Background: Type 2 diabetes is a major challenge for Canadian public health authorities, and regular physical activity is a key factor in the management of this disease. Given that less than half of people with type 2 diabetes in Canada are sufficiently active to meet the Canadian Diabetes Association's guidelines, effective programs targeting the adoption of regular physical activity are in demand for this population. Many researchers have argued that Web-based interventions targeting physical activity are a promising avenue for insufficiently active populations; however, it remains unclear if this type of intervention is effective among people with type 2 diabetes.
Objective: This research project aims to evaluate the effectiveness of two Web-based interventions targeting the adoption of regular aerobic physical activity among insufficiently active adult Canadian Francophones with type 2 diabetes.
Methods: A 3-arm, parallel randomized controlled trial with 2 experimental groups and 1 control group was conducted in the province of Quebec, Canada. A total of 234 participants were randomized at a 1:1:1 ratio to receive an 8-week, fully automated, computer-tailored, Web-based intervention (experimental group 1); an 8-week peer support (ie, Facebook group) Web-based intervention (experimental group 2); or no intervention (control group) during the study period.
Results: The primary outcome of this study is self-reported physical activity level (total min/week of moderate-intensity aerobic physical activity). Secondary outcomes are attitude, social influence, self-efficacy, type of motivation, and intention. All outcomes are assessed at baseline and 3 and 9 months after baseline with a self-reported questionnaire filled directly on the study websites.
Conclusions: By evaluating and comparing the effectiveness of 2 Web-based interventions characterized by different behavior change perspectives, findings of this study will contribute to advances in the field of physical activity promotion in adult populations with type 2 diabetes.
Trial Registration: International Standard Randomized Controlled Trial Number (ISRCTN): ISRCTN15747108; http://www.isrctn.com/ISRCTN15747108 (Archived by WebCite at http://www.webcitation.org/6eJTi0m3r)
In 2009, 2.4 million Canadians were living with type 2 diabetes (T2D), and this number is expected to grow to 3.7 million by 2019 . Scientific evidence increasingly shows the importance of regular moderate-intensity aerobic physical activity (PA) as a means of reducing the risk of complications associated with T2D [ - ]. Despite this body of evidence, self-reported measures suggest that only 30 to 40% of Canadians with T2D are meeting the Canadian Diabetes Association's guidelines [ , ]. Accordingly, innovative strategies aimed at promoting the adoption and maintenance of regular PA among Canadians with T2D are needed [ ], and information technologies such as the Internet are a promising avenue for the development of large-scale interventions with behavior change purposes [ - ]. The evolution of Canadian Internet use also suggests that public health organizations should take advantage of this technology to develop and deliver services [ - ], including services targeting populations with chronic diseases [ ] and adults ranging from 18 to 65 years of age [ - ].
To date, a significant number of scientific reviews have studied the effectiveness of Web-based interventions designed for PA adoption and/or maintenance, with evidence showing that they can be effective across various populations and settings [- ]. However, only a few studies have attempted to develop Web-based PA-oriented interventions specifically for people with T2D, which have only produced mixed results so far [ - ]. Among these studies, the most successful interventions have been characterized by the following [ , ]: (1) they were theory-based; (2) they included goal-setting activities; (3) they included interactive tracking and personalized feedback components; and (4) they provided opportunities for peer support. In line with these insights, two web-based interventions using different behavior change perspectives will be evaluated and compared in this study regarding their effectiveness in promoting the adoption and maintenance of regular moderate-intensity aerobic PA in adults with T2D.
The first intervention consists of a fully automated, computer-tailored, Web-based intervention developed from the perspective of offering tracking and personalized feedback. In eHealth research, the technology underlying the tracking and personalized feedback component of interventions is known as “computer tailoring,” which can be defined as the generation of personalized feedback by a computer program based on prior individual assessment(s) . This technology follows principles of face-to-face counselling and assesses individuals’ perceived health behavior status as well as the determinants that influence their motivation and behavior. Next, tailored feedback is provided about these determinants based on individuals’ answers and personal characteristics. According to the Elaboration Likelihood Model [ ], the provision of tailored feedback will result in more thoughtful information processing via the central route of persuasion, since tailored messages are perceived as being personally relevant and thus encourage the person to pursue the desired behavior [ ]. The technology of computer tailoring has proven to be more effective than more general types of health behavior change interventions, including those targeting PA adoption [ , - ]. Yet, to our knowledge, only a few studies to date have used computer-tailoring technologies for the purpose of promoting regular moderate-intensity aerobic PA among adults with T2D [ - ].
The second intervention consists of a peer support, Web-based intervention that was developed from the perspective of offering Web-based opportunities for peer support. Within a health care context, peer support is defined as “the provision of emotional, appraisal, and informational assistance by a created social network member who possesses experiential knowledge of specific behavior or stressor and similar characteristics as the target population, to address a health-related issue of a potentially or actually stressed focal person” . Social networking sites, of which Facebook is the most popular platform [ ], can offer an innovative way of delivering peer support interventions to promote the adoption of health-related behaviors [ ]. In this regard, the use of Facebook as a means of offering opportunities for peer support for promoting PA is relatively new. Evaluative studies published to date demonstrate the potential of this platform among young adult cancer survivors [ ], healthy adults [ ], female undergraduate students [ ], female college freshmen [ ], and government employees with metabolic syndrome [ ]. Yet, to our knowledge, no study to date has used this platform for the purpose of enabling adults with T2D to share their experiences, concerns, and progress with their peers regarding the adoption of regular moderate-intensity aerobic PA specifically.
A substantial body of evidence stresses the importance of using a theoretical framework to inform the development of behavior change interventions [, , ]. The development of both interventions was thus based on the I-Change Model [ ]. The I-Change Model is a theoretical framework that integrates the main concepts of several social cognitive theories (eg, Theory of Planned Behavior [ ], Social Cognitive Theory [ ], Health Belief Model [ ], and the Transtheoretical Model [ ]). The I-Change Model distinguishes 3 phases and their corresponding determinants in the behavior change process. For instance, to promote regular moderate-intensity aerobic PA in adults with T2D, an intervention should (1) increase a person’s awareness about the importance of practicing moderate-intensity aerobic PA; (2) motivate the person to practice moderate-intensity aerobic PA; and (3) transfer this motivation to the adoption of regular moderate-intensity aerobic PA [ ]. Furthermore, the I-Change Model assumes that the determinants of the 3 phases mentioned above are affected by, among other influences, channel factors, which refer to the methods used to deliver messages. In this research project, the computer-tailoring component and peer support component are considered to be two different channels for message delivery. It is hypothesized that these channels will foster the adoption of regular moderate-intensity aerobic PA through cognitive changes (eg, attitude, social influence, self-efficacy, intention) [ , ].
In addition, to explore a current trend in Web-based interventions with health behavior change purposes [- ], concepts derived from the Self-Determination Theory (SDT) [ ] and Motivational Interviewing (MI) [ ] are added to the I-Change Model as elements of the theoretical framework. SDT is a theory focusing on types of motivation rather than solely on the amount of motivation [ ], while MI is a counseling approach using a collaborative style of communication to strengthen motivation and commitment to change in individuals [ ]. As it has the potential to foster more autonomous motivation toward adopting and maintaining health behaviors in individuals [ ], the integration and application of SDT and MI into behavior change interventions have been extensively discussed and encouraged in the PA domain in recent years [ - ]. Recent positive findings from a randomized controlled trial highlighted that more research on the effects of SDT and MI in Web-based PA behavior change interventions is needed [ ].
