Document Type


Degree Name

Master of Kinesiology (MKin)


Kinesiology and Physical Education


Faculty of Science

First Advisor

Dr. Robertson-Wilson

Advisor Role



Prolonged sedentary behaviour (SB) poses health risks independent of physical activity (PA) levels (Owen et al., 2010). University students in particular are at risk of engaging in prolonged SB due to the demands of school. Due to the pervasiveness of smartphones, and ability of mobile applications (apps) to target SB (Bond et al., 2014), apps may be used to encourage less SB in this population. Apps for PA have been coded for behaviour change techniques (BCTs) (Conroy et al., 2014; Middelweerd et al., 2014; Yang et al., 2015), however, apps for SB have yet to be assessed for BCTs.

The purpose of this study was two-fold. The first aim was review smartphone apps designed to reduce SB for the presence of BCTs. The second aim was to gain an understanding of university students’ SB, PA and experiences with apps, and trial an SB app as a pilot intervention in this population.

To address the first aim, systematic searches of the iTunes App and Google Play stores were completed using keyword searches. Two reviewers independently coded free (n=36) and paid (n=14) app descriptions using a taxonomy of 93 BCTs (Michie et al., 2012). A subsample (n=4) of free apps were trialed for one week by the reviewers and coded for the presence of BCTs. In the free and paid app descriptions, only 10 of 93 BCTs were present with a mean of 2.42 BCTs (range 0-6) per app. The BCTs coded most frequently were “prompts/cues” (n=43), “information about health consequences” (n=31), and “self-monitoring of behaviour” (n=17). For the four free apps that were trialed, three additional BCTs were coded that were not coded in the descriptions: “graded tasks”, “focus on past successes”, and “behaviour substitution”. These SB apps have fewer BCTs compared to PA apps(Conroy et al., 2014; Middelweerd et al., 2014; Yang et al., 2015) and traditional (i.e., non-app) PA and healthy eating interventions (Michie et al., 2009).

To address the second aim, students from WLU (n=177) completed an online survey of questions about self-report levels of PA, SB, and experiences with and perceptions of apps. Following this, participants were asked to participate in a follow-up study and were randomly assigned to a trial group (n=53) or a control group (n=74). The trial group was asked to use the app Rise & Recharge® for two weeks. After two weeks, participants in trial (n=18) and control groups (n=37) completed a second online survey that repeated the self-report PA and SB questions. Participants in the trial group also responded to additional questions about their app experience. A two-way mixed repeated measures ANOVA found no significant difference in PA in either group from ‘time 1’ to ‘time 2’ (p>0.05). However, another two-way mixed repeated measures ANOVA for SB determined there was no main effect of time or group (p>0.05), but a significant interaction between group and time (F(1,33)=6.81, p=0.014, ηp2= 0.171), in which the trial group (n=11) decreased in SB from ‘time 1’ to ‘time 2’, whereas the control group (n=24) increased in ‘time 1’ to ‘time 2’. Despite this, participants in the trial group rated the app as only ‘slightly influential’. Further, students’ open-ended responses showed that they perceive a lack of control over their own SB due to the demands of university.

Overall, the present study sheds light on behaviour change potential of SB apps and provides practical insight about coding for BCTs in apps, and provides insight into PA an SB among university students and into the potential of using apps to influence this behaviour.

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