C 2007) Journal of Behavioral Medicine, Vol. 30, No. 2, April 2007 ( DOI: 10.1007/s10865-006-9092-1
Personal Digital Assistants are Comparable to Traditional Diaries for Dietary Self-Monitoring During a Weight Loss Program Bethany A. Yon,1 Rachel K. Johnson,1,3 Jean Harvey-Berino,1 Beth Casey Gold,1 and Alan B. Howard2 Accepted for publication: December 6, 2006 Published online: January 10, 2007
Dietary self-monitoring is considered the core of behavioral weight control programs. As software for personal digital assistants (PDA) has become more available, this study investigated whether the use of a PDA would improve dietary self-monitoring frequency and subsequent weight loss over the use of traditional paper diaries. One-hundred-seventy-six adults (BMI 25–39.9) participated in a 6-month behavioral weight control program. Treatment subjects (n = 61) were provided with a PalmZire 21 with Calorie King’s Diet Diary software installed. Their self-monitoring habits and weight loss were compared with the results from a previous program (n = 115) which followed the same protocol using paper diaries for selfmonitoring. No significant differences in weight loss or dietary self-monitoring were found. More frequent self-monitoring correlated with weight loss in both groups (p < .001). People seeking to lose weight should be encouraged to self-monitor and be matched with a mode of self-monitoring that is fitting to their lifestyle and skills. KEY WORDS: technology; handheld computer; obesity treatment; electronic dietary records; compliance.
INTRODUCTION
The frequency of dietary self-monitoring is an important predictor of success with weight loss (Stevens et al., 1989; Streit et al., 1991). Studies conclude that subjects lose more weight during the weeks they self-monitor with the highest level of consistency and completeness (Baker and Kirschenbaum, 1993, 1998; Boutelle and Kirschenbaum, 1998; Boutelle et al., 1999). Thus, self-monitoring is seen as an important outcome for weight loss as opposed to simply a process, therefore it is important that ways of increasing compliance be identified. It has been suggested that the self-monitoring process be made as simple as possible in order to improve consistency and compliance (Baker and Kirschenbaum, 1993; Boutelle et al., 1999; O’Neil, 2001; Borg et al., 2004). Computer technology and the Internet may help to simplify the monitoring process. Several studies have laid the groundwork for using technology to increase compliance or strengthen the correlation between self-monitoring and weight
Dietary self-monitoring is frequently referred to as the “cornerstone” of all behavioral weight control programs (Foreyt and Goodrick, 1993) and is seen as an important step in behavior change for weight loss as the recording of food/beverages helps to manage eating habits. The specific addition of systematic caloric self-monitoring and charting of eating behaviors to a behavioral weight control program increases weight loss and reduces attrition rates (Sperduto et al., 1986). Even in the absence of a group therapist, dietary self-monitoring is effective in producing weight loss (Romanczyk, 1974). 1 Department
of Nutrition and Food Sciences, University of Vermont, 108 Morrill Hall, Burlington, VT 05405. 2 Academic Computing Services, University of Vermont, Burlington. 3 To whom correspondence should be addressed e-mail: Rachel.
[email protected].
165 C 2007 Springer Science+Business Media, LLC 0160-7715/07/0400-0165/0
166 loss. A study comparing computerized and paper/ pencil self-monitoring was too small and short in duration to draw conclusions, but did observe that those subjects using computerized methods submitted more food records/day and recorded more days (Heetderks-Cox et al., 2001). The use of the Internet for weight maintenance support also yielded significantly higher self-monitoring rates compared to participants meeting in-person, suggesting that further investigation of electronic self-monitoring could be of value (Harvey-Berino et al., 2004). Subjects using a specially designed laptop computer for dietary self-monitoring lost more weight than paper/pencil users; however self-monitoring compliance was not examined (Burnett et al., 1985). The frequency of on-line dietary self-monitoring was correlated with improved weight loss during a weight loss program delivered using Internet technology (Tate et al., 2001). Handheld computers or personal digital assistants (PDAs) have been used extensively for data collection in clinical trials for over 15 years and have both strengths and weaknesses (Koop and Mosges, 2002). Medical fields using handhelds include nutrition (Greeno et al., 2000), diabetes (Tsang et al., 2001), pain (Honkoop et al., 1999), and asthma (Hyland et al., 1993). These studies and others generally found an improvement in the quality of data collected due to fewer errors, and improved consistency and completeness. The portability and programmability of handhelds has the potential to both enable and simplify data collection in a free-living environment. Electronic diaries have provided innovative methods for collecting dietary reports in smoking and food intake studies (Battig et al., 1994)). The programmability of the handheld allowed for varied sampling of appetite in studies of binge antecedents in obese women, instead of restricting measurements to just prior to eating (Greeno et al., 2000). A traditional behavioral weight loss therapy program using early handheld technology was compared to a computer therapy program combined with group support and found no differences in weight loss, and no improvements in dietary self-monitoring (Agras et al., 1990). A computer-assisted therapy program using a pocket computer programmed with a weight loss and exercise program did not yield better weight loss results and while self-monitoring began well, after 12 weeks, it dropped to 50% (Taylor et al., 1991). A recent pilot study concluded that handheld computers improve dietary self-monitoring based on self-reports and could be a
