## 2 Literature Review
### Studies Assessing SI Using Sensors  
Recent studies have increasingly utilized sensor-based technologies to objectively measure and predict SI. A scoping review by [PERSON] et al. [17], comprising eight studies, focused on SI as an objective measure with OAs residing in the community. Studies involving OAs in congregate living settings, such as nursing homes, were excluded. The findings suggested that future research should broaden the range of behavioral and social features derived from sensors for the development of predictive models. Furthermore, this scoping review indicates that longitudinal studies to explore meaningful feature extraction from sensing modalities are needed to determine the significant associates of extracted features with SI. Both of these directions are required to enhance the accuracy of future predictive models.  
[PERSON] et al. [18] explored the relationship between SI and OAs using both cross-sectional and longitudinal approaches. In their research, Social Isolation Scale (SIS) [19] was used to assess subjective and objective SI, and sleep data collected from community-dwelling OAs were used for cross-sectional (\(n=108\)) and longitudinal (\(n=1554\)) analyses. A strong association was found between SI and sleep metrics in OAs. Self-reported SI was directly linked to self-reported sleep quality, while the objective SI was correlated with objective sleep metrics. The results from the longitudinal analysis showed that loneliness significantly affected the relationship between SI and sleep over time, independent of demographic factors among the participants.  
In the work of [PERSON] et al. (2018), a physiological sensor for measuring electrocardiography was used to collect data from 4888 participants. The age range of participants was 50 years old and older (2242 males, 2646 females). In this study, the Berkman Social Disengagement Index (2018) was used to assess SI by measuring the presence of a spouse, the number of in-person contacts with three or more relatives and close friends, and the number of non-visual (online or over the phone) yearly contact with 10 or more relatives and close friends. This scale also measures the frequency of participation in social activities. The study concluded that there is a negative correlation between social network size and the resting heart rate.  
The research presented by [PERSON] et al. (2018) focused on detecting SI using motion sensors. In this study, SI was described as the absence of interpersonal contacts with society. The data were collected from 46 participants above 60 years old (19 males, 27 females). The scale used in this work for assessing SI was the Lubben Social Network Scale (LSNS) (2018). The results from the study indicated a correlation between the average time spent outside of the home with the overall SI level. [PERSON] et al. (2018) used seven sensors to collect data from 22 participants over 140 weeks across the United States of America and Japan. A total of 22 features were extracted from the sensor data in order to predict loneliness caused by the SI intensified by COVID-19 pandemic. The UCLA loneliness score (2018) was collected on a weekly basis and then correlated using [PERSON]'s (P) and [PERSON]'s (\(\rho\)) correlations with the sensor data. The LSNS (2018) was also used at the baseline. The results of the study indicated that the sleep mattress and the temperature humidity sensors were the most predictive ones in detecting loneliness.  
These studies collectively underscore the importance of comprehensive, longitudinal approaches to feature extraction and analysis to enhance the accuracy of SI predictive models and improve interventions for OAs in the community.