Comparison of 10 Nights’ Sleep Analysis between Actiwatch 2 and Vivosmart 4

Comparison of 10 Nights’ Sleep Analysis between Actiwatch 2 and Vivosmart 4

In our previous posts, we reverse-engineered the Actiwatch 2 algorithm for deriving sleep/wake patterns and sleep parameters from raw acceleration (ACC) data. Now we've applied the same algorithm to 10 nights of raw-ACC data obtained from a consumer wearable, Vivosmart 4, and compared it with Actiwatch 2 results.

Nov 4, 2022
By Drs. Francis Hsu, Han-Ping Huang and Andrew Ahn
10 nights sleep analysis

Abstract

In the previous two posts, we reverse-engineered the Actiwatch 2 algorithm for deriving sleep/wake patterns  and sleep parameters  from raw acceleration (ACC) data. This post applied the same algorithms to 10 nights of raw-ACC data obtained from Vivosmart 4 and compared the results with Actiwatch 2.

As described here, we found both the sleep/wake patterns and sleep parameters derived from Vivosmart 4 to be consistent with that of Actiwatch 2. The result suggests that an affordable, consumer wearable such as Vivosmart 4 can reproduce the results from a more expensive, medical-grade actigraphic device like Actiwatch 2.

1. Comparison of Sleep/Wake Detection and Sleep Parameters

Sleep/Wake Patterns of 10 nights derived from Actiwatch 2 and Vivosmart 4
Figure 1. Sleep/Wake Patterns of 10 nights derived from Actiwatch 2 and Vivosmart 4.

Figure 1 illustrates the sleep/wake patterns of the 10 nights derived from Actiwatch 2 and Vivosmart 4 (the method was introduced in a previous post). The two vertical dashed lines in each subplot indicated the Actiwatch 2 scored sleep onset and offset. Table 1 lists the consistent and inconsistent results between Actiwatch 2 and Vivosmart 4. The number in each cell is the total minutes within sleep onset and offset in the 10 nights. Treating the result from Actiwatch 2 as a standard, the sensitivity (to detect sleep), specificity, and accuracy of Vivosmart 4 are near 1.00, 0.84, and 0.99, respectively.

Table 1. Confusion matrices of sleep/wake detection
Confusion matrices of sleep/wake detection

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

Table 2. Differences in sleep parameters
Differences in sleep parameters

2. Conclusion

We reverse-engineered the full algorithm utilized in the Actiwatch 2 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 Actiwatch 2. The result suggests that an affordable, consumer wearable such as Vivosmart 4 can reproduce the results from a more expensive, medical-grade actigraphic device.

3. References

[1] K. Y. Chen and D. R. Bassett, “The technology of accelerometry-based activity monitors: current and future,” Med. Sci. Sports Exer., vol. 37, pp. S490–S500, Nov. 2005.

[2] CamNtech, “The MotionWatch 8 and MotionWare User Guide,” Available at http://www.medicoimpianti.it/files/The- MotionWatch-User-Guide.pdf, 2018.

[3] Respironics, “Instruction manual of Actiwatch Communication and Sleep Analysis Software. Instruction manual. “Available at https://johnawinegarden.files.wordpress.com/2015/03/actiwatchsoftware.pdf, 2015

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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.

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.

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