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Arroyo KM, Carpenter CA, Krukowski RA, Ross KM. Identification of minimum thresholds for dietary self-monitoring to promote weight-loss maintenance. Obesity (Silver Spring) 2024; 32:655-659. [PMID: 38529540 PMCID: PMC10972539 DOI: 10.1002/oby.23994] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Revised: 12/18/2023] [Accepted: 12/27/2023] [Indexed: 03/27/2024]
Abstract
OBJECTIVE Reduced schedules of dietary self-monitoring are typically recommended after the end of behavioral weight-loss programs; however, there exists little empirical evidence to guide these recommendations. METHODS We explored potential thresholds for dietary self-monitoring during a 9-month maintenance period following a 3-month weight-loss program in 74 adults with overweight or obesity (mean [SD] age = 50.7 [10.4] years, BMI = 31.2 [4.5] kg/m2) who were encouraged to self-monitor weight, dietary intake, and physical activity daily and report their adherence to self-monitoring each week via a study website. RESULTS Greater self-monitoring was correlated with less weight regain for thresholds of ≥3 days/week, with the largest benefit observed for thresholds of ≥5 to ≥6 days/week (all p < 0.05); significant weight gain was observed for thresholds of ≥1 to ≥2 days/week, whereas no change in weight was observed for thresholds of ≥3 to ≥4 days/week, and weight loss was observed with thresholds of ≥5 or more days/week. CONCLUSIONS Results demonstrate that self-monitoring at least 3 days/week may be beneficial for supporting long-term maintenance, although greater benefit (in relation to weight loss) may be realized at thresholds of 5 to 6 days/week. Future research should investigate whether individuals who were randomized to self-monitor at these different thresholds demonstrate differential patterns of weight-loss maintenance.
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Affiliation(s)
- Kelsey M Arroyo
- Department of Clinical & Health Psychology, University of Florida, Gainesville, Florida, USA
- Center for Integrative Cardiovascular and Metabolic Disease, University of Florida, Gainesville, Florida, USA
| | - Chelsea A Carpenter
- Department of Clinical & Health Psychology, University of Florida, Gainesville, Florida, USA
| | - Rebecca A Krukowski
- Department of Public Health Sciences, University of Virginia, Charlottesville, Virginia, USA
| | - Kathryn M Ross
- Department of Clinical & Health Psychology, University of Florida, Gainesville, Florida, USA
- Center for Integrative Cardiovascular and Metabolic Disease, University of Florida, Gainesville, Florida, USA
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Li S, Du Y, Miao H, Sharma K, Li C, Yin Z, Brimhall B, Wang J. Understanding Heterogeneity in Individual Responses to Digital Lifestyle Intervention Through Self-Monitoring Adherence Trajectories in Adults With Overweight or Obesity: Secondary Analysis of a 6-Month Randomized Controlled Trial. J Med Internet Res 2024; 26:e53294. [PMID: 38506903 PMCID: PMC10993111 DOI: 10.2196/53294] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2023] [Revised: 01/01/2024] [Accepted: 01/31/2024] [Indexed: 03/21/2024] Open
Abstract
BACKGROUND Achieving clinically significant weight loss through lifestyle interventions for obesity management is challenging for most individuals. Improving intervention effectiveness involves early identification of intervention nonresponders and providing them with timely, tailored interventions. Early and frequent self-monitoring (SM) adherence predicts later weight loss success, making it a potential indicator for identifying nonresponders in the initial phase. OBJECTIVE This study aims to identify clinically meaningful participant subgroups based on longitudinal adherence to SM of diet, activity, and weight over 6 months as well as psychological predictors of participant subgroups from a self-determination theory (SDT) perspective. METHODS This was a secondary data analysis of a 6-month digital lifestyle intervention for adults with overweight or obesity. The participants were instructed to perform daily SM on 3 targets: diet, activity, and weight. Data from 50 participants (mean age: 53.0, SD 12.6 y) were analyzed. Group-based multitrajectory modeling was performed to identify subgroups with distinct trajectories of SM adherence across the 3 SM targets. Differences between subgroups were examined for changes in clinical outcomes (ie, body weight, hemoglobin A1c) and SDT constructs (ie, eating-related autonomous motivation and perceived competence for diet) over 6 months using linear mixed models. RESULTS Two distinct SM trajectory subgroups emerged: the Lower SM group (21/50, 42%), characterized by all-around low and rapidly declining SM, and the Higher SM group (29/50, 58%), characterized by moderate and declining diet and weight SM with high activity SM. Since week 2, participants in the Lower SM group exhibited significantly lower levels of diet (P=.003), activity (P=.002), and weight SM (P=.02) compared with the Higher SM group. In terms of clinical outcomes, the Higher SM group achieved a significant reduction in body weight (estimate: -6.06, SD 0.87 kg; P<.001) and hemoglobin A1c (estimate: -0.38, SD 0.11%; P=.02), whereas the Lower SM group exhibited no improvements. For SDT constructs, both groups maintained high levels of autonomous motivation for over 6 months. However, the Lower SM group experienced a significant decline in perceived competence (P=.005) compared with the Higher SM group, which maintained a high level of perceived competence throughout the intervention (P=.09). CONCLUSIONS The presence of the Lower SM group highlights the value of using longitudinal SM adherence trajectories as an intervention response indicator. Future adaptive trials should identify nonresponders within the initial 2 weeks based on their SM adherence and integrate intervention strategies to enhance perceived competence in diet to benefit nonresponders. TRIAL REGISTRATION ClinicalTrials.gov NCT05071287; https://clinicaltrials.gov/study/NCT05071287. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) RR2-10.1016/j.cct.2022.106845.
