.

Can Wearable Devices and Machine Learning Techniques Be Used for Recognizing and Segmenting Modified Physical Performance Test Items?

Assessment of physical performance is essential to predict the frailty level of older adults. The modified Physical Performance Test (mPPT) clinically assesses the performance of nine activities: standing balance, chair rising up & down, lifting a book, putting on and taking off a jacket, picking up a coin, turning 360°, walking, going upstairs, and going downstairs. The activity performing duration is the primary evaluation standard. In this study, wearable devices are leveraged to recognize and predict mPPT items' duration automatically. This potentially allows frequent follow up of physical performance, and facilitates more appropriate interventions. Five devices, including accelerometers and gyroscopes, were attached to the waist, wrists and ankles of eight younger adults. The system was experimented within three aspects: machine learning models, sensor placement, and sampling frequencies, to which the non-causal six-stages temporal convolutional network using 6.25 Hz signals from the left wrist and right ankle obtained the optimal performance. The duration prediction error ranged from 0.63±0.29 s (turning 360°) to 8.21±16.41 s (walking). The results suggest the potential for the proposed system in the automatic recognition and segmentation of mPPT items. Future work includes improving the recognition performance of lifting a book and implementing the frailty score prediction.

Predictive Accuracy of Digital Biomarker Technologies for Detection of Mild Cognitive Impairment and Pre-Frailty Amongst Older Adults: A Systematic Review and Meta-Analysis

Digital biomarker technologies coupled with predictive models are increasingly applied for early detection of age-related potentially reversible conditions including mild cognitive impairment (MCI) and pre-frailty (PF). We aimed to determine the predictive accuracy of digital biomarker technologies to detect MCI and PF with systematic review and meta-analysis. A computer-assisted search on major academic research databases including IEEE-Xplore was conducted. Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines were adopted reporting in this study. Summary receiver operating characteristic curve based on random-effect bivariate model was used to evaluate overall sensitivity and specificity for detection of the respective age-related conditions. A total of 43 studies were selected for final systematic review and meta-analysis. 26 studies reported on detection of MCI with sensitivity and specificity of 0.48-1.00 and 0.55-1.00, respectively. On the other hand, there were 17 studies that reported on the detection of PF with reported sensitivity of 0.53-1.00 and specificity of 0.61-1.00. Meta-analysis further revealed pooled sensitivities of 0.84 (95% CI: 0.79-0.88) and 0.82 (95% CI: 0.74-0.88) for in-home detection of MCI and PF, respectively, while pooled specificities were 0.85 (95% CI: 0.80-0.89) and 0.82 (95% CI: 0.75-0.88), respectively. Besides MCI, and PF, in this work during systematic review, we also found one study which reported a sensitivity of 0.93 and a specificity of 0.57 for detection of cognitive frailty (CF). The meta-analytic result, for the first time, quantifies the predictive efficacy of digital biomarker technologies for detection of MCI and PF. Additionally, we found the number of studies for detection of CF to be notably lower, indicating possible research gaps to explore predictive models on digital biomarker technology for detection of CF.

Effects of Healthcare Technologies on the Promotion of Physical Activities in Older Persons: A Systematic Review

This study aimed to explore the effects of health technologies on the promotion of health through physical activities of older persons. Following PRISMA guidelines, a systematic review of relevant articles published prior to 2020 was conducted from selected indices such as COCHRANE, PubMed, Science Direct, Proquest, including the use of hand search procedure. Twenty-seven articles were analyzed with significant findings influential to older people nursing: types of health technologies used for promoting physical activity; effects of technology use in promoting physical activity of older person care; and aspects that need to be considered in technology use among older persons. Characteristics of technologies were accuracy, usefulness, reliability, comfort, safety, and relevancy. Most technologies promoting physical activities for older people were wearable technologies that use artificial intelligence. Altogether, these technologies influenced overall healthcare behaviors of older persons. With healthcare technology efficiencies, proficiencies, and dependencies, technology-based healthcare have served older people well. Most technologies for older people care, such as wearables, reliably produce characteristics enhancing dependency and accuracy of bio-behavioral information influencing physical activities of older persons. Health technologies foster the values of physical activities among older persons thereby promoting healthy living.

