Length of remedy in a weight reduction program utilizing a mobil


Overweight and obesity are risk factors for noncommunicable diseases, such as type 2 diabetes, cardiovascular disease, and some cancers.1–4 Globally, the prevalence of obesity and overweight has significantly risen over the past three decades,5 and the projected obesity trends are expected to enhance its economic burden.6 In Taiwan, the prevalence of obesity in adults has also increased over the past two decades, and approximately half of adults in Taiwan have become overweight or obese.7,8 Weight loss through lifestyle modification can significantly produce health benefits.9–12 However, only a small proportion of patients with obesity undergoing lifestyle modification achieve clinically significant weight loss and weight loss maintenance.13,14 To date, no country has succeeded in reversing the current obesity pandemic.15

Since coronavirus disease 2019 (COVID-19) was declared a global pandemic,16 several studies conducted in different countries have shown lower diet quality, higher overeating frequency, less physical activity, and weight gain during the COVID-19 pandemic compared to before the COVID-19 pandemic.17–20 Furthermore, obesity increases the risk of severe COVID-19 illness, hospitalization, and mortality.21–24 However, weight management programs for obesity have been reduced during the COVID-19 pandemic to ensure the safety of patients and healthcare professionals.25 Thus, remote approaches instead of face-to-face interactions for weight management are required.

Prior to the COVID-19 pandemic, many studies demonstrated that telehealth and mobile health (mHealth) technologies are effective tools for weight loss treatment.26 Weight loss treatment delivered by smartphone applications (apps) may lead to higher adherence and greater weight loss compared to that delivered by websites or paper diaries.27 In the Asian population, the use of a mobile app applied to weight loss may also have benefits for improving diet quality and weight loss self-efficacy.28,29 During the COVID-19 pandemic, the use of telehealth increased by almost 200% in the US.30 Recently, a randomized clinical trial, using the mHealth app to promote ketogenic diet during the COVID-19 pandemic, found that self-reported dietary adherence is an important predictor of weight-loss success.31 Thus, telehealth technologies may be a feasible choice for weight management during the COVID-19 pandemic.

Although a nation-wide lockdown was not implemented until May 18, 2021, a social distancing policy was implemented after a locally-transmitted COVID-19 infection was reported on January 21, 2020 in Taiwan.32 Assembly and gathering were restricted according to public assembly guidelines published by the Taiwan Centers for Disease Control. Since September 1, 2020, the Taichung Veteran General Hospital (VGH) provided a weight management program, which combined self-monitoring of meal tracking and weight tracking as well as person-to-person individualized feedback, via a mobile app during follow-up in the Weight Control Outpatient Department in the Taichung VGH.

The present study retrospectively assessed data from real-life practice to examine the degree of weight loss during a mobile weight management program in the Taichung VGH during the COVID-19 pandemic and explored independent factors associated with successful weight loss.

Materials and Methods


We retrospectively collected the medical information of subjects who had undergone weight loss treatment at the Weight Control Outpatient Department in the Taichung VGH between September 2020 and May 2021. The inclusion criteria were: (1) age between 20 and 64 years; (2) initial body mass index (BMI) ≥ 24 kg/m2; (3) patients registering the homecare app between September 2020 and May 2021. The exclusion criteria were: (1) changing any medications within 3 months before the first visit; (2) the use of anti-obesity drugs within 3 months before the first visit; (3) the use of glucagon-like peptide 1 agonists; (4) a history of bariatric surgery; (5) weight loss by > 5% in 6 months prior to baseline measurements; (6) a history of endocrine disorders; (7) a history of drug addiction or psychological disorders; (8) the use of systemic steroids; (9) pregnancy; and (10) no any meal photo uploaded onto the homecare app. Finally, we enrolled participants in our analyses only if they had at least two anthropometric measurements at different visits during the program (Figure 1).

Figure 1 Flow diagram of the enrollment of study subjects.

Abbreviation: VGH, Veteran General Hospital.

