Rollo ME, Aguiar EJ, Pursey KM, Morgan PJ, Plotnikoff RC, Young MD, Collins CE, Callister R. Impact on dietary intake of a self-directed, gender-tailored diabetes prevention program in men. World J Diabetes 2017; 8(8): 414-421
Corresponding Author of This Article
Megan E Rollo, PhD, BHlthSci (Nutr and Diet), BAppSci, Postdoctoral Research Fellow, Priority Research Centre for Physical Activity and Nutrition, School of Health Sciences, Faculty of Health and Medicine, University of Newcastle, ATC Building, University Drive, Callaghan, NSW 2308, Australia. email@example.com
Checklist of Responsibilities for the Scientific Editor of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
Impact on dietary intake of a self-directed, gender-tailored diabetes prevention program in men
Megan E Rollo, Elroy J Aguiar, Kirrilly M Pursey, Philip J Morgan, Ronald C Plotnikoff, Myles D Young, Clare E Collins, Robin Callister
Megan E Rollo, Kirrilly M Pursey, Clare E Collins, Priority Research Centre for Physical Activity and Nutrition, School of Health Sciences, Faculty of Health and Medicine, University of Newcastle, Callaghan, NSW 2308, Australia
Elroy J Aguiar, Department of Kinesiology, School of Public Health and Health Sciences, University of Massachusetts Amherst, Amherst, MA 01003, United States
Philip J Morgan, Ronald C Plotnikoff, Myles D Young, Priority Research Centre for Physical Activity and Nutrition, School of Education, Faculty of Education and Arts, University of Newcastle, Callaghan, NSW 2308, Australia
Robin Callister, Priority Research Centre for Physical Activity and Nutrition, School of Biomedical Sciences and Pharmacy, Faculty of Health and Medicine, University of Newcastle, Callaghan, NSW 2308, Australia
ORCID number: $[AuthorORCIDs]
Author contributions: Aguiar EJ, Morgan PJ, Plotnikoff RC, Young MD, Collins CE and Callister R conceptualised and designed the study and resources; Aguiar EJ was responsible for the administration of the study protocol and data collection; Pursey KM performed the statistical analysis and Rollo ME drafted the manuscript; all authors provided critical revisions of the manuscript and approved the final manuscript.
Supported by Hunter Medical Research Institute Lions Club District 201N3 Diabetes Foundation (HMRI Project Grant 11-12); and Hunter Medical Research Institute Aurizon (HMRI Project Grant 12-05).
Institutional review board statement: This study received ethical approval from the University of Newcastle’s Human Ethics Research Committee (H-2012-0232).
Clinical trial registration statement: The trial is registered at https://anzctr.org.au/Trial/Registration/TrialReview.aspx?ACTRN=12612000721808. The registration identification number is ACTRN12612000721808.
Informed consent statement: All study participants provided informed written consent prior to study enrolment.
Conflict-of-interest statement: All authors declare no conflict of interest.
Data sharing statement: No additional data are available.
Open-Access: This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
Correspondence to: Megan E Rollo, PhD, BHlthSci (Nutr and Diet), BAppSci, Postdoctoral Research Fellow, Priority Research Centre for Physical Activity and Nutrition, School of Health Sciences, Faculty of Health and Medicine, University of Newcastle, ATC Building, University Drive, Callaghan, NSW 2308, Australia. firstname.lastname@example.org
Telephone: +61-2-49215649 Fax: +61-2-49217053
Received: December 20, 2016 Peer-review started: December 25, 2016 First decision: January 16, 2017 Revised: March 3, 2017 Accepted: March 23, 2017 Article in press: March 24, 2017 Published online: August 15, 2017
To investigate changes in dietary intake following a 6-mo randomised controlled trial of the self-directed, gender-tailored type 2 diabetes mellitus (T2DM) Prevention Using LifeStyle Education (PULSE) program in men.
Men aged 18-65 years, with a body mass index (BMI) 25-40 kg/m2, and at high risk for developing T2DM were recruited from the Hunter Region of New South Wales, Australia. Eligible participants were randomised into one of two groups: (1) waitlist control; or (2) PULSE intervention. Dietary intake was assessed at baseline and immediately post-program using the Australian Eating Survey food frequency questionnaire and diet quality measured using the Australian Recommended Food Score (ARFS).
