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    Original Paper

    Examining Participant Engagement in an Information Technology-Based Physical Activity and Nutrition Intervention for Men: The Manup Randomized Controlled Trial

    1Center for Physical Activity Studies, Institute for Health and Social Science Research, Central Queensland University, Rockhampton, Australia

    2Kansas State University, Department of Human Nutrition, Kansas State University, Manhattan, KS, United States

    3University of British Columbia, Faculty of Health and Social Development, University of British Columbia, Kelowna, BC, Canada

    4The Australian eHealth Research Center, ICT Center, CSIRO, Herston, Australia

    5University of Western Sydney, School of Science and Health, Sydney, Australia

    6University of Western Sydney, School of Computing, Engineering, and Mathematics, Sydney, Australia

    7Food and Nutritional Sciences, CSIRO, Adelaide, Australia

    Corresponding Author:

    Camille E Short, PhD

    Center for Physical Activity Studies

    Institute for Health and Social Science Research

    Central Queensland University

    Building 18

    Bruce Highway

    Rockhampton, 4702

    Australia

    Phone: 61 749232689

    Fax:61 749306402

    Email:


    ABSTRACT

    Background: Males experience a shorter life expectancy and higher rates of chronic diseases compared to their female counterparts. To improve health outcomes among males, interventions specifically developed for males that target their health behaviors are needed. Information technology (IT)-based interventions may be a promising intervention approach in this population group, however, little is known about how to maximize engagement and retention in Web-based programs.

    Objective: The current study sought to explore attributes hypothesized to influence user engagement among a subsample of participants from the ManUp study, a randomized controlled trial testing the efficacy of an interactive Web-based intervention for promoting physical activity and nutrition among middle-aged males.

    Methods: Semistructured interviews were conducted and audiotaped with 20 of the ManUp participants. Interview questions were based on a conceptual model of engagement and centered on why participants took part in the study, what they liked and did not like about the intervention they received, and how they think the intervention could be improved. Interview recordings were transcribed and coded into themes.

    Results: There were five themes that were identified in the study. These themes were: (1) users’ motives, (2) users’ desired outcomes, (3) users’ positive experiences, (4) users’ negative emotions, and (5) attributes desired by user.

    Conclusions: There is little research in the field that has explored user experiences in human-computer interactions and how such experiences may relate to engagement, especially among males. Although not conclusive, the current study provides some insight into what personal attributes of middle-aged males (such as their key motives and goals for participating) and attributes of the intervention materials (such as usability, control, and interactivity) may impact on user engagement in this group. These findings will be helpful for informing the design and implementation of future health behavior interventions for males.

    Trial Registration: Australian New Zealand Clinical Trials Registry: ACTRN12611000081910; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?ACTRN=12611000081910 (Archived by WebCite at http://www.webcitation.org/6M4lBlvCA).

    JMIR Res Protoc 2014;3(1):e2)

    doi:10.2196/resprot.2776

    KEYWORDS



    Introduction

    Engaging Men in Healthy Lifestyles

    Despite strong evidence that physical inactivity and poor nutrition are associated with an increased risk of chronic diseases and mortality [1,2], the majority of the adult population in western societies continues to live an unhealthy lifestyle [3-7]. Interventions that promote healthy lifestyles at a population level are needed to address this public health issue [1,8]. Of particular concern is how to engage males in such interventions. Compared to their female counterparts, males are less likely to engage in lifestyle modification programs, utilize health services, and participate in intervention research [9,10]. This may be a contributing factor to the shorter life expectancy and higher age-specific rates of chronic diseases, such as heart disease, Type 2 diabetes, obesity, and mental health issues experienced by males compared to females [11-13]. To effectively reach males and have them engage in health promotion initiatives, interventions that appeal to males and meet their specific needs are required.

    There is a clear gap in the research literature addressing the physical activity and nutritional behaviors of males. Little male-specific research has been undertaken, and best practice approaches have not been identified [12,14,15]. Overall, males receive less educational advice than women from health professionals about modifiable lifestyle behaviors associated with disease risk [16], and there are a lack of resources available that are targeted specially at males [17,18]. There is strong evidence that behavior change interventions that address characteristics and behavior change determinants unique to particular subgroups are more effective than generic “one-size-fits all” approaches [19]. Given the difference in social norms regarding masculine and feminine gender roles and the influence of these norms on behavior [16], the development of resources targeted specially at men is required.