Wearable-Based Assessment of Frailty Trajectories During Cardiac Rehabilitation After Open-Heart Surgery

Frailty in patients after open-heart surgery influences the type and intensity of a cardiac rehabilitation program. The response to tailored exercise training can be different, requiring convenient tools to assess the effectiveness of a training program routinely. The study aims to investigate whether kinematic measures extracted from the acceleration signals can provide information about frailty trajectories during rehabilitation. One hundred patients after open-heart surgery, assigned to the equal-sized intervention and control groups, participated in exercise training during inpatient rehabilitation. After rehabilitation, the intervention group continued exercise training at home, whereas the control group was asked to maintain the usual physical activity regimen. Stride time, cadence, movement vigor, gait asymmetry, Lissajous index, and postural sway were estimated during the clinical walk and stair-climbing tests before and after inpatient rehabilitation as well as after home-based exercise training. Frailty was assessed using the Edmonton frail scale. Most kinematic measures estimated during walking improved after rehabilitation along with the improvement in frailty status, i.e., stride time, cadence, postural sway, and movement vigor improved in 71%, 77%, 81%, and 83% of patients, respectively. Meanwhile, kinematic measures during stair-climbing improved to a lesser extent compared to walking. Home-based exercise training did not result in a notable change in kinematic measures which agrees well with only a negligible deterioration in frailty status. The study demonstrates the feasibility to follow frailty trajectories during inpatient rehabilitation after open-heart surgery based on kinematic measures extracted using a single wearable sensor.

Physical Activity Behavior of Patients at a Skilled Nursing Facility: Longitudinal Cohort Study

BACKGROUND

On-body wearable sensors have been used to predict adverse outcomes such as hospitalizations or fall, thereby enabling clinicians to develop better intervention guidelines and personalized models of care to prevent harmful outcomes. In our previous work, we introduced a generic remote patient monitoring framework (Sensing At-Risk Population) that draws on the classification of human movements using a 3-axial accelerometer and the extraction of indoor localization using Bluetooth low energy beacons, in concert. Using the same framework, this paper addresses the longitudinal analyses of a group of patients in a skilled nursing facility. We try to investigate if the metrics derived from a remote patient monitoring system comprised of physical activity and indoor localization sensors, as well as their association with therapist assessments, provide additional insight into the recovery process of patients receiving rehabilitation.

OBJECTIVE

The aim of this paper is twofold: (1) to observe longitudinal changes of sensor-based physical activity and indoor localization features of patients receiving rehabilitation at a skilled nursing facility and (2) to investigate if the sensor-based longitudinal changes can complement patients' changes captured by therapist assessments over the course of rehabilitation in the skilled nursing facility.

METHODS

From June 2016 to November 2017, patients were recruited after admission to a subacute rehabilitation center in Los Angeles, CA. Longitudinal cohort study of patients at a skilled nursing facility was followed over the course of 21 days. At the time of discharge from the skilled nursing facility, the patients were either readmitted to the hospital for continued care or discharged to a community setting. A longitudinal study of the physical therapy, occupational therapy, and sensor-based data assessments was performed. A generalized linear mixed model was used to find associations between functional measures with sensor-based features. Occupational therapy and physical therapy assessments were performed at the time of admission and once a week during the skilled nursing facility admission.

RESULTS

Of the 110 individuals in the analytic sample with mean age of 79.4 (SD 5.9) years, 79 (72%) were female and 31 (28%) were male participants. The energy intensity of an individual while in the therapy area was positively associated with transfer activities (β=.22; SE 0.08; P=.02). Sitting energy intensity showed positive association with transfer activities (β=.16; SE 0.07; P=.02). Lying down energy intensity was negatively associated with hygiene activities (β=-.27; SE 0.14; P=.04). The interaction of sitting energy intensity with time (β=-.13; SE 0.06; P=.04) was associated with toileting activities.

CONCLUSIONS

This study demonstrates that a combination of indoor localization and physical activity tracking produces a series of features, a subset of which can provide crucial information to the story line of daily and longitudinal activity patterns of patients receiving rehabilitation at a skilled nursing facility. The findings suggest that detecting physical activity changes within locations may offer some insight into better characterizing patients' progress or decline.

Characteristics and Quality of Diagnostic and Risk Prediction Models for Frailty in Older Adults: A Systematic Review

Several prediction models for frailty in older adults have been published, but their characteristics and methodological quality are unclear. This review aims to summarize and critically appraise the prediction models. Studies describing multivariable prediction models for frailty among older adults were included. PubMed, Embase, Web of Science, and PsycINFO were searched from outset to Feb 21, 2021. Methodological and reporting quality of included models were evaluated by PROBAST and TRIPOD, respectively. All results were descriptively summarized. Twenty articles including 39 models were identified. The included models showed good predictive discrimination with C indices ranging from 0.70 to 0.98. However, all studies except one were assessed as high risk of bias mainly due to inappropriate analysis; meanwhile, poor reporting quality was also frequently observed. Few mature prediction models can be used in practice. Researchers should pay more attention to external validation and improving the quality both in methodology and reporting.

