towards a balanced and active lifestyle
Reinoud Achterkamp is a PhD student in the department of Biomedical Signals and Systems (BSS) research group. His supervisors are prof.dr. M.M.R. Vollenbroek-Hutten and prof.dr.ir. H.J. Hermens both from the faculty of Electrical Engineering, Mathematics and Computer Science (EWI).
The main aim of this thesis was to increase our understanding about whether it is useful to incorporate tailoring in mobile, technology-supported physical activity enhancing applications, if so, how to incorporate this, and to provide first insights in what happens on physical activity and self-efficacy levels, when overweight subjects are provided with such an application. More specific, we aim to answer the following three research questions:
1) What is the relation between self-efficacy, stage of change, and objectively measured level of physical activity in patients and healthy adults, and can typical users be identified?
2) What is the effect of a feedback strategy that is delivered through technology and applies self-efficacy increasing techniques on self-efficacy and task performance?
3) Does two-week use of a mobile, technology-supported physical activity application by overweight adults lead to changes in goal achievement, self-efficacy, or level of physical activity over a two-week period or in an interval of fifteen minutes after a feedback message has been prompted?
To answer these questions four studies were performed: one on data previously retrieved in various observational cohort studies, two experimental laboratory-setting studies, and one small cohort field study.
Chapter 1 started with a description of the central problem in this thesis; the number of overweight subjects is increasing to alarming levels. Improving level of physical activity is presented as a solution, for which a definition was presented. Interventions aimed to increase level of physical activity were discussed and showed that in overweight subject samples, the majority of the physical activity interventions are designed as face-to-face interventions, only using accelerometers to assess effectiveness of face-to-face interventions, and not as part of a stand-alone self-management intervention. Furthermore, it was found that interventions for general subject samples that did include accelerometer data and set up interventions as self-management, did not tailor feedback to constructs from behavioural sciences, which was hypothesized a flaw and an opportunity for improvement; earlier research, not using (mobile) technology, had already proven the effectiveness of tailored feedback based on construct from behavioural sciences. Finally, various behavioural change theories and constructs were presented and discussed, of which self-efficacy was identified as an important component that needs to be assessed at the start of an intervention and if necessary, be increased.
Regarding RQ 1, in Chapter 2, relations between self-efficacy, stage of change and physical activity were investigated, in order to define the constructs on which feedback should be tailored and to develop feedback strategies that can be used to further improve the effectiveness of mobile physical activity coaching and feedback interventions. To this end, data from previously performed experiments using 3D-accelerometry were used. Results showed that higher self-efficacy was related to higher activity levels. Patients were less active than healthy controls and showed a larger drop in physical activity over the day. Patients in the maintenance stage of change were more active than patients in lower stages of change, but showed an equally large drop in level of physical activity at the end of the day. The above lead to the statement that coaching should at least be tailored to level of self-efficacy, stage of change and physical activity pattern. Eight typical users were identified, for which six feedback strategies were developed.
As a first step to test aspects of the developed feedback strategies in practice and with respect to RQ 2, Chapter 3 describes a laboratory setting study. The experiment was designed to test whether using feedback strategies that focus on success experience and are provided through technology can influence self-efficacy regarding a specific task, just as in non-technology-supported interventions. Subjects were asked to walk from A to B in exactly 14, 16 or 18 seconds, wearing scuba fins and a blindfold. The task guaranteed an equal level of task experience among all subjects at the start of the experiment and made it difficult for subjects to estimate their performance accurately. This allowed for manipulation of feedback and success experience through technology-supported feedback. Results showed that subjects’ self-efficacy regarding the task decreased when experiencing little success and that self-efficacy regarding the task increased when experiencing success. This effect did not transfer to level of self-efficacy regarding physical activity in general. The above lead to the conclusion that mastery experience, i.e. experiencing success, is a promising strategy to use in technology-supported interventions that aims at changing behaviour, like mobile physical activity applications.
