Tele-Monitoring of Cancer Patients’ Rhythms during Daily Life Identifies Actionable Determinants of Circadian and Sleep Disruption
Abstract
:1. Introduction
2. Results
2.1. Study Flow, Participants’ Characteristics, Compliance, and Data Quality
2.1.1. Cancer Patients
2.1.2. Controls
2.1.3. Compliance
2.2. Tele-Transmitted Rest-Activity Patterns
2.3. Tele-Transmitted Chest Surface Temperature Patterns
2.4. Lifestyle, Cortisol and Dim Light Melatonin Patterns
2.5. Sleep
2.6. Physical Activity
2.7. Relevance of Age, Sex and Cancer on Circadian Parameters
2.8. Regression Analysis to Identify Main Actionable Determinants of (I < O)
- The day-to-day variability in sleep duration estimated with hidden Markov modelling (HMM) (r = −0.53, p = 0.009);
- The self-reported exercise (r = 0.48, p = 0.02), the rest-activity circadian amplitude (r = 0.73, p < 0.0001), the median activity out-of-bed (r = 0.68, p = 0.0003), and the level of activity (r = 0.56, p = 0.005);
- The day-to-day variability in the self-reported retiring time (r = 0.49, p = 0.02);
- The physiologic chest temperature rhythm (24-h dominant period and nocturnal acrophase between 22:01 and 07:00, p = 0.03);
- The chronotype score (r = −0.43, p = 0.04).
- The HMM-estimated sleep duration variability (r = −0.53, p = 0.002);
- The rest-activity circadian amplitude (r = 0.36, p = 0.04);but in addition age (r = −0.48, p = 0.006).
3. Discussion
4. Materials and Methods
4.1. Study Designs and Participants
4.2. Data Collection and Management
4.3. Statistical Methods
4.3.1. Circadian Parameters
- The mid values of the MA and HA states which indicate daily activity levels;
- The rhythm index (RI), with values ranging between 1, corresponding to best average quality and regularity of the IA state, and 0, corresponding to poor quality and absence of a consistent rest state in the pattern;
- The average center-of-rest time of the IA state;
- P1-1, the estimated probability of staying in the IA state (state 1); when having previously been in this state. P1-1 is positively correlated with I < O, while the probability [1-(P1-1)] serves as an estimate of rest interruption.
4.3.2. Spectral Analysis and Cosinor Modelling
4.3.3. DLMO Computation
4.3.4. Analysis of RACR Domain
4.4. Study Approval
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
Appendix A
Appendix B
References
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Number of Participants | Cancer Patients 25 | (I < O)low Cancer Group 13 | (I < O)high Cancer Group 12 | Controls 33 | p-Values 1 | |
---|---|---|---|---|---|---|
(I < O)low vs. (I < O)high (Cancer) | (I < O)high (Cancer) vs. Controls | |||||
Sex | ||||||
M | 21 (84.0) | 11 (84.6) | 10 (83.3) | 15 (45.5) | 1 | 0.04 * |
F | 4 (16.0) | 2 (15.4) | 2 (16.7) | 18 (54.5) | ||
Age (years) | ||||||
Median | 66 | 66 | 70 | 35 | 0.568 | <0.0001 **** |
Range | 40–82 | 40–80 | 42–82 | 21–78 | ||
BMI | ||||||
Median | 27.5 | 27.5 | 26.9 | 24.4 | 0.511 | 0.216 |
Range | 19.8–38.8 | 22.4–38.8 | 19.8–33.7 | 18.9–42.0 | ||
(I < O)72h (%) | ||||||
Median | 97.4 | 90.4 | 98.7 | NA | 0.0017 ** | NA |
