No abstract

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.

About 422 million people worldwide have diabetes and approximately one-third of them have a major risk factor for diabetic foot ulcers, including poor sensation in their feet from peripheral neuropathy and/or poor perfusion to their feet from peripheral artery disease. The current healthcare ecosystem, which is centered on the treatment of established foot disease, often fails to adequately control key reversible risk factors to prevent diabetic foot ulcers leading to unacceptable high foot disease amputation rate, 40% recurrence of ulcers rate in the first year, and high hospital admissions. Thus, the latest diabetic foot ulcer guidelines emphasize that a paradigm shift in research priority from siloed hospital treatments to innovative integrated community prevention is now critical to address the high diabetic foot ulcer burden. The widespread uptake and acceptance of wearable and digital health technologies provide a means to timely monitor major risk factors associated with diabetic foot ulcer, empower patients in self-care, and effectively deliver the remote monitoring and multi-disciplinary prevention needed for those at-risk people and address the health care access disadvantage that people living in remote areas. This narrative review paper summarizes some of the latest innovations in three specific areas, including technologies supporting triaging high-risk patients, technologies supporting care in place, and technologies empowering self-care. While many of these technologies are still in infancy, we anticipate that in response to the Coronavirus Disease 2019 pandemic and current unmet needs to decentralize care for people with foot disease, we will see a new wave of innovations in the area of digital health, smart wearables, telehealth technologies, and “hospital-at-home” care delivery model. These technologies will be quickly adopted at scale to improve remote management of diabetic foot ulcers, smartly triaging those who need to be seen in outpatient or inpatient clinics, and supporting acute or subacute care at home.

Background: A critical factor in healing diabetic foot ulcers is patient adherence to offloading devices. We tested a smart offloading boot (SmartBoot) combined with a smartwatch app and cloud dashboard to remotely monitor patient adherence and activity. In addition, the impact of SmartBoot on balance, gait, and user experience was investigated. Methods: Fourteen volunteers (31.6±8.7 years; 64% female) performed natural activities (eg, sitting, standing, walking) with and without the SmartBoot for approximately 30 minutes. All participants completed balance tests, 10-meter walking tests at slow, normal, and fast pace while wearing the SmartBoot, and a user experience questionnaire. The accuracy of real-time adherence reporting was assessed by comparing the SmartBoot and staff observation. Center of mass (COM) sway and step counts were measured using a validated wearable system. Results: Average sensitivity, specificity, and accuracy for adherence and non-adherence were 90.6%, 88.0%, and 89.3%, respectively. The COM sway area was significantly smaller with the SmartBoot than without the SmartBoot regardless of test condition. Step count error was 4.4% for slow waking, 36.2% for normal walking, 16.0% for fast walking. Most participants agreed that the SmartBoot is easy to use, relatively comfortable, nonintrusive, and innovative. Conclusions: To our knowledge, this is the first smart offloading system that enables remote patient monitoring and real-time adherence and activity reporting. The SmartBoot enhanced balance performance, likely due to somatosensory feedback. Questionnaire results highlight SmartBoot’s technical and clinical potential. Future studies warrant clinical validation of real-time non-adherence alerting to improve wound healing outcomes in people with diabetic foot ulcers.

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.