Taichung VGH Homecare App

The Taichung VGH homecare app is a non-commercial app consisting of a patient interface and a professional interface. The patient interface allows subjects to record their blood pressure, blood glucose, body weight, and meal contents via a smartphone. Particularly, meal contents can be uploaded as photos with text annotations and patient users can obtain feedback messages from professional users. The professional interface is the backstage management system, accessible by professional users. Professional users monitor the records of blood pressure, blood glucose, body weight, and meal contents uploaded by participants. Notably, professional users can send person-to-person messages in response to meal photos through the professional interface.

Mobile Weight Management Program

The mobile weight management program was provided, beginning September 1, 2020. Patients in the first visit to the Weight Control Outpatient Department registered in the Taichung VGH homecare app. Patients were assigned to a personalized meal plan with a specific calorie and macronutrient goal based on their initial weight. This program used daily weight tracking, meal tracking, and personalized feedback for meal tracking on the homecare app. Patients were encouraged to participate in weight tracking every day and upload all food and beverages consumed throughout the program without a limit on the number of food logs entered daily. Patients logged meals, snacks, treats, and beverages, including their photo, description, and quantity. Each photo of an individual meal, snack, treat, or beverage was considered a single food log entry.

Professional users, four physicians and one dietitian, checked the weight and meal tracking records on the professional interface and sent personalized feedback messages, including mental health support and diet education based on each meal photo, to the patient-user interface. Diet education included estimation of calorie, knowledge and estimation of macronutrients, and suggestions for dietary adjustments. Patient users received messages on the homecare app from professional users, and were encouraged to adjust their diet content based on recommendations to meet their personalized meal goals (Figure 2).

Figure 2 Circuit of mobile weight management program on the app.

Outpatient follow-up visits were requested in an interval of less than 3 months, and anthropometric measures (body weight, waist circumference, hip circumference, body fat mass, skeletal muscle mass, body fat mass percentage, and fat-free mass) were evaluated on every follow-up visit. Patients who did not attend the next outpatient follow-up visits within 3 months were defined as being at end of weight loss treatment.


Baseline data obtained from the medical records of the initial face-to-face interview included age, sex, and anthropometric measures (body height, body weight, waist circumference, hip circumference, body fat mass, skeletal muscle mass, body fat mass percentage, and fat-free mass). Body weight, body fat mass, body fat mass percentage, skeletal muscle mass, and fat-free mass were measured at every follow-up visit with a calibrated bioelectrical impedance scale (InBody 230 multifrequency analyzer, Biospace Corp., Seoul, Korea). BMI was calculated through the following equation: weight (kg)/(height [m])2. Waist circumference was measured to the midpoint between the lower border of the rib cage and the iliac crest. Hip circumference was measured at the level of the greatest protrusion of the buttocks when the subject was standing erect with feet together.

Type 2 diabetes was defined according to a recorded diagnosis of type 2 diabetes or use of antidiabetic drugs, an HbA1c level ≥ 6.5%, or a fasting glucose level ≥ 126 mg/dL before the date of the first visit. Prediabetes was defined according to a recorded diagnosis of prediabetes, an HbA1c between 5.7 and 6.4% or glucose between 100 and 125 mg/dL before the date of the first visit in subjects who did not meet the criteria for diabetes. Hypertension was defined according to a recorded diagnosis of hypertension or use of anti-hypertensive drugs.

Educational status, marital status, fertility status, history of weight loss, exercise habits, snacking habits, and times of eating out per week were all categorized and obtained at the first weight control outpatient visit.

Outcome Measures

The primary outcome was a successful weight loss, defined as a final weight reduction by ≥ 5% of the initial body weight.33 Changes in other anthropometric measures were defined as follows: (the last observed anthropometric measurements) – (the first observed anthropometric measurements). Duration of treatment was defined as the interval between the date of the first and last observed in-person exams. The number of dietary records was defined as the number of meal photos tracked over the duration of treatment. The number of diet records per week was defined as follows: (the number of dietary records)/(duration of treatment). The number of days of weight recording was defined as the number of days of weight tracking over the duration of treatment. The completion ratio of daily weight recording was defined as follows: (days of weight recording/days of the duration of treatment) ×100%. The number of outpatient visits was defined as the number of outpatient visits over the duration of treatment. The interval of outpatient visits was defined as follows: (weeks of treatment duration)/(number of outpatient visits).