Aim of the Study and Hypotheses
The primary aim of this study is to evaluate the independent effectiveness of two Web-based interventions in promoting regular moderate-intensity aerobic PA among insufficiently active adult Canadian Francophones with T2D. In line with this aim, it is hypothesized that the Web-based computer-tailored intervention (H1) and the Web-based peer support intervention (H2) will be more effective at increasing moderate-intensity aerobic PA levels compared to a control group at 3 and 9 months after baseline. Another aim of this study is to compare the relative effectiveness of the two Web-based interventions at increasing moderate-intensity aerobic PA levels. Hence, the third hypothesis is that participants in both intervention groups will show a different level of moderate-intensity aerobic PA at 3 and 9 months after baseline. This hypothesis is formulated bilaterally, because no study to date has compared these two methods.
The study design is a parallel, randomized controlled trial (RCT) with the following 3 study arms: (1) an experimental group receiving an 8-week, fully automated, computer-tailored, Web-based intervention; (2) an experimental group receiving an 8-week, peer support, Web-based intervention; and (3) a control group that does not receive any intervention during the study period. A baseline measurement (T0) and two follow-up measurements at three (T1) and nine (T2) months after baseline are included. All three arms follow the same study timeline and are invited to complete the assessments during the same period. The protocol was developed in accordance with the CONSORT-EHEALTH checklist  and the SPIRIT statement 2013 [ ].
Eligibility and Recruitment
The target population for this research project is Canadian men and women with self-reported T2D residing in the province of Quebec. Other inclusion criteria to be eligible for the research project are not meeting the Canadian Diabetes Association guidelines on moderate-intensity aerobic PA , being able to understand French, having access to the Internet, being between 18 and 65 years of age, and not having medical indications limiting the practice of PA. Eligibility was evaluated with a self-reported questionnaire administered to potential participants via the intervention websites during the study registration process.
As for the recruitment process, the research project was implemented in partnership with Diabète Québec, a recognized association for people with T2D in the province of Québec, Canada. Following a joint agreement between both parties, Diabète Québec sent 2 invitation emails over a 2-week period (from 2014 September 15 to 28) to all Canadian Francophones with T2D meeting the age criteria and who subscribed to the association’s newsletter.
Two websites were created for this study: one to host activities related to the computer-tailored intervention, and the other to host activities for the peer support intervention and those of the control group. Randomization with a 1:1:1 allocation ratio was performed in three steps. First, all Diabète Québec newsletter recipients with T2D who were between 18 and 65 years of age received invitation emails to participate in the study, resulting in 6425 potential participants. Next, 35% of these 6425 potential participants were randomly sampled by Diabète Québec via a computer algorithm to receive emails with a URL link redirecting them to the first website (computer tailoring group). Meanwhile, the remaining 65% of potential participants received emails giving them access to a URL redirecting them to the second website. Finally, potential participants who had been redirected to the second website were then allocated randomly by the web solution company responsible for the website development to either the peer support intervention group or the control group via a computer algorithm. The randomization procedures mentioned above were conducted independently of the research team. This is an open-label RCT, meaning that participants and investigators know who has received which intervention.
Sample Size Calculation
Calculation of the sample size was achieved with GPower 3.0.10, and instructions for selecting different criteria were taken from Erdfelder et al . Thus, according to a mixed-plan, 3 (2 experimental groups and 1 control group) x 3 (baseline and 3- and 9-month follow-ups) design, assuming a statistical power of .80 and an alpha value of .05, a total sample size of 204 participants is necessary to detect a conservative effect size of d=.20 [ ]. This effect size was chosen with reference to results observed in the field of computer-tailored interventions [ , ] and considering the modest evidence observed to date for Web-based interventions integrating online opportunities for peer support [ , ]. Moreover, to obtain the calculated sample size at the end of the study (N=204), it was estimated that at least 6000 participants needed to be solicited through the invitation emails sent by Diabète Québec. This estimate was based on a similar study [ ], and a review of additional literature: (1) from the 6000 participants solicited, 30% (1800/6000) will agree to participate in the study [ , ]; (2) of this number, almost 100% (1800/1800) will have T2D, since preselection of potential participants with this type of diabetes will be executed by Diabète Québec; (3) 85% (1530/1800) will not have medical indications limiting the practice of moderate-intensity aerobic PA [ ]; (4) 70% (1071/1530) will not meet the Canadian Diabetes Association guidelines related to aerobic PA [ ]; (5) 70% (749/1071) will have access to the Internet [ ]; and (6) 60% (449/749) will complete the study [ ]. However, this algorithm has overestimated the number of participants enrolled at baseline, which was 234.
The Web-based computer-tailored intervention and the Web-based peer support intervention followed a rigorous theory-based and evidence-based development process involving a literature review, focus groups with T2D patients, a usability testing phase, and multidisciplinary expertise. As outlined in the background section, both interventions are based on the I-Change Model, in which concepts borrowed from SDT and the MI approach are integrated as part of the theoretical framework (). These concepts are as follows: (1) type of motivation (SDT-related); (2) importance ruler (MI-related); (3) confidence ruler (MI-related); and (4) MI-SDT concepts application. In short, feedback messages about the type of motivation aim to inform participants in both interventions that controlled or autonomous forms of motivation can influence the adoption and maintenance of regular PA [ ]. Importance ruler and confidence ruler are used mainly to provide feedback to participants with MI-related strategies. The remaining added construct, MI-SDT concepts application, aims to provide messages to participants using a style of communication that is consistent with both SDT and MI across all of the interventions’ features.
Content Specific to the Computer-Tailored Intervention
A key component of the computer-tailored intervention is that it uses recent techniques borrowed from computer-tailoring research to provide messages in a personalized fashion to its participants [, ]. Eight computer-tailored motivational sessions based on PA-related cognitions and current PA level were offered to participants. These sessions provided messages matching how individual participants scored on each cognitive construct and were complemented with personalization (eg, name) and different types of feedback (eg, descriptive, comparative, or evaluative) [ ]. One motivational session per week was made available to participants over an 8-week period. The order in which each of these motivational sessions was offered in the intervention was established so as to fit the theoretical framework and its 3 related phases. First, motivational session 1 aimed to raise awareness among participants regarding how their insufficient PA level could impact their health (ie, tailored feedback on risk perception and current PA level). This corresponds to the first phase of our theoretical framework: increasing the importance of change in participants [ ]. Next, all other sessions aimed to motivate participants toward increasing their PA level over the course of the intervention by providing them with tailored feedback related to their attitude, social influence, self-efficacy, type of motivation, and intention. This corresponds to the second phase of our theoretical framework, which was to develop participants’ motivation for making the desired change [ ]. Moreover, all computer-tailored motivational sessions were built such that they ended by querying participants about their desire to build a PA action plan for the week, which aimed to help them take action, and thus, increase their PA level. For instance, if a participant told the software he wanted to build an action plan, he was redirected to the action planning tool available on the website to build his plan. This last step is in concordance with the final phase of our model, in which motivation is transferred into the adoption of the desired behavior [ ]. To integrate SDT and MI within the computer-tailoring component, all motivational sessions were also interactively geared so that participants could develop an autonomous motivation to practice more PAs. Furthermore, all sessions used various techniques associated with MI and its basic interview skills: (1) asking open questions, (2) affirming, (3) reflecting, (4) summarizing, and (5) informing and advising [ ] (see ) [ - ]. Finally, readers are encouraged to read Moreau et al [ ] for an in-depth description of the algorithms behind the tailoring component of the intervention and examples of messages received by participants during computer-tailored motivational sessions.
