Sensor-based characterization of daily walking: a new paradigm in pre-frailty/frailty assessment

Gait parameters and daily physical activity for distinguishing pre-frail, frail, and non-frail older adults: A scoping review

OBJECTIVE

This scoping review aimed to gather current knowledge on accurately identifying and distinguishing between non-frail, pre-frail, and frail older adults using gait and daily physical activity (DPA) parameters and/or models that combine gait with DPA parameters in both controlled and daily life environments.

METHODS

Following PRISMA-ScR guidelines, a systematic search was conducted across seven databases using key terms: "frail", "gait or walk", "IMU", and "age". Studies were included if they focused on gait analysis using Inertial Measurement Units (IMUs) for walking distances greater than 10 meters. Extracted data included study design, gait and DPA outcomes, walking conditions, and classification model performance. Gait parameters were grouped into four domains: spatio-temporal, frequency, amplitude, and dynamic gait. DPA parameters were synthesized into three categories: postural and transition, variability, and physical activity pattern.

RESULTS

A total of 15 cross-sectional studies involving 2,366 participants met the inclusion criteria. Gait analysis showed (pre)frail individuals had slower, shorter steps with longer stride times compared to non-frail individuals. Pre-frail individuals showed distinct gait patterns in periodicity, magnitude range, and variability. In daily activities, (pre)frail individuals displayed shorter, fragmented walking periods and longer transitions between positions. Walking variation identified pre-frail status, showing progressive decreases from non-frail to frail states. Combined gait and daily physical activity models achieved over 97% accuracy, sensitivity and specificity in distinguishing between groups.

DISCUSSION

This review provides an updated synthesis of the relationship between various gait and/or DPA parameters and physical frailty, highlighting gaps in pre-frailty detection and the variability in measurement protocols. It underscores the potential of long-term, sensor-based monitoring of daily physical activity for advancing pre-frailty screening and guiding future clinical trials. Structured Abstract BACKGROUND: Changes in gait and physical activity are critical indicators of frailty. With advancements in wearable sensor technology, long-term gait analysis using acceleration data has become more feasible. However, the contribution of parameters beyond gait speed, such as gait dynamics and daily physical activity (DPA), in identifying frail and pre-frail individuals remains unclear.

OBJECTIVE

This scoping review aimed to gather knowledge on accurately identifying and differentiating physical pre-frail and frail individuals from non-frail individuals using gait parameters alone or models that combine gait and DPA parameters, both in controlled settings and daily life environments.

METHODS

The review followed PRISMA-ScR guidelines. A search strategy incorporating key terms-"frail", "gait or walk", "IMU", and "age"-was applied across seven databases from inception to March 1, 2024. Studies were included if they focused on gait analysis in controlled or daily environments using Inertial Measurement Units (IMUs) and involved walking distances longer than 10 meters. Data on walking conditions, gait outcomes, classification methods, and results were extracted. Gait parameters were categorized into four domains: spatio-temporal, frequency, amplitude, and dynamic gait. DPA parameters were synthesized into three categories: postural and transition, variability, physical activity pattern.

RESULTS

A total of 15 cross-sectional observational studies met the eligibility criteria, covering 2,366 participants, with females representing 27%-80% of the sample and ages ranging from 60 to 92 years. Regarding gait parameters, (pre)frail individuals exhibited longer stride times, slower walking speeds, shorter steps, and reduced cadence compared to non-frail individuals. In three studies, pre-frail could be distinguished from the non-frail and frail group through gait periodicity, range of magnitude, and gait variability. DPA patterns differed between groups, with (pre)frail individuals showing shorter and more fragmented walking periods, brief walking bouts and longer postural transitions. Walking bout variation (CoV) effectively identified pre-frail status, decreasing 53.73% from non-frail to pre-frail, and another 30.87% from pre-frail to frail. Models combining both gait and DPA parameters achieved the highest accuracy (97.25%), sensitivity (98.25%), and specificity (98.25%) in distinguishing between groups.

