**6. Conclusions**

We reported correlations among important sleep metrics for four different sleep tracker devices and correlated the results with self-reported questionnaires and cognitive metrics, specifically the n-back. Difficulty in participant enrollment and engagement led to new ideas about recruitment design and participant engagement design. Exploiting existing technology such as ReasearchKit or HealthKit from Apple can have a twofold benefit for recruiting people remotely (with an e-consent feature built into ResearchKit) and sharing electronic health records (EHR). By further combining this with additional data stores present in the HealthApp, participant eligibility screening can be improved [32]. In consideration of the missing data in the questionnaires and active tasks prescribed, we promote the use of as many passive collection procedures as possible. One such option is a smart mirror [33], which can be more passive than using a smartphone for data (e.g., imaging) collection. Finally, the weak correlation among devices opens new challenges for accurate interpretation and data portability for the end user. How will device-specific findings from various studies be taken in context to one another? The results from the current study can hopefully highlight the need for better standardization for sleep-related metrics across devices in order to make any robust and accurate conclusions.

**Supplementary Materials:** The following are available online at http://www.mdpi.com/1424-8220/20/5/1378/s1, S1: Demographic questionnaire, S2: SF-36 questionnaire, S3: MEQ questionnaire, S4: PSQI questionnaire, S5: Results table of all correlation analyses, S6: Results table of univariate analyses comparing morning cognitive scores to device data, S7: Results table of univariate analyses comparing afternoon cognitive scores to device data, S8: Results table of univariate analyses comparing evening cognitive scores to device data, S9: Results table of univariate analyses comparing morning cognitive scores to participant summary data, S10: Results table of univariate analyses comparing afternoon cognitive scores to participant summary data, S11: Results table of univariate analyses comparing evening cognitive scores to participant summary data.

**Author Contributions:** Conceptualization, G.S., N.Z., B.P., and J.T.D.; methodology, M.S., J.S., B.P., and N.Z.; software, J.S., D.S., M.S., and G.S.; formal analysis, F.F.C, M.D., and B.S.G.; investigation, G.B. and E.G.; validation, R.M., M.D., J.K.D.F., I.P., and G.N.N.; data curation, F.F.C., M.D., J.S., M.S., and N.Z.; writing—original draft preparation, F.F.C., M.D., E.G., B.P., N.Z., and B.S.G; writing—review and editing, all authors.; visualization, F.F.C., M.D., J.S., N.Z., and B.S.G.; supervision, J.T.D. and B.S.G.; project administration, G.B., E.G., and P.G.; funding acquisition, G.S. and J.T.D. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research: and The Harris Center for Precision Wellness, was funded by generous gifts from Joshua and Marjorie Harris of the Harris Family Charitable Foundation and Julian Salisbury.

**Acknowledgments:** We acknowledge David E. Stark for assistance in designing the cognitive battery and Christopher Cowan for design and development of the HC mobile app.

**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
