. Author manuscript; available in PMC: 2021 Apr 15.

Published in final edited form as: Lancet Digit Health. 2020 Jan 16;2(2):e85–e93. doi: 10.1016/S2589-7500(19)30222-5

Table 4:

Pearson correlations from model 1 restricted to influenza season only comparing CDC ILI rates with predicted rates in naive, null, and full negative binomial models and comparing change in CDC ILI rates with change in Fitbit data with a 1-week lag and a 1-week lead

	Negative binomial model predicting ILI case counts				Linear regression model predicting weekly change in ILI rates
	m_naive	m_abs,H0	m_abs,H1	p value^*	m_change	m_change (1-week lag)	m_change(1-week lead)
California	0·91	0·61	0·97	<0·0001	0·71^†	0·32^†	0·33^†
Texas	0·72	0·79	0·89	<0·0001	0·27^†	0·20	0·11
New York	0·31	0·71	0·79	<0·0001	0·15	0·21	−0·07
Illinois	0·61	0·71	0·88	<0·0001	0·42^†	0·37^†	0·13
Pennsylvania	0·34	0·71	0·85	<0·0001	0·29^†	0·16	−0·11

Influenza season is defined as week 40 to week 20 in the following year. Individuals were classified as having a week with abnormal Fitbit data if their weekly average exceeded a given threshold: a sleep time that was longer than 0·5 SD below their overall average and an RHR that was 0·5 SD (model 1) above their overall average. Naive models included just Fitbit data. H₀ models assumed the ILI case count was not affected by the proportion of users with abnormal Fitbit data, whereas H₁ models assumed that it was. CDC=US Centers for Disease Control and Prevention. ILI=influenza-like illness. RHR=resting heart rate.

p value comparing H₀ to H₁ models.

^†

Pearson correlations were significant (p<0·05).