Yon, Johnson, Harvey-Berino, Gold, and Howard powerful tool to promote dietary adherence (Glanz et al., 2006). Limited data exist comparing the effectiveness of traditional paper/pencil diaries with PDAs for dietary self-monitoring and weight loss. Unpublished data described by O’Neil (2001), suggest that the use of a PDA self-monitoring system results in greater weight loss. A number of related studies have compared compliance and use of electronic diaries (PDA’s) and paper diaries with varied results. Patients monitoring pain symptoms over a 24-day period were significantly more compliant (94%) when using an electronic diary (Stone et al., 2003). A smaller but longer trial monitoring pain using palmtop computers also found better compliance (89%) among patients using an electronic diary when compared to the paper diaries (55% compliant) (Jamison et al., 2001). Patients using the electronic diary received a message each time they uploaded their data and reported that they felt as though they were being closely monitored and that someone cared for them. Diabetes patients who used an electronic diary to record dietary intake showed significant improvement in glycemic control and found the handheld both useful and easy to use (Tsang et al., 2001). A PDA dietary assessment program was comparable with a 24-h recall in measuring energy and macronutrient intakes (Beasley et al., 2005). A fourday food record kept using a pocket computer agreed well with a food frequency questionnaire; given the positive acceptance by the research subjects and overall reliability, it was concluded that electronic diaries could be suitable for dietary intake studies (Kos and Battig, 1996). While PDAs have been shown to be a reliable tool for dietary self-monitoring and have improved compliance and health indicators in related studies, limited research has been conducted on the use of a PDA for self-monitoring behavior and potential impact on weight loss as a part of a behavioral weight control program. This is particularly relevant as more PDA software has become available. Therefore the purpose of this study was to investigate whether the use of a PDA for dietary self-monitoring would improve self-monitoring frequency and subsequent weight loss at the end of a 24-week behavioral weight loss program. We hypothesized that subjects self-monitoring their eating and exercise habits using a PDA would submit more weekly food records and lose more weight compared to controls using paper/pencil diaries.
Personal Digital Assistants are Comparable to Traditional Diaries RESEARCH METHODS AND PROCEDURES Subjects Sixty-one (56 women and 5 men) overweight and obese adults were recruited through newspaper advertisements placed throughout southwestern Vermont. Interested participants were asked to enroll via a secure Website developed for this study that screened out volunteers who did not meet the basic study criteria which included: over age 18, BMI >25 and ≤ 39 kg/m2 , and regular access to a computer (not less than three years old with CD Rom drive, Internet connection, at least 64 Megabytes of RAM, 350 MHz processor speed, and Windows 98 or higher as a computer operating system). After this initial screening, 98 participants were further screened by telephone. They were deemed ineligible if they planned a pregnancy or to move from the area within the next 12 months, had a history of major medical or psychiatric problems, took medications that had implications for weight loss, were unable to participate in a mild to moderate exercise program, or unable to regularly attend weekly meetings. Control subjects (n = 115, 96 women and 19 men) were recruited through newspaper advertisements placed in northwestern Vermont, asked to enroll via a secure Website, and subsequently telephone screened in the same manner as the PDA subjects. All participants agreed not to participate in other weight loss treatment programs during the study.
Design A total of 87 eligible participants were invited to attend an orientation that described the study protocol in detail and 71 signed an informed consent. Only one participant owned an eligible PDA (Palm Operating System ≥ 3.3 and 2 Megabytes free memory); the remaining subjects were provided with a Palm Zire21 (Palm Inc., Sunnyvale, CA). The food database and self-monitoring software, Calorie King’s Handheld Diet Diary software v3.2.2 (Family Health Network, Costa Mesa, CA), were installed as a two-week free trial version on each PDA. Software registration codes were e-mailed to each continuing participant during a two-week orientation period. Participants attended a second orientation at which time baseline data were collected, and techni-
167
cal support and assistance for the PDA and Calorie King software were provided. The 24-week behavioral weight loss treatment group began the following week with 61 participants, meeting in-person for one hour, in three separate groups with group sizes of 20–25. Existing data from a previous 24-week behavioral weight loss program, where subjects used paper diaries for dietary self-monitoring, were used for the control data (n = 115) (Fig. 1). This study was approved by the Committee on Human Research at the University of Vermont. Behavioral Weight Loss Program The 24-week weight loss program focused on the modification of eating and exercise habits through the use of behavioral strategies and self-management skills. Participants were given printed lessons for review each week. The facilitator, a master’s level nutrition graduate student trained in behavior modification techniques, led the intervention groups. The control group, two cohorts of a larger intervention (Harvey-Berino et al., 2004), also met weekly for 24weeks, in-person, following the same sequence of lessons with either a master’s level nutrition graduate student or registered dietitian. Participants were instructed to reduce their energy intake by up to 1000 kcal/day, as determined by their baseline body weight. Individual calorie goals were determined by multiplying baseline weight in pounds by 12 in order to estimate current energy requirements, and then subtracting 1000 kcal establishing a calorie goal (1200–2200 kcal/day) to produce a weight loss of approximately one to two pounds each week. This particular method for determining a calorie goal has been used successfully in previous behavioral weight control studies (Harvey-Berino et al., 2002, 2004). The dietary intervention did not provide specific menu plans, but rather emphasized a decrease in total calorie consumption. Food intake patterns consistent with the 2000 Dietary Guidelines for Americans, Food Guide Pyramid (USDA, 2000) and D.A.S.H. Diet (Appel et al., 1997) were recommended. Graded goals for programmed physical activity (i.e., walking) were used throughout the program, and subjects were encouraged to expend at least 1000 kcal/week (walking approximately 10 miles/week). Participants were also encouraged to increase engagement in lifestyle exercise, such as taking the stairs and walking instead of driving.
