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Effects of meal preparation training on body weight, glycemia, and blood pressure: results of a phase 2 trial in type 2 diabetes.
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MedLine Citation:
PMID:  23075398     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
BACKGROUND: Modest reductions in weight and small increases in step- related activity (e.g., walking) can improve glycemic and blood pressure control in type 2 diabetes mellitus (DM2). We examined changes in these parameters following training in time- efficient preparation of balanced, low- energy meals combined with pedometer- based step count monitoring.
METHODS: Seventy- two adults with DM2 were enrolled in a 24- week program (i.e., 15 three- hour group sessions). They prepared meals under a chef's supervision, and discussed eating behaviours/nutrition with a registered dietitian. They maintained a record of pedometer- assessed step counts. We evaluated changes from baseline to 24 weeks in terms of weight, step counts, hemoglobin A1c (HbA1c, glycemic control), blood pressure, and eating control ability (Weight Efficacy Lifestyle WEL Questionnaire). 53 participants (73.6%) completed assessments.
RESULTS: There were improvements in eating control (11.2 point WEL score change, 95% CI 4.7 to 17.8), step counts (mean change 869 steps/day, 95% CI 198 to 1,540), weight (mean change -2.2%; 95% CI -3.6 to -0.8), and HbA1c (mean change -0.3% HbA1c, 95% CI -0.6 to -0.1), as well as suggestion of systolic blood pressure reduction (mean change -3.5 mm Hg, 95% CI -7.8 to 0.9). Findings were not attributable to medication changes. In linear regression models (adjusted for age, sex, ethnicity, insulin use, season), a -2.5% weight change was associated with a -0.3% HbA1c change (95% CI -0.4 to -0.2) and a -3.5% systolic blood pressure change (95% CI -5.5 to -1.4).
CONCLUSIONS: In this 'proof of concept' study, persistence with the program led to improvements in eating and physical activity habits, glycemia reductions, and suggestion of blood pressure lowering effects. The strategy thus merits further study and development to expand the range of options for vascular risk reduction in DM2.
Authors:
Kaberi Dasgupta; Samantha Hajna; Lawrence Joseph; Deborah Da Costa; Stavroula Christopoulos; Rejeanne Gougeon
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Publication Detail:
Type:  Clinical Trial, Phase II; Journal Article; Randomized Controlled Trial; Research Support, Non-U.S. Gov't     Date:  2012-10-17
Journal Detail:
Title:  The international journal of behavioral nutrition and physical activity     Volume:  9     ISSN:  1479-5868     ISO Abbreviation:  Int J Behav Nutr Phys Act     Publication Date:  2012  
Date Detail:
Created Date:  2013-01-14     Completed Date:  2013-04-16     Revised Date:  2013-07-11    
Medline Journal Info:
Nlm Unique ID:  101217089     Medline TA:  Int J Behav Nutr Phys Act     Country:  England    
Other Details:
Languages:  eng     Pagination:  125     Citation Subset:  IM    
Affiliation:
Department of Medicine, McGill University, Montreal, QC, Canada. kaberi.dasgupta@mcgill.ca
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MeSH Terms
Descriptor/Qualifier:
Aged
Blood Pressure*
Body Mass Index
Body Weight*
Diabetes Mellitus, Type 2 / therapy*
Diet
Energy Intake
Female
Glycemic Index*
Health Promotion / methods
Hemoglobin A, Glycosylated / analysis
Humans
Intervention Studies
Life Style
Linear Models
Male
Meals*
Middle Aged
Patient Education as Topic*
Questionnaires
Socioeconomic Factors
Walking
Grant Support
ID/Acronym/Agency:
//Canadian Institutes of Health Research
Chemical
Reg. No./Substance:
0/Hemoglobin A, Glycosylated
Comments/Corrections

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine

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Journal Information
Journal ID (nlm-ta): Int J Behav Nutr Phys Act
Journal ID (iso-abbrev): Int J Behav Nutr Phys Act
ISSN: 1479-5868
Publisher: BioMed Central
Article Information
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Copyright ©2012 Dasgupta et al.; licensee BioMed Central Ltd.
open-access:
Received Day: 11 Month: 4 Year: 2012
Accepted Day: 11 Month: 10 Year: 2012
collection publication date: Year: 2012
Electronic publication date: Day: 17 Month: 10 Year: 2012
Volume: 9First Page: 125 Last Page: 125
PubMed Id: 23075398
ID: 3543247
Publisher Id: 1479-5868-9-125
DOI: 10.1186/1479-5868-9-125

Effects of meal preparation training on body weight, glycemia, and blood pressure: results of a phase 2 trial in type 2 diabetes
Kaberi Dasgupta123 Email: kaberi.dasgupta@mcgill.ca
Samantha Hajna2 Email: samantha.hajna@mail.mcgill.ca
Lawrence Joseph2 Email: lawrence.joseph@mcgill.ca
Deborah Da Costa1 Email: deborah.dacosta@mcgill.ca
Stavroula Christopoulos1 Email: stavroula.christopoulos@mcgill.ca
Rejeanne Gougeon1 Email: rejeanne.gougeon@mcgill.ca
1Department of Medicine, McGill University, Montreal, QC, Canada
2Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, QC, Canada
3Division of Clinical Epidemiology, Department of Medicine, McGill University Health Centre, 687 Pine Avenue West, V-Building (V1.08), Montreal, Canada

Introduction

Type 2 diabetes mellitus (DM2) confers a two- to four- fold risk increase for myocardial infarction (MI) and stroke and is a leading cause of renal injury and visual impairment [1]. A net weight loss of 5% or more leads to improvements in both blood pressure and glycemia, [2-6] and there is even evidence for improvements with net weight loss of as little as 2 to 5% in persons with DM2 [2]. Similarly, small increases in walking and other forms of ‘stepping activity’ have the potential to reduce vascular risk through blood pressure lowering, as we [7,8] and others [9] have previously demonstrated.

Weight loss is challenging, however, in a socio- environmental context characterized by shifts in food consumption patterns, from home- prepared regular meals to more erratic, ‘on the run’ intake of ‘supersized’ prepared meals [10-12]. Moreover, increased use of computers in the workplace, communication, and leisure has led to a marked decline in ‘daily steps [13]. Further, there is evidence that individuals with DM2 have greater difficulty losing weight and increasing physical activity levels compared to those without DM2 [14].

Nonetheless, in the ongoing Look AHEAD trial, important weight losses in individuals with DM2 have been achieved through a dietary intervention that incorporates meal replacements and strong behavioural therapy elements [15]. Not all individuals with DM2, however, may be willing to use meal replacements, because of limited variety and flexibility. There is thus a need for development of a variety of strategies to allow for ‘fit’ with preferences.