One hundred and one participants (n = 48, control; n = 53, intervention, mean age 52.3 ± 9.7 years, BMI of 32.6 ± 3.3 kg/m2) commenced the study. Following the active phase, differences between groups were observed for proportion of total energy consumed from healthful (core) foods (+7.6%EI, P < 0.001), energy-dense, nutrient-poor foods (-7.6%EI, P < 0.001), sodium (-369 mg, P = 0.047), and diet quality (ARFS) (+4.3, P = 0.004), including sub-scales for fruit (+1.1, P = 0.03), meat (+0.9, P = 0.004) and non-meat protein (+0.5, P = 0.03).
The PULSE prevention program’s nutrition messages led to significant improvements in dietary intake in men at risk of T2DM.
Core tip: In the context of type 2 diabetes mellitus (T2DM) prevention programs, only recently has the effect on diet quality been reported. However, no studies have examined the effect on diet of a program designed exclusively for men. This study reports on the dietary outcomes following the self-directed T2DM Prevention Using LifeStyle Education (PULSE) program. Following completion of the PULSE program, men receiving the intervention significantly reduced intake of energy-dense, nutrient-poor foods and portion size. In addition, the intervention group increased overall diet quality and greater variety within healthful food groups.
Citation: Rollo ME, Aguiar EJ, Pursey KM, Morgan PJ, Plotnikoff RC, Young MD, Collins CE, Callister R. Impact on dietary intake of a self-directed, gender-tailored diabetes prevention program in men. World J Diabetes 2017; 8(8): 414-421
Despite concerted public health efforts, the prevalence of diabetes continues to increase worldwide. Between 2013 to 2015, the worldwide prevalence of diabetes increased by 33 million to an estimated 415 million adults[1,2], with the prevalence expected to increase to 642 million in the next 25 years. The economic burden on national health systems attributed to diabetes is significant, with USD 612 billion spent worldwide or 11% of total health expenditure. In higher income countries, approximately 87%-91% of those with diabetes have type 2 diabetes mellitus (T2DM). Further, global estimates place 318 million adults at risk of developing the condition, with an additional 163 million individuals estimated to have impaired glucose tolerance by 2040.
Lifestyle risk factors such as a high body mass index (BMI), sub-optimal diet quality and lack of physical activity are key targets underpinning T2DM prevention programs, such as the Diabetes Prevention Program in the United States and the Diabetes Prevention Study in Finland. In the years following these seminal studies, many diabetes prevention programs have been evaluated including successful adaptations for various populations and different settings[6-8].
These T2DM prevention programs promote moderate weight loss by improving physical activity and dietary behaviours. Programs containing a combination of diet and physical activity components have demonstrated efficacy with regard to weight loss and improvements in glucose regulation[9,10]. Specific dietary changes following the diabetes prevention programs have included a reduction in total energy intake and favourable shifts in macronutrient composition (e.g., reduction in total and saturated fat intake). Only recently have changes in diet quality or amounts of foods (as opposed to energy or nutrients) been reported both immediately following completion of the intervention[11,12], as well as over the long-term for periods of 5-10 years later[13,14]. To date, no studies have reported the effect on diet of a diabetes prevention program tailored for men. Therefore, this study examines changes in dietary intake, in particular diet quality, among Australian men following the 6-mo randomised controlled trial of the self-directed, gender-tailored Prevention Using LifeStyle Education (PULSE) program.
MATERIALS AND METHODS
The PULSE randomised controlled trial evaluated the efficacy of a self-directed, diabetes prevention program tailored for men at risk of T2DM. The study was conducted in Newcastle, Australia in 2012-2013 after receiving ethical approval (H-2012-0232) from the University of Newcastle Human Research Ethics Committee and registration with the Australian New Zealand Clinical Trials Registry (ACTRN 12612000721808).
Detailed information regarding the rationale, study design and methods are reported elsewhere[15,16]. Briefly, emerging evidence supports the use of gender-tailored approaches, in particular for weight loss[17,18]. Our previous research has found gender-tailored programs to be effective in producing weight loss in men[19-21]. However, T2DM prevention programs predominantly involve both men and women with results reported collectively. Therefore, the PULSE program aimed to address this gap in the evidence through the evaluation of a T2DM prevention program designed exclusively for men.
Males aged 18-65 years, with a BMI 25-40 kg/m2 and at high risk for developing T2DM (with a score ≥ 12 as self-reported via the Australian Diabetes risk assessment tool) were recruited. Men with pre-existing diabetes or other serious medical conditions, recent significant weight loss (> 5% in past 6 mo), currently participating in other weight loss programs, and without access to a mobile phone were ineligible. Study recruitment used advertisements across various modes including print and online, workplace emails and research participant registers. Participants were stratified by age and BMI, and randomised to either the intervention group or waitlist control. The active intervention period was 6 mo with the waitlist control group receiving the PULSE program at the completion of active phase.