Next to mastery experience, vicarious experience is a known effective technique in traditional, non-technology-supported interventions, to influence self-efficacy. Regarding RQ 2, Chapter 4 builds on the experiment described in Chapter 3, this time investigating whether self-efficacy regarding a specific task can be influenced through vicarious experience provided through mobile technology. Subjects were again asked to walk from A to B in exactly 14, 16, or 18 seconds, wearing scuba fins and a blindfold, but before each trial, subjects in the experimental group viewed a video on a smartphone of a subject successfully performing the task, whereas subjects in the control group did not view a video. Results showed that despite that subjects found the video helpful to perform the task, subjects’ level of self-efficacy regarding the task, as well as task performance, did not differ significantly between the two groups. However, a secondary outcome parameter did indicate a possible difference between how subjects walked forward while wearing the scuba fins; either shuffling forward, or raising their knees high up, depending on the strategy that the model in the instructional video applied.
Results from the two laboratory studies were promising. However, they were not close to a representation of daily life. As such, a field study in a more ecologically valid setting was designed regarding RQ 3. In Chapter 5, results are presented from a study on a mobile physical activity monitoring and feedback application used as self-management tool, without professional face-to-face contact. The goal of this exploratory study was to obtain insights regarding goal achievement, self-efficacy regarding physical activity, and daily level of physical activity of overweight adults using a mobile physical activity application. Secondarily, differences between the effects of feedback tailored to self-efficacy versus general feedback messages were investigated. For fourteen days, thirty overweight subjects used a mobile, technology-supported physical activity application that provides frequent, automated, real-time feedback messages to subjects based on their level of physical activity. The first seven days were used as baseline measurement. During the second period of seven days subjects received a personalized daily goal that was ten percent higher than their baseline level of physical activity and motivational feedback messages to achieve their goal. Results showed that over the seven feedback days on which the thirty subjects could achieve their goal (n=210), subjects succeeded a total of 77 times (36.7%) and the number of subjects achieving their goal increased over time. The conclusion stated that overall goal achievement was moderate, but importantly, subjects’ level of self-efficacy increased during the two-week intervention; this is promising considering the strong mediating role of self-efficacy in achieving a sufficient level of physical activity. Self-efficacy precedes increased levels of physical activity, but we hypothesized that two weeks is a too short period to demonstrate according changes in level of physical activity.
Whereas Chapter 5 focuses on global intervention parameters such as increase in self-efficacy over two weeks, and changes in level of physical activity over two weeks, Chapter 6 focuses on the immediate effect of feedback messages, as part of RQ 3. Specifically, the primary outcome parameter was the difference between level of physical activity in an interval of fifteen minutes before, and fifteen minutes after having provided a textual feedback message based on real-time level of physical activity. The same method and data sample was used as presented in Chapter 5. Only data from the second week of measurement, the intervention week, was used in this study. Results showed that feedback messages led to significant changes in level of physical activity in an interval of fifteen minutes after the message was prompted to subjects; encouraging messages led to an increase, whereas neutral and discouraging messages led to a decrease. No differences were found between the effect of messages based on tailored feedback strategies and messages not based on these strategies. Considering this, it was concluded that real-time textual feedback messages based on objective level of physical activity are smart options to incorporate in mobile, technology-supported physical activity monitoring and feedback applications for overweight adults, but we failed to identify significant differences between tailored feedback versus general feedback.
In Chapter 7, general conclusions of the research presented in the current thesis were summarized and critically discussed. Three subsections followed, firstly describing future steps for tailoring; we concluded that self-efficacy increasing techniques should be incorporated alongside, or as part of, other known effective theories or models from behavioural sciences in modern-day physical activity applications or interventions. Two examples of such models are presented and we presented suggestions on how to incorporate these into modern day physical activity applications. Secondly, implications and recommendations for daily practice were discussed, in which it was stated that, in our opinion, blended care has most potential with respect to clinical practice in short term. However, in the long term, it is not unimaginable that these types of technology-supported systems are handed out by a healthcare professional and thereafter function as stand-alone, self-management application, with all aspects as baseline measurements, goal-setting, feedback and coaching programmed into an adaptive, learning and personalized physical activity application. Lastly, we proposed that health applications of the future not only focus on physical activity, but also take into account, biological, psychological, and social factors.