Through a Phase 2 trial, we aimed to empower adults with DM2 to meet the challenges of improving weight control and increasing step counts in our modern ‘obesogenic’ environment. We developed a program that combined self-monitoring strategies (e.g., weight, step counts) with culinary skills training to apply nutrition- related education. No meal replacement products were employed. We tested the intervention through a pre- intervention/post- intervention design. Specifically, among overweight adults followed for DM2, we aimed to assess the mean weight change during our 24- week intervention, as well as mean changes in step count, glycemic control (hemoglobin A1c, HbA1c), blood pressure, and control of eating. We also sought to quantify relationships between changes in weight and step counts with changes in HbA1c and blood pressure. The findings reported herein provide important ‘proof of concept’ information and justification for further study and development.


Methods
Participants

Recruitment (1 April 2009 to 8 May 2010) occurred at hospital-based outpatient clinics affiliated with McGill University (Montreal, Canada; McGill University Health Centre MUHC, Sir Mortimer Davis Jewish General Hospital, St. Mary’s Hospital), through a combination of posters, newsletters, and staff referrals. Requirements for inclusion were age ≥ 18 years; a physician diagnosis of DM2 with regular follow- up; body mass index in the overweight to stage 2 obesity range (i.e., BMI of 25 to 40 kg/m2); and ability to communicate and read in English or French. Criteria for exclusion were multiple daily insulin doses (basal insulin permitted); food allergies, intolerances or dietary restrictions; smoking (i.e., past 12 months), and co-morbid illness (e.g., malignancy) or medication (e.g. steroids, sibutramine, orlistat) that could importantly impact body weight during the 24- week intervention period.

Procedures and investigations
Intervention procedure

The main focus of the intervention was on nutritional issues. A series of fifteen core group-based sessions (three hours per session) were delivered over a 24- week period. Each group included 10 to 12 participants. Sessions were held at one of two local grocery stores, in their kitchen workshops. These workshops, normally used for birthday parties and community group meetings, are equipped with a stove, oven, microwave, sink, and several large tables.

At the first session, each participant received a recipe book [16,17] endorsed by the Canadian Diabetes Association (CDA) and detailing its dietary guidelines. They also received a Yamax SW-701 pedometer, [18,19] were instructed in its positioning, and tested it on- site, during a 10- minute indoor group walk. Participants were encouraged to track daily step counts, and progressively increase these by 500 steps/day each week, with an aim of ultimately achieving at least 8,000 to 10,000 steps/day [20-22]. At the beginning of each of the subsequent sessions, the registered dietitian collected a record of step counts since the last session, and weighed each participant.

At all sessions, participants prepared a balanced meal under a professional chef’s supervision, in smaller groups of 3 to 4, using recipes from the book provided. The chef provided guidance on techniques for selecting fresh produce and performing preparatory work (e.g., cutting vegetables on weekends to expedite preparation during the week), as well as actual food preparation techniques (e.g., slicing, steaming, sautéing, etc.).

Meals were ‘self-served’, with guidance from the dietitian with respect to proportions (i.e., half plate vegetables, quarter plate grains, quarter plate protein/substitute) and portion sizes (i.e., unlimited vegetables, grains filling one’s ‘cupped hand’, protein portion the size of palm) [1]. An interactive nutritional education discussion with the dietitian followed [16,17]. Participants were encouraged to prepare a similar meal at home, and to try other recipes from the book provided.

Investigations

At baseline and following the intervention, participants underwent evaluations at the McGill University Health Centre. Demographic information and medication (i.e., antihypertensive and antihyperglycemic medications) use were evaluated. Direct measures included height (without shoes SECA 214 stadiometer), body weight (light clothes, without shoes, post-void, calibrated SECA 882 electronic scale), and circumference of waist (standing position, midway between the lateral lower ribs and the iliac crests after a moderate expiration) and hip (widest level, over the greater trochanters) [23]. Systolic blood pressure (SBP) and diastolic blood pressure (DBP) (two consecutive measurements were taken and averaged for each, Omron HEM-747 IC), and HbA1c (Bio-Rad Variant II) were evaluated. HbA1c, reported as the proportion of hemoglobin molecules that are glycosylated, provides an indication of ambient glucose levels over a two to three month period, the average life span of a red blood cell.

Dietary intake information was derived from a self-administered Food Frequency Questionnaire [24] that assessed total energy, salt, carbohydrate, protein, total fat and saturated fat intakes. Hours dedicated to meal preparation on weekdays and weekend days were queried. Self- perceived cooking ability was evaluated through a series of questions used in a previous study [25]. Control of eating behaviour was evaluated using the Weight Efficacy Lifestyle (WEL) Questionnaire [26,27]. The 20-item WEL instrument scores each item on a 10-point Likert scale (0 to 9), where a higher score is indicative of a greater ability to avoid eating under various circumstances. The five circumstances under which ability to resist eating is assessed include 1) food availability (e.g., attending a party), 2) negative emotions (e.g., anxiety), 3) physical discomfort (e.g., pain), 4) positive activities (e.g., reading or while watching television), and 5) social pressure (e.g., being unable to refuse an invitation to eat). Readiness for weight change was evaluated using the Weight Stages of Change-Short Form [28,29]. Depressive symptomology was assessed using the Center for Epidemiological Studies Depression Scale.

Daily step counts at baseline were estimated from mean values based on records collected at sessions 1 to 3; final step counts were derived from step counts records collected at sessions 13 to 15.

Ethics

The study was conducted with approval from Institutional Review Board (IRB) of McGill University and at participating institutions (McGill University Health Centre MUHC, Sir Mortimer Davis Jewish General Hospital, St. Mary’s Hospital). All participants provided written informed consent.

Statistical methods

Baseline characteristics (means and standard deviations (SD) or proportions, as appropriate) were examined separately for those who did and did not complete final assessments. An implausible energy intake was defined as outside the range of 500 to 3500 kcal/d for women and 800 to 4000 kcal/d for men, and these were excluded [30]. Mean changes with 95% confidence intervals (CI) were computed for weight (i.e., percent change from baseline), BMI, step counts, SBP, DBP, HbA1c, dietary intakes (total energy, macronutrient proportion, fibre, sodium), WEL scores and subscale scores, time dedicated to meal preparation, and eating- out frequency. The proportion achieving a 5% or greater weight reduction was computed overall and for those who attended 75% or more of sessions.

Linear regression analysis was used to assess for associations between changes in weight from baseline with corresponding changes in SBP, DBP, and HbA1c. A similar approach was used to assess for associations between changes in step counts and changes in SBP, DBP, and HbA1c. Potential confounders/co-variates considered for inclusion in these models included demographic factors and baseline characteristics. Final models were adjusted for age, sex, ethnocultural background, insulin use (indicator of diabetes ‘severity’), and season (spring/summer vs. fall/winter) [21]. Sensitivity analyses were conducted wherein those with medication changes were excluded (i.e., antihypertensive medications for blood pressure models, antihyperglycemic medications for HbA1c models).

