Table 1.

Priority	Description
Sleep metrics	Improved and standardized approaches to the definition of sleep intervals (ie, when deliberate sleep opportunities exist, such as times in and out of bed). Improved detail regarding performance of technology to determine when sleep opportunities exist (eg, when individuals are in or out of bed and/or attempting to sleep) with and/or without user input.
	Improved and standardized approaches to the classification of sleep continuity variables, including time in and out of bed, sleep latency, awakenings, wake time after sleep onset, etc
	Improved and standardized approaches to the classification of sleep stages.
	Improved characterizations of daytime sleep, unintended sleep, and/or naps, including differentiation between sedentary behavior and sleep and specification for timing and duration criteria in the classification of naps whether intentional or unintentional.
	Oxygen desaturation and sleep-related breathing, particularly in at-home settings.
Study populations	Evaluations in populations where activity patterns and relationships between activity and other physiological measures may vary from “young healthy adults” that are routinely included in studies.
	Example priority populations include individuals with pacemakers, those undergoing medical treatments affecting motion and/or cardiovascular hemodynamics, people with physical (eg, back injury) and mental (eg, depressive disorders) conditions and sleep disorders (eg, restless legs syndrome, narcolepsy, insomnia disorder, sleep disordered breathing), individuals from underrepresented cultural backgrounds, individuals with atypical sleep-wake patterns (eg, nursing home residents, shift workers), individuals with atypical sleeping arrangements (eg, people who don’t sleep in a bed).
Characteristics and/or confounders	Exploration of the role of contextual factors on measurement properties, including sex-specific variables (eg, menstrual cycle effects on sleep, or on primary data for algorithm development), age-related variables (eg, studies conducted during puberty, transition to nursing home), and other demographic variables (eg, race, ethnicity, skin color, body size), environmental variables (eg, temperature and light, seasonal changes).
	Evaluations in conditions of pronounced variation in the targeted variable of interest (eg, accuracy of a sleep technology in measuring wake in condition of low and high sleep fragmentation).
	Evaluations under different conditions known to alter the physiological features used in sleep classification (eg, evaluating the impact of caffeine/alcohol consumption on performance).
	Examples of factors affecting sleep technology performance are outlined in de Zambotti et al.12
Data source	Evaluation of accuracy and reliability of raw signals derived from the sensors versus derived and/or summarized signals.
	Unidimensional vs. multidimensional approaches to sleep and sleep event classification.
	Evaluation of outputs integrating multiple physiological measurements (eg, oxygen desaturation, heart rate variability [HRV]) to improve (and extend) activity-based measurements.