Self-efficacy can add to patient motivation; by contrast, a lack of self-efficacy can degrade motivation. Currently, self-efficacy measures are also somewhat underrepresented on the application market; however, paid apps make it more likely that the buyer will maintain self-monitoring. Defining self-efficacy as belief in one’s ability to succeed in certain situations or accomplish specific tasks implies that users can actively interact with their apps through self-report, manually or automatically logging physical activity and monitoring the achievement of goals.
A study by Glynn et al. (2014) demonstrated that short-term use of an app that includes physical activity tracking and goal-setting helped patients increase physical activity levels by around 1000 steps per day. Kirwan, Duncan, Vandelanotte, and Mummery (2012) found that participant engagement produced behavior change via increased physical activity for the duration of their 90-day study. Based on the literature, it seems that the patient or participant has to play an active role in self-monitoring of lifestyle modifications, and applications can
help this. Feldman et al. (2018) concluded that as they continue research into apps, clinicians can consider making endorsement that patients use specific apps that incorporate selected BCT. The apps should work on several levels, not only educating, but enabling social, behavioral, and decision support, all while developing user self-efficacy. Feldman et al.
(2018) concluded that patients should actively track physical activity behaviors in addition to using passive pedometer tracking. They also showed that patients would gain from the integration of social interaction onto the mHealth platform. A lasting change in behavior requires collaboration between clinician and patient, where progress towards a short-term activity goals can be monitored over time. The mActive trial, where smart texts using the name of the patient’s when sending out positive reimbursement for patients reaching daily goals, and words of encouragement for patients infrequently surpassing their goals (Martin et al., 2015). By leveraging the patient-clinician relationship, the mActive trial saw nearly twice as many participants in the text-receiving arm achieve their goal of 10 000 steps per day (Martin et al., 2015).
According to Feldman et al. (2018), it is not until a patient can demonstrate that he/she can consistently achieve short-term goals that a clinician can confidently prescribe the type of long-term goals than can actually lower CVD risk.
A review by Muellmann et al. (2018) pointed out that eHealth interventions effectively promote physical activity in older adults (<55), but evidence regarding long-term effects was missing. Haberlin et al. (2018) also found in their review that the use of eHealth to promote physical activity in cancer survivors was effective. The ten studies reviewed reported improvement in physical activity, with eight out of ten studies reporting statistically significant changes. In a review of the most important BCTs implemented in the eHealth interventions, the three most used were information about health consequences, goal setting and in joint third place self-monitoring of behavior and social support (Duff et al., 2017).
In a longitudinal RCT study, the health and well-being effect of wearable self-tracking devices was examined (Stiglbauer, Weber, & Batinic, 2019). The results show that fitness tracker usage increased perceived physical health and performance, and the effect was more pronounced if users additionally used the accompanying app.
Since most physical activity interventions take place with older, sick or obese participants, the aim of this study is to shed light on the possible effects of such interventions on a group of people who struggles with sedentary behavior. A further novel aspect of this study is also the testing of the PAI measure in such an intervention. The aim of this master’s thesis is to draw
on both Bandura’s self-efficacy theory and Feldman’s mHealth behavior change model as a framework to study the effects of a mHealth exercise intervention. The research question will be as following:
Can the implementation of a mHealth service improve exercise self-efficacy and increase daily physical activity among groups with high levels of sedentary behavior?
As mHealth apps and activity trackers are intended to foster health and well-being in a holistic sense, a positive impact of health-related self-monitoring technology use may be expected.
- Hypothesis 0: Implementation of mHealth behavior change application in an exercise intervention will not lead to greater exercise self-efficacy in the intervention group than in the control group which receives standard care, i.e. instructions to follow national guidelines for physical activity without further supervision.
- Hypothesis 1: Implementation of a mHealth behavior change application in an exercise intervention will lead to greater exercise self-efficacy in intervention group than in the control group which receives standard care, i.e. instructions to follow national guidelines for physical activity without further supervision.
6.0 Method
6.1 Subjects
The first step towards testing the potential of a mHealth behavior change application in an exercise intervention for people with sedentary behavior was to publish a recruitment poster in CERG (Cardiac Exercise Research Group) channels (Facebook and web-page) with an invitation to join the study. The poster had information about the study aim and further practical details. It was pointed out that to be included in the study it was important that the person considered him/herself as low-active and wished to become active. After registration, all subjects were phoned to ascertain whether they were actually motivated for the study. As soon as a large enough sample was reached, we stopped recruiting and started incorporating patients into the study.
The inclusion criteria for the study were (1) to be low-active, with a wish to become more active, (2) to have little experience of using exercise apps to self-monitor one’s activity. (3) To be 21 years or above. Exclusion criteria were set to make sure the participants were able to perform the planned physical activity in the study period. (1) To include patients in the study wasn’t allowed by Norwegian center for science-data (NSD) and was therefore an exclusion criterion, and (2) persons with limitations for being physically active, e.g. according to their doctor was excluded. Inclusion was terminated as soon as 26 persons had agreed to join the study. When the study started to reach the desired number of persons, a lack of male participants was detected, which made including males important at the end. The study
Included N= 26 Interested
N= 60
Study group
N= 13
Control group
N= 13
Figure 3: Flow chart of inclusion
seemed to attract female participants more than men. Only 14 out of 60 interested persons were men, and since four of them had conditions that made them medically unfit, and two of them had experience with self-monitoring physical activity with activity armbands and applications they were only eight left.
The sample of 26 persons thus consisted of eight males and 18 women, with a mean birth decade of 1970-79. In the questionnaire, age was defined in decade intervals to provide anonymity. Response 1 was for those born between 1920-1929, 2 for 1930-1939, 3 for 1940-1949, etc. until 8 for 1990-1999 which was the youngest age to be included in the study.