Content Specific to the Peer Support Intervention
A key component of the peer support intervention is that it provides opportunities for participants to support each other by using a Web-based discussion group. More precisely, the online peer support component can be viewed as a semi-guided private Facebook group moderated by a clinical nurse. At their convenience, participants had the opportunity to visit either the intervention website or to interact with other participants via the Facebook group during an 8-week intervention period. Participants randomized into the peer support intervention were exposed to the same content as participants in the computer-tailored intervention regarding PA-related cognitions. However, the peer support intervention was targeted, meaning that the same content was provided to all participants, regardless of their individual characteristics beyond being adults with T2D. For the first 4 weeks, participants received 2 web articles per week that provided information on PA-related topics (eg, risk perception, PA guidelines, attitude), which were made available directly on the website and posted simultaneously in the Facebook group. For weeks 5, 6, and 7, participants received 1 web article per week that provided information on other PA-related topics (eg, self-efficacy, intention, and action planning). No article was offered during the last week, allowing the nurse-moderator to discuss participants’ appreciation for the intervention through the Facebook group. Similar to the computer-tailored intervention, the order in which each web article was made available was established to correspond to the theoretical framework of the study. All topics in each web article are presented in.
The clinical nurse moderated the group using a method we developed that integrates SDT concepts and MI techniques, aiming to encourage participants to focus their discussions on PA-related topics and develop their own reasons for practicing more moderate-intensity aerobic PAs  (see ). The following steps explain the method used by the moderator: (1) participants were first greeted by the nurse-moderator when they joined the Facebook group; (2) they were then invited to read the introductory text describing the underlying MI spirit sought for the group, which was accessible in the group’s description section; (3) when posting a new web article to the Facebook group’s page, the nurse-moderator always simultaneously posted 1-2 open-ended questions on the group’s wall that were related to the article topic (see ) and aimed to encourage participants to develop their own motivation; (4) 2 days after the questions were posted, the nurse-moderator summarized the answers posted by participants, with an emphasis on comments made in favor of adopting the desired behavior; (5) for the last 4 weeks of the intervention, since only 1 article was posted per week during this period (or no article during the last week), the nurse-moderator spent at least 1-2 hours per day, 3 days a week, replying to specific participants’ comments to show interest, encourage further self-exploration, and favor group participation [ ]. Despite this standardized method adopted by the moderator, it is important to note that participants were part of a peer support intervention and most interactions in the group were between themselves. While participants had the opportunity to talk about the web articles posted on the Facebook group’s page, they could also discuss any topic or concern they wanted to share with other participants.
Despite their distinct core content, both interventions possessed the following similar features: (1) the appearance of the website home page, (2) a tool for self-monitoring of PA behavior, (3) tools for goal setting and action planning, (4) tabs providing safety tips on how to practice PA properly, and (5) access to technical support via email. The designs of the websites were different except for the home page (see), as they were developed by different web solution companies at different times during the study development phase. Additionally, other evidence-based behavior change techniques according to Michie et al’s taxonomy [ ] were integrated into both interventions and can be viewed in detail in [ - ]. These were identified in the literature review preceding the development of both interventions [ ]. Lastly, the computer-tailored and peer support interventions both started on 29 September 2014 and ended on 22 November 2014.
presents a broad overview of the intervention components for each experimental condition. presents a more detailed overview of both interventions, including timelines.
|Peer support |
|Peer support opportunities||xb|
|PA: cognitions messages|
|Attitude (pros and cons)||x||x|
|Type of motivation||x||x|
|Self-monitoring of PA||x||x|
|Periodic email promptsc||x||x||x|
aContemporary computer-tailoring terminology summarized by Harrington and Noar .
bDistinctive components of each intervention
cEmails were sent only to stimulate participants to reuse the interventions or to complete posttest evaluations.
Participants in this group did not participate in any intervention for the duration of the study. Following the last follow-up questionnaire, participants from this group were offered the fully automated, computer-tailored, Web-based intervention.
Emails were used to invite participants to complete baseline, 3-month, and 9-month follow-up questionnaires on the intervention websites. Participants were invited to complete their baseline questionnaire during the enrolment period between 2014 September 15 to 2014 September 18. Participants were invited to complete their 3-month and 9-month follow-up questionnaires during the period between 2014 December 16 and 2015 January 4 and during the period between 2015 June 5 and 2015 July 4, respectively. As it is well known that Internet interventions generally suffer from a high attrition rate , a financial incentive was offered to participants for completing the 3-month ($20) and 9-month ($10) follow-ups.
PA level was evaluated with an adapted version of the Godin Leisure Time Exercise Questionnaire (GLTEQ) [, ]. An independent evaluation of this instrument indicated that it compared favorably in terms of reliability and validity to 9 other self-report measures of PA on test-retest methods, objective measure of PA, and fitness indexes [ ]. The GLTEQ had a one-month test-retest reliability of .62 and concurrent validity coefficients of .32 with an accelerometer and .56 with maximum oxygen uptake [ ]. Participants were asked (1) the frequency with which they practiced intensity-specific aerobic PAs (ie, low, moderate, vigorous) in a typical 7-day week and (2) the average duration in minutes of these activities for each intensity category. Final PA levels were obtained by calculating the converted total minutes per week of moderate-intensity aerobic PA that participants engaged in during a typical week; only moderate (4 metabolic equivalents [METS]) to vigorous (7.5 METS) types of aerobic PA were considered, and the total minutes of vigorous aerobic PA/week were considered as 1.875 × minutes of moderate aerobic PA/week (7.5 METS/4 METS = 1.875). Using this method, a participant was considered “regularly active” with a score greater than or equal to 600 METs-min/week. This threshold is the equivalent of 150 min of moderate-intensity aerobic PA per week and corresponds to the recommendations of the Canadian Diabetes Association [ ].
PA-related cognitions were also assessed to identify the mediating processes through which behavior change occurred in participants according to the answers they provided during the baseline, 3-month, and 9-month follow-up assessments on the study’s websites. The items used for the assessment of attitude, social influence, self-efficacy/perceived behavioral control, and intention were developed according to the guidelines from Ajzen's Theory of Planned Behavior  ( ). A test-retest procedure was conducted over a 2-week period with a subgroup of 29 individuals with T2D. The mean age of the participants was 57.3 years (SD 10.8), 48% (14/29) were male, and 45% (13/29) had completed postsecondary education. The Cronbach alpha coefficients and intraclass correlation coefficients are reported in . As for the items assessing the type of motivation of participants, a modified, validated, French-language version of the BREQ-2 [ ] (Behavioural Regulation in Exercise Questionnaire, version 2) was used to measure all possible types of motivation a participant could experience, including integrated motivation, which is normally absent in the standard BREQ-2 [ ]. The investigators who developed the questionnaire assessed the construct and discriminant validity with confirmatory factor analysis. Nomological and convergent validity were also assessed with correlation analysis. Questionnaire validation results are reported elsewhere [ ].
|Variable||Items||Scale||Internal consistency a||Test-retest reliability c|
|I intend to be regularly active in the next month.||Unlikely (+1)/likely (+7)|
|My plans are to practice physical activities regularly in the next month.||Disagree (+1)/agree (+7)|
|I estimate that my chances of practicing physical activities regularly over the next month are...||Weak (+1)/good (+7)|
|I think that practicing physical activities regularly in the next month would be…||Bad (+1)/good (+7)|
|Useless (+1)/useful (+7)|
|Unenjoyable (+1)/enjoyable (+7)|
|Unpleasant (+1)/pleasant (+7)|
|I feel capable of practicing physical activities regularly in the next month.||Disagree (+1)/agree (+7)|
|For me, practicing physical activities regularly in the next month would be…||Difficult (+1)/easy (+7)|
|How much control do you feel you have over your ability to practice physical activities regularly?||No control (+1)/control (+7)|
|Social influence (subjective norm)||.87b||.77|
|People who are important to me would support me if I were to practice physical activities regularly in the next month.||Disagree (+1)/agree (+7)|
|People who are important to me think I should practice physical activities regularly in the next month.||Disagree (+1)/agree (+7)|
|Social influence (descriptive norm) d||—||—|
|How many people who are part of your daily life (children, spouse/partner, physician, friends, colleagues, etc) are physically active on a regular basis?||Nobody (+1); a minority (+2); half (+3); a majority (+4); everybody (+5)|
aInternal consistency reported as Cronbach alpha coefficient for variables of 3 items or more.
bInternal consistency reported as Spearman’s correlation coefficient for variables of 2 items.
cIntraclass correlation coefficient.
dNo data provided for descriptive norm, given the change of scale following the test-retest.