Statistical Analyses

All continuous data are presented as the mean ± standard deviation (SD). Categorical data are presented as numbers and percentages. A Mann–Whitney U-test was conducted to detect significant differences in continuous variables between groups. A Wilcoxon Signed Rank test was conducted to detect significant differences in continuous variables before and after treatment. Chi-square tests were conducted to detect differences in categorical variables. Backward-stepwise logistic regression was used to identify the factors associated with successful weight loss (weight loss by ≥ 5%). Statistical analyses were performed using SPSS version 22.0 software (IBM Corp., Armonk, NY, USA).


In a total of 45 subjects enrolled in the study, a mean weight reduction of 4.1 ± 4.4 kg (P < 0.001) was observed over a mean duration of 11.0 ± 7.3 weeks. There were 24 (53.3%) subjects in the unsuccessful weight loss group with weight loss < 5%, and 21 (46.7%) subjects in the successful weight loss group, including: 14 subjects (31.1%) with weight loss between 5% and 9.9%, 5 (11.1%) subjects with weight loss between 10% and 14.9%, and 2 (4.4%) subjects with weight loss ≥ 15% (Figure 3).

Figure 3 Proportions of patients categorized by weight loss of their baseline body weight.

The characteristics of enrolled subjects are shown in Table 1. Subjects in the successful weight loss group had lower baseline body fat percentages compared to those in the unsuccessful weight loss group (37.5 ± 6.9% vs 41.6 ± 6.3%, P = 0.032). There were no significant differences in age (37.4 ± 11.2 years vs 43.1 ± 12.3 years, P = 0.108) and sex (47.6% female vs 75% female, P = 0.114) between the successful weight loss and the unsuccessful weight loss groups, respectively. There were no significant differences in baseline body weight, BMI, waist circumference, hip circumference, body fat mass, skeletal muscle mass, body fat mass percentage, fat-free mass, number of subjects with type 2 diabetes, prediabetes, hypertension, antidiabetic drug history, educational status, marital status, fertility status, history of weight loss, exercise habits, snacking habits, and times of eating out per week between these two groups.

Table 1 Baseline Characteristics of the Enrolled Patients

The use of the mobile app and other aspects of the interventions are shown in Table 2.

Table 2 The Clinical Practice in the Weight Loss Program

Subjects in the successful weight loss group had longer duration of treatment (14.6 ± 6.5 weeks vs 6.9 ± 6.0 weeks, P < 0.001), higher number of dietary records (109.2 ± 84.7 vs 54.7 ± 58.8, P = 0.002), higher number of outpatient visits (6.1 ± 2.7 vs 3.7 ± 2.3, P < 0.001), and higher interval of outpatient visits (2.5 ± 1.1 weeks vs 1.8 ± 0.9 weeks, P = 0.003) than those in the unsuccessful weight loss group. There were no significant differences in the number of dietary records per week, days of weight recording, and completion ratio of daily weight recording between these two groups.

Table 3 shows the changes in the anthropometric measurements. Subjects in the successful weight loss group had higher weight loss in percentage (8.6 ± 3.4% vs 1.3 ± 2.4%, P < 0.001), higher weight loss (7.6 ± 3.4 kg vs 1.1 ± 2.4 kg, P < 0.001), higher BMI reduction (2.7 ± 1.1 kg/m2 vs 0.4 ± 0.8 kg/m2, P < 0.001), higher waist circumference reduction (7.0 ± 3.7 cm vs 1.9 ± 3.2 cm, P < 0.001), higher hip circumference reduction (5.4 ± 3.4 cm vs 0.7 ± 3.3 cm, P < 0.001), higher body fat mass reduction (6.2 ± 3.0 kg vs 1.0 ± 1.1 kg, P < 0.001), higher skeletal muscle mass reduction (0.9 ± 1.1 kg vs 0 ± 1.1 kg, P = 0.018), higher body fat percentage reduction (4.4 ± 3.0% vs 0.7 ± 1.1%, P < 0.001), and higher fat-free mass reduction (1.5 ± 1.9 kg vs 0.1 ± 1.9 kg, P = 0.021) than those in the unsuccessful weight loss group.

