Introduction

The World Health Organization (WHO) estimates that more than 220 million people worldwide have diabetes [1]. Among individuals with type 2 diabetes mellitus (T2DM), participation in regular physical activity (PA) has been linked to improved blood glucose control [2–6], reduced progression of diabetic neuropathy [7], cardiovascular disease risk [8], and all-cause mortality [9, 10]. In spite of these benefits, current estimates indicate the majority of adults with T2DM remain either sedentary or insufficiently active to achieve health benefits [11, 12].

Several recent randomized trials have demonstrated that supervised, facility-based exercise training improves glycemic control and other cardiovascular risk factors in adults with T2DM [13–15]. However, such programs are often resource-intensive, only available in urban centers, and their long-term sustainability is uncertain. Innovative methods are needed to encourage this population to become more active. An effective, individually tailored intervention that could increase PA in large numbers of people without requiring much labour/cost per recipient would be beneficial in terms of public health impact.

It is generally accepted that behavior change programs for PA that are theoretically grounded are more efficacious/effective than atheoretical strategies [16]. A number of PA interventions of at least 12 months in duration using social–cognitive theories and some form of individual tailoring have been implemented using print materials and/or telephone counseling [17–23]; the results of these studies have produced mixed results in producing behavior change among adults with T2DM. While some of the studies have found theory-based counseling was effective for promoting PA [17, 21], other studies did not report significant changes in PA [20, 23]. There are however, limited PA theory-based, individually tailored, long-term studies with large samples targeting the adult T2DM population.

In this paper, we present the main results of the Alberta Diabetes and Physical Activity Trial (ADAPT), which examined the effectiveness of public health PA interventions delivered through print and telephone modes compared to standard PA educational materials [24]. We hypothesized that intervention strategies utilized in this study would lead to higher levels of PA and better health outcomes for the participants compared to standard PA educational materials. A secondary objective was to explore the moderating effects of gender and age on the study outcomes.

Methods

Experimental Design Details

Design details of the study, development of intervention materials, participant recruitment, randomization and measures used in the study were published previously [24]. Briefly, 287 participants were recruited using a multi-strategy approach (mainly using general advertising strategies), and were randomly assigned to three groups. Study inclusion criteria were participants being 18 years or older, having been diagnosed with T2DM, having regular access to a telephone, and without an English language barrier.

The relative advantages and disadvantages of the modes of delivery used in ADAPT (i.e., printed material and telephone counseling) were considered in the development of the trial groups. For example, the benefits of using print materials include: the promotion of self-initiated change; low cost; potential to reach large numbers of individuals; decreased staff and participant burden; minimized time barriers as individuals can read the material when they have time; and, the material can be used as a reference tool at a later date. Conversely, the disadvantages to using print materials include: difficulty determining dose-response; the material may not be seen as personally relevant nor engaging; lack of social support due to lack of personal contact; and uncertainty that individuals will read the material. The telephone counseling mode of PA promotion may be particularly relevant to a diabetic population as telephone-mediated interventions appear to be effective in specialized populations, particularly those that may be highly burdened by co-morbidities (such as individuals with T2DM).

Group 1

Group 1 received standard print PA educational materials provided by the Canadian Diabetes Association (i.e., control group).

Group 2

Group 2 received the Canadian Diabetes Association PA guidelines as well as stage-based, print materials developed to address issues specific to the PA stage in which they were currently assessed. These materials were based on our Integrated Stage Model [24], which took into account strong data validating the magnitude of theoretical robustness of a number of social–cognitive constructs and items from the Theory of Planned Behaviour, the Health Belief Model, Protection Motivation Theory, Social Cognitive Theory, and the Transtheoretical Model to predict forward PA behavior stage of change transitions. The materials were also tailored to be season-specific (i.e., winter, spring, summer and autumn versions) and were mailed every 3 months for 12 months. The purpose of the materials was to help address specific issues related to each stage (i.e., Pre-contemplation, Contemplation, Preparation, Action and Maintenance). (The developed materials can be obtained from http://www.newcastle.edu.au/research-centre/pan/ or by contacting the first author.) Therefore, based on the five stages and four seasons, 20 combinations of booklets were available for supporting stage progress or retention. Study participants completed a stage measure at baseline, 3, 6 and 9 months, and were then mailed at each of these time points corresponding stage-matched materials based on their responses to the staging algorithm. Participants in this group also received a pedometer, logbook and calendar to chart their progress. (There was no specific intervention recommendation regarding steps per week.)

