Abstract

The Internet can access a large number of consumers in a more cost-effective manner than other information delivery channels. In this pilot study, we assessed whether an online weight reduction program including dietary advice plus exercise (ED) was more effective in reducing weight than an exercise-only program (EX) >12 weeks. Participants were randomized to either the ED or EX group and attended a center for anthropometric measurements and dietary assessment. Both groups wore a pedometer and set weekly goals to increase daily steps through an interactive Web site. The ED group set weekly dietary goals via the Web site and received tailored e-mail assistance. Seventy-three participants commenced and 53 (73%) completed the study [EX n = 26; ED n = 27; body mass index—mean (standard deviation): 29.7 (2.5) kg m−2, age 46.3 (10.8); 21% male]. Percent weight changes were EX, 2.1 (0.6)% and ED, 0.9 (0.6)% (P = 0.15). Both groups increased their daily steps with no difference between groups. Only the ED group significantly reduced their energy intake. Despite a greater fall in energy intake reported by the ED group and a similar increase in physical activity in both groups, setting individual dietary goals did not enhance weight loss.

Introduction

In 2004/05, for adult Australians, the prevalence of overweight was 33% and obesity 17% [1]. Obesity and its associated risk factors can be treated or prevented through lifestyle changes that include diet and physical activity modifications. While there is currently no ‘gold standard’ for the dissemination of lifestyle advice for maximal, long-term effectiveness for weight management, one of the most effective intervention methods involves individual counseling with face-to-face contact [2]. However, this method is time intensive and can be costly for patients, in both time and money. Recently, Internet-based interventions and education programs have been developed. Although the Internet is limited in its access to certain population groups, it has the ability to access a large number of consumers in a more cost-effective manner than other information delivery channels. Not only do consumers appear to prefer the Internet as a source of health information [3], they say it is important that they can access health information at a time that is convenient to them [4]. While the general effectiveness of Internet-based lifestyle counseling is still being considered, the use of the Internet has been effective in increasing consumer knowledge in the area of nutrition [5] and has resulted in improved behavioral outcomes related to health [5]. More specifically, the Internet has been found to be an effective tool for aiding in weight loss [6, 7], maintenance of weight loss [8] and increasing physical activity in the short term [9]. While it is clear that personally tailored information and feedback are more effective than non-tailored information [10], the question remains whether detailed dietary plans are required and are more effective in achieving weight loss in motivated individuals than simpler plans that focus on increasing physical activity. There is evidence that motivated people who set exercise goals may also make positive dietary changes [11].

The use of pedometers as an intervention to aid in increasing physical activity is becoming increasingly popular. Pedometers have shown to be effective in assisting participants to increase daily step counts over time as part of a physical activity intervention [12–19]. The use of a pedometer in combination with an online program may be a simple and cost-effective method to assist large groups to increase daily physical activity.

The aims of this study were to determine whether an Internet-based, online weight reduction program that includes dietary advice plus exercise (ED) was more effective in reducing weight and results in more positive lifestyle changes than an exercise-only program (EX) over a 12-week period. The positive lifestyle changes were defined as decreased overall energy intake, reduced percentage energy consumed from saturated fat, increased fruit and vegetable intake as well as increased exercise by means of walking (as calculated by a pedometer). We hypothesized that the ED group would lose more weight than the EX group. Previous studies have found that dietary change achieves more weight loss than exercise advice [20, 21] and that diet and exercise achieve more weight loss than diet alone [22].

Methods

Participants

Male and female participants were recruited through advertisements placed in local and Melbourne-based newspapers as well as flyers placed in local community centers, libraries and health centers. All potential participants were directed either to a Web site or to phone the primary researcher for further information. Interested persons then completed a screening questionnaire either online or over the phone. An appointment was made with all eligible participants for the first face-to-face visit (baseline). Participants were included if their body mass index (BMI) was between 24.5 and 37 kg m−2 and if they had regular Internet access. Participants were excluded if they were <18 years of age, were pregnant or lactating or were currently receiving medications for Type 1 or Type 2 diabetes. This study was approved by the Deakin University Human Research Ethics Committee (EC 60-2005) and all participants provided informed consent.