Table 3 Changes in Anthropometric Outcome Measures

Multivariate logistic regression analyses demonstrated that duration of treatment was an independent factor associated with successful weight loss (odds ratio = 1.23, 95% confidence interval: 1.08–1.41, P = 0.003). The association was significant in both the crude and adjusted models (Table 4).

Table 4 Univariate and Backward Multivariate Logistic Analysis Showing Odds Ratios for Successful Weight Loss


The present study showed a mean weight loss of 4.7% of baseline body weight during an average 11 weeks of treatment using a mobile app. There were 46.7% of patients who had successful weight loss ≥ 5% of baseline weight. The duration of treatment was an independent factor associated with successful weight loss. Setting a weight loss goal is important for obesity management.34 According to the Report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines and The Obesity Society in 2013, a loss of ≥ 5% of the initial body weight can be a target for successful weight loss.33 In line with our findings, the duration of treatment was reported as the only independent factor of successful weight loss defined by ≥ 5% baseline weight, and all other assessed variables were no longer significantly associated with successful weight loss after adjustment for duration of treatment based on the data retrospectively extracted from electronic medical records in The Wharton Weight Management Clinic.35 Hu et al demonstrated that enrollment length in the digital Foodsmart platform was significantly associated with successful weight loss of 5% in a retrospective cohort.36 Similarly, 52-week weight loss programs showed significantly greater weight losses than 12-week programs based on a randomized controlled trial using life-style modification.37 Notably, early attrition and drop out, defined as a participant not attending a scheduled visit, were negative predictors of successful weight loss during clinical trials.38

It was previously reported that a higher frequency of face-to-face attendance is a significant predictor of greater weight loss in conventional weight loss programs.39–42 In contrast to the conventional weight loss programs, we observed a longer interval of outpatient visits and more outpatient visits in the successful weight loss group than in the unsuccessful weight loss group. None of above outpatient visit profile was independent factor after adjustment for the duration of treatment in the present study. Since self-monitoring and diet education are conducted via the mobile app, the goal of weight loss can be achieved without frequent face-to-face visits during the COVID-19 pandemic.

Dietary self-monitoring is an important component of weight loss programs.43–45 This relationship between a dietary diary and weight loss has been revealed via use of online support.46 In the Livongo Diabetes Prevention Program, food logging had the most important impact on weight loss.47 Falkenhain et al also reported that self-reported dietary adherence was the most important metric to predict weight loss during the COVID-19 pandemic, based on a secondary analysis of a randomized clinical trial using a mHealth ketogenic diet app intervention.31 Although the number of dietary records in the ≥ 5% weight loss group was significantly greater than that in the < 5% weight loss group in the present study, the difference became nonsignificant after standardizing the number of dietary records by treatment time, ie the number of dietary records per week. Carter et al reported that a long duration was significantly associated with improvement of weight loss via electronic dietary self-monitoring using a mobile app.48 In line with our findings, weight loss via an app using a photo feature as a dietary record method showed that a greater weight loss is mediated by increased duration and logged days.49 Notably, several dietary-based programs for weight loss demonstrated that greater initial weight loss is associated with a lower study dropout rate.50–54 Initial weight loss is a known predictor of successful weight loss and greater initial weight loss may be the motivating factor leading to longer treatment length and less attrition.55

