Comparison of 6 Nights’ Sleep Analysis between Motionlogger and Vivosmart 4

Comparison of 6 Nights’ Sleep Analysis between Motionlogger and Vivosmart 4

In our previous posts, we reverse-engineered the Motionlogger algorithm for deriving sleep/wake patterns and sleep parameters from raw acceleration (ACC) data. Now we've applied the same algorithm to 6 nights of raw-ACC data obtained from a consumer wearable, Vivosmart 4, and compared it with Motionlogger results.

Dec 11, 2022
By Drs. Andrew Ahn, Francis Hsu and Han-Ping Huang
Sleep Analysis Motionlogger vs Actiwatch

Abstract

In the previous two posts, we reverse-engineered the Motionlogger algorithm for deriving sleep/wake patterns and sleep parameters from raw acceleration (ACC) data. This post applied the same algorithms to the 6 nights of raw-ACC data obtained from Vivosmart 4 and compared the results with Motionlogger.

As described here, we found both the sleep/wake patterns and sleep parameters derived from Vivosmart 4 to be consistent with that of Motionlogger. The result suggests that an affordable, consumer wearable such as Vivosmart 4 can potentially reproduce the results from a more expensive, medical-grade actigraphic device like Motionlogger. Further validations for more data, especially for people with different sleep conditions, are still needed.

1. Comparison in Sleep/Wake Detection and Sleep Parameters

sleep/wake patterns of the 6 nights derived from Motionlogger and Vivosmart 4
Figure 1. Sleep/Wake Patterns of 6 nights derived from Motionlogger and Vivosmart 4. The labels indicate the awake time.

Figure 1 illustrates the sleep/wake patterns of the 6 nights derived from Motionlogger and Vivosmart 4 (the method was introduced in a previous post). The vertical dashed line in each subplot indicated the Motionlogger scored sleep onset.

Table 1 lists the consistent and inconsistent results between Motionlogger and Vivosmart 4. The number in each cell represents the total sleep/wake minutes contained within the time intervals between sleep onset and offset for the 6 nights. Treating the Motionlogger result as the gold standard, the sensitivity (to detect sleep), specificity, negative predictive value, and accuracy of Vivosmart 4 are near 0.99, 0.93, 0.77, and 0.99, respectively.


the consistent and inconsistent results between Motionlogger and Vivosmart 4
Table 1. Confusion matrices of sleep/wake detection.

Table 2 compares the differences in sleep parameters (introduced in the previous post) between Motionlogger and Vivosmart 4. The two watches showed good consistency as the mean differences of most of the parameters are smaller than 5 mins.

table comparing the differences in sleep parameters between Motionlogger and Vivosmart 4
Table 2. Differences in sleep parameters.

2. Conclusion

We reverse-engineered the full algorithm utilized in the Motionlogger to derive nighttime sleep/wake patterns and sleep parameters from the raw acceleration signal. Both the sleep/wake patterns and sleep parameters derived from Vivosmart 4 are consistent with that of Motionlogger. The result suggests that an affordable, consumer wearable such as Vivosmart 4 can potentially reproduce the results from a more expensive, medical-grade actigraphic device. Further validations for more data, especially for people with different sleep conditions, are still needed.

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3. References

[1] D. Fekedulegn, M. E. Andrew, M. Shi, J. M. Violanti, S. Knox, and K. E. Innes, "Actigraphy-based assessment of sleep parameters," Annals of Work Exposures and Health, vol. 64, no. 4, pp. 350-367, 2020.

[2] Ambulatory Monitoring, Inc., “Motionlogger user guide,” Available at https://agingresearchbiobank.nia.nih.gov/studies/sof/documents/download/Protocols/Visit_8/Motionlogger.pdf/.

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Dr. Andrew Ahn
Dr. Andrew Ahn
Chief Medical/Science Officer

Dr. Ahn is an internal medicine physician with a background in physics/engineering and physiological signal analyses. He is the Chief Medical Officer at Labfront and an Assistant Professor in Medicine & Radiology at Harvard Medical School. Dr. Ahn is passionate about democratizing health sciences and exploring health from an anti-disciplinary perspective.

Dr. Francis Hsu, PhD
Dr. Francis Hsu, PhD
Research Lead

Francis is a research Lead at Labfront, responsible for data validation and analysis. He is interested in applying physics or math to medical research.

Dr. Han-Ping Huang, PhD
Dr. Han-Ping Huang, PhD
Research Lead

Research Lead (and Designated Plant Caretaker) at Labfront. Han-Ping is a researcher who seeks interesting phenomena, especially the interdisciplinary ones. His dream is to make all the riveting research be easily explored.

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