## 6 Discussion and Conclusions  
The findings in this paper suggest that different dimensions of health, such as number of social interactions, sense of belonging, and physical mobility, may be moderately interconnected with physiological measurements and behaviors, although the findings are nuanced and not straightforward. The strong correlations between the questions from the Oxford Hip and Knee scores and the sensor data highlight the direct impact of physical health status on measurable daily physical activities. The findings from the daily correlation analysis suggest that as OAs experience improvements or declines in joint function and pain, the sensors are able to capture these changes in their movement and level of activity. Comparing the results between daily and biweekly correlation analyses, it can be seen that similar correlations between the clinical data and features extracted from the sensor data were found. Looking at Tables 7 and 11, item 6 on the SIS has a daily moderate correlation and a biweekly strong correlation with duration of sleep (0.36 and 0.68, respectively). A similar occurrence can be seen between item 6 on SIS and the step-sum (total step count) with 0.42 and 0.62 correlation values of daily and biweekly, respectively. The repeated correlation, although with different values, gives strength to the findings in this paper. The consistency in correlation results suggests that sensor data can provide reliable insights into changes in participants' SI levels over time. From both Tables 7 and 11, sleep metrics have repeatedly shown correlations with the SIS metrics, marking the importance of sleep metrics in assessing SI, which is consistent with the previous literature [18; 45; 46]. Tables 6, 8, 9, 12 and 13 show that the Oxford Knee Score was strongly correlated with the daily and biweekly features extracted from the sensor data in comparison to the Oxford Hip Score. Therefore, the Oxford Knee Score can be considered as a superior instrument for evaluating the correlation between functional recovery and sensor data when addressing various types of lower limb fractures. The outcomes of daily and biweekly correlations between SI and sensor data are similar, both demonstrating a relationship between SIS scores and sleep data with various degrees of correlation. This similarity also exists with the findings from daily and biweekly correlations between FD and sensor data, in that they are both correlated with heart rate and motion features. However, in the biweekly correlation of FD and SI with sensor data, strong correlations exist between the clinical data and acceleration (i.e., _l2_-max, \(l\)2-mean) and position features (distance traveled from home), which is not found in daily correlations. This finding shows that the changes in acceleration and position features are not visible on a day-to-day basis but rather on a longer, 2-week period. There are limitations in this research. A limited sample size from only one recruitment site may limit the findings of our study. Psychological state or pre-existing conditions of depression or anxiety disorders of the participants were not tracked and controlled in this study. Another limitation is the gender and ethnicity of the patients who participated in the study. Although the inclusion criteria were not limited to any specific ethnicity or gender,the consented population turned out to be mostly females and Caucasians. Efforts to diversify the recruitment sites for this study can mitigate this potential bias. Further, when collecting GPS data, the locations the participants traveled to were not considered a feature as this would require more inputs from the participants and risk overburdening them. One of the features that was explored and supported by the work of [PERSON] et al. (2020) was the frequency of daytime mapping. Day time napping was originally considered as a feature, but as participants took naps sometimes on the couch or other places other than the bed, it was difficult to accurately account for the midday naps.