High education is helpful in adopting a healthy diet and associated with a good repose to weight loss program.56 Despite high education was associated with successful weight loss under univariable analysis, the association became nonsignificant after adjustment for the duration of treatment using mobile app in the present study. It has been reported that self-reporting weight has a potential social desirability bias.57 The strength of our study was that the measurements of weight was performed using the same InBody machine for body-component analyses. However, the present study had several limitations: First, to promote adherence to the program, subjects were not followed by a fixed time schedule, and diet education via the app did not use a fixed formula in this study program. Therefore, our results cannot be applied to the weight-reduction programs with fixed-visit schedules. Second, the case number was relatively small in the present study. Third, changes in adherence-related profiles are important for successful weight loss. The self-weighing trajectories are reported to be significantly associated with weight-loss maintenance.58 However, we did not perform these analyses due to limited case numbers and different visit intervals in each participant. Fourth, the present study had an observational design, which did not allow the determination of a causal effect. A randomized controlled trial is warranted to help determine causal direction.


We present a retrospective analysis of a weight management program using an app with self-monitoring dietary records, weight tracking, and person-to-person individualized feedback in real-world practice during the COVID-19 pandemic. The duration of treatment was an independent factor associated with successful weight loss. The numbers of dietary records and outpatient visits were not significantly associated with successful weight loss in multivariate logistic analysis.

Ethical Approval and Informed Consent

The study complied with the Declaration of Helsinki. The Institutional Review Board of the Taichung Veterans General Hospital approved the protocol (ethical approval code: CE21241B) and waived the need for informed consent due to the retrospective collection of data. Anonymous medical record data were obtained from the Clinical Informatics Research & Development Center of Taichung Veterans General Hospital after delinking the identification code.


This work was supported by grants from the Taichung Veterans General Hospital, Taichung, Taiwan (grant number TCVGH-1103503C) and the Ministry of Science and Technology, Taiwan (grant number MOST 110-2314-B-075A-004-MY3). The funders had no role in the decision to submit the manuscript for publication.


The authors report no conflicts of interest in this work.


1. National Task Force on the Prevention and Treatment of Obesity. Overweight, obesity, and health risk. Arch Intern Med. 2000;160(7):898–904. doi:10.1001/archinte.160.7.898

2. Flegal KM, Graubard BI, Williamson DF, Gail MH. Excess deaths associated with underweight, overweight, and obesity. JAMA. 2005;293(15):1861–1867. doi:10.1001/jama.293.15.1861

3. Ogden CL, Carroll MD, McDowell MA, Flegal KM. Obesity among adults in the United States–no statistically significant change since 2003–2004. NCHS Data Brief; 2007:1–8.

4. Ogden CL, Yanovski SZ, Carroll MD, Flegal KM. The epidemiology of obesity. Gastroenterology. 2007;132(6):2087–2102. doi:10.1053/j.gastro.2007.03.052

5. Ng M, Fleming T, Robinson M, et al. Global, regional, and national prevalence of overweight and obesity in children and adults during 1980–2013: a systematic analysis for the Global Burden of Disease Study 2013. Lancet. 2014;384(9945):766–781. doi:10.1016/S0140-6736(14)60460-8

6. Wang YC, McPherson K, Marsh T, Gortmaker SL, Brown M. Health and economic burden of the projected obesity trends in the USA and the UK. Lancet. 2011;378(9793):815–825. doi:10.1016/S0140-6736(11)60814-3

7. Chang HC, Yang HC, Chang HY, et al. Morbid obesity in Taiwan: prevalence, trends, associated social demographics, and lifestyle factors. PLoS One. 2017;12(2):e0169577. doi:10.1371/journal.pone.0169577

8. Yeh CJ, Chang HY, Pan WH. Time trend of obesity, the metabolic syndrome and related dietary pattern in Taiwan: from NAHSIT 1993–1996 to NAHSIT 2005–2008. Asia Pac J Clin Nutr. 2011;20(2):292–300.