Group 3

Group 3 received the same intervention materials as specified in Groups 1 and 2. In addition, Group 3 also received telephone counseling. Telephone counseling was carried out by five individuals with relevant degree qualifications related to PA promotion and/or counseling and who received a 1-day, 7-h training course provided by the study team in the following key areas: type 2 diabetes, PA, older adults, communication approaches including motivational interviewing techniques [25], and psychological underpinnings for behavior change including Social Cognitive Theory [26] and the Transtheoretical Model [27]. For the first month, calls were weekly, the next month calls were biweekly, and for the remainder of the intervention calls were monthly. This tapered schedule was to allow participants to become less reliant on counselor support as the intervention progressed. The study protocol was 15 min per call.

The study coordinator with relevant experience and qualifications supervised the counselors and was also available to the study participants. Participants were contacted by the study co-ordinator at months 1, 6 and 12 to (1) confirm call frequency and to (2) assess counseling satisfaction using a simple analogue scale (1 = very dissatisfied; 10 = very satisfied). Overall, the participant satisfaction with the study counselors was high; the scores for the five counselors ranged between 8.3 and 9.0 in the first 6 months, and between 7.5 and 8.7 in the second 6 months.

Allocation of study participants was concealed to all study investigators. Only one Research Assistant who was not directly involved in the study had access to the database and the coding of the three study groups. After all the recruited individuals were randomized by the above-mentioned Research Assistant, the participant’s group assignments along with their study IDs were passed on to the research coordinator. Research staff (other than the above Research Assistant) and investigators were unaware of the treatment allocation prior to randomization. Participants were notified by telephone of their group allocation. A log was maintained by the Research Assistant of all randomization encounters. The study used a Relational Database Management System (RDBMS; Microsoft Access) to organize the large quantity of data generated.

The stated objective in the three intervention groups was to have participants achieve a minimum of 150 min of moderate minutes of PA per week as stated by current guidelines [4]. Ethical approval for this study was granted from an institutional review board at the University of Alberta, Edmonton, Canada.

The study’s co-primary outcomes were: (1) PA (i.e., moderate and vigorous [weighted × 2] PA minutes per week [MVPA]; and 3-day step count); and (2), glycosylated hemoglobin (A1c). The 12-month assessment was the study’s primary time-point. Secondary outcomes of other additional clinical measures and health-related quality of life were also investigated, along with changes in the co-primary and secondary outcomes across other secondary time points.

Measures

Self-reported PA was collected at baseline, then at 3, 6, 9, 12 and 18 months follow-up using a modified version [12] of the validated Godin Leisure Time Exercise Questionnaire [28], that asked participants to report the average number of times per week, in the past month, they engaged in vigorous (rapid heartbeats, sweating), moderate (not exhausting, light perspiration) and mild (minimal effort and no perspiration) intensity PA, for a minimum of 10 min per session. The Godin Leisure Time Exercise Questionnaire measure has also been found to compare favorably with nine other self-reported measures of PA based on various criteria, including test–retest scores, objective activity monitors, and fitness indices [29].

Participant responses for the moderate and vigorous activity categories were added; vigorous activity time was multiplied by two as suggested by the 2008 American Public Health Guidelines [30] to obtain MVPA minutes per week. Further, participants were classified as meeting guidelines if they achieved 150 min of moderate and/or vigorous activity per week [4].

Total daily steps were collected with a pedometer over 3 days. Participants in all three intervention groups were sent a pedometer, and were instructed to wear it and record total daily step-counts in a log for 3 consecutive days within the week for the baseline, 6, 12 and 18-month assessments. A recent review concluded that measuring steps for 3 days is an appropriate number of days to reliably estimate weekly steps [31]. The standard/control group was instructed not to use the pedometers between the assessment periods.

Clinical measures were assessed at baseline, 12 and 18 months (6 months post-intervention) using common laboratory procedures [32] and included A1c, insulin, glucose, total cholesterol, low- and high-density lipoproteins and triglycerides. Health-related quality of life was assessed at baseline, 12 and 18 months using the SF-12 Physical and Mental scales [33] and the EQ-5D index [34].

