Abstract Background Time-use estimates are typically used to describe 24-hour movement behaviours. However, these behaviours can additionally be characterised by other easily measured metrics. These include sleep quality (e.g., sleep efficiency), 24-hour rest-activity rhythmicity (e.g., between-day rhythm variability), and directly measured acceleration metrics (e.g., intensity gradient). Associations between these characteristics and youth mental health are unclear. This study aimed to [1] compare 24-hour movement behaviour characteristics by sex and age groups, [2] determine which movement behaviour characteristics were most strongly associated with mental health outcomes, and [3] investigate the optimal time-use behaviour compositions for different mental health outcomes. Methods Three-hundred-and-one children and adolescents (age 9–13 y; 60% girls) wore accelerometers for 24-hours/day over 7-days. Overall mental health, externalising, and internalising problems were self-reported using the Strengths and Difficulties Questionnaire. 24-hour movement behaviour characteristics were categorised as time-use estimates, sleep quality, 24-hour activity rhythmicity, and directly measured acceleration. Linear mixed models and compositional data analysis were used to analyse the data in alignment with the study aims. Results Time-use estimates, directly measured accelerations, and 24-hour rest-activity rhythm metrics indicated that children were significantly more physically active (p = .01-<0.001) than adolescents. Children were also less sedentary (p < .01), slept longer (p = .02-0.01), and had lower sleep efficiency. Boys were significantly more active than girls (p < .001) who in turn accrued more time in sleep (p = .02). The timing of peak activity was significantly later among adolescents (p = .047). Overall mental health and externalising problems were significantly associated with sleep, sedentary time, sleep efficiency, amplitude, and inter-daily stability (p = .04-0.01). The optimal time-use compositions were specific to overall mental health and externalising problems and were characterised by more sleep, light and vigorous physical activity, and less sedentary time and moderate physical activity than the sample’s mean time-use composition. Conclusions Extracting and examining multiple movement behaviour characteristics from 24-hour accelerometer data can provide a more rounded picture of the interplay between different elements of movement behaviours and their relationships with mental health than single characteristics alone, such as time-use estimates. Applying multiple movement behaviour characteristics to the translation of research findings may enhance the impact of the data for research users.