    Purpose of the ManUp Study

    The purpose of the ManUp study [20] was to address this issue by developing and testing innovative physical activity and nutrition modification strategies that would be appealing and effective for middle-aged males. As part of this study, comprehensive reviews of the literature were undertaken to identify strategies that have worked in previous interventions targeting males [21,22]. Original research exploring male’s intervention preferences [23] and factors influencing physical activity and nutrition behavior among males were also conducted [24]. From this, Web- and mobile phone-based interventions that include the delivery of quantitative information and feedback and encourage goal setting and self-monitoring were identified as a promising intervention approach for targeting and engaging males. This was due to Web-based interventions with these features successfully improving physical activity and nutrition behaviors in previous studies [21,22], and the potential for Web-based interventions to be accessed by large numbers of individuals without time of day or geographic restrictions, which was considered necessary for overcoming key barriers to participating in healthy lifestyle behaviors reported by males (such as work and family commitments) [24]. On this basis, a Web-based social cognitive [25,26] and self-regulation theory-based [27] intervention, designed to increase knowledge and self-efficacy via information provision, goal setting, and self-monitoring, was developed and evaluated in a two-arm randomized controlled trial [20].

    Issues With Participant Engagement and Retention

    Despite the comprehensive and systematic approach to intervention development, issues with engagement and retention of participants were experienced. Few participants logged on to the website regularly (median number of log-ins over the 9 month intervention period was 2 per participant) (interquartile range-IQR=6), and engagement in key behavior change components was low overall (median number of self-monitoring entries was 1 per participant) (IQR=20); median number of challenges initiated was 1 per participant (IQR=3). The dropout was high across both intervention groups, but was significantly higher in the Web-based intervention arm (52/96, 54%, had dropped out at the 9 month follow-up) compared to the print-based positive intervention control arm (96/205, 46.8%, had dropped out at the 9 month follow-up). This is in line with findings from other Web-based-delivered health behavior intervention studies [14,28]. If such interventions are to be effective public health tools, a greater research focus on user engagement is needed [28,29]. This should include research examining the underlying theoretical mechanisms of engagement.

    Prior research examining user experiences in human-computer interactions suggests that engagement consists of four distinct stages: (1) the point of engagement, (2) a period of sustained engagement, (3) disengagement, and (4) (possibly) reengagement [30]. According to O’Brien and Toms’ [30] conceptual framework of engagment with technology, the point of engagement is initiated by the aesthetic appeal and/or novel presentation of the interface, the users' motivations and interests, the users' ability and desire to be situated in the interaction, and to perceive that there is sufficient time to use the application. At this phase, users typically have a goal in mind for what they would like to gain from the interaction. Engagement is sustained when users are able to maintain their attention and interest in the application and is characterized by positive emotions. Attention and interest are perpetuated by the interactivity of the computer environment (physical, social, and cognitive), the usability of the interface, and how well these features match the users' attention, motivation, interest, and need for aesthetic and sensory appeal, novelty, control, and challenge. Users must be made to feel a part of the interaction through an awareness of what the system is doing (via feedback), by feeling connected to the technology (via interactivity) or to other people, and by feeling in control over what is happening. Disengagement can occur for many reasons, such as the usability of the technology (ie, challenge and interactivity) and distractions in the user’s environment. This stage, depending on the outcome, will result in either positive (user’s needs and motivations are satisfied and they feel successful) or negative emotions (user feels frustration, overwhelmed by challenges or information, loss of interest or motivation). Users may cycle through the stages of engagement several times during a single session or over several sessions, thus demonstratating reingagement. Reingagement shares the same attributes as the point of engagement [30].

    The aim of the current study was to explore participant engagement in the ManUp intervention using this framework and to generate directions for future research in the field. The behavioral outcomes of the ManUp randomized trial, relating to changes in diet and physical activity behavior, are forthcoming and will be published in a separate manuscript.


    Methods

    The ManUp Trial

    A detailed description of the ManUp randomized controlled trials (RCT) protocol and study sample has been published previously [20]. Briefly, 317 middle-aged men (35-54 years old) recruited from two areas in Central Queensland, Australia were randomly allocated to one of two intervention arms: (1) an Information Technology (IT)-based intervention arm (Web- and mobile phone-based), or (2) a print-based positive-intervention control arm. A print-based intervention was selected as the positive control due to evidence that print-based interventions can be effective among adults, and because they are low-cost and wide-reaching and therefore may be of equal public health potential to IT-based approaches [31,32]. A positive intervention control was employed rather than a standard or true control (eg, wait list), as this approach has been recommended in order to more readily inform public health policy and practice [32,33]. Allocation was conducted on a 2:1 ratio in favor of the IT-based intervention. This was to control for attrition and ensure adequate power [20]. An overview of the interventions is provided below and a screenshot of the ManUp intervention can be found in Figure 1. This study was registered retrospectively (ACTRN12611000081910).

