Abstract
Objective:
Dietary self-monitoring is consistently related to both short- and long-term weight loss. The purpose of this study was to quantify the time spent and the daily frequency of self-monitoring necessary for weight-loss success.
Methods:
Participants in a 24-week, online, behavioral weight-control intervention recorded daily dietary intake using a Web-based dietary analysis program. Time spent self-monitoring and frequency of dietary journal page access were captured. Weight loss (kilograms) and the proportion of participants losing ≥ 5% and ≥ 10% of baseline weight were assessed at 6 months.
Results:
Participants (n = 142; BMI 35.8 kg/m2; 90.8% female; 23.2% African American) spent an average of 23.2 minutes per day self-monitoring in month 1 and 14.6 minutes in month 6. For those still recording any minutes self-monitoring by month 6 (65.5%), there were no significant differences in time spent based on weight loss; however, those losing either ≥ 5% or ≥ 10% logged in to the journal Web page significantly more times per day (1.6 vs. 2.4, P < 0.001 for < 5% vs. ≥ 5%; 1.7 vs. 2.7, P < 0.001 for < 10% vs. ≥ 10%).
Conclusions:
The frequency of self-monitoring is significantly related to weight loss, with the time needed to be successful diminishing during the intervention.
Introduction
Dietary self-monitoring is consistently related to successful short- and long-term weight loss (1–3). Daily dietary self-monitoring consists of recording all foods and beverages consumed as well as the portion size and preparation method. Weight-loss program participants are often instructed to record foods as soon as they are consumed, or to “write it when you bite it,” although we are aware of very little research supporting this recommendation. The advent of nutrient analysis websites and smartphone applications allows for the easy tabulation of total calories and fat consumed, thus lowering the burden of numerical calculation. This is important, as an ongoing tabulation of calories consumed provides an awareness of whether goals are being achieved. Unfortunately, many people engaged in weight-loss interventions fail to record their food intake proximal to consumption, and self-monitoring overall has been shown to consistently decrease over time (4). This is despite repeated encouragement to continue the behavior to attain weight-loss success.
Evidence has suggested that the accuracy and completeness of self-monitoring diaries are not as important as the frequency with which they are completed (5). Daily diary completion for a typical 24-week weight-loss intervention is perceived by participants as onerous and can represent a significant time commitment. Little is known, however, about exactly how much time individuals successful at weight loss actually spend self-monitoring. Moreover, it is not clear whether individuals become more efficient with practice, thus becoming able to decrease the time they spend self-monitoring in order to get the same result. Previous methods of dietary self-monitoring (i.e., paper and pencil diaries) did not lend themselves to an analysis of time spent engaged in the behavior. Moreover, it was not generally possible to evaluate the timing and frequency of recording during a single day without relying on self-report, which is notoriously unreliable. Electronic self-monitoring options now provide opportunities to continue to evaluate the details of self-monitoring behavior without relying on self-report. Therefore, the purpose of this study was to quantify the time spent and the daily frequency of dietary self-monitoring for participants engaged in an online 24-week behavioral weight-control intervention. Given that adherence to self-monitoring predicts weight-loss success yet declines over time, it is important to understand the thresholds of engagement necessary to produce clinically significant weight loss in order to establish meaningful goals and expectations for program participants.
Methods
Participants
Participants enrolled in the final two cohorts of the Internet Obesity Treatment Enhanced with Motivational Interviewing (iReach2) trial (6) were the focus of this substudy. To be eligible, participants had to be in generally good health, be at least 18 years old, and have a BMI (weight in kilograms divided by height in meters squared) between 25 and 50. Individuals were ineligible if they took medications that might affect weight loss, reported substantial recent weight loss, had a history of bariatric surgery or were enrolled in another weight-reduction program, or had a condition for which weight loss was contraindicated. All participants had to have access to a computer (at home or work) and the Internet. Additional details on inclusion and exclusion criteria, recruitment and screening procedures, weight-loss outcomes, and attrition have been published previously (6).
