Do individual, online motivational interviewing chat sessions enhance weight loss in a group‐based, online weight control program?

West, Delia Smith; Harvey, Jean; Krukowski, Rebecca A.; Prewitt, T. Elaine; Priest, Jeffrey S.; Ashikaga, Takamaru

doi:10.1002/oby.21645

Cited by 34 publications

(61 citation statements)

References 26 publications

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“…Behavioral strategies offered to assist in making habit changes included self‐monitoring, goal setting, problem solving, and relapse prevention . There were no weight‐loss or adherence differences by condition ; 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.…”

Section: Methodsmentioning

confidence: 99%

Log Often, Lose More: Electronic Dietary Self‐Monitoring for Weight Loss

Harvey

Krukowski

Priest

et al. 2019

Obesity

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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.

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Section: Methodsmentioning

confidence: 99%

Log Often, Lose More: Electronic Dietary Self‐Monitoring for Weight Loss

Harvey

Krukowski

Priest

et al. 2019

Obesity

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“…[ 11 – 16 ]. Research also suggests the efficacy of weekly professionally led group chat visits as part of an internet program [ 17 , 18 ] but less is known about periodic human support delivered either in person or online.…”

Section: Introductionmentioning

confidence: 99%

Randomized trial comparing group size of periodic in-person sessions in a remotely delivered weight loss intervention

Tate

Valle

Crane

et al. 2017

Int J Behav Nutr Phys Act

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BackgroundFew randomized studies have examined differential effects of group size in behavioral weight control, especially in hybrid programs that include Internet treatment approaches.MethodsRandomized controlled trial (n = 195) comparing a 4 month hybrid internet weight loss program coupled with monthly face to face groups of 100 persons (Large Group, LG; 1 group) or to the same approach with monthly groups of 20 persons (Small Group, SG; 4 groups). Repeated-measures mixed-model analysis with age and race as covariates were used to estimate primary (weight) and secondary outcomes, and to test group differences in change over time.ResultsThe sample was 46.3 years old ±10.4, 90.3% female, and 51.9% non-white, with BMI 37.9 ± 8.4 kg/m2. Participants in the LG were more likely to return for the 4-month assessment visit than those in the SG (p = 0.04). Participants randomized to both the LG and SG conditions experienced significant WL over time (no between group difference: −4.1 kg and −3.7 kg, respectively) and weight loss was positively associated with attendance at monthly meetings and logins to the website. Satisfaction with the program was high and similar in both groups (94.4% reported that they were “satisfied” or “very satisfied”).ConclusionsUsing a hybrid approach of in-person and online weight loss interventions may be an effective way to reach larger and more diverse populations. Delivering the face to face component of the intervention in groups larger than those traditionally delivered (20–25 people) could increase the cost-effectiveness of group-based behavioral weight loss interventions.Clinical trials registration number NCT01615471. Registered June 6, 2012. Registered retrospectively.

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“…This study was part of a larger study designed to evaluate the benefit of adding motivational interviewing to an online behavioral weight loss intervention. The treatment intervention and outcomes have been described previously . Briefly, participants were recruited from research sites in Vermont and Arkansas using community‐based resources (newspaper ads, radio notices, television), targeted emails, and recruitment materials in primary care offices.…”

Section: Methodsmentioning

confidence: 99%

“…The treatment intervention and outcomes have been described previously. 8,9 Briefly, participants were recruited from research sites in Vermont and Arkansas using community-based resources (newspaper ads, radio notices, television), targeted emails, and recruitment materials in primary care offices. Individuals were included if they were at least 18 years old, had a BMI ࣙ25 (kg/m 2 ), had no major medical problems, were not taking medications that may have interfered with their ability to lose weight, not currently pregnant or lactating, and not enrolled in another weight loss program.…”

Section: Study Population and Sampling Designmentioning

confidence: 99%

Weight Loss Success of Participants Residing in Rural and Urban Areas

Mench

West

Krukowski

et al. 2017

The Journal of Rural Health

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Purpose Rural residents are significantly more likely to be overweight and obese than are urban residents. However, few data have compared weight control program responses in these population groups. Therefore, the primary aim of this study was to evaluate the weight loss outcomes of participants in a multi-site, on-line behavioral intervention by residential location (rural vs. urban) and, secondly, assess any possible differences in adherence to treatment goals. Methods 492 adult participants (Mean BMI = 35.7 kg/m2; 90 % Female; 24% Minority) were categorized based on their home zip code and the 2010 US Census Urban and Rural Classification criteria (58.3% Rural; 41.7% Urban). Weight (kg) was measured in-person at baseline and 6 months after participating in a behavioral weight control intervention. Adherence to physical activity and calorie and fat intake goals was assessed based on weekly self-monitoring journals. Weekly online chat attendance and completion of weekly online self-monitoring journals were recorded. Presence of obesogenic foods in the home was self-reported at baseline and 6 months. Findings There were no statistically significant differences in weight loss between rural and urban participants (−6.1 kg vs. −5.3 kg, P = .16), nor were there differences in chat attendance, self-monitoring journals, self-reported physical activity, calorie intake or obesogenic foods reported in the household. Conclusions Overall, there was no difference in weight loss and adherence to treatment goals for rural and urban participants. Further research on rural and urban residents is necessary to explore the factors responsible for the disparity in obesity prevalence.

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Do individual, online motivational interviewing chat sessions enhance weight loss in a group‐based, online weight control program?

Cited by 34 publications

References 26 publications

Log Often, Lose More: Electronic Dietary Self‐Monitoring for Weight Loss

Log Often, Lose More: Electronic Dietary Self‐Monitoring for Weight Loss

Randomized trial comparing group size of periodic in-person sessions in a remotely delivered weight loss intervention

Weight Loss Success of Participants Residing in Rural and Urban Areas

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