    Both interventions comprised three main components: (1) educational materials that were designed to enhance health literacy by clearly communicating the health benefits associated with physical activity and a healthy diet, show the risks associated with inactivity and an unhealthy diet, and show the amount or type of physical activity and nutrition behaviors required to achieve health benefit; (2) ManUp physical activity, nutrition personal, and group “challenges” constructed to provide participants with specific, measureable, and time-based goals and to encourage self-monitoring behaviors, and; (3) self-monitoring resources, providing participants with the ability to record progress and keep themselves informed of progress towards completing these challenges.

    Participants in the print-delivered intervention arm received these components via a hard copy booklet that contained a series of log sheets that could be used to monitor their progress and/or successful completion of any of the ManUp physical activity or nutrition challenges. Participants in the IT-delivered intervention arm were given access to a password protected website containing these components across six separate sections which participants could navigate (ie, My Profile, My Progress, My Mates, My Groups, My Weight, and Information Center). The website contained additional components that reflected the ability of the IT-based intervention to deliver automated feedback on challenge progress. Participants were also able to record and receive feedback on their body weight (kg), Body Mass Index (BMI) (kg/m2), and waist circumference (cm), as well as view summaries of all data recorded, schedule activities, search for “mates,” write on “mates” profile pages, and take part in group challenges. Additionally, participants with Internet access on their mobile phones were given access to a mobile phone application, facilitating self-monitoring behaviors by allowing them to quickly and conveniently start a new ManUp physical activity or healthy eating challenge, record progress, and view progress towards completing challenges.

    Figure 1. Screenshot of ManUP intervention website (participant profile).
    View this figure

    Recruitment

    Before approaching ManUp participants an estimate of the required sample size to reach saturation was conducted [34]. As the study was relatively narrow in scope and based on participants’ direct experiences, we expected that the sample size needed to reach saturation would be small and aimed to recruit 30 ManUp participants (15 from each intervention arm) [34-36]. ManUp participants were contacted by telephone (in alphabetical order) and invited to take part in the study. Up to three call attempts were made to participants before labeling them as a nonresponder. It was intended that calls would continue to be made until 30 interviews had been conducted. However, after contacting 60 ManUp participants, with 20 participants consenting (a response rate of 33%), a decision was made to delay inviting additional men to participate until a judgment about theoretical saturation could be made on the already recruited sample. Theoretical saturation arises when no new data occurs after continuing sampling and analyzing data [36]. After conducting interviews with this sample, it was agreed that further interviews were unlikely to result in new information, and as such, no further attempts to recruit participants were made.

    Procedure

    Semistructured telephone interviews were conducted and audiotaped by research assistants working on the ManUp project. During each interview, written notes were taken in as much detail as possible to aid in the interpretation of the recordings. Each interview ran for approximately 10 minutes. Telephone interviews were chosen over other interview methods due to the geographical dispersion of participants and evidence that this method can provide rich data [37].

    Prior to conducting interviews with participants, training interviews were conducted on a convenience sample (n=5) to provide the interviewers with an opportunity to practice interview skills and refine the interview materials if necessary. Feedback was provided to the interviewers by the lead investigator (MJD) and necessary changes were made to the interview protocol prior to data collection.

    Discussion Guide

    Interview questions were designed to explore attributes associated with participant engagement and disengagement in the intervention materials. The development of the open-ended questions was guided by O’Brien and Toms’ [30] conceptual model of engagement and centered on why participants took part in the study, what they liked and did not like about the intervention they received, and how they think the intervention could be improved. Leading questions about specific intervention attributes were avoided, and, instead, questions were designed to allow participants to voice their own views, values, and experiences. Participants were prompted to expand on their answers and give as many details as possible using standard prompting techniques (eg, requesting more information, paraphrasing, and using affirmative noises). The interview questions in the context of the conceptual model are presented in Table 1.

    Table 1. Hypothesized attributes influencing engagement and related interview questions.
    View this table

    Data Analysis

    Data collection was conducted from April 2012 to June 2012. Data extraction was conducted between January and February 2013. Interview recordings were transcribed verbatim by a research assistant and analyzed thematically using a theory-driven code [38] into a tabular format (based on O’Brien and Toms’ [30] preexisiting description of attributes influencing engagement; see Table 1). During the coding process frequencies of each issue raised were noted. Data were analysed in this way to enable the investigators to identify patterns and facilitate discovery of the most prevalent themes. However, in accordance with qualitative research methodology, where the focus is on richness and not representativeness, frequencies are not reported in the text [35,36]. There were two researchers (CS, MWD) that conducted the coding process independently to ensure the trustworthiness of the themes. Results of the independent analyses were compared and discussed until agreement was reached.