Lifestyle interventions
The iReach2 study randomized participants to an 18-month, online, group-based lifestyle intervention for obesity with the following two conditions: Behavior Therapy alone or Behavior Therapy plus Motivational Interviewing (MI). All group meetings, as well as MI sessions, were conducted via Internet chat. Participants met in closed groups once per week during the first 6 months and monthly for the remaining 12 months. Six individual MI sessions were conducted over the 18-month period for those in the Behavior Therapy plus MI condition. All participants were encouraged to adhere to a reduced-calorie lowfat diet and complete 200 minutes per week of moderate to vigorous exercise. Behavioral strategies offered to assist in making habit changes included self-monitoring, goal setting, problem solving, and relapse prevention (7–9). There were no weight-loss or adherence differences by condition (6); therefore, data have been combined for the purposes of this analysis. Because participants were not asked to self-monitor daily after the first 24 weeks, the present analysis is restricted to the first 6 months of the intervention. For additional details regarding the design and methods of iReach2, see West et al. and Krukowski et al. (6,10). The trial was approved by the Institutional Review Boards of the University of Vermont and the University of Arkansas for Medical Sciences.
Measures
Height and weight.
Weight was measured in street clothes, without shoes, on a calibrated digital scale (Tanita, Tokyo, Japan). Height was measured using a wall-mounted stadiometer (Seca Corp., Hamburg, Germany). Both height and weight were measured in person by a trained research assistant. The dependent variable was weight change measured as a continuous variable and converted to BMI as well as percent of participants achieving a 5% or 10% weight loss (categorical variable). These cutoffs were chosen as they are related to improvements in prevalence and incidence of chronic disease (11).
Dietary self-monitoring.
Dietary self-monitoring was the independent variable expressed as frequency of log-in sessions to the journal page per day as well as minutes spent on the journal page per day. Participants were instructed to record dietary intake daily over the 24-week intervention using the iReach2 study website. A self-monitoring journal tool, based on US Department of Agriculture dietary analysis data, with a personalized dietary monitoring feature was embedded on the website. Participants were provided a goal for a calorie-restricted diet and given a dietary fat goal corresponding to ≤ 25% of calories from fat. The website offered real-time feedback on total calories consumed in the day based on the foods and portion sizes recorded as well as calories remaining to reach the participant’s personal daily goal. Participants were encouraged to monitor their intake throughout the day (“write when you bite”) and check the balance of calories remaining to meet (and not exceed) their daily goal to help them plan their eating for the remainder of the day. Time spent daily on the website journal page, which included exercise minutes, weight reporting, and dietary entries, as well as number of log-ins each day to this particular page was captured electronically. The capacity to electronically record engagement with the journaling page of the iReach2 website was operational only in the later part of the trial, which is why only the final two cohorts are included in these analyses.
Statistical analysis
Descriptive statistics were calculated for all study variables. Using a multiple imputation method, intent-to-treat analyses were conducted to evaluate change in weight from baseline to 6 months. The fully conditional specification method was used in SAS PROC MI (SAS Institute Inc., Cary, North Carolina) to conduct six imputations of missing data. This method utilized regression of continuous variables and logistic regression of classification variables to estimate missing parameters. Predictors of missingness included baseline weight and weight at 6 months, site, treatment condition, cluster, sex, education, African American identity, age at baseline, depression score, and obesity status. Weight change was calculated as total kilograms lost as well as the proportion of participants losing ≥ 5% and 10% of baseline weight. Pearson correlation coefficients were used to explore the relationship between time spent self-monitoring and log-ins to the journal Web page and weight change. Because the purpose of the study was to assess whether frequency of self-monitoring and time spent self-monitoring were associated with successful weight loss, participants who had 0 minutes of self-monitoring each month were excluded from analysis. We examined sociodemographic factors that were potentially associated with not self-monitoring to explore whether certain groups may be more prone to struggle with self-monitoring and may need a tailored intervention. The total number of minutes spent self-monitoring as well as the total number of self-monitoring log-in sessions per month was calculated. These data were then converted to average daily minutes and log-ins each month. Differences between minutes and sessions by percent weight loss were assessed using the Wilcoxon rank sum test. All analyses were conducted using SAS version 9.4. Across all tests, statistical significance was defined as P < 0.05 (two-tailed).