Descriptive statistics will be used to summarize the baseline characteristics of the participants for sociodemographic variables (ie, origin, occupation, gender, age, and education), anthropometric variables (ie, height, weight, and body mass index), psychosocial variables (ie, attitude, social influence, self-efficacy, type of motivation, and intention), and PA level. In line with the hypotheses, linear mixed model analyses for repeated measures will be used to examine the effectiveness of the computer-tailored intervention (H1) and peer support intervention (H2) relative to the control group and also to compare both interventions to each other (H3). A linear mixed-model approach is very well suited for modelling data containing repeated measures from several participants  and has the advantage of providing better capabilities for handling missing observations when data are missing at random, compared to more traditional approaches such as repeated measures analysis of variance [ , ]. In accordance with the statistical procedure and for each analysis, group, time, and time × group will be included as independent variables and minutes per week of moderate-intensity aerobic PA will be included as the dependent variable. Analyses will be adjusted for sociodemographic characteristics and other relevant variables that significantly differ between the 3 study arms at baseline. In the presence of statistically significant interaction terms, post-hoc tests will be applied to compare differences between all pairs of means. All pairwise tests of the computer-tailored or peer support interventions compared to the control group will be 1-tailed, and tests comparing both interventions together will be 2-tailed. Finally, potential mediators of the interventions’ effects will be explored. Based on the theoretical framework ( ), a mediation analysis will be conducted involving a multicategorical independent variable (both interventions relative to control group), with type of motivation and intention as mediator variables in serial, and PA behavior as the dependent variable. Hayes and Preacher’s SPSS macro will be used for exploring the potential mediators [ ]. The statistical analyses will be conducted with SPSS 22.0.
Ethics Approval and Participant Consent
Ethics approval was obtained from the Ethics Committee of Research with Humans from the Université du Québec à Trois-Rivières on 2014 September 12 (CER–13–194–08.03.04). All participants in the study gave their informed consent online via the study websites during the registration process.
The project was funded in 2011 and enrolment was completed in September 2014. Data analysis is currently under way and the first results are expected to be submitted for publication in 2016.
The purpose of this study is to assess the effectiveness of two Web-based interventions, each of which was developed based on different behavior change perspectives, to promote regular moderate-intensity aerobic PA among adults living with T2D. Several meta-analyses of traditional T2D self-management education programs have demonstrated the effectiveness of certain lifestyle programs [- ]. In Canada [ ], as elsewhere [ , ], these programs have mostly been developed in group or individual format sessions. However, in the province of Québec, where the study is being conducted, diabetes education centers cannot meet the demand for traditional self-management education programs serving adults diagnosed with the disease, particularly in large urban regions and remote areas [ ]. This observation, combined with the ever-increasing prevalence of diabetes [ , ] and the fact that still only 50% of Canadian patients with T2D meet the recommended glycated hemoglobin target of ≤ 7.0% [ ], provide a clear call-to-action for the implementation of wide-reaching programs helping people with T2D to improve their lifestyles. With that purpose in mind, computer-tailored interventions and peer support interventions offered through the Internet and focusing on lifestyle changes represent two highly credible solutions that deserve attention.
The proposed study has several strengths. First, a rigorous theory-based and evidence-based intervention development process was employed. Notably, considerable efforts were devoted to base the intervention offering online peer support on an evidence-based theoretical framework, which is less frequent for this type of intervention . Second, the integration of SDT and MI in a Web-based PA promotion context is innovative and has been attempted by few research teams to date. Positive results suggest that additional research should explore this path in the future [ ]. Third, another strength of this study is its rigorous reporting of the interventions’ components, behavioral change techniques, and theoretical framework used; such rigor is increasingly required for comprehensiveness, reproducibility, and comparison among trials [ , , - ].
However, this study is not without limitations. First, given its open-label design (ie, not blinded), although quite common in Web-based interventions delivered under free-living conditions , participants in all 3 study groups and researchers were aware of the participants’ group assignment. This has the potential to introduce performance bias, defined as “systematic differences between groups in the care that is provided, or in exposure to factors other than the interventions of interest” [ ]. All participants were, however, unaware of the hypotheses of the study and were only told that the researchers wanted to assess the effect of different Web-based interventions on PA behavior in adults with T2D. Second, given the use of self-reported measures for the outcome assessments, even from validated questionnaire items, this has the potential to introduce detection bias, defined as “systematic differences between groups in how outcomes are determined” [ ]. However, outcome data assessment was automated through the study websites and was thus blinded to the research team, which should reduce the possibility of a differential bias between intervention groups and the control group. Furthermore, the third hypothesis regarding the comparative effectiveness of the interventions was formulated bilaterally, reducing the possibility of bias on the investigators’ part. Finally, one possible limitation across Web-based interventions is the attrition phenomenon [ , ]. Despite a smaller than expected number of participants recruited at baseline and the impossibility of fully preventing a high attrition rate, some evidence demonstrated that adding personalization, computer tailoring, and peer support components could contribute to reducing the attrition rate in Web-based interventions [ , ]. Financial compensation associated with follow-up completion might also have helped reduce the attrition rate of our study [ ].
To the best of our knowledge, this trial represents one of the first studies seeking to compare a Web-based computer tailoring intervention and a Web-based peer support intervention that are both PA-focused and designed for patients with T2D. Results of this study have the potential to inform future developers about the effectiveness of PA-focused, Web-based interventions for patients with T2D, including which of the two behavior change perspectives employed should be prioritized. If positive results are observed, developers could also use information provided in this manuscript to gain insights about which additional components to include in their Web-based interventions.
This study was made possible through a grant awarded to FB by the Fonds de Recherche en Santé du Québec (FRQS-22855) and the Diabète Québec organization. We would also like to thank all the main external collaborators on the intervention development, including Gilles Tanguay from AlphaZero Inc, Véronique Raby, Sébastien Talbot, Mark Lardinois from Overnite Software Europe, John William Kayser from PsyMontreal Inc, Sylvie Croteau from RA Malatest & Associates Ltd, Marie-Pierre Gagnon from Laval University, Omnimedia Inc and Point Bleu Productions. Finally, we would like to thank Hein de Vries from Maastricht University and two of his graduate students (Daniela Schulz and Michel Walthouwer) for their relevant contribution to the development of the fully automated, computer-tailored intervention.
FB and MM are equal contributors and co-first authors of this paper. Both were primarily in charge of drafting the manuscript. FB was the original author of the study design, which was complemented with input from MM and JC. MM was primarily in charge of developing the fully automated, computer-tailored intervention with relevant contributions from FB. FB was primarily in charge of developing the peer support intervention with relevant contributions from MM.