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Affiliation(s)
- Shiyu Li
- Department of Kinesiology, The Pennsylvania State University, University Park, PA, United States
| | - Yan Du
- School of Nursing, The University of Texas Health Science Center at San Antonio, San Antonio, TX, United States
| | - Hongyu Miao
- College of Nursing, Florida State University, Tallahassee, FL, United States
| | - Kumar Sharma
- Center for Precision Medicine, Long School of Medicine, The University of Texas Health Science Center at San Antonio, San Antonio, TX, United States
| | - Chengdong Li
- College of Nursing, Florida State University, Tallahassee, FL, United States
| | - Zenong Yin
- Department of Public Health, The University of Texas at San Antonio, San Antonio, TX, United States
| | - Bradley Brimhall
- Department of Pathology and Laboratory Medicine, Long School of Medicine, The University of Texas Health Science Center at San Antonio, San Antonio, TX, United States
| | - Jing Wang
- College of Nursing, Florida State University, Tallahassee, FL, United States
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Li S, Du Y, Meireles C, Song D, Sharma K, Yin Z, Brimhall B, Wang J. Decoding Heterogeneity in Data-Driven Self-Monitoring Adherence Trajectories in Digital Lifestyle Interventions for Weight Loss: A Qualitative Study. RESEARCH SQUARE 2024:rs.3.rs-3854650. [PMID: 38313251 PMCID: PMC10836100 DOI: 10.21203/rs.3.rs-3854650/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/06/2024]
Abstract
Background Data-driven trajectory modeling is a promising approach for identifying meaningful participant subgroups with various self-monitoring (SM) responses in digital lifestyle interventions. However, there is limited research investigating factors that underlie different subgroups. This qualitative study aimed to investigate factors contributing to participant subgroups with distinct SM trajectory in a digital lifestyle intervention over 6 months. Methods Data were collected from a subset of participants (n = 20) in a 6-month digital lifestyle intervention. Participants were classified into Lower SM Group (n = 10) or a Higher SM (n = 10) subgroup based on their SM adherence trajectories over 6 months. Qualitative data were obtained from semi-structured interviews conducted at 3 months. Data were thematically analyzed using a constant comparative approach. Results Participants were middle-aged (52.9 ± 10.2 years), mostly female (65%), and of Hispanic ethnicity (55%). Four major themes with emerged from the thematic analysis: Acceptance towards SM Technologies, Perceived SM Benefits, Perceived SM Barriers, and Responses When Facing SM Barriers. Participants across both subgroups perceived SM as positive feedback, aiding in diet and physical activity behavior changes. Both groups cited individual and technical barriers to SM, including forgetfulness, the burdensome SM process, and inaccuracy. The Higher SM Group displayed positive problem-solving skills that helped them overcome the SM barriers. In contrast, some in the Lower SM Group felt discouraged from SM. Both subgroups found diet SM particularly challenging, especially due to technical issues such as the inaccurate food database, the time-consuming food entry process in the Fitbit app. Conclusions This study complements findings from our previous quantitative research, which used data-drive trajectory modeling approach to identify distinct participant subgroups in a digital lifestyle based on individuals' 6-month SM adherence trajectories. Our results highlight the potential of enhancing action planning problem solving skills to improve SM adherence in the Lower SM Group. Our findings also emphasize the necessity of addressing the technical issues associated with current diet SM approaches. Overall, findings from our study may inform the development of practical SM improvement strategies in future digital lifestyle interventions. Trial registration The study was pre-registered at ClinicalTrials.gov (NCT05071287) on April 30, 2022.