9. Liao CC, Sheu WH, Lin SY, Lee WJ, Lee IT. The relationship between abdominal body composition and metabolic syndrome after a weight reduction program in adult men with obesity. Diabetes Metab Syndr Obes. 2020;13:1–8. doi:10.2147/DMSO.S228954

10. Franz MJ, VanWormer JJ, Crain AL, et al. Weight-loss outcomes: a systematic review and meta-analysis of weight-loss clinical trials with a minimum 1-year follow-up. J Am Diet Assoc. 2007;107(10):1755–1767. doi:10.1016/j.jada.2007.07.017

11. Anderson JW, Konz EC. Obesity and disease management: effects of weight loss on comorbid conditions. Obes Res. 2001;Suppl 9(S11):326S–334S. doi:10.1038/oby.2001.138

12. American Diabetes Association and National Institute of Diabetes, Digestive and Kidney Diseases. The prevention or delay of type 2 diabetes. Diabetes Care. 2002;25(4):742–749. doi:10.2337/diacare.25.4.742

13. Nordmo M, Danielsen YS, Nordmo M. The challenge of keeping it off, a descriptive systematic review of high-quality, follow-up studies of obesity treatments. Obes Rev. 2020;21(1):e12949. doi:10.1111/obr.12949

14. McGuire MT, Wing RR, Hill JO. The prevalence of weight loss maintenance among American adults. Int J Obes Relat Metab Disord. 1999;23(12):1314–1319. doi:10.1038/sj.ijo.0801075

15. Roberto CA, Swinburn B, Hawkes C, et al. Patchy progress on obesity prevention: emerging examples, entrenched barriers, and new thinking. Lancet. 2015;385(9985):2400–2409. doi:10.1016/S0140-6736(14)61744-X

16. World Health Organization. WHO Director-General’s opening remarks at the media briefing on COVID-19-11; 2020 [cited March 11, 2020]. Available from: https://www.who.int/director-general/speeches/detail/who-director-general-s-opening-remarks-at-the-media-briefing-on-covid-19—11-march-2020. Accessed June 2, 2022.

17. Di Renzo L, Gualtieri P, Pivari F, et al. Eating habits and lifestyle changes during COVID-19 lockdown: an Italian survey. J Transl Med. 2020;18(1):229. doi:10.1186/s12967-020-02399-5

18. Sidor A, Rzymski P. Dietary choices and habits during COVID-19 lockdown: experience from Poland. Nutrients. 2020;12(6):1657. doi:10.3390/nu12061657

19. Cheikh Ismail L, Osaili TM, Mohamad MN, et al. Eating habits and lifestyle during COVID-19 lockdown in the United Arab Emirates: a cross-sectional study. Nutrients. 2020;12(11):3314. doi:10.3390/nu12113314

20. Robinson E, Boyland E, Chisholm A, et al. Obesity, eating behavior and physical activity during COVID-19 lockdown: a study of UK adults. Appetite. 2021;156:104853. doi:10.1016/j.appet.2020.104853

21. Kass DA, Duggal P, Cingolani O. Obesity could shift severe COVID-19 disease to younger ages. Lancet. 2020;395(10236):1544–1545. doi:10.1016/S0140-6736(20)31024-2

22. Caussy C, Pattou F, Wallet F, et al. Prevalence of obesity among adult inpatients with COVID-19 in France. Lancet Diabetes Endocrinol. 2020;8(7):562–564. doi:10.1016/S2213-8587(20)30160-1

23. Williamson EJ, Walker AJ, Bhaskaran K, et al. Factors associated with COVID-19-related death using OpenSAFELY. Nature. 2020;584(7821):430–436. doi:10.1038/s41586-020-2521-4

24. Ho FK, Celis-Morales CA, Gray SR, et al. Modifiable and non-modifiable risk factors for COVID-19, and comparison to risk factors for influenza and pneumonia: results from a UK Biobank prospective cohort study. BMJ Open. 2020;10(11):e040402. doi:10.1136/bmjopen-2020-040402

25. England PH. Weight management services during COVID-19: Phase 1 insights; 2020 [cited September 7, 2020]. Available from: https://www.gov.uk/government/publications/weight-management-services-during-covid-19-phase-1-insights. Accessed June 2, 2022.