Range | 67.6–100 | 67.6–97.4 | 97.7–100 | NA | ||
Work status | ||||||
Employed or self-employed | 8 (32.0) | 5 (38.5) | 3 (25.0) | 15 (45.5) | 0.673 | 0.002 ** |
Student | 0 (0) | 0 (0) | 0 (0) | 11 (33.3) | ||
Retired or not working | 17 (68.0) | 8 (61.5) | 9 (75.0) | 7 (21.2) | ||
Chronotype | ||||||
Definite morning | 3 (12) | 1 (8) | 2 (17) | 5 (15.2) | 0.408 | 0.594 |
Moderate morning | 12 (48) | 8 (62) | 4 (33) | 10 (30.3) | ||
Intermediate | 9 (36) | 4 (31) | 5 (42) | 15 (45.5) | ||
Moderate evening | 0 (0) | 0 (0) | 0 (0) | 3 (9.1) | ||
Not available | 1 (4) | 0 (0) | 1 (8) | 0 (0) | ||
Ongoing medical condition (other than cancer) | ||||||
None | 8 (32.0) | 3 (23.1) | 5 (41.7) | 25 (75.8) | 0.362 | 0.003 ** |
1–2 | 8 (32.0) | 6 (46.2) | 2 (16.7) | 7 (21.2) | ||
≥ 3 | 9 (36.0) | 4 (30.8) | 5 (41.7) | 1 (3.0) | ||
Concurrent medications (apart from cancer treatments) | ||||||
0 | 3 (12.0) | 1 (7.7) | 2 (16.7) | 25 (75.8) | 0.866 | < 0.0001 **** |
1–2 | 12 (48.0) | 7 (53.8) | 5 (41.7) | 8 (24.2) | ||
≥ 3 | 10 (40.0) | 5 (38.5) | 5 (41.7) | 0 (0) | ||
Site of primary tumor | ||||||
Colorectal | 14 (56.0) | 5 (38.5) | 9 (75.0) | NA | 0.111 | NA |
Other | 11 (44.0) | 8 (61.5) | 3 (25.0) | NA | ||
Cancer status | ||||||
No residual tumor | 2 (8.0) | 2 (15.4) | 0 (0) | NA | 0.561 | NA |
Locally advanced cancer | 6 (24.0) | 3 (23.1) | 3 (25.0) | NA | ||
Metastatic disease | 17 (68.0) | 8 (61.5) | 9 (75.0) | NA | ||
Number of metastatic sites | ||||||
0 | 8 (32.0) | 5 | 3 | NA | 0.861 | NA |
1–2 | 13 (52.0) | 6 | 7 | NA | ||
≥ 3 | 4 (16.0) | 2 | 2 | NA | ||
Main metastatic sites | ||||||
Liver | 9 (36.0) | 3 (23.1) | 6 (50.0) | NA | 0.721 | NA |
Lymph nodes | 9 (36.0) | 5 (38.5) | 4 (33.3) | NA | ||
Lungs | 8 (32.0) | 5 (38.5) | 3 (25.0) | NA | ||
Other | 5 (20.0) | 2 (15.4) | 3 (25.0) | NA | ||
Prior cancer treatments | ||||||
Surgery | 13 (52.0) | 6 (46.1) | 7 (58.3) | NA | 0.508 | NA |
Radiotherapy | 8 (32.0) | 2 (15.4) | 6 (50.0) | NA | ||
Chemotherapy | 20 (80.0) | 10 (76.9) | 10 (83.3) | NA |
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Lévi, F.; Komarzynski, S.; Huang, Q.; Young, T.; Ang, Y.; Fuller, C.; Bolborea, M.; Brettschneider, J.; Fursse, J.; Finkenstädt, B.; et al. Tele-Monitoring of Cancer Patients’ Rhythms during Daily Life Identifies Actionable Determinants of Circadian and Sleep Disruption. Cancers 2020, 12, 1938. https://doi.org/10.3390/cancers12071938
Lévi F, Komarzynski S, Huang Q, Young T, Ang Y, Fuller C, Bolborea M, Brettschneider J, Fursse J, Finkenstädt B, et al. Tele-Monitoring of Cancer Patients’ Rhythms during Daily Life Identifies Actionable Determinants of Circadian and Sleep Disruption. Cancers. 2020; 12(7):1938. https://doi.org/10.3390/cancers12071938
Chicago/Turabian StyleLévi, Francis, Sandra Komarzynski, Qi Huang, Teresa Young, Yeng Ang, Claire Fuller, Matei Bolborea, Julia Brettschneider, Joanna Fursse, Bärbel Finkenstädt, and et al. 2020. "Tele-Monitoring of Cancer Patients’ Rhythms during Daily Life Identifies Actionable Determinants of Circadian and Sleep Disruption" Cancers 12, no. 7: 1938. https://doi.org/10.3390/cancers12071938