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Affiliation(s)
- Shiyu Li
- Department of Kinesiology, Pennsylvania State University
| | - Yan Du
- School of Nursing, UT Health San Antonio
| | | | - Dan Song
- College of Nursing, Florida State University
| | | | - Zenong Yin
- Department of Public Health, The University of Texas at San Antonio
| | | | - Jing Wang
- College of Nursing, Florida State University
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Mavragani A, Cleare AE, Smith CM, Rosas LG, King AC. Detailed Versus Simplified Dietary Self-monitoring in a Digital Weight Loss Intervention Among Racial and Ethnic Minority Adults: Fully Remote, Randomized Pilot Study. JMIR Form Res 2022; 6:e42191. [PMID: 36512404 PMCID: PMC9795401 DOI: 10.2196/42191] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Revised: 10/29/2022] [Accepted: 11/04/2022] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND Detailed self-monitoring (or tracking) of dietary intake is a popular and effective weight loss approach that can be delivered via digital tools, although engagement declines over time. Simplifying the experience of self-monitoring diet may counteract this decline in engagement. Testing these strategies among racial and ethnic minority groups is important as these groups are often disproportionately affected by obesity yet underrepresented in behavioral obesity treatment. OBJECTIVE In this 2-arm pilot study, we aimed to evaluate the feasibility and acceptability of a digital weight loss intervention with either detailed or simplified dietary self-monitoring. METHODS We recruited racial and ethnic minority adults aged ≥21 years with a BMI of 25 kg/m2 to 45 kg/m2 and living in the United States. The Pacific time zone was selected for a fully remote study. Participants received a 3-month stand-alone digital weight loss intervention and were randomized 1:1 to either the detailed arm that was instructed to self-monitor all foods and drinks consumed each day using the Fitbit mobile app or to the simplified arm that was instructed to self-monitor only red zone foods (foods that are highly caloric and of limited nutritional value) each day via a web-based checklist. All participants were instructed to self-monitor both steps and body weight daily. Each week, participants were emailed behavioral lessons, action plans, and personalized feedback. In total, 12 a priori benchmarks were set to establish feasibility, including outcomes related to reach, retention, and self-monitoring engagement (assessed objectively via digital tools). Acceptability was assessed using a questionnaire. Weight change was assessed using scales shipped to the participants' homes and reported descriptively. RESULTS The eligibility screen was completed by 248 individuals, of whom 38 (15.3%) were randomized, 18 to detailed and 20 to simplified. At baseline, participants had a mean age of 47.4 (SD 14.0) years and BMI of 31.2 (SD 4.8) kg/m2. More than half (22/38, 58%) were identified as Hispanic of any race. The study retention rate was 92% (35/38) at 3 months. The detailed arm met 9 of 12 feasibility benchmarks, while the simplified arm met all 12. Self-monitoring engagement was moderate to high (self-monitoring diet: median of 49% of days for detailed, 97% for simplified; self-monitoring steps: 99% for detailed, 100% for simplified; self-monitoring weight: 67% for detailed, 80% for simplified). Participants in both arms reported high satisfaction, with 89% indicating that they would recommend the intervention. Weight change was -3.4 (95% CI -4.6 to -2.2) kg for detailed and -3.3 (95% CI -4.4 to -2.2) kg for simplified. CONCLUSIONS A digital weight loss intervention that incorporated either detailed or simplified dietary self-monitoring was feasible, with high retention and engagement, and acceptable to racial and ethnic minority adults. TRIAL REGISTRATION ASPREDICTED #66674; https://aspredicted.org/ka478.pdf.
