Testing innovative methods to improve the reach and effectiveness of web-based physical activity interventions

Insufficient levels of physical activity increase the risk of developing cardiovascular disease, some cancers, diabetes, osteoporosis, depression, anxiety and obesity. However, less than half of the Australian population meet the minimum physical activity guidelines of accumulating 30 minutes of moderate intensity physical activity on most days of the week. This increases the burden of disease, lowers quality of life and costs the health care system over AUD $719 million per year. Therefore, there is an urgent need for effective population based interventions to increase physical activity at low cost for large numbers of people. The Internet provides a good platform to deliver physical activity interventions as it can reach large numbers of people at low cost. Whilst the short-term effectiveness of web-based interventions has been established, effective promotion of web-based interventions, as well as long-term participant engagement and retention into web-based interventions, can be problematic and needs to be addressed in order to improve the long-term effectiveness of these kinds of interventions. Therefore, the first study examined the cost-effectiveness of web-based advertising methods compared to traditional print-based advertising methods, as they have the potential to attract large numbers of people into a web-based physical activity intervention at a lower cost. For the second study, a 3-group randomised controlled trial was conducted to determine the effectiveness (in terms of retention, adherence, website engagement, satisfaction, physical activity changes and quality of life changes) of using online video-coaching (using Skype) in addition to personally-tailored physical activity advice. For the third study the effectiveness of video-tailored advice to improve attention and recall of the physical activity message was compared to basic text-tailored advice using eyetracking technology and a recall questionnaire. Findings from the recruitment evaluation (study 1) revealed that the cost-effectiveness of both the web-based and print-based methods varied substantially. Newspaper articles and community calendars cost the least per sign-up, but resulted in a small number of sign-ups (17 and 6 respectively). The targeted Facebook advertisements were the next most cost-effective method (AUD$45 per sign up), and reached the most number of sign-ups (184). People reached through the targeted Facebook advertisements were on average older and had a higher BMI than people reached through the other methods. Google advertisements and newspaper advertisements were not cost effective. Further research is needed to determine the effectiveness of Facebook advertising for attracting specific population groups and evaluate the use of mass-media to attract larger numbers to population level interventions. The intervention trial (study 2) revealed that the tailored advice + video-coaching group significantly improved their physical activity in comparison to a wait-list control group. However due to a low adherence to the coaching sessions, the tailored advice + video-coaching group did not improve their physical activity more than the tailoring only group. Participants who participated in the video-coaching sessions were nonetheless satisfied and had higher program and website engagement. Further research using online video-coaching should investigate ways to improve coaching adherence. The eye-tracking study (study 3) demonstrated that video-tailored advice leads to improved user-engagement compared to text-tailored advice (i.e., video participants paid more attention and interacted with the website for longer). However no group differences in recall of the physical activity message were found. More research is needed to determine how recall of computer-tailored advice can be improved and whether video-tailored advice can lead to greater health behaviour change than text-tailored advice. In summary, the findings from this PhD add valuable knowledge to the literature about improving the promotion, engagement and effectiveness of web-based physical activity interventions, and inform the development of the next generation of interventions.</p

Testing innovative methods to improve the reach and effectiveness of web-based physical activity interventions