Although changes in weight and changes in step count may not be independent (e.g., increases in step counts may enhance weight loss), we assessed the effects of adjusting for changes in step counts in models examining associations of changes in weight with changes in SBP, DBP, and HbA1c. Similarly, we report the effects of adjusting for weight change in models examining relationships between changes in step counts with changes in SBP, DBP, and HbA1c. These analyses were conducted to discern the independent effects of changes in weight and changes in step counts. Data were analyzed using SAS 9.2 (SAS Institute Inc., Cary, NC, USA).

To account for potential effects of those who did not present for final assessments, the mean weight change was re- computed in two sensitivity analyses: (i) carrying baseline weights forward and (ii) carrying forward the weight recorded at last session attended. Multiple imputations were examined but are not reported herein because they did not substantially alter the final estimates.


Results
Participants

Among 112 candidates who underwent preliminary assessment of eligibility, 74 were interested and potentially eligible, 72 completed baseline assessment procedures, and 53 completed final evaluations. Among the 19 who did not complete the final assessment, more than half reported time- related barriers (i.e., 6 work- related; 7 unspecified time constraints). Other reasons included unspecified health issues (2), lack of interest in content (3), and moving out of the city (1). Those who did not complete final assessments were (Table  1) younger, had a higher BMI, were more frequently men, were less often on insulin, reported greater ability to withstand eating pressures (Table  2), and dedicated roughly one hour per day to meal preparation.

Among the 53 completing final assessments (Tables  1 and 2), three- quarters were Europid, roughly half Canada- born, and nearly 60% were employed. They were on average at the stage 1 level of obesity with abdominal obesity (mean waist circumference (SD) 103 (10.4) cm in women and 109 (11) cm in men; waist to hip 0.88 (0.07) in women and 0.98 (0.05) in men). Average step counts were in the low active range (based on 43 who provided records), average blood pressure values were close to recommended levels, and average HbA1c was above recommended targets, with one quarter on insulin. Fewer than half were ‘competent’ cooks and WEL scores indicated intermediate level ability to regulate intake in the context of food availability and negative emotions and higher ability to modulate intake in the contexts of physical discomfort, positive activities, and social pressures. The median number of classes attended was 13.

Changes during intervention

There was a net weight reduction (mean weight change −2.2%; 95% CI −3.6 to −0.8; Table  3) overall, and this remained conclusive when the baseline values were carried forward (mean change −1.6%, 95% CI −2.6 to −0.6) as well as when the last in-session observation was carried forward (mean change −1.9%, 95% CI −3.1 to −0.7). A net weight loss of at least 5% was achieved by nearly one fifth (18.9%, 95% CI: 9.4 to 32.0) of those who completed study procedures (Table  3). Close to one third (28.6%, 95% CI 15 to 46) of those who attended at least 75% of the scheduled sessions achieved a 5% or greater net weight reduction. There were no conclusive changes in waist/hip or waist circumference.

There were reductions in total energy (−239 kcal/day, 95% CI −408 to −70) and salt intake (mean change −384 mg/day, 95% CI −646 to −122). There was a 30-minute increase in time dedicated to meal preparation on weekdays. Eating-out frequency did not change, remaining at an average of twice per week. There was an important increase in both overall (mean change 11.2, 95% CI 4.7 to 17.8) and subscale WEL scores, indicating better ability to control eating behaviours.

A total of 43 individuals provided the records needed to compute step count changes. In these individuals, daily step counts increased by 869 (95% CI 198 to 1,540), with greater increases in those who started the intervention in the fall/winter and ended in the spring/summer (1,366 steps/day increase) compared to those who did the reverse (436 steps/day increase).

HbA1c values decreased overall, with an average absolute change of −0.3% (95% CI −0.6 to −0.1). There was a signal for a SBP reduction (−3.5 mm Hg, 95% CI −7.8 to 0.9), but there did not appear to be an important change in DBP (−0.1 mm Hg, 95% CI −3.0 to 2.8). Mean changes in HbA1c, SBP and DBP were more apparent in those who entered the study in fall/winter (−0.5% and −8.7/-1.9 mm Hg, respectively) than in those who entered in the spring/summer (−0.2% and 1.2/1.5 mm Hg, respectively).

Associations of changes in weight and step counts with Hba1c and blood pressure

Changes in HbA1c were linked to changes in weight (Table  4) but not to changes in step count (Table  5). Across the models examined, a 2.5% weight reduction, close to the average weight change in this study, was associated with a 0.3% absolute reduction in HbA1c (Table  4; e.g., HbA1c change −0.3%, 95% CI −0.4% to −0.2% in model adjusted for age, sex,ethnicity, insulin use, and season). This was similar to the actual reductions observed (Table  3). These values were not appreciably altered by adjustment for change in step counts. An identical estimate was derived when analyses were restricted to those without any changes in antihyperglycemic medications (Table  4).

Changes in both SBP and DBP were related to changes in weight (Table  4). After adjustment for age, sex, ethnicity, insulin use, and season, a 2.5% weight reduction was associated with a −3.5/-2.3 mm Hg mean change in SBP/DBP (95% CI −5.5 to −1.4/-3.6 to −0.9). These values were not appreciably altered by adjustment for change in step counts. In contrast, although changes in both SBP and DBP also appeared to be related to changes in step count (Table  5), the magnitude of these associations declined with adjustment for weight change and an absence of association could not be excluded. A 1,000 step per day increment was associated with a −2.3/-1.6 mm Hg mean change in SBP/DBP in models adjusted for age, sex, ethnicity, insulin use, and season (95% CI-4.5 to −0.1/-3.1 to −0.2) but with adjustment for weight change, these values were −1.4/-1.2 (95% CI −3.2 to 0.4/-2.6 to 0.2) Changes in salt intake were not linked to changes in blood pressure levels. When analyses were restricted to individuals without changes in antihypertensive medications (Tables  4 and 5), the point estimates remained similar.


Discussion

Among adults with DM2, we conducted a phase 2 trial to assess the effects of a 24- week 15- core session nutritional education program that focused on meal preparation training and integrated pedometer-based self-monitoring. In the 74% of participants who completed assessments, our findings demonstrate small but conclusive reductions in weight (mean weight change −2.2%; 95% CI −3.6 to −0.8) with a nearly 1- point decrease in BMI (mean change −0.9, 95% CI −1.3 to −0.4) and a net increase in step counts (mean change 869 steps/day, 95% CI, 198 to 1,540) in those who provided step count records. Although eating- out frequency did not decline, there were reductions in reported energy intake and salt consumption, as well as improvements across a range of behaviours related to the ability to control eating, and increases in time dedicated to meal preparation on weekdays. Most importantly, there was a definitive improvement in glycemic control (mean HbA1c change - 0.3%, 95% CI −0.6 to −0.1) that was not attributable to medication changes, confirming the physiological impact of the intervention. Further, there was a strong suggestion of systolic blood pressure improvement (mean change −3.5 mm Hg, 95% CI −7.8 to 0.9). We believe that these findings indicate that the intervention holds promise and merits further development and testing.