DISCUSSION

This scoping review provides an updated overview of the current knowledge and gaps in understanding the relationship between gait parameters across different domains and DPA parameters along with physical frailty. Significant variability in gait measurement methods and protocols complicates direct comparisons between studies. The review emphasizes the need for further research, particularly in pre-frailty screening, and underscores the potential of inertial sensor-based long-term monitoring of daily physical activity for future clinical trials.

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.

Effects of Remote Exercise on Physical Function in Pre-Frail Older Adults: A Randomized Controlled Trial

BACKGROUND Remote exercise have emerged as a promising solution to overcome barriers to physical activity participation in pre-frail older adults, such as limited mobility and accessibility issues. Pre-frail older adults often face barriers to physical activity due to limited mobility and accessibility, underscoring the need for remote exercise alternatives. This study aimed to evaluate and compare the efficacy of remote versus in-person exercise in improving physical function in pre-frail older adults. MATERIAL AND METHODS Ninety pre-frail older adults aged 65 years and above were recruited, and randomly assigned to 3 groups: the remote exercise group (REG, n=30), the in-person exercise group (IPEG, n=30), and the control group (CG, n=30). The REG and IPEG groups underwent identical exercise, including balance, strength, and gait training, conducted twice weekly for 8 weeks. The REG received live, real-time instructions via video conferencing, while the IPEG participated in identical sessions conducted at a local facility. Outcome measures included assessments of balance, lower-limb strength, gait ability, and fall efficacy. RESULTS Both the REG and IPEG groups demonstrated significant improvements in balance, gait ability, lower-limb strength, and fall efficacy compared to the CG (P<0.05). No significant differences were found between the REG and IPEG groups across all outcome measures, indicating that remote exercise were as effective as in-person sessions. CONCLUSIONS Remote exercise effectively enhanced balance, strength, gait, and fall efficacy in pre-frail older adults, providing a viable alternative to traditional in-person programs and addressing healthcare disparities.

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.

Real-world mobility recovery after hip fracture: secondary analyses of digital mobility outcomes from four randomized controlled trials

BACKGROUND

The main focus of rehabilitation following hip fracture is to regain mobility.

OBJECTIVES

To estimate the progression of real-world mobility the first year after hip fracture using digital mobility outcomes.

DESIGN

An exploratory, prospective cohort study with pooled data from four previously conducted clinical trials.

SETTING AND SUBJECTS

We combined data from the Trondheim Hip Fracture Trial and Eva-Hip Trial in Trondheim, Norway, and the PROFinD 1 and PROFinD 2 trials in Stuttgart and Heidelberg, Germany, resulting in a sample of 717 hip fracture patients aged ≥65 years.

METHODS

Each of the trials assessed mobility using body-fixed sensors (activPAL) at three time points, collectively providing observations across the entire first year post-surgery. The following 24-h DMOs were calculated: total walking duration (minutes), maximum number of steps within a walking bout, and number of sit-to-stand-to-walk transfers. Continuous 1-year progression of the median, the 25th percentile, and the 75th percentile were estimated using quantile regression models with splines.

RESULTS

The dataset contained 5909 observation days. The median daily total walking duration increased until 36 weeks post-surgery reaching 40 min; daily maximum number of steps within a walking bout increased during the first eight weeks and then stabilized at less than 100 steps; daily sit-to-stand-to-walk transfers reached a plateau after 6 weeks with less than 40 transfers.

CONCLUSIONS

The three DMOs progressed differently and attained plateau levels at varying times during the first year after hip fracture, indicating that these Digital Mobility Outcomes provide complementary information about different aspects of mobility recovery.

Remote Gait Analysis Using Ultra-Wideband Radar Technology Based on Joint Range-Doppler-Time Representation

OBJECTIVE

In recent years, radar technology has been extensively utilized in contactless human behavior monitoring systems. The unique capabilities of ultra-wideband (UWB) radars compared to conventional radar technologies, due to time-of-flight measurements, present new untapped opportunities for in-depth monitoring of human movement during overground locomotion. This study aims to investigate the deployability of UWB radars in accurately capturing the gait patterns of healthy individuals with no known walking impairments.

