Published on in Vol 23, No 9 (2021): September

Preprints (earlier versions) of this paper are available at, first published .
Use of a Mobile Lifestyle Intervention App as an Early Intervention for Adolescents With Obesity: Single-Cohort Study

Use of a Mobile Lifestyle Intervention App as an Early Intervention for Adolescents With Obesity: Single-Cohort Study

Use of a Mobile Lifestyle Intervention App as an Early Intervention for Adolescents With Obesity: Single-Cohort Study

Original Paper

1KK Women's and Children's Hospital, Singapore, Singapore

2ReMark Group, Singapore, Singapore

3National Institute of Education, Singapore, Singapore

4Duke-NUS Medical School, Health Services and Systems Research, Singapore, Singapore

Corresponding Author:

Chu Shan Elaine Chew, MBBS, MRCPCH, MCI

KK Women's and Children's Hospital

100 Bukit Timah Road

Singapore, 229899


Phone: 65 62255554


Background: Effective, resource-efficient treatment is urgently needed to address the high rates of pediatric and adolescent obesity. This need has been accelerated by the COVID-19 pandemic. The use of a mobile health tool as an early intervention before a clinic-based multidisciplinary weight management program could be an effective treatment strategy that is appropriate during a pandemic.

Objective: This study aims to assess the effectiveness of and adolescent engagement with a mobile app–based lifestyle intervention program as an early intervention before enrollment in a clinic-based multidisciplinary weight management program.

Methods: This prospective single-cohort study involved adolescents, aged 10-16 years, who were overweight and obese (defined as BMI percentile above the 85th percentile). Participants used the mobile Kurbo app as an early intervention before enrolling in a clinic-based multidisciplinary weight management program. Kurbo’s health coaches provided weekly individual coaching informed by a model of supportive accountability via video chat, and participants self-monitored their health behavior. The implementation of Kurbo as an early intervention was evaluated using the reach, effectiveness, adoption, implementation, and maintenance framework by reach (number who consented to participate out of all patients approached), implementation (Kurbo engagement and evaluation), and effectiveness as measured by the primary outcome of the BMI z-score at 3 months. Secondary outcome measures included changes in body fat percentage, nutrition and physical activity levels, and quality of life at 3 months. Maintenance was defined as the outcome measures at 6-month follow-up.

Results: Of the 73 adolescents who were approached for enrollment, 40 (55%) of adolescents were recruited. The mean age was 13.8 (SD 1.7) years, and the mean BMI z-score was 2.07 (SD 0.30). In the multiethnic Asian sample, 83% (33/40) of the participants had household incomes below the national median. Kurbo engagement was high, with 83% (33/40) of participants completing at least 7 coaching sessions. In total, 78% (18/23) of participants rated the app as good to excellent and 70% (16/23) stated that they would recommend it to others. There were no statistically significant changes in BMI z-scores at 3 months (P=.19) or 6 months (P=.27). Participants showed statistically significant improvements in measured body fat percentage, self-reported quality of life, and self-reported caloric intake from the 3-day food diaries at 3 and 6 months.

Conclusions: The use of Kurbo before enrollment in an outpatient multidisciplinary clinical care intervention is a feasible strategy to expand the reach of adolescent obesity management services to a low-income and racially diverse population. Although there was no significant change in BMI z-scores, the use of Kurbo as an early intervention could help to improve quality of life and reduce body fat percentage and total caloric intake.

J Med Internet Res 2021;23(9):e20520




Mirroring global trends, the prevalence of overweight among Singaporean adolescents increased from 2.2% in 1975 to 15.9% in 2016. Overweight adolescents are at a higher risk for adult obesity as well as short- and long-term medical and psychosocial complications [1]. To address this concern in a resource-efficient manner, experts have proposed a staged care approach for the management of adolescents who are overweight and obese. Those who are less successful in reaching a healthy weight through less-intensive interventions, such as mobile health (mHealth) apps [2], are then recommended for higher stage interventions. mHealth apps have the potential for broad reach and adoption among Singaporean youth because of their low cost, widespread internet availability, and high levels of smartphone ownership [3-5]. Given the current COVID-19 pandemic, mHealth is a timely and essential tool to engage adolescents when in-person services are unavailable and when COVID-19 response strategies contribute to decreased physical activity and increase other unhealthy lifestyle behaviors [6,7,8].

