The use of accelerometer-based wearable activity monitors in clinical settings: current practice, barriers, enablers, and future opportunities

Stepping up recovery: integrating patient reported outcome measures and wearable technology for 90-day rehabilitation following total hip arthroplasty

INTRODUCTION

There is conflicting data in the literature regarding the clinical utility of wearable devices. This study examined the association between patient reported outcome measures (PROMs) and step and stair flight counts obtained from wearable devices in postoperative total hip arthroplasty (THA) patients.

METHODS

Data was collected from a multicenter prospective longitudinal cohort study from October 2018 to February 2022. A smartphone-based platform with smartwatch was utilized for collection of daily step and stair flight counts. Subjects (N = 1644) completed the Hip disability and Osteoarthritis Outcome Score for Joint Replacement (HOOS JR) and numerical rating scale (NRS) pain scores preoperatively and at 1 and 3 months postoperatively. Patients who reported living in a multi-level home (N = 931) were included in analysis of stair flight counts. Pearson correlation coefficients were calculated to determine correlations between step and stair flight counts with NRS pain and HOOS JR scores.

RESULTS

Step counts demonstrated a weak negative correlation to NRS pain scores at preoperative (r = - 0.15, p < 0.0001), 1-month (r = - 0.15, p < 0.0001), and 3-months follow-up (r = - 0.06, p = 0.05). Step counts demonstrated a weak positive correlation with HOOS JR scores at preoperative (r = 0.16, p < 0.0001), 1-month (r = 0.15, p < 0.0001), and 3-months (r = 0.13, p < 0.0001). Stair flight counts demonstrated a weak negative correlation with NRS pain preoperatively (r = - 0.19, p < 0.0001) and at 1-month (r = - 0.11, p = 0.003). Stair flight counts positively correlated with HOOS JR scores at preoperative (r = 0.24, p < 0.0001), 1-month (r = 0.15, p < 0.0001), and 3-months (r = 0.09, p = 0.02).

CONCLUSION

The utilization of wearable technology can enhance the evaluation of patient outcomes after THA, primarily due to the observed correlation between data collected from wearables and PROMs. Our study highlights the importance of the use of objective data, in addition to subjective patient reported data, when analyzing postoperative patient progress, which propels forward the field of postoperative THA patient care.

Classifying physical activity levels using Mean Amplitude Deviation in adults using a chest worn accelerometer: validation of the Vivalink ECG Patch

BACKGROUND

The development of readily available wearable accelerometers has enabled clinicians to objectively monitor physical activity (PA) remotely in the community, a superior alternative to patient self-reporting measures. Critical to the value of these monitors is the ability to reliably detect when patients are undergoing ambulatory activity. Previous studies have highlighted the strength of using mean amplitude deviation (MAD) as a universal measure for analysing raw accelerometery data and defining cut-points between sedentary and ambulatory activities. Currently however there is little evidence surrounding the use of chest-worn accelerometers which can provide simultaneous monitoring of other physiological parameters such as heart rate (HR), RR intervals, and Respiratory Rate alongside accelerometery data. We aimed to calibrate the accelerometery function within the VivaLink ECG patch to determine the cut-point MAD value for differentiating sedentary and ambulatory activities.

METHODS

We recruited healthy volunteers to undergo a randomised series of 9 activities that simulate typical free-living behaviours, while wearing a VivaLink ECG Patch (Campbell, California). MAD values were applied to a Generalised Linear Mixed Model to determine cut-points between sedentary and ambulatory activities. We constructed a Receiver Operating Characteristic (ROC) curve to analyse the sensitivity and specificity of the cut-off MAD value.

RESULTS

Eighteen healthy adults volunteered to the study and mean MAD values were collected for each activity. The optimal MAD cut-point between sedentary and ambulatory activities was 47.73mG. ROC curve analysis revealed an area under the curve of 0.99 (p < 0.001) for this value with a sensitivity and specificity of 98% and 100% respectively.

CONCLUSION

In conclusion, the MAD cut-point determined in our study is very effective at categorising sedentary and ambulatory activities among healthy adults and may be of use in monitoring PA in the community with minimal burden. It will also be useful for future studies aiming to simultaneously monitor PA with other physiological parameters via chest worn accelerometers.