As previously reported, the program was effective in reducing clinical risk factors of T2DM. In summary, a significant mean difference favouring the intervention group over control was observed for change in body weight (-5.5 kg and -5.3%, both P < 0.001), BMI (-1.8 kg/m2, P < 0.001), and waist circumference (umbilicus -5.4 cm, P < 0.001; narrowest point -6.2 cm; P < 0.001). In addition, significant changes were found for measures of glucose regulation in the intervention group with improvements in HbA1c (-0.2%, P = 0.002), fasting insulin (-3.0 mIU/L, P = 0.002) and measures of insulin resistance (HOMA-IR; -0.4, P = 0.002) and insulin sensitivity (QUICKI; +0.02, P = 0.006).
The PULSE program
The current paper reports the secondary dietary outcome data, and therefore the methods focus on the intervention components used to target changes in diet and eating behaviours. Underpinned by Social Cognitive Theory, the PULSE program aimed to promote modest weight loss through changes in key dietary and physical activity behaviours. The program addressed key theoretical constructs relating to goal setting and planning, positive outcome expectations, seeking social support, promoting behavioural self-monitoring, and increasing self-efficacy, as described in detail previously[15,16]. Following randomisation, participants in the intervention group were provided with information and equipment resource packs and briefly orientated to the contents by a research team member[15,16].
The PULSE handbook contained dietary information and focused on four main messages: (1) key nutrients and their role in the body; (2) dietary composition, focusing on amount and quality of carbohydrate [i.e., lower glycaemic index (GI)], fat, protein and fibre, and using the plate model to represent appropriate meal portion sizes; (3) variety within core (healthful) foods, particularly vegetables; and (4) suggestions for the composition (e.g., low-moderate GI) of breakfast, lunch and dinner. In addition, participants were provided with the Self-Help, Exercise and Diet using Information Technology (SHED-IT) program[19,23]. This handbook provided information on general weight loss principles including setting daily energy intake targets, goal setting for eating and activity behaviours, self-monitoring tools for tracking weight, waist circumference and step counts. In addition, participants were provided with a calorie counter and instructions for the accompanying CalorieKing website (http://www.calorieking.com.au ). Participants were encouraged to self-monitor dietary intake and physical activity (both for at least 4 d per week) and weight (once per week).
Assessments occurred at baseline and following the program (6 mo). Usual dietary intake was assessed using the validated, semi-quantitative food frequency questionnaire, the Australian Eating Survey (AES) at baseline and following the program completion. The AES comprises 120 food items with 15 supplementary questions on food behaviours. Standard adult portion sizes for each food item were derived from National Nutrition Survey data or from the product standard serving size (e.g., slice of bread). Participants were asked to recall the frequency of food consumption over the past 3 mo, with individual responses for each food or food type. Frequency options ranged from “Never” up to “≥ 4 times/d”, but varied depending on the food, with some drinks items up to “≥ 7 glasses/d”. Nutrient intakes were computed from the most current food composition database of Australian foods, the Australian AusNut 1999 database (All Foods) Revision 17, primarily and AusFoods (Brands) Revision 5.
The validated Australian Recommended Food Score (ARFS) assesses diet quality and variety within the food groups relative to the Australian Guide to Healthy Eating within the Australian Dietary Guidelines (ADGs). The ARFS uses a sub-set of 70 AES food items and comprises eight sub-scales from a range of healthful or core food groups (e.g., vegetables, fruit, grains, meats, non-meat proteins, dairy) with total score ranging from 0 to 73. For most items AES frequency response options are collapsed into two categories “once per week or more” or “less than once per week or never”. A higher total score is indicative of more optimal nutrient intakes[27,29], greater variety within the core food groups and alignment with ADGs.
Portion size for four common foods (potato, vegetables, steak and casserole) was assessed separately using food photographs from the Dietary Questionnaire for Epidemiological Studies, version 2[30,31]. Three photographs are displayed, representing the 25th, 50th (median), and 75th percentiles of portion sizes for adult men and women to indicate the portion size typically consumed, with eight response options ranging from “not eating the food at all” up to “more than the amount represented”.
Statistical analysis was conducted using Stata version 13. Between group differences in completers (those who did complete all 6-mo follow-up measures) vs non-completers (those who did not complete all 6-mo follow-up measures, including those lost to follow-up) were assessed using t tests for continuous data and χ2 tests for categorical data.