METHODS

A novel algorithm was developed that can extract ten clinical spatiotemporal gait features using the Doppler information captured from three monostatic UWB radar sensors during a 6-meter walking task. Key gait events are detected from lower-extremity movements based on the joint range-Doppler-time representation of recorded radar data. The estimated gait parameters were validated against a gold-standard optical motion tracking system using 12 healthy volunteers.

RESULTS

On average, nine gait parameters can be consistently estimated with 90-98% accuracy, while capturing 94.5% of participants' gait variability and 90.8% of inter-limb symmetry. Correlation and Bland-Altman analysis revealed a strong correlation between radar-based parameters and the ground-truth values, with average discrepancies consistently close to 0.

CONCLUSION

Results prove that radar sensing can provide accurate biomarkers to supplement clinical human gait analysis, with quality similar to gold standard assessment.

SIGNIFICANCE

Radars can potentially allow a transition from expensive and cumbersome lab-based gait analysis tools toward a completely unobtrusive and affordable solution for in-home deployment, enabling continuous long-term monitoring of individuals for research and healthcare applications.

Characteristics of cadence during continuous walking in daily life

Despite the acknowledged relationship between the usual (preferred) walking speed (UWS) and health, there is currently no practical method available to reliably and accurately detect slight changes in UWS. This study aimed to explore whether either of the following two phenomena occurs during continuous daily walking in various periods: (a) Similarity between the most frequent cadences in the two periods. (b) The occurrence of the most frequent cadence in at least one of the two periods during the other period, with a frequency close to that of the most frequent cadence. In August 2021, invitations to participate in the study were extended via email to participants that took part in the Japan COVID-19 and Society Internet Surveys (JACSIS). A mobile phone application that collected step data during continuous walking was provided to the participants, and data were collected from December 1, 2021, to January 31, 2022. While 1022 participants installed the phone application, only 505 had measurement data for ten days or more in each of the two months of the study duration. The cadence during continuous walking was automatically measured daily from 05:00 to 21:00. Most participants exhibited at least one of the phenomena mentioned above, confirming a common, notably frequent, invariant cadence over time. Overall, this method allows for the identification of minor reductions and lower bounds of decline in UWS. This study illustrates the potential for tracking a decreasing trend in UWS. Early detection of a downward trend permits individuals to take timely remedial action, as recovery is relatively easy, and the confirmation of even a slight recovery bolsters recovery motivation.

Association between frailty and sleep quality in people living with multiple sclerosis and obesity: An observational cross-sectional study

BACKGROUND

A majority of the people with multiple sclerosis (pwMS) experience sleep disturbances. Frailty is also common in pwMS. The geriatric literature strongly suggests that frailty is associated with worse sleep outcomes in community-dwelling older adults, but this association has yet to be explored among pwMS. This study focused on examining the association between frailty and sleep quality in pwMS.

METHODS

Seventy-six people with both MS and obesity (mean age: 47.6 ± 10.9 years, 81.6 % female, mean body mass index (BMI): 37.10 ± 5.5 kg/m2, mean Patient Determined Disease Steps (PDDS): 0.82 ± 1.20) were included in this cross-sectional secondary analysis. A comprehensive frailty index (FI) based on 41 health deficits from various health domains was calculated based on standardized procedures. Sleep quality was determined by the Pittsburgh Sleep Quality Index questionnaire (PSQI).

RESULTS

Overall, 67.1 % of the participants were identified as non-frail (FI ≤ 0.25), and 32.9 % were identified as frail (FI > 0.25). A significant correlation was observed between FI scores and global PSQI scores (ρ = 0.43, p < 0.05). Cross-tabulation analyses revealed that frail participants had worse subjective sleep quality, sleep latency, habitual sleep efficiency, sleep disturbances, daytime dysfunction, and higher use of sleep medications compared to non-frail participants (p < 0.05).

CONCLUSIONS

The current study identified a significant association between frailty and sleep quality in people with both MS and obesity with minimal disability. These findings underscore the importance of untangling the relationship between frailty and sleep quality in pwMS. These results could lead to a more targeted approach for rehabilitation interventions aiming to improve frailty in MS.

[Criteria of syndrome frailty in Parkinson´s disease]

OBJECTIVE

To evaluate syndrome frailty by the Fried phenotype in patients with Parkinson's disease (PD).