Establishing a Consensus-Based Framework for the Use of Wearable Activity Trackers in Health Care: Delphi Study

BACKGROUND

Physical activity (PA) plays a crucial role in health care, providing benefits in the prevention and management of many noncommunicable diseases. Wearable activity trackers (WATs) provide an opportunity to monitor and promote PA in various health care settings.

OBJECTIVE

This study aimed to develop a consensus-based framework for the optimal use of WATs in health care.

METHODS

A 4-round Delphi survey was conducted, involving a panel (n=58) of health care professionals, health service managers, and researchers. Round 1 used open-response questions to identify overarching themes. Rounds 2 and 3 used 9-point Likert scales to refine participants' opinions and establish consensus on key factors related to WAT use in health care, including metrics, device characteristics, clinical populations and settings, and software considerations. Round 3 also explored barriers and mitigating strategies to WAT use in clinical settings. Insights from Rounds 1-3 informed a draft checklist designed to guide a systematic approach to WAT adoption in health care. In Round 4, participants evaluated the draft checklist's clarity, utility, and appropriateness.

RESULTS

Participation rates for rounds 1 to 4 were 76% (n=44), 74% (n=43), 74% (n=43), and 66% (n=38), respectively. The study found a strong interest in using WATs across diverse clinical populations and settings. Key metrics (step count, minutes of PA, and sedentary time), device characteristics (eg, easy to charge, comfortable, waterproof, simple data access, and easy to navigate and interpret data), and software characteristics (eg, remote and wireless data access, access to multiple patients' data) were identified. Various barriers to WAT adoption were highlighted, including device-related, patient-related, clinician-related, and system-level issues. The findings culminated in a 12-item draft checklist for using WATs in health care, with all 12 items endorsed for their utility, clarity, and appropriateness in Round 4.

CONCLUSIONS

This study underscores the potential of WATs in enhancing patient care across a broad spectrum of health care settings. While the benefits of WATs are evident, successful integration requires addressing several challenges, from technological developments to patient education and clinician training. Collaboration between WAT manufacturers, researchers, and health care professionals will be pivotal for implementing WATs in the health care sector.

Stepping Up Recovery: Integrating Patient-reported Outcome Measures and Wearable Technology for Rehabilitation Following Knee Arthroplasty

Improvement after knee arthroplasty (KA) is often measured using patient-reported outcome measures (PROMs). However, PROMs are limited due to their subjectivity. Therefore, wearable technology is becoming commonly utilized to objectively assess physical activity and function. We assessed the correlation between PROMs and step/stair flight counts in total (TKA) and partial knee arthroplasty (PKA) patients.Analysis of a multicenter, prospective, longitudinal cohort study investigating the collection of average daily step and stair flight counts, was performed. Subjects (N = 1,844 TKA patients and N = 489 PKA patients) completed the Knee Injury and Osteoarthritis Outcome Score for Joint Replacement (KOOS JR) and provided numerical rating scale pain scores pre- and postoperatively. Only patients who reported living in a multilevel home environment (N = 896 TKA patients and N = 258 PKA patients) were included in analysis of stair flight counts. Pearson correlation coefficients were calculated to determine correlations between variables.Among TKA patients, pain scores demonstrated a negative correlation to mean step counts at preoperative (r = -0.14, p < 0.0001) and 1-month follow-up (r = -0.14, p < 0.0001). Similar negative correlations were true for pain and stair flight counts at preoperative (r = -0.16, p < 0.0001) and 1-month follow-up (r = -0.11, p = 0.006). KOOS JR scores demonstrated weak positive correlations with mean step counts at preoperative (r = 0.19, p < 0.0001) and 1-month postoperative (r = 0.17, p < 0.0001). Similar positive correlations were true for KOOS JR scores and stair flight counts preoperatively (r = 0.13, p = 0.0002) and at 1-month postoperatively (r = 0.10, p = 0.0048). For PKA patients, correlations between pain and KOOS JR with step/stair counts demonstrated similar directionality.Given the correlation between wearable-generated data and PROMs, wearable technology may be beneficial in evaluating patient outcomes following KA. By combining subjective feedback with the objective data, health care providers can gain a holistic view of patients' progress and tailor treatment plans accordingly.