Changes in dietary intake over the 6-mo intervention were analysed using linear mixed models according to the intention-to-treat principle. Outcomes were assessed for the impact of treatment (intervention compared to control), time [baseline and immediately post program (i.e., 6 mo)] and group by time interaction. Models were adjusted for participant age and socioeconomic status (SES), which were specified a priori. The coefficient and P-value from the mixed model testing the difference between groups in change from baseline to 6 mo was used to determine the effect of the intervention on each outcome (significance level, P < 0.05). The statistical methods of this study were reviewed by Daniel Barker from the University of Newcastle.
A total of 101 participants were recruited to the study. Participant baseline dietary intakes are reported in Table 1. Nineteen participants (n = 6 control; n = 13 intervention) were lost to follow-up (not able to attend the assessment sessions or were unable to be contacted). An additional two participants in the intervention group attended the follow-up session, but did not complete the AES at 6-mo, leaving a total of 21 non-completers and 80 completers from the original 101 participants whom commenced the study. At baseline, non-completers reported lower intakes of total energy, protein and carbohydrates compared to completers. The mean age of the participants was 52.3 ± 9.7 years (range 20-66 years) and the most frequently reported highest education level was a certificate or trade qualification (60%). The mean BMI of the sample was 32.6 ± 3.3 kg/m2, and ranged from 25.7-41.0 kg/m2.
Data is presented as mean ± SD. Portion size coded as per Hodge et al as follows: Never eat = 0, less than image A = 0.4, equal to image A = 0.5, between images A and B = 0.75, equal to image B = 1.0, between images B and C = 1.5, equal to image C = 2.0, greater than image C = 2.5.
1n = 100 participants;
2n = 97 participants;
3n = 99 participants. EI: Energy intake; ARFS: Australian Recommended Food Score.
Changes in dietary intakes of participants are reported in Table 2. At follow-up, significant mean differences between groups favouring the intervention group were identified for %EI from healthful (core) foods (+7.6%EI; P < 0.001), energy-dense, nutrient-poor foods (-7.6%EI, P < 0.001), protein (+1.3%EI, P = 0.03), and polyunsaturated fat (-0.4%EI, P = 0.02), as well as sodium intake (-369 mg, P = 0.047). Between group differences were observed in ARFS diet quality, with the intervention group achieving a greater improvement in mean total score (+4.3, P = 0.004) and subscales of fruit (+1.1, P = 0.03), meat (+0.9, P = 0.004) and non-meat protein (+0.5, P = 0.03). Greater reductions in portion sizes were achieved in the intervention group for potato (-0.9, P = 0.002), steak (-0.9, P = 0.002) and casserole (-0.7, P = 0.01) compared to controls, however there was no change in vegetable portion size.
Table 2 Change in dietary intake by group at 6 mo (n = 101).
Significant differences within and between groups:
aP < 0.05;
bP < 0.01;
dP< 0.001. Portion size coded as per Hodge et al as follows: Never eat = 0, less than image A = 0.4, equal to image A = 0.5, between images A and B = 0.75, equal to image B = 1.0, between images B and C = 1.5, equal to image C = 2.0, greater than image C = 2.5. EI: Energy intake; ARFS: Australian Recommended Food Score.
The PULSE self-directed T2DM prevention program for men resulted in significant improvements in usual dietary intake, including a reduction in intakes of energy-dense, nutrient-poor foods and increased overall diet quality and variety within healthful food groups and fruit, non-meat protein and meat ARFS subscales. Of note portion sizes for potatoes, steak and casserole were significantly reduced in the intervention group vs the control group. Though not significant, changes in the desired direction were observed for increased vegetable variety and decreased alcohol intake (%EI).
For men in the PULSE program intervention group, the increased diet quality was accompanied by an increased percentage of total energy from healthful foods and a %EI from energy-dense, nutrient-poor foods. Despite a reduction in mean total energy intake of approximately 1300 kJ/d (P = 0.08), dietary macronutrient composition remained relatively stable, with a small but significant increase of +1.3% in %EI from protein in the intervention group. This is in contrast to the United States Diabetes Prevention Program which reported a similar reduction in total energy intake among those in the lifestyle intervention group, however total fat intake decreased by 6.6% of total energy intake following the first 6 mo. Similar reductions in total energy were also observed in the Finnish Diabetes Prevention Study following the intensive phase at 1 year. These findings, combined with the increases in diet quality, suggest individuals in the PULSE intervention program replaced energy-dense, nutrient-poor foods with healthful food choices.