Results
The 142 participants enrolled and randomized in the final two recruitment waves of the larger iReach2 study were, on average, 48.9 ± 10.5 years old with an average BMI of 35.8 ± 5.9 kg/m2. They were predominantly female (90.8%); 81.7% were classified as having obesity (BMI ≥ 30), and 23.2% identified as African American. The subset of participants in this substudy did not differ at baseline from the remaining participants in the overall study sample (n = 256; data not shown). They also did not differ in the amount of weight lost or overall retention at 6 months (data not shown). Clinic-obtained weight-change outcome data at 6 months were available for 88.7% (n = 126) of randomized participants in the substudy, and 65.5% (n = 93) were still self-monitoring in month 6. Total minutes spent self-monitoring were significantly correlated with total number of log-ins to the self-monitoring Web page (r = 0.79, P < 0.0001). Weight change (kilograms) was also significantly correlated with number of minutes spent self-monitoring (r = 0.61, P < 0.0001) and with total self-monitoring log-ins (r = 0.64, P < 0.0001). Those with no minutes of self-monitoring in month 6 were significantly younger than those with any minutes of self-monitoring (P < 0.01) (Table 1). Participants who identified as African American had significantly higher rates of not recording any minutes of self-monitoring (35% vs. 17%; P < 0.001) by month 6. Neither gender nor BMI was a significant predictor of lack of self-monitoring by month 6.
TABLE 1.
Age, BMI, race, and sex for those with any minutes and 0 minutes of self-monitoring at 6 months
| Any minutes (n = 93) |
No minutes (n = 49) |
|
|---|---|---|
| Sex (%) | ||
| Female | 92.5 | 87.7 |
| Male | 7.5 | 12.2 |
| Race (%) | ||
| African American | 17.2 | 34.7* |
| Caucasian | 82.8 | 65.3 |
| Age, y (± SD) | 51.2 ± 10.7 | 44.5 ± 8.8** |
| BMI (± SD) | 35.8 ± 5.5 | 35.8 ± 6.2 |
P < 0.01.
P < 0.001.
Participants spent an average of 23.2 ± 10 minutes per day self-monitoring during month 1 and 14.6 ± 9.6 minutes per day at month 6 (Table 2). Among those still self-monitoring, there were no significant differences in time spent monitoring based on weight loss, with those losing ≥ 5% or ≥ 10% spending as much time as those not losing a clinically significant amount of weight. There were, however, significant differences at month 6 in weight loss among those who still monitored during the month compared with those who did not monitor at all, such that there were very few participants who were successful at reaching the ≥ 5% or ≥ 10% weight-loss thresholds with no minutes of self-monitoring.
TABLE 2.