Conflicts of Interest
Multimedia Appendix 1
Motivational Interviewing (MI) and Self-Determination Theory (SDT) concepts application to both interventions.PDF File (Adobe PDF File), 401KB
Multimedia Appendix 2
Trial overview: timeline and intervention components.PDF File (Adobe PDF File), 403KB
Multimedia Appendix 3
Moderator's questions: peer support intervention.PDF File (Adobe PDF File), 247KB
Multimedia Appendix 4
Layout of websites.PDF File (Adobe PDF File), 778KB
Multimedia Appendix 5
Behavior change techniques for both interventions.PDF File (Adobe PDF File), 447KB
- Public Health Agency of Canada. Ottawa. Diabetes in Canada: Facts and Figures from a Public Health Perspective URL: http://www.phac-aspc.gc.ca/cd-mc/publications/diabetes-diabete/facts-figures-faits-chiffres-2011/pdf/facts-figures-faits-chiffres-eng.pdf [accessed 2015-12-04] [WebCite Cache]
- Thomas DE, Elliott EJ, Naughton GA. Exercise for type 2 diabetes mellitus. Cochrane Database Syst Rev 2006(3):CD002968. [CrossRef] [Medline]
- Boulé NG, Haddad E, Kenny GP, Wells GA, Sigal RJ. Effects of exercise on glycemic control and body mass in type 2 diabetes mellitus: A meta-analysis of controlled clinical trials. JAMA 2001 Sep 12;286(10):1218-1227. [Medline]
- Kodama S, Tanaka S, Heianza Y, Fujihara K, Horikawa C, Shimano H, et al. Association between physical activity and risk of all-cause mortality and cardiovascular disease in patients with diabetes: A meta-analysis. Diabetes Care 2013 Feb;36(2):471-479 [FREE Full text] [CrossRef] [Medline]
- Zhang X, Geiss LS, Caspersen CJ, Cheng YJ, Engelgau MM, Johnson JA, et al. Physical activity levels and differences in the prevalence of diabetes between the United States and Canada. Prev Med 2010;50(5-6):241-245. [CrossRef] [Medline]
- Plotnikoff RC, Johnson ST, Loucaides CA, Bauman AE, Karunamuni ND, Pickering MA. Population-based estimates of physical activity for adults with type 2 diabetes: A cautionary tale of potential confounding by weight status. J Obes 2011;2011 [FREE Full text] [CrossRef] [Medline]
- Kohl LF, Crutzen R, de Vries NK. Online prevention aimed at lifestyle behaviors: A systematic review of reviews. J Med Internet Res 2013;15(7):e146 [FREE Full text] [CrossRef] [Medline]
- Bennett GG, Glasgow RE. The delivery of public health interventions via the Internet: Actualizing their potential. Annu Rev Public Health 2009;30:273-292. [CrossRef] [Medline]
- Kamel Boulos MN, Wheeler S. The emerging Web 2.0 social software: An enabling suite of sociable technologies in health and health care education. Health Info Libr J 2007 Mar;24(1):2-23 [FREE Full text] [CrossRef] [Medline]
- Lintonen TP, Konu AI, Seedhouse D. Information technology in health promotion. Health Educ Res 2008 Jun;23(3):560-566 [FREE Full text] [CrossRef] [Medline]
- Buntin MB, Burke MF, Hoaglin MC, Blumenthal D. The benefits of health information technology: A review of the recent literature shows predominantly positive results. Health Aff (Millwood) 2011 Mar;30(3):464-471 [FREE Full text] [CrossRef] [Medline]
- Statistics Canada. 2010. Table 358-0126 - Canadian Internet Use Survey, Internet Use, by Location of Access and Income Quartile 2010 URL: http://www5.statcan.gc.ca/cansim/pick-choisir?lang=eng&id=3580126&pattern=3580126&searchTypeByValue=1 [accessed 2015-01-14] [WebCite Cache]
- Statistics Canada. 2013. Table 358-0171 - Canadian Internet Use Survey, Households with Access to the Internet at Home, Canada, Provinces and Census Metropolitan Areas (CMA), Occasional (percent) URL: http://www5.statcan.gc.ca/cansim/a26?lang=eng&retrLang=eng&id=3580171&paSer=&pattern=&stByVal=1&p1=1&p2=31&tabMode=dataTable&csid= [accessed 2015-01-14] [WebCite Cache]
- Statistics Canada. 2013. Table 358-0167 - Canadian Internet Use Survey, Household Access to the Internet at Home, by Household Income Quartile, Canada and Provinces, Occasional (percent) URL: http://www5.statcan.gc.ca/cansim/a26?lang=eng&retrLang=eng&id=3580167&paSer=&pattern=&stByVal=1&p1=1&p2=31&tabMode=dataTable&csid= [accessed 2015-01-14] [WebCite Cache]
- Lear SA, Araki Y, Maric B, Kaan A, Horvat D, British Columbia Alliance on Telehealth PolicyResearch. Prevalence and characteristics of home Internet access in patients with cardiovascular disease from diverse geographical locations. Can J Cardiol 2009 Oct;25(10):589-593 [FREE Full text] [Medline]
- Aalbers T, Baars MA, Rikkert MG. Characteristics of effective Internet-mediated interventions to change lifestyle in people aged 50 and older: A systematic review. Ageing Res Rev 2011 Sep;10(4):487-497. [CrossRef] [Medline]
- Vandelanotte C, Kirwan M, Rebar A, Alley S, Short C, Fallon L, et al. Examining the use of evidence-based and social media supported tools in freely accessible physical activity intervention websites. Int J Behav Nutr Phys Act 2014;11:105 [FREE Full text] [CrossRef] [Medline]
- Statistics Canada. 2014. Diabetes, by Age Group and Sex (Number of Persons) URL: http://www.statcan.gc.ca/tables-tableaux/sum-som/l01/cst01/health53a-eng.htm [accessed 2015-01-14] [WebCite Cache]
- Müller AM, Khoo S. Non-face-to-face physical activity interventions in older adults: A systematic review. Int J Behav Nutr Phys Act 2014;11(1):35 [FREE Full text] [CrossRef] [Medline]
- Norman GJ, Zabinski MF, Adams MA, Rosenberg DE, Yaroch AL, Atienza AA. A review of eHealth interventions for physical activity and dietary behavior change. Am J Prev Med 2007 Oct;33(4):336-345 [FREE Full text] [CrossRef] [Medline]
- van den Berg MH, Schoones JW, Vliet Vlieland TP. Internet-based physical activity interventions: A systematic review of the literature. J Med Internet Res 2007;9(3):e26 [FREE Full text] [CrossRef] [Medline]
- 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]
- 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]
- Foster C, Richards J, Thorogood M, Hillsdon M. Remote and web 2.0 interventions for promoting physical activity. Cochrane Database Syst Rev 2013 Sep 30;9:CD010395 [FREE Full text] [CrossRef] [Medline]
- Cotter AP, Durant N, Agne AA, Cherrington AL. Internet interventions to support lifestyle modification for diabetes management: A systematic review of the evidence. J Diabetes Complications 2014;28(2):243-251 [FREE Full text] [CrossRef] [Medline]
- Stellefson M, Chaney B, Barry AE, Chavarria E, Tennant B, Walsh-Childers K, et al. Web 2.0 chronic disease self-management for older adults: A systematic review. J Med Internet Res 2013 Feb;15(2):e35 [FREE Full text] [CrossRef] [Medline]
- Ramadas A, Quek KF, Chan C K Y, Oldenburg B. Web-based interventions for the management of type 2 diabetes mellitus: A systematic review of recent evidence. Int J Med Inform 2011 Jun;80(6):389-405. [CrossRef] [Medline]
- de Vries H, Brug J. Computer-tailored interventions motivating people to adopt health promoting behaviours: Introduction to a new approach. Patient Educ Couns 1999 Feb;36(2):99-105. [Medline]
- Petty R, Cacioppo J. Communication and Persuasion: Central and Peripheral Routes to Attitude Change. New York: Springer-Verlag; 1986.
