Frequency of Nutritional Follow-Up and Its Impact on Weight Loss in Adults with Overweight or Obesity

Background: Frequent professional followup is often cited as a predictor of successful weight loss, yet its specific impact has not been consistently quantified in real-world settings. Objective: To evaluate whether followup frequency is associated with improved clinical outcomes in adults with overweight or obesity. Methods: This retrospective analysis reviewed 191 anonymized medical records from patients treated between 2010 and 2023, both in-person (Argentina, Colombia) and remotely (Spain, United States, Chile). Patients were grouped by average followup interval (≤15 vs. >15 days). Student’s ttest and linear regression were used to evaluate weight loss and waist circumference changes. Results: Those with followups ≤15 days lost significantly more weight (6.17 ± 4.94 kg vs. 2.10 ± 7.03 kg; p < 0.01) and reduced their waist circumference more (7.38 ± 5.18 cm vs. 2.43 ± 7.13 cm; p = 0.01). A negative correlation was found between visit interval and weight loss (r = –0.32; p < 0.01). Regression analysis confirmed an independent association. Conclusion: Frequent followup is strongly associated with better weight and waist circumference outcomes, supporting its role as a key factor in obesity management. 1. Wadden TA, Tronieri JS, Butryn ML. Lifestyle modification approaches for the treatment of obesity in adults. Am Psychol. 2020;75(2):235–51. https://doi.org/10.1037/amp0000517 2. Tronieri JS, Wadden TA, Chao AM, Berkowitz RI. Primary care interventions for obesity: review of the evidence. Curr Obes Rep. 2021;10(3):324–32. https://doi.org/10.1007/s13679-021-00439-3 3. Thomas JG, Bond DS, Raynor HA, Papandonatos GD, Wing RR. Frequency of self-monitoring behaviors and long-term weight loss maintenance. Obesity (Silver Spring). 2023;31(1):56–64. https://doi. org/10.1002/oby.23582 4. Aronne LJ, Hall KD, Jakicic JM, et al. Describing the weight-reduced state: physiology, behavior, and interventions. Obesity (Silver Spring). 2021;29(S1):S9–24. https://doi.org/10.1002/oby.23098 5. Burke LE, Wang J, Sevick MA. Self-monitoring in weight loss: a systematic review ofthe literature. JAm DietAssoc. 2011;111(1):92–102. 6. Rotunda W, Rains C, Jacobs SR, et al. Weight loss in short-term interventions for physical activity and nutrition among adults with overweight or obesity: a systematic review and meta-analysis. Prev ChronicDis. 2024;21:230347. https://doi.org/10.5888/pcd21.230347 7. Moreira MFS, de Azevedo BEF, Beretta MV, Busnello FM. Nutritional counseling based on mindful eating for the eating behavior of people living with overweight and obesity: a randomized clinical trial. Nutrients. 2024;16(24):4388. https://doi.org/10.3390/nu16244388 8. The impact of technology-enabled medical nutrition therapy on weight loss: a retrospective cohort study. JMIR Mhealth Uhealth. 2025;13:e70228. https://doi.org/10.2196/70228 9. A guideline-directed approach to obesity treatment. Diabetes Spectr. 2024;37(4):281–9. https://doi.org/10.2337/dsi24-0001 10. Blüher M. New insights into the treatment of obesity. Diabetes Obes Metab. 2023;25(8):1971–83. https://doi.org/10.1111/dom.15017 11. Jones RA, Lawlor ER, Birch JM, et al. The impact of adult behavioural weight management interventions on mental health: a systematic review and meta-analysis. Obes Rev. 2021;22(4):e13150. https://doi. org/10.1111/obr.13150 12. Brown RE, Kuk JL. Consequences of obesity and weight loss: a devil’s advocate position. Obes Rev. 2014;16(1):77–87. https://doi. org/10.1111/obr.12232 13. Baer HJ, Rozenblum R, De La Cruz BA, et al. Effect of an online weight management program integrated with population health management on weight change: a randomized clinical trial. JAMA. 2020;324(17):1737–46. https://doi.org/10.1001/jama.2020.19349 14. Swancutt D, Tarrant M, Ingram W, et al. A group-based behavioural intervention for weight management (PROGROUP) versus usual care in adults with severe obesity: a feasibility randomised controlled trial protocol. Pilot Feasibility Stud. 2022;8:206. https://doi.org/10.1186/ s40814-022-01167-0 15. Patel ML, Hopkins CM, Bennett GG, et al. Self-monitoring via digital health in weight loss interventions: a systematic review among adults with overweight or obesity. Obesity (Silver Spring). 2021;29(3):478– 99. https://doi.org/10.1002/oby.23089 16. Arroyo KM, Ross KM, Krukowski RA, et al. Identification of minimum thresholds for dietary self-monitoring to promote weight-loss maintenance. Obesity (Silver Spring). 2024;32(4):712–20. https://doi. org/10.1002/oby.23894 17. Krukowski RA, Ross KM, Carpenter C, Arroyo K. A primary care–based weight navigation program: cohort study. JAMA Netw Open. 2024;7(5):e2418868. https://doi.org/10.1001/ jamanetworkopen.2024.18868 18. Elmaleh-Sachs A, Schwartz JL, Bramante CT, et al. Obesity management in adults: a review. JAMA. 2023;330(20):2000–15. https://doi. org/10.1001/jama.2023.19897 19. Krukowski RA, Denton AH, König LM, et al. Impact of feedback generation and presentation on self-monitoring behaviors, dietary intake, physical activity, and weight: a systematic review and metaanalysis. Int J Behav Nutr Phys Act. 2024;21(1):3. https://doi. org/10.1186/s12966-023-01555-6 20. Keseko EA, Bell A, Turner-McGrievy GM. Behavioral and dietary strategies for weight loss and weight loss maintenance among Black/ African American adults and the potential role of media: a narrative review. Nutrients. 2025;17(4):617. https://doi.org/10.3390/ nu17040617 21. Antoun J, Itani H, Alarab N, Elsehmawy A. The effectiveness of combining nonmobile interventions with the use of smartphone apps with various features for weight loss: systematic review and metaanalysis. JMIR Mhealth Uhealth. 2022;10(4):e35479. https://doi. org/10.2196/35479 22. Virtanen P, Gommers R, Oliphant TE, et al. SciPy 1.0: Fundamental algorithms for scientific computing in Python. Nat Methods. 2020;17(3):261–72. https://doi.org/10.1038/s41592-019-0686-2 23. Seabold S, Perktold J. Statsmodels: Econometric and statistical modeling with Python. In: van der Walt S, Millman J, eds. Proceedings of the 9th Python in Science Conference. 2010;57–61. https://doi. org/10.25080/Majora-92bf1922-011.