Average number of days per month of dietary self-monitoring, daily self-monitoring minutes, and daily log-ins to self-monitoring Web page for total sample and among those who lost ≥ 5% and ≥ 10% of baseline weight (using imputed weight) at 6 months
| All | < 5% WL | ≥ 5% WL | P value | < 10% WL | ≥ 10% WL | P value | |
|---|---|---|---|---|---|---|---|
| Month 1 | |||||||
| n | 142 | 71 | 71 | 108 | 34 | ||
| Minutes/day | 23.2 ± 10.1 | 22.0 ± 8.1 | 24.5 ± 11.7 | 0.279 | 23.2 ± 10.6 | 23.3 ± 8.4 | 0.466 |
| Sessions/day | 2.5 ± 1.2 | 2.3 ± 1.0 | 2.8 ± 1.3 | < 0.001 | 2.3 ± 0.9 | 3.2 ± 1.7 | < 0.001 |
| Days/month | 19.4 ± 7.5 | 17.4 ± 7.1 | 21.3 ± 7.4 | 0.001 | 18.0 ± 7.5 | 23.7 ± 5.7 | < 0.001 |
| Number with no log-ins | 0 | 0 | 0 | 0 | 0 | ||
| Month 2 | |||||||
| n | 134 | 66 | 68 | 100 | 34 | ||
| Minutes/day | 19.3 ± 9.1 | 19.0 ± 9.2 | 19.5 ± 9.0 | 0.566 | 19.0 ± 9.1 | 20.0 ± 9.1 | 0.468 |
| Sessions/day | 2.3 ± 1.2 | 2.0 ± 0.8 | 2.7 ± 1.5 | 0.001 | 2.1 ± 0.8 | 3.2 ± 1.8 | < 0.001 |
| Days/month | 17.9 ± 9.0 | 14.3 ± 9.0 | 21.3 ± 7.7 | < 0.001 | 15.8 ± 8.7 | 23.8 ± 7.4 | < 0.001 |
| Number with no log-ins | 8 (6%) | 5 (7%) | 3 (4%) | 0.719 | 8 (7%) | 0 | 0.199 |
| Month 3 | |||||||
| n | 124 | 58 | 66 | 91 | 33 | ||
| Minutes/day | 18.1 ± 10.7 | 18.0 ± 11.2 | 18.2 ± 10.3 | 0.859 | 18.3 ± 11.3 | 17.7 ± 9.0 | 0.897 |
| Sessions/day | 2.2 ± 1.2 | 1.8 ± 0.7 | 2.5 ± 1.5 | < 0.001 | 1.8 ± 0.6 | 3.2 ± 1.9 | < 0.001 |
| Days/month | 16.0 ± 9.9 | 10.9 ± 8.8 | 20.4 ± 8.7 | < 0.001 | 12.9 ± 8.8 | 24.5 ± 7.6 | < 0.001 |
| Number with no log-ins | 18 (13%) | 13 (18%) | 5 (7%) | 0.075 | 17 (16%) | 1 (3%) | 0.073 |
| Month 4 | |||||||
| n | 111 | 47 | 64 | 79 | 32 | ||
| Minutes/day | 15.6 ± 8.6 | 14.9 ± 9.4 | 16.1 ± 7.9 | 0.422 | 15.3 ± 8.8 | 16.2 ± 8.0 | 0.534 |
| Sessions/day | 2.1 ± 1.2 | 1.7 ± 0.5 | 2.5 ± 1.4 | < 0.001 | 1.8 ± 0.6 | 2.9 ± 1.9 | 0.001 |
| Days/month | 15.9 ± 9.4 | 11.5 ± 8.1 | 19.1 ± 9.0 | < 0.001 | 12.7 ± 8.1 | 23.9 ± 7.5 | < 0.001 |
| Number with no log-ins | 31 (22%) | 24 (34%) | 7 (10%) | 0.001 | 29 (27%) | 2 (6%) | 0.009 |
| Month 5 | |||||||
| n | 98 | 37 | 61 | 65 | 33 | ||
| Minutes/day | 16.1 ± 9.0 | 15.4 ± 9.3 | 16.5 ± 8.9 | 0.420 | 16.1 ± 9.2 | 15.9 ± 8.7 | 0.907 |
| Sessions/day | 2.1 ± 1.1 | 1.7 ± 0.6 | 2.3 ± 1.3 | 0.004 | 1.8 ± 0.6 | 2.7 ± 1.6 | 0.001 |
| Days/month | 14.2 ± 10.1 | 9.4 ± 8.1 | 17.1 ± 10.1 | < 0.001 | 10.9 ± 9.0 | 20.8 ± 8.9 | < 0.001 |
| Number with no log-ins | 44 (31%) | 34 (48%) | 10 (14%) | < 0.001 | 43 (40%) | 1 (3%) | < 0.001 |
| Month 6 | |||||||
| n | 93 | 34 | 59 | 61 | 32 | ||
| Minutes/day | 14.6 ± 9.6 | 13.7 ± 8.6 | 15.1 ± 10.1 | 0.786 | 13.9 ± 9.9 | 16.0 ± 8.8 | 0.141 |
| Sessions/day | 2.1 ± 1.1 | 1.6 ± 0.5 | 2.4 ± 1.2 | 0.001 | 1.7 ± 0.5 | 2.7 ± 1.4 | < 0.001 |
| Days/month | 14.5 ± 10.1 | 9.0 ± 8.2 | 17.6 ± 9.8 | < 0.001 | 11.2 ± 8.9 | 20.8 ± 9.2 | < 0.001 |
| Number with no log-ins | 49 (34%) | 37 (52%) | 12 (17%) | < 0.001 | 47 (43%) | 2 (6%) | < 0.001 |
Results for minutes/day, sessions/day, and days/month include only participants with at least 1 minute of self-reported monitoring within the month. Comparisons for minutes/day, sessions/day, and days/month were conducted using the Wilcoxon rank sum. Comparisons for number with no log-ins was conducted using Fisher exact test. WL, weight loss.