The commercially available mHealth weight management program, Kurbo, has provided individualized health coaching, educational videos on nutrition and physical activity, and self-monitoring to improve diet and physical activity behaviors [9]. Prior research among Kurbo users showed that increased engagement with the Kurbo features, including the web-based coaching sessions, was associated with greater weight loss, suggesting that Kurbo may be appropriate as an early-stage intervention among adolescents with obesity [10].


Therefore, the primary objective of this study is to examine the implementation of Kurbo as an early intervention for adolescents with obesity before enrollment in a clinic-based multidisciplinary weight management program in Singapore. The evaluation used the relevant dimensions of the reach, effectiveness, adoption, implementation, and maintenance evaluation framework [11].

Study Design

This was a prospective, single-arm study that conducted evaluations at three time points: baseline, 3 months, and 6 months. A total of 40 participants, with informed parental consent and child assent, were enrolled in the study at the point of referral to the KK Women’s and Children’s Hospital (KKH) weight management clinic (WMC). Participants were enrolled between October 2018 and March 2019. All study procedures were approved by the Singhealth Centralized Institutional Review Board. The study was registered at (NCT03561597).

Multidisciplinary WMC Clinic

KKH is an 830-bed tertiary pediatric teaching hospital that provides two-third of the government-subsidized pediatric care in Singapore. The adolescent WMC is a physician-led multidisciplinary clinic where adolescents with overweight and their families engage with a multidisciplinary care team consisting of physicians, dietitians, exercise physiologists, and psychologists to set and monitor behavioral goals to manage obesity-related comorbidities. The KKH WMC protocols and outcomes have been previously published, with a historical dropout rate of 58% [12].

The usual waiting time for a first visit to the WMC clinic varies from 4 to 8 weeks after the initial referral. For this study, WMC providers had access to information about participants’ Kurbo progress through an administrator site during this period. This allowed for monitoring safety concerns and guiding discussions during clinic visits. After the first WMC visit, dietary recommendations and counseling were provided by the dietitian according to the recommended nutritional guidelines. Physical activity counseling was also performed by exercise trainers based on the World Health Organization guidelines on physical activity and sedentary behavior.

Study Participants

Adolescents, referred to the WMC, aged 10-17 years with a BMI percentile above the 85th percentile [13], were eligible for enrollment. Adolescents with secondary causes of obesity, such as Cushing syndrome, those whose parents were non-English speakers, and those without smartphone access were not eligible for enrollment. Study participants were asked to download the Kurbo app onto their mobile phones and enroll in the app at the point of study enrollment. The participants had free access to the Kurbo program.

Kurbo Program

Kurbo is a mobile app developed to aid adolescents and their families with weight management through dietary self-monitoring and weekly coaching sessions (Figure 1). The details of the Kurbo mobile app and use of supportive accountability in the coaching program have been described previously [10,14]. In short, the Kurbo program consists of a mobile app for self-monitoring of eating, physical activity behaviors, and weight and individualized coaching sessions by Kurbo-certified behavioral coaches. The mobile app’s data-driven platform provides users with feedback via push notifications, text messages, and emails. Using the traffic light diet to categorize foods [15], Kurbo promotes the gradual reduction of high-calorie (red) foods over time. Study participants were asked to use the mobile app to log their daily food intake and were encouraged to gradually reduce their red food consumption and increase consumption of green food. They were also recommended to monitor their daily physical activity levels and to work toward the recommended 60 minutes of moderate-to-vigorous physical activity (MVPA) each day. Weight tracking and uploading into the app were recommended at least weekly. Individualized coaching included a weekly check-in with the coach for 15 minutes via video, phone, or text over a 12-week period. Participants were paired with the same coach for the duration of the program. Coaches monitored participants’ self-report of weight, physical activity, and dietary behaviors and provided individualized feedback based on the information provided. After each coaching session, coaches emailed a copy of the session summary and a tailored plan for the coming week.