MATERIAL AND METHODS

Seventy-three patients over 65 years of age with Hoehn and Yahr stage 2-4 PD were tested for the presence of subjective criteria of the Fried phenotype of syndrome frailty: fatigue, difficulty in performing habitual activities, weight loss and objective criteria: grip strength and walking speed. The relationships of the objective criteria of Fried with indicators of age, sex, sports history, prescription of PD, the number of medications, blood pressure and MDS UPDRS part III scores, the severity of depression on the Beck scale and cognitive disorders on the MOCA were evaluated.

RESULTS

All patients complained of fatigue, difficulties in performing habitual activities. Four people noted a decrease in body weight of more than 5 kg per year. Objective criteria of Fried were absent in 38 (51%) patients, 23 (32%) people had one objective criterion: reduced walking speed (less than 0.8 m/s) or hand strength (less than 16 kg for women and 26 kg for men), in 12 (17%) people both objective criteria were reduced. The values of objective criteria of weakness were correlated with age, sex and MDS UPDRS part III total scores.

CONCLUSION

Frailty syndrome is difficult to diagnose in patients with PD due to the coincidence of complaints of the underlying disease and the syndrome. Objective criteria of the Fried phenotype suggest the presence of syndrome frailty in patients. The increase in the age of the patient, the female sex and the severity of PD are interrelated with the presence of objective criteria for the frailty of an elderly person.

The prevalence and predictors of geriatric giants in community-dwelling older adults: a cross-sectional study from the Middle East

The term "geriatric giants" refers to the chronic disabilities of senescence leading to adverse health outcomes. This study aimed to investigate the prevalence and predictors of geriatric giants in Southern Iran. The participants were selected from Bushehr city using a multistage cluster random sampling method. Demographic data were collected through interviews. Frailty, incontinence, immobility, depression, cognitive impairment, and malnutrition were measured by questionnaires and instruments. Finally, data from 2392 participants were analyzed. The prevalence of fecal incontinence was less than 1% among all participants and similar in men and women. In contrast, compared with men, women had higher prevalence of urinary incontinence (36.44% vs. 17.65%), depression (39.05% vs. 12.89%), anorexia and malnutrition (2.35% vs. 0.82%), immobility (8.00% vs. 2.5%), frailty (16.84 vs. 7.34), and pre-frailty (54.19 vs. 38.63%). The prevalence of dependence and cognitive impairment was also higher in women and considerably increased with the age of participants. In total, 12.07% of subjects were frail, and 46.76% were pre-frail. The prevalence of frailty exponentially increased in older age, ranging from 4.18% among those aged 60-64 years to 57.35% in those aged ≥ 80 years. Considering 95% confidence interval (CI), multivariate logistic regression revealed that low physical activity [odds ratio (OR) 31.73 (18.44-54.60)], cancer (OR 3.28 (1.27-8.44)), depression [OR 2.42 (1.97-2.98)], age [OR 1.11 (1.08-1.14)], waist circumference [OR 1.03 (1.01-1.06)], BMI [OR 1.07 (1.01-1.14)], MNA score [OR 0.85 (0.79-0.92)], polypharmacy [OR 2.26 (1.30-3.95)] and male gender [OR 0.63 (0.42-0.93)] were independently associated with frailty. White blood cell count (WBC), smoking, marital status, and number of comorbidities were not independently associated with frailty. Low physical activity was the strongest predictor of frailty, which may need more attention in geriatric care. Frailty, its predictors, and other components of geriatric giants were considerably more common among women and older ages.

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.

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.

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.

A Human-in-the-loop Segmented Mixed-effects Modeling Method For Analyzing Wearables Data

Wearables are an important source of big data as they provide real-time high-resolution data logs of health indicators of individuals. Higher-order associations between pairs of variables is common in wearables data. Representing higher-order association curves as piece-wise linear segments in a regression model makes them more interpretable. However, existing methods for identifying the change points for segmented modeling either overfit or have low external validity for wearables data containing repeated measures. Therefore, we propose a human-in-the-loop method for segmented modeling of higher-order pairwise associations between variables in wearables data. Our method uses the smooth function estimated by a generalized additive mixed model to allow the analyst to annotate change point estimates for a segmented mixed-effects model, and thereafter employs the Brent's constrained optimization procedure to fine-tuning the manually provided estimates. We validate our method using three real-world wearables datasets. Our method not only outperforms state-of-the-art modeling methods in terms of prediction performance but also provides more interpretable results. Our study contributes to health data science in terms of developing a new method for interpretable modeling of wearables data. Our analysis uncovers interesting insights on higher order associations for health researchers.