There was also a significant difference in the average number of daily log-ins to self-monitor every month, with those losing either ≥ 5% or ≥ 10% of baseline weight having more self-monitoring log-ins than those who lost ≤ 5% or < 10% (Table 2). Those participants who lost either ≥ 5% or ≥ 10% logged in to the journal Web page significantly more times per day (1.6 vs. 2.4, P < 0.001 for < 5% vs. ≥ 5%; 1.7 vs. 2.7, P < 0.001 for < 10% vs. ≥ 10%).
It would appear that to be successful, self-monitoring needs to occur at least two to three times a day rather than during a single sitting.
Finally, consistency in daily self-monitoring was also important, as those with successful weight losses (i.e., ≥ 5% and ≥ 10% weight loss) recorded on significantly more days out of the month both early in the program (month 1) as well as later in treatment (month 6) than did those who lost < 5%. Though the number of days with any dietary self-monitoring diminishes slightly over time, participants who were most successful (≥ 10% weight loss) were still recording on an average of 20 days per month in month 6.
Discussion
Successful participants, those losing ≥ 5% or ≥ 10% of baseline weight, were spending about 23 to 24 minutes per day recording dietary intake during the initial month of the program, but this was reduced to only 15 to 16 minutes per day by month 6 of the program. For those still recording, although the time spent journaling was not significantly different based on weight-loss category, the number of times during the course of the day that participants logged in to record their intake was significantly different. The data suggest that logging in to record intake at least twice a day is associated with greater weight-loss success. It would appear that to be successful, self-monitoring needs to occur about three times a day rather than during a single sitting, which suggests that those logging in about three times per day may have been self-monitoring more proximal to their eating episodes and may have had a better idea of their running balance of calories throughout the day, consistent with the program’s recommendations.
In fact, those losing ≥ 10% of their baseline weight were journaling approximately three times a day throughout the program. Therefore, it would appear that self-monitoring more frequently is likely related to increased weight-loss success.
These findings related to the importance of the frequency of self-monitoring within each day as well as the consistency throughout a month are similar to previous research which found that frequency of self-monitoring during the course of a day only helped to reduce weight regain when it was coupled with high consistency (> 3 days/week) (12). Krukowski and colleagues (13) similarly found that consistent periods of self-monitoring (recording ≥ 6 days in a row), particularly early in a weight-loss program, were associated with greater likelihood of achieving > 5% weight loss. Though the current study did not evaluate the number of continuous days of self-monitoring, it did demonstrate that journaling on more days in the month was significantly associated with better weight loss. This is consistent with an extensive literature associating the number of days of self-monitoring with weight-loss outcomes (4).