Most studies reporting on dietary outcomes immediately following major diabetes prevention program interventions have only reported on changes in total energy and/or nutrient intakes. Few studies have evaluated changes in diet quality as assessed using an a priori defined diet quality index or score, or intakes of individual food groups. Miller et al reported significant within group improvements in overall diet quality scores (+4.6, P < 0.01) measured using the Alternative Healthy Eating Index following a 4-mo group-based diabetes prevention program. In another study, Block et al reported changes in food habits associated with higher diet quality, such as significant changes in the consumption frequency of fruits and vegetables (increases) and sweets and refined carbohydrate foods (decreases) found among those with prediabetes who received a 6-mo automated web-based diabetes prevention program.
Findings from interventions aimed at the prevention of T2DM, including the current PULSE trial, are consistent with meta-analyses[33,34] which support a significant association between higher overall diet quality, as assessed by a score or index, and a decreased risk of T2DM. Specifically in men with a high BMI, diets of higher quality have been associated with a reduction in the incidence of T2DM. Findings from the current study also add to the emerging evidence for a lower risk of developing T2DM in those with a higher diet quality[36,37]. Greater dietary diversity within core food groups has also been associated with a lower risk of metabolic syndrome, and to be a predictor of weight loss and reduced waist circumference within a weight loss intervention. In particular, diets that are diverse in the variety of fruits and vegetables consumed are associated with a lower risk of T2DM. Recent meta-analyses on the amounts of fruits and vegetables consumed in relation to T2DM risk confirm the positive relationship between higher intakes of fruits and vegetables and lower risk of developing diabetes[40,41], including a dose-response relationship with a 6% and 13% lower risk for each 1 serve increase in fruit intake and each additional 0.2 serve of green-leafy vegetables, respectively.
The changes in diet in those receiving the PULSE intervention program are encouraging, especially given the self-directed nature of the program and compare favourably to traditional intensive T2DM prevention programs delivered using one-on-one or group or combination approaches. Given the increasing prevalence of T2DM and associated risk factors, such as excess body weight, poor diet and physical inactivity, evidence of the effectiveness of low intensity programs are gaining momentum. However, modifications to the traditional diabetes prevention programs are considered necessary for translation to non-research or “real-world” settings and to promote sustainability of effective programs through delivery of these programs at scale. Delivery of programs using various media and technologies, such as DVD or web- or mobile-based platforms, have demonstrated effectiveness in terms of weight loss[6,11,43], while other diabetes prevention programs have begun to provide higher intensity interventions through the supplementation of mobile applications with remote support from a trained health coach[44,45].
The dietary changes observed by men in the PULSE intervention group support the observed improvements in weight status, waist circumference and measures of glucose regulation. These findings demonstrate that participants adhered to the dietary messages contained in the PULSE program, in particular increasing variety within healthful food groups and reducing portion sizes. This indicates that a gender-tailored, self-directed program can result in desirable dietary changes that reduce T2DM risk. Despite, the benefits demonstrated by the PULSE program immediately following completion, the long-term impact on the maintenance of healthy eating behaviours remain to be established.
Although a validated food frequency questionnaire was used to measure dietary intake, the use of a self-administered measure is subject to inherent errors of self-reported dietary data and is a limitation of the current study. The effect of measurement error in the context of intervention studies is an emerging area of research and recommendations to use a biomarker of dietary intake to calibrate the primary measure of intake were outside the scope of the PULSE pilot study. While the dietary outcome findings should be interpreted in light of this, the use of objective measures for the anthropometric and biochemical outcomes is a strength of the current study, and improvements in these variables are likely to reflect the positive dietary changes observed for the intervention group.
In addition to reductions in anthropometric measures and improved glucose regulation, the self-directed, gender-tailored PULSE program resulted in significant improvements in dietary intake. This included a reduction in intake of energy-dense, nutrient-poor foods and portion size, increased overall diet quality and greater variety of healthful foods, especially within fruit, meat, and non-meat protein food groups.
The authors would like to thank the men who participated in this study and members of the Human Performance Laboratory at The University of Newcastle for their assistance with data collection. Ronald C Plotnikoff and Clare E Collins are supported by Senior Research Fellowships from the National Health and Medical Research Council of Australia. The authors would like to acknowledge the Hunter Medical Research Institute, Lions Club District 201N3 Diabetes Foundation, and Aurizon for supporting this project. These funding groups had no role in the design of the trial, data collection process, analysis or interpretation of data, or in the writing and submission of this manuscript. The authors thank Professor Graham Giles of the Cancer Epidemiological Centre of the Cancer Council Victoria, for permission to use the pictures from the Dietary Questionnaire for Epidemiological Studies (version 2). Melbourne: The Cancer Council Victoria, 1996. In addition, the authors wish to acknowledge the statistical support provided by Daniel Barker of the University of Newcastle.