We acknowledge that the weight loss effects that we observed are less than reported in some other intervention studies. Intervention studies that have focused primarily on nondiabetic individuals have achieved a net reduction of 5% or greater in a larger proportion of participants [6,31-33] compared to the one fifth of our study participants. This is likely partly attributable to our focus on individuals with DM2, perhaps underscoring the particular challenge that weight loss poses to DM2 patients [14].

Impressively, and specifically in adults with DM2, the Look AHEAD trial achieved a 5% or greater weight loss in roughly 70% of participants at one year [34]. The most notable difference between our strategy and that employed in Look AHEAD is the use of meal replacements in Look AHEAD [35]. During the first 20 weeks of the Look AHEAD intervention, participants are prescribed two meal replacements per day, one portion- controlled snack, and one self- selected meal. After 20 weeks, they are prescribed one meal replacement and two meals of self-selected foods, and use of weight- lowering medication is considered if weight loss is deemed insufficient. The use of meal replacements has been demonstrated to be an important predictor of weight loss in the Look AHEAD cohort [15]. However, it does potentially limit variety and flexibility in dietary intake and may therefore not be uniformly accepted, hence the need for the development of alternative strategies. Additionally, the long term feasibility of sustained used of meal replacements has not been reported; there is a very real possibility that while meal replacements may lead to greater initial weight loss, once these are discontinued, this benefit may not persist. This underscores the need for studies such as ours that endeavour to develop and test alternatives to meal replacement-based strategies.

It should also be noted that in Look AHEAD failure to complete the run- in for dietary intake and exercise was an exclusion criterion; [35] we did not use a run-in period. This may also have contributed to the lesser degree of weight loss observed in our study.

In our strategy, by integrating step count self- monitoring, study participants who provided step count records achieved an 869 average increase in daily step counts, roughly one third that achieved in interventions that focus primarily on increasing walking and other step- related activity [22]. Given that the main focus of the intervention was on nutritional issues, the increase in step counts was notable. The step count increase was greater in those who started the intervention in fall/winter and ended in spring/summer compared to those who did the opposite (1,366 vs.436 steps/day). This is consistent with a previous observational study wherein we demonstrated that during fall/winter, step counts decline by 758 steps/day [21]. Thus, in the present study, those who started the intervention during the spring/summer were not only able to offset the usual fall/winter decline, they were also able to achieve a net increase. Arguably, a mean increase of at least1,000 steps/day may be needed to demonstrate definite health effects, as supported by our own observational studies [7,8]. Nonetheless, the 869 steps/day increase is promising, and could potentially rise to 1,000 steps/day or more with greater focus on this aspect of behavioural change.

A key finding was the demonstration of a definitive 0.3% reduction in HbA1c that was closely associated with the weight changes observed (Table  4). This reduction was determined to be unrelated to changes in antihyperglycemic agents. In terms of magnitude, it is roughly one third of the HbA1c reduction in the intensive glycemic control with metformin arm of the United Kingdom Prospective Diabetes Study (UKPDS), an arm with a 42% relative reduction in diabetes- related deaths. [36] Based on UKPDS analyses, Tricco and colleagues [37] recently estimated that a 0.33% HbA1c reduction may be associated with 21% fewer deaths, 14% fewer myocardial infarctions, and 37% fewer microvascular complications (retinopathy, nephropathy, neuropathy) at the population level. This estimate supports the clinical importance of the 0.3% HbA1c reduction that we observed.

Thus, even with a mean weight reduction of less than 5%, our intervention demonstrated clinically- important improvements in glycemic control. Such physiological effects of small weight changes have been demonstrated in other DM2 intervention trials, including the group that had a net weight change of 2 to 5% over a one-year period in the Look AHEAD trial [2].

Another important signal of potential for physiological impact was the SBP reduction, although this was not definitive (95% mean change – 3.5 mm Hg, 95% CI −7.8 to 0.9), likely because of insufficient sample size. In regression analyses, however, definitive association with weight change were demonstrated: a −2.5% weight change was associated with a 3.5 mm Hg SBP reduction (95% CI −5.5 to −1.4). There was also a signal that in some individuals the increase in step counts may have contributed to blood pressure reductions; however, this was not clearly independent of weight change, and was not the primary focus of the strategy. Greater emphasis on methods to increase step counts may enhance the impact of this aspect of the intervention. For example, a recent trial suggests that encouraging stepping during television commercial breaks may importantly increase daily steps [38].

Both participants who began the 24-week intervention in the spring/summer and those who started in the fall/winter experienced HbA1c reductions (−0.2% and −0.5%, respectively). Blood pressure– lowering was apparent for those who started in the fall/winter (SBP/DBP −8.7/-1.9 mm Hg) but those who started in the spring/summer experienced a small increase (1.2/1.5 mmHg): this must be interpreted, however, in the context of a usual fall/winter 4 mmHg SBP increase, as we have previously documented in this patient population [21]. Thus the intervention appears to have attenuated the usual fall/winter increase.

The main limitations of our study are related to sample size and use of a single-arm intervention rather than a randomized controlled design. We would emphasize, however, that a single- arm trial- i.e., a Phase2 trial- was deliberately pursued in order to assess for feasibility and evidence of some impact. A single-arm design permitted each participant to act as his/her own control, reducing confounding and increasing the precision of estimates. In contrast, small trials risk unbalanced treatment arms and limited scope for statistical adjustments. Another potential limitation relates to use of participant reports to estimate dietary intake and step counts. While we did detect reductions in energy intake and salt intake as well as increases in step counts (Table  3), we acknowledge that self-report of dietary intake may be subject to either over or under reporting as it relies on an individual’s memory and ability to estimate portion sizes. [39] Similarly, use of step count records to monitor physical activity requires participants to reliably wear a pedometer and accurately record values. Indeed, not all of those who completed final assessments reliably provided step count records. Thus, both the estimates of dietary intake and step counts are subject to some degree of measurement error. We would emphasize, however, that the main parameters of interest in this study- changes in weight, blood pressure, and HbA1c- were all objectively-assessed.