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Affiliation(s)
| | | | | | - Lisa Goldman Rosas
- Department of Epidemiology & Population Health, Stanford University School of Medicine, Stanford, CA, United States
| | - Abby C King
- Stanford Prevention Research Center, Department of Medicine, Stanford University School of Medicine, Palo Alto, CA, United States.,Department of Epidemiology & Population Health, Stanford University School of Medicine, Stanford, CA, United States
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Tate DF, Lutes LD, Bryant M, Truesdale KP, Hatley KE, Griffiths Z, Tang TS, Padgett LD, Pinto AM, Stevens J, Foster GD. Efficacy of a Commercial Weight Management Program Compared With a Do-It-Yourself Approach: A Randomized Clinical Trial. JAMA Netw Open 2022; 5:e2226561. [PMID: 35972742 PMCID: PMC9382439 DOI: 10.1001/jamanetworkopen.2022.26561] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
IMPORTANCE Given the prevalence of obesity, accessible and effective treatment options are needed to manage obesity and its comorbid conditions. Commercial weight management programs are a potential solution to the lack of available treatment, providing greater access at lower cost than clinic-based approaches, but few commercial programs have been rigorously evaluated. OBJECTIVE To compare the differences in weight change between individuals randomly assigned to a commercial weight management program and those randomly assigned to a do-it-yourself (DIY) approach. DESIGN, SETTING, AND PARTICIPANTS This 1-year, randomized clinical trial conducted in the United States, Canada, and United Kingdom between June 19, 2018, and November 30, 2019, enrolled 373 adults aged 18 to 75 years with a body mass index (BMI; calculated as weight in kilograms divided by height in meters squared) of 25 to 45. Assessors were blinded to treatment conditions. INTERVENTIONS A widely available commercial weight management program that included reduced requirements for dietary self-monitoring and recommendations for a variety of DIY approaches to weight loss. MAIN OUTCOMES AND MEASURES The primary outcomes were the difference in weight change between the 2 groups at 3 and 12 months. The a priori hypothesis was that the commercial program would result in greater weight loss than the DIY approach at 3 and 12 months. Analyses were performed on an intention-to-treat basis. RESULTS The study include 373 participants (272 women [72.9%]; mean [SD] BMI, 33.8 [5.2]; 77 [20.6%] aged 18-34 years, 74 [19.8%] aged 35-43 years, 82 [22.0%] aged 44-52 years, and 140 [37.5%] aged 53-75 years). At 12 months, retention rates were 88.8% (166 of 187) for the commercial weight management program group and 95.7% (178 of 186) for the DIY group. At 3 months, participants in the commercial program had a mean (SD) weight loss of -3.8 (4.1) kg vs -1.8 (3.7) kg among those in the DIY group. At 12 months, participants in the commercial program had a mean (SD) weight loss of -4.4 (7.3) kg vs -1.7 (7.3) kg among those in the DIY group. The mean difference between groups was -2.0 kg (97.5% CI, -2.9 to -1.1 kg) at 3 months (P < .001) and -2.6 kg (97.5% CI, -4.3 to -0.8 kg) at 12 months (P < .001). A greater percentage of participants in the commercial program group than participants in the DIY group achieved loss of 5% of body weight at both 3 months (40.7% [72 of 177] vs 18.6% [34 of 183]) and 12 months (42.8% [71 of 166] vs 24.7% [44 of 178]). CONCLUSIONS AND RELEVANCE Adults randomly assigned to a commercial weight management program with reduced requirements for dietary self-monitoring lost more weight and were more likely to achieve weight loss of 5% at 3 and 12 months than adults following a DIY approach. This study contributes data on the efficacy of commercial weight management programs and DIY weight management approaches. TRIAL REGISTRATION ClinicalTrials.gov Identifier: NCT03571893.
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Affiliation(s)
- Deborah F. Tate
- Department of Nutrition, University of North Carolina at Chapel Hill
- Department of Health Behavior, University of North Carolina at Chapel Hill
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill
| | - Lesley D. Lutes
- Department of Psychology, University of British Columbia, Okanagan Campus, Kelowna, British Columbia, Canada
| | - Maria Bryant
- Department of Health Sciences, University of York, York, United Kingdom
- The Hull York Medical School, University of York, York, United Kingdom
| | | | - Karen E. Hatley
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill
| | | | - Tricia S. Tang
- Department of Medicine, University of British Columbia, Vancouver Campus, Vancouver, British Columbia, Canada
| | - Louise D. Padgett
- Department of Health Sciences, University of York, York, United Kingdom
| | - Angela M. Pinto
- Department of Psychology, Baruch College/City University of New York, New York
| | - June Stevens
- Department of Nutrition, University of North Carolina at Chapel Hill
- Department of Epidemiology, University of North Carolina at Chapel Hill
| | - Gary D. Foster
- Center for Weight and Eating Disorders, Perelman School of Medicine, University of Pennsylvania, Philadelphia
- WW, Maidenhead, Berkshire, UK
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