Study design

Following baseline measurements, participants were randomized to one of two groups, an EX or ED group. The duration of each program was 12 weeks. Participants attended a face-to-face visit with the study researcher at baseline and then again at completion of the program. Goal-setting theory, developed by Locke and Latham [23], was used as part of the design of the study to maximize success in achieving dietary and physical activity change. One review of behavioral interventions for the modification of dietary fat intake and fruit and vegetable intake found goal setting (protocol not specified) as one of the most effective strategies for dietary change [24]. Goal setting has also been found to be effective in promoting physical activity behavior [25–28].

Anthropometry measurements

At each visit, weight was measured in kilograms to two decimal places on an A&D Personal Precision Scale (UC-321, A&D Co. Ltd, Tokyo, Japan). Height was measured once without shoes, in centimeters to one decimal place on a Surgical and Medical Products™ stadiometer. Waist circumference was measured using a Birch, 300 cm × 20 mm, fiberglass tape measure and was measured anteriorly halfway between the ileac crest and the lowest lateral portion of the rib cage. One researcher was responsible for all anthropometric measurements.

Dietary assessment

A 24-hour recall

Subjects completed a 24-hour dietary recall for the day prior to their visit with the primary researcher at baseline and Week 12. Our 24-hour recall also provided the option for subjects to write down the amount and time of food eaten prior to the visit. This information was checked for accuracy by the researcher. Recall of the previous 24-hour intake is considered to be valid and reliable in adults; however, it does tend to underestimate overall intake [29, 30]. Although it provides limited information on usual intake, it has a low participant burden and can provide some indication of macronutrient composition and energy intake. Dietary information was entered into a dietary analysis program (Foodworks, Professional Edition, Version 3.02, Xyris Software) to calculate daily energy and nutrient intakes.

Physical activity assessment

Physical activity questionnaire

A physical activity questionnaire was completed online at their first login, prior to receiving any education material and Week 12. This questionnaire assessed the frequency and intensity of physical activity conducted in the previous week. The questionnaire was adapted from the Active Australia Survey from the Australian Institute of Health and Welfare and has shown reliability, face validity, criterion validity and acceptability [31]. This questionnaire was used to determine changes in physical activity that may not be detected by the pedometer (e.g. swimming).

Pedometer use

All participants were provided with a YAMAX digiwalker SW700/701 pedometer prior to logging on for the first time. At the initial login, participants were asked to record their steps for 4 days over the following week. The average number of daily steps recorded was used as their baseline measure of walking activity. A pedometer is a small device about the size of a matchbox that clips on to waistbands at the hip and calculates the number of steps taken. The model YAMAX SW700/701 pedometers have been deemed suitable for research purposes when compared with other popular commercial models available [32]. The participants were asked to keep to their usual routine during Week 1 and the 4-day average number of steps was calculated for baseline (Week 1) and Weeks 2–4 and Weeks 9–12.

Background and health questionnaire

A background and health questionnaire was completed at the baseline visit. The background questionnaire included questions on marital status, highest education level, household income, employment status and self-identified cultural group.

Web site

Each participant received a login identification code and a password which gave them access to either the EX or the ED program, according to randomization group. Any data entered were recorded on the server without the participant names.

Exercise advice

Participants were encouraged to wear their pedometer on all or most days of the week for every week of the study in order to meet their goals; however, step data were only collected for the first 4 and last 4 weeks of the study. This information was entered online.

EX: Web site and goal setting

When participants first logged on to the Web site, they were guided through a series of steps. Participants first completed the physical activity questionnaire, then a stage of change questionnaire [33] before they received information on exercise and began to use their pedometer. They were asked to record their daily steps and enter these data each week into the Web site. After each week, the program calculated the participants' daily average for that week and recommends a level to increase to for the next week. The step recommendations are based on the National Heart Foundation of Australia guidelines [34]. If the participants were taking <10 000 steps on average per day, then they were asked to aim to increase their steps by 1000 steps per day each week. If they began the study taking close to 8000 steps per day or more, then they were recommended to increase their steps by 10% a day each week until they reached a maximum level that they could maintain on a daily basis. From this recommendation, the participant set a goal to increase their steps for the following week. If the set goal was not within the set guidelines (no >1000 steps per day or 10%), then they were asked to reset their goal. Each subsequent week they entered their steps, their daily average for that week was calculated and a new goal was set. Each participant was recommended to increase their steps to a maximum amount that they could maintain on a daily basis.