168
Yon, Johnson, Harvey-Berino, Gold, and Howard
Overweight/Obese Adults BMI 25-39.9, n=176
PDA Group n=61
Control n=115
24-wk Behavioral Weight Control Program (in-person)
24-wk Behavioral Weight Control Program (in-person)
April-September, 2004
Sept 2000-February 2001 May- November, 2001
-Self-monitor with PDA -Websync records weekly -Weekly e-mail feedback
-Self-monitor Paper/Pencil -Hand in records weekly -Weekly written feedback
57 Completers (93%)
93 Completers (81%) Fig. 1. Study protocol.
Mode of Self-Monitoring Control group participants were provided with small (3.5 × 6 ) paper weekly diaries and a book with calorie listings of foods (The Calorie Counter, 2nd Ed, Pocket Books, 2000) to record their food and calorie intake, as well as daily programmed exercise and calories expended. Subjects were encouraged to seek out additional resources for calorie databases such as restaurant websites and cookbooks with calorie listings. The food and exercise paper diaries were handed in to the group facilitator at weekly meetings, who reviewed the journals and provided written positive feedback, support and nutritional advice. PDA participants were instructed to selfmonitor their food intake and exercise using the Calorie King Handheld Diet Diary software installed on their PalmZire 21. During the first few group meetings, time was spent trouble-shooting use of both the PDA and Diet Diary software. Instruction was provided on how to customize and add foods to the database, as well as the software’s “memorize” function for common meals enabling easy recall into a new food record.
Calorie King established both a master account for the group facilitator, and individual online accounts for each of the study participants. The individual accounts (username and password protected with sole access by the facilitator) were anonymous, bearing the name assigned to the PDA by the research team. Participants electronically submitted their food and exercise diary weekly at the group meeting using the PDA’s hotsync function, at which time the PDA programs and databases were copied in the form of a backup to the study computer. The Calorie King software was also programmed at this computer to “websync,” copying all food and exercise records from the PDA to each subject’s online account at the Calorie King website. The input of the account user name and password was a pre-programmed conduit at the Palm desktop so that participants were unable to view that information. The group facilitator then logged onto the master Calorie King account to view each participant’s weekly food and exercise records. As with the control group, positive feedback on the self-monitoring behavior and support was provided via weekly e-mail messages from the group facilitator to each subject.
Personal Digital Assistants are Comparable to Traditional Diaries Assessments The following measures were obtained at baseline and six months. Body weight was measured on a Taylor Professional scale (Taylor Precision Products LP, Oak Brook, IL) placed on a flat, hard surface with participants in street clothes. Height was selfreported for the control group and measured against a wall for the PDA group. Body mass index was calculated using the formula, weight in kg/(height in m)2 . Energy intake was measured using the Block 98.2 Food Frequency Questionnaire (Block, 1982) and analyzed using the Block Dietary Data Systems (Berkeley, CA). Energy expended in physical activity was measured using the Paffenbarger Physical Activity Questionnaire (Paffenbarger et al., 1978). A number of process measures were used in an effort to identify the mediators of change in weight. Computer abilities and comfort were assessed at baseline and 6 months, where subjects were asked to choose one of five statements to describe their computer ability (1 = novice with little/no experience; 5 = computer professional). They were also asked to rate their comfort with computers and computer technology on a scale of one to ten (1 = profoundly uncomfortable; 10 = extremely comfortable). PDA subjects were also asked to rate their comfort with PDAs and their applications on a scale of one to ten (1 = profoundly uncomfortable; 10 = extremely comfortable). Attendance at group meetings, and compliance with calorie and exercise goals were recorded weekly. Adherence to self-monitoring was assessed by tracking the number of weeks food diaries were submitted (paper diaries handed in for the control group and weekly journals submitted via the websync for the PDA group). For analysis purposes, the frequency with which participants submitted weekly food records was measured as a percentage, as there were slight variations in the number of eligible attendance/submission weeks both within the control group (two cohorts which met during different time periods) and between the PDA and control group. The PDA group was asked about their use and attitudes towards the PDA and software at six months.
