Hostname: page-component-78c5997874-j824f Total loading time: 0 Render date: 2024-11-05T15:42:15.282Z Has data issue: false hasContentIssue false

Changes of dietary patterns during participation in a web-based weight-reduction programme

Published online by Cambridge University Press:  28 September 2015

Eva Luger*
Affiliation:
Institute of Social Medicine, Centre for Public Health, Medical University of Vienna, Kinderspitalgasse 15/I, 1090 Vienna, Austria
Rosa Aspalter
Affiliation:
KiloCoach Internetportale GmbH, Vienna, Austria
Maria Luger
Affiliation:
Division of Endocrinology and Metabolism, Department of Internal Medicine III, Medical University of Vienna, Vienna, Austria Special Institute for Preventive Cardiology And Nutrition SIPCAN, Salzburg, Austria
Rita Longin
Affiliation:
KiloCoach Internetportale GmbH, Vienna, Austria
Anita Rieder
Affiliation:
Institute of Social Medicine, Centre for Public Health, Medical University of Vienna, Kinderspitalgasse 15/I, 1090 Vienna, Austria
Thomas Ernst Dorner
Affiliation:
Institute of Social Medicine, Centre for Public Health, Medical University of Vienna, Kinderspitalgasse 15/I, 1090 Vienna, Austria
*
*Corresponding author: Email [email protected]
Rights & Permissions [Opens in a new window]

Abstract

Objective

To examine the weight-loss success associated with distinct dietary patterns and to determine changes of these dietary patterns during participation in a web-based weight-reduction programme.

Design

Factor analysis was used to identify the dietary patterns of twenty-two food groups that were administered in 14 d dietary protocols at baseline and after 3 months. Successful weight loss (≥5 % of initial weight) and BMI were calculated. Logistic regression analyses were used to assess the rates of weight-loss success from each dietary pattern and changing or remaining in the initial dietary pattern. A generalised linear mixed model was used to estimate the effects of changing or staying in a dietary pattern on change in BMI.

Subjects

Adults (n 1635) aged 18–81 years.

Setting

Users of a web-based weight-reduction programme (2006–2012).

Results

Participants who aligned to a healthful dietary pattern at baseline (OR=1·8; 95 % CI 1·5, 2·3) and after 3 months (OR=1·5; 95 % CI 1·2, 1·9) had a greater chance of successfully losing weight. After adjusting for age, sex, initial dietary pattern and BMI, participants who started with or changed to the healthful dietary pattern had a greater chance of being successful (OR=1·4; 95 % CI 1·1, 1·7) and a higher BMI reduction of 0·30 (95 % CI 0·2, 0·5) kg/m2 compared with those who started with or changed to the energy-dense or high-carbohydrate dietary pattern.

Conclusions

A favourable healthful dietary pattern at the beginning and after 3 months was positively associated with anthropometry. However, successful weight loss was feasible in each dietary pattern.

Type
Research Papers
Copyright
Copyright © The Authors 2015 

In Europe, more than 50 % of men and women are overweight( 1 ). A BMI above 30 kg/m2 is present in 21 % of women and 22 % of men aged 20 years or older( 2 ). Moreover, worldwide, the prevalence of obesity has almost doubled between 1980 and 2008( 1 ). Obesity is an important health risk for diseases such as diabetes mellitus type 2, hypertension, CVD and others( Reference Van Gaal and Maggioni 3 ). Nevertheless, weight is adjustable, and a moderate weight reduction of 5–10 % is associated with enhanced health benefits( Reference Pi-Sunyer 4 ). Weight gain is the result of an imbalance between energy intake and expenditure; however, the effect of different dietary factors on weight gain or weight loss remains unclear( Reference Hall, Sacks and Chandramohan 5 ). Because of the complexity of the diet, dietary pattern analysis is useful because it simultaneously reflects various aspects of the diet, has the potential to capture interactions between food and nutrients( Reference Lassale, Fezeu and Andreeva 6 , Reference McNaughton 7 ) and is an alternative to traditional methods of examining single foods or nutrients( Reference Newby and Tucker 8 ). The dietary pattern approach has intuitive appeal because the human diet does not consist of a single nutrient or food, but instead represents a complex set of highly correlated dietary exposures( Reference van Dam 9 Reference Wirt and Collins 11 ). Statistical methods, such as factor analysis, can be used to generate dietary patterns from food group data, by which various dietary factors can be reduced to a convincing set of dietary patterns describing how people actually eat. A few longitudinal studies have examined the association between changes in dietary patterns and a reduction in BMI( Reference Togo, Osler and Sorensen 12 Reference Collins, Sibbritt and Patterson 16 ). There is evidence in the literature that several web-based weight-loss interventions are efficient at supporting weight loss( Reference Collins, Morgan and Jones 17 Reference Truby, Hiscutt and Herriot 24 ). In this regard, these programmes can support participants by identifying dietary patterns and can also be used for collecting precise long-term and comprehensive nutrition data( Reference O’Brien, Hutchesson and Jensen 25 ). Moreover, weight self-monitoring has been documented as an impactful tool for weight loss and control( Reference Baker and Kirschenbaum 26 Reference Burke, Wang and Sevick 28 ). Additionally, recording dietary intake, physical activity and body weight is associated with successful weight loss and weight control( Reference Boutelle, Kirschenbaum and Baker 27 , Reference VanWormer, Martinez and Martinson 29 Reference Krukowski, Harvey-Berino and Ashikaga 31 ).

The aims of the present study were to: (i) characterise the dietary patterns of participants in a web-based weight-reduction programme; (ii) examine the weight-loss success associated with the distinct dietary patterns; and (iii) determine the changes in these dietary patterns over time.

Methods

Programme description

The web-based weight-reduction programme KiloCoachTM ( 32 ) has the main goal of encouraging changes in lifestyle regarding nutrition and physical activity that lead to weight loss by using self-monitoring combined with information about dietary intake and tailored feedback( Reference Postrach, Aspalter and Elbelt 23 ). Self-monitoring consists of a dietary protocol and height and weight records. The dietary protocol is an electronic version of the common written protocols for recording food intake( Reference Postrach, Aspalter and Elbelt 23 ). In our study, height and weight were self-reported; however, it has previously been shown that self-reported weight data in online programmes are comparable to an in-person assessment( Reference Harvey-Berino, Krukowski and Buzzell 33 ). Participants were advised to continuously document their behaviour regarding dietary intake and physical activity units for at least 5 d/week for sixty consecutive days and to update their body weight once weekly. Energy and nutrient intakes were calculated immediately by the programme after the participant electronically documented all of the food items and drinks, based on a food database of more than 40 000 items. Moreover, an upper threshold for daily energy intake (kcal), on the basis of self-reported data (sex, age, height and body weight) and energy expenditure (physical activity units), is automatically calculated by the programme( Reference Postrach, Aspalter and Elbelt 23 ) and therefore provides immediate feedback. The calculated energy expenditure is then subtracted from the energy intake and provides an individualised energy recommendation. The programme offers assistance regarding an individual’s weight-loss goal so that the weight reduction should be equal to or less than 1 kg/week to be in accordance with the European Clinical Practice Guidelines; this rate of weight loss is realistic and desirable( Reference Tsigos, Hainer and Basdevant 34 ). Therefore, the weight-loss programme fulfils this criterion. Additionally, the programme is based on a healthy diet and encourages participants to increase their physical activity. Recommendations for a healthy diet include achieving a balance in macronutrients( Reference Deutsche, Österreichische and Schweizerische 35 ); for physical activity, the participants should undertake 150 or 75 min of respectively moderate-intensity or vigorous-intensity exercise per week and a muscle-strengthening activity for a minimum of 2 d/week( Reference Titze, Ring-Dimitriou and Schober 36 ). Additionally, the web-based weight-reduction programme provides graphically displayed reports, e.g. on components of the participant’s diet (macronutrient balance of proteins, carbohydrates and fats) and the most consumed food groups.