Dietary advice

ED: Web site plus goal setting

The ED worked through the same procedure as the EX with an additional series of dietary questions assessing their dietary intake: fruits and vegetables, wholegrain breads and cereals, high-fat snack foods, legumes, rice, pasta, beverages and takeaway foods. Answers were recorded and participants set their weekly dietary goals on the areas that could be improved. They were asked to set a maximum of one goal for each week, with a minimum of four dietary goals across the 12-week program (maximum 12). For each dietary goal set, the following week they were asked to report on whether they met the goal and received positive reinforcement if appropriate, if not they were asked to identify barriers to change and identify strategies to address the issues. Participants received a number of fact sheets and work sheets which included information on dietary fiber, legumes, dietary fats, seafood, alcohol, takeaway foods (how to make healthier choices) and tips on how to modify intakes of these. The work sheets included information and questions on reading labels, understanding health claims, healthier cooking options and modifying recipes.

Reinforcement and dietary goal feedback

Information on the individual dietary goals set and successes and failures were automatically e-mailed to the primary researcher each week and participants received individual feedback on at least three occasions throughout the program.

Online forum

The Web site also offered an area for the participants to post and respond to messages for all participants and researchers to see. This might include suggestions for other participants, questions for other participants or any other comments they may have. Each group had their own separate message board to prevent contamination.

Participant feedback

At the Week 12 face-to-face visit, each participant was asked to complete a qualitative and quantitative feedback questionnaire evaluating the Web site as well as the 12-week program.

Statistical analysis

All statistical analysis was performed using SPSS for Windows (SPSS Inc., Chicago, IL, USA, release 11.5.2.1, Chicago). Between-subject effects (anthropometric change, step change and diet change compared between groups) were analyzed using univariate analyses of variance. Difference in dietary intake from baseline to Week 12 was analyzed using paired t-tests. Changes in daily steps over time were calculated using repeated measures analysis of variance. Pearson chi-square was used to determine differences between grouped variables (gender, education level, income, BMI category, whether weight was lost or gained, age groups, etc.). Pearson correlations were used to determine correlations between weight change and change in waist circumference for groups (gender, age and intervention group).

Results

Participants

One hundred and thirty-six people responded to the advertisements, and of these, 109 completed a screening questionnaire. Seventy-six interested and eligible persons (according to self-reported height and weight) were invited to the first face-to-face visit, and of these, three did not meet the entry criteria [BMI too high (n = 2) and too low (n = 1)]. Seventy three who met the entry criteria were randomized to either the EX or ED group. Eleven participants dropped out before logging on to the Web site and beginning of Week 1, including all seven who dropped out from the EX group. Nine participants dropped out after commencing the study, all of these were in the ED group; two dropped out after Week 1, two after Week 2 and five after Week 4. Reasons given for dropping out included a lack of time (n = 2), being too lazy (n = 2), having problems with their computer (n = 2) and unknown (n = 3).

Of the 53 participants (26 EX and 27 ED) who completed the study, 11 (5 EX and 6 ED) were male (21%). Eighty-one percent were Anglo-Australian, 42% were employed full time (34% part time), 57% were married (23% in a de facto relationship) and 51% held tertiary education qualifications (32% with high school level education). Twenty-one percent had a household income of up to $30 000, 42% had an income between $30 001 and $50 000 and 30% had an income >$50 001. The median full time income in Australia is $55 000.

There was no difference in baseline BMI, age or change in weight or waist circumference between males and females and so they were combined in all analyses. At baseline, there was no difference in age, BMI, weight or waist circumference between the groups (Table I). Males had a higher waist circumference than females at baseline (by 13.2 ± 2.3 cm, P < 0.001).

Table I.