26. Ufholz K, Bhargava D. A review of telemedicine interventions for weight loss. Curr Cardiovasc Risk Rep. 2021;15(9):17. doi:10.1007/s12170-021-00680-w

27. Carter MC, Burley VJ, Nykjaer C, Cade JE. Adherence to a smartphone application for weight loss compared to website and paper diary: pilot randomized controlled trial. J Med Internet Res. 2013;15(4):e32. doi:10.2196/jmir.2283

28. Ang SM, Chen J, Liew JH, et al. Efficacy of interventions that incorporate mobile apps in facilitating weight loss and health behavior change in the asian population: systematic review and meta-analysis. J Med Internet Res. 2021;23(11):e28185. doi:10.2196/28185

29. Cavero-Redondo I, Martinez-Vizcaino V, Fernandez-Rodriguez R, Saz-Lara A, Pascual-Morena C, Álvarez-Bueno C. Effect of behavioral weight management interventions using lifestyle mhealth self-monitoring on weight loss: a systematic review and meta-analysis. Nutrients. 2020;12(7):1977. doi:10.3390/nu12071977

30. Demeke HB, Merali S, Marks S, et al. Trends in use of telehealth among health centers during the COVID-19 pandemic – United States, June 26-November 6, 2020. MMWR Morb Mortal Wkly Rep. 2021;70(7):240–244. doi:10.15585/mmwr.mm7007a3

31. Falkenhain K, Locke SR, Lowe DA, et al. Use of an mHealth ketogenic diet app intervention and user behaviors associated with weight loss in adults with overweight or obesity: secondary analysis of a randomized clinical trial. JMIR mHealth UHealth. 2022;10(3):e33940. doi:10.2196/33940

32. Liu YC, Liao CH, Chang CF, et al. A locally transmitted case of SARS-CoV-2 infection in Taiwan. N Engl J Med. 2020;382:1070–1072. doi:10.1056/NEJMc2001573

33. Jensen MD, Ryan DH, Apovian CM, et al. 2013 AHA/ACC/TOS guideline for the management of overweight and obesity in adults: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines and The Obesity Society. Circulation. 2014;129(25_suppl_2):S102–138. doi:10.1161/01.cir.0000437739.71477.ee

34. Wharton S, Lau DCW, Vallis M, et al. Obesity in adults: a clinical practice guideline. CMAJ. 2020;192:E875–E891. doi:10.1503/cmaj.191707

35. Jiandani D, Wharton S, Rotondi MA, Ardern CI, Kuk JL. Predictors of early attrition and successful weight loss in patients attending an obesity management program. BMC Obes. 2016;3:14. doi:10.1186/s40608-016-0098-0

36. Hu EA, Nguyen V, Langheier J, et al. Weight reduction through a digital nutrition and food purchasing platform among users with obesity: longitudinal study. J Med Internet Res. 2020;22:e19634. doi:10.1016/S0140-6736(20)31024-2

37. Ahern AL, Wheeler GM, Aveyard P, et al. Extended and standard duration weight-loss programme referrals for adults in primary care (WRAP): a randomised controlled trial. Lancet. 2017;389(10085):2214–2225. doi:10.1016/S0140-6736(17)30647-5

38. Ponzo V, Scumaci E, Goitre I, et al. Predictors of attrition from a weight loss program. A study of adult patients with obesity in a community setting. Eat Weight Disord. 2021;26(6):1729–1736. doi:10.1007/s40519-020-00990-9

39. Kerrigan SG, Clark M, Convertino A, Forman EM, Butryn ML. The association between previous success with weight loss through dietary change and success in a lifestyle modification program. J Behav Med. 2018;41(2):152–159. doi:10.1007/s10865-017-9883-6

40. Batra P, Das SK, Salinardi T, et al. Eating behaviors as predictors of weight loss in a 6 month weight loss intervention. Obesity. 2013;21(11):2256–2263. doi:10.1002/oby.20404

41. Piernas C, MacLean F, Aveyard P, et al. Greater attendance at a community weight loss programme over the first 12 weeks predicts weight loss at 2 years. Obes Facts. 2020;13(4):349–360. doi:10.1159/000509131