Despite some potential limitations, the findings provide good justification for further development and ultimately testing of this innovative strategy through a large randomized controlled trial. Importantly, such a trial should be of sufficient duration to allow examination of longer term effects, adherence, and sustainability (i.e., one year and beyond). Indeed we are presently attempting to tailor a similar strategy to women with a history of gestational diabetes mellitus (Canadian Institutes of Health Research, Principal Investigator K. Dasgupta, CAI 117789); this younger patient population at risk for DM2 may be able to achieve greater changes in weight and step counts, potentially translating into even greater vascular health improvements. Further, contingent on focus group discussions, we are considering the implementation of a tapering course of meal replacements concurrent with the acquisition of meal preparation skills. This could diminish long term reliance on meal replacement products, while allowing initial benefit from their use.

In this study, the average cost per participant per session (and covered through the study) was roughly $50 CDN (room rental, chef, groceries, dietitian), with a 15- session program costing approximately $750.00 CDN per participant. In an actual program, such a fee could be prohibitively high for some persons with DM2, given the higher prevalence of DM2 in more disadvantaged populations [40]. Translating such a strategy into a sustainable program will thus require important cooperation/contributions from key stakeholders (diabetes associations, local businesses, government agencies). While sessions were held at kitchen workshops of a grocery chain, other options would include kitchens at schools and community centres.

The findings reported in this study provide evidence to support the merits of a group-based dietary education strategy that is centered on preparing a meal, eating it together, and discussing both optimal eating habits and the challenges of adopting these. In individuals followed for DM2, the strategy tested demonstrated reductions in weight as well as effects on glycemic and blood pressure control- even in the context of medication therapy. There is a need to further develop and target the intervention to achieve a greater magnitude of effect. Nonetheless, our findings support the merit of this line of enquiry. The prevention of overweight- related complications requires a range of creative management options, allowing a ‘fit’ between patient and approach. Our strategy may emerge as one such option, empowering overweight persons with or at- risk for DM2 with the tools to develop a healthy relationship with food and better physical activity habits in the modern obesogenic environment.


Abbreviations

DM2: Type 2 diabetes mellitus; HbA1c: Glycosylated hemoglobin; WEL: Weight efficacy lifestyle (Questionnaire); MI: Myocardial infarction; BMI: Body mass index; CDA: Canadian diabetes association; SBP: Systemic blood pressure; DBP: Diastolic blood pressure; SD: Standard deviation; CI: Confidence Interval.


Competing interests

The author(s) declare that they have no competing interests.


Authors’ contributions

KD, RG, LJ, and DD designed research; KD and RG conducted research; SH and LJ analyzed data; KD wrote the paper; SC and RG interpreted and edited the paper; KD had primary responsibility for final content. All authors read and approved the final manuscript.


Authors’ information

Kaberi Dasgupta is a Physician Scientist and Associate Professor of Medicine at McGill University. Samantha Hajna is a doctoral student in Epidemiology at McGill University. Lawrence Joseph is a Professor of Medicine at McGill University and a senior biostatistician. Deborah Da Costa is Associate Professor of Medicine at McGill University and a clinical psychologist and researcher. Stavroula Christopoulos is an endocrinologist at the Sir Mortimer Davis Jewish General Hospital in Montreal. Rejeanne Gougeon is Associate Professor of Medicine at McGill University and an expert in Human Nutrition. She has led the development of the nutrition guidelines of the Canadian Diabetes Association.


Acknowledgements

Deborah Chan played a key role in all aspects of project management, with invaluable assistance from Amanda Wilkins and Rita Motchula. Registered dietitians involved in the intervention included Gilda Bastasi, along with Lisa Kaouk, Marissa Ranallo, Laura Dytynyshyn, Stephanie Shabat, Geraldine St. Jules, Mary Hendrickson- Nelson, and Ani Tchakrian. Dr. Soghra Jarvandi and Abeer Bader developed important refinements to the nutritional education component. The chefs who co- led the intervention sessions included Nicolina Servello, Philip Chang, Martin Patenaude, and Dahlia Gold. Paul Shubin and Martin Patenaude were the community liaisons/events coordinators at the grocery stores (Loblaws) where the sessions were held, providing critical logistic support. Sehar Manji provided important support in the preparation of this manuscript.

Preliminary findings were presented at the 29th Annual Scientific Meeting of The Obesity Society, Obesity 2011. Orlando, Florida, U.S.A., October 1–5, 2011.

Funding

This research was supported by a grant from the McGill University Office of the Vice-Principal (Research) to K. Dasgupta. At the time of this study, she held a New Investigator career award from Canadian Institutes of Health Research (CIHR) and she now holds the Fonds de recherché Sante Québec-Société québécoise d’hypertension arterielle- Jacques de Champlain Clinician Scientist career award. D. DaCosta also holds a career award from the FRQS and S. Hajna holds a doctoral award from the CIHR.


References
Canadian Diabetes Association Clinical Practice Guidelines Expert CommitteeCanadian Diabetes Association 2008 clinical practice guidelines for the prevention and management of diabetes in CanadaCanadian Journal of DiabetesYear: 200832suppl 1S40
Wing RR,Lang W,Wadden TA,Safford M,Knowler WC,Bertoni AG,et al. Benefits of modest weight loss in improving cardiovascular risk factors in overweight and obese individuals with type 2 diabetesDiabetes CareYear: 20113471481148610.2337/dc10-241521593294
Blackburn G,Effect of degree of weight loss on health benefitsObes ResYear: 19953Suppl 2211s216s8581779
Vidal J,Updated review on the benefits of weight lossInt J Obes Relat Metab DisordYear: 200226Suppl 4S25S2812457296
Lau DC,Douketis JD,Morrison KM,Hramiak IM,Sharma AM,Ur E,2006 Canadian clinical practice guidelines on the management and prevention of obesity in adults and childrenCMAJYear: 20071768S1S1310.1503/cmaj.06140917420481
Christian JG,Tsai AG,Bessesen DH,Interpreting weight losses from lifestyle modification trials: using categorical dataInt J Obes (Lond)Year: 201034120720910.1038/ijo.2009.21319823184
Manjoo P,Joseph L,Pilote L,Dasgupta K,Sex Differences in Step Count-Blood Pressure Association: A Preliminary Study in Type 2 DiabetesPLoS OneYear: 20105e1408610.1371/journal.pone.001408621124929
Manjoo P,Joseph L,Dasgupta K,Abdominal Adiposity and Daily Step Counts as Determinants of Glycemic Control in Cohort of Patients with Type 2 Diabetes MellitusNutrition and DiabetesYear: 20122e2510.1038/nutd.2011.2223154681
Bates JH,Serdula MK,Khan LK,Jones DA,Gillespie C,Ainsworth BE,Total and leisure-time walking among U.S. adults should every step count?Am J Prev MedYear: 2005291465010.1016/j.amepre.2005.03.01115958251
Hill JO,Wyatt HR,Reed GW,Peters JC,Obesity and the environment: where do we go from here?ScienceYear: 2003299560885385510.1126/science.107985712574618
James WP,The epidemiology of obesity: the size of the problemJ Intern MedYear: 2008263433635210.1111/j.1365-2796.2008.01922.x18312311
Glanz K,Resnicow K,Seymour J,Hoy K,Stewart H,Lyons M,et al. How major restaurant chains plan their menus: the role of profit, demand, and healthAm J Prev MedYear: 200732538338810.1016/j.amepre.2007.01.00317478263
Dasgupta K,Re-engineering Steps Into Daily Life: A Critical Issue in Diabetes ManagementCanadian Journal of DiabetesYear: 2011353245246
Wing R,Marcus M,Epstein L,Salata R,Type II diabetic subjects lose less weight than their overweight nondiabetic spousesDiabetes CareYear: 198710556356610.2337/diacare.10.5.5633677974
Wadden TA,Neiberg RH,Wing RR,Clark JM,Delahanty LM,Hill JO,et al. Four-year weight losses in the Look AHEAD study: factors associated with long-term successObesity (Silver Spring)Year: 201119101987199810.1038/oby.2011.23021779086
Younker KE,Complete Canadian Diabetes CookbookYear: 2005Toronto, Ontario: Robert Rose Inc
Younker KE,Nos meilleures recettes pour mieux vivre avec le diabète: de l'entrée au dessertYear: 2005FrenchOutremont, Quebec: Editions du Trécarré
Schneider P,Crouter S,Bassett D,Pedometer measures of free-living physical activity: comparison of 13 modelsMed Sci Sports ExercYear: 200436233133510.1249/01.MSS.0000113486.60548.E914767259
Crouter S,Schneider P,Karabulut M,Bassett D Jr,Validity of 10 electronic pedometers for measuring steps, distance, and energy costMed Sci Sports ExercYear: 20053581455146012900704
Tudor-Locke C,Bassett DR Jr,How many steps/day are enough? Preliminary pedometer indices for public healthSports MedYear: 20043411810.2165/00007256-200434010-0000114715035
Dasgupta K,Joseph L,Pilote L,Strachan I,Sigal RJ,Chan C,Daily steps are low year-round and dip lower in fall/winter: findings from a longitudinal diabetes cohortCardiovasc DiabetolYear: 201098110.1186/1475-2840-9-8121118567
Bravata DM,Smith-Spangler C,Sundaram V,Gienger AL,Lin N,Lewis R,et al. Using pedometers to increase physical activity and improve health: a systematic reviewJAMAYear: 2007298192296230410.1001/jama.298.19.229618029834
Kamel E,McNeill G,Van Wijk M,Change in intra-abdominal adipose tissue volume during weight loss in obese men and women: correlation between magnetic resonance imaging and anthropometric measurementsInt J Obes Relat Metab DisordYear: 200024560761310.1038/sj.ijo.080120410849583
Shatenstein B,Nadon S,Godin C,Ferland G,Development and validation of a food frequency questionnaireCan J Diet Pract ResYear: 2005662677510.3148/66.2.2005.6715975195
Beardsworth A,Bryman A,Keil T,Goode J,Haslam CLE,Women, men and food: the significance of gender for nutritional attitudes and choicesBr Food JYear: 2002104747049110.1108/00070700210418767
Clark MM,Cargill BR,Medeiros ML,Pera V,Changes in self-efficacy following obesity treatmentObes ResYear: 19964217918110.1002/j.1550-8528.1996.tb00531.x8681051
Fontaine KR,Cheskin LJ,Self-efficacy, attendance, and weight loss in obesity treatmentAddict BehavYear: 199722456757010.1016/S0306-4603(96)00068-89290865
O'Connell D,Velicer WF,A decisional balance measure and the stages of change model for weight lossInt J AddictYear: 19882377297503192340
Rossi JS,Rossi SR,Velicer WF,Prochaska JO,Allison DBMotivational readiness to control weightHandbook of Assessment Methods for Eating Behaviors and wight related ProblemsYear: 1995Thousand Oaks, CA: Sage Publications387430
Willet W,Nutritional EpidemiologyYear: 19982New York: Oxford University Press
Wadden TA,Volger S,Sarwer DB,Vetter ML,Tsai AG,Berkowitz RI,et al. A Two-Year Randomized Trial of Obesity Treatment in Primary Care PracticeN Engl J MedYear: 2011365211969197910.1056/NEJMoa110922022082239
Jebb SA,Ahern AL,Olson AD,Aston LM,Holzapfel C,Stoll J,et al. Primary care referral to a commercial provider for weight loss treatment versus standard care: a randomised controlled trialLancetYear: 201137898011485149210.1016/S0140-6736(11)61344-521906798
Appel LJ,Clark JM,Yeh HC,Wang NY,Coughlin JW,Daumit G,et al. Comparative Effectiveness of Weight-Loss Interventions in Clinical PracticeN Engl J MedYear: 2011365211959196810.1056/NEJMoa110866022085317
Wing RR,Long-term effects of a lifestyle intervention on weight and cardiovascular risk factors in individuals with type 2 diabetes mellitus: four-year results of the Look AHEAD trialArch Intern MedYear: 2010170171566157520876408
Ryan D,Espeland M,Foster G,Haffner S,Hubbard V,Johnson K,et al. Look AHEAD (Action for Health in Diabetes): design and methods for a clinical trial of weight loss for the prevention of cardiovascular disease in type 2 diabetesControl Clin TrialsYear: 200324561062810.1016/S0197-2456(03)00064-314500058
UK Prospective Diabetes Study GroupEffect of intensive blood glucose control with metformin on complications in overweight patients with type 2 diabetes (UKPDS 34)LancetYear: 199835291318548659742977
Tricco AC,Ivers NM,Grimshaw JM,Moher D,Turner L,Galipeau J,et al. Effectiveness of quality improvement strategies on the management of diabetes: a systematic review and meta-analysisLancetYear: 201237998332252226110.1016/S0140-6736(12)60480-222683130
Steeves JA,Bassett DR,Fitzhugh EC,Raynor HA,Thompson DL,Can sedentary behavior be made more active? A randomized pilot study of TV commercial stepping versus walkingInt J Behav Nutr Phys ActYear: 2012919510.1186/1479-5868-9-9522866941
Poslusna K,Ruprich J,de Vries JH,Jakubikova M,Van't Veer P,Misreporting of energy and micronutrient intake estimated by food records and 24 hour recalls, control and adjustment methods in practiceBr J NutrYear: 2009101Suppl 2S73S8519594967
Dasgupta K,Khan S,Ross N,Type 2 diabetes in Canada: concentration of risk among most disadvantaged in men but inverse social gradient across groups in womenDiabetic MedYear: 20102752253110.1111/j.1464-5491.2010.02982.x20536947

Tables
[TableWrap ID: T1] Table 1 

Socio- demographic characteristics, clinical measures, and medications at baseline