The programme’s food database consists of food groups that are based on the nutrient profile (e.g. high in carbohydrates) and type of food (e.g. fruit or vegetables). These food groups contain basic products (e.g. plain vegetables), dishes prepared using the main food components (e.g. vegetable dishes) and types of dishes (e.g. bakery products and pastries). Twenty-two food groups were formed: ‘bakery products and pastries’, ‘bread and buns’, ‘diet products and food supplements’, ‘eggs and pasta’, ‘convenience products’, ‘fish and seafood’, ‘meat’, ‘vegetables, mushrooms, soya and herbs’, ‘alcoholic beverages’, ‘non-alcoholic beverages’, ‘cereals’, ‘spices and condiments’, ‘milk and dairy products’, ‘nuts and seeds’, ‘fruits and fruit products’, ‘oils and fats’, ‘salads’, ‘soups and soup garnishes’, ‘candies and sweets’, ‘sweet dishes’, ‘poultry, venison and offal’ and ‘sausages, spreads and spicy snacks’. The total consumption for each food group was determined by summing the total amount of each item (in grams) within the group.

Study design

The design is a pre-and-post intervention study without a control group, investigating users within a web-based weight-loss programme. The programme contains self-monitoring of diet and body weight, combined with information about dietary intake and tailored feedback to change lifestyle regarding nutrition and physical activity. Data sets of users were available between 2006 and 2012.

Study participants

The duration of the weight-loss period was defined by the chosen membership length (at least 3 months) for each participant. Six thousand seven hundred and seventeen data sets were available from participants who signed up for the weight-reduction programme between 7 February 2006 and 8 January 2012. The inclusion criteria for the present analysis were age ≥18 years (leaving a sample size of n 6715), available self-reported weight records after 3 months of participation (n 1987) and continuous daily dietary records over a period of 60 d (n 1635). The act of continuously recording dietary intake for more than 60 d has a significant impact on weight loss; this impact was previously described by Longin et al. ( Reference Longin, Grasse and Aspalter 21 ). Therefore, we excluded participants with less than 60 d of continuous dietary recording. These selection criteria yielded a study sample size of 1635 participants. All of the data sets included individual age, sex, height and body weight.

Factor analysis

To derive dietary and food patterns, the twenty-two main food groups at two time points, baseline and after 3 months, were entered separately into a principal component analysis (PCA) with orthogonal rotation (varimax) as the absolute weight in grams( Reference McNaughton 7 ). We calculated the daily food intake for each individual at both time points from the average dietary intake of each food group over two weeks of dietary records. The extraction of the number of components was determined by applying the following criteria: eigenvalue >1, identification of a break in the scree plot and interpretability of the components( Reference Schulze, Hoffmann and Kroke 37 ). Furthermore, food items with absolute factor loadings that were ≥0·25 accounted for each component. These items correlated intensely with the identified component. This fact is in accordance with the previous literature( Reference McCann, Marshall and Brasure 38 Reference McNaughton, Mishra and Stephen 40 ). Food groups were not included if they had an absolute loading of <0·25. We labelled the dietary patterns according to the food items. Hence, foods that did not fit in a category or did not load on any factor were not used for further analysis, such as ‘diet products and food supplements’, ‘eggs and pasta’, ‘convenience products’, ‘spices and condiments’, ‘nuts and seeds’, ‘oils and fats’ and ‘soups and soup garnishes’.

The factor score coefficients at baseline and after 3 months were calculated by summing the amount of daily intake at each time point for each food group and were weighted by the loading factor determined by PCA at baseline( Reference Schulze, Hoffmann and Kroke 37 , Reference Crozier, Robinson and Godfrey 41 Reference Northstone and Emmett 43 ). Each participant was given a factor score coefficient for each defined dietary pattern. The purpose of this calculation was to describe the initial dietary patterns and the change of dietary patterns after 3 months( Reference Crozier, Robinson and Godfrey 41 Reference Northstone and Emmett 43 ).

Anthropometrics

Total body weight loss was calculated as a percentage based on the initial weight. A cut-off of 5 % weight loss was used to classify all of the participants as either successful (≥5 %) or unsuccessful (<5 %). We have chosen this classification because a weight reduction of lower than 5 % is considered to be insignificant( Reference Tsigos, Hainer and Basdevant 34 ). BMI was calculated from height and weight as [weight (kg)]/[height (m)]2. Normal weight, overweight and obesity were classified according to the cut-off points from the WHO( 2 ).

Definition of under-reporters of energy intake

The average daily energy intake (EI; kcal/d) was assessed within the weight-loss programme. The BMR was not measured and therefore was calculated with the Schofield equation( Reference Schofield 44 ). Moreover, EI:BMR was determined to evaluate the under- and over-reporters of energy intake by using the values defined by Goldberg et al.( Reference Goldberg, Black and Jebb 45 ). For estimating energy expenditure, the programme considered the minimum energy requirement and coefficients for physical activity levels that were suggested by FAO/WHO/United Nations University( 46 ). EI:BMR<1·35 was considered under-reporting( Reference Johansson, Solvoll and Bjorneboe 47 ) and EI:BMR≥2·4 suggested over-reporting( Reference Black, Coward and Cole 48 ) as the maximum for a sustainable lifestyle( Reference Johansson, Solvoll and Bjorneboe 47 ). Nevertheless, these cut-offs fail to take the true energy expenditure of each individual into account. Therefore, in the current analysis, an EI:BMR range of 1·35 to 2·39 is categorised as plausible reporting regarding energy intake.

Statistical analyses

Statistical analyses were performed with the statistical software package IBM® SPSS® Statistics for Windows, Version 22. P values <0·05 were considered statistically significant and all tests were two-sided. Data are presented as means and standard deviations for continuous variables or as percentages for categorical variables, as appropriate. All of the results are stratified by sex.

The t test and the Mann–Whitney U test for continuous variables and the χ 2 test for categorical variables were used to detect differences between groups.

To ensure consistency, the Pearson product-moment correlation (r) was used to cross-check factor score coefficients after 3 months against the dietary pattern results at baseline, which is in agreement with the previously published literature( Reference Crozier, Robinson and Godfrey 41 , Reference Mishra, McNaughton and Bramwell 42 ).

The predictive value of each dietary pattern on weight-loss success was assessed by logistic regression analysis. Three 0–1 dummy variables were constructed for each dietary pattern; the first dummy variable was 1 for a healthful pattern and 0 for an energy-dense and high-carbohydrate pattern. The second dummy variable was 1 for an energy-dense pattern and 0 for a healthful and high-carbohydrate pattern, and the third dummy variable was 1 for a high-carbohydrate pattern and 0 for a healthful and energy-dense pattern. Success in the programme was defined as the dependent variable and the dummy variables for the dietary patterns were defined as the independent variables adjusted for age and initial BMI.