Baseline characteristics of participantsa

 Range (n = 53) EX (n = 26) ED (n = 27) 
Age (years) 25–70 46.2 (9.2) 46.4 (12.5) 
BMI [weight (kg)/height (m2)] 24.5–36.8 29.9 (2.7) 29.0 (2.3) 
Weight (kg) 65.5–117.3 84.3 (11.3) 80.5 (8.6) 
Waist circumference (cm) 81.9–120.6 96.9 (10.0) 95.6 (8.4) 
 Range (n = 53) EX (n = 26) ED (n = 27) 
Age (years) 25–70 46.2 (9.2) 46.4 (12.5) 
BMI [weight (kg)/height (m2)] 24.5–36.8 29.9 (2.7) 29.0 (2.3) 
Weight (kg) 65.5–117.3 84.3 (11.3) 80.5 (8.6) 
Waist circumference (cm) 81.9–120.6 96.9 (10.0) 95.6 (8.4) 
a

Mean (SD).

Weight and waist circumference change

The EX group lost 1.9 ± 3.1 kg weight (P < 0.01) and ED group lost 0.7 ± 2.0 kg (P = 0.09) (Table II). Percentage weight loss was twice as greater among the EX group compared with the ED group; however, the difference was not statistically significant (Table II). Sixty-nine percent of the EX group and 48% of the ED group lost weight of ≥0.5 kg and 23 and 30% gained weight, respectively. Among the participants who lost weight, the EX group lost a mean of 3.1 ± 2.9 kg and the ED group lost a mean of 2.3 ± 1.9 kg.

Table II.

Mean weight change from baseline to completion by groupa

 EX (n = 26) ED (n = 27) EX versus ED
 
 Mean (SD) Range Mean (SD) Range P value 
Weight change (%) 2.1 (3.4) −2.9–14.2 0.9 (2.5) 3.0–8.6 0.143 
Weight change (kg) −1.9 (3.1)** −12.3–2.1 −0.7 (2.0)**** −7.5–2.6 0.094 
Waist change (cm) −4.5 (4.5)*** −12.5–4.7 −3.2 (2.9)*** −8.7–2.2 0.239 
BMI change −0.67 (1.1)** −4.4–0.83 −0.30 (0.75)* −2.7–0.93 0.152 
 EX (n = 26) ED (n = 27) EX versus ED
 
 Mean (SD) Range Mean (SD) Range P value 
Weight change (%) 2.1 (3.4) −2.9–14.2 0.9 (2.5) 3.0–8.6 0.143 
Weight change (kg) −1.9 (3.1)** −12.3–2.1 −0.7 (2.0)**** −7.5–2.6 0.094 
Waist change (cm) −4.5 (4.5)*** −12.5–4.7 −3.2 (2.9)*** −8.7–2.2 0.239 
BMI change −0.67 (1.1)** −4.4–0.83 −0.30 (0.75)* −2.7–0.93 0.152 
a

Mean (SD) difference between groups. *P ≤ 0.5, **P ≤ 0.01, ***P ≤ 0.001, ****P = 0.08, difference from baseline.

There was a significant fall in waist circumference and BMI in both groups (Table II). There were no differences in mean waist or BMI change between groups (Table II). Seventy-seven percent of the EX group and 70% of the ED group lost ≥1 cm off their waist circumference. Twenty-one percent of participants moved from having a waist circumference in the very high-risk category (waist circumference ≥102 cm for males and ≥88 cm for females) to a low-risk category (P < 0.01, Wilcoxon signed-ranks test). There was no difference in change in weight or waist circumference by income level, education level or employment status or between males and females (data not shown).

Changes in weight were correlated with change in waist circumference and did not differ between diet groups (Pearson correlation; EX r = 0.60, ED r = 0.66, P < 0.01) or by gender (male r = 0.68, P < 0.05; female r = 0.62, P < 0.01).

Physical activity change

There was a significant increase in the number of daily steps taken from Week 1 to Week 12 with no difference in step change between the groups (Table III). Steps increased from nearly 9000 steps per day in Week 1 to >12000 steps per day in Week 12 for both groups (Table III). Participants significantly increased their daily steps from Week 1 to Week 4 and then maintained this increase until Week 12 (Fig. 1). There were no significant changes or differences in other physical activities between the groups as assessed by the questionnaire (data not shown).

Table III.