    Results

    Participants

    Twenty out of the 60 individuals contacted took part in the study. A summary of participant characteristics is presented in Table 2 (data obtained using Web-based surveys as part of baseline assessment in the ManUp study) [20]. Overall, the majority of participants were professional or white collar workers, with moderate health literacy for physical activity and nutrition topics, were classified as overweight or obese (based on BMI), and were partaking in some physical activity, but not at a level sufficient to meet the national guidelines (ie, 150 minutes/week of moderate-vigorous physical activity spread across at least five days) [39]. There were no significant differences between ManUp participants who participated in the RCT and those who participated in the current study in terms of age, income, health literacy, physical activity, and diet behavior (P>.05). However, a higher proportion of interviewed participants were employed in professional and white collar occupations than those who were not interviewed (P=.03), and of those that received the IT-based intervention, interview participants of the current study had higher median log-in rates as compared to those who did not participate (P=.01).

    Themes from the theory-driven analysis are summarized here. Representative quotes relating to each theme are provided in Tables 3 and 4.

    Table 2. Participant characteristics.
    View this table

    Users’ Motivations

    The most common motivating factors among participants were weight loss and gaining the necessary tools and incentives to self-manage one’s health. In particular, participants expressed a desire to “gauge” their health and fitness levels and to be provided with feedback and advice on how to control their weight and maintain healthy lifestyle habits. For a few participants, participation was driven by external factors, such as pressure to participate from their partner.

    Users’ Desired Outcomes

    Overall, the outcomes people anticipated from engaging in the program largely reflected their motives for participating. Most were expecting to receive guidance and counseling from the project team to help them enhance their diet and participation in physical activity. For some, this guidance and advice were expected to be specific, such as a prescriptive diet plan to follow or support for their particular sport and activities. For others, the type of advice and support expected were described more generally, such as “tips and suggestions” to live a healthier life. Strategies to help participants stay disciplined and to take action were also expected, such as the provision of materials to record diet and physical activity behaviors. A few participants also expressed that they expected to improve their lifestyle behaviors and/or weight status as a result of participating in the study.

    Users’ Positive Emotions

    Participants from both intervention groups liked that the information they were provided with was easy to read, use, and had an appropriate tone (ie, not derogatory). Participants from both groups liked that the materials could be used as a benchmark and reference tool when thinking about their own health. Participants who received the IT-based intervention liked the ability to record and view a visual summary of their progress.

    Users’ Negative Emotions

    Some participants who received the printed information found that the booklet was too long and that some of the text was long-winded. Furthermore, there were a few participants who expressed disappointment with the level of interaction and feedback provided and felt they would have done better with the “Web-based stuff.” Among those who received the IT-based intervention, some participants expressed that they would have liked functional aspects of the website to be improved, such as the ability to enter and keep track of different types of activities, the ability to enlarge text, and the progress calendar. Furthermore, a few participants raised sustaining self-monitoring as an issue, especially when personal physical activity routines did not change (ie, self-monitoring via the website was considered less useful) or when in out-of-service areas (ie, when self-monitoring could not be done immediately and conveniently due to a lack of Internet connection). A few participants also expressed disappointment with the intervention content, with some participants reporting that the physical activity and nutrition content was not prescriptive enough, and others reporting that they would have liked to have received more personalized information and feedback about how their changes in health behavior were likely to impact on their health.

    Attributes Desired by Users

    Suggestions on how to improve the print-based intervention included providing more tips and helpful hints that are based on the experiences of their peers, and transferring the intervention onto a Web or mobile phone platform to make it more interesting and accessible. Suggestions on how to improve the IT-based intervention were more varied and included both suggestions on how to improve the website usability and for improving intervention content. Specific functional components requested by participants included a facility to report IT-based issues, reminder emails offering direct links to participant profiles (without logging in), the capacity to use the mobile app when there is no Internet connection, and to sync the data with the website at a later date. Suggestions on how to improve intervention content included providing links to nutrition and physical activity information on a separate page of the website, allowing participants to set their own challenge metrics, providing more detailed and iterative feedback, and providing access to other useful tools, such as a calorie conversion calculator. Some of these suggestions, namely a facility to report IT-based issues and links providing further lifestyle information, were actually included on the website.