- Kreuter MW, Bull FC, Clark EM, Oswald DL. Understanding how people process health information: A comparison of tailored and nontailored weight-loss materials. Health Psychol 1999 Sep;18(5):487-494. [Medline]
- Broekhuizen K, Kroeze W, van Poppel MN, Oenema A, Brug J. A systematic review of randomized controlled trials on the effectiveness of computer-tailored physical activity and dietary behavior promotion programs: An update. Ann Behav Med 2012 Oct;44(2):259-286 [FREE Full text] [CrossRef] [Medline]
- Webb TL, Joseph J, Yardley L, Michie S. Using the internet to promote health behavior change: A systematic review and meta-analysis of the impact of theoretical basis, use of behavior change techniques, and mode of delivery on efficacy. J Med Internet Res 2010;12(1):e4 [FREE Full text] [CrossRef] [Medline]
- Krebs P, Prochaska JO, Rossi JS. A meta-analysis of computer-tailored interventions for health behavior change. Prev Med 2010;51(3-4):214-221 [FREE Full text] [CrossRef] [Medline]
- Glasgow RE, Kurz D, King D, Dickman JM, Faber AJ, Halterman E, et al. Twelve-month outcomes of an Internet-based diabetes self-management support program. Patient Educ Couns 2012 Apr;87(1):81-92 [FREE Full text] [CrossRef] [Medline]
- Lim S, Kang SM, Kim KM, Moon JH, Choi SH, Hwang H, et al. Multifactorial intervention in diabetes care using real-time monitoring and tailored feedback in type 2 diabetes. Acta Diabetol 2015 May 5. [CrossRef] [Medline]
- Tang PC, Overhage JM, Chan AS, Brown NL, Aghighi B, Entwistle MP, et al. Online disease management of diabetes: Engaging and motivating patients online with enhanced resources-diabetes (EMPOWER-D), a randomized controlled trial. J Am Med Inform Assoc 2013 May 1;20(3):526-534 [FREE Full text] [CrossRef] [Medline]
- Dennis C. Peer support within a health care context: A concept analysis. Int J Nurs Stud 2003 Mar;40(3):321-332. [Medline]
- Statista. Leading Social Networks Worldwide as of March 2015, Ranked by Number of Active Users (in millions) 2015 URL: http://www.statista.com/statistics/272014/global-social-networks-ranked-by-number-of-users/ [accessed 2015-04-30] [WebCite Cache]
- Chou WS, Prestin A, Lyons C, Wen K. Web 2.0 for health promotion: Reviewing the current evidence. Am J Public Health 2013 Jan;103(1):e9-18. [CrossRef] [Medline]
- Valle CG, Tate DF, Mayer DK, Allicock M, Cai J. A randomized trial of a Facebook-based physical activity intervention for young adult cancer survivors. J Cancer Surviv 2013 Sep;7(3):355-368 [FREE Full text] [CrossRef] [Medline]
- Maher C, Ferguson M, Vandelanotte C, Plotnikoff R, De Bourdeaudhuij I, Thomas S, et al. A web-based, social networking physical activity intervention for insufficiently active adults delivered via Facebook app: Randomized controlled trial. J Med Internet Res 2015;17(7):e174 [FREE Full text] [CrossRef] [Medline]
- Cavallo DN, Tate DF, Ries AV, Brown JD, DeVellis RF, Ammerman AS. A social media-based physical activity intervention: A randomized controlled trial. Am J Prev Med 2012 Nov;43(5):527-532 [FREE Full text] [CrossRef] [Medline]
- Rote AE, Klos LA, Brondino MJ, Harley AE, Swartz AM. The efficacy of a walking intervention using social media to increase physical activity: A randomized trial. J Phys Act Health 2015 Jun;12 Suppl 1:S18-S25. [CrossRef] [Medline]
- Chee H, Hazizi A, Barakatun NM, Mohd NM. A randomised controlled trial of a Facebook-based physical activity intervention for government employees with metabolic syndrome. Malays J Nutrition 2014;20(2):165-181.
- van Vugt M, de Wit M, Cleijne WH, Snoek FJ. Use of behavioral change techniques in web-based self-management programs for type 2 diabetes patients: Systematic review. J Med Internet Res 2013;15(12):e279 [FREE Full text] [CrossRef] [Medline]
- de Vries H, Kremers SP, Smeets T, Brug J, Eijmael K. The effectiveness of tailored feedback and action plans in an intervention addressing multiple health behaviors. Am J Health Promot 2008;22(6):417-425. [CrossRef] [Medline]
- Ajzen I. The theory of planned behavior. Organ Behav Hum Decis Process 1991 Dec;50(2):179-211. [CrossRef]
- Bandura A. Social cognitive theory: An agentic perspective. Annu Rev Psychol 2001;52:1-26. [CrossRef] [Medline]
- Rosenstock IM. The Health Belief Model and preventive health behavior. Health Educ Behav 1974 Dec 01;2(4):354-386. [CrossRef]
- Prochaska JO, Velicer WF. The transtheoretical model of health behavior change. Am J Health Promot 1997;12(1):38-48. [Medline]
- de Vries H. 2008. I-Change Model URL: http://www.maastricht-university.eu/hein.devries/interests/i-change-model [accessed 2014-11-28] [WebCite Cache]
- van Stralen MM, de Vries H, Mudde AN, Bolman C, Lechner L. The long-term efficacy of two computer-tailored physical activity interventions for older adults: Main effects and mediators. Health Psychol 2011 Jul;30(4):442-452. [CrossRef] [Medline]
- Resnicow K, Davis RE, Zhang G, Konkel J, Strecher VJ, Shaikh AR, et al. Tailoring a fruit and vegetable intervention on novel motivational constructs: Results of a randomized study. Ann Behav Med 2008 Apr;35(2):159-169. [CrossRef] [Medline]
- Friederichs SA, Oenema A, Bolman C, Guyaux J, van Keulen HM, Lechner L. Motivational interviewing in a web-based physical activity intervention: Questions and reflections. Health Promot Int 2015 Sep;30(3):803-815. [CrossRef] [Medline]
- Friederichs SA, Oenema A, Bolman C, Guyaux J, van Keulen HM, Lechner L. I Move: Systematic development of a web-based computer tailored physical activity intervention, based on motivational interviewing and self-determination theory. BMC Public Health 2014;14:212 [FREE Full text] [CrossRef] [Medline]
- Joseph CL, Ownby DR, Havstad SL, Saltzgaber J, Considine S, Johnson D, et al. Evaluation of a web-based asthma management intervention program for urban teenagers: Reaching the hard to reach. J Adolesc Health 2013 Apr;52(4):419-426 [FREE Full text] [CrossRef] [Medline]
- DiIorio C, Escoffery C, Yeager KA, McCarty F, Henry TR, Koganti A, et al. WebEase: Development of a Web-based epilepsy self-management intervention. Prev Chronic Dis 2009 Jan;6(1):A28 [FREE Full text] [Medline]
- Walters ST, Ondersma SJ, Ingersoll KS, Rodriguez M, Lerch J, Rossheim ME, et al. MAPIT: Development of a web-based intervention targeting substance abuse treatment in the criminal justice system. J Subst Abuse Treat 2014 Jan;46(1):60-65 [FREE Full text] [CrossRef] [Medline]
- Deci EL, Ryan RM. Self-determination theory: A macrotheory of human motivation, development, and health. Can Psychol/Psychol Can 2008;49(3):182-185. [CrossRef]
- Miller WR, Rollnick S. Motivational Interviewing, 3rd edition: Helping People Change. New York: The Guilford Press; 2012.
