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

Park, Catherine; Mishra, Ram Kinker; Golledge, Jonathan; Najafi, Bijan

doi:10.3390/s21165289

Cited by 34 publications

(29 citation statements)

References 45 publications

(95 reference statements)

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“…For physical activity assessments, all participants were asked to wear a pendant sensor (PAMSys™, BioSensics, Newton, MA, USA) at their sternum level for 48 h (i.e., two consecutive days) ( Figure 1 B) [ 25 , 26 , 27 ]. The 48 h duration was determined based on the results of our previous studies [ 25 , 26 , 27 ]. The PAMSys™ is small (3.5 cm (W) × 3.5 cm (H) × 1.5 cm (D)), light in weight (24 g), easy to use, and it can run for 200 h without charging.…”

Section: Methodsmentioning

confidence: 99%

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

Park

Atique

Mishra

et al. 2022

IJERPH

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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.

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Section: Methodsmentioning

confidence: 99%

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

Park

Atique

Mishra

et al. 2022

IJERPH

Self Cite

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“…In terms of frailty, comparable studies report ROC AUC values between 0.72 and 0.86, based on wearable sensors [51][52][53] . These results were primarily obtained on the basis of gait and physical activity measures.…”

Section: Discussionmentioning

confidence: 99%

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

et al. 2022

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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.

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“…A reduced number of motor tasks may improve the feasibility of using the platform in a non-clinical setting and for remote patient monitoring. Digital health technologies are poised to become an integral part of modern health care, expedited by the surge in remote care necessitated by the COVID-19 pandemic [ 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 ]. The proposed solution paves the way to integrating digital health technology (i.e., wearables) to enhance the utility of objective measures of ataxia.…”

Section: Discussionmentioning

confidence: 99%

Objective Assessment of Upper-Extremity Motor Functions in Spinocerebellar Ataxia Using Wearable Sensors

Mohammadi-Ghazi

Nguyen

Mishra

et al. 2022

Sensors

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The study presents a novel approach to objectively assessing the upper-extremity motor symptoms in spinocerebellar ataxia (SCA) using data collected via a wearable sensor worn on the patient’s wrist during upper-extremity tasks associated with the Assessment and Rating of Ataxia (SARA). First, we developed an algorithm for detecting/extracting the cycles of the finger-to-nose test (FNT). We extracted multiple features from the detected cycles and identified features and parameters correlated with the SARA scores. Additionally, we developed models to predict the severity of symptoms based on the FNT. The proposed technique was validated on a dataset comprising the seventeen (n = 17) participants’ assessments. The cycle detection technique showed an accuracy of 97.6% in a Bland–Altman analysis and a 94% accuracy (F1-score of 0.93) in predicting the severity of the FNT. Furthermore, the dependency of the upper-extremity tests was investigated through statistical analysis, and the results confirm dependency and potential redundancies in the upper-extremity SARA assessments. Our findings pave the way to enhance the utility of objective measures of SCA assessments. The proposed wearable-based platform has the potential to eliminate subjectivity and inter-rater variabilities in assessing ataxia.

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

Cited by 34 publications

References 45 publications

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

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

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

Objective Assessment of Upper-Extremity Motor Functions in Spinocerebellar Ataxia Using Wearable Sensors

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