The Home-Based Sleep Laboratory

Sleep disturbances are prevalent in neurodegenerative diseases in general, and in Parkinson's disease (PD) in particular. Recent evidence points to the clinical value of sleep in disease progression and improving quality of life. Therefore, monitoring sleep quality in an ongoing manner at the convenience of one's home has the potential to improve clinical research and to contribute to significantly better personalized treatment. Further, precise mapping of sleep patterns of each patient can contribute to a better understanding of the disease, its progression and the appropriate medical treatment. Here we review selective, state-of-the-art, home-based devices for assessing sleep and sleep related disorders. We highlight the large potential as well as the main challenges. In particular, we discuss medical validity, standardization and regulatory concerns that currently impede widespread clinical adoption of existing devices. Finally, we propose a roadmap with the technological and scientific steps that are required to impact PD research and treatment.

Cognitive Frailty: An Update

This review article provides an update of the empirical research on cognitive fragility conducted in the last four years. The studies retrieved were classified in four different categories. The first category includes articles relating cognitive frailty to cognitive reserve and which continue to highlight the importance of educational level. The second category includes recent research on cognitive fragility biomarkers, involving neuroimaging, metabolism and, in a novel way, microbiota. The third category includes research on how cognitive frailty is related to motor development and physical functioning, exploring e.g. the use of technology to study motor markers of cognitive frailty. Finally, in the fourth category, research clarifying the difference between reversible frailty and potentially reversible cognitive frailty has led to new interventions aimed at reducing cognitive frailty and preventing negative health outcomes. Interventions based on physical activity and multicomponent interventions are particularly emphasized. In addition, recent research explores the long-term effects of dual interventions in older adults living in nursing homes. In summary, research on cognitive frailty has increased in recent years, and applied aspects have gained importance.

Harnessing Digital Health to Objectively Assess Functional Performance in Veterans with Chronic Obstructive Pulmonary Disease

INTRODUCTION

An early detection of impaired functional performance is critical to enhance symptom management for patients with chronic obstructive pulmonary disease (COPD). However, conventional functional measures based on walking assessments are often impractical for small clinics where the available space to administrate gait-based test is limited. This study examined the feasibility and effectiveness of an upper-extremity frailty meter (FM) in identifying digital measures of functional performance and assessing frailty in COPD patients.

METHODS

Forty-eight patients with COPD (age = 68.8 ± 8.5 years, body mass index [BMI] = 28.7 ± 5.8 kg/m2) and 49 controls (age = 70.0 ± 3.0 years, BMI = 28.7 ± 6.1 kg/m2) were recruited. All participants performed a 20-s repetitive elbow flexion-extension test using a wrist-worn FM sensor. Functional performance was quantified by FM metrics, including speed (slowness), range of motion (rigidity), power (weakness), flexion and extension time (slowness), as well as speed and power reduction (exhaustion). Conventional functional measures, including timed-up-and-go test, gait and balance tests, and 5 repetition sit-to-stand test, were also performed.

RESULTS

Compared to controls, COPD patients exhibited deteriorated performances in all conventional functional assessments (d = 0.64-1.26, p < 0.010) and all FM metrics (d = 0.45-1.54, p < 0.050). FM metrics had significant agreements with conventional assessment tools (|r| = 0.35-0.55, p ≤ 0.001). FM metrics efficiently identified COPD patients with pre-frailty and frailty (d = 0.82-2.12, p < 0.050).

CONCLUSION

This study proposes the feasibility of using a 20-s repetitive elbow flexion-extension test and wrist-worn sensor-derived frailty metrics as an alternative and practical solution to evaluate functional performance in COPD patients. Its simplicity and low risk for test administration may also facilitate its application for remote patient monitoring. Furthermore, in settings where the administration of walking test is impractical, for example, when ventilator support is needed or space is limited, FM may be used as an alternative solution. Future studies are encouraged to use the FM to quantitatively monitor the progressive decline in functional performance and quantify outcomes of rehabilitation interventions.

Quantifying Steps During a Timed Up and Go Test Using a Wearable Sensor System: A Laboratory-Based Validation Study in Healthy Young and Older Volunteers

Mobility is an important factor in maintaining health and independence in an aging population. Facilitating community-dwelling older adults to independently identify signs of functional decline could help reduce disability and frailty development. Step-count from a body-worn sensor system was compared with a criterion measure in healthy young (n = 10) and healthy older adults (n = 10) during a Timed Up and Go test under different conditions. Spearman's rank correlation coefficient indicated strong agreement between the sensor-obtained step-count and that of the criterion measure in both age groups, in all mobility tests. A body-worn sensor system can provide objective, quantitative measures of step-count over short distances in older adults. Future research will examine if step-count alone can be used to identify functional decline and risk of frailty.Clinical Relevance-This demonstrates the correlation between step-count derived from a wearable sensor and a criterion measure over a short distance in older adults.