Slowness as a Predictor of Functional Decline in Older Adults: Comparison of Moberg Picking-Up Test and Walking Speed

OBJECTIVES

Slowness, generally assessed by walking speed (WS), is an estimator of frailty and its outcomes. Because of potential difficulties in assessing WS, the Moberg picking-up test (MPUT) might be an alternative. This study investigated the capacity of slowness measurements (WS and MPUT) to predict nonfatal adverse consequences of frailty: primarily, decline in basic activities of daily living (BADL); and secondarily, decline in instrumental activities of daily living (IADL), fall, hospitalization, and incident disease.

DESIGN

Observational (prospective longitudinal study).

SETTING AND PARTICIPANTS

This study used data from the population-based Lausanne cohort 65+. At baseline, 1887 individuals (aged 72-77 years) completed both WS (time to walk 20 m at usual pace) and MPUT (time to pick up 12 objects) assessments.

METHODS

All outcomes, assessed at 1- and 4-year follow-ups, were entered in separate logistic regression models with adjustment for age, sex, and respective values at baseline. The prediction of all outcomes by either WS or MPUT was assessed using area under the receiver operating characteristic curve and compared by χ² tests.

RESULTS

There were positive associations between slowness either assessed by WS [relative risk (RR) = 2.48; P < .001] or MPUT (RR = 1.91; P < .001) and decline in BADL at 1-year follow-up. These associations remained significant at 4-year follow-up for both WS (RR = 2.28; P < .001) and MPUT (RR = 1.95; P < .001). There was no significant difference between predictive values of slow WS and MPUT for decline in BADL at 1-year (P = .328) and 4-year follow-ups (P = .413). The prediction was not significantly different for secondary outcomes, except for decline in IADL for which the prediction was slightly better for WS.

CONCLUSIONS AND IMPLICATIONS

MPUT may be an alternative measurement of slowness with predictive value of functional decline. No significant difference in predictive capabilities of MPUT and WS for specific adverse consequences of frailty is promising in favor of using MPUT for measuring slowness.

A systems approach towards remote health-monitoring in older adults: Introducing a zero-interaction digital exhaust

Using connected sensing devices to remotely monitor health is a promising way to help transition healthcare from a rather reactive to a more precision medicine oriented proactive approach, which could be particularly relevant in the face of rapid population ageing and the challenges it poses to healthcare systems. Sensor derived digital measures of health, such as digital biomarkers or digital clinical outcome assessments, may be used to monitor health status or the risk of adverse events like falls. Current research around such digital measures has largely focused on exploring the use of few individual measures obtained through mobile devices. However, especially for long-term applications in older adults, this choice of technology may not be ideal and could further add to the digital divide. Moreover, large-scale systems biology approaches, like genomics, have already proven beneficial in precision medicine, making it plausible that the same could also hold for remote-health monitoring. In this context, we introduce and describe a zero-interaction digital exhaust: a set of 1268 digital measures that cover large parts of a person’s activity, behavior and physiology. Making this approach more inclusive of older adults, we base this set entirely on contactless, zero-interaction sensing technologies. Applying the resulting digital exhaust to real-world data, we then demonstrate the possibility to create multiple ageing relevant digital clinical outcome assessments. Paired with modern machine learning, we find these assessments to be surprisingly powerful and often on-par with mobile approaches. Lastly, we highlight the possibility to discover novel digital biomarkers based on this large-scale approach.