Measurement properties of activity monitoring for a rehabilitation (AMoR) platform in post-stroke individuals in a simulated home environment

AIM

The aim of this study was to evaluate the measurement properties of activity monitoring for a rehabilitation (AMoR) platform for step counting, time spent in sedentary behavior, and postural changes during activities of daily living (ADLs) in a simulated home environment.

METHODS

Twenty-one individuals in the post-stroke chronic phase used the AMoR platform during an ADL protocol and were monitored by a video camera. Spearman's correlation coefficient, mean absolute percent error (MAPE), intraclass correlation coefficient (ICC), and Bland-Altman plot analyses were used to estimate the validity and reliability between the AMoR platform and the video for step counting, time spent sitting/lying, and postural changes from sit-to-stand (SI-ST) and sit-to-stand (ST-SI).

RESULTS

Validity of the platform was observed with very high correlation values for step counting (rs = 0.998) and time spent sitting/lying (rs = 0.992) and high correlation for postural change of SI-ST (rs = 0.850) and ST-SI (rs = 0.851) when compared to the video. An error percentage above 5% was observed only for the SI-ST postural change (7.13%). The ICC values show excellent agreement for step counting (ICC3, k = 0.999) and time spent sitting/lying (ICC3, k = 0.992), and good agreement for SI-ST (ICC3, k = 0.859) and ST-SI (ICC3, k = 0.936) postural change. Values of the differences for step counting, sitting/lying time, and postural change were within the limits of agreement according to the analysis of the Bland-Altman graph.

CONCLUSION

The AMoR platform presented validity and reliability for step counting, time spent sitting/lying, and identification of SI-ST and ST-SI postural changes during tests in a simulated environment in post-stroke individuals.

Associations of Physical Activity and Heart Rate Variability from a Two-Week ECG Monitor with Cognitive Function and Dementia: The ARIC Neurocognitive Study

Low physical activity (PA) measured by accelerometers and low heart rate variability (HRV) measured from short-term ECG recordings are associated with worse cognitive function. Wearable long-term ECG monitors are now widely used, and some devices also include an accelerometer. The objective of this study was to evaluate whether PA or HRV measured from long-term ECG monitors was associated with cognitive function among older adults. A total of 1590 ARIC participants had free-living PA and HRV measured over 14 days using the Zio® XT Patch [aged 72-94 years, 58% female, 32% Black]. Cognitive function was measured by cognitive factor scores and adjudicated dementia or mild cognitive impairment (MCI) status. Adjusted linear or multinomial regression models examined whether higher PA or higher HRV was cross-sectionally associated with higher factor scores or lower odds of MCI/dementia. Each 1-unit increase in the total amount of PA was associated with higher global cognition (β = 0.30, 95% CI: 0.16-0.44) and executive function scores (β = 0.38, 95% CI: 0.22-0.53) and lower odds of MCI (OR = 0.38, 95% CI: 0.22-0.67) or dementia (OR = 0.25, 95% CI: 0.08-0.74). HRV (i.e., SDNN and rMSSD) was not associated with cognitive function. More research is needed to define the role of wearable ECG monitors as a tool for digital phenotyping of dementia.

The Wearable Activity Tracker Checklist for Healthcare (WATCH): a 12-point guide for the implementation of wearable activity trackers in healthcare

Increasing physical activity in patients offers dual benefits, fostering improved patient health and recovery, while also bolstering healthcare system efficiency by minimizing costs related to extended hospital stays, complications, and readmissions. Wearable activity trackers offer valuable opportunities to enhance physical activity across various healthcare settings and among different patient groups. However, their integration into healthcare faces multiple implementation challenges related to the devices themselves, patients, clinicians, and systemic factors. This article presents the Wearable Activity Tracker Checklist for Healthcare (WATCH), which was recently developed through an international Delphi study. The WATCH provides a comprehensive framework for implementation and evaluation of wearable activity trackers in healthcare. It covers the purpose and setting for usage; patient, provider, and support personnel roles; selection of relevant metrics; device specifications; procedural steps for issuance and maintenance; data management; timelines; necessary adaptations for specific scenarios; and essential resources (such as education and training) for effective implementation. The WATCH is designed to support the implementation of wearable activity trackers across a wide range of healthcare populations and settings, and in those with varied levels of experience. The overarching goal is to support broader, sustained, and systematic use of wearable activity trackers in healthcare, therefore fostering enhanced physical activity promotion and improved patient outcomes.