Figure 1. Screenshot of Kurbo app.
View this figure


Demographic and parental characteristics were obtained at the baseline. Questionnaires and food diaries were obtained through self-reports using physical forms during the study visits.


The implementation of the Kurbo program as an early intervention was evaluated using the relevant dimensions of reach, effectiveness, adoption, implementation, and maintenance framework [11] as follows: (1) reach, number who consented to participate out of all patients approached; (2) implementation, program engagement as detailed for the 12-week program and program evaluation; (3) effectiveness, measured by 3-month BMI z-score changes from baseline and changes in blood pressure, nutritional intake physical activity levels, quality of life, and disordered eating behaviors as defined below; and (4) maintenance, measured using the identical outcomes at 6 months and 3 months after the program conclusion. Throughout the 12-week program, we also recorded the weekly frequency of information entered for meal consumption, body weight, physical activity, and coaching sessions. We evaluated satisfaction with the Kurbo program through solicitation of participants’ feedback on ease of program navigation and overall use as well as their likelihood to recommend Kurbo to others.

Anthropometric and Blood Pressure Measurements

Height, weight, waist circumference [16], body fat percentage [17], and blood pressure were measured by trained staff at each study visit in the clinic. Height was measured to the nearest 0.1 cm via a stadiometer (Seca, Model 220). Weight was measured to the nearest 0.1 kg using a medical weighing scale without shoes and in light clothing. Body fat percentage was assessed using bioimpedance analysis (Impedimed, DF50 Body Composition Analyser). Blood pressure was measured using an electronic sphygmomanometer (Dinamap model 8101, Critikon Inc). Anthropometric measurements were taken at baseline and at 3 and 6 months.

Nutritional Intake

Adolescents’ daily total caloric intake and fruit and vegetable consumption were assessed using a 3-day food diary that has been previously validated for use with Singaporean adolescents [18,19]. The food diary was administered at baseline and at 3 and 6 months.

Physical Activity Levels

Physical activity was assessed using a wGT3X+ ActiGraph accelerometer. Participants wore the accelerometer for a 7-day period at baseline, 3 months, and 6 months. The ActiGraph data were processed using ActiLife 6 software. The Puyau cutoff point of 3200 counts per minute was used to estimate the time spent in MVPA. When 20 minutes of consecutive zeros were present in the accelerometry data, it was assumed that the monitor was not being worn at that time. All days with >500 minutes of valid data were included in the analysis [20-22].

Psychosocial Outcomes

Self-reported questionnaires were administered to adolescents at baseline, 3 months, and 6 months. The Pediatric Quality of Life Inventory (PedsQoL; UK version 4) was administered to evaluate physical, emotional, school, and social functioning. The Eating Pattern Inventory for Children (EPI-C) [23] was used to assess four dimensions of psychological eating behavior: external eating, emotional eating, dietary restraint, and parental pressure to eat. Higher scores were indicative of concerning behavior in each dimension. Both the PedsQoL and EPI-C have been validated in adolescent cohorts, including overweight adolescents [24,25].

Statistical Analysis

Data were analyzed using SPSS version 9.3 for Windows (IBM Corp). BMI was computed as kg/m2, and the BMI z-score was calculated using the L, M, and S parameters published by the Centers for Disease Control and Prevention [13]. Baseline demographic, anthropometric measurements, and parent characteristics were compared between study completers and those who dropped out of the study using the Wilcoxon rank-sum test and two-sample, 2-tailed t tests for nonnormal and normal continuous variables, respectively, and the Fisher exact test for categorical variables. Changes in anthropometric measurements, quality of life, disordered eating behaviors, MVPA, total caloric intake, and blood pressure measurements between the first visit and months 3 and 6 were compared using paired-sample t tests. Linear regression was used to determine the association between BMI z-scores at 3 months (primary outcome) and the number of WMC visits attended during the Kurbo program. The level of statistical significance was set at P<.05.