Gait-based Frailty Assessment using Image Representation of IMU Signals and Deep CNN

Frailty is a common and critical condition in elderly adults, which may lead to further deterioration of health. However, difficulties and complexities exist in traditional frailty assessments based on activity-related questionnaires. These can be overcome by monitoring the effects of frailty on the gait. In this paper, it is shown that by encoding gait signals as images, deep learning-based models can be utilized for the classification of gait type. Two deep learning models (a) SS-CNN, based on single stride input images, and (b) MS-CNN, based on 3 consecutive strides were proposed. It was shown that MS-CNN performs best with an accuracy of 85.1%, while SS-CNN achieved an accuracy of 77.3%. This is because MS-CNN can observe more features corresponding to stride-to-stride variations which is one of the key symptoms of frailty. Gait signals were encoded as images using STFT, CWT, and GAF. While the MS-CNN model using GAF images achieved the best overall accuracy and precision, CWT has a slightly better recall. This study demonstrates how image encoded gait data can be used to exploit the full potential of deep learning CNN models for the assessment of frailty.

Detecting Fall Risk and Frailty in Elders with Inertial Motion Sensors: A Survey of Significant Gait Parameters

In the elderly, geriatric problems such as the risk of fall or frailty are a challenge for society. Patients with frailty present difficulties in walking and higher fall risk. The use of sensors for gait analysis allows the detection of objective parameters related to these pathologies and to make an early diagnosis. Inertial Measurement Units (IMUs) are wearables that, due to their accuracy, portability, and low price, are an excellent option to analyze human gait parameters in health-monitoring applications. Many relevant gait parameters (e.g., step time, walking speed) are used to assess motor, or even cognitive, health problems in the elderly, but we perceived that there is not a full consensus on which parameters are the most significant to estimate the risk of fall and the frailty state. In this work, we analyzed the different IMU-based gait parameters proposed in the literature to assess frailty state (robust, prefrail, or frail) or fall risk. The aim was to collect the most significant gait parameters, measured from inertial sensors, able to discriminate between patient groups and to highlight those parameters that are not relevant or for which there is controversy among the examined works. For this purpose, a literature review of the studies published in recent years was carried out; apart from 10 previous relevant reviews using inertial and other sensing technologies, a total of 22 specific studies giving statistical significance values were analyzed. The results showed that the most significant parameters are double-support time, gait speed, stride time, step time, and the number of steps/day or walking percentage/day, for frailty diagnosis. In the case of fall risk detection, parameters related to trunk stability or movements are the most relevant. Although these results are important, the total number of works found was limited and most of them performed the significance statistics on subsets of all possible gait parameters; this fact highlights the need for new frailty studies using a more complete set of gait parameters.

Screening prefrailty in Japanese community-dwelling older adults with daily gait speed and number of steps via tri-axial accelerometers

Prefrailty is an intermediate stage between non-frailty and frailty. It is associated with an increased risk of progression to frailty, which makes it important to screen older adults for prefrailty at an early stage. This study verified whether daily gait speed and number of steps measured using a tri-axial accelerometer could be used to identify prefrailty. In total, 1692 Japanese community-dwelling older adults were divided into robust (n = 1032) and prefrail (n = 660) groups based on the Kihon Checklist, which is a self-administered questionnaire. Both daily gait speed and number of steps were measured for two weeks using tri-axial accelerometers. We also calculated the area under the ROC curve and the cut-off values for these parameters. Our results showed that the cut-off value for daily gait speed was 106.3 cm/s, while that for number of steps was 6342.2. In addition, we found that the combined assessment of both cut-off values was a more effective way to screen older adults with prefrailty status compared to either parameter alone. This is also considered an effective way to reduce national expenditures for daily care assistance.

Variability in Hourly Activity Levels: Statistical Noise or Insight Into Older Adult Frailty?

BACKGROUND

Frailty is associated with lower mean activity; however, hourly activity is highly variable among older individuals. We aimed to relate frailty to hourly activity variance beyond frailty's association with mean activity.

METHOD

Using the 2010-2011 National Social Life, Health and Aging Project wrist accelerometry data (n = 647), we employed a mixed-effects location scale model to simultaneously determine whether an adapted phenotypic frailty scale (0-4) was associated with the log10-mean hourly counts per minute (cpm) and between-and within-subject hourly activity variability, adjusting for demographics, health characteristics, season, day-of-week, and time-of-day. We tested the significance of a Frailty × Time-of-day interaction and whether adjusting for sleep time altered relationships.