Logistic regression analyses were used to assess the predictive value of changing or staying in the initial dietary pattern on the successful group. Success in the programme was defined as the dependent variable and changing or staying in the initial dietary pattern as the independent variable. This procedure provides odds ratios for the effect of changing or staying in the dietary pattern on successful weight loss that are adjusted for age, sex, initial BMI and initial dietary pattern.

Furthermore, changes in the initial BMI were obtained by calculating the difference between the BMI after 3 months and at baseline. The predicted value of the change in BMI, as the dependent variable, on changing or maintaining the dietary pattern was assessed by a univariate generalised linear mixed model and was adjusted for age, sex, initial BMI and initial dietary pattern.

Results

Characteristics of the study participants

A total of 1108 women and 527 men, aged 18–81 years, were included in the present analysis. At baseline, women had a statistically significant lower initial BMI than men (P<0·001; Table 1).

Table 1 Characteristics of participants included in the analysis; adults (n 1635) aged 18–81 years, users of a web-based weight-reduction programme, Austria, 2006–2012

Independent-samples t test or Mann–Whitney U test, as appropriate.

Weight change after 3 months

After 3 months of participation in the web-based weight-reduction programme, women had a mean BMI of 28·9 (sd 5·3) kg/m2 and men had a mean BMI of 29·5 (sd 4·5) kg/m2 (P<0·001). Overall, 25 % of the population had a normal weight after 3 months (32 % of women and 11 % of men; P<0·001), representing a change of 10 %. Twenty-eight per cent of women and 36 % of men were obese at follow-up, representing an overall change of 12 %. More than half of the participants (52 % of women and 58 % of men) lost 5 % or more of their initial weight and thus were considered to be successful in the programme (Table 1). Significantly more women changed from the overweight to the normal weight group after 3 months of participation in the web-based weight-reduction programme compared with men (27·3 % v. 15·7 %; P<0·001).

Under-reporters

At baseline, 68 % were under-reporters (EI:BMR<1·35) and 1 % were over-reporters (EI:BMR≥2·4). Compared with plausible reporters, under-reporters had significantly higher mean BMI (28·2 (sd 4·5) v. 30·9 (sd 5·5) kg/m2; P<0·001) and the majority were overweight or obese (89 %). Regarding weight change after 3 months, there was no significant difference between under-, plausible and over-reporters. Participants who over-reported their energy intake demonstrated a significantly lower BMR. Regarding the dietary pattern, both time points (at baseline and after 3 months) showed significantly higher proportions of under-reporters in the energy-dense pattern (at baseline: 41 % under-reporters, 18 % plausible reporters, 21 % over-reporters, P<0·001; after 3 months: 45 % under-reporters, 22 % plausible reporters, 21 % over-reporters, P<0·001). The distribution of under-, plausible and over-reporters in the three dietary patterns remained constant over time (e.g. healthy dietary pattern at baseline: 30 % under-reporters, 38 % plausible reporters, 33 % over-reporters; after 3 months: 30 % under-reporters, 32 % plausible reporters, 46 % over-reporters).

Dietary patterns

At baseline and after 3 months, PCA revealed three dietary patterns (Table 2). The first pattern was characterised as healthful (HP) and included the food groups ‘vegetables, mushrooms, soya and herbs’, ‘cereals’, ‘milk and dairy products’, ‘fruits and fruit products’ and ‘salads’. The second pattern, which included the food groups ‘fish and seafood’, ‘meat’, ‘alcoholic beverages’, ‘poultry, venison and offal’ and ‘sausages, spreads and spicy snacks’, was called energy-dense pattern (EDP). The third pattern was considered to be a high-carbohydrate pattern (HCP) and contained ‘bakery products and pastries’, ‘bread and buns’, ‘non-alcoholic beverages’, ‘candies and sweets’ and ‘sweet dishes’. All three patterns explained 36 % and 42 % of the variation of food intake at baseline and after 3 months, respectively. PCA after 3 months revealed similar dietary patterns to those identified at baseline with respect to component loadings for each food group (HP: r=0·594, P<0·001; EDP: r=0·606, P<0·001; HCP: r=0·622, P<0·001). The factor score coefficients compared with baseline were statistically significantly lower after 3 months in all participants and in males and females, which could be attributed to a decreased food intake (P<0·001). Supplemental Table 1 (see online supplementary material) illustrates all food groups in grams per day at baseline and after 3 months.

Table 2 Factor-loading matrix for the three dietary patterns and their foods or food groups

HP, healthful pattern; EDP, energy-dense pattern; HCP, high-carbohydrate pattern.

Food items and groups with an absolute component loading ≥0·25 comprised the dietary patterns at baseline and after 3 months; only these are included in the analysis.

As shown in Fig. 1, these dietary patterns were similarly distributed at the start of the programme, with 32 % in the HP, 34 % in the EDP and 34 % in the HCP. However, there was a marked difference between men and women. Whereas the HP and the HCP were the most prominent dietary patterns in women, it was the EDP for men. Sixty-three per cent of the total population remained in their baseline dietary pattern during the observation period of 3 months. Fifty-nine per cent of participants with an initial HP, 70 % with an EDP and 59 % with an HCP stayed in their initial dietary pattern. Fifty-nine per cent of women and 61 % of men with an initial HP stayed in the HP. Furthermore, men were more likely to remain in the EDP than women (77 % v. 64 %). In addition, 17 % of all participants changed from the EDP or the HCP to the HP (Fig. 1).

Fig. 1 Assignment changes of dietary patterns (, healthful pattern (HP); , energy-dense pattern (EDP); , high-carbohydrate pattern (HCP)) after 3 months compared with baseline for (a) the total population, (b) women and (c) men; adults (n 1635) aged 18–81 years, users of a web-based weight-reduction programme, Austria, 2006–2012

Dietary patterns and weight loss

Table 3 shows the proportion of successful participants in relation to their initial dietary pattern or change in their dietary patterns. Sixty-three per cent of all participants who showed an HP at baseline were successful in losing weight (P<0·001; Table 3). Participants in the EDP and in the HCP groups were quite similar in successfully losing weight (47 %, P<0·001 v. 50 %, P<0·001) compared with those who failed to be successful.

Table 3 Success of weight loss (>5 % of initial weight after 3 months) in relation to dietary patterns either at baseline or after 3 months; adults (n 1635) aged 18–81 years, users of a web-based weight-reduction programme, Austria, 2006–2012

HP, healthful pattern; EDP, energy-dense pattern; HCP, high-carbohydrate pattern.

Significant differences tested between successful and unsuccessful weight-loss groups using the χ 2 test for categorical variables.

Participants who changed to or remained in the HP (31 %) had a greater chance of being successful (OR=1·4; 95 % CI 1·1, 1·7). Participants who aligned with the HP lost a statistically significant greater amount of weight compared with those who changed to the EDP and HCP (6·3 (sd 4·5) v. 5·2 (sd 5·3) and 6·1 (sd 3·7) %; P=0·032) and had a significantly higher BMI reduction of 6·3 (sd 4·5) % (P=0·034) compared with those participants with an EDP or HCP at baseline or who changed to those patterns (data not shown). This analysis was adjusted for age, sex, initial BMI and initial dietary pattern.

Furthermore, we observed significant sex differences; in all three dietary patterns, men lost more weight than did women (HP: 7·5 (sd 4·6) v. 5·2 (sd 4·0) %, P<0·001; EDP: 4·7 (sd 4·2) v. 3·9 (sd 4·5) %, P=0·022; HCP: 5·7 (sd 3·5) v. 4·9 (sd 3·9) %, P=0·029). Seventy-one per cent of men and 57 % of women who were in the HP after 3 months were successful, with a statistically significant difference between sexes (P<0·001).