    Table 3. Representative quotes from participants relating to each theme.
    View this table
    Table 4. Representative quotes from participants relating to each theme.
    View this table

    Discussion

    Aim of the Study

    Poor user engagement in Web-based health behavior interventions is consistently reported in the literature [14,28]. Despite this, very little research in the field has explored user experiences in human-computer interactions and how such experiences may relate to engagement, especially among males who are in general harder to engage than women in behavior change interventions [9,10]. The aim of the current study was to explore participant engagement in the ManUp intervention using a conceptual framework [30] and to inform future research in the field. Although not conclusive, the current study provides some insight into what personal attributes of middle-aged males (such as their key motives and goals for participating) and attributes of the intervention materials (such as usability, control, and interactivity) may impact on user engagement in this group. When considered in the context of previous research, these findings will be helpful for informing the design and implementation of future health behavior interventions for males and assist researchers and practitioners to engage males in health behaviors.

    Comparison to Previous Research

    The desirable intervention components reported in this study are similar to those reported in previous (atheoretical) qualitative studies exploring participant’s experiences with Web-based interventions [40,41]. Participants in Morgan et al’s [40] Australian self-help, exercise, diet, and information technology (SHED-IT) intervention, an Internet-based weight loss program designed specifically for men [41], found the use of a calorie converter website that provided instant and visual feedback an invaluable part of the program, especially for education about foods and for self-monitoring. Participants also reported that email feedback was helpful, but felt they would have benefited more if the information provided was more specific to them personally. SHED-IT participants were not interested in the forum facility of the website, which was designed to provide them with an opportunity to engage in discussions with other men about weight loss. Participants in Ferney and Marshall’s [42] study (including both males and females) reported that the overall usability of the website was extremely important to them and that engagement in the website would be better sustained if there were more interactive features such as self-reported progress charts allowing users to set goals and monitor their progress, and if they received regular newsletters via email. They also suggested that a forum would help encourage engagement by offering a social support network along with other helpful advice and suggestions. Interestingly, in randomized trials associated with both of these previous qualitative studies, participants did not use forum features of the website [41,43]. The current study did not include a forum feature, rather the ability to post comments on their own or another user's profile page was included, and this was not widely used by participants. It may be that utilization of the forum is dependent on social environmental factors such as the presence of a forum moderator for participants to interact with. In the current study, the low usage of the comments feature may be due to participants not knowing each other and being reluctant to comment or interact with unknown people. Future research is needed to explore this further. One website component that was not discussed by participants in the current study, but did emerge as an important factor in both of the aforementioned studies, was the speed of the website. In both of these previous studies, participants reported dissatisfaction and reduced use of the website if the loading time was slow [27,28].

    Implications for Practice

    The findings of this study provide some insight into how we may improve engagement among males in Web-based behavior change interventions targeting physical activity and nutrition. First, as weight management appears to be a key motivator for many males participating in Web-based programs, participant engagement (in terms of recruitment and content) may be higher if weight loss is also highlighted as an intervention outcome. Findings from the SHED-IT trial showed that how the weight loss intervention is framed is important and suggest that programs that are framed as nonintrusive and flexible are most appealing [27]. Second, as participant’s experience of the intervention (positive and negative) seemed to be guided by their initial expectations, managing outcome expectations from the outset and avoiding violations of these expectations may help to improve engagement, retention, and maintenance [30,31]. This could be achieved in the following ways: (1) by ensuring recruitment information adequately explains the aim, content, and structure of the intervention program; (2) by educating participants about the health outcomes associated with adhering to the intervention protocol; (3) by allowing participants to set their own goals; and (4) by ensuring participants set goals that are both achievable and in line with their desired outcomes. The use of positive reinforcement (internal or external) may also be helpful, especially in cases where outcomes may not be immediate (eg, disease prevention) [30,31]. Finally, participants provided several suggestions regarding how to improve website usability and intervention content which may improve efficacy, engagement, and maintenance in future interventions. As some of these suggestions (namely a facility to report IT-based issues and links providing further lifestyle information) were actually included on the website, this may suggest potential user navigation issues, communication issues (eg, use of the word “bug” for computer issue), lack of use of the IT-based platform, and/or that the website did not sufficiently “grab” the attention of some participants to encourage more elaborate use of the site. These issues highlight that researchers and practitioners need to consider the attributes of user disengagement (as well as engagement) when designing websites. This is particularly so for disengagement that occurs due to negative emotions, such as feeling frustrated due to usability issues with the website or feeling bored due to lack of interactivity and/or novelty of the content.