Associations of Physical Activity and Heart Rate Variability from a Two-Week ECG Monitor with Cognitive Function and Dementia: the ARIC Neurocognitive Study

BACKGROUND

Low physical activity (PA) measured from accelerometers and low heart rate variability (HRV) measured from short-term ECG recordings are associated with worse cognitive function. Wearable long-term ECG monitors are now widely used. These monitors can provide long-term HRV data and, if embedded with an accelerometer, they can also provide PA data. Whether PA or HRV measured from long-term ECG monitors is associated with cognitive function among older adults is unknown.

METHODS

Free-living PA and HRV were measured simultaneously over 14-days using the Zio ® XT Patch among 1590 participants in the Atherosclerosis Risk in Communities Study [aged 72-94 years, 58% female, 32% Black]. Total amount of PA was estimated by total mean amplitude deviation (TMAD) from the 14-day accelerometry raw data. HRV indices (SDNN and rMSSD) were measured from the 14-day ECG raw data. Cognitive factor scores for global cognition, executive function, language, and memory were derived using latent variable methods. Dementia or mild cognitive impairment (MCI) status was adjudicated. Linear or multinomial regression models examined whether higher PA or higher HRV was cross-sectionally associated with higher factor scores or lower odds of MCI/dementia. Models were adjusted for demographic and medical comorbidities.

RESULTS

Each 1-unit higher in total amount of PA was significantly associated with 0.30 higher global cognition factor scores (95% CI: 0.16-0.44), 0.38 higher executive function factor scores (95% CI: 0.22-0.53), and 62% lower odds of MCI (OR: 0.38, 95% CI: 0.22-0.67) or 75% lower odds of dementia (OR: 0.25, 95% CI: 0.08-0.74) versus unimpaired cognition. Neither HRV measure was significantly associated with cognitive function or dementia.

CONCLUSIONS

PA derived from a 2-week ECG monitor with an embedded accelerometer was significantly associated with higher cognitive test performance and lower odds of MCI/dementia among older adults. By contrast, HRV indices measured over 2 weeks were not significantly associated with cognitive outcomes. More research is needed to define the role of wearable ECG monitors as a tool for digital phenotyping of dementia.

CLINICAL PERSPECTIVE

What Is New?: This cross-sectional study evaluated associations between physical activity (PA) and heart rate variability (HRV) measured over 14 days from a wearable ECG monitor with cognitive function.Higher total amount of PA was associated with higher global cognition and executive function, as well as lower odds of mild cognitive impairment or dementia.HRV indices measured over 2 weeks were not significantly associated with cognitive outcomes.What Are the Clinical Implications?: These findings replicate positive associations between PA and cognitive function using accelerometer data from a wearable ECG monitor with an embedded accelerometer.These findings raise the possibility of using wearable ECG monitors (with embedded accelerometers) as a promising tool for digital phenotyping of dementia.

A Privacy-Preserving Desk Sensor for Monitoring Healthy Movement Breaks in Smart Office Environments with the Internet of Things

Smart workplace Internet of Things (IoT) solutions rely on several sensors deployed efficiently in the workplace environment to collect accurate data to meet system goals. A vital issue for these sensor-based IoT solutions is privacy. Ideally, the occupants must be monitored discreetly, and the strategies for maintaining privacy are dependent on the nature of the data required. This paper proposes a new sensor design approach for IoT solutions in the workplace that protects occupants' privacy. We focus on a novel sensor that autonomously detects and captures human movements in the office to monitor a person's sedentary behavior. The sensor guides an eHealth solution that uses continuous feedback about desk behaviors to prompt healthy movement breaks for seated workers. The proposed sensor and its privacy-preserving characteristics can enhance the eHealth solution system's performance. Compared to self-reporting, intrusive, and other data collection techniques, this sensor can collect the information reliably and timely. We also present the data analysis specific to this new sensor that measures two physical distance parameters in real-time and uses their difference to determine human actions. This architecture aims to collect precise data at the sensor design level rather than to protect privacy during the data analysis phase.