42. Karlsen TI, Søhagen M, Hjelmesæth J. Predictors of weight loss after an intensive lifestyle intervention program in obese patients: a 1-year prospective cohort study. Health Qual Life Outcomes. 2013;11:165. doi:10.1186/1477-7525-11-165

43. Burke LE, Wang J, Sevick MA. Self-monitoring in weight loss: a systematic review of the literature. J Am Diet Assoc. 2011;111(1):92–102. doi:10.1016/j.jada.2010.10.008

44. Boutelle KN, Kirschenbaum DS. Further support for consistent self-monitoring as a vital component of successful weight control. Obes Res. 1998;6(3):219–224. doi:10.1002/j.1550-8528.1998.tb00340.x

45. Taylor CB, Agras WS, Losch M, Plante TG, Burnett K. Improving the effectiveness of computer-assisted weight loss. Behav Ther. 1991;22(2):229–236. doi:10.1016/S0005-7894(05)80178-9

46. Johnson F, Wardle J. The association between weight loss and engagement with a web-based food and exercise diary in a commercial weight loss programme: a retrospective analysis. Int J Behav Nutr Phys Act. 2011;8:83. doi:10.1186/1479-5868-8-83

47. Painter SL, Lu W, Schneider J, James R, Shah B. Drivers of weight loss in a CDC-recognized digital diabetes prevention program. BMJ Open Diabetes Res Care. 2020;8(1):e001132. doi:10.1136/bmjdrc-2019-001132

48. Carter MC, Burley VJ, Cade JE. Weight loss associated with different patterns of self-monitoring using the mobile phone app my meal mate. JMIR mHealth UHealth. 2017;5(2):e8. doi:10.2196/mhealth.4520

49. Ben Neriah D, Geliebter A. Weight loss following use of a smartphone food photo feature: retrospective cohort study. JMIR mHealth UHealth. 2019;7(6):e11917. doi:10.2196/11917

50. Yackobovitch-Gavan M, Steinberg DM, Endevelt R, Benyamini Y. Factors associated with dropout in a group weight-loss programme: a longitudinal investigation. J Hum Nutr Diet. 2015;28(Suppl 2):33–40. doi:10.1111/jhn.12220

51. Colombo O, Ferretti VV, Ferraris C, et al. Is drop-out from obesity treatment a predictable and preventable event? Nutr J. 2014;13:13. doi:10.1186/1475-2891-13-13

52. Handjieva-Darlenska T, Holst C, Grau K, et al. Clinical correlates of weight loss and attrition during a 10-week dietary intervention study: results from the NUGENOB project. Obes Facts. 2012;5(6):928–936. doi:10.1159/000345951

53. Batterham M, Tapsell LC, Charlton KE. Predicting dropout in dietary weight loss trials using demographic and early weight change characteristics: implications for trial design. Obes Res Clin Pract. 2016;10(2):189–196. doi:10.1016/j.orcp.2015.05.005

54. Greenberg I, Stampfer MJ, Schwarzfuchs D, Shai I. Adherence and success in long-term weight loss diets: the dietary intervention randomized controlled trial (DIRECT). J Am Coll Nutr. 2009;28(2):159–168. doi:10.1080/07315724.2009.10719767

55. Chopra S, Malhotra A, Ranjan P, et al. Predictors of successful weight loss outcomes amongst individuals with obesity undergoing lifestyle interventions: a systematic review. Obes Rev. 2021;22(3):e13148. doi:10.1111/obr.13148

56. Zheng Y, Sereika SM, Danford CA, et al. Trajectories of weight change and predictors over 18-month weight loss treatment. J Nurs Scholarsh. 2017;49(2):177–184. doi:10.1111/jnu.12283

57. Burke MA, Carman KG. You can be too thin (but not too tall): social desirability bias in self-reports of weight and height. Econ Hum Biol. 2017;27(Pt A):198–222. doi:10.1016/j.ehb.2017.06.002

58. Zheng Y, Burke LE, Danford CA, et al. Patterns of self-weighing behavior and weight change in a weight loss trial. Int J Obes. 2016;40:1392–1396. doi:10.1038/ijo.2016.68

Comments are closed.