Estimation of Hand Grip Strength Using Foot motion Measured by In-shoe Motion Sensor

There is a strong need to assess frailty in daily living. Hand grip strength (HGS) has been proven to be a very important factor for identifying frailty, however it is always assessed under the guidance of facility clinicians. Our purpose is to demonstrate the possibility of providing HGS estimation by using foot-motion signals measured by an in-shoe motion sensor (IMS) embedded in an insole to achieve high precision HGS assessment in daily living. The foot-motion signals were collected from 62 elder participants (27 men and 35 women). Their HGSs were assessed by a hand dynamometer. Gait parameters, individual properties, and predictors derived from foot-motion signal features in one gait cycle were selected as candidates. Statistical parametric mapping analyses were used to generate predictors from the foot-motion signals. Prior to estimation construction, least absolute shrinkage and selection operator was applied to reduce redundant predictors from candidates. Linear regression models for HGS estimation of men and women were constructed. As the results, we discovered new effective predictors for HGS estimation from foot motions and successfully constructed HGS estimation models that achieved "excellent" agreement with the reference according to intra-class coefficients, and mean absolute errors of 2.96 and 2.57 kg for men and women in leave-one-subject-out cross-validation, respectively. These results suggest that HGS can be estimated with high precision by IMS-measured foot motion and more effective frailty identification in daily living is possible through wearing an IMS.

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.

Impact of medium-chain triglycerides on gait performance and brain metabolic network in healthy older adults: a double-blind, randomized controlled study

Nutritional supplementation with medium-chain triglycerides (MCTs) has the potential to increase memory function in elderly patients with frailty and dementia. Our aim was to investigate the effects of MCT on cognitive and gait functions and their relationships with focal brain metabolism and functional connectivity even in healthy older adults. Participants were blindly randomized and allocated to two groups: 18 g/day of MCT oil and matching placebo formula (control) administered as a jelly stick (6 g/pack, ingested three times a day). Gait analysis during the 6-m walk test, cognition, brain focal glucose metabolism quantified by ¹⁸F-fluorodeocyglucose positron emission tomography, and magnetic resonance imaging-based functional connectivity were assessed before and after a 3-month intervention. Sixty-three healthy, normal adults (females and males) were included. Compared with the control group, the MCT group showed better balance ability, as represented by the lower Lissajous index (23.1 ± 14.4 vs. 31.3 ± 18.9; P < 0.01), although no time × group interaction was observed in cognitive and other gait parameters. Moreover, MCT led to suppressed glucose metabolism in the right sensorimotor cortex compared with the control (P < 0.001), which was related to improved balance (r = 0.37; P = 0.04) along with increased functional connectivity from the ipsilateral cerebellar hemisphere. In conclusion, a 3-month MCT supplementation improves walking balance by suppressing glucose metabolism, which suggests the involvement of the cerebro-cerebellar network. This may reflect, at least in part, the inverse reaction of the ketogenic switch as a beneficial effect of long-term MCT dietary treatment.

Kinematic characteristics during gait in frail older women identified by principal component analysis

Frailty is associated with gait variability in several quantitative parameters, including high stride time variability. However, the associations between joint kinematics during walking and increased gait variability with frailty remain unclear. In the current study, principal component analysis was used to identify the key joint kinematics characteristics of gait related to frailty. We analyzed whole kinematic waveforms during the entire gait cycle obtained from the pelvis and lower limb joint angle in 30 older women (frail/prefrail: 15 participants; non-frail: 15 participants). Principal component analysis was conducted using a 60 × 1224 input matrix constructed from participants’ time-normalized pelvic and lower-limb-joint angles along three axes (each leg of 30 participants, 51 time points, four angles, three axes, and two variables). Statistical analyses revealed that only principal component vectors 6 and 9 were related to frailty. Recombining the joint kinematics corresponding to these principal component vectors revealed that frail older women tended to exhibit greater variability of knee- and ankle-joint angles in the sagittal plane while walking compared with non-frail older women. We concluded that greater variability of knee- and ankle-joint angles in the sagittal plane are joint kinematic characteristics of gait related to frailty.