- Teixeira PJ, Silva MN, Mata J, Palmeira AL, Markland D. Motivation, self-determination, and long-term weight control. Int J Behav Nutr Phys Act 2012;9:22 [FREE Full text] [CrossRef] [Medline]
- Deci EL, Ryan RM. Self-determination theory in health care and its relations to motivational interviewing: A few comments. Int J Behav Nutr Phys Act 2012;9:24 [FREE Full text] [CrossRef] [Medline]
- Miller WR, Rollnick S. Meeting in the middle: Motivational interviewing and self-determination theory. Int J Behav Nutr Phys Act 2012;9:25 [FREE Full text] [CrossRef] [Medline]
- Vansteenkiste M, Williams GC, Resnicow K. Toward systematic integration between self-determination theory and motivational interviewing as examples of top-down and bottom-up intervention development: Autonomy or volition as a fundamental theoretical principle. Int J Behav Nutr Phys Act 2012;9:23 [FREE Full text] [CrossRef] [Medline]
- Markland D, Ryan RM, Tobin VJ, Rollnick S. Motivational interviewing and self-determination theory. J Soc Clin Psychol 2005 Sep;24(6):811-831. [CrossRef]
- Patrick H, Williams GC. Self-determination theory: Its application to health behavior and complementarity with motivational interviewing. Int J Behav Nutr Phys Act 2012;9:18 [FREE Full text] [CrossRef] [Medline]
- Patrick H, Canevello A. Methodological overview of a self-determination theory-based computerized intervention to promote leisure-time physical activity. Psychol Sport Exerc 2011 Jan;12(1):13-19 [FREE Full text] [CrossRef] [Medline]
- Chatzisarantis NL, Hagger MS. Effects of an intervention based on self-determination theory on self-reported leisure-time physical activity participation. Psychol Health 2009 Jan;24(1):29-48. [CrossRef] [Medline]
- Friederichs SA, Oenema A, Bolman C, Lechner L. Long term effects of self-determination theory and motivational interviewing in a web-based physical activity intervention: Randomized controlled trial. Int J Behav Nutr Phys Act 2015;12(1):101 [FREE Full text] [CrossRef] [Medline]
- Eysenbach G. CONSORT-EHEALTH: Improving and standardizing evaluation reports of Web-based and mobile health interventions. J Med Internet Res 2011;13(4):e126 [FREE Full text] [CrossRef] [Medline]
- Chan A, Tetzlaff JM, Gotzsche PC, Altman DG, Mann H, Berlin JA, et al. SPIRIT 2013 explanation and elaboration: Guidance for protocols of clinical trials. BMJ 2013;346:e7586 [FREE Full text] [Medline]
- Canadian Diabetes Association. Canadian Journal of Diabetes. 2013. The Canadian Diabetes Association Clinical Practice Guidelines for the Prevention and Management of Diabetes in Canada URL: http://guidelines.diabetes.ca/app_themes/cdacpg/resources/cpg_2013_full_en.pdf [accessed 2015-12-04] [WebCite Cache]
- Erdfelder E, Faul F, Buchner A. GPOWER: A general power analysis program. Behavior Research Methods, Instruments, & Computers 1996 Mar;28(1):1-11. [CrossRef]
- Cohen J. A power primer. Psychol Bull 1992 Jul;112(1):155-159. [Medline]
- Noar SM, Benac CN, Harris MS. Does tailoring matter? Meta-analytic review of tailored print health behavior change interventions. Psychol Bull 2007 Jul;133(4):673-693. [CrossRef] [Medline]
- Laranjo L, Arguel A, Neves AL, Gallagher AM, Kaplan R, Mortimer N, Lau Annie Y S. The influence of social networking sites on health behavior change: A systematic review and meta-analysis. J Am Med Inform Assoc 2015 Jan;22(1):243-256. [CrossRef] [Medline]
- Boudreau F, Godin G, Poirier P. Effectiveness of a computer-tailored print-based physical activity intervention among French Canadians with type 2 diabetes in a real-life setting. Health Educ Res 2011 Aug;26(4):573-585 [FREE Full text] [CrossRef] [Medline]
- Marcus BH, Lewis BA, Williams DM, Dunsiger S, Jakicic JM, Whiteley JA, et al. A comparison of Internet and print-based physical activity interventions. Arch Intern Med 2007 May 14;167(9):944-949. [CrossRef] [Medline]
- Ford ES, Herman WH. Leisure-time physical activity patterns in the U.S. diabetic population. Findings from the 1990 National Health Interview Survey--Health Promotion and Disease Prevention Supplement. Diabetes Care 1995 Jan;18(1):27-33. [Medline]
- Moreau M, Gagnon M, Boudreau F. Development of a fully automated, web-based, tailored intervention promoting regular physical activity among insufficiently active adults with type 2 diabetes: Integrating the I-change model, self-determination theory, and motivational interviewing components. JMIR Res Protoc 2015;4(1):e25 [FREE Full text] [CrossRef] [Medline]
- Harrington NG, Noar SM. Reporting standards for studies of tailored interventions. Health Educ Res 2012 Apr;27(2):331-342 [FREE Full text] [CrossRef] [Medline]
- Hawkins RP, Kreuter M, Resnicow K, Fishbein M, Dijkstra A. Understanding tailoring in communicating about health. Health Educ Res 2008 Jun;23(3):454-466 [FREE Full text] [CrossRef] [Medline]
- Walthouwer MJ, Oenema A, Candel M, Lechner L, de Vries H. Eating in moderation and the essential role of awareness. A Dutch longitudinal study identifying psychosocial predictors. Appetite 2015 Apr;87:152-159. [CrossRef] [Medline]
- Hagger M, Chatzisarantis N, Biddle S. A meta-analytic review of the theories of reasoned action and planned behavior in physical activity: Predictive validity and the contribution of additional variables. J Sport Exerc Psychol 2002;24(1):3-32.
- Sniehotta FF, Scholz U, Schwarzer R. Bridging the intention–behaviour gap: Planning, self-efficacy, and action control in the adoption and maintenance of physical exercise. Psychol Health 2005 Apr;20(2):143-160. [CrossRef]
- Deci E, Ryan RM. Intrinsic Motivation and Self-Determination in Human Behavior. New York: Plenum; 1985.