Mean daily steps and step change from Week 1 to Week 12a

 Week 1 Week 12 Change (Week 1 to Week 12) P value change from baseline 
EX 9151.5 (3289.9); 3559, 16623 12299.6 (3514.7); 6214, 19246 3148.1 (4214.8); −7026, 10602 <0.001 
ED 8673.3 (3567.3); 2784, 15202 12198.8 (4121.8); 6650, 22572 3525.5 (3010.4); −2733, 10687 <0.01 
Overall 8925.7 (3382.6); 2784, 16623 12252.0 (3757.9); 6214, 22572 3326.3 (3649.0); −7206, 10687 <0.001 
 Week 1 Week 12 Change (Week 1 to Week 12) P value change from baseline 
EX 9151.5 (3289.9); 3559, 16623 12299.6 (3514.7); 6214, 19246 3148.1 (4214.8); −7026, 10602 <0.001 
ED 8673.3 (3567.3); 2784, 15202 12198.8 (4121.8); 6650, 22572 3525.5 (3010.4); −2733, 10687 <0.01 
Overall 8925.7 (3382.6); 2784, 16623 12252.0 (3757.9); 6214, 22572 3326.3 (3649.0); −7206, 10687 <0.001 
a

Mean (SD); range.

Fig. 1.

Difference between groups weekly mean (± SEM) daily steps for the ED (▪) and EX (•) groups during the intervention period. *Difference in mean daily steps between Week 1 and Week 4 (paired samples t-test) for both groups, P < 0.001. ∧Difference in mean daily steps between Week 1 and Week 12 (paired samples t-test) for ED P < 0.001, for EX P < 0.01.

Fig. 1.

Difference between groups weekly mean (± SEM) daily steps for the ED (▪) and EX (•) groups during the intervention period. *Difference in mean daily steps between Week 1 and Week 4 (paired samples t-test) for both groups, P < 0.001. ∧Difference in mean daily steps between Week 1 and Week 12 (paired samples t-test) for ED P < 0.001, for EX P < 0.01.

Physical activity goals

Step count increased by a mean of 956 ± 121 steps per day until Week 4 and was maintained at this level until Week 12. Seventy percent increased within 10% of the suggested 1000 steps per day (>900 steps per day) from Week 1 to Week 2, 64% from Week 2 to Week 3 and 37% from Week 3 to Week 4. The mean step increase, overall, from Week 1 to Week 4 of 2906 ± 411 steps per day was maintained until Week 12 (3233 ± 730 steps per day), P = 0.564 (unpaired t-test). Seventy-two percent of participants maintained the goal of 10 000 steps or more per day from Week 4.

Step and weight/waist/BMI change

There was an association between step change and waist circumference change from Week 1 to Week 12 for both groups (EX r = 0.486, ED r = 0.595, P < 0.05 for both).

After splitting the group into those who gained or remained weight stable (weight change of less than or equal to −0.49 kg) and those who lost weight (weight change ≥0.5 kg), there was no difference detected by age, gender, education level, income or employment status. However, the participants in the ED group who lost weight increased their mean daily steps by a significantly greater amount (5797 ± 2606 steps per day) than those who gained weight or experienced no weight change (1936 ± 2183 steps per day) (P < 0.01). When both groups were combined, those who lost weight increased their steps by 4595 ± 3591 (48%) steps per day compared with an increase of 1741 ± 3148 (21%) steps per day among those who did not lose weight (P < 0.05). Similarly for waist circumference, those who lost ≥1 cm off their waist by Week 12 increased their steps by 4189 ± 3273 steps per day compared with the others 306 ± 3442 steps per day (P < 0.01).

Diet change

Assessing the 24-hour records, no difference in baseline energy intake was found between the groups. The ED group reduced their total energy intake and percent of energy from fat across the 12 weeks (Table IV). There was no change in kilojoule intake across the 12 weeks by the EX group and no change in fat intake. For either group, there was no change in protein, dietary fiber, alcohol or carbohydrate intake.

Table IV.