Sample Size

To detect a ‘medium’ effect size (f = .25), which corresponds to a difference of 0.61 standard deviation (SD) between groups on the PA measures at post-intervention, a sample size of 76 participants per group (power = 0.81; alpha = 0.01) was required (Cohen 1988: p. 355; Table 8.3.2., p. 355; and, formula 8.2.8., p. 277 [35]). Based on recent PA trials in this population [36–38], this sample size was also adequate to detect a 12.6 % relative decrease in A1c (assuming an SD of 11.1 %) with alpha of 0.01 (power >0.80) for each comparison between the groups. The alpha was set at 0.01 to adjust for the multiple co-primary outcomes.

Statistical Analyses

Using Linear Mixed Model (LMM) analyses, changes in the co-primary outcomes (MVPA, steps and A1c) from baseline to 12 months in each of the intervention groups were compared to changes in the control group. (LMM uses all available data and provides valid estimates for any missing data.) Secondary analyses examined changes in body mass index (BMI) and other clinical variables (high sensitivity C-reactive protein, blood insulin, fasting glucose, triglyceride, HDL and LDL cholesterol) and health-related quality of life. Assessment of MVPA at other time points (i.e., 3, 6, 9 and 18 months, and clinical outcomes including health-related quality of life at 18 months), were also investigated as secondary study outcomes.

To limit the effect of outliers on distributional properties of the outcome variables, extreme cases for any given outcome were re-assigned values equal to 3.29 SDs from the original meanFootnote 1 [39]. All analyses were adjusted for baseline values.

Prior to analyses, dummy variables were created containing cases with missing and non-missing values on PA at each time point, and a series of logistic regressions were carried out to examine if any of the potential independent variables (i.e., age, gender, diabetes duration, BMI, marital status, educational level, and gross annual and family income) were linked to participants dropping out.

Further, prior to conducting the LMM analyses, regression models (planned analyses) examined if there were any statistically significant interactions for changes in the co-primary outcomes between the groups for: (1) gender and (2) age (considered separately). These secondary, stratified analyses were carried out only for significant interactions.

Subsidiary analyses were carried out for the subgroup that was not meeting PA guidelines (achieving at least 150 min of moderate PA per week) at baseline (n = 188) as well, and for the subgroup of those having high A1c values (>7.0 %; n = 134). Separate gender analyses were not conducted for these subgroup analyses due to limited power.

Additionally, correlation analyses were carried out to determine the association between the pedometer steps with MVPA.

Results

Figure 1 provides a flow diagram of the study including data collection time points, and the number of participants at each time point that provided data on various measures. At 12 months, approximately 80 % of the participants provided PA and clinical data, respectively; while 74 % and 71 % of participants provided PA and clinical data, respectively, at the study’s 18-month follow-up assessment. A total of 175 participants (61 %) provided data at all study time periods. (There were no systematic differences in data gathering between the completions of the pedometer versus questionnaire data.) Individuals who provided complete data across the study were more active than those who did not (MET min per week at baseline for those who provided complete data and those who did not were 1,113.5 and 842.0, respectively; F = 4.9; p < 0.05). Out of the study’s three groups, fewer individuals dropped out of the control group compared to the other two groups (χ 2 = 7.4; p = 0.025).

Fig. 1
figure 1

Study flow diagram for Alberta Diabetes and Physical Activity Trial (ADAPT). PM print material, TC telephone counseling, SC socio-cognitive variables

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Demographic characteristics of the participants at baseline are presented in Table 1.

Table 1 Sample characteristics at baseline
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At baseline, none of the displayed variables were significantly different between the three groups. In the total sample, 35 % were meeting PA guidelines, and men were more active than women; MVPA for men and women at baseline were 185.0 and 125.2, respectively; t = 2.3; p < 0.05. Figure 2 displays MVPA minutes and steps for men and women across the study’s time-points in the three study groups.

Fig. 2
figure 2

Physical activity (MVPA weekly minutes and 3-day step total) for women and men. Note: unadjusted means values are plotted

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Participants were asked at the end of the intervention period (i.e., 12 months) the extent to which (1 = not at all; 5 = very much) they read the print materials. The respective means to this question were 3.60 (SD = 1.22), 4.08 (SD = 0.94) and 4.11 (SD = 0.96) for Group 1, Group 2 and Group 3. Based on our fidelity checks with both counselors and Group 3 participants, over 90 % of the 15-min Group 3 calls were completed to schedule in the first 6 months of the study, while over 80 % were to scheduled in the studies second 6-month period.