Success ratios

On the one hand, participants who aligned to an HP at baseline (OR=1·8; 95 % CI 1·5, 2·3) and after 3 months (OR=1·5; 95 % CI 1·2, 1·9) had a greater chance at successfully losing weight. Moreover, the odds of successful weight loss were greater in men compared with women (Table 4). On the other hand, in those participants who complied with the EDP at baseline (OR=0·7; 95 % CI 0·6, 0·8) and after 3 months (OR=0·6; 95 % CI 0·5, 0·7), we observed a lower chance of successfully losing weight (Table 4). Female participants in the HCP at baseline had lower odds to be successful (OR=0·7; 95 % CI 0·6, 0·9). There were no significant odds ratios in all of the other distributions of the HCP (Table 4).

Table 4 Association between successful weight loss (>5 % of initial weight after 3 months) and the dietary pattern (0–1 dummy variables) at baseline and after 3 months; adults (n 1635) aged 18–81 years, users of a web-based weight-reduction programme, Austria, 2006–2012

HP, healthful pattern; EDP, energy-dense pattern; HCP, high-carbohydrate pattern.

*P<0·05, **P<0·01, ***P<0·001.

Odds ratio of successful weight loss (dependent variable) for dietary patterns with 0–1 dummy variables (independent variables) adjusted for age and initial BMI. The reference group for the 0–1 dummy variables is the relevant dietary pattern (e.g. HP dummy variable: 1=having an HP, 0=having an EDP or an HCP).

Discussion

Our results revealed that more than half of the participants successfully lost weight (≥5 % of their initial weight after 3 months) during participation in the web-based weight-reduction programme. Using twenty-two food groups, we identified three major dietary patterns derived from PCA, which we labelled healthful (HP), energy-dense (EDP) and high-carbohydrate (HCP). Deriving the amount of food from dietary records represents the ‘gold standard’ for dietary assessments. In our investigation, we used web-based food records for assessing energy intake; this method appears to be consistent with other published methods( Reference Hutchesson, Truby and Callister 49 ). Alignment to the HP, reflecting high intakes of vegetables, fruits, grains, dairy products and salads, was associated with a higher chance of being successful and a significantly higher BMI reduction. In contrast, alignment to the EDP was associated with lower odds of successfully losing weight.

With high accessibility to the Internet, a rising number of web-based weight-reduction programmes are available and have been shown to produce significant weight loss( Reference Collins, Morgan and Jones 17 , Reference Johnson and Wardle 20 Reference Postrach, Aspalter and Elbelt 23 ). To date, three studies have reported that early weight loss, as in the present study at 3 months, is favourably associated with the final weight loss amount( Reference Postrach, Aspalter and Elbelt 23 , Reference Handjieva-Darlenska, Handjiev and Larsen 50 Reference Fabricatore, Wadden and Moore 52 ).

Very frequently in weight-reduction programmes, the participation rates of men and women are disproportional because more females participate( Reference Young, Morgan and Plotnikoff 53 , Reference Williams, Wood and Collins 54 ). The reason for this unequal distribution of participants might be an enhanced lifestyle and health triggers to lose weight for women( Reference Gorin, Phelan and Hill 55 ). In our investigation, we analysed men and women separately because other trials have found sex differences in using web-based programmes and in factors associated with successfully losing weight( Reference Presnell, Pells and Stout 56 ). We observed a higher average weight loss in men compared with women (6·3 kg v. 4·3 kg). This might be caused by a higher BMI at baseline, fewer prior weight-loss attempts compared with women, or greater self-efficacy regarding weight loss( Reference Mo, Malik and Coulson 57 ).

More than two-thirds of the participants were classified as under-reporters, while less than 2 % were considered over-reporters; under-reporters demonstrated a higher BMI( Reference Livingstone and Black 58 ). The under-reporting of energy intake was not uniformly distributed among the three dietary patterns and might be higher among participants within the rather unfavourable dietary pattern (EDP). Nevertheless, under- and over-reporting have been shown to be subject specific( Reference Black and Cole 59 ). However, the focus of the current analysis is weight-loss success and therefore the bias may be attenuated because there was no significant difference between under- and over-reporters.

The frequency of the HP in our study population is in agreement with other studies on dietary patterns in other populations( Reference Newby, Weismayer and Akesson 15 , Reference van Dam, Rimm and Willett 60 Reference Schulz, Nothlings and Hoffmann 64 ). Additionally, in agreement with previous studies( Reference Schulz, Nothlings and Hoffmann 64 Reference Newby, Muller and Hallfrisch 67 ), alignment to the HP from our study was associated with both a significantly higher chance to be successful and a greater weight reduction. Both women and men eating the HP lost more weight compared with those eating unhealthful dietary patterns, such as the EDP and HCP. Noticeably, men lost more weight in spite of the dominant EDP, and men in the HP lost more weight than women. This is in agreement with another study among adult cohorts( Reference Schulze, Fung and Manson 68 ). Schulze et al.( Reference Schulze, Fung and Manson 68 ) described that strong adherence to a Western pattern (derived by PCA), characterised by a high intakes of refined grains, sweets and desserts, was associated with greater weight gain in women.

In two recent randomised controlled intervention trials regarding diet quality and weight loss( Reference O’Brien, Hutchesson and Jensen 25 , Reference Champagne, Broyles and Moran 69 ), a higher diet quality was associated with greater weight loss. In our investigation, the high number of participants in the HP may be trying to adopt a healthier eating pattern to lose weight or prevent further weight gain( Reference Reedy, Wirfalt and Flood 70 ). These individuals might also implement healthier foods but may not adopt recommended portion sizes. Further research will be needed to determine whether these dietary patterns are consistent and maintain the same associations with weight over a longer time period.

To the best of our knowledge, the majority of studies involving dietary patterns use data from an FFQ or 24 h dietary recalls( Reference Devlin, McNulty and Nugent 71 , Reference Wirfalt, Drake and Wallstrom 72 ); a minimal number of studies have used dietary records( Reference Newby, Muller and Hallfrisch 67 , Reference Fialkowski, McCrory and Roberts 73 ). Furthermore, dietary assessment tools will most likely always face some level of error and the ability of individuals to precisely self-report their dietary intake may be challenging( Reference Champagne, Bray and Kurtz 74 , Reference Mahabir, Baer and Giffen 75 ). However, applied factor analyses propose that derived dietary patterns account for a modest part of the total variance of whole food intake( Reference Schulze, Hoffmann and Kroke 76 ). Our three strongest dietary patterns accounted for only 36 % of the total variance in food intake with all types of reporters (under-, over- and plausible reporters), which is in the range of previous reports( Reference Fung, Willett and Stampfer 77 , Reference Hu, Rimm and Stampfer 78 ) and is comparable to other studies( Reference Schulze, Hoffmann and Kroke 76 ). The variance explained by individual factors is a function of both the number of variables included in the analysis and the correlation matrix itself( Reference Schwerin, Stanton and Riley 79 ) and might be expected to vary across investigations. Moreover, considering only plausible reporters, dietary patterns accounted for 43 % of the total variance in food intake.

The key finding of our study implicates the consistency of dietary patterns over time because approximately two-thirds stayed in their initial dietary pattern. An explanation for this finding might be the relatively short observation period of 3 months. Nevertheless, according to participants who successfully lost weight, a change of a rather unfavourable dietary pattern to a healthful pattern might be challenging, especially for overweight and obese individuals and with respect to the short duration. Accordingly, the best diet for those persons might depend on taste preference and ease of adherence( Reference McCullough 80 ).