  Final Assessement Attended n=53 [a] Final Assessment Not Attended (n=19) [b]
Socio-demographic Characteristics
 
 
Age, years, Mean (SD)
59.7 (10.6)
57.2 (9.9)
Women, No. (%)
36 (67.9)
9 (47.4)
Europid, No. (%)
40 (75.5)
15 (79.0)
Born in Canada, No. (%)
27 (50.9)
10 (52.6)
Ever smoked, No. (%)
27 (50.9)
8 (42.1)
Income < $50,000, No. (%)
29 (56.9)
6 (33.3)
Employed, [c] No. (%)
27 (50.9)
15 (79.0)
Education (>high school), No. (%)
42 (79.3)
16 (84.2)
Living Alone, No. (%)
19 (35.9)
3 (15.8)
Clinical Measures
 
 
Weight, kg, Mean (SD)
88.3 (14.6)
100.3 (16.3)
BMI, kg/m2b, Mean (SD)
33.1 (5.0)
35.4 (5.3)
Systolic blood pressure, mm Hg, Mean (SD)
133.4 (12.5)
137.8 (13.2)
Diastolic blood pressure, mm Hg, Mean (SD)
80.4 (9.0)
83.6 (8.6)
HbA1c, %, Mean (SD)
8.0 (1.7)
8.2 (1.7)
Years with diabetes, Mean (SD)
8.5 (8.7)
8.6 (6.2)
Medication Use
 
 
Antihypertensive agents, No. (%)
 
 
One
14 (26.9)
5 (26.3)
Two
9 (17.3)
1 (5.3)
More than two
11 (21.2)
2 (10.5)
Antihyperglycemic agents, No. (%)
 
 
One oral Agent
22 (42.3)
4 (21.1)
Two oral Agents
14 (26.9)
9 (47.4)
More than two oral agents
4 (7.7)
3 (15.8)
Insulin 13 (25.0) 2 (10.5)

a Income was based on a sample size of 51; Years with diabetes was based on a sample size of 51; Medication use data were based on a sample size of 52.

b Income was based on a sample size of 18; Years with diabetes was based on a sample size of 17 represent the average steps per day taken during weeks 1 to 3; Frequency of eating out (days/week) was based in a sample size of 8 and represents the average frequency of eating out during weeks 1 to 3.

c Compared to individuals who are unemployed or on sick leave.


[TableWrap ID: T2] Table 2 

Step counts, dietary intakes, and psychobehavioural characteristics at baseline


  Final Assessment Attended (n=53) [d] Final Assessment Not Attended (n=19) [e]
Step Counts, steps/day, Mean (SD)
7,272 (2,777)
6,408 (2,923)
Dietary Intake
 
 
Energy, kcal/day, Mean (SD)
2,024 (839)
1,576 (486)
Protein, % of total energy intake, [f] Mean (SD)
19.2 (3.7)
20.7 (5.5)
Carbohydrate, % of total energy intakec, Mean (SD)
44.3 (6.8)
40.6 (8.2)
Fat, % of total energy intakec, Mean (SD)
36.7 (6.1)
37.9 (6.1)
Saturated fat, % of total energy intakef, Mean (SD)
11.1 (2.3)
11.3 (2.2)
Fibre, g/day, Mean (SD)
21.2 (10.3)
15.0 (4.4)
Sodium, mg/day, Mean (SD)
2,903 (1255)
2,277 (714)
Alcohol Consumption (< once per week), No. (%)
43 (81.1)
15 (79.0)
Weight Efficacy Life-Style Scores, [g] Mean (SD)
 
 
Availability Subscale
20.3 (7.8)
22.2 (5.1)
Negative Emotions Subscale
20.2 (9.1)
20.9 (8.4)
Physical Discomfort Subscale
25.4 (7.3)
27.1 (5.3)
Positive Activities Subscale
25.1 (7.6)
26.8 (4.6)
Social Pressure Subscale
23.9 (7.5)
25.8 (4.8)
Total WEL Score
115.0 (32.9)
122.8 (22.6)
Hours/day spent cooking and washing up, Mean (SD)
 
 
Weekdays
1.5 (1.0)
0.9 (0.8)
Weekend days
1.7 (1.2)
1.1 (1.0)
Self-perception as competent cook [h] , No. (%)
21 (39.6)
10 (52.6)
Frequency of eating out (days/week), Mean (SD)
1.9 (1.8)
3.2 (2.7)
Stage of Change for Weight Change Efforts, No. (%)
 
 
Pre-contemplation
2 (3.8)
0 (0)
Contemplation
13 (24.5)
4 (21.1)
Action
29 (54.7)
13 (68.4)
Maintenance
9 (17.0)
2 (10.5)
Depressive Symptomology (CES-S score >16), No. (%) 15 (28.3) 6 (31.6)

d Steps counts per day were based on a sample size of 52 and represent the average steps per day taken during weeks 1 to 3; Frequency of eating out (days/week) was based on a sample size of 48 and represents the average frequency of eating out during weeks 1 to 3.

e Steps counts per day were based on a sample size of 14 and represent the average steps per day taken during weeks 1 to 3; Frequency of eating out (days/week) was based in a sample size of 8 and represents the average frequency of eating out during weeks 1 to 3.

f Assuming that 1 gram of protein equals 4 kcal, 1 gram of carbohydrates equals 4 kcal, 1 gram of total fat equals 9 kcal and 1 gram of saturated fat equals 9 kcal.

g A higher score is indicative of a greater ability to resist eating under the given condition. WEL subscale scores range from 0 to 36. The Total WEL Score ranges from 0 to 180.

h Compared to individuals who perceived that they can cook with a recipe, can prepare simple meals, it is not their role to cook or do not know how to cook.


[TableWrap ID: T3] Table 3 

Changes from baseline to the final assessment (n=53)


    95% CI [i]
Changes in Clinical Measures
 
 
≥5% weight loss, % (No.)
18.9 (10)
9.4, 32
Percent weight change, Mean (SD)
−2.2 (5.1)
−3.6, -0.8
BMI, kg/m2b, Mean (SD)
−0.9 (1.8)
−1.3, -0.4
Systolic blood pressure, mm Hg, Mean (SD)
−3.5 (15.8)
−7.8, 0.9
Diastolic blood pressure, mm Hg, Mean (SD)
−0.1 (10.4)
−3.0, 2.8
HbA1c, %, Mean (SD) [j]
−0.3 (1.0)
−0.6, -0.1
Changes in Dietary Intakes
 
 
Total energy, kcal/day, Mean (SD)
−239 (612)
−408, -70
Protein, % of total energy intake, [k] Mean (SD)
0.8 (3.7)
−0.2, 1.8
Carbohydrate, % of total energy intakec, Mean (SD)
−2.1 (7.4)
−4.2, -0.1
Fat, % of total energy intakec, Mean (SD)
1.2 (7.2)
−0.8, 3.2
Saturated fat, % of total energy intake3, Mean (SD)
0.5 (2.6)
−0.2, 1.3
Fibre, g/day, Mean (SD)
−1.8 (7.3)
−3.8, 0.2
Sodium, mg/day, Mean (SD)
−384 (951)
−646, -122
Changes in Step Counts
 