Dietary outcomes following diabetes prevention programs have primarily focused on reporting changes in energy and macronutrient intakes; effects on diet quality have only begun to be investigated. Also, most past programs have included both men and women, were not gender-tailored, and had little or no separate reporting of effects on men and women. This study investigated effects on dietary intakes and diet quality of the self-directed, gender-tailored Prevention Using LifeStyle Education (PULSE) program for type 2 diabetes mellitus (T2DM) prevention in men.
Emerging evidence supported the use of gender-tailored programs for men in the context of weight loss, however no programs developed for T2DM prevention had been specifically tailored for men. The PULSE program aimed to address this gap.
Innovations and breakthroughs
The PULSE program’s nutrition messages led to significant improvements in dietary intake and diet quality, as well as decreasing clinical risk factors for T2DM, in men at risk of T2DM, suggesting that gender-tailoring in this group may be important for achieving healthful dietary behaviour changes in men.
These findings offer support for the use of gender-tailored dietary messages in the context of dietary advice for the prevention of T2DM, in particular in relation to increasing variety within healthful food groups.
Diet quality assessment provides an indication of adherence to dietary guidelines and healthy eating patterns. Diet quality can be assessed via indices or scores that evaluate types of foods consumed and variety within food groups, and in some instances, intakes of selected nutrients.
The study is very interesting from a clinical practice point of view.
Manuscript source: Invited manuscript
Specialty type: Endocrinology and metabolism
Country of origin: Australia
Peer-review report classification
Grade A (Excellent): A, A, A
Grade B (Very good): B
Grade C (Good): 0
Grade D (Fair): D
Grade E (Poor): 0
P- Reviewer: García-Mayor RV, Gómez-Sáez GM, Qi L, Tasci I, Xie ZJ S- Editor: Ji FF L- Editor: A E- Editor: Lu YJ
International Diabetes Federation. IDF Diabetes Atlas, 7th ed. Brussels, Belgium: International Diabetes Federation 2015; .
Guariguata L, Whiting DR, Hambleton I, Beagley J, Linnenkamp U, Shaw JE. Global estimates of diabetes prevalence for 2013 and projections for 2035.Diabetes Res Clin Pract. 2014;103:137-149.
da Rocha Fernandes J, Ogurtsova K, Linnenkamp U, Guariguata L, Seuring T, Zhang P, Cavan D, Makaroff LE. IDF Diabetes Atlas estimates of 2014 global health expenditures on diabetes.Diabetes Res Clin Pract. 2016;117:48-54.
Knowler WC, Barrett-Connor E, Fowler SE, Hamman RF, Lachin JM, Walker EA, Nathan DM. Reduction in the incidence of type 2 diabetes with lifestyle intervention or metformin.N Engl J Med. 2002;346:393-403.
Tuomilehto J, Lindström J, Eriksson JG, Valle TT, Hämäläinen H, Ilanne-Parikka P, Keinänen-Kiukaanniemi S, Laakso M, Louheranta A, Rastas M. Prevention of type 2 diabetes mellitus by changes in lifestyle among subjects with impaired glucose tolerance.N Engl J Med. 2001;344:1343-1350.
Ali MK, Echouffo-Tcheugui J, Williamson DF. How effective were lifestyle interventions in real-world settings that were modeled on the Diabetes Prevention Program?Health Aff (Millwood). 2012;31:67-75.
Aziz Z, Absetz P, Oldroyd J, Pronk NP, Oldenburg B. A systematic review of real-world diabetes prevention programs: learnings from the last 15 years.Implement Sci. 2015;10:172.
Dunkley AJ, Bodicoat DH, Greaves CJ, Russell C, Yates T, Davies MJ, Khunti K. Diabetes prevention in the real world: effectiveness of pragmatic lifestyle interventions for the prevention of type 2 diabetes and of the impact of adherence to guideline recommendations: a systematic review and meta-analysis.Diabetes Care. 2014;37:922-933.
Aguiar EJ, Morgan PJ, Collins CE, Plotnikoff RC, Callister R. Efficacy of interventions that include diet, aerobic and resistance training components for type 2 diabetes prevention: a systematic review with meta-analysis.Int J Behav Nutr Phys Act. 2014;11:2.
Balk EM, Earley A, Raman G, Avendano EA, Pittas AG, Remington PL. Combined Diet and Physical Activity Promotion Programs to Prevent Type 2 Diabetes Among Persons at Increased Risk: A Systematic Review for the Community Preventive Services Task Force.Ann Intern Med. 2015;163:437-451.