A Sensor-Based mHealth Platform for Remote Monitoring and Intervention of Frailty Patients at Home

Frailty syndrome is an independent risk factor for serious health episodes, disability, hospitalization, falls, loss of mobility, and cardiovascular disease. Its high reversibility demands personalized interventions among which exercise programs are highly efficient to contribute to its delay. Information technology-based solutions to support frailty have been recently approached, but most of them are focused on assessment and not on intervention. This paper describes a sensor-based mHealth platform integrated in a service-based architecture inside the FRAIL project towards the remote monitoring and intervention of pre-frail and frail patients at home. The aim of this platform is constituting an efficient and scalable system for reducing both the impact of aging and the advance of frailty syndrome. Among the results of this work are: (1) the development of elderly-focused sensors and platform; (2) a technical validation process of the sensor devices and the mHealth platform with young adults; and (3) an assessment of usability and acceptability of the devices with a set of pre-frail and frail patients. After the promising results obtained, future steps of this work involve performing a clinical validation in order to quantify the impact of the platform on health outcomes of frail patients.

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.

Real-world gait speed estimation, frailty and handgrip strength: a cohort-based study

Gait speed is a reliable outcome measure across multiple diagnoses, recognized as the 6th vital sign. The focus of the present study was on assessment of gait speed in long-term real-life settings with the aim to: (1) demonstrate feasibility in large cohort studies, using data recorded with a wrist-worn accelerometer device; (2) investigate whether the walking speed assessed in the real-world is consistent with expected trends, and associated with clinical scores such as frailty/handgrip strength. This cross-sectional study included n = 2809 participants (1508 women, 1301 men, [45-75] years old), monitored with a wrist-worn device for 13 consecutive days. Validated algorithms were used to detect the gait bouts and estimate speed. A set of metrics were derived from the statistical distribution of speed of gait bouts categorized by duration (short, medium, long). The estimated usual gait speed (1-1.6 m/s) appears consistent with normative values and expected trends with age, gender, BMI and physical activity levels. Speed metrics significantly improved detection of frailty: AUC increase from 0.763 (no speed metrics) to 0.798, 0.800 and 0.793 for the 95th percentile of individual's gait speed for bout durations < 30, 30-120 and > 120 s, respectively (all p < 0.001). Similarly, speed metrics also improved the prediction of handgrip strength: AUC increase from 0.669 (no speed metrics) to 0.696, 0.696 and 0.691 for the 95th percentile of individual's gait speed for bout durations < 30, 30-120 and > 120 s, respectively (all p < 0.001). Forward stepwise regression showed that the 95th percentile speed of gait bouts with medium duration (30-120 s) to be the best predictor for both conditions. The study provides evidence that real-world gait speed can be estimated using a wrist-worn wearable system, and can be used as reliable indicator of age-related functional decline.

Early diagnosis of frailty: Technological and non-intrusive devices for clinical detection

This work analyses different concepts for frailty diagnosis based on affordable standard technology such as smartphones or wearable devices. The goal is to provide ideas that go beyond classical diagnostic tools such as magnetic resonance imaging or tomography, thus changing the paradigm; enabling the detection of frailty without expensive facilities, in an ecological way for both patients and medical staff and even with continuous monitoring. Fried's five-point phenotype model of frailty along with a model based on trials and several classical physical tests were used for device classification. This work provides a starting point for future researchers who will have to try to bridge the gap separating elderly people from technology and medical tests in order to provide feasible, accurate and affordable tools for frailty monitoring for a wide range of users.

Between-day repeatability of sensor-based in-home gait assessment among older adults: assessing the effect of frailty

BACKGROUND

While sensor-based daily physical activity (DPA) gait assessment has been demonstrated to be an effective measure of physical frailty and fall-risk, the repeatability of DPA gait parameters between different days of measurement is not clear.

AIMS

To evaluate test-retest reliability (repeatability) of DPA gait performance parameters, representing the quality of walking, and quantitative gait measures (e.g. number of steps) between two separate days of assessment among older adults.

METHODS

DPA was acquired for 48-h from older adults (age ≥ 65 years) using a tri-axial accelerometer. Continuous walking bouts (≥ 60 s) were identified from acceleration data and used to extract gait performance parameters, including time- and frequency-domain gait parameters, representing walking speed, variability, and irregularity. To assess repeatability, intraclass correlation coefficient (ICC) was calculated using two-way mixed effects F-test models for day-1 vs. day-2 as the independent random effect. Repeatability tests were performed for all participants and also within frailty groups (non-frail and pre-frail/frail identified using Fried phenotype).