Insufficient levels of physical activity increase the risk of developing cardiovascular disease, some cancers, diabetes, osteoporosis, depression, anxiety and obesity. However, less than half of the Australian population meet the minimum physical activity guidelines of accumulating 30 minutes of moderate intensity physical activity on most days of the week. This increases the burden of disease, lowers quality of life and costs the health care system over AUD $719 million per year. Therefore, there is an urgent need for effective population based interventions to increase physical activity at low cost for large numbers of people. The Internet provides a good platform to deliver physical activity interventions as it can reach large numbers of people at low cost. Whilst the short-term effectiveness of web-based interventions has been established, effective promotion of web-based interventions, as well as long-term participant engagement and retention into web-based interventions, can be problematic and needs to be addressed in order to improve the long-term effectiveness of these kinds of interventions. Therefore, the first study examined the cost-effectiveness of web-based advertising methods compared to traditional print-based advertising methods, as they have the potential to attract large numbers of people into a web-based physical activity intervention at a lower cost. For the second study, a 3-group randomised controlled trial was conducted to determine the effectiveness (in terms of retention, adherence, website engagement, satisfaction, physical activity changes and quality of life changes) of using online video-coaching (using Skype) in addition to personally-tailored physical activity advice. For the third study the effectiveness of video-tailored advice to improve attention and recall of the physical activity message was compared to basic text-tailored advice using eyetracking technology and a recall questionnaire. Findings from the recruitment evaluation (study 1) revealed that the cost-effectiveness of both the web-based and print-based methods varied substantially. Newspaper articles and community calendars cost the least per sign-up, but resulted in a small number of sign-ups (17 and 6 respectively). The targeted Facebook advertisements were the next most cost-effective method (AUD$45 per sign up), and reached the most number of sign-ups (184). People reached through the targeted Facebook advertisements were on average older and had a higher BMI than people reached through the other methods. Google advertisements and newspaper advertisements were not cost effective. Further research is needed to determine the effectiveness of Facebook advertising for attracting specific population groups and evaluate the use of mass-media to attract larger numbers to population level interventions. The intervention trial (study 2) revealed that the tailored advice + video-coaching group significantly improved their physical activity in comparison to a wait-list control group. However due to a low adherence to the coaching sessions, the tailored advice + video-coaching group did not improve their physical activity more than the tailoring only group. Participants who participated in the video-coaching sessions were nonetheless satisfied and had higher program and website engagement. Further research using online video-coaching should investigate ways to improve coaching adherence. The eye-tracking study (study 3) demonstrated that video-tailored advice leads to improved user-engagement compared to text-tailored advice (i.e., video participants paid more attention and interacted with the website for longer). However no group differences in recall of the physical activity message were found. More research is needed to determine how recall of computer-tailored advice can be improved and whether video-tailored advice can lead to greater health behaviour change than text-tailored advice. In summary, the findings from this PhD add valuable knowledge to the literature about improving the promotion, engagement and effectiveness of web-based physical activity interventions, and inform the development of the next generation of interventions

Testing innovative methods to improve the reach and effectiveness of web-based physical activity interventions

Insufficient levels of physical activity increase the risk of developing cardiovascular disease, some cancers, diabetes, osteoporosis, depression, anxiety and obesity. However, less than half of the Australian population meet the minimum physical activity guidelines of accumulating 30 minutes of moderate intensity physical activity on most days of the week. This increases the burden of disease, lowers quality of life and costs the health care system over AUD $719 million per year. Therefore, there is an urgent need for effective population based interventions to increase physical activity at low cost for large numbers of people. The Internet provides a good platform to deliver physical activity interventions as it can reach large numbers of people at low cost. Whilst the short-term effectiveness of web-based interventions has been established, effective promotion of web-based interventions, as well as long-term participant engagement and retention into web-based interventions, can be problematic and needs to be addressed in order to improve the long-term effectiveness of these kinds of interventions. Therefore, the first study examined the cost-effectiveness of web-based advertising methods compared to traditional print-based advertising methods, as they have the potential to attract large numbers of people into a web-based physical activity intervention at a lower cost. For the second study, a 3-group randomised controlled trial was conducted to determine the effectiveness (in terms of retention, adherence, website engagement, satisfaction, physical activity changes and quality of life changes) of using online video-coaching (using Skype) in addition to personally-tailored physical activity advice. For the third study the effectiveness of video-tailored advice to improve attention and recall of the physical activity message was compared to basic text-tailored advice using eyetracking technology and a recall questionnaire. Findings from the recruitment evaluation (study 1) revealed that the cost-effectiveness of both the web-based and print-based methods varied substantially. Newspaper articles and community calendars cost the least per sign-up, but resulted in a small number of sign-ups (17 and 6 respectively). The targeted Facebook advertisements were the next most cost-effective method (AUD$45 per sign up), and reached the most number of sign-ups (184). People reached through the targeted Facebook advertisements were on average older and had a higher BMI than people reached through the other methods. Google advertisements and newspaper advertisements were not cost effective. Further research is needed to determine the effectiveness of Facebook advertising for attracting specific population groups and evaluate the use of mass-media to attract larger numbers to population level interventions. The intervention trial (study 2) revealed that the tailored advice + video-coaching group significantly improved their physical activity in comparison to a wait-list control group. However due to a low adherence to the coaching sessions, the tailored advice + video-coaching group did not improve their physical activity more than the tailoring only group. Participants who participated in the video-coaching sessions were nonetheless satisfied and had higher program and website engagement. Further research using online video-coaching should investigate ways to improve coaching adherence. The eye-tracking study (study 3) demonstrated that video-tailored advice leads to improved user-engagement compared to text-tailored advice (i.e., video participants paid more attention and interacted with the website for longer). However no group differences in recall of the physical activity message were found. More research is needed to determine how recall of computer-tailored advice can be improved and whether video-tailored advice can lead to greater health behaviour change than text-tailored advice. In summary, the findings from this PhD add valuable knowledge to the literature about improving the promotion, engagement and effectiveness of web-based physical activity interventions, and inform the development of the next generation of interventions

