## 4 Results
* Without the use of the sensor network, 61.1% of the people who presented low total sleep hours had medium-low snoring, equivalent to 22 people. On the other hand, with the use of the sensor network, 61.3% of the people who presented high total sleep hours had low snoring, equivalent to 19 people. This pair of patterns may indicate that, following the algorithm's recommendations, people increased their total hours of sleep and reduced their level of snoring. * Without the use of the sensor network, 65.5% of the people who presented medium-high REM sleep had medium-low snoring, equivalent to 19 people with this association. * With the use of the sensor network, 66.7% of the people who presented medium-high temperature had medium-high REM sleep, equivalent to 24 people. * Without the use of the sensor network, 69.2% of the people who presented high temperature and medium-low REM sleep had medium-low snoring, equivalent to 18 people. On the other hand, with the sensor network, a pattern with the same three metrics in a different order was found: 63% of the people with medium-low temperature and medium-low snoring had medium-high REM sleep, equivalent to 17 people. This pair of patterns may suggest an association between REM sleep and ambient temperature. Following the algorithm's recommendations, lowering ambient temperature increases REM sleep levels. * Without the use of the sensor network, 68% of the people with low HRV and low snoring had medium-low total sleep hours, equivalent to 17 people. * Without the use of the sensor network, 61.5% of the people who presented medium-low REM sleep and medium-low total sleep hours had low HRV, equivalent to 16 people. * Without the use of the sensor network, 64% of the people who presented low HRV and medium-low snoring had medium-low REM sleep, equivalent to 16 people. * Without the use of the sensor network, 65.2% of the people who presented high temperature and low HRV had medium-low REM sleep, equivalent to 15 people. * Without the use of the sensor network, 65.5% of the people who presented high temperature and low HRV had medium-low total sleep hours, equivalent to 15 people. * Without the use of the sensor network, 68.2% of the people who presented high temperature and medium-low total sleep hours had low HRV, equivalent to 15 people. * With the use of the sensor network, 60% of the people who presented medium-high total sleep hours and medium-low snoring had medium-high REM sleep, equivalent to 18 people. * Without the use of the sensor network, 65.5% of the people who presented medium-high REM sleep and medium-high total sleep hours had medium-low snoring, equivalent to 18 people. Finally, once the metrics that improved or did not improve with the sensor network were identified, contingency tables were created, where four significant relationships were observed: DS with S; BF with S; HRV with T; and HR with T. In Figures 5-8, the relationships are observed. The intensity of red shows a relationship where fewer incidents were observed than expected, whereas the intensity of blue indicates a relationship where more incidents were observed than expected. Figure 5: Sieve diagram for the variables deep sleep (DS) and snoring (S). Figure 6: Sieve diagram for the variables breathing frequency (BF) and snoring (S).