Moreover, most programmes rely on changes in diet and physical activity with the aim of energy intake reduction and energy expenditure increase( Reference Lyznicki, Young and Riggs 81 , Reference Huang, Drewnosksi and Kumanyika 82 ). According to the literature, studies have proposed that a change in behaviour (dietary patterns) or in the principles of social learning, such as goal-setting and self-monitoring, can improve the efficacy of weight-loss approaches( Reference Shaw, O’Rourke and Del Mar 83 Reference Baulch, Chester and Brennan 85 ). Moreover, these factors, particularly behavioural changes and self-monitoring, have been found to be associated with weight-loss success( Reference Morgan, Lubans and Collins 86 , Reference Polzien, Jakicic and Tate 87 ). Studies of persons who successfully maintain weight loss after self-directed or weight-loss programmes showed that long-term self-monitoring is a crucial component of weight-loss maintenance( Reference Klem, Wing and McGuire 88 , Reference Colvin and Olson 89 ). Therefore self-monitoring may serve as an effective behavioural intervention.

Some limitations and strengths of the present study need to be mentioned. First, the weight-change outcomes are based on self-reports, and weight is generally under-reported from overweight and obese individuals and women in particular( Reference Connor Gorber, Tremblay and Moher 90 ). However, it has been found that self-reported weight recorded online( Reference Pursey, Burrows and Stanwell 91 ) and by web-based weight-loss programme participants( Reference Harvey-Berino, Krukowski and Buzzell 33 ) is accurate compared with measured weight( Reference Harvey-Berino, Krukowski and Buzzell 33 , Reference Pursey, Burrows and Stanwell 91 ). The consumption of food is self-reported, and diet reporting is generally under-reported. These findings could lead to non-random misclassifications and can either underestimate or overestimate the association between dietary exposure and weight-loss success( Reference Livingstone and Black 58 , Reference Black and Cole 59 ). A possible factor for under-reporting the diet could be the general climate of knowledge about food and health( Reference Livingstone and Black 58 ). There is evidence that the act of self-monitoring is more important than the particular approach and is beneficial for weight control. Electronic and paper diaries seem to be comparable( Reference Yon, Johnson and Harvey-Berino 92 ) with regard to the level of entry details( Reference Helsel, Jakicic and Otto 93 ) or whether the programme participants have received training on accurately recording their dietary intake( Reference Lowe, Tappe and Annunziato 94 ). Second, there might be a potential bias associated with weight loss regarding follow-up and attrition. We only analysed the ‘completers’ who recorded their diet for a minimum of 60 d. Third, the use of PCA to identify dietary patterns involves some subjectivity in the criteria employed to retain factors. Fourth, it is not clear whether the patterns that we derived would have a different biological effect in individuals with altered educational levels or ethnicities. Dietary patterns might differ by sex( Reference Rogers and Longnecker 95 Reference Wirfalt and Jeffery 98 ) because women tend to have healthier dietary patterns compared with men( Reference Kanter and Caballero 99 Reference Martin 102 ). Fifth, physical activity levels were not taken into account in the present analysis, and activity levels might be closely related to weight loss. Physical activity might therefore be a confounder regarding outcome parameters, such as the success of weight loss and dietary intake. Sixth, the study is a pre-and-post intervention study without a control group; therefore this might be a possible bias. The changes of dietary pattern after 3 months could be attributed to participating in the weight-loss programme. Furthermore, an association may exist between dietary pattern and weight loss, due to an intervention effect of self-monitoring.

The present study also has several strengths, including its prospective nature covering a participation period of 3 months with assessments of detailed dietary and anthropometric data and thus our ability to describe changes in the diet. To characterise habitual dietary intake, it is necessary to record dietary intake for more than 8 d to minimise the effect of random errors (day-to-day variation in dietary intake) in a cohort of overweight and obese men and women( Reference Jackson, Byrne and Magarey 103 ); the dietary intake was recorded for over 14 d. Additionally, the effects of paying for a weight-loss programme, as was the case for participants in the present analysis, are not particularly obvious. Regarding previous trials, it is reasonable that paying for a weight-loss programme might affect motivation and outcomes( Reference Gold, Burke and Pintauro 18 , Reference Truby, Baic and deLooy 104 ). Nevertheless, our findings were strengthened by adjusting for the many potential confounders in our regression models, such as sex, age, initial BMI and initial dietary pattern. Furthermore, individual feedback and information about nutrition and physical activity within the web-based weight-loss programme could be ascribed as an advantage of the programme( Reference Hutchesson, Truby and Callister 49 ).

Conclusion

In summary, more than half of the participants successfully lost weight during their 3-month participation in the web-based weight-reduction programme. In the current investigation, alignment to the healthful dietary pattern was related to a higher chance of being successful and a significant BMI reduction compared with other unfavourable dietary patterns. Nevertheless, the majority of the participants remained in their initial dietary pattern, which could be related to the duration of the observation period or the fact that changing from a rather unfavourable dietary pattern to a healthful pattern might be challenging.

Acknowledgements

Financial support: This research received no specific grant from any funding agency in the public, commercial or not-for-profit sectors. Conflict of interest: R.A. is the co-owner of the commercial programme KiloCoachTM. R.A. provided the study team with user data and declares that the submitted data have been unselected and unmodified. R.L. is an employee of KiloCoach Internetportale GmbH. KiloCoach Internetportale GmbH will not have direct financial benefit from publication. No author has any conflict of interest. Authorship: T.E.D. designed the research. E.L., M.L. and T.E.D. analysed the data and performed statistical analyses. T.E.D. supervised the statistical analysis. E.L., M.L. and T.E.D. drafted the manuscript. E.L. had prime responsibility for the final manuscript content. A.R., R.A. and R.L. provided critical input on all versions of the manuscript. All authors read and approved the final manuscript. Language and quality control editors of the Elsevier Editing Service have revised the manuscript. Ethics of human subject participation: This study was conducted according to the guidelines laid down in the Declaration of Helsinki and all procedures involving subjects were approved by the ethics committee at the Medical University of Vienna (EC 1021/2012).

Supplementary Material

To view supplementary material for this article, please visit http://dx.doi.org/10.1017/S1368980015002852

