Back in 2011 when I was completing my Masters in Human Movement (research) at Federation U in Ballarat I was intent on testing the effectiveness of a mobile app and wearable product I had co-developed with Roger Nesbitt and Mike Elmsly of NZ. It was called SPUTNIK. It used available 3 rd party sensors which we integrated into a vest, a mobile app. running on SYMBIAN O/S and a Facebook app. This latter part of the architecture was interesting as it was originally built 3 years before that so in effect our work dates back to 2008; well before the likes of fitbit. The Facebook app provided goal management, graphical analyses and Comment support among users. The system collected, managed and shared HR (heart rate) speed, skin temp. and respiration rate although for the trial I am about to explain we restricted the functional spread to speed and HR measures (max min avg).
We set up a small trial using a simple experimental study design with participants comprising 16 field hockey players randomly allocated to a technology intervention or a control group and subjected to pre- and post-tests of repeated sprint performance using a standardised Yo-Yo test. Both groups completed a 5-week field hockey-specific HIIT program. The technology intervention group used the technology to import HIIT session data from body sensors via a mobile phone app, set and edit goals, view session performance graphically and interact with others in the group using this information. The control group trained in isolation from others and used a standard heart rate monitor and pedometer with a paper-based diary to record their exercise session data.
The findings indicated a significant increase in performance scores from pre- to post-test for the technology intervention group, T = 0, z = –2.53 (corrected for ties), p = .01, r = .63, with no significant differences for the control group, T = 13.5, z = –0.09 (corrected for ties), p = .93, r = .02 the technology intervention group adhered significantly more to the HIIT program than the control group, U =9.00, z = –2.44 (not corrected for ties), p = .01, r = .61.
We concluded that despite the limitation of such as small sample size, socialising physiological performance data leads to improved fitness and increased adherence to a HIIT intervention program with field hockey players.
Now, this small-time original pilot did not use standardised psycho-social scales from established health behaviour change theories such as Bandura's Social Cognitive Model or Ryan & Deci's Self Determination Theory (SDT) to identify and measure any change in key attributes comprising motivation level and type.
The smoke of the possible association of online social support with adherent behaviour may point to the system helping satisfy the basic psychological need of relatedness to others as specified by Deci & Ryan in SDT. Satisfaction of this need is positively associated with autonomous motivation for exercise and long term sustainability of exercise regimes.
In a nutshell, the much mooted social support benefits of on line social network systems in general and wearable exercise trackers in particular may have some grounds in proof. For coaches monitoring the training loads, performance measures, video analyses and health of athletes the provision of in situ wearables may be an effective means to not only gather data but provide the athlete with real support both social in terms of peers but also from you the coach trusting them to gather and manage their training activities autonomously. Again, pointing to the evidence accrued by researchers using SDT in coaching environments, the provision of autonomy support and relatedness to others are positively associated with long term productive exercise behaviour. Services such as coach-logic.com are improving coaching efficacy by way of technology provisioning and may be adding additional value through enhanced athlete motivation.