Implementation of Kurbo Program as an Early Intervention


Of the 73 eligible participants, 40 (55%) were consented and enrolled in the study. The mean age of the participants was 13.8 (SD 1.7) years. Among this, 58% (23/40) of the enrolled adolescents were male, 45% (18/40) were Chinese, 33% (13/40) were Malay, and 13% (5/40) were Indian. Of 40 participants, 32 (80%) were referred to WMC as a result of opportunistic screening during medical visits for nonobesity-related conditions and 8 (20%) were referred specifically for obesity-related comorbidities. Moreover, 65% (26/40) of participants had a family history of metabolic diseases, and 83% (33/40) of accompanying parents were overweight or obese. Overall, 83% (33/40) of participants had household income less than the national median monthly household income of SGD 9500 (US $7125) [26], and approximately one-third had a monthly household income below SGD 2000 (US $1500). Half of the accompanying parents had a secondary school–level education or less. Other baseline characteristics of the participants are summarized in Table 1. There were no significant differences in baseline characteristics between those who completed the study and those who dropped out (Table 1). In total, 53% (21/40) of adolescents completed the 3-month assessment, and 50% (20/40) completed the 6-month assessment (Figure 2).

Table 1. Baseline characteristics of participants in the study (N=40).
CharacteristicsTotal (N=40)Completers (n=20)Drop-outs (n=20)P value
Baseline characteristics

Gender (male), n (%)23 (58)12 (60)11 (55).79

Ethnicity, n (%).25

Chinese18 (45)12 (60)6 (30)

Malay13 (33)3 (15)11 (55)

Indian5 (13)2 (10)3 (15)

Other4 (10)4 (35)0 (0)

Weight category, n (%).32

Overweight8 (20)5 (25)3 (15)

Obesity32 (80)15 (75)17 (85)

Family history of metabolic diseases, n (%)26 (65)12 (60)14 (74).37

Age (years), mean (SD)13.8 (1.7)14.6 (1.5)13.4 (1.8).62

Body mass (kg), mean (SD)81.2 (17.2)83.8 (19.1)78.6 (15.1).34

Height (cm), mean (SD)161.9 (11.4)163.3 (12.2)160.4 (10.7).43

BMI (kg/m2), mean (SD)30.7 (3.9)30.6 (4.3)30.3 (3.5).79

BMI z-scores, mean (SD)2.07 (0.30)2.05 (0.34)2.09 (0.25).68

Waist-to-height ratio, mean (SD)0.61 (0.06)0.60 (0.06)0.61 (0.05).80

Body fat percentage (%), mean (SD)43.3 (5.9)42.8 (5.8)44 (6).53

Blood pressure (mm Hg), mean (SD)

Systolic121 (13)120 (13)117 (10).26

Diastolic69 (9)68 (10)67 (5).60
Accompanying parental baseline characteristics

Age (years), mean (SD)43.6 (5.3)44.7 (5.2)42.5 (5.2).19

Gender (female), n (%)33 (82)17 (85)16 (80).68

Weight status, n (%)

Overweight11 (28)7 (35)4 (20).57

Obesity22 (55)10 (50)12 (60).57

Marital status, n (%).79

Married parent33 (82)17 (85)16 (80)

Single parent7 (18)3 (15)4 (20)

Highest education level, n (%).11

Secondary school (equivalent to 10 years of education)21 (53)8 (40)13 (65)

Diploma11 (27)8 (40)3 (15)

Bachelor’s degree and above8 (20)4 (20)4 (20)

Monthly household income (SGD; US $), n (%).27

Below 1500 (1125)13 (33)4 (20)9 (45)

1500-4499 (1125-3374.25)12 (31)9 (45)3 (15)

4500-7500 (3375-5625)11 (26)6 (30)5 (25)

Not reported4 (10)1 (5)3 (15)
Figure 2. CONSORT (Consolidated Standards of Reporting Trials) diagram showing the flow of participants through each stage of the trial. WMC: weight management clