Affiliations: [a] School of Medicine, University of Glasgow, Glasgow, UK | [b] School of Health and Life Science, Glasgow Caledonian University, Glasgow, UK | [c] Institute of Health and Wellbeing, University of Glasgow, Glasgow, UK | [d] School of Computing Science, University of Glasgow, Glasgow, UK | [e] Institute of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow, UK
Correspondence:
[*]
Corresponding author: Evan Campbell, School of Medicine, University of Glasgow, Glasgow, UK. Tel.: +44 0141 330 7154; E-mail: [email protected].
Abstract: BACKGROUND: Smartphone sensors are underutilised in rehabilitation. OBJECTIVE: To validate the step count algorithm used in the STARFISH smartphone application. METHODS: Twenty-two healthy adults (8 male, 14 female) walked on a treadmill for 5 minutes at 0.44,
0.67, 0.90 and 1.33 m⋅s-1
. Each wore an activPAL
TM
and four Samsung Galaxy
S3TM smartphones, with the STARFISH application running, in: 1) a belt carrycase,
2) a trouser or skirt pocket), 3a) a handbag on shoulder for females or 3b) shirt pocket for males and
4) an upper arm strap. Step counts of the STARFISH application and the activPALTM
were compared at corresponding speeds and Bland-Altman statistics used to assess level of agreement (LOA). RESULTS: The LOA between the STARFISH application and activPALTM varied across the four speeds and positions, but improved as speed increased. The LOA ranged from 105–177% at 0.44 m⋅s-1; 50–98% at 0.67 m⋅s-1; 19–67% at 0.9 m⋅s-1 and 8–53% at 1.33 m⋅s-1. The best LOAs were at 1.33 m⋅s-1 in the shirt pocket (8%) and upper arm strap (12%) positions. CONCLUSIONS: Step counts measured by the STARFISH smartphone application are valid in most body positions especially at walking speeds of 0.9 m⋅s-1 and above.