    Strengths and Limitations

    A major strength of this study is that it provides a greater understanding of what males want in terms of Web-based health behavior interventions. Males are a hard-to-reach population [9,10]. As such, little male specific research has been undertaken and best practice intervention approaches targeting males have not been identified. This research directs some insight into what recruitment and intervention strategies may enhance a male’s engagement, retention, and maintenance in these interventions. This is useful information, considering the paucity of research in this area, a male’s preference for IT-based strategies compared to face-to-face strategies, and the potential public health impact of such interventions if engagement and maintenance can be achieved. However, there are some limitations that should be acknowledged when interpreting these findings. As not all participants agreed to be contacted to take part in follow-up interviews, the results presented in this manuscript may not be representative of the whole ManUp sample. In addition, the ManUp Trial participants were primarily employed in white-collar and professional occupations, as were the participants of the current study. As such, the findings may not be representative of those in lower socioeconomic positions. While interesting outcomes have been reported, due to the exploratory and qualitative nature of the study (where the focus is on information richness rather than representativeness) [21,22], more comprehensive methodologies, such as intervention research, are now needed to confirm the outcomes revealed by the present study.

    Compared to their female counterparts, males have higher age-specific rates of chronic disease (such as heart disease, Type 2 diabetes, obesity, and mental health issues) [11-13] and are less likely to engage in lifestyle modification programs and utilize health services [9,10]. Given this, interventions that appeal to males, which meet their specific needs and that are not resource intensive are required. The development and evaluation of lifestyle modification programs that have a strong theoretical and evidence base, grounded in sound behavioural and communication-based research, and that are tailored to specific at risk subgroups are an important public health response to the growing prevalence of chronic disease and the survival deficit experienced by males. Our study highlights intervention and participant attributes that impacted on the engagement of males participating in a lifestyle modification program. These findings will help inform the design and implementation of future health behavior interventions for males and assist researchers and practitioners to engage males in health behaviors.

    Acknowledgments

    Queensland Health provided funding to conduct the ManUp project. CV is supported by a National Health and Medical Research Council of Australia (#519778) and a National Heart Foundation of Australia (#PH 07B 3303) postdoctoral research fellowship. CES and MJD are supported by fellowships provided by the Institute of Health and Social Science Research, CQUniversity. MWD was funded by a student internship provided by the Institute of Health and Social Science Research, CQUniversity.

    Conflicts of Interest

    None declared.

