Social Support and Technology Use and Their Association With Mental and Physical Health During the COVID-19 Pandemic Among Asian Americans: The COMPASS Cross-sectional Study.

BACKGROUND: The global COVID-19 pandemic disproportionately affected Asian Americans and Pacific Islanders (AAPIs) and revealed significant health disparities with reports of increased discrimination and xenophobia. Among AAPIs, the pandemic exacerbated their social, linguistic, and geographic isolation. Social support may be especially important for AAPIs given the salience of collectivism as a cultural value. Another mechanism for support among AAPIs was technology use, as it is generally widespread among this population. However, older adults may not perceive the same benefits. OBJECTIVE: We examined social support and technology use and their relationships with mental and physical health outcomes through the COVID-19 pandemic among AAPIs. METHODS: Data were drawn from the COVID-19 Effects on the Mental and Physical Health of AAPI Survey Study (COMPASS) for the time period of October 2020 to February 2021. COMPASS was a cross-sectional, multilingual, national survey conducted online, by phone, and in person with AAPI adults who were ≥18 years of age, in collaboration with academic and community partners in the United States. Data were analyzed using multivariable linear regression using the outcome variables of mental and physical health with various predictors such as social support and technology use. We tested for interactions specific to age and ethnicity. RESULTS: Among 4631 AAPIs (mean age 45.9, SD 16.3 years; 2992/4631, 63.1% female), we found that (1) increased social support was associated with better physical health, (2) total social support was positively associated with better mental health, (3) higher technology use was associated with poorer mental health and inversely associated with poorer physical health, (4) the association of technology use with mental health was weaker among those with low social support (vs those with high social support), (5) adults younger than 60 years old (vs ≥60 years old) were more negatively affected with social support and mental health, and (6) Korean Americans appeared to be a high-risk group for poor physical health with increased technology use. CONCLUSIONS: Our paper identified mental and physical health needs along with supportive therapies observed among AAPIs during the pandemic. Future research on how social support can be leveraged, especially among AAPIs younger than 60 years old, and how various types of technology are being utilized are important to guide the recovery efforts to address both mental and physical disparities across communities as a result of the COVID-19 pandemic.

ATTENTIV: Instrumented Peripheral Catheter for the Detection of Catheter Dislodgement in IV Infiltration.

Intravenous (IV) infiltration is a common problem associated with IV infusion therapy in clinical practice. A multitude of factors can cause the leakage of IV fluids into the surrounding tissues, resulting in symptoms ranging from temporary swelling to permanent tissue damage. Severe infiltration outcomes can be avoided or minimized if the patient's care provider is alerted of the infiltration at its earliest onset. However, there is a lack of real-time, continuous infiltration monitoring solutions, especially those suited for clinical use for critically ill patients. Our design of the sensor-integrated ATTENTIV catheter allows direct detection of catheter dislodgement, a root cause of IV infiltration. We verify two detection methods: blood-tissue differentiation with a support vector machine and signal peak identification with a thresholding algorithm. We present promising preliminary testing results on biological and phantom models that utilize bioimpedance as the sensing modality. Clinical relevance- The sensor-embedded ATTENTIV catheter demonstrates potential to automate IV infiltration detection in lieu of using traditional infusion catheters and manual detection methods.

Offline and Real-Time Implementation of a Terrain Classification Pipeline for Pushrim-Activated Power-Assisted Wheelchairs.

Pushrim-activated power-assisted wheelchairs (PAPAWs) are assistive technologies that provide propulsion assist to wheelchair users and enable access to various indoor and outdoor terrains. Therefore, it is beneficial to use PAPAW controllers that adapt to different terrain conditions. To achieve this objective, terrain classification techniques can be used as an integral part of the control architecture. Previously, the feasibility of using learning-based terrain classification models was investigated for offline applications. In this paper, we examine the effects of three model parameters (i.e., feature characteristics, terrain types, and the length of data segments) on offline and real-time classification accuracy. Our findings revealed that Random Forest classifiers are computationally efficient and can be used effectively for real-time terrain classification. These classifiers have the highest performance accuracy when used with a combination of time- and frequency-domain features. Additionally, we found that increasing the number of data points used for terrain estimation improves the prediction accuracy. Finally, our results revealed that classification accuracy can be improved by considering terrains with similar characteristics under one umbrella group. These findings can contribute to the development of real-time adaptive controllers that enhance PAPAW usability on different terrains.

Development of A Learning-Based Terrain Classification Framework for Pushrim-Activated Power-Assisted Wheelchairs.

Pushrim-activated power-assisted wheels (PAPAWs) are assistive technologies that provide on-demand torque assistance to wheelchair users. Although the available power can reduce the physical load of wheelchair propulsion, it may also cause maneuverability and controllability issues. Commercially-available PAPAW controllers are insensitive to environmental changes, leading to inefficient and/or unsafe wheelchair movements. In this regard, adaptive velocity/torque control strategies could be employed to improve safety and stability. To investigate this objective, we propose a context-aware sensory framework to recognize terrain conditions. In this paper, we present a learning-based terrain classification framework for PAPAWs. Study participants performed various maneuvers consisting of common daily-life wheelchair propulsion routines on different indoor and outdoor terrains. Relevant features from wheelchair frame-mounted gyroscope and accelerometer measurements were extracted and used to train and test the proposed classifiers. Our findings revealed that a one-stage multi-label classification framework has a higher accuracy performance compared to a two-stage classification pipeline with an indoor-outdoor classification in the first stage. We also found that, on average, outdoor terrains can be classified with higher accuracy (90%) compared to indoor terrains (65%). This framework can be used for real-time terrain classification applications and provide the required information for an adaptive velocity/torque controller design.