The effectiveness of e-& mHealth interventions to promote physical activity and healthy diets in developing countries: A systematic review

Background: Promoting physical activity and healthy eating is important to combat the unprecedented rise in NCDs in many developing countries. Using modern information-and communication technologies to deliver physical activity and diet interventions is particularly promising considering the increased proliferation of such technologies in many developing countries. The objective of this systematic review is to investigate the effectiveness of e-& mHealth interventions to promote physical activity and healthy diets in developing countries. Methods: Major databases and grey literature sources were searched to retrieve studies that quantitatively examined the effectiveness of e-& mHealth interventions on physical activity and diet outcomes in developing countries. Additional studies were retrieved through citation alerts and scientific social media allowing study inclusion until August 2016. The CONSORT checklist was used to assess the risk of bias of the included studies. Results: A total of 15 studies conducted in 13 developing countries in Europe, Africa, Latin-and South America and Asia were included in the review. The majority of studies enrolled adults who were healthy or at risk of diabetes or hypertension. The average intervention length was 6.4 months, and text messages and the Internet were the most frequently used intervention delivery channels. Risk of bias across the studies was moderate (55.7 % of the criteria fulfilled). Eleven studies reported significant positive effects of an e-& mHealth intervention on physical activity and/or diet behaviour. Respectively, 50 % and 70 % of the interventions were effective in promoting physical activity and healthy diets. Conclusions: The majority of studies demonstrated that e-& mHealth interventions were effective in promoting physical activity and healthy diets in developing countries. Future interventions should use more rigorous study designs, investigate the cost-effectiveness and reach of interventions, and focus on emerging technologies, such as smart phone apps and wearable activity trackers

The facilitators and barriers of physical activity among Aboriginal and Torres Strait Islander regional sport participants

Background: Disparities in health perspectives between Indigenous and non-Indigenous populations are major concerns in many of the world's well-developed nations. Indigenous populations are largely less healthy, more prone to chronic diseases, and have an earlier overall mortality than non-Indigenous populations. Low levels of physical activity (PA) contribute to the high levels of disease in Indigenous Australians. Method: Qualitative analysis of structured one-on-one interviews discussing PA in a regional setting. Participants were 12 Indigenous Australian adults, and 12 non-Indigenous Australian adults matched on age, sex, and basketball division. Results: Most participants reported engaging in regular exercise; however, the Indigenous group reported more barriers to PA. These factors included cost, time management and environmental constraints. The physical facilitators identified by our Indigenous sample included social support, intrinsic motivation and role modelling. Conclusion: Findings describe individual and external factors that promote or constraint PA as reported by Indigenous Australian adults. Results indicate that Indigenous people face specific barriers to PA when compared to a non-Indigenous sample. Implications for public health: This study is the first to compare the perspective of Indigenous Australians to a matched group of non-Indigenous Australians and provides useful knowledge to develop public health programs based on culturally sensitive data. © 2017 The Author

My Activity Coach – using video-coaching to assist a web-based computer-tailored physical activity intervention : a randomised controlled trial protocol

Background: There is a need for effective population-based physical activity interventions. The internet provides a good platform to deliver physical activity interventions and reach large numbers of people at low cost. Personalised advice in web-based physical activity interventions has shown to improve engagement and behavioural outcomes, though it is unclear if the effectiveness of such interventions may further be improved when providing brief video-based coaching sessions with participants. The purpose of this study is to determine the effectiveness, in terms of engagement, retention, satisfaction and physical activity changes, of a web-based and computer-tailored physical activity intervention with and without the addition of a brief video-based coaching session in comparison to a control group. Methods/Design: Participants will be randomly assigned to one of three groups (tailoring + online video-coaching, tailoring-only and wait-list control). The tailoring + video-coaching participants will receive a computer-tailored web-based physical activity intervention (‘My Activity Coach’) with brief coaching sessions with a physical activity expert over an online video calling program (e.g. Skype). The tailoring-only participants will receive the intervention but not the counselling sessions. The primary time point’s for outcome assessment will be immediately post intervention (week 9). The secondary time points will be at 6 and 12 months post-baseline. The primary outcome, physical activitychange, will be assessed via the Active Australia Questionnaire (AAQ). Secondary outcome measures include correlates of physical activity (mediators and moderators), quality of life (measured via the SF-12v2), participant satisfaction, engagement (using web-site user statistics) and study retention. Discussion: Study findings will inform researchers and practitioners about the feasibility and effectiveness of brief online video-coaching sessions in combination with computer-tailored physical activity advice. This may increase intervention effectiveness at an acceptable cost and will inform the development of future web-based physical activity interventions

