Use of mobile-stroke risk scale and lifestyle guidance promote healthy lifestyles and decrease stroke risk factors

Basic psychological needs satisfaction of stroke patients: a qualitative study

BACKGROUND

Previous studies have shown that the satisfaction of basic psychological needs is related to psychological well-being. Improving satisfaction will increase personal well-being, promote positive health outcomes, and improve disease recovery. However, no research has focused on the basic psychological needs of stroke patients. Therefore, this study aims to determine the basic psychological needs experience, satisfaction, and its influencing factors of stroke patients.

METHODS

12 males and 6 females in the non-acute phase with stroke were recruited in the Department of Neurology, Nanfang Hospital. The individual, semi-structured interviews were conducted in a separate room. The data were imported to Nvivo 12 and analyzed using the directed content analysis approach.

RESULTS

Three main themes consisting of 9 sub-themes were derived from the analysis. These three main themes focused on the needs for autonomy, competence, and relatedness of stroke patients.

CONCLUSION

Participants have different degrees of satisfaction of their basic psychological needs, which may be related to their family environment, work environment, stroke symptoms, or other factors. Stroke symptoms can significantly reduce the patients' needs for autonomy and competence. However, the stroke seems to increase the patients' satisfaction of the need for relatedness.

Trends and Challenges of Wearable Multimodal Technologies for Stroke Risk Prediction

We review in this paper the wearable-based technologies intended for real-time monitoring of stroke-related physiological parameters. These measurements are undertaken to prevent death and disability due to stroke. We compare the various characteristics, such as weight, accessibility, frequency of use, data continuity, and response time of these wearables. It was found that the most user-friendly wearables can have limitations in reporting high-precision prediction outcomes. Therefore, we report also the trend of integrating these wearables into the internet of things (IoT) and combining electronic health records (EHRs) and machine learning (ML) algorithms to establish a stroke risk prediction system. Due to different characteristics, such as accessibility, time, and spatial resolution of various wearable-based technologies, strategies of applying different types of wearables to maximize the efficacy of stroke risk prediction are also reported. In addition, based on the various applications of multimodal electroencephalography-functional near-infrared spectroscopy (EEG-fNIRS) on stroke patients, the perspective of using this technique to improve the prediction performance is elaborated. Expected prediction has to be dynamically delivered with high-precision outcomes. There is a need for stroke risk stratification and management to reduce the resulting social and economic burden.