What is perhaps the most novel contribution here is insights into the actual time required for participants to self-monitor their dietary intake. With these data, it is now possible to estimate for participants the time commitment they should anticipate to enhance the likelihood that they will achieve clinically meaningful weight loss in a behavioral weight-control program. Participants who lost ≥ 10% spent, on average, 23 minutes per day self-monitoring during month 1 and 16 minutes per day during month 6. Twenty-three minutes per day represents about a 2.5-hour time commitment per week for the first month in a behavioral weight-loss program when the participants are learning the self-monitoring system and accumulating their commonly consumed foods. By the sixth month of the program, according to our data, the time engaged in self-monitoring among those who are successful in achieving weight loss may decrease to less than 2 hours per week.
Total minutes spent journaling decreased every month, even for those who continued to engage in the behavior. It is not clear whether this time decrease was because they got “better,” and therefore more efficient at the behavior, or they were recording fewer foods or less detail. This data set does not permit evaluation of the number and types of foods reported; however, previous research suggested that participants reported fewer foods as time goes on (12). Fortunately, very detailed or comprehensive dietary self-monitoring journals may not be necessary after the first few months of a weight-loss program (5,12). Individuals who scale back the detail in their self-monitoring were shown to still be very successful in their weight loss (5). The act of self-monitoring consistently may be the key.
In light of this, it is important to keep people engaged in the process of self-monitoring over time. Unfortunately, however, participation in the behavior was shown to decrease (14). The same was true in the current study, with 34.5% of subjects not registering any journaling minutes by month 6. Results from the Diabetes Prevention Program (15) suggested that for every self-monitoring journal submitted during the first 16 weeks of the program, the chances of meeting the 7% weight-loss goal increased by 8%. Therefore, cumulatively, a month of self-monitoring diaries increases the odds of achieving a 7% weight-loss goal by 32%. These are impressive odds, particularly when considered with the time commitment necessary to self-monitor. Increasing the odds of achieving this benchmark weight loss by 32% by just spending about 9 hours a month on self-monitoring would seem a real deal. We also demonstrated that younger individuals and participants who identified as African American were more likely to have stopped self-monitoring by month 6, which is consistent with previous research indicating that younger individuals (16) and those who identify as African American (15) tend to have lower rates of weight loss in behavioral weight-loss programs. Though it is hard to know the direction of the relationship (i.e., whether lack of weight-loss success leads participants to give up on self-monitoring or whether lack of self-monitoring leads to lower weight losses), this finding suggests that there may need to be targeted interventions focused on increasing self-monitoring adherence in these populations.
This study had several strengths and limitations. The sample represents two geographic areas with a high proportion of participants who identified as African American, both men and women included, although the sample size was not specifically powered to examine sociodemographic differences in self-monitoring behaviors. It is also the case that all self-monitoring was done online, and therefore these results cannot be generalized to other forms of dietary self-monitoring. However, electronic forms of self-monitoring (via websites or applications) are far more common in behavioral weight-control programs than are paper and pencil diaries. In fact, these newer systems may take even less time than our Web-based system did. The assessment of time and frequency of self-monitoring in the current study was limited to the first 24 weeks of the intervention and should therefore be extrapolated to self-monitoring behaviors during the weight-loss initiation phase of the study rather than to weight maintenance. It is also the case that participants in this study were enrolled in a weight-loss clinical trial, and results may not be the same for people with obesity who are not engaged in a behavioral weight-loss research program. Finally, we were not able, with our website configuration, to match the number of foods recorded on a specific day to the time spent or number of log-ins to the journal page on that particular day. Despite this, the present analysis provides important new information about time taken to self-monitor and frequency of self-monitoring as they relate to weight-loss success.
Conclusion
The results of this study confirmed that the frequency of self-monitoring is significantly related to weight-loss success in an online behavioral weight-loss intervention, with both the number of days per month of self-monitoring and the number of times per day that the self-monitoring website is accessed associated with weight-loss outcomes (i.e., two to three times per day). Additionally, the time participants need to spend to achieve clinically meaningful weight loss decreases over time, with approximately 20 minutes per day needed early in the program and only 15 minutes required later in the program. This new information allows for the development of very specific self-monitoring goals as well as expectations about the time commitment required.