Statistical Analysis Data were analyzed using the Statistical Package for the Social Sciences (SPSS for Windows, Version
169
10.0.5 SPSS Inc, Chicago, IL), with statistical significance set at p < .05. Descriptive statistics (means and standard deviations for continuous variables, cross tabulation tables for ordinal and nominal variables) were used to describe the characteristics of each group. Differences between groups at baseline were examined using independent samples t-tests for continuous variables and Chi-Square analyses and Fisher’s exact tests for categorical variables. Paired samples t-tests were used to examine differences in PDA comfort levels from baseline to 6-months. Pearson product moment correlations were used to explore relationships between program components, weight loss and dietary self-monitoring. Repeated measures analysis of variance was used to test group differences and temporal changes in data from the food frequency and exercise questionnaires. Ordinal data were compared within the PDA group using the non-parametric Wilcoxon Signed Rank test. An intent-to-treat analysis, carrying forward baseline weights for non-completers, was used when comparing weight change between groups. Analysis of covariance was used to detect differences in weight loss between groups, adjusting for significant baseline differences in BMI, as well as to examine the relationship between self-monitoring mode, frequency and weight loss. A number of other possible covariates were examined (marital status, education, computer comfort and fat intake) in these weight loss analyses, but none were statistically significant. Thus, these covariates are not reported in the final analyses. A power analysis was conducted prior to recruiting for the PDA group. In order to detect a weight loss difference between groups at 6 months of 2 kg, and 11% improvement in dietary self-monitoring behavior between the PDA and control group, at alpha of .05 and a power of 80%, it was determined that a sample size of 75 participants per group was needed.
RESULTS Baseline characteristics of the study participants are shown in Table I by group. Subjects were exclusively white and predominately female, married, well educated, and working full or part-time for pay. The PDA group had a significantly higher BMI (t(174) = − 2.54, p = .01). Significant differences were also found in education level between the two groups (χ2 (4, n = 176) = 10.35, p = .03) with the PDA group having more people with a high
170
Yon, Johnson, Harvey-Berino, Gold, and Howard Table I. Subject Characteristics at Baseline
Mean age (SD) Mean weight, kg (SD) Mean BMI, kg/m2 (SD) Mean calorie intake/day (SD)c Mean fat intake, g/day (SD)c Mean fat intake, %/day (SD)c Mean exercise, kcal/day (SD)d Mean computer comfort (SD) Gender (%) Female Male Education (%)∗ High school/Voc Some college College degree Grad/Prof degree Marital status (%)∗∗∗ Married/civil union Separated/Divorce/Widow Never married Computer ability (%) Novice/Basic Software Software + Internet Hobbyist Professional
PDAa (n = 61)
Controlb (n = 115)
48.2 (8.7) 90.2 (14.0) 32.3 (3.4)∗ 2069 (859) 94.5 (44.5) 41.1 (7.7)∗∗∗ 1234 (1199) 6.7 (2.3)∗
46.1 (9.2) 86.4 (13.7) 30.9 (3.5)∗ 2026 (754) 83.7 (40.8) 36.3 (6.8)∗∗∗ 1578 (1358) 7.5 (1.7)∗
56 (92) 5 (8)
96 (84) 19 (16)
10 (16) 11 (18) 18 (30) 22 (36)
7 (6) 30 (26) 34 (30) 44 (38)
43 (70) 13 (22) 5 (8)
86 (75) 29 (25) 0
10 (17) 41 (67) 5 (8) 5 (8)
8 (7) 77 (67) 25 (22) 5 (4)
a Data
from all subjects who began the PDA/Intervention group. from all subjects who began the Control group. c From analysis of Block Food Frequency Questionnaire. d From Paffenbarger Physical Activity Questionnaire. ∗ p < .05. ∗∗ p < .01. ∗∗∗ p < .001. b Data
school and vocational school education while the control group had more people who had completed some college. The control group was also more comfortable with computers and computer technology (t(174) = 2.51, p = .02).
Table II.