RESULTS

Each additional frailty point was associated with a 7.6% (10-0.0343, β = -0.0343; 95% CI: -0.05, -0.02) lower mean hourly cpm in the morning, mid-day, and late afternoon but not evening. Each frailty point was also associated with a 24.5% (e0.219, β = 0.219; 95% CI: 0.09, 0.34) greater between-subject hourly activity variance across the day; a 7% (e0.07, β = 0.07; 95% CI: 0.01¸ 0.13), 6% (e0.06, β = 0.06; 95% CI: 0, 0.12), and 10% (e0.091, β = 0.091; 95% CI: 0.03, 0.15) greater within-subject hourly activity variance in the morning, mid-day, and late afternoon, respectively; and a 6% (e-0.06, β = -0.06; 95% CI: -0.12, -0.003) lower within-subject hourly activity variance in the evening. Adjusting for sleep time did not alter results.

CONCLUSIONS

Frail adults have more variable hourly activity levels than robust adults, a potential novel marker of vulnerability. These findings suggest a need for more precise activity assessment in older adults.

Digital Biomarkers of Physical Frailty and Frailty Phenotypes Using Sensor-Based Physical Activity and Machine Learning

Remote monitoring of physical frailty is important to personalize care for slowing down the frailty process and/or for the healthy recovery of older adults following acute or chronic stressors. Taking the Fried frailty criteria as a reference to determine physical frailty and frailty phenotypes (slowness, weakness, exhaustion, inactivity), this study aimed to explore the benefit of machine learning to determine the least number of digital biomarkers of physical frailty measurable from a pendant sensor during activities of daily living. Two hundred and fifty-nine older adults were classified into robust or pre-frail/frail groups based on the physical frailty assessments by the Fried frailty criteria. All participants wore a pendant sensor at the sternum level for 48 h. Of seventeen sensor-derived features extracted from a pendant sensor, fourteen significant features were used for machine learning based on logistic regression modeling and a recursive feature elimination technique incorporating bootstrapping. The combination of percentage time standing, percentage time walking, walking cadence, and longest walking bout were identified as optimal digital biomarkers of physical frailty and frailty phenotypes. These findings suggest that a combination of sensor-measured exhaustion, inactivity, and speed have potential to screen and monitor people for physical frailty and frailty phenotypes.

How wearable sensors have been utilised to evaluate frailty in older adults: a systematic review

BACKGROUND

Globally the population of older adults is increasing. It is estimated that by 2050 the number of adults over the age of 60 will represent over 21% of the world's population. Frailty is a clinical condition associated with ageing resulting in an increase in adverse outcomes. It is considered the greatest challenge facing an ageing population affecting an estimated 16% of community-dwelling populations worldwide.

AIM

The aim of this systematic review is to explore how wearable sensors have been used to assess frailty in older adults.

METHOD

Electronic databases Medline, Science Direct, Scopus, and CINAHL were systematically searched March 2020 and November 2020. A search constraint of articles published in English, between January 2010 and November 2020 was applied. Papers included were primary observational studies involving; older adults aged > 60 years, used a wearable sensor to provide quantitative measurements of physical activity (PA) or mobility and a measure of frailty. Studies were excluded if they used non-wearable sensors for outcome measurement or outlined an algorithm or application development exclusively. The methodological quality of the selected studies was assessed using the Appraisal Tool for Cross-sectional Studies (AXIS).

RESULTS

Twenty-nine studies examining the use of wearable sensors to assess and discriminate between stages of frailty in older adults were included. Thirteen different body-worn sensors were used in eight different body-locations. Participants were community-dwelling older adults. Studies were performed in home, laboratory or hospital settings. Postural transitions, number of steps, percentage of time in PA and intensity of PA together were the most frequently measured parameters followed closely by gait speed. All but one study demonstrated an association between PA and level of frailty. All reports of gait speed indicate correlation with frailty.

CONCLUSIONS

Wearable sensors have been successfully used to evaluate frailty in older adults. Further research is needed to identify a feasible, user-friendly device and body-location that can be used to identify signs of pre-frailty in community-dwelling older adults. This would facilitate early identification and targeted intervention to reduce the burden of frailty in an ageing population.

Information and Communication Technologies for Managing Frailty: A Systematic Literature Review

Frailty is a prevalent condition among Canadians; over one million are diagnosed as medically frail, and in the next ten years this number will double. Information and telecommunication technologies can provide a low-cost method for managing frailty more proactively. This study aims to examine the range and extent of information and telecommunication technologies for managing frailty in older adults, their technology readiness level, the evidence, and the associated outcomes. A systematic literature review was conducted. Four databases were searched for studies: Medline, EMBASE, CINAHL, and Web of Science. In total, we included 19 studies (out of 9,930) for the data abstraction. Overall, our findings indicate that (1) the proposed frailty phenotype is the most common ground truth to be used for assessing frailty; (2) the most common uses of information and telecommunication technologies for managing frailty are detection, and monitoring and detection, while interventional studies on frailty are very rare; (3) the five main types of information and telecommunication technologies for managing frailty in older adults are information and telecommunication technology-based platforms, smartphones, telemonitoring (home monitoring), wearable sensors and devices (commercial off-the-shelf), and multimedia formats for online access; (4) the technology readiness level of information and telecommunication technologies for managing frailty in older adults is the “Technology Demonstration” level, i.e., not yet ready to be operated in an actual operating environment; and (5) the level of evidence is still low for information and telecommunication technology studies that manage frailty in older adults. In conclusion, information and telecommunication technologies for managing frailty in the older adult population are not yet ready to be full-fledged technologies for this purpose.