8-year trends in physical activity, nutrition, TV viewing time, smoking, alcohol and BMI: A comparison of younger and older Queensland adults

Lifestyle behaviours significantly contribute to high levels of chronic disease in older adults. The aims of the study were to compare the prevalence and the prevalence trends of health behaviours (physical activity, fruit and vegetable consumption, fast food consumption, TV viewing, smoking and alcohol consumption), BMI and a summary health behaviour index in older (65+ years) versus younger adults (18-65 years). The self-report outcomes were assessed through the Queensland Social Survey annually between 2007-2014 (n=12,552). Regression analyses were conducted to compare the proportion of older versus younger adults engaging in health behaviours and of healthy weight in all years combined and examine trends in the proportion of younger and older adults engaging in health behaviours and of healthy weight over time. Older adults were more likely to meet recommended intakes of fruit and vegetable (OR=1.43, 95%CI=1.23-1.67), not consume fast food (OR=2.54, 95%CI=2.25-2.86) and be non-smokers (OR=3.02, 95%CI=2.53-3.60) in comparison to younger adults. Conversely, older adults were less likely to meet the physical activity recommendations (OR=0.86, 95%CI= 0.78-0.95),watch less than 14 hours of TV per week (OR=0.65, 95%CI=0.58-0.74) and be a healthy weight (OR=1.11, 95%CI=1.00-1.24). Overall, older adults were more likely to report engaging in 3, or at least 4 out of 5 healthy behaviours. The proportion of both older and younger adults meeting the physical activity recommendations (OR=0.97, 95%CI=0.95-0.98 and OR=0.94, 95%CI=0.91-0.97 respectively), watching less than 14 hours of TV per week (OR=0.96, 95%CI=0.94-0.99 and OR=0.94, 95%CI=0.90-0.99 respectively) and who were a healthy weight (OR=0.95, 95%CI=0.92-0.99 and OR=0.96, 95%CI=0.94-0.98 respectively) decreased over time. The proportion of older adults meeting the fruit and vegetable recommendations (OR=0.90, 95%CI=0.84-0.96) and not consuming fast food (OR=0.94, 95%CI=0.88-0.99) decreased over time. Although older adults meet more health behaviours than younger adults, the decreasing prevalence of healthy nutrition behaviours in this age group needs to be addressed

8-year trends in physical activity, nutrition, TV viewing time, smoking, alcohol and BMI: A comparison of younger and older Queensland adults

Lifestyle behaviours significantly contribute to high levels of chronic disease in older adults. The aims of the study were to compare the prevalence and the prevalence trends of health behaviours (physical activity, fruit and vegetable consumption, fast food consumption, TV viewing, smoking and alcohol consumption), BMI and a summary health behaviour index in older (65+ years) versus younger adults (18-65 years). The self-report outcomes were assessed through the Queensland Social Survey annually between 2007-2014 (n=12,552). Regression analyses were conducted to compare the proportion of older versus younger adults engaging in health behaviours and of healthy weight in all years combined and examine trends in the proportion of younger and older adults engaging in health behaviours and of healthy weight over time. Older adults were more likely to meet recommended intakes of fruit and vegetable (OR=1.43, 95%CI=1.23-1.67), not consume fast food (OR=2.54, 95%CI=2.25-2.86) and be non-smokers (OR=3.02, 95%CI=2.53-3.60) in comparison to younger adults. Conversely, older adults were less likely to meet the physical activity recommendations (OR=0.86, 95%CI= 0.78-0.95),watch less than 14 hours of TV per week (OR=0.65, 95%CI=0.58-0.74) and be a healthy weight (OR=1.11, 95%CI=1.00-1.24). Overall, older adults were more likely to report engaging in 3, or at least 4 out of 5 healthy behaviours. The proportion of both older and younger adults meeting the physical activity recommendations (OR=0.97, 95%CI=0.95-0.98 and OR=0.94, 95%CI=0.91-0.97 respectively), watching less than 14 hours of TV per week (OR=0.96, 95%CI=0.94-0.99 and OR=0.94, 95%CI=0.90-0.99 respectively) and who were a healthy weight (OR=0.95, 95%CI=0.92-0.99 and OR=0.96, 95%CI=0.94-0.98 respectively) decreased over time. The proportion of older adults meeting the fruit and vegetable recommendations (OR=0.90, 95%CI=0.84-0.96) and not consuming fast food (OR=0.94, 95%CI=0.88-0.99) decreased over time. Although older adults meet more health behaviours than younger adults, the decreasing prevalence of healthy nutrition behaviours in this age group needs to be addressed