At baseline, participants’ stage classification was as follows: Pre-contemplation 3 %; Contemplation 23 %; Preparation 17 %; Action 10 %; and Maintenance 47 %. The stage distributions were similar between the three groups. The proportion of stage progression (i.e., forward progression by at least one stage, or retention in the maintenance stage) from: baseline to 6 months was 40 % (Group 1), 73 % (Group 2) and 77 % (Group 3); and 6 to 12 months was 53 % (Group 1), 56 % (Group 2) and 69 % (Group 3).

Logistic regression analyses revealed that for the dependent variable of PA, none of the variables tested were significantly associated with participants dropping out of the study (all p values ≥0.05).

Following the results of the regression interaction analyses, all LMM analyses were stratified by gender, as significant (p < 0.05) gender by study group interactions for PA change scores were observed.

Finally, we found significant positive correlations between MVPA and steps for all three time points (r = ~0.30; p < 0.01).

Co-primary Outcomes

Table 2 (panel 1) displays the main results for the study’s co-primary outcome variables (changes at 12 months for MVPA, steps and A1c). Changes from baseline to 12 months in each of the intervention groups was compared to changes for the same period in the control group after adjusting for baseline differences. MVPA did not change significantly at 12 months, compared to changes in the control group, the mean difference for this variable at 12 months for Group 2 and Group 3 were: 29.2 min/week; 95 % confidence interval (CI) = −37.1 to 95.6; p = 0.387; η 2 = 0.01 and 13.7 min/week 95 % CI = −52.1 to 79.6; p = 0.683; η 2 = 0.01, respectively. For the 3-day step count, the mean difference at 12 months for Group 2 and Group 3 (versus controls) were: 854 steps; 95 % CI = −2,015 to 3,723; p = 0.559; η 2 = 0.01 and 1,467 steps 95 % CI = −1,348 to 4,281; p = 0.307; η 2 = 0.00, respectively. A1c did not change (versus the control group; mean differences at 12 months for Group 2 and Group 3 were: −0.07 %; 95 % CI = −0.31 to 0.17; p = 0.576, and 0.18 % CI = −0.07 to 0.42; p = 0.153, respectively).

Table 2 Changes in co-primary (panel 1) and secondary (panel 2) outcomes, at 12 months across study groups for the study’s overall population
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Secondary Outcomes

Clinical variables did not change significantly for the two intervention groups (versus the control group; see Table 2, panel 2). For those not meeting the guideline of at least 150 min of moderate PA/week at baseline, MVPA increased in Group 3 at 3 months (mean differences from baseline were: 77.2 min/week; 95 % CI = 10.0 to 144.5; p = 0.024). For the subgroup of individuals with baseline A1c levels >7 % (n = 134), MVPA did not change significantly at any of the time points.

Stratified Analyses (Gender)

Stratified analyses for gender are displayed in Table 3; age did not have any moderating effects for any of the study outcomes. For women, steps increased in Group 3 (versus controls) at both 12 months (primary end-point) and 18 months. The mean differences for the 3-day step count for the two time periods were: 5,964 steps; 95 % CI = 1,540 to 10,388; p = 0.008; η 2 = 0.13 and 5,044 steps; 95 % CI = 553 to 9,535; p = 0.028; η 2 = 0.07, respectively.

Table 3 Changes in co-primary outcomes (PA and A1cs), at 12 months across study groups displayed separately for women and men
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In terms of the secondary time-points, MVPA increased in Group 2 (versus controls) at 3 and 9 months. The mean difference for these two time-points was 84.0 min/week; 95 % CI = 0.4 to 167.7; p = 0.049; η 2 = 0.04, and 87.2 min/week; 95 % CI = 0.8 to 173.5; p = 0.048; η 2 = 0.04. In terms of secondary variables, LDL increased at 18 months, in Group 3 (versus controls) mean difference: 0.4 mmol/l; 95 % CI = 0.1 to 0.8; p = 0.026.

The only reported difference for men was the SF-12: physical scale values significantly increased for men at 6 months in Groups 2 and 3 (versus controls); the respective mean differences were 3.6 units 95 % CI = 0.4 to 6.9; p = 0.029 and 4.2 units 95 % CI = 0.8 to 7.6; p = 0.016.