Attrition was very low for the PDA group (7%) with 57 participants completing all questionnaires (93%), however final weight data at 6-months were obtained for only 56 participants (Fig. 1). The control group experienced a higher rate of attrition (19%) with 93 participants completing all data points at 6-months (81%). At baseline, the 150 study completers had a higher BMI (M = 31.7 (3.4) kg/m2 ) than the 26 non-completers (M = 29.6 (3.3) kg/m2 ) (t(174) = 2.79, p = .006). The completers were also engaged in less exercise (M = 1328 (1081) kcal/day) with lower Paffenbarger scores at baseline than the non-completers (M = 2212 (2095) kcal/day) (t(174) = − 2.10, p = .04). Otherwise, no significant differences were found between the intervention and control groups, subsequently, with the exception of weight loss outcomes, results are presented for only those 150 subjects who completed the six-month measures. Body Weight Change An analysis of covariance (ANCOVA) using baseline BMI as a covariate, found no significant differences in weight loss between groups for those subjects who completed all 6-month measures, (PDA n = 56, Control n = 93) (F(1, 145) = .17, p = .68) (Table II). An intent-to-treat analysis also found no significant differences in weight loss between groups for all subjects (PDA n = 61, Control n = 115) (F(1, 172) = .04, p = .84) (Table II). Adherence to Treatment Goals Results are summarized in Fig. 2 for differences between the two groups for adherence to the following treatment goals: dietary self-monitoring,
Weight Change, Adjusted for Baseline BMI, at Six Months for Completers and All Subjects
Completersa
PDA (n = 56)
Control (n = 93)
Mean weight Loss, kg (SD) Mean % weight loss (SD)
6.3 (6.1) 7.0% (6.5)
7.2 (5.2) 8.3% (5.8)
All subjectsb Mean Weight Loss, kg (SD)
PDA (n = 61) 5.8 (6.1)
Control (n = 115) 5.8 (5.5) (F(1, 172) = .04, p = .84)
(F(1, 145) = .17, p = .68) (F(1, 146) = .99, p = .32)
Note. No Significant Differences, controlling for baseline BMI. a Completers includes weight change data only from those subjects for whom weight data were collected at six months. b Intent to treat analysis where no weight change from baseline is assumed for those subjects from whom a weight could not be assessed at six months.
Personal Digital Assistants are Comparable to Traditional Diaries
171
80% 70% 60% 50% PDA, n=57
40%
Control, n=93
30% 20% 10% 0% Self-monitoring
Attendance
Calorie Goals
Exercise Goals
Fig. 2. Adherence to program treatment by condition for completers. ∗ p = .05. Selfmonitoring: % of weekly food records submitted. Attendance: % of weeks subjects attended group meetings. Calorie and Exercise goals: % of weeks subjects complied with prescribed goals.
attendance at group meetings, and compliance with calorie and exercise goals. Student’s t-tests showed no significant differences in frequency of dietary selfmonitoring, attendance, or compliance with calorie goals between groups. The Control group met more of their exercise goals when compared to the PDA group (t(148) = 1.98, p = .05). ANCOVA showed that dietary self-monitoring (a covariate) was strongly associated with weight loss outcomes for completers in both groups, also including baseline BMI as a covariate. Thirty-two percent of the weight loss was explained by the frequency of dietary self-monitoring (F(1, 144) = 72.45, p < .001), however the relationship was not different between the two groups (Fig. 3). Additional ANCOVAs found that there was also a significant overall relationship between attendance and weight loss (F(1, 144) = 87.52, p < .001), between compliance with calorie goals and weight loss (F(1, 144) = 66.62, p < .001), and between exercise goals and weight loss (F(1, 144) = 72.75, p < .001). Repeated measures ANOVA (measurements taken at baseline and at 6 months) found that there was a significant overall decrease in caloric intake, as measured by the Food Frequency Questionnaire, (Time main effect: F(1, 148) = 99.58, p < .001) but this decrease was not significantly different between groups (Time × Group interaction: F(1, 148) = .14, p = .71). There was also a significant overall decrease in fat intake and percent calories consumed from fat, but again the decrease was not significantly different between groups. Repeated measures ANOVA also found that there was a significant overall increase in exercise,
as measured by the Paffenbarger Physical Activity Questionnaire (Time main effect: F(1, 148) = 30.60, p < .001), but the increase in exercise was not significantly different between groups (Time × Group interaction: F(1, 148) = .09, p = .77). Computer and PDA Abilities The PDA group categorized themselves as having higher computer abilities (n = 57, Wilcoxon, p = .03) at six-months in comparison with baseline, so that there were no longer any significant differences in the two groups’ abilities at the end of the treatment program (χ2 (4, N = 150) = 6.21, p = .18). They also reported higher comfort levels with the PalmZire21 and its applications at 6 months (t(56) = − 6.15, p < .001). There was a significant relationship between the participant’s comfort level with the PDA at 6 months and the frequency of selfmonitoring (r = .32, p = .01). PDA Use and Attitudes When subjects used the PDA, most reported making entries either regularly throughout the day or at the end of the day (61%). Subjects indicated that they entered most or all of the foods they consumed into the PDA (80%) and most or all of the programmed exercise they completed (71%). The following aspects of the PDA and Calorie King program were liked by the participants: the ability to look up calorie and nutrition information (65%), its portability (47%), convenience (34%), and the ease of
172
Yon, Johnson, Harvey-Berino, Gold, and Howard 50
40
Weight Loss (Lb)
30
20
10
0
-10
-20 0%
20%
40%
60%
80%
100%
Frequency PDA group, n=56
Control group, n=93
Fig. 3. Frequency of Self-Monitoring and Weight loss outcomes, adjusted for baseline BMI. No significant difference in trend lines between groups, F(1, 144) = .07, p = .80.
