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|Title: ||Novel sedentary behaviour measurement methods: application for self-monitoring in adults|
|Authors: ||Sanders, James P.|
|Keywords: ||Wearable technology|
|Issue Date: ||2017|
|Publisher: ||© James Patrick Sanders|
|Abstract: ||With the introduction of the technological age, increasing mechanisation has led to labour saving devices which have all-but engineered physical activity out of our lives and sedentary behaviour has now become the default behaviour during waking hours. Interventions that previously focused on improving levels of physical activity are now attempting to concurrently increase levels of physical activity and decrease time spent in sedentary behaviour. One method that has shown promise in interventions to increase physical activity and healthy eating in adults is the behaviour change technique of self-monitoring. There is now a robust set of literature indicating self-monitoring as the most promising behaviour change technique in this area. Self-monitoring is tied inherently into the recent rise in wearable technology. These new devices have the ability to track a variety of behavioural and physiological parameters and immediately make the information returnable to the user via connected mobile applications. The potential pervasive nature of these technologies and their use of robust behaviour change techniques could make them a useful tool in interventions to reduce sedentary behaviour. Therefore the overall purpose of this three study dissertation was to identify and validate technology that can self-monitor sedentary behaviour and to determine its feasibility in reducing sedentary behaviour.
Purpose: The aim of this study was to review the characteristics and measurement properties of currently available self-monitoring devices for sedentary behaviour and/or physical activity. Methods: To identify technologies, four scientific databases were systematically searched using key terms related to behaviour, measurement, and population. Articles published through October 2015 were identified. To identify technologies from the consumer electronic sector, systematic searches of three Internet search engines were also performed through to October 1st, 2015. Results: The initial database searches identified 46 devices and the Internet search engines identified 100 devices yielding a total of 146 technologies. Of these, 64 were further removed because they were currently unavailable for purchase or there was no evidence that they were designed for, had been used in, or could readily be modified for self-monitoring purposes. The remaining 82 technologies were included in this review (73 devices self-monitored physical activity, 9 devices self-monitored sedentary time). Of the 82 devices included, this review identified no published articles in which these devices were used for the purpose of self-monitoring physical activity and/or sedentary behaviour; however, a number of technologies were found via Internet searches that matched the criteria for self-monitoring and provided immediate feedback on physical activity (ActiGraph Link, Microsoft Band, and Garmin Vivofit) and sedentary behaviour (activPAL VT, the LumoBack, and Darma). Conclusions: There are a large number of devices that self-monitor physical activity; however, there is a greater need for the development of tools to self-monitor sedentary time. The novelty of these devices means they have yet to be used in behaviour change interventions, although the growing field of wearable technology may facilitate this to change.
Purpose: The aim of this study was to examine the criterion and convergent validity of the LumoBack as a measure of sedentary behaviour compared to direct observation, the ActiGraph wGT3X+ and the activPAL under laboratory and free-living conditions in a sample of healthy adults. Methods: In the laboratory experiment, 34 participants wore a LumoBack, ActiGraph and activPAL monitor and were put through seven different sitting conditions. In the free-living experiment, a sub-sample of 12 participants wore the LumoBack, ActiGraph and activPAL monitor for seven days. Validity were assessed using Bland-Altman plots, mean absolute percentage error (MAPE), and intraclass correlation coefficient (ICC). T-test and Repeated Measures Analysis of Variance were also used to determine any significant difference in measured behaviours. Results: In the laboratory setting, the LumoBack had a mean bias of 76.2, 72.1 and -92.3 seconds when compared to direct observation, ActiGraph and activPAL, respectively, whilst MAPE was less than 4%. Furthermore, the ICC was 0.82 compared to the ActiGraph and 0.73 compared to the activPAL. In the free-living experiment, mean bias was -4.64, 8.90 and 2.34 seconds when compared to the activPAL for sedentary behaviour, standing time and stepping time respectively. Mean bias was -38.44 minutes when compared to the ActiGraph for sedentary time. MAPE for all behaviours were <9%, and the ICC were all >0.75. Conclusion: The LumoBack has acceptable validity and reliability as a measure of sedentary behaviour.
Purpose: The aim of this study was to explore the use of the LumoBack as a behaviour change tool to reduce sedentary behaviour in adults. Methods: Forty-two participants (≥25 years) who had an iPhone 4S or later model wore the LumoBack without any feedback for one week for baseline measures of behaviour. Participants then wore the LumoBack for a further five weeks whilst receiving feedback on sedentary behaviour via a sedentary vibration from the device and feedback on the mobile application. Sedentary behaviour, standing time, and stepping time were objectively assessed using the LumoBack. Differences in behaviour were determined between baseline, week 1 and week 5. Participant engagement with the LumoBack was determined using Mobile app analytics software. Results: There were no statistically significant differences in behaviour between baseline and the LumoBack intervention period (p>0.05). Participants engaged most with the Steps card on the LumoBack app with peaks in engagement seen at week 5. Conclusion: This study indicates that using the LumoBack on its own was not effective in reducing sedentary behaviour in adults. Self-monitoring and feedback may need to be combined with other behaviour change strategies such as environmental restructuring to be effective.
This thesis found that there are currently an abundance of technologies which self-monitors physical activity but a lack of devices which measuring sedentary behaviour. One such device, the LumoBack, has shown to have acceptable validity as a measure of sedentary behaviour. Whilst the use of the LumoBack as a behaviour change tool did not elicit any significant changes, its ability to be a pervasive behavioural intervention and the use of user-defined nudging can make the LumoBack, and other similar low cost, valid objective sedentary behaviour self-monitors key components in multi-faceted interventions.|
|Description: ||A Doctoral Thesis. Submitted in partial fulfilment of the requirements for the award of Doctor of Philosophy of Loughborough University.|
|Appears in Collections:||PhD Theses (Sport, Exercise and Health Sciences)|
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