Discussion

The primary aim of this study was to explore the effectiveness of two PA behavioral intervention strategies developed using an Integrated Stage Model with a polytheoretical approach and presented through print and telephone modes of delivery. The study’s co-primary outcomes of PA and A1c did not significantly change for the two intervention groups (versus controls) at 12 months.

In terms of secondary outcome variables, we found relatively few significant changes. However, the stratified analyses that were carried out as part of our secondary investigation revealed that MVPA increased at several time points in Group 3 (print and telephone counseling group) versus controls, for women only.

Other randomized trials that have investigated theory-based, tailored interventions for adult populations with T2DM have reported mixed results in improving PA behaviors. For example, Kirk et al. [23] reported that neither a theory-driven PA consultation delivered by a person nor when provided in written form were better than a standard care leaflet for increasing all measures of PA over 6 and 12 months among inactive individuals with T2DM (n = 134).

Similarly, in a 12-month telephone counseling intervention for patients with T2DM, conducted in primary care clinics in a socioeconomically disadvantaged community, the counseled group did not achieve a significantly greater increase in PA at 4 or 12 months compared to the control group [20]. Other randomized trials in this population have observed increases in PA following intervention. Using a prospective two-armed design, Plotnikoff et al. [17] randomly assigned 96 adults with T2DM to either standard care (diabetes education program) or standard care supplemented with an individualized counseling and community-based PA component. This study reported a significant increase of 654 MET min (p < 0.01) at 12 months. Furthermore, Di Loreto et al. [21] conducted a study (n = 158) where the intervention group received an initial individualized counseling session followed 1 month later, by a telephone call at home and, every 3 months, by an appointment in the Outpatient Diabetes Clinic. After 24 months, the intervention group increased energy expenditure through voluntary PA from 1.0 ± 0.3 to 27.1 ± 2.0 METs × h/week (p < 0.001), a value that was 7-fold greater (p < 0.001) than in the control group that received general advice and brochures.

Our stratified analyses revealed the two interventions were effective for women in increasing their PA. The 3-day step counts increased from baseline to all time points for women in both intervention groups, although the increase was statistically significant only in Group 3 at 12 months (primary end point) and 18 months. Furthermore, MVPA increased in Group 2 (versus controls) at 3 and 9 months. Indeed, with a greater sample size, we may have been able to detect even greater effects on the study’s outcomes for women.

Most of the previous intervention studies testing PA promotion strategies among the diabetes population [8, 17, 20, 21, 23] have not reported results by gender. Among those that did, some demonstrated that telephone counseling is an effective intervention for women [19, 40], while others found no gender effect in terms of intervention effectiveness [22]. Furthermore, in a T2DM prevention initiative, Albright et al., [40] showed that women in a telephone and mail counseling condition had significantly greater increases in estimated total energy expenditure compared to women in a mail only support condition (p < 0.05). A randomized trial that targeted inactive patients (with and without diabetes) attending primary care facilities [41] found that an intervention involving counseling was more effective in increasing fitness in women but not in men. Evidence suggesting that men and women operationalize PA messages differently [42, 43] may help to explain the findings in the current study. Considering that telephone counseling provides some social support for participants, it is interesting to note social support correlates positively with recommended diabetes control outcomes for women, but not for men [44]. However, there is also a possibility that women may have been more susceptible to social desirability in completing the step data. The telephone counseling study protocol was up to 15 min per call (approximately 4 h per individual over the course of the 12-month intervention). Although this may be considered labour intensive relative to other behavior change strategies, it is less resource intensive than medical and drug therapies.

The significant changes in PA observed in this study reported effect sizes in the small to moderate range following Cohen’s [35] guidelines. For example, for women, MVPA increased in Group 2 (versus controls) at 3 and 9 months, by 84.0 and 87.2 min/week, respectively. It should be noted, however, that small changes in behavior may be important from a public health perspective. Small changes at the individual level can translate to substantial changes within the population if the changes are distributed across the entire target (T2DM) population. Health promotion experts advocate that practical, low/minimal intensity interventions that might not have large clinical effects, but can be delivered to large numbers of participants, are more likely to have a broader health impact [45].