entering food and exercise data (26%). However 44% reported that they disliked the PDA and Calorie King software because they were unable to find commonly eaten foods, as well as it was hard to see the PDA screen (39%). When subjects were asked if they used a different tool for self-monitoring (such as a paper/pencil diary) if they did not use the PDA regularly, only 10% of the completers responded yes, with 50% of subjects (n = 28) reporting that not only did they not use the PDA regularly, they didn’t use any other tools for self-monitoring. Those subjects who used self-monitoring tools other than the PDA lost significantly more weight (M = 5.3 (4.9) kg) than those who reported not using any form of self-monitoring (M = 3.5 (5.0) kg) (F(1, 55), p < .001). The remaining subjects who reported using the PDA regularly lost the most amount of weight (M = 10.2 (5.7) kg), three times more than those who reported not selfmonitoring regularly (M = 3.5 (5.0) kg).
DISCUSSION This study confirmed the already well-documented strong relationship between dietary self-monit-
oring and weight loss; however the use of a PDA did not improve that relationship. Subjects who selfreported regularly using dietary self-monitoring tools lost significantly more weight. While we hypothesized that the use of technology would simplify the process of self-monitoring and thus increase the frequency of dietary self-monitoring, the technology itself presented a barrier to regular use that some subjects were unable to overcome. The PalmZire21 is considered to be an “entry-level” PDA device and subsequently lacked more user-friendly options. The data screen is black and white and is not back-lit, making it hard to see and read. Our PDA subjects were essentially non-PDA users and only moderately comfortable with computer technology and not comfortable with PDA technology at the outset. We experienced several incidences of crashes and data losses early on as subjects became familiar with the use and care of the PDA. While comfort levels with the PDA improved from baseline to six months, at six months it was still less than seven on a scale of one to ten. Subjects either really liked using the PDA or remained uncomfortable with the technology and stopped using it altogether. Anecdotally, subjects who enjoyed using the PDA reported they liked being able to use the PDA throughout the day
Personal Digital Assistants are Comparable to Traditional Diaries to record meals. They perceived that their friends, family and colleagues wouldn’t know that they were keeping a food diary since so many other people regularly used a PDA in public settings. While regular support was provided by both the group facilitator and group members on the use of the Calorie King software program, subjects continued to have difficulty using the search function, navigating the database, and ultimately finding the foods they had consumed. The inability to find commonly eaten foods was the largest complaint among the PDA users, possibly impacting the frequency of selfmonitoring. In spite of the technology challenges, the PDA group had outcomes similar to those seen in the literature for both self-monitoring and weight loss. A 52% rate of self-monitoring is consistent with rates seen in other clinical programs (Tate et al., 2001). Perhaps the length of the intervention had an impact on self-monitoring rates. Many dietary selfmonitoring studies track frequency for 8–12 weeks (Baker and Kirschenbaum, 1993). Online food diary submissions have been seen to decline after a threemonth period (Tate et al., 2001). Self-monitoring 75% of the time, or five out of seven days, has been recommended for weight loss success (Boutelle and Kirschenbaum, 1998), and while the PDA group did not achieve that level of dietary self-monitoring, as a group they were successful with weight loss. On average the group lost seven percent of their initial body weight and no significant differences in weight loss or percent weight loss were found between groups, adjusting for baseline differences in BMI. There are several limitations to this study, beginning with our inability to recruit the minimum of 75 subjects for the PDA group. A retrospective analysis of power indicated that for the dependent variable weight loss, the observed power was .147. With such a small weight loss difference between groups and large standard deviation, we would have needed to recruit over 650 subjects for each group in order to detect a significant difference with a power of 80%. The retrospective analysis of selfmonitoring frequency yielded unwieldy recruitment requirements of over 64,000 people in each group to detect any significant differences, and a retrospective power of .05, again due to the very small differences between groups and a relatively large standard deviation. The quasi-experimental design presented a limitation, in that recruiting only for the intervention group led to baseline group differences that needed to be controlled for in our analyses.