    References

    1. World Health Organization. Global strategy of diet, physical activity, and health. 2004. Diet and physical activity: A public health priority   URL: http://www.who.int/dietphysicalactivity/en/ [accessed 2013-12-16] [WebCite Cache]
    2. Lee IM, Shiroma EJ, Lobelo F, Puska P, Blair SN, Katzmarzyk PT, Lancet Physical Activity Series Working Group. Effect of physical inactivity on major non-communicable diseases worldwide: An analysis of burden of disease and life expectancy. Lancet 2012 Jul 21;380(9838):219-229 [FREE Full text] [CrossRef] [Medline]
    3. Bauman A, Bull F, Chey T, Craig CL, Ainsworth BE, Sallis JF, et al. The international prevalence study on physical activity: Results from 20 countries. Int J Behav Nutr Phys Act 2009;6:21 [FREE Full text] [CrossRef] [Medline]
    4. Macniven R, Bauman A, Abouzeid M. A review of population-based prevalence studies of physical activity in adults in the Asia-Pacific region. BMC Public Health 2012;12:41 [FREE Full text] [CrossRef] [Medline]
    5. Centers for Disease Control and Prevention (CDC). Prevalence of regular physical activity among adults-United States, 2001 and 2005. MMWR Morb Mortal Wkly Rep 2007 Nov 23;56(46):1209-1212 [FREE Full text] [Medline]
    6. Centers for Disease Control and Prevention (CDC). Prevalence of fruit and vegetable consumption and physical activity by race/ethnicity-United States, 2005. MMWR Morb Mortal Wkly Rep 2007 Apr 6;56(13):301-304 [FREE Full text] [Medline]
    7. Macera CA, Ham SA, Yore MM, Jones DA, Ainsworth BE, Kimsey CD, et al. Prevalence of physical activity in the United States: Behavioral risk factor surveillance system, 2001. Prev Chronic Dis 2005 Apr;2(2):A17 [FREE Full text] [Medline]
    8. World Cancer Research Fund, American Institute of Cancer Research. Food, nutrition, physical activity, and the prevention of cancer: A global perspective. Washington, DC: World Cancer Research Fund/ Institute for Cancer Research   URL: http://www.dietandcancerreport.org/ [accessed 2013-12-17] [WebCite Cache]
    9. Deeks A, Lombard C, Michelmore J, Teede H. The effects of gender and age on health related behaviors. BMC Public Health 2009;9:213 [FREE Full text] [CrossRef] [Medline]
    10. Waters LA, Galichet B, Owen N, Eakin E. Who participates in physical activity intervention trials? J Phys Act Health 2011 Jan;8(1):85-103. [Medline]
    11. Mathers CD, Vos ET, Stevenson CE, Begg SJ. Bulletin of the World Health Organization. 2001. The burden of disease and injury in Australia   URL: http://www.scielosp.org/scielo.php?script=sci_arttext&pid=S0042-96862001001100013&lng=en&nrm=iso [WebCite Cache]
    12. Australian Government, Department of Health. National male health policy: Building on the strengths of Australian males. Australian   URL: http://www.healthissuescentre.org.au/documents/items/2013/05/751293-upload-00001.pdf [accessed 2013-12-19] [WebCite Cache]
    13. Malcher G. The state of men's health in Europe. BMJ 2011 Nov 29;343:d7054-d7054. [CrossRef]
    14. Vandelanotte C, Spathonis KM, Eakin EG, Owen N. Website-delivered physical activity interventions: A review of the literature. Am J Prev Med 2007 Jul;33(1):54-64. [CrossRef] [Medline]
    15. Kumanyika SK, Obarzanek E, Stettler N, Bell R, Field AE, Fortmann SP, American Heart Association Council on Epidemiology and Prevention‚ Interdisciplinary Committee for Prevention. Population-based prevention of obesity: The need for comprehensive promotion of healthful eating, physical activity, and energy balance: A scientific statement from American Heart Association Council on Epidemiology and Prevention, Interdisciplinary Committee for Prevention (formerly the expert panel on population and prevention science). Circulation 2008 Jul 22;118(4):428-464 [FREE Full text] [CrossRef] [Medline]
    16. Courtenay WH. Constructions of masculinity and their influence on men's well-being: A theory of gender and health. Soc Sci Med 2000 May;50(10):1385-1401. [Medline]
    17. Latimer AE, Brawley LR, Bassett RL. A systematic review of three approaches for constructing physical activity messages: What messages work and what improvements are needed? Int J Behav Nutr Phys Act 2010;7:36 [FREE Full text] [CrossRef] [Medline]
    18. Hatchell AC, Bassett-Gunter RL, Clarke M, Kimura S, Latimer-Cheung AE. Messages for men: The efficacy of EPPM-based messages targeting men's physical activity. Health Psychol 2013 Jan;32(1):24-32. [CrossRef] [Medline]
    19. Noar SM, Harrington NG, Van Stee SK, Aldrich RS. Tailored health communication to change lifestyle behaviors. American Journal of Lifestyle Medicine 2010 Nov 19;5(2):112-122. [CrossRef]
    20. Duncan MJ, Vandelanotte C, Rosenkranz RR, Caperchione CM, Ding H, Ellison M, et al. Effectiveness of a website and mobile phone based physical activity and nutrition intervention for middle-aged males: Trial protocol and baseline findings of the ManUp Study. BMC Public Health 2012;12:656 [FREE Full text] [CrossRef] [Medline]
    21. George ES, Kolt GS, Duncan MJ, Caperchione CM, Mummery WK, Vandelanotte C, et al. A review of the effectiveness of physical activity interventions for adult males. Sports Med 2012 Apr 1;42(4):281-300. [CrossRef] [Medline]
    22. Taylor PJ, Kolt GS, Vandelanotte C, Caperchione CM, Mummery WK, George ES, et al. A review of the nature and effectiveness of nutrition interventions in adult males-A guide for intervention strategies. Int J Behav Nutr Phys Act 2013;10:13 [FREE Full text] [CrossRef] [Medline]