References

1. World Health Organization (2013) Obesity and overweight. Fact sheet no. 311. http://www.who.int/mediacentre/factsheets/fs311/en/ (accessed February 2014).Google Scholar
2. World Health Organization (2006) Global database on body mass index. http://apps.who.int/bmi/index.jsp (accessed February 2014).Google Scholar
3. Van Gaal, LF & Maggioni, AP (2013) Overweight, obesity, and outcomes: fat mass and beyond. Lancet 383, 935936.CrossRefGoogle ScholarPubMed
4. Pi-Sunyer, F (1998) Clinical guidelines on the identification, evaluation, and treatment of overweight and obesity in adults – the evidence report. National Institutes of Health. Obes Res 6, Suppl. 2, 51S209S.Google Scholar
5. Hall, KD, Sacks, G, Chandramohan, D et al. (2011) Quantification of the effect of energy imbalance on bodyweight. Lancet 378, 826837.CrossRefGoogle ScholarPubMed
6. Lassale, C, Fezeu, L, Andreeva, VA et al. (2012) Association between dietary scores and 13-year weight change and obesity risk in a French prospective cohort. Int J Obes (Lond) 36, 14551462.Google Scholar
7. McNaughton, SA (2010) Dietary patterns and diet quality: approaches to assessing complex exposures in nutrition. Australas Epidemiol 17, 3537.Google Scholar
8. Newby, PK & Tucker, KL (2004) Empirically derived eating patterns using factor or cluster analysis: a review. Nutr Rev 62, 177203.Google Scholar
9. van Dam, RM (2007) New approaches to the study of dietary patterns. Br J Nutr 93, 573574.Google Scholar
10. Marchioni, DML, Latorre, MdRDdO, Eluf-Neto, J et al. (2005) Identification of dietary patterns using factor analysis in an epidemiological study in São Paulo. Sao Paulo Med J 123, 124127.CrossRefGoogle Scholar
11. Wirt, A & Collins, CE (2009) Diet quality – what is it and does it matter? Public Health Nutr 12, 24732492.Google Scholar
12. Togo, P, Osler, M, Sorensen, TI et al. (2004) A longitudinal study of food intake patterns and obesity in adult Danish men and women. Int J Obes Relat Metab Disord 28, 583593.Google Scholar
13. Quatromoni, PA, Copenhafer, DL, D’Agostino, RB et al. (2002) Dietary patterns predict the development of overweight in women: the Framingham Nutrition Studies. J Am Diet Assoc 102, 12391246.Google Scholar
14. Newby, PK, Weismayer, C, Akesson, A et al. (2006) Long-term stability of food patterns identified by use of factor analysis among Swedish women. J Nutr 136, 626633.Google Scholar
15. Newby, PK, Weismayer, C, Akesson, A et al. (2006) Longitudinal changes in food patterns predict changes in weight and body mass index and the effects are greatest in obese women. J Nutr 136, 25802587.Google Scholar
16. Collins, C, Sibbritt, D, Patterson, A et al. (2011) The association between diet quality and weight change in adults over time: a systematic review of studies with follow up. JBI Database Syst Rev Implement Rep 9, 16 Suppl., 9.Google Scholar
17. Collins, CE, Morgan, PJ, Jones, P et al. (2012) A 12-week commercial web-based weight-loss program for overweight and obese adults: randomized controlled trial comparing basic versus enhanced features. J Med Internet Res 14, 57.Google Scholar
18. Gold, BC, Burke, S, Pintauro, S et al. (2007) Weight loss on the web: a pilot study comparing a structured behavioral intervention to a commercial program. Obesity (Silver Spring) 15, 155164.Google Scholar
19. Heshka, S, Greenway, F, Anderson, JW et al. (2000) Self-help weight loss versus a structured commercial program after 26 weeks: a randomized controlled study. Am J Med 109, 282287.CrossRefGoogle Scholar
20. Johnson, F & Wardle, J (2011) The association between weight loss and engagement with a web-based food and exercise diary in a commercial weight loss programme: a retrospective analysis. Int J Behav Nutr Phys Act 8, 83.Google Scholar
21. Longin, R, Grasse, M, Aspalter, R et al. (2012) Effectiveness of the online weight reduction program KiloCoach and comparison with other evaluated commercial direct intervention and online programs. Obes Facts 5, 372383.Google Scholar
22. Neve, M, Morgan, PJ & Collins, CE (2011) Weight change in a commercial web-based weight loss program and its association with website use: cohort study. J Med Internet Res 13, 83.CrossRefGoogle Scholar
23. Postrach, E, Aspalter, R, Elbelt, U et al. (2013) Determinants of successful weight loss after using a commercial web-based weight reduction program for six months: cohort study. J Med Internet Res 15, 219.CrossRefGoogle ScholarPubMed
24. Truby, H, Hiscutt, R, Herriot, AM et al. (2008) Commercial weight loss diets meet nutrient requirements in free living adults over 8 weeks: a randomised controlled weight loss trial. Nutr J 7, 25.Google Scholar
25. O’Brien, KM, Hutchesson, MJ, Jensen, M et al. (2014) Participants in an online weight loss program can improve diet quality during weight loss: a randomized controlled trial. Nutr J 13, 82.Google Scholar
26. Baker, RC & Kirschenbaum, DS (1998) Weight control during the holidays: highly consistent self-monitoring as a potentially useful coping mechanism. Health Psychol 17, 367370.Google Scholar
27. Boutelle, KN, Kirschenbaum, DS, Baker, RC et al. (1999) How can obese weight controllers minimize weight gain during the high risk holiday season? By self-monitoring very consistently. Health Psychol 18, 364368.Google Scholar
28. Burke, LE, Wang, J & Sevick, MA (2011) Self-monitoring in weight loss: a systematic review of the literature. J Am Diet Assoc 111, 92102.CrossRefGoogle ScholarPubMed
29. VanWormer, JJ, Martinez, AM, Martinson, BC et al. (2009) Self-weighing promotes weight loss for obese adults. Am J Prev Med 36, 7073.Google Scholar
30. Butryn, ML, Phelan, S, Hill, JO et al. (2007) Consistent self-monitoring of weight: a key component of successful weight loss maintenance. Obesity (Silver Spring) 15, 30913096.Google Scholar
31. Krukowski, RA, Harvey-Berino, J, Ashikaga, T et al. (2008) Internet-based weight control: the relationship between web features and weight loss. Telemed J E Health 14, 775782.Google Scholar
32. KiloCoach™ (2014) KiloCoach. http://kilocoach.com/at-de/ (accessed July 2014).Google Scholar
33. Harvey-Berino, J, Krukowski, RA, Buzzell, P et al. (2011) The accuracy of weight reported in a web-based obesity treatment program. Telemed J E Health 17, 696699.Google Scholar
34. Tsigos, C, Hainer, V, Basdevant, A et al. (2008) Management of obesity in adults: European clinical practice guidelines. Obes Facts 1, 106116.Google Scholar
35. Deutsche, Gesellschaft für Ernährung, Österreichische, Gesellschaft für Ernährung, Schweizerische, Gesellschaft für Ernährungsforschung et al. (2000) D-A-CH Nutrient Intake Recommendations. Frankfurt am Main: Umschau/Braus.Google Scholar
36. Titze, S, Ring-Dimitriou, S, Schober, P et al. (2010) Österreichische Empfehlungen für gesundheitswirksame Bewegung. Vienna: (Hrsg.) Bundesministerium für Gesundheit, Gesundheit Österreich GmbH and Geschäftsbereich Fonds Gesundes Österreich.Google Scholar
37. Schulze, MB, Hoffmann, K, Kroke, A et al. (2003) An approach to construct simplified measures of dietary patterns from exploratory factor analysis. Br J Nutr 89, 409419.Google Scholar