173
The PDA group experienced a lower rate of attrition (7%) than the control group (19%). While the three group facilitators had similar backgrounds and training with the behavioral weight control program, scores on a Working Alliance Inventory did find the PDA group felt a stronger bonding and relationship with their facilitator in comparison to the control. This relationship may have contributed to the lower attrition rate, but clearly had no impact on weight loss outcomes or self-monitoring rates. The control group’s attrition rate was still less than attrition rates of 20–30% reported in other clinical studies (Honas et al., 2003; Wadden and Osei, 2002). No significant differences were found in weight loss regardless of the analysis method, intent to treat or observing only the completers. Additionally, very few baseline differences were found between the subjects who completed the study and those who did not. The lack of racial/ethnic diversity in our groups, with primarily well-educated, Caucasian women as subjects, may limit the ability to make generalizations based on the reported results. The study protocol presented some limitations, specifically around the measurement of dietary selfmonitoring. Baker and Kirschenbaum (1993) state that dietary self-monitoring varies tremendously both between people, as well as at the individual level. That is, one person may self-monitor regularly for a period of time, stop, and then resume the practice. Our protocol measured food record submissions on a weekly basis, however it would have been interesting to explore more detailed patterns of submission by measuring the actual number of days that food records were turned in or web-synced. While no differences were found in self-monitoring frequency between groups, the strong correlation to weight loss was still supported for both groups and most certainly within the PDA group where the self-reported “regular users” lost three times the weight of the non-self-monitors. While we did not track the use of other features of the Diet Diary software, one might speculate that those people using the PDA regularly may also have used additional features that would have supported their monitoring, such as the BMI calculator and graphing functions to monitor weight loss. The validity of energy reporting was not improved by the use of this technology and is reported elsewhere (Yon et al., 2006). Given the strong and consistent correlation between self-monitoring and weight loss, technology and electronic data collection are indeed useful tools for dietary self-monitoring. With further training, PDAs
174 may also be effective in increasing self-monitoring rates, especially among people who are familiar and comfortable with technology. Participants in a weight control program should be highly encouraged to selfmonitor and be matched to self-monitoring methods that are appropriate to their skills and lifestyle. ACKNOWLEDGMENTS This study was supported by NIH Grant R01DK056746 awarded to Dr. Harvey-Berino and the Vermont Agricultural Experiment Station. We thank CalorieKing for their support, Paul Buzzell, MS, for assistance with the Website and setting up the PDAs, and Amy Starinski for help with data coding. REFERENCES Agras, W. S., Taylor, C. B., Feldman, D. E., Losch, M., and Burnett, K. F. (1990). Developing computer-assisted therapy for the treatment of obesity. Behav. Ther. 21: 99–109. Appel, L. K., Moore, T. J., Obarzanek, E., Vollmer, W. M., Svetkey, L. P., Sacks, F. M., Bray, G. A., Vogt, T. M., Cutler, J. A., Windhauser, M. M., Lin, P. H., and Karanja, N. (1997). A clinical trial of the effects of dietary patterns on blood pressure. DASH Collaborative Research Group. N. Engl. J. Med. 336: 1117–1124. Baker, R. C., and Kirschenbaum, D. S. (1993). Self-monitoring may be necessary for successful weight control. Behav. Ther. 24: 377–394. Baker, R. C., and Kirschenbaum, D. S. (1998). Weight control during the holidays: highly consistent self-monitoring as a potentially useful coping mechanism. Health Psychol. 17: 367–370. Battig, K., Kos J., and Hasenfratz, M. (1994). Smoking and food intake in a field study: Continuous actometer/heart rate recording and pocket computer assisted dietary reports, subjective self-assessments, and mental performance. Drug Dev. Res. 31: 59–70. Beasley, J., Riley, W. T., and Jean-Mary, J. (2005) Accuracy of a PDA-based dietary assessment program. Nutrition 21: 672– 677. Block, G. (1982). A review of validations of dietary assessment methods. Am. J. Epidemiol. 115: 492–505. Borg, P., Fogelholm, M., and Kukkonen-Harjula, K. (2004). Food selection and eating behaviour during weight maintenance intervention and 2-y follow-up in obese men. Int. J. Obes. 28: 1548–1554. Boutelle, K. N., and Kirschenbaum, D. S. (1998). Further support for consistent self-monitoring as a vital component of successful weight control. Obes. Res. 6: 219–224. Boutelle, K. N., Kirschenbaum, D. S., Baker, R. C., and Mitchell, M. E. (1999). How can obese weight controllers minimize weight gain during the high risk holiday season? By selfmonitoring very consistently. Health Psychol. 18: 364–368. Burnett, K. F., Taylor, C. B., and Agras, W. S. (1985). Ambulatory computer-assisted therapy for obesity: A new frontier for behavior therapy. J. Consult Clin. Psychol. 53: 698–703. Foreyt, J. P., and Goodrick, G. K. (1993). Evidence for success of behavior modification in weight loss and control. Ann. Intern. Med. 119: 698–701. Glanz, K., Murphy, S., Moylan, J., Evensen, D., and Curb, J. D. (2006). Improving dietary self-monitoring and adherence with