- Ryan RM, Deci EL. Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. Am Psychol 2000 Jan;55(1):68-78. [Medline]
- Ryan RM. Psychological needs and the facilitation of integrative processes. J Pers 1995 Sep;63(3):397-427. [Medline]
- Michie S, Richardson M, Johnston M, Abraham C, Francis J, Hardeman W, et al. The behavior change technique taxonomy (v1) of 93 hierarchically clustered techniques: Building an international consensus for the reporting of behavior change interventions. Ann Behav Med 2013 Aug;46(1):81-95. [CrossRef] [Medline]
- Olander EK, Fletcher H, Williams S, Atkinson L, Turner A, French DP. What are the most effective techniques in changing obese individuals' physical activity self-efficacy and behaviour: A systematic review and meta-analysis. Int J Behav Nutr Phys Act 2013;10:29 [FREE Full text] [CrossRef] [Medline]
- Avery L, Flynn D, van WA, Sniehotta FF, Trenell MI. Changing physical activity behavior in type 2 diabetes: A systematic review and meta-analysis of behavioral interventions. Diabetes Care 2012 Dec;35(12):2681-2689 [FREE Full text] [CrossRef] [Medline]
- Ashford S, Edmunds J, French DP. What is the best way to change self-efficacy to promote lifestyle and recreational physical activity? A systematic review with meta-analysis. Br J Health Psychol 2010 May;15(Pt 2):265-288. [CrossRef] [Medline]
- Michie S, Abraham C, Whittington C, McAteer J, Gupta S. Effective techniques in healthy eating and physical activity interventions: A meta-regression. Health Psychol 2009 Nov;28(6):690-701. [CrossRef] [Medline]
- Bird E, Baker G, Mutrie N, Ogilvie D, Sahlqvist S, Powell J. Behavior change techniques used to promote walking and cycling: A systematic review. Health Psychol 2013 Aug;32(8):829-838 [FREE Full text] [CrossRef] [Medline]
- Williams SL, French DP. What are the most effective intervention techniques for changing physical activity self-efficacy and physical activity behaviour--and are they the same? Health Educ Res 2011 Apr;26(2):308-322 [FREE Full text] [CrossRef] [Medline]
- Forbes CC, Plotnikoff RC, Courneya KS, Boulé NG. Physical activity preferences and type 2 diabetes: Exploring demographic, cognitive, and behavioral differences. Diabetes Educ 2010;36(5):801-815. [CrossRef] [Medline]
- Eysenbach G. The law of attrition. J Med Internet Res 2005;7(1):e11 [FREE Full text] [CrossRef] [Medline]
- Godin G, Shephard R. Godin Leisure-Time Exercise Questionnaire 1997 URL: http://www.dapa-toolkit.mrc.ac.uk/documents/en/God/Godin_Leisure-Time_Exercise_Q.pdf [accessed 2014-11-28] [WebCite Cache]
- Plotnikoff RC, Karunamuni N, Courneya KS, Sigal RJ, Johnson JA, Johnson ST. The Alberta Diabetes and Physical Activity Trial (ADAPT): A randomized trial evaluating theory-based interventions to increase physical activity in adults with type 2 diabetes. Ann Behav Med 2013 Feb;45(1):45-56. [CrossRef] [Medline]
- Jacobs DR, Ainsworth BE, Hartman TJ, Leon AS. A simultaneous evaluation of 10 commonly used physical activity questionnaires. Med Sci Sports Exerc 1993 Jan;25(1):81-91. [Medline]
- Chamberland P, Miquelon P, Gareau A. Poster presented at the 74th annual convention of the Canadian Psychological Association, June 13 to 15, Quebec City, Canada. 2013. French validation of the integrated motivation scale for the Behavioural Regulation in Exercise Questionnaire II (BREQ-II) URL: https://drive.google.com/file/d/0B_cuU6hPPfCKSkF6eTczcmJNQzg/view?usp=sharing [accessed 2015-10-28] [WebCite Cache]
- Markland D, Tobin V. A modification to the behavioural regulation in exercise questionnaire to include an assessment of amotivation. J Sport Exerc Psychol 2004;26(2):191-196.
- Hayat MJ, Hedlin H. Modern statistical modeling approaches for analyzing repeated-measures data. Nurs Res 2012;61(3):188-194. [CrossRef] [Medline]
- Gueorguieva R, Krystal JH. Move over ANOVA: Progress in analyzing repeated-measures data and its reflection in papers published in the Archives of General Psychiatry. Arch Gen Psychiatry 2004 Mar;61(3):310-317. [CrossRef] [Medline]
- Krueger C, Tian L. A comparison of the general linear mixed model and repeated measures ANOVA using a dataset with multiple missing data points. Biol Res Nurs 2004 Oct;6(2):151-157. [CrossRef] [Medline]
- Hayes AF, Preacher KJ. Statistical mediation analysis with a multicategorical independent variable. Br J Math Stat Psychol 2014 Nov;67(3):451-470. [CrossRef] [Medline]
- Norris SL, Zhang X, Avenell A, Gregg E, Bowman B, Serdula M, et al. Long-term effectiveness of lifestyle and behavioral weight loss interventions in adults with type 2 diabetes: A meta-analysis. Am J Med 2004 Nov 15;117(10):762-774. [CrossRef] [Medline]
- Gary TL, Genkinger JM, Guallar E, Peyrot M, Brancati FL. Meta-analysis of randomized educational and behavioral interventions in type 2 diabetes. Diabetes Educ 2003;29(3):488-501. [Medline]
- Ellis SE, Speroff T, Dittus RS, Brown A, Pichert JW, Elasy TA. Diabetes patient education: A meta-analysis and meta-regression. Patient Educ Couns 2004 Jan;52(1):97-105. [Medline]
- Pal K, Eastwood SV, Michie S, Farmer AJ, Barnard ML, Peacock R, et al. Computer-based diabetes self-management interventions for adults with type 2 diabetes mellitus. Cochrane Database Syst Rev 2013;3:CD008776. [CrossRef] [Medline]
- Hwee J, Cauch-Dudek K, Victor JC, Ng R, Shah BR. Diabetes education through group classes leads to better care and outcomes than individual counselling in adults: A population-based cohort study. Can J Public Health 2014;105(3):e192-e197. [Medline]
- Rickheim PL, Weaver TW, Flader JL, Kendall DM. Assessment of group versus individual diabetes education: A randomized study. Diabetes Care 2002 Feb;25(2):269-274. [Medline]
- Vadstrup ES, Frølich A, Perrild H, Borg E, Røder M. Health-related quality of life and self-related health in patients with type 2 diabetes: Effects of group-based rehabilitation versus individual counselling. Health Qual Life Outcomes 2011;9:110 [FREE Full text] [CrossRef] [Medline]
- Bélanger A, Ékoé J, Kous S, Morris D, Yale J, Aras M. Diabète Québec. 2009. Rapport 2009 du Groupe d'Experts sur la Situation du Diabète au Québec URL: http://www.diabete.qc.ca/fr/comprendre-le-diabete/ressources/getdocumentutile/rapport-experts2009Db-au-Qc.pdf [accessed 2015-12-04] [WebCite Cache]
- Statistics Canada. 2014. Diabetes, by Sex, Provinces and Territories (Number of People) URL: http://www.statcan.gc.ca/tables-tableaux/sum-som/l02/cst01/health54a-fra.htm [accessed 2015-01-14] [WebCite Cache]
- Leiter LA, Berard L, Bowering CK, Cheng AY, Dawson KG, Ekoé J, et al. Type 2 diabetes mellitus management in Canada: Is it improving? Can J Diabetes 2013 Apr;37(2):82-89. [CrossRef] [Medline]
- Michie S, Fixsen D, Grimshaw JM, Eccles MP. Specifying and reporting complex behaviour change interventions: The need for a scientific method. Implement Sci 2009;4:40 [FREE Full text] [CrossRef] [Medline]
- Michie S, Abraham C, Eccles MP, Francis JJ, Hardeman W, Johnston M. Strengthening evaluation and implementation by specifying components of behaviour change interventions: A study protocol. Implement Sci 2011;6:10 [FREE Full text] [CrossRef] [Medline]
- El-Gayar O, Timsina P, Nawar N, Eid W. Mobile applications for diabetes self-management: Status and potential. J Diabetes Sci Technol 2013;7(1):247-262 [FREE Full text] [Medline]
- Higgins J, Green S. Cochrane handbook for systematic reviews of interventions. Chichester, England: Wiley-Blackwell; 2008.
- Yu CH, Bahniwal R, Laupacis A, Leung E, Orr MS, Straus SE. Systematic review and evaluation of web-accessible tools for management of diabetes and related cardiovascular risk factors by patients and healthcare providers. J Am Med Inform Assoc 2012;19(4):514-522 [FREE Full text] [CrossRef] [Medline]
- Gingerich SB, Anderson DR, Koland H. Impact of financial incentives on behavior change program participation and risk reduction in worksite health promotion. Am J Health Promot 2012;27(2):119-122. [CrossRef] [Medline]
|DEF: Diabète en Forme|
|MI: motivational interviewing|
|PA: physical activity|
|SDT: self-determination theory|
|T2D: type 2 diabetes|
Edited by G Eysenbach; submitted 10.08.15; peer-reviewed by J Wright; comments to author 31.08.15; revised version received 10.11.15; accepted 29.11.15; published 11.02.16Copyright
©François Boudreau, Michel Moreau, José Côté. Originally published in JMIR Research Protocols (http://www.researchprotocols.org), 11.02.2016.
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