Change in energy and percent fat intake from Week 1 to Week 12 (mean ± SE)

 EX groupa ED groupa P valueb 
Energy (kJ) 131.1 ± 759.7 −1812.6 ± 729.9* 0.066 
Fat (g) −7.2 ± 8.1 −24.3 ± 7.7* 0.132 
% Fat −2.7 ± 2.0 −4.0 ± 1.9** 0.642 
% Carbohydrates 0.5 ± 2.3 −3.3 ± 2.2 0.239 
% Protein −1.9 ± 1.5 −1.4 ± 1.5 0.832 
% Alcohol −1.3 ± 1.7 0.7 ± 1.7 0.414 
 EX groupa ED groupa P valueb 
Energy (kJ) 131.1 ± 759.7 −1812.6 ± 729.9* 0.066 
Fat (g) −7.2 ± 8.1 −24.3 ± 7.7* 0.132 
% Fat −2.7 ± 2.0 −4.0 ± 1.9** 0.642 
% Carbohydrates 0.5 ± 2.3 −3.3 ± 2.2 0.239 
% Protein −1.9 ± 1.5 −1.4 ± 1.5 0.832 
% Alcohol −1.3 ± 1.7 0.7 ± 1.7 0.414 
a

Difference from Week 1, *P < 0.05, **P < 0.01.

b

Difference between groups.

Dietary goals

Participants set a mean of 4.7 individual dietary goals over the 12 weeks (minimum = 3 and maximum = 9). There was no evidence of a relationship between number of goals set and amount of weight lost. From the dietary goals, 56% of participants chose to increase their vegetable intake; 48% chose to reduce their intake of cakes, pastries, chocolate and cream biscuits; 44% chose to increase their intake of legumes; 37% chose to switch to whole-grain-based cereals; 33% chose to decrease their alcohol intake and 30% chose to increase their fruit intake. Other goals included decreasing intake of fats and oils (26%), fatty meats (19%), cheese and ice cream (15%) and switching to whole-grain/whole-meal breads (26%). A minimum of four dietary goals were set as a target for each participant. Twenty-six percent of participants met and maintained two goals, an additional 22% met and maintained at least four dietary goals and 33% met and maintained up to one goal only. Fifty-one percent of the goals that were set and had been met on at least one occasion, but were not maintained across the following weeks. The most frequently set dietary goals were to increase intake of vegetables, limit intake of cakes, snacks, pastries, chocolate, etc. to increase legume intake and to increase fruit intake. The majority of participants found the feedback on goal setting (71%) to be useful.

Stage of change

At baseline, 6% were in the pre-contemplation stage for physical activity change, 21% were in contemplation, 32% were in preparation, 14% were in action and 22% were in the maintenance stage. For diet change at baseline, there were no participants in pre-contemplation, 16% were in the contemplation stage, 25% were in preparation, 12% were in action and 40% were classified as being in the maintenance stage. There was no difference between groups or between completers versus non-completers at baseline. There was no difference in weight change, waist circumference change, step change or dietary intake between the different stages.

Discussion

This study investigated the effectiveness of two online, individualized interventions that included goal setting and feedback: one included both physical activity and dietary modifications (ED) and the other included only modification to physical activity (EX) >12 weeks. The strength of this study is that the anthropometric measurements were actually recorded and we did not rely on self-report. It was surprising that there was a trend for a greater weight loss in the EX group of 2 kg versus <1 kg in the ED group, although not statistically significant a 1-kg difference is of interest. One would have expected that combination of diet and exercise would result in a greater weight loss than exercise only. The number of steps and incremental change in steps did not differ between the groups; therefore, the additional weight loss in the exercise group may have been due to undetectable increases in physical activities other than walking or reductions in energy intake that we were unable to detect using our measurement tools.

The 12 000 steps that were achieved in the present study were higher than that achieved in other free-living studies [12–14, 16, 18]. The number of daily steps taken clearly played a major role in the loss of weight among participants. For all participants in both groups, those who lost weight also increased their daily steps by a greater amount (4500 steps per day) than those who did not lose weight (1700 steps per day). Thus, it appears increased walking was the major factor contributing to weight loss among this group. Additionally, among those who experienced a reduction in waist circumference, increased their steps by nearly 4000 per day more than those who did not experience a reduction in waist circumference. However, the number of steps achieved in the first week was high (8500–9000 steps per day) suggesting that the group were already quite active. While it is likely that we recruited a motivated group due to the nature of the recruitment, this number of steps may not be a true representation of their usual number of daily steps due to the additional motivation the pedometer may have provided in this first week.