Block G, Azar KM, Romanelli RJ, Block TJ, Palaniappan LP, Dolginsky M, Block CH. Improving diet, activity and wellness in adults at risk of diabetes: randomized controlled trial.Nutr Diabetes. 2016;6:e231.
Miller CK, Weinhold KR, Nagaraja HN. Impact of a Worksite Diabetes Prevention Intervention on Diet Quality and Social Cognitive Influences of Health Behavior: A Randomized Controlled Trial.J Nutr Educ Behav. 2016;48:160-169.e1.
Jaacks LM, Ma Y, Davis N, Delahanty LM, Mayer-Davis EJ, Franks PW, Brown-Friday J, Isonaga M, Kriska AM, Venditti EM. Long-term changes in dietary and food intake behaviour in the Diabetes Prevention Program Outcomes Study.Diabet Med. 2014;31:1631-1642.
Lindahl B, Nilssön TK, Borch-Johnsen K, Røder ME, Söderberg S, Widman L, Johnson O, Hallmans G, Jansson JH. A randomized lifestyle intervention with 5-year follow-up in subjects with impaired glucose tolerance: pronounced short-term impact but long-term adherence problems.Scand J Public Health. 2009;37:434-442.
Aguiar EJ, Morgan PJ, Collins CE, Plotnikoff RC, Young MD, Callister R. The PULSE (Prevention Using LifeStyle Education) trial protocol: a randomised controlled trial of a Type 2 Diabetes Prevention programme for men.Contemp Clin Trials. 2014;39:132-144.
Aguiar EJ, Morgan PJ, Collins CE, Plotnikoff RC, Young MD, Callister R. Efficacy of the Type 2 Diabetes Prevention Using LifeStyle Education Program RCT.Am J Prev Med. 2016;50:353-364.
Robertson C, Avenell A, Boachie C, Stewart F, Archibald D, Douglas F, Hoddinott P, van Teijlingen E, Boyers D. Should weight loss and maintenance programmes be designed differently for men? A systematic review of long-term randomised controlled trials presenting data for men and women: The ROMEO project.Obes Res Clin Pract. 2016;10:70-84.
Young MD, Morgan PJ, Plotnikoff RC, Callister R, Collins CE. Effectiveness of male-only weight loss and weight loss maintenance interventions: a systematic review with meta-analysis.Obes Rev. 2012;13:393-408.
Morgan PJ, Callister R, Collins CE, Plotnikoff RC, Young MD, Berry N, McElduff P, Burrows T, Aguiar E, Saunders KL. The SHED-IT community trial: a randomized controlled trial of internet- and paper-based weight loss programs tailored for overweight and obese men.Ann Behav Med. 2013;45:139-152.
Morgan PJ, Lubans DR, Collins CE, Warren JM, Callister R. 12-month outcomes and process evaluation of the SHED-IT RCT: an internet-based weight loss program targeting men.Obesity (Silver Spring). 2011;19:142-151.
Morgan PJ, Warren JM, Lubans DR, Collins CE, Callister R. Engaging men in weight loss: Experiences of men who participated in the male only SHED-IT pilot study.Obes Res Clin Pract. 2011;5:e169-e266.
Morgan PJ, Collins CE, Plotnikoff RC, McElduff P, Burrows T, Warren JM, Young MD, Berry N, Saunders KL, Aguiar EJ. The SHED-IT community trial study protocol: a randomised controlled trial of weight loss programs for overweight and obese men.BMC Public Health. 2010;10:701.
Borushek A, Calorie fat and carbohydrate counter.
Nedlands:Australia Family Health Publications, 2012.
Collins CE, Boggess MM, Watson JF, Guest M, Duncanson K, Pezdirc K, Rollo M, Hutchesson MJ, Burrows TL. Reproducibility and comparative validity of a food frequency questionnaire for Australian adults.Clin Nutr. 2014;33:906-914.
Australian Bureau of Statistics. National nutrition survey: nutrient intakes and physical measurements (No.: 4805.0). Canberra, Australia: Australian Bureau of Statistics 1998; .
Collins CE, Burrows TL, Rollo ME, Boggess MM, Watson JF, Guest M, Duncanson K, Pezdirc K, Hutchesson MJ. The comparative validity and reproducibility of a diet quality index for adults: the Australian Recommended Food Score.Nutrients. 2015;7:785-798.
National Health and Medical Research Council of Australia, Eat For Health - Educators Guide.
National Health and Medical Canberra: Research Council, 2013.
Collins CE, Young AF, Hodge A. Diet quality is associated with higher nutrient intake and self-rated health in mid-aged women.J Am Coll Nutr. 2008;27:146-157.
