Table 3

Summary of analytical approaches’ (including descriptor, mathematical transformation and statistical model) strengths and limitations in relation to closure, collinearity, relation-shape assumptions and interpretation relative to public health guidelines

 Descriptor CoDA transform Statistical modelling Risk of closure?* Risk of collinearity? Handles closure? Handles collinearity? Relationship assumptions Allow investigation of longitudinal associations (eg, Cox regression) Interpretation relative to guidelines? (eg, 150 min/week of MVPA) Average acceleration No Linear No No NA NA Linear Yes No Time-use descriptors No Linear Yes Yes No No Linear Yes Yes Yes Linear Yes Yes Yes In part† Log-linear Yes Yes No ISO Yes Yes Yes No Linear Yes Yes No MPA Yes Yes No No Linear Not at the moment Yes Yes MPA Yes Yes Yes Yes Log-linear Not at the moment Yes Intensity spectrum No Linear Yes Yes No No Linear Yes Yes‡ Yes Linear Yes Yes Yes In part† Log-linear Yes Yes‡ No ISO Yes Yes Yes No Linear Yes Yes‡ No MPA Yes Yes No No Linear Not at the moment Yes‡ Yes MPA Yes Yes Yes Yes Log-linear Not at the moment Yes‡ Intensity gradient No Linear No No NA NA Linear Yes No No FDA No No NA NA Fewer assumptions than other models Yes Yes§ MX metrics No Linear Yes Yes No No Linear Yes Yes‡ No MPA Yes Yes No Yes Linear Not at the moment Yes‡ Other acceleration functions No FDA No No NA NA Fewer assumptions than other models Yes Yes§
• *Closure refers to whether a certain descriptor is a specific part of the daily time constraint (ie, it is measured in time per day).

• †Indicates that it solves the collinearity due to the closure, but collinearity can still exist across the CoDA-transformed variables.

• ‡Indicates that the interpretation is made through a post-hoc application of validated cut-points to identify the PA intensity (eg, MVPA).

• §Indicates that more work is needed on the interpretation of functional data analysis, an example can be found elsewhere.39

• CoDA, compositional data analysis; FDA, functional data analysis; ISO, isotemporal substitution models; MPA, multivariate pattern analysis; MVPA, moderate-to-vigorous PA; MX, acceleration above which a person’s most active X minutes/time are spent; NA, not applicable; PA, physical activity.