    23. Vandelanotte C, Caperchione CM, Ellison M, George ES, Maeder A, Kolt GS, et al. What kinds of website and mobile phone-delivered physical activity and nutrition interventions do middle-aged men want? J Health Commun 2013;18(9):1070-1083. [CrossRef] [Medline]
    24. Caperchione CM, Vandelanotte C, Kolt GS, Duncan M, Ellison M, George E, et al. What a man wants: Understanding the challenges and motivations to physical activity participation and healthy eating in middle-aged Australian men. Am J Mens Health 2012 Nov;6(6):453-461. [CrossRef] [Medline]
    25. Bandura A. A social cognitive theory. In: Social foundations of thought and action. Englewood Cliffs, N.J: Prentice-Hall; 1986.
    26. Bandura A. Health promotion by social cognitive means. Health Educ Behav 2004 Apr;31(2):143-164. [CrossRef] [Medline]
    27. Maes S, Karoly P. Self-regulation assessment and intervention in physical health and illness: A review. Applied Psychology 2005 Apr;54(2):267-299. [CrossRef]
    28. Davies CA, Spence JC, Vandelanotte C, Caperchione CM, Mummery WK. Meta-analysis of Internet-delivered interventions to increase physical activity levels. Int J Behav Nutr Phys Act 2012;9:52 [FREE Full text] [CrossRef] [Medline]
    29. Kirwan M, Duncan MJ, Vandelanotte C, Mummery WK. Using smartphone technology to monitor physical activity in the 10,000 Steps program: A matched case-control trial. J Med Internet Res 2012;14(2):e55 [FREE Full text] [CrossRef] [Medline]
    30. O'Brien HL, Toms EG. What is user engagement? A conceptual framework for defining user engagement with technology. J. Am. Soc. Inf. Sci 2008 Apr;59(6):938-955. [CrossRef]
    31. Marcus BH, Lewis BA, Williams DM, Whiteley JA, Albrecht AE, Jakicic JM, et al. Step into motion: A randomized trial examining the relative efficacy of Internet vs print-based physical activity interventions. Contemp Clin Trials 2007 Nov;28(6):737-747. [CrossRef] [Medline]
    32. Short CE, James EL, Plotnikoff RC, Girgis A. Efficacy of tailored-print interventions to promote physical activity: A systematic review of randomised trials. Int J Behav Nutr Phys Act 2011;8:113 [FREE Full text] [CrossRef] [Medline]
    33. Courneya KS. Efficacy, effectiveness, and behavior change trials in exercise research. Int J Behav Nutr Phys Act 2010;7:81 [FREE Full text] [CrossRef] [Medline]
    34. Mason M. Qualitative Social Research. 2010. Sample size and saturation in PhD studies using qualitative interviews   URL: http://www.qualitative-research.net/index.php/fqs/article/view/1428 [accessed 2013-12-19] [WebCite Cache]
    35. Guest GB, Arwen G, Johnson L. How many interviews are enough? An experiment with data saturation and variability. Field Methods 2006;18(1):59-82 [FREE Full text] [CrossRef]
    36. Morse JM. Determining sample size. Qualitative Health Research 2000;10(1):3-5 [FREE Full text] [CrossRef]
    37. Sturges JE, Hanrahan KJ. Comparing telephone and face-to-face qualitative interviewing: A research note. Qualitative Research 2004;4(1):107-118 [FREE Full text] [CrossRef]
    38. Boyatzis R. Developing themes using theory-driven and prior-research driven method and then applying the code: An example using a critical incident interview. In: Transforming qualitative information: Thematic analysis and code development. Thousand Oaks, CA: Sage Publications; 1998.
    39. Australian Government, Department of Health. An active way to better health: National physical activity guidelines for adults.   URL: http:/​/www.​health.gov.au/​internet/​main/​publishing.nsf/​Content/​phd-physical-activity-adults-pdf-cnt.​htm [accessed 2013-12-17] [WebCite Cache]
    40. Morgan PJ, Warren JM, Lubans DR, Collins CE, Callister R. Engaging men in weight loss: Experiences of men who participated in the male only SHED-IT pilot study. Obesity Research & Clinical Practice 2011 Jul;5(3):e239-e248. [CrossRef]
    41. Morgan PJ, Lubans DR, Collins CE, Warren JM, Callister R. The SHED-IT randomized controlled trial: Evaluation of an Internet-based weight-loss program for men. Obesity (Silver Spring) 2009 Nov;17(11):2025-2032. [CrossRef] [Medline]
    42. Ferney SL, Marshall AL. Website physical activity interventions: Preferences of potential users. Health Educ Res 2006 Aug;21(4):560-566 [FREE Full text] [CrossRef] [Medline]
    43. Ferney SL, Marshall AL, Eakin EG, Owen N. Randomized trial of a neighborhood environment-focused physical activity website intervention. Prev Med 2009 Feb;48(2):144-150. [CrossRef] [Medline]


    Abbreviations

    BMI: body mass index
    IQR: interquartile range
    IT: information technology
    RCT: randomized controlled trials
    SHED-IT: self-help, exercise, diet, and information technology


    Edited by G Eysenbach; submitted 18.06.13; peer-reviewed by S Wyke, A Wagemakers, B Irwin, R Gerrits; comments to author 12.07.13; revised version received 08.09.13; accepted 03.11.13; published 03.01.14

    ©Camille E Short, Corneel Vandelanotte, Marcus W Dixon, Richard Rosenkranz, Cristina Caperchione, Cindy Hooker, Mohan Karunanithi, Gregory S Kolt, Anthony Maeder, Hang Ding, Pennie Taylor, Mitch J Duncan. Originally published in JMIR Research Protocols (http://www.researchprotocols.org), 03.01.2014.

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