RESULTS

Data was analyzed from 63 older adults (29 non-frail and 34 pre-frail/frail). Most of the time- and frequency-domain gait performance parameters showed good to excellent repeatability (ICC ≥ 0.70), while quantitative parameters, including number of steps and walking duration showed poor repeatability (ICC < 0.30). Among majority of the gait performance parameters, we observed higher repeatability among the pre-frail/frail group (ICC > 0.78) compared to non-frail individuals (0.39 < ICC < 0.55).

CONCLUSION

Gait performance parameters, showed higher repeatability compared to quantitative measures. Higher repeatability among pre-frail/frail individuals may be attributed to a reduced functional capacity for performing more intense and variable physical tasks.

TRIAL REGISTRATION

The clinical trial was retrospectively registered on June 18th, 2013 with ClinicalTrials.gov, identifier NCT01880229.

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.

Unobtrusive Sensors for the Assessment of Older Adult's Frailty: A Scoping Review

Ubiquity (devices becoming part of the context) and transparency (devices not interfering with daily activities) are very significant in healthcare monitoring applications for elders. The present study undertakes a scoping review to map the literature on sensor-based unobtrusive monitoring of older adults' frailty. We aim to determine what types of devices comply with unobtrusiveness requirements, which frailty markers have been unobtrusively assessed, which unsupervised devices have been tested, the relationships between sensor outcomes and frailty markers, and which devices can assess multiple markers. SCOPUS, PUBMED, and Web of Science were used to identify papers published 2010-2020. We selected 67 documents involving non-hospitalized older adults (65+ y.o.) and assessing frailty level or some specific frailty-marker with some sensor. Among the nine types of body worn sensors, only inertial measurement units (IMUs) on the waist and wrist-worn sensors comply with ubiquity. The former can transparently assess all variables but weight loss. Wrist-worn devices have not been tested in unsupervised conditions. Unsupervised presence detectors can predict frailty, slowness, performance, and physical activity. Waist IMUs and presence detectors are the most promising candidates for unobtrusive and unsupervised monitoring of frailty. Further research is necessary to give specific predictions of frailty level with unsupervised waist IMUs.

Wearable Sensors Technology as a Tool for Discriminating Frailty Levels During Instrumented Gait Analysis

Background and objectives: One of the greatest challenges facing the healthcare of the aging population is frailty. There is growing scientific evidence that gait assessment using wearable sensors could be used for prefrailty and frailty screening. The purpose of this study was to examine the ability of a wearable sensor-based assessment of gait to discriminate between frailty levels (robust, prefrail, and frail). Materials and methods: 133 participants (≥60 years) were recruited and frailty was assessed using the Fried criteria. Gait was assessed using wireless inertial sensors attached by straps on the thighs, shins, and feet. Between-group differences in frailty were assessed using analysis of variance. Associations between frailty and gait parameters were assessed using multinomial logistic models with frailty as the dependent variable. We used receiver operating characteristic (ROC) curves to calculate the area under the curve (AUC) to estimate the predictive validity of each parameter. The cut-off values were calculated based on the Youden index. Results: Frailty was identified in 37 (28%) participants, prefrailty in 66 (50%), and no Fried criteria were found in 30 (23%) participants. Gait speed, stance phase time, swing phase time, stride time, double support time, and cadence were able to discriminate frailty from robust, and prefrail from robust. Stride time (AUC = 0.915), stance phase (AUC = 0.923), and cadence (AUC = 0.930) were the most sensitive parameters to separate frail or prefrail from robust. Other gait parameters, such as double support, had poor sensitivity. We determined the value of stride time (1.19 s), stance phase time (0.68 s), and cadence (101 steps/min) to identify individuals with prefrailty or frailty with sufficient sensitivity and specificity. Conclusions: The results of our study show that gait analysis using wearable sensors could discriminate between frailty levels. We were able to identify several gait indicators apart from gait speed that distinguish frail or prefrail from robust with sufficient sensitivity and specificity. If improved and adapted for everyday use, gait assessment technologies could contribute to frailty screening and monitoring.