Author: Christine Roberts1, Louise Phillips2, Clare Cooper3, Stuart Gray4, Roy Soiza5, Julia Allan3
Affiliation: <sup>1</sup> Sport & Exercise Team, University of Aberdeen, Aberdeen, United Kingdom.
<sup>2</sup> School of Psychology, University of Aberdeen, Aberdeen, United Kingdom.
<sup>3</sup> Institute of Applied Health Sciences, School of Medicine, Medical Sciences & Nutrition, University of Aberdeen, Aberdeen, United Kingdom.
<sup>4</sup> Institute of Cardiovascular & Metabolic Health, University of Glasgow, Glasgow, United Kingdom.
<sup>5</sup> Institute of Medical Sciences, School of Medicine, Medical Sciences & Nutrition, University of Aberdeen, Aberdeen, United Kingdom.
Conference/Journal: PLoS One
Date published: 2023 Oct 19
Other:
Volume ID: 18 , Issue ID: 10 , Pages: e0291782 , Special Notes: doi: 10.1371/journal.pone.0291782. , Word Count: 264
Different physical activity types vary in metabolic demand (intensity), but also in non-metabolic physical demand (balance, co-ordination, speed and flexibility), cognitive demand (attention, memory and decision making), and social demand (social interaction). Activity types with different combinations of demands may have different effects on health outcomes but this cannot be formally tested until such demands can be reliably quantified. The present Delphi expert consensus study aimed to objectively quantify the cognitive, physical and social demands of different core physical activity types and use these scores to create a formal Physical Activity Demand (PAD) typology. International experts (n = 40; experts in cognitive science, psychology, sports science and physiology; 7 different nationalities; 18 male/22 female; M = 13.75 years of disciplinary experience) systematically rated the intrinsic cognitive, physical and social demands of 61 common activity types over 2-rounds of a modified Delphi (expert consensus) study. Consensus (>70% agreement) was reached after 2 rounds on the demands of 59/61 activity types. Cognitive, physical and social demand scores were combined to create an overall non-metabolic demand rating for each activity type, and two-step cluster-analysis was used to identify groups of activities with comparable demand profiles. Three distinct clusters of activities were identified representing activity types with low (n = 12 activities; e.g. domestic cleaning), moderate (n = 23 activities; e.g. tai-chi) and high (n = 24 activities; e.g. football) total non-metabolic demands. These activity types were then organised into a formal typology. This typology can now be used to test hypotheses about if and why physical activity types with different combinations of cognitive, physical and social demands affect health outcomes in different ways.
PMID: 37856505 PMCID: PMC10586621 DOI: 10.1371/journal.pone.0291782