Decrease in Mobility during the COVID-19 Pandemic and Its Association with Increase in Depression among Older Adults: A Longitudinal Remote Mobility Monitoring Using a Wearable Sensor

Background: Social isolation during COVID-19 may negatively impact older adults’ wellbeing. To assess its impact, we measured changes in physical activity and sleep among community-dwelling older adults, from pre-to post-pandemic declaration. Method: Physical activity and sleep in older adults (n = 10, age = 77.3 ± 1.9 years, female = 40%) were remotely assessed within 3-month pre-to 6-month post-pandemic declaration using a pendant-wearable system. Depression was assessed pre-and post-pandemic declaration using the Center for Epidemiologic Studies Depression scale and was compared with 48 h continuous physical activity monitoring data before and during pandemic. Results: Compared to pre-pandemic, post-pandemic time spent in standing declined by 32.7% (Cohen’s d = 0.78, p < 0.01), walking by 52.2% (d = 1.1, p < 0.01), step-counts by 55.1% (d = 1.0, p = 0.016), and postural transitions by 44.6% (d = 0.82, p = 0.017) with increase in sitting duration by 20.5% (d = 0.5, p = 0.049). Depression symptoms increased by 150% (d = 0.8, p = 0.046). Interestingly, increase in depression was significantly correlated with unbroken-prolong sitting bout (ρ = 0.677, p = 0.032), cadence (ρ = −0.70, p = 0.024), and sleep duration (ρ = −0.72, p = 0.019). Conclusion: This is one of the early longitudinal studies highlighting adverse effect of the pandemic on objectively assessed physical activity and sleep in older adults. Our observations showed need for timely intervention to mitigate hard to reverse consequences of decreased physical activity such as depression.

Exploration of Confounding Due to Poor Health in an Accelerometer-Mortality Study

PURPOSE

Confounding due to poor health is a concern in accelerometer-based studies of physical activity and health, but detailed investigations of this source of bias are lacking.

METHODS

US adults (n = 4840) from the National Health and Nutrition Examination Survey (2003 to 2006) wore an accelerometer for 1 to 7 d (mean = 5.7 d) and were followed for mortality through 2015. Logistic regression was used to examine odds ratios between poor health (chronic conditions, self-reported health, mobility limitations, frailty) and low physical activity levels; Cox models were used to estimate adjusted hazard ratios (HR) and 95% CI for mortality associations for a 1 h·d increase in moderate-to-vigorous-intensity physical activity (MVPA) using two commonly used cut-points (MVPA760, MVPA2020). Modeling scenarios with shorter and longer follow-up time, increasing adjustment for poor health, by age group, and after excluding early years of follow-up were used to assess bias.

RESULTS

Over a mean of 10.1 yr of follow-up, 1165 deaths occurred. Poor health was associated with low MVPA760 levels and increased mortality risk. In fully adjusted MVPA760 models, HR was 26% stronger comparing 0 to 4 yr (HR = 0.46) with 0 to 12 yr of follow-up (HR = 0.62), particularly in older adults (65 yr and older). Increasing statistical adjustment for poor health attenuated MVPA760 associations by 13% to 15%, and exclusion of the first 2 yr of follow-up had limited effects. Comparable results were obtained with the MVPA2020 cut-point.

CONCLUSIONS

We did not find evidence that confounding by health status resulted in entirely spurious MVPA-mortality associations; however, potential bias was appreciable in modeling scenarios involving shorter follow-up (<6 yr), older adults, and more limited statistical adjustment for poor health. The strength of MVPA-mortality associations in studies reflecting these scenarios should be interpreted cautiously.

Objectively measured physical activity is associated with frailty in community-dwelling older adults: A systematic review

OBJECTIVE

The later-age shift towards physical inactivity and sedentary behaviour is associated with comorbidity and reduced function: markers of frailty. Whether these behaviours relate to frailty has yet to be thoroughly studied using objective measurements. This study aimed to summarise the associations of objectively measured habitual physical activity and sedentary behaviour with frailty in community-dwelling older adults.