Maintaining PA is an important element in the success of achieving and maintaining the maximum benefit of exercise. However, adhering to PA can be difficult because the benefits of this behavior are often not immediate; one often needs to continue the activity for quite some time in order to achieve the rewards. This is reflected by the finding from a meta-analysis of 127 PA intervention studies that report approximately half (40–65 %) of adults dropped out within the first 3 to 6 months of starting an exercise program [46]. The ADAPT study dropout at 6 months was approximately 10 %. The reason for the relatively lower dropout rate might be due to this specialized (T2DM) population of individuals who are generally aware of the benefits of being active to control their disease, and that these individuals often receive recommendations to engage in PA. Only 8 % of our sample had newly diagnosed diabetes (duration of less than 2 years), and therefore most individuals were probably aware of the benefits of PA.

We did not observe a significant change in A1c levels even in the sub-group of women where PA increases were seen. In Canadian primary care centres, the median A1c level is approximately 7 % [47], which is comparable to our study population. We might have found more favourable results for this clinical variable among individuals with poorer glycemic control. In other words, among women who showed increased PA and did not show improved glycemic control, a floor effect may have occurred because these individuals were well controlled at baseline (mean A1cg of 7.1 ± 1.1 %). In the Diabetes Aerobic and Resistance Exercise (DARE) trial (15), only the combination of aerobic and resistance exercise reduced A1c in subjects with baseline A1c under 7.5 %; either aerobic or resistance exercise alone did not. In the present trial, we did not attempt to promote resistance training, and this might have accounted in part for the lack of effect on A1c. Further, our study results did not control for medication use; the buffering effect of physicians backing off treatment in the face of lifestyle improvement could also help to explain the negative results.

The recruitment for this study was conducted employing a multi-strategy approach including general advertising strategies [24] as opposed to recruiting through community-based diabetes clinics, which may account in part for the differing results from other studies, especially those that have found positive results. The study by Kirk et al. [23] employed a multi-strategy approach for recruitment including general advertisement strategies and had null finding in terms of PA change, in contrast to an earlier study by the same authors [22], in which patients were recruited from a diabetes clinic. It is possible those who respond to public advertising (e.g., media) may be a biased group who are more active and take “better care” of themselves and indeed individuals who volunteer to participate in these types of studies are healthier on average compared to the background population [48]. At baseline, men were more active than women, which is in line with findings from previous investigations [11, 49, 50]. However, none of the PA variables were significantly different in the three groups, indicating that the random allocation of the individuals into the different groups was successful. Further, individuals who remained in the study tended to be those who were relatively active at baseline. In other words, the small changes that we observed for PA may also have in part been due to a ceiling effect (i.e., already active). Considering that the men in the study were significantly more active at baseline, such ceiling effects may have especially applied to men. The low PA levels in the dropouts suggest these individuals most needy of PA interventions did not participate in the full study. Future trials should recruit larger samples of adults with T2DM to adequately examine low resource – broad public health approaches effects at the population-level including those individuals who are inactive and most in need of interventions.

Considering that there are limited PA theory-based, individually tailored, long-term studies targeting the adult T2DM population, this study adds to the current literature for promoting PA among adults with T2DM. This study utilized a unique theoretical approach (i.e., employing an Integrated Stage Model based on a polytheoretical approach), investigating a multi-strategy, easily administrative intervention in a sample over an 18-month period. The study’s co-primary outcomes of PA and A1c did not significantly change for the two intervention groups. It is possible that the theoretical approach and the intervention strategies employed in this study were not sufficient to promote change in the study outcomes, and also not particularly suitable for this overall study population. The combined print materials, pedometer and telephone counseling strategies however, appeared to be effective in part for increasing PA for women, but not men. Future studies are required to discover and determine the level of effect sizes for women with this intervention strategy.

Limitations of the study include the use of a self-report measure and 3-day (self-report) pedometer counts to assess PA; future studies should include accelerometry and consider objective assessments beyond 3 days including weekend days. Further, losses to follow-up for the study’s main outcome assessments may have biased the study toward positive findings as participants with poorer responses/outcomes may be more likely to drop out than their successful counterparts. Although our loss to follow-up for the co-primary outcome assessments are historically similar to behavioral interventions [20], recent well-funded, landmark studies (e.g., Diabetes Prevention Project, Look AHEAD [51, 52]) have indeed set an appropriately more rigorous standards for outcome assessments. Finally, given the nature of the intervention in relation to other more intense interventions, this study may have been under powered as it was not powered to detect smaller, and perhaps more realistic, changes for the main outcomes.