*5.2. Considerations Related to Cognitive Metrics and Self-Reported Sleep Quality Indices*

PSQI has been shown to be a poor screening measure of PSG [29]. This may explain why self-reported one-time PSQI sleep quality variation was not well explained by much of the device data. However, the Oura ring's measurements of efficiency and sleep duration did explain variation in the one-time PSQI with statistical significance. These Oura tracking metrics may merit further investigation. Also, it is important to note that poor tracking metrics and a low number of participants could also be the reason more device data was not able to explain variation in PSQI. In terms of the SRSMs, specifically TSD, we found significant (*p* < 0.05), albeit low (range: 0.31–0.58) correlations between all devices.

Evidence of using the n-back test as a fluid intelligence metric is contentiously accepted, with some critics citing low correlation between n-back and other fluid intelligence tests [30]. The cognition metric, taken from the N-back results, and results from participant summary data had statistically significant associations. This provides a direction for further studies to investigate with larger samples. Ultimately, higher statistical power is needed to help understand these relationships. A recent study showed that poor sleep or deprivation may cause local deficits, specifically for tasks of an emotional nature [31]. This may suggest implementing a metric for wellbeing in addition to fluid intelligence tasks. Of particular note was Withings latency, which was statistically significant for the afternoon and evening cognitive scores (*p* < 0.05). Due to the low sample size, the importance of this is uncertain, but hopefully with subsequent studies could build on this work by further comparing latency with cognitive scores.

Insight from response rate based on MEQ segmentation into three categories (early, intermediate, and late preference) could help future study designs. Across all MEQ groups, n-back test response rates were highest in the afternoon. This suggests that crucial surveys should be administered around this time if possible. Another finding of note is that late-preferred participants had the highest n-back test response rate on average in the morning and afternoon timepoints. This finding suggests that participants who are not late-preferred may need extra motivating factors to increase their response rates.