STUDY DESIGN AND SETTING

Six databases were searched from inception to July 21st 2020. Articles analyzing objectively measured physical activity and/or sedentary behaviour with frailty in community-dwelling adults ≥60 years old were included. Synthesis of included articles was performed using effect direction heat maps and albatross plots.

RESULTS

The search identified 23 articles across 18 cohorts, including 7,696 total participants with a mean age of 69.3±8.1 years, and 56.9% female. All but one article were cross-sectional. Lower moderate-to-vigorous and total physical activity, steps, postural transitions, and energy expenditure were associated with frailty. The use of multifactorial or physical frailty definitions did not alter associations. Median effect sizes for the associations of all physical activity and sedentary behaviour measures with frailty were β = -0.272 [-0.381, -0.107] and β = 0.100 [0.001, 0.249], respectively.

CONCLUSION

Objective measures of physical activity are associated with frailty, regardless of frailty definition.

Harnessing digital health to objectively assess cancer-related fatigue: The impact of fatigue on mobility performance

Objective

Cancer-related fatigue (CRF) is highly prevalent among cancer survivors, which may have long-term effects on physical activity and quality of life. CRF is assessed by self-report or clinical observation, which may limit timely diagnosis and management. In this study, we examined the effect of CRF on mobility performance measured by a wearable pendant sensor.

Methods

This is a secondary analysis of a clinical trial evaluating the benefit of exercise in cancer survivors with chemotherapy-induced peripheral neuropathy (CIPN). CRF status was classified based on a Functional Assessment of Chronic Illness Therapy-Fatigue (FACIT-F) score ≤ 33. Among 28 patients (age = 65.7±9.8 years old, BMI = 26.9±4.1kg/m², sex = 32.9%female) with database variables of interest, twenty-one subjects (75.9%) were classified as non-CRF. Mobility performance, including behavior (sedentary, light, and moderate to vigorous activity (MtV)), postures (sitting, standing, lying, and walking), and locomotion (e.g., steps, postural transitions) were measured using a validated pendant-sensor over 24-hours. Baseline psychosocial, Functional Assessment of Cancer Therapy–General (FACT-G), Falls Efficacy Scale–International (FES-I), and motor-capacity assessments including gait (habitual speed, fast speed, and dual-task speed) and static balance were also performed.

Results

Both groups had similar baseline clinical and psychosocial characteristics, except for body-mass index (BMI), FACT-G, FACIT-F, and FES-I (p<0.050). The groups did not differ on motor-capacity. However, the majority of mobility performance parameters were different between groups with large to very large effect size, Cohen’s d ranging from 0.91 to 1.59. Among assessed mobility performance, the largest effect sizes were observed for sedentary-behavior (d = 1.59, p = 0.006), light-activity (d = 1.48, p = 0.009), and duration of sitting+lying (d = 1.46, p = 0.016). The largest correlations between mobility performance and FACIT-F were observed for sitting+lying (rho = -0.67, p<0.001) and the number of steps per day (rho = 0.60, p = 0.001).

Conclusion

The results of this study suggest that sensor-based mobility performance monitoring could be considered as a potential digital biomarker for CRF assessment. Future studies warrant evaluating utilization of mobility performance to track changes in CRF over time, response to CRF-related interventions, and earlier detection of CRF.

Systematic review on the application of wearable inertial sensors to quantify everyday life motor activity in people with mobility impairments

BACKGROUND

Recent advances in wearable sensor technologies enable objective and long-term monitoring of motor activities in a patient's habitual environment. People with mobility impairments require appropriate data processing algorithms that deal with their altered movement patterns and determine clinically meaningful outcome measures. Over the years, a large variety of algorithms have been published and this review provides an overview of their outcome measures, the concepts of the algorithms, the type and placement of required sensors as well as the investigated patient populations and measurement properties.

METHODS

A systematic search was conducted in MEDLINE, EMBASE, and SCOPUS in October 2019. The search strategy was designed to identify studies that (1) involved people with mobility impairments, (2) used wearable inertial sensors, (3) provided a description of the underlying algorithm, and (4) quantified an aspect of everyday life motor activity. The two review authors independently screened the search hits for eligibility and conducted the data extraction for the narrative review.

RESULTS

Ninety-five studies were included in this review. They covered a large variety of outcome measures and algorithms which can be grouped into four categories: (1) maintaining and changing a body position, (2) walking and moving, (3) moving around using a wheelchair, and (4) activities that involve the upper extremity. The validity or reproducibility of these outcomes measures was investigated in fourteen different patient populations. Most of the studies evaluated the algorithm's accuracy to detect certain activities in unlabeled raw data. The type and placement of required sensor technologies depends on the activity and outcome measure and are thoroughly described in this review. The usability of the applied sensor setups was rarely reported.