38. McCann, SE, Marshall, JR, Brasure, JR et al. (2001) Analysis of patterns of food intake in nutritional epidemiology: food classification in principal components analysis and the subsequent impact on estimates for endometrial cancer. Public Health Nutr 4, 989997.Google Scholar
39. McNaughton, SA, Ball, K, Mishra, GD et al. (2008) Dietary patterns of adolescents and risk of obesity and hypertension. J Nutr 138, 364370.Google Scholar
40. McNaughton, SA, Mishra, GD, Stephen, AM et al. (2007) Dietary patterns throughout adult life are associated with body mass index, waist circumference, blood pressure, and red cell folate. J Nutr 137, 99105.Google Scholar
41. Crozier, SR, Robinson, SM, Godfrey, KM et al. (2009) Women’s dietary patterns change little from before to during pregnancy. J Nutr 139, 19561963.CrossRefGoogle ScholarPubMed
42. Mishra, GD, McNaughton, SA, Bramwell, GD et al. (2006) Longitudinal changes in dietary patterns during adult life. Br J Nutr 96, 735744.Google Scholar
43. Northstone, K & Emmett, PM (2008) A comparison of methods to assess changes in dietary patterns from pregnancy to 4 years post-partum obtained using principal components analysis. Br J Nutr 99, 10991106.Google Scholar
44. Schofield, WN (1985) Predicting basal metabolic rate, new standards and review of previous work. Hum Nutr Clin Nutr 39, Suppl. 1, 541.Google Scholar
45. Goldberg, GR, Black, AE, Jebb, SA et al. (1991) Critical evaluation of energy intake data using fundamental principles of energy physiology: 1. Derivation of cut-off limits to identify under-recording. Eur J Clin Nutr 45, 569581.Google ScholarPubMed
46. World Health Organization (1985) Energy and Protein Requirements. Report of a Joint FAO/WHO/UNU Expert Consultation. WHO Technical Report Series no. 724. Geneva: WHO.Google Scholar
47. Johansson, L, Solvoll, K, Bjorneboe, GE et al. (1998) Under- and overreporting of energy intake related to weight status and lifestyle in a nationwide sample. Am J Clin Nutr 68, 266274.Google Scholar
48. Black, AE, Coward, WA, Cole, TJ et al. (1996) Human energy expenditure in affluent societies: an analysis of 574 doubly-labelled water measurements. Eur J Clin Nutr 50, 7292.Google Scholar
49. Hutchesson, MJ, Truby, H, Callister, R et al. (2013) Can a web-based food record accurately assess energy intake in overweight and obese women? A pilot study. J Hum Nutr Diet 26, Suppl. 1, 140144.Google Scholar
50. Handjieva-Darlenska, T, Handjiev, S, Larsen, TM et al. (2010) Initial weight loss on an 800-kcal diet as a predictor of weight loss success after 8 weeks: the Diogenes study. Eur J Clin Nutr 64, 994999.Google Scholar
51. Elfhag, K & Rossner, S (2010) Initial weight loss is the best predictor for success in obesity treatment and sociodemographic liabilities increase risk for drop-out. Patient Educ Couns 79, 361366.Google Scholar
52. Fabricatore, AN, Wadden, TA, Moore, RH et al. (2009) Predictors of attrition and weight loss success: results from a randomized controlled trial. Behav Res Ther 47, 685691.CrossRefGoogle ScholarPubMed
53. Young, MD, Morgan, PJ, Plotnikoff, RC et al. (2012) Effectiveness of male-only weight loss and weight loss maintenance interventions: a systematic review with meta-analysis. Obes Rev 13, 393408.Google Scholar
54. Williams, RL, Wood, LG, Collins, CE et al. (2015) Effectiveness of weight loss interventions – is there a difference between men and women: a systematic review. Obes Rev 16, 171186.Google Scholar
55. Gorin, AA, Phelan, S, Hill, JO et al. (2004) Medical triggers are associated with better short- and long-term weight loss outcomes. Prev Med 39, 612616.Google Scholar
56. Presnell, K, Pells, J, Stout, A et al. (2008) Sex differences in the relation of weight loss self-efficacy, binge eating, and depressive symptoms to weight loss success in a residential obesity treatment program. Eat Behav 9, 170180.Google Scholar
57. Mo, PK, Malik, SH & Coulson, NS (2009) Gender differences in computer-mediated communication: a systematic literature review of online health-related support groups. Patient Educ Couns 75, 1624.Google Scholar
58. Livingstone, MB & Black, AE (2003) Markers of the validity of reported energy intake. J Nutr 133, Suppl. 3, 895S920S.Google Scholar
59. Black, AE & Cole, TJ (2001) Biased over- or under-reporting is characteristic of individuals whether over time or by different assessment methods. J Am Diet Assoc 101, 7080.Google Scholar
60. van Dam, RM, Rimm, EB, Willett, WC et al. (2002) Dietary patterns and risk for type 2 diabetes mellitus in US men. Ann Intern Med 136, 201209.Google Scholar
61. Balder, HF, Virtanen, M, Brants, HA et al. (2003) Common and country-specific dietary patterns in four European cohort studies. J Nutr 133, 42464251.CrossRefGoogle ScholarPubMed
62. Drogan, D, Hoffmann, K, Schulz, M et al. (2007) A food pattern predicting prospective weight change is associated with risk of fatal but not with nonfatal cardiovascular disease. J Nutr 137, 19611967.Google Scholar
63. Weikert, C, Hoffmann, K, Dierkes, J et al. (2005) A homocysteine metabolism-related dietary pattern and the risk of coronary heart disease in two independent German study populations. J Nutr 135, 19811988.Google Scholar
64. Schulz, M, Nothlings, U, Hoffmann, K et al. (2005) Identification of a food pattern characterized by high-fiber and low-fat food choices associated with low prospective weight change in the EPIC-Potsdam cohort. J Nutr 135, 11831189.Google Scholar
65. Fung, TT, Rimm, EB, Spiegelman, D et al. (2001) Association between dietary patterns and plasma biomarkers of obesity and cardiovascular disease risk. Am J Clin Nutr 73, 6167.Google Scholar
66. Murtaugh, MA, Herrick, JS, Sweeney, C et al. (2007) Diet composition and risk of overweight and obesity in women living in the southwestern United States. J Am Diet Assoc 107, 13111321.Google Scholar
67. Newby, PK, Muller, D, Hallfrisch, J et al. (2004) Food patterns measured by factor analysis and anthropometric changes in adults. Am J Clin Nutr 80, 504513.CrossRefGoogle ScholarPubMed
68. Schulze, M, Fung, T, Manson, J et al. (2006) Dietary patterns and changes in body weight in women. Obesity (Silver Spring) 14, 14441453.Google Scholar
69. Champagne, CM, Broyles, ST, Moran, LD et al. (2011) Dietary intakes associated with successful weight loss and maintenance during the Weight Loss Maintenance trial. J Am Diet Assoc 111, 18261835.Google Scholar
70. Reedy, J, Wirfalt, E, Flood, A et al. (2010) Comparing 3 dietary pattern methods – cluster analysis, factor analysis, and index analysis – with colorectal cancer risk: the NIH–AARP Diet and Health Study. Am J Epidemiol 171, 479487.Google Scholar
71. Devlin, UM, McNulty, BA, Nugent, AP et al. (2012) The use of cluster analysis to derive dietary patterns: methodological considerations, reproducibility, validity and the effect of energy mis-reporting. Proc Nutr Soc 71, 599609.Google Scholar
72. Wirfalt, E, Drake, I & Wallstrom, P (2013) What do review papers conclude about food and dietary patterns? Food Nutr Res 2013, 57.Google Scholar
73. Fialkowski, MK, McCrory, MA, Roberts, SM et al. (2012) Dietary patterns are associated with dietary recommendations but have limited relationship to BMI in the Communities Advancing the Studies of Tribal Nations Across the Lifespan (CoASTAL) cohort. Public Health Nutr 15, 19481958.Google Scholar