Acknowledgments
The authors would like to thank all of the research participants of the iReach2 study as well as Mr. Paul Buzzell who maintained the iR2 website and journal page.
Funding agencies: This study was funded by NIH/NIDDK R01 DK056746 to JH and DW.
Footnotes
Disclosure: The authors declared no conflict of interest.
Clinical trial registration:ClinicalTrials.gov identifer .
References
- 1.Gold BC, Burke S, Pintauro S, Buzzell P, Harvey-Berino J. Weight loss on the web: a pilot study comparing a structured behavioral intervention to a commercial program. Obesity (Silver Spring) 2007;15:155–164. [DOI] [PubMed] [Google Scholar]
- 2.Boutelle KN, Kirschenbaum DS. Further support for consistent self-monitoring as a vital component of successful weight control. Obes Res 1998;6:219–224. [DOI] [PubMed] [Google Scholar]
- 3.Burke LE, Sereika SM, Music E, Warziski M, Styn MA, Stone A. Using instrumented paper diaries to document self-monitoring patterns in weight loss. Contemp Clin Trials 2008;29:182–193. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Burke LE, Wang J, Sevick MA. Self-monitoring in weight loss: a systematic review of the literature. J Am Diet Assoc 2011;111:92–102. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Helsel DL, Jakicic JM, Otto AD. Comparison of techniques for self-monitoring eating and exercise behaviors on weight loss in a correspondence-based intervention. J Am Diet Assoc 2007;107:1807–1810. [DOI] [PubMed] [Google Scholar]
- 6.West DS, Harvey JR, Krukowski RA, Prewitt TE, Priest J, Ashikaga T. Do individual, online motivational interviewing chat sessions enhance weight loss in a group-based, online weight control program? Obesity (Silver Spring) 2016;24:2334–2340. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Bandura A, Simon KM. The role of proximal intentions in self-regulation of refractory behavior. Cognit Ther Res 1977;1:177–193. [Google Scholar]
- 8.D’Zurilla TJ, Goldfried MR. Problem solving and behavior modification. J Abnorm Psychol 1971;78:107–126. [DOI] [PubMed] [Google Scholar]
- 9.Haaga DA, Davison GC. Cognitive change methods In: Kanfer FH, Goldstein AP, Helping People Change: A Textbook of Methods. Elmsford, NY: Pergamon Press; 1991:248–304. [Google Scholar]
- 10.Krukowski RA, DiLillo V, Ingle K, Harvey JR, West DS. Design and methods of a synchronous online motivational interviewing intervention for weight management. JMIR Res Protoc 2016;5:e69. doi: 10.2196/resprot.5382 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Wing RR, Lang W, Wadden TA, et al. Benefits of modest weight loss in improving cardiovascular risk factors in overweight and obese individuals with type 2 diabetes. Diabetes Care 2011;34:1481–1486. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Peterson ND, Middleton KR, Nackers LM, Medina KE, Milsom VA, Perri MG. Dietary self-monitoring and long-term success with weight management. Obesity (Silver Spring) 2014;22:1962–1967. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Krukowski RA, Harvey-Berino J, Bursac Z, Ashikaga T, West DS. Patterns of success: online self-monitoring in a web-based behavioral weight control program. Health Psychol 2013;32:164. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Pellegrini CA, Conroy DE, Phillips SM, Pfammatter AF, McFadden G, Spring B. Daily and seasonal influences on dietary self-monitoring using a smartphone application. J Nutr Educ Behav 2016;50:56–61. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Diabetes Prevention Program Research Group. Achieving weight and activity goals among Diabetes Prevention Program lifestyle participants. Obes Res 2004;12:1426–1434. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Wadden TA, West DS, Neiberg RH, et al. One-year weight losses in the LookAHEAD study: factors associated with success. Obesity (Silver Spring) 2009;17:713–722. [DOI] [PMC free article] [PubMed] [Google Scholar]