Table 3.

Mathematical approaches to quantifying variability of mood data

Statistical method	Assumptions	Limitations
Time domain e.g., RMSSD	Normally distributed data	Influenced by extreme scores No estimate of the width of the distribution Do not distinguish different signals Examples of datasets with identical means, SDs and RMSSDs with very different underlying data structure
Frequency domain/ Spectral analysis	Data considered a sum of sinusoidal oscillations with distinct frequencies Analyses require stationarity within data	Long data series required
Entropy	Considered a measure of randomness/irregularity Should be calculated on non- normalized time series	Accuracy reduced in short time series Sinusoidal trends are detrimental Spikes in the data can impair linear estimates