 
Step Counts, steps/day, Mean (SD)a
869 (2180)
198, 1540
Changes in Psychobehavioural Characteristics
 
 
Weight Efficacy Life-Style Scores, [l] Mean (SD)
 
 
Availability Subscale
2.3 (6.9)
0.4, 4.2
Negative Emotions Subscale
2.7 (6.6)
0.9, 4.5
Physical Discomfort Subscale
2.1 (5.4)
0.6, 3.6
Positive Activities Subscale
2.3 (5.2)
0.8, 3.7
Social Pressure Subscale
1.9 (6.0)
0.3, 3.6
Total WEL Score
11.2 (23.7)
4.7, 17.8
Hours/day spent cooking and washing up, Mean (SD)
 
 
Weekdays
0.5 (1.7)
0.03, 1.0
Weekend days
0.04 (1.3)
−0.3, 0.4
Frequency of eating out (days/week), Mean (SD)1 0.1 (1.0) −0.3, 0.4

i 95% confidence intervals.

j. Change in HbA1c was based on a sample size of 51; Change in step counts was based on a sample size of 43 and represented the difference between the average steps per day taken during weeks 13 to 15 and the average steps per day taken during weeks 1 to 3; Change in frequency of eating out was based on a sample size of 33 and represented the difference between the average percentage of days eating out in a week during weeks 13 to 15 and the average percentage of days eating out in a week during weeks 1 to 3.

k. Assuming that 1 gram of protein equals 4 kcal, 1 gram of carbohydrates equals 4 kcal, 1 gram of total fat equals 9 kcal and 1 gram of saturated fat equals 9 kcal.

l A higher score is indicative of a greater ability to resist eating under the given condition. WEL subscale scores range from 0 to 36. The Total WEL Score ranges from 0 to 180.


[TableWrap ID: T4] Table 4 

Associations of weight change during intervention with changes in blood pressure and HbA1c


 
Weight Loss Increment
  n −2.5% 95% CI [m]
All with final assessment
 
 
 
Change in systolic blood pressure, mm Hg
 
 
 
Weight change
53
−3.4
−5.4, -1.5
Weight change + age + sex + ethnicity + insulin use + season
52
−3.5
−5.5, -1.4
Weight change + step change + age + sex + ethnicity + season
41
−4.1
−6.1, -2.2
Change in diastolic blood pressure, mm Hg
 
 
 
Weight change
53
−2.5
−3.7, -1.2
Weight change + age + sex + ethnicity + insulin use + season
52
−2.3
−3.6, -0.9
Weight change + step change + age + sex + ethnicity + season
41
−2.5
−3.9, -1.0
Change in HbA1c, %
 
 
 
Weight change
51
−0.3
−0.4, -0.2
Weight change + age + sex + ethnicity + insulin use + season
50
−0.3
−0.4, -0.2
Weight change + step change + age + sex + ethnicity + season
39
−0.3
−0.4, -0.2
Without antihypertensive medication changes
 
 
 
Change in systolic blood pressure, mm Hg
 
 
 
Weight change
42
−3.2
−5.5, -1.0
Weight change + age + sex + ethnicity + insulin use + season
42
−3.5
−5.9, -1.1
Weight change + step change + age + sex + ethnicity + season
31
−4.2
−6.5, -2.0
Change in diastolic blood pressure, mm Hg
 
 
 
Weight change
42
−2.4
−3.8, -1.0
Weight change + age + sex + ethnicity + insulin use + season
42
−2.2
−3.8, -0.6
Weight change + step change + age + sex + ethnicity + season
31
−2.4
−4.1, -0.7
Without antihyperglycemic medication changes
 
 
 
Change in HbA1c, %
 
 
 
Weight change
32
−0.3
−0.4, -0.2
Weight change + age + sex + ethnicity + insulin use + season
32
−0.3
−0.4, -0.2
Weight change + step change + age + sex + ethnicity + season 25 −0.3 −0.4, -0.2

m 95% confidence intervals.


[TableWrap ID: T5] Table 5 

Associations of step count change during intervention with changes in blood pressure and HbA1c [n]


 
Step Count Increment
  n 1,000 95% CI*
All with final assessment
 
 
 
Change in systolic blood pressure, mm Hg
 
 
 
Step count change
41
−2.9
−4.9, -0.8
Step count change + age + sex + ethnicity + insulin use + season
40
−2.3
−4.5, -0.1
Step count change + weight change + age + sex + ethnicity + season
41
−1.4
−3.2, 0.4
Change in diastolic blood pressure, mm Hg
 
 
 
Step count change
41
−1.8
−3.2, -0.4
Step count change + age + sex + ethnicity + insulin use + season
40
−1.6
−3.1, -0.2
Step count change + weight change + age + sex + ethnicity + season
41
−1.2
−2.6, 0.2
Change in HbA1c,%
 
 
 
Step count change
39
−0.1
−0.2, 0.1
Step count change + age + sex + ethnicity + insulin use + season
38
−0.1
−0.2, 0.1
Step count change + weight change + age + sex + ethnicity + season
39
0.01
−0.1, 0.1
Without antihypertensive medication changes
 
 
 
Change in systolic blood pressure, mm Hg
 
 
 
Step count change
31
−2.5
−4.7, -0.3
Step count change + age + sex + ethnicity + insulin use + season
31
−2.2
−4.6, 0.2
Step count change + weight change + age + sex + ethnicity + season
31
−1.4
−3.3, 0.6
Change in diastolic blood pressure, mm Hg
 
 
 
Step count change
31
−1.5
−0.3, 0.03
Step count change + age + sex + ethnicity + insulin use + season
31
−1.5
−3.1, 0.2
Step count change + weight change + age + sex + ethnicity + season
31
−1.0
−2.5, 0.5
Without antihyperglycemic medication changes
 
 
 
Change in HbA1c,%
 
 
 
Step count change
25
−0.03
−0.1, 0.1
Step count change + age + sex + ethnicity + insulin use + season
25
−0.01
−0.2, 0.1
Step count change + weight change + age + sex + ethnicity + season 25 −0.001 −0.1, 0.1

n Change in step counts represented the difference between the average steps per day taken during weeks 13 to 15 and the average steps per day taken during weeks 1 to 3. Although 43 individuals provided step count records required, 2 among these experienced technical difficulties during blood pressure assessment.



Article Categories:
  • Research

Keywords: Body weight, Physical activity, Patient education, Risk factors, Type 2 diabetes.

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