CONCLUSION

This systematic review provides a comprehensive overview of applications of wearable inertial sensors to quantify everyday life motor activity in people with mobility impairments. It summarizes the state-of-the-art, it provides quick access to the relevant literature, and it enables the identification of gaps for the evaluation of existing and the development of new algorithms.

What Features and Functions Are Desired in Telemedical Services Targeted at Polish Older Adults Delivered by Wearable Medical Devices?-Pre-COVID-19 Flashback

The emerging wearable medical devices open up new opportunities for the provision of health services and promise to accelerate the development of novel telemedical services. The main objective of this study was to investigate the desirable features and applications of telemedical services for the Polish older adults delivered by wearable medical devices. The questionnaire study was conducted among 146 adult volunteers in two cohorts (C.1: <65 years vs. C.2: ≥65 years). The analysis was based on qualitative research and descriptive statistics. Comparisons were performed by Pearson’s chi-squared test. The questionnaire, which was divided into three parts (1-socio-demographic data, needs, and behaviors; 2-health status; 3-telemedicine service awareness and device concept study), consisted of 37 open, semi-open, or closed questions. Two cohorts were analyzed (C.1: n = 77; mean age = 32 vs. C.2: n = 69; mean age = 74). The performed survey showed that the majority of respondents were unaware of the telemedical services (56.8%). A total of 62.3% of C.1 and 34.8% of C.2 declared their understanding of telemedical services. The 10.3% of correct explanations regarding telemedical service were found among all study participants. The most desirable feature was the detection of life-threatening and health-threatening situations (65.2% vs. 66.2%). The findings suggest a lack of awareness of telemedical services and the opportunities offered by wearable telemedical devices.

Consumer-Grade Wearable Device for Predicting Frailty in Canadian Home Care Service Clients: Prospective Observational Proof-of-Concept Study

Background

Frailty has detrimental health impacts on older home care clients and is associated with increased hospitalization and long-term care admission. The prevalence of frailty among home care clients is poorly understood and ranges from 4.0% to 59.1%. Although frailty screening tools exist, their inconsistent use in practice calls for more innovative and easier-to-use tools. Owing to increases in the capacity of wearable devices, as well as in technology literacy and adoption in Canadian older adults, wearable devices are emerging as a viable tool to assess frailty in this population.

Objective

The objective of this study was to prove that using a wearable device for assessing frailty in older home care clients could be possible.

Methods

From June 2018 to September 2019, we recruited home care clients aged 55 years and older to be monitored over a minimum of 8 days using a wearable device. Detailed sociodemographic information and patient assessments including degree of comorbidity and activities of daily living were collected. Frailty was measured using the Fried Frailty Index. Data collected from the wearable device were used to derive variables including daily step count, total sleep time, deep sleep time, light sleep time, awake time, sleep quality, heart rate, and heart rate standard deviation. Using both wearable and conventional assessment data, multiple logistic regression models were fitted via a sequential stepwise feature selection to predict frailty.

Results

A total of 37 older home care clients completed the study. The mean age was 82.27 (SD 10.84) years, and 76% (28/37) were female; 13 participants were frail, significantly older (P<.01), utilized more home care service (P=.01), walked less (P=.04), slept longer (P=.01), and had longer deep sleep time (P<.01). Total sleep time (r=0.41, P=.01) and deep sleep time (r=0.53, P<.01) were moderately correlated with frailty. The logistic regression model fitted with deep sleep time, step count, age, and education level yielded the best predictive performance with an area under the receiver operating characteristics curve value of 0.90 (Hosmer-Lemeshow P=.88).

Conclusions

We proved that a wearable device could be used to assess frailty for older home care clients. Wearable data complemented the existing assessments and enhanced predictive power. Wearable technology can be used to identify vulnerable older adults who may benefit from additional home care services.

Defining the vulnerable patient with myeloma-a frailty position paper of the European Myeloma Network

As the treatment landscape continues to evolve towards the application of precision medicine in multiple myeloma (MM), there is a clear need to identify those patients who are at risk of not achieving the maximum benefit whilst exposed to the highest level of toxicity. This group of patients, defined as frail, is an unmet clinical need. However, how we define such a vulnerable group of patients with MM remains to be clarified. An integral aspect of this is to define the physiological age and capacity of patients with MM to deal with the burden of their disease and it's treatment. Such assessments may include not only functional and clinical assessments but also laboratory-based biomarkers of frailty, aging and senescent cellular burden. A need to develop, test and validate clinical screening scores before their adoption into clinical practice is mandated. This position paper from the European Myeloma Network aims to review what is known about defining frailty in MM, and how we can advance this knowledge for the design of clinical trials and ultimately how we deliver treatment in the clinic.