# Smart Home Technology Solutions for Cardiovascular Diseases: A Systematic Review
## 4 Discussion
Furthermore, wearable sensors and ML could play a vital role in post-stroke rehabilitation through activity recognition, movement classification, and clinical assessment emulation [93]. High BP is directly associated with a high risk for CVD [94]. With the advancements in technology, BP could be monitored at fixed intervals during sleep and at different intervals during the day and could determine BP in response to a specific trigger, such as increased heart rate [95]. BP monitoring at home could assist in predicting the onset of CVD events, enabling proactive interventions to avert adverse outcomes [95]. Sleep disturbance could be a common factor that might increase CVD risk; participants with short sleep duration had significantly higher CVD and hypertension; on the contrary, participants with long sleep durations had no increase in CVD, CHD, myocardial infarction, or hypertension prevalence [96]. Furthermore, sleep onset timing could initiate CVD risk; hence, collecting sleep parameters via accelerometry-capable wearable devices may serve as novel CVD risk indicators [97]. In addition, a study identified three sleep-cardiovascular health phenogroups, such as resilient (non-adequate sleep and ideal cardiovascular health), uncoupled (adequate sleep and non-ideal cardiovascular health), and concordant (sleep and cardiovascular health metrics were aligned), highlighting the advantage of incorporating sleep assessments into studies of cardiovascular health [98]. Finally, with sleep disturbances and disorders implicated in CVD morbidity and mortality, there is an imminent need for a transdisciplinary research framework integrating knowledge, methods, and measures of psychology and sleep research to advance CVD prevention and treatment [99]. Monitoring patients using technologies, including telemedicine, creates a void in evaluating vital parameter recordings and could result in misdiagnosis [100]. Moreover, with many forced to stay at home for a prolonged period [101], and with the associated risk surrounding CVD patients and the restrictions in healthcare services due to COVID, there is a greater need for remote monitoring of CVD patients in real time, especially while they are at their homes [100; 102]. In addition, we observed that the SHT functionalities and components, including sensors, communication systems, and end-user applications, differed; however, all the systems lacked real-time data processing, analysis, and reporting capabilities [67; 68; 69; 70; 71]. With 53.6% of global households and 84% of households in developed countries having internet access [103], there are research prospects to develop SHT to monitor CVD patients in real time. Wearable devices are finding widespread applications in healthcare in health and safety monitoring, chronic disease management, disease diagnosis and treatment, and rehabilitation [104]. A study observed that activity data from wearables could monitor CVD patients remotely, enabling safer and higher resolution monitoring of patients [105]. However, on the downside, although current studies highlight the wearables' potential to monitor cardiovascular events, the lack of a real data set and proper systematic and prospective evaluation hampers their deployment as a diagnostic or prognostic cardiovascular clinical tool [106]. Additionally, to date, wearable devices possess challenges in cardiovascular care, such as device accuracy, clinical validity, a lack of standardised regulatory policies and concerns for patient privacy hindering the widespread adoption of smart wearable technologies in clinical practice [32]. With an imperative need to develop affordable and reliable real-time SHT CVD monitoring systems, IoT-based healthcare systems could be an effective solution.