. 2024 Sep 30;31(11):2496–2506. doi: 10.1093/jamia/ocae230

Table 3.

Data and metadata attributes computation.

Data attribute—type	Description	Contextual relevance	Source of data	Type and range of values
Part of the day—metadata	Splits the day into 4 segments	Helps detect energy loss in PwMS with fatigue during the day, a common symptom.	Computed values	Categorical: morning, afternoon, evening, night Additional granularity within a segment: 1-minute, 15-minute, or 1-hour
Part of the week—metadata	Indicates on which weekdays specific patterns occur, applicable across all 4 granularity levels	Many PwMS are pursuing jobs or have family duties, leading to different physical activity patterns between weekdays and weekends.	Computed values	Categorical: Monday, Tuesday, Wednesday, Thursday, Friday, Saturday, Sunday
PwMS distribution—metadata	Anonymized PwMS IDs help identify if activity patterns are common to specific PwMS or are balanced in distribution across PwMS.	Identifies specific PwMS activity patterns, helping to manage relevance and cardinality. In the interface, the top 3 PwMS for each pattern are highlighted.	BarKA-MS³⁹	Categorical: Anonymized PwMS IDs
Weather condition—metadata	Weather data from a public API is integrated for multimodal analysis of PwMS sequences, while maintaining patient anonymity	Weather may impose additional barriers for physical activity, as heat can worsen specific MS symptoms.	Openweathermap Public API⁴⁰	Categorical: sunny, rainy, cloudy, snowy, etc.
Heart rate—data	Wearables-based measurement of PwMS heart rate	Approximates sleep and stress evaluations	BarKA-MS³⁹	Numerical
Steps—data	Wearables-based measurement of PwMS step count	Approximates level of activity, and indicates possible fatigue episode	BarkaMS³⁹	Numerical