[Research progress on wearable physiological parameter monitoring and its clinical applications]

Analysis of Factors Influencing the Willingness of Chinese Older Adults to Use mHealth Devices: Nationwide Cross-Sectional Survey Study

BACKGROUND

In addition to standard older adult care services, mobile medical devices have proved to be an effective tool for controlling the health of older adults. However, little is known about the variables driving the acceptance of these gadgets and the willingness of older adults in China to use them.

OBJECTIVE

This study aims to explore the factors that affect the use of mobile health (mHealth) devices by older adults in China, focusing on individual, social, and family influences.

METHODS

The Psychology and Behavior Investigation of Chinese Residents survey database provided the data for this study. The survey was conducted in 148 Chinese cities between June 20 and August 31, 2022. The parameters linked to older persons' desire to use mobile medical devices were determined by this study using a combination model of multiple stepwise linear regression and a classification and regression tree decision tree.

RESULTS

In total, 4085 older adults took part in the poll. On a scale of 0 to 100, the average score for willingness to adopt mHealth devices was 63.70 (SD 25.11). The results of the multiple stepwise linear regression showed that having a postgraduate degree and higher (β=.040; P=.007), being unemployed (β=.037; P=.02), having a high social status (β=.085; P<.001), possessing high health literacy (β=.089; P<.001), demonstrating high self-efficacy (β=.043; P=.02), not living with children (β=.0340; P=.02), having a household per capita monthly income of >Y4000 (US $550) (β=.048; P=.002), experiencing high perceived social support (β=.096; P<.001), reporting a high quality of life (β=.149; P<.001), having higher levels of family communication (β=-.071; P<.001), having an identity bubble (β=.085; P<.001), not having chronic diseases (β=.049; P=.001), and experiencing mild depression (β=-.035; P=.02) were associated with older adults' willingness to use mHealth devices. The classification and regression tree decision tree model's findings demonstrated that the primary determinants of older adults' desire to use mHealth devices are quality of life, identity bubble, social status, health literacy, family health, and perceived social support.

CONCLUSIONS

This study uses the Andersen Healthcare Utilization Model to investigate the effects of demand variables, enabling resources, and predisposing traits on older persons' propensity to use mHealth devices. These results offer reference data for the marketing and use of mHealth devices for older individuals in the future. The ultimate goal of this strategy is to create a balanced and harmonious integration of technology and humanistic care.

[Research progress on the application of novel sensing technologies for sleep-related breathing disorder monitoring at home]

Sleep-related breathing disorder (SRBD) is a sleep disease with high incidence and many complications. However, patients are often unaware of their sickness. Therefore, SRBD harms health seriously. At present, home SRBD monitoring equipment is a popular research topic to help people get aware of their health conditions. This article fully compares recent state-of-art research results about home SRBD monitors to clarify the advantages and limitations of various sensing techniques. Furthermore, the direction of future research and commercialization is pointed out. According to the system design, novel home SRBD monitors can be divided into two types: wearable and unconstrained. The two types of monitors have their own advantages and disadvantages. The wearable devices are simple and portable, but they are not comfortable and durable enough. Meanwhile, the unconstrained devices are more unobtrusive and comfortable, but the supporting algorithms are complex to develop. At present, researches are mainly focused on system design and performance evaluation, while high performance algorithm and large-scale clinical trial need further research. This article can help researchers understand state-of-art research progresses on SRBD monitoring quickly and comprehensively and inspire their research and innovation ideas. Additionally, this article also summarizes the existing commercial sleep respiratory monitors, so as to promote the commercialization of novel home SRBD monitors that are still under research.

[Research progress on intelligent assessment system for upper limb function of stroke patients]

At present, the upper limb function of stroke patients is often assessed clinically using a scale method, but this method has problems such as time-consuming, poor consistency of assessment results, and high participation of rehabilitation physicians. To overcome the shortcomings of the scale method, intelligent upper limb function assessment systems combining sensors and machine learning algorithms have become one of the hot research topics in recent years. Firstly, the commonly used clinical upper limb functional assessment methods are analyzed and summarized. Then the researches on intelligent assessment systems in recent years are reviewed, focusing on the technologies used in the data acquisition and data processing parts of intelligent assessment systems and their advantages and disadvantages. Lastly, the current challenges and future development directions of intelligent assessment systems are discussed. This review is hoped to provide valuable reference information for researchers in related fields.