My Activity Coach – using video-coaching to assist a web-based computer-tailored physical activity intervention : a randomised controlled trial protocol

Background: There is a need for effective population-based physical activity interventions. The internet provides a good platform to deliver physical activity interventions and reach large numbers of people at low cost. Personalised advice in web-based physical activity interventions has shown to improve engagement and behavioural outcomes, though it is unclear if the effectiveness of such interventions may further be improved when providing brief video-based coaching sessions with participants. The purpose of this study is to determine the effectiveness, in terms of engagement, retention, satisfaction and physical activity changes, of a web-based and computer-tailored physical activity intervention with and without the addition of a brief video-based coaching session in comparison to a control group. Methods/Design: Participants will be randomly assigned to one of three groups (tailoring + online video-coaching, tailoring-only and wait-list control). The tailoring + video-coaching participants will receive a computer-tailored web-based physical activity intervention (‘My Activity Coach’) with brief coaching sessions with a physical activity expert over an online video calling program (e.g. Skype). The tailoring-only participants will receive the intervention but not the counselling sessions. The primary time point’s for outcome assessment will be immediately post intervention (week 9). The secondary time points will be at 6 and 12 months post-baseline. The primary outcome, physical activitychange, will be assessed via the Active Australia Questionnaire (AAQ). Secondary outcome measures include correlates of physical activity (mediators and moderators), quality of life (measured via the SF-12v2), participant satisfaction, engagement (using web-site user statistics) and study retention. Discussion: Study findings will inform researchers and practitioners about the feasibility and effectiveness of brief online video-coaching sessions in combination with computer-tailored physical activity advice. This may increase intervention effectiveness at an acceptable cost and will inform the development of future web-based physical activity interventions

Web-based video-coaching to assist an automated computer-tailored physical activity intervention for inactive adults: A randomized controlled trial

Background: Web-based physical activity interventions that apply computer tailoring have shown to improve engagement and behavioral outcomes but provide limited accountability and social support for participants. It is unknown how video calls with a behavioral expert in a Web-based intervention will be received and whether they improve the effectiveness of computer-tailored advice. Objective: The purpose of this study was to determine the feasibility and effectiveness of brief video-based coaching in addition to fully automated computer-tailored advice in a Web-based physical activity intervention for inactive adults. Methods: Participants were assigned to one of the three groups: (1) tailoring + video-coaching where participants received an 8-week computer-tailored Web-based physical activity intervention (“My Activity Coach”) including 4 10-minute coaching sessions with a behavioral expert using a Web-based video-calling program (eg, Skype; n=52); (2) tailoring-only where participants received the same intervention without the coaching sessions (n=54); and (3) a waitlist control group (n=45). Demographics were measured at baseline, intervention satisfaction at week 9, and physical activity at baseline, week 9, and 6 months by Web-based self-report surveys. Feasibility was analyzed by comparing intervention groups on retention, adherence, engagement, and satisfaction using t tests and chi-square tests. Effectiveness was assessed using linear mixed models to compare physical activity changes between groups. Results: A total of 23 tailoring + video-coaching participants, 30 tailoring-only participants, and 30 control participants completed the postintervention survey (83/151, 55.0% retention). A low percentage of tailoring + video-coaching completers participated in the coaching calls (11/23, 48%). However, the majority of those who participated in the video calls were satisfied with them (5/8, 71%) and had improved intervention adherence (9/11, 82% completed 3 or 4 modules vs 18/42, 43%, P=.01) and engagement (110 minutes spent on the website vs 78 minutes, P=.02) compared with other participants. There were no overall retention, adherence, engagement, and satisfaction differences between tailoring + video-coaching and tailoring-only participants. At 9 weeks, physical activity increased from baseline to postintervention in all groups (tailoring + video-coaching: +150 minutes/week; tailoring only: +123 minutes/week; waitlist control: +34 minutes/week). The increase was significantly higher in the tailoring + video-coaching group compared with the control group (P=.01). No significant difference was found between intervention groups and no significant between-group differences were found for physical activity change at 6 months. Conclusions: Only small improvements were observed when video-coaching was added to computer-tailored advice in a Web-based physical activity intervention. However, combined Web-based video-coaching and computer-tailored advice was effective in comparison with a control group. More research is needed to determine whether Web-based coaching is more effective than stand-alone computer-tailored advice