Giles GG, Ireland PD. Dietary Questionnaire for Epidemiological Studies (Version 2). Melbourne: Cancer Council Victoria 1996; .
Hodge A, Patterson AJ, Brown WJ, Ireland P, Giles G. The Anti Cancer Council of Victoria FFQ: relative validity of nutrient intakes compared with weighed food records in young to middle-aged women in a study of iron supplementation.Aust N Z J Public Health. 2000;24:576-583.
Lindström J, Louheranta A, Mannelin M, Rastas M, Salminen V, Eriksson J, Uusitupa M, Tuomilehto J. The Finnish Diabetes Prevention Study (DPS): Lifestyle intervention and 3-year results on diet and physical activity.Diabetes Care. 2003;26:3230-3236.
Esposito K, Chiodini P, Maiorino MI, Bellastella G, Panagiotakos D, Giugliano D. Which diet for prevention of type 2 diabetes? A meta-analysis of prospective studies.Endocrine. 2014;47:107-116.
Schwingshackl L, Hoffmann G. Diet quality as assessed by the Healthy Eating Index, the Alternate Healthy Eating Index, the Dietary Approaches to Stop Hypertension score, and health outcomes: a systematic review and meta-analysis of cohort studies.J Acad Nutr Diet. 2015;115:780-800.e5.
de Koning L, Chiuve SE, Fung TT, Willett WC, Rimm EB, Hu FB. Diet-quality scores and the risk of type 2 diabetes in men.Diabetes Care. 2011;34:1150-1156.
Conklin AI, Monsivais P, Khaw KT, Wareham NJ, Forouhi NG. Dietary Diversity, Diet Cost, and Incidence of Type 2 Diabetes in the United Kingdom: A Prospective Cohort Study.PLoS Med. 2016;13:e1002085.
Cooper AJ, Sharp SJ, Lentjes MA, Luben RN, Khaw KT, Wareham NJ, Forouhi NG. A prospective study of the association between quantity and variety of fruit and vegetable intake and incident type 2 diabetes.Diabetes Care. 2012;35:1293-1300.
Vadiveloo M, Parekh N, Mattei J. Greater healthful food variety as measured by the US Healthy Food Diversity index is associated with lower odds of metabolic syndrome and its components in US adults.J Nutr. 2015;145:564-571.
Vadiveloo M, Sacks FM, Champagne CM, Bray GA, Mattei J. Greater Healthful Dietary Variety Is Associated with Greater 2-Year Changes in Weight and Adiposity in the Preventing Overweight Using Novel Dietary Strategies (POUNDS Lost) Trial.J Nutr. 2016;146:1552-1559.
Li M, Fan Y, Zhang X, Hou W, Tang Z. Fruit and vegetable intake and risk of type 2 diabetes mellitus: meta-analysis of prospective cohort studies.BMJ Open. 2014;4:e005497.
Wang PY, Fang JC, Gao ZH, Zhang C, Xie SY. Higher intake of fruits, vegetables or their fiber reduces the risk of type 2 diabetes: A meta-analysis.J Diabetes Investig. 2016;7:56-69.
Johnson M, Jones R, Freeman C, Woods HB, Gillett M, Goyder E, Payne N. Can diabetes prevention programmes be translated effectively into real-world settings and still deliver improved outcomes? A synthesis of evidence.Diabet Med. 2013;30:3-15.
Sepah SC, Jiang L, Peters AL. Long-term outcomes of a Web-based diabetes prevention program: 2-year results of a single-arm longitudinal study.J Med Internet Res. 2015;17:e92.
Michaelides A, Raby C, Wood M, Farr K, Toro-Ramos T. Weight loss efficacy of a novel mobile Diabetes Prevention Program delivery platform with human coaching.BMJ Open Diabetes Res Care. 2016;4:e000264.
Chen F, Su W, Becker SH, Payne M, Castro Sweet CM, Peters AL, Dall TM. Clinical and Economic Impact of a Digital, Remotely-Delivered Intensive Behavioral Counseling Program on Medicare Beneficiaries at Risk for Diabetes and Cardiovascular Disease.PLoS One. 2016;11:e0163627.
Subar AF, Freedman LS, Tooze JA, Kirkpatrick SI, Boushey C, Neuhouser ML, Thompson FE, Potischman N, Guenther PM, Tarasuk V. Addressing Current Criticism Regarding the Value of Self-Report Dietary Data.J Nutr. 2015;145:2639-2645.
Keogh RH, Carroll RJ, Tooze JA, Kirkpatrick SI, Freedman LS. Statistical issues related to dietary intake as the response variable in intervention trials.Stat Med. 2016;35:4493-4508.