Yon, Johnson, Harvey-Berino, Gold, and Howard hand-held computers: a pilot study. Am. J. Health Promot. 20: 165–169. Greeno, C. G., Wing, R. R., and Shiffman, S. (2000). Binge antecedents in obese women with and without binge eating disorder. J. Consult Clin. Psychol. 68: 95–102. Harvey-Berino, J., Pintauro, S., Buzzell, P., DiGiulio, M., Gold, B. C., Moldovan, C., and Ramirez, E. (2002). Does using the Internet facilitate the maintenance of weight loss? Int. J. Obes. 26: 1254–1260. Harvey-Berino, J., Pintauro, S., Buzzell, P., and Gold, E. C. (2004). Effect of Internet support on the long-term maintenance of weight loss. Obes. Res. 12: 320–329. Heetderks-Cox, M. J., Alford, B. B., Bednar, C. M., Heiss, C. J., Tauai, L. A., and Edgren, K. K. (2001). CD-ROM nutrient analysis database assists self-monitoring behavior of active duty Air Force personnel receiving nutrition counseling for weight loss. J. Am. Diet Assoc. 101: 1041–1046. Honas, J. J., Early, J. L., Frederickson, D. D., and O’Brien, M. S. (2003). Predictors of attrition in a large clinic-based weightloss program. Obes Res. 11: 888–894. Honkoop, P. C., Sorbi, M. J., Godaert, G. L. R., and Spierings, E. L. H. (1999). High-density assessment of the HIS classification criteria for migraine without aura: a prospective study. Cephalalgia 19: 201–206. Hyland, M. E., Kenyon, C. A. P., Allen, R., and Howarth, P. (1993). Diary keeping in asthma: comparison of written and electronic methods. BMJ 306: 487–489. Jamison, R. N., Raymond, S. A., Levine, J. G., Slawsby, E. A., Nedeljkovic, S. S., and Katz, N. P. (2001). Electronic diaries for monitoring chronic pain: 1-year validation study. Pain 91: 277–285. Koop, A., and Mosges, R. (2002). The use of handheld computers in clinical trials. Control Clin. Trials 23: 469–480. Kos, J., and Battig, K. (1996). Comparison of an electronic food diary with a nonquantitative food frequency questionnaire in male and female smokers and nonsmokers. J. Am. Diet Assoc. 96: 283–285. O’Neil, P. M. (2001). Assessing dietary intake in the management of obesity. Obes. Res. 9(Suppl 5): 361S–374S. Paffenbarger, R. S., Wing, A. L., and Hyde, R. T. (1978). Physical activity as an index of heart attack risk in college alumni. Am. J. Epidemiol. 108: 161–175. Romanczyk, R. G. (1974). Self-monitoring in the treatment of obesity: parameters of reactivity. Behav. Ther. 5: 531–540. Sperduto, W. A., Thompson, H. S., and O’Brien, R. M. (1986). The effect of target behavior monitoring on weight loss and completion rate in a behavior modification program for weight reduction. Addict. Behav. 11: 337–340. Stevens, V. J., Rossner, J., Greenlick, M., Stevens, N., Frankel, H. M., and Craddick, S. (1989). Freedom from Fat: a contemporary multi-component weight loss program for the general population of obese adults. J. Am. Diet Assoc. 89: 1254–1258. Stone, A. A., Shiffman, S., Schwartz, J. E., Broderick, J. E., and Hufford, M. R. (2003). Patient compliance with paper and electronic diaries. Control Clin. Trails 24: 182–199. Streit, K. J., Stevens, N. H., Stevens, V. J., and Rossner, J. (1991). Food records: a predictor and modifier of weight change in a long-term weight loss program. J. Am. Diet Assoc. 91: 213– 216. Tate, D. F., Wing, R. R., and Winett, R. A. (2001). Using Internet technology to deliver a behavioral weight loss program. JAMA 285: 1172–1177. Taylor, C. B., Agras, W. S., Losch, M., Plante, T.G., and Burnett, K. (1991). Improving the effectiveness of computer-assisted weight loss. Behav. Ther. 22: 229–236. Tsang, M. W., Mok, M., Kam, G., Jung, M., Tang, A., Chan, U., Chu, C. M., Li, I., and Chan, J. (2001). Improvement in diabetes control with a monitoring system based on a hand-held, touch-screen electronic diary. J. Telemed & Telecare 7: 47–50.
Personal Digital Assistants are Comparable to Traditional Diaries US Department of Agriculture. (2000). Nutrition and Your Health: Dietary Guidelines for Americans. Home and Garden Bulletin No 232. U.S. Government Printing Office, Washington, DC. Wadden, T. A., and Osei, S. (2002). The treatment of obesity: an overview. In: Wadden, T. A., Stunkard, A. J. (Eds.), Hand-
175
book of obesity treatment. The Guilford Press, New York pp. 229–248. Yon, B. A., Johnson, R. K., Harvey-Berino, J., and Gold, B. C. (2006). The use of a personal digital assistant for dietary selfmonitoring does not improve the validity of self-reports of energy intake. J. Am. Diet Assoc. 106: 1256–1259.