74. Champagne, CM, Bray, GA, Kurtz, AA et al. (2002) Energy intake and energy expenditure: a controlled study comparing dietitians and non-dietitians. J Am Diet Assoc 102, 14281432.Google Scholar
75. Mahabir, S, Baer, DJ, Giffen, C et al. (2006) Calorie intake misreporting by diet record and food frequency questionnaire compared to doubly labeled water among postmenopausal women. Eur J Clin Nutr 60, 561565.Google Scholar
76. Schulze, MB, Hoffmann, K, Kroke, A et al. (2001) Dietary patterns and their association with food and nutrient intake in the European Prospective Investigation into Cancer and Nutrition (EPIC)-Potsdam study. Br J Nutr 85, 363373.Google Scholar
77. Fung, TT, Willett, WC, Stampfer, MJ et al. (2001) Dietary patterns and the risk of coronary heart disease in women. Arch Intern Med 161, 18571862.Google Scholar
78. Hu, FB, Rimm, EB, Stampfer, MJ et al. (2000) Prospective study of major dietary patterns and risk of coronary heart disease in men. Am J Clin Nutr 72, 912921.Google Scholar
79. Schwerin, HS, Stanton, JL, Riley, AM Jr et al. (1981) Food eating patterns and health: a reexamination of the Ten-State and HANES I surveys. Am J Clin Nutr 34, 568580.Google Scholar
80. McCullough, ML (2014) Diet patterns and mortality: common threads and consistent results. J Nutr 144, 795796.Google Scholar
81. Lyznicki, JM, Young, DC, Riggs, JA et al. (2001) Obesity: assessment and management in primary care. Am Fam Physician 63, 21852196.Google Scholar
82. Huang, TT, Drewnosksi, A, Kumanyika, S et al. (2009) A systems-oriented multilevel framework for addressing obesity in the 21st century. Prev Chronic Dis 6, 82.Google Scholar
83. Shaw, K, O’Rourke, P, Del Mar, C et al. (2005) Psychological interventions for overweight or obesity. Cochrane Database Systematic Rev, issue 2, CD003818.Google Scholar
84. Baker, RC & Kirschenbaum, DS (1993) Self-monitoring may be necessary for successful weight control. Behav Ther 24, 377394.Google Scholar
85. Baulch, J, Chester, A & Brennan, L (2008) Treatment alternatives for overweight and obesity: the role of online interventions. Behav Change 25, 114.Google Scholar
86. Morgan, PJ, Lubans, DR, Collins, CE et al. (2009) The SHED-IT randomized controlled trial: evaluation of an Internet-based weight-loss program for men. Obesity (Silver Spring) 17, 20252032.CrossRefGoogle ScholarPubMed
87. Polzien, KM, Jakicic, JM, Tate, DF et al. (2007) The efficacy of a technology-based system in a short-term behavioral weight loss intervention. Obesity (Silver Spring) 15, 825830.Google Scholar
88. Klem, ML, Wing, RR, McGuire, MT et al. (1997) A descriptive study of individuals successful at long-term maintenance of substantial weight loss. Am J Clin Nutr 66, 239246.Google Scholar
89. Colvin, RH & Olson, SB (1983) A descriptive analysis of men and women who have lost significant weight and are highly successful at maintaining the loss. Addict Behav 8, 287295.CrossRefGoogle ScholarPubMed
90. Connor Gorber, S, Tremblay, M, Moher, D et al. (2007) A comparison of direct vs. self-report measures for assessing height, weight and body mass index: a systematic review. Obes Rev 8, 307326.Google Scholar
91. Pursey, K, Burrows, TL, Stanwell, P et al. (2014) How accurate is web-based self-reported height, weight, and body mass index in young adults? J Med Internet Res 16, e4.Google Scholar
92. Yon, BA, Johnson, RK, Harvey-Berino, J et al. (2006) The use of a personal digital assistant for dietary self-monitoring does not improve the validity of self-reports of energy intake. J Am Diet Assoc 106, 12561259.Google Scholar
93. Helsel, DL, Jakicic, JM & Otto, AD (2007) Comparison of techniques for self-monitoring eating and exercise behaviors on weight loss in a correspondence-based intervention. J Am Diet Assoc 107, 18071810.Google Scholar
94. Lowe, MR, Tappe, KA, Annunziato, RA et al. (2008) The effect of training in reduced energy density eating and food self-monitoring accuracy on weight loss maintenance. Obesity (Sliver Spring) 16, 20162023.Google Scholar
95. Rogers, AE & Longnecker, MP (1988) Dietary and nutritional influences on cancer: a review of epidemiologic and experimental data. Lab Invest 59, 729759.Google Scholar
96. Patterson, RE, Haines, PS & Popkin, BM (1994) Health lifestyle patterns of US adults. Prev Med 23, 453460.Google Scholar
97. Randall, E, Marshall, JR, Graham, S et al. (1990) Patterns in food use and their associations with nutrient intakes. Am J Clin Nutr 52, 739745.CrossRefGoogle ScholarPubMed
98. Wirfalt, AK & Jeffery, RW (1997) Using cluster analysis to examine dietary patterns: nutrient intakes, gender, and weight status differ across food pattern clusters. J Am Diet Assoc 97, 272279.Google Scholar
99. Kanter, R & Caballero, B (2012) Global gender disparities in obesity: a review. Adv Nutr 3, 491498.Google Scholar
100. Vandevijvere, S, De Vriese, S, Huybrechts, I et al. (2009) The gap between food-based dietary guidelines and usual food consumption in Belgium, 2004. Public Health Nutr 12, 423431.Google Scholar
101. Prattala, R, Paalanen, L, Grinberga, D et al. (2007) Gender differences in the consumption of meat, fruit and vegetables are similar in Finland and the Baltic countries. Eur J Public Health 17, 520525.Google Scholar
102. Martin, S (2002) Surprise! Women eat healthier than men. CMAJ 167, 913.Google Scholar
103. Jackson, KA, Byrne, NM, Magarey, AM et al. (2008) Minimizing random error in dietary intakes assessed by 24-h recall, in overweight and obese adults. Eur J Clin Nutr 62, 537543.Google Scholar
104. Truby, H, Baic, S, deLooy, A et al. (2006) Randomised controlled trial of four commercial weight loss programmes in the UK: initial findings from the BBC ‘diet trials’. BMJ 332, 13091314.Google Scholar
Figure 0

Table 1 Characteristics of participants included in the analysis; adults (n 1635) aged 18–81 years, users of a web-based weight-reduction programme, Austria, 2006–2012

Figure 1

Table 2 Factor-loading matrix for the three dietary patterns and their foods or food groups

Figure 2

Fig. 1 Assignment changes of dietary patterns (, healthful pattern (HP); , energy-dense pattern (EDP); , high-carbohydrate pattern (HCP)) after 3 months compared with baseline for (a) the total population, (b) women and (c) men; adults (n 1635) aged 18–81 years, users of a web-based weight-reduction programme, Austria, 2006–2012

Figure 3

Table 3 Success of weight loss (>5 % of initial weight after 3 months) in relation to dietary patterns either at baseline or after 3 months; adults (n 1635) aged 18–81 years, users of a web-based weight-reduction programme, Austria, 2006–2012

Figure 4

Table 4 Association between successful weight loss (>5 % of initial weight after 3 months) and the dietary pattern (0–1 dummy variables) at baseline and after 3 months; adults (n 1635) aged 18–81 years, users of a web-based weight-reduction programme, Austria, 2006–2012

Supplementary material: File

Luger supplementary material

Table S1

Download Luger supplementary material(File)
File 56.3 KB