A unique quality of the present study was the combined pedometer use with an individualized Internet program. One previous study used the Internet as an option to record daily steps; however, the actual intervention was conducted by other means including weekly face-to-face meetings [19]. Another two studies combined pedometer use with e-counseling or weekly e-mail contact. Participants in these studies managed to increase their steps to an average of >10 000 per day [16, 17]. The present study is one of the first known studies assessing pedometer use via an online program and appears to be an effective way to promote activity.

Additionally, we assessed the effectiveness of online goal setting with e-mail assistance. While goal setting has been used in previous Web-based nutrition and/or physical activity interventions [35–37], the actual outcomes of the goal setting technique have not been previously evaluated. Our study found that less than one-quarter of participants maintained four dietary changes and one-third maintained one or less dietary change.

One of the appealing factors of online programs is their relative cost-effectiveness if they reach large populations and confer health benefits. While the initial construction of the Web site itself is relatively costly (at least $20 000 AUD), ongoing costs are minimal. Based on the minimum cost of consulting a dietitian in a public hospital, an initial 1-hour consultation ($70) plus fortnightly 30-min follow-up consultations ($35) would total $280 over a period of 12 weeks. A 0.5-hour initial consultation ($35), followed by plus 15 min week−1 ($30 per hour) input from a nutritionist using this Internet-based program would total only $125 for the 12-week program. There would also be additional cost savings for participants, e.g. elimination of travel costs and travel time, with Internet costs being minimal for those already connected.

The effect on weight loss this online program had was slightly lower (0.17 kg week−1 for the EX group and 0.08 kg week−1 for the ED group) than other individual-based weight loss interventions that lasted for a similar period of time (0.2–0.5 kg week−1) [38–41]. Longer term interventions (6–12 months) demonstrate a lesser weight change (0.02–0.2 kg week−1) [42–45]. It would be worthwhile to evaluate the cost-effectiveness of maintaining weight loss in the long term with Internet support.

Limitations

We only compared the effect of two interventions, and without a control group, we can therefore only make conclusions related to the difference between exercise alone and exercise plus diet education. It is possible that as the EX group was aware that the other group was receiving the diet information, they may have worked harder in achieving weight loss, or it could be that changing both diet and increasing physical activity might be too demanding. We could not detect a difference in physical activity (by pedometer or questionnaire) or dietary intake between the groups, although the EX group tended to lose more weight: this may due to the limitations of our measurement tools to detect small differences. Although we acknowledge that a greater number of measurements would have been preferable, we wished to minimize subject burden. Additionally, the variability of the data may limit the generalizability of these results; however, we feel that the results warrant further investigation.

In this study, weight was actually measured and self-report was not used. However, it is acknowledged that the fact participants knew they would be required to be weighed at the end of the study may have enhanced compliance. It should also be acknowledged that this group may be different from other Internet users as they had to agree to come in and be weighed and the majority were female.

Conclusions

This pilot study had a 27% drop-out rate which is similar to other online programs and is not much higher than other face-to-face weight loss programs (∼20%) [46]. An Internet-based program with goal setting resulted in a mean weight loss of 1–2% >12 weeks. The combined exercise and dietary modifications did not result in a greater weight loss when compared with exercise alone. It may be that those randomized to the exercise group made additional lifestyle changes that we were unable to detect.

This study was able to increase daily steps above the recommendations to a level similar to or higher than most other pedometer-based traditional interventions. We also found that goal setting for increasing exercise appears to be more effective and sustainable than goal setting for dietary change in the short term, when conducted as part of an online weight loss program. Longer term studies with staggered diet and exercise goals may improve dietary goal compliance. The small participant numbers and large variance in data are likely to have contributed to these results. A larger, controlled study needs to be conducted to confirm our findings. An online program has the potential to reach a large population at relatively little cost and did induce a clinically significant reduction in waist circumference which would reduce cardiovascular risk if maintained in the long term.

Funding

Strategic Teaching and Learning Grants Scheme, Deakin University.

Conflict of interest statement

None declared.

References

1.
Australian Bureau of Statistics
2004–05 National Health Survey: Summary of Results
 , 
2006
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Author notes

1
Present address: Nutrition and Metabolism Research Group, School of Medicine and Dentistry, Queens University Belfast, Lower Ground Floor, Pathology Building, Grosvenor Road, Royal Victoria Hospital Site, Belfast BT12 6BJ, Northern Ireland

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