Background Light
exposure regulates the human circadian system and more widely affects
health, well-being, and performance. With the rise in field studies on
light exposure’s effects, the amount of data collected through wearable
loggers and dosimeters has also grown. These data are more complex than
stationary laboratory measurements. Determining sample sizes in field
studies is challenging, as the literature shows a wide range of sample
sizes (between 2 and 1,887 from a recent review of the field and
approaching 105 participants in first studies using
large-scale ‘biobank’ databases). Current decisions on sample size for
light exposure data collection lack a specific basis rooted in power
analysis. Therefore, there is a need for clear guidance on selecting
sample sizes.
Methods Here, we introduce a novel
procedure based on hierarchical bootstrapping for calculating
statistical power and required sample size for wearable light and
optical radiation logging data and derived summary metrics, taking into
account the hierarchical data structure (mixed-effects model) through
stepwise resampling. Alongside this method, we publish a dataset that
serves as one possible basis to perform these calculations: one week of
continuous data in winter and summer, respectively, for 13 early-day
shift-work participants (collected in Dortmund, Germany; lat. 51.514° N,
lon. 7.468° E).
Results Applying our method on the
dataset for twelve different summary metrics (luminous exposure,
geometric mean, and standard deviation, timing/time above/below
threshold, mean/midpoint of darkest/brightest hours, intradaily
variability) with a target comparison across winter and summer, reveals
required sample sizes ranging from as few as 3 to more than 50. About
half of the metrics–those that focus on the bright time of day–showed
sufficient power already with the smallest sample. In contrast, metrics
centered around the dark time of the day and daily patterns required
higher sample sizes: mean timing of light below mel EDI of 10 lux (5),
intradaily variability (17), mean of darkest 5 hours (24), and mean
timing of light above mel EDI of 250 lux (45). The geometric standard
deviation and the midpoint of the darkest 5 hours lacked sufficient
power within the tested sample size.
Conclusions Our
novel method provides an effective technique for estimating sample size
in light exposure studies. It is specific to the used light exposure or
dosimetry metric and the effect size inherent in the light exposure data
at the basis of the bootstrap. Notably, the method goes beyond typical
implementations of bootstrapping to appropriately address the structure
of the data. It can be applied to other datasets, enabling comparisons
across scenarios beyond seasonal differences and activity patterns. With
an ever-growing pool of data from the emerging literature, the utility
of this method will increase and provide a solid statistical basis for
the selection of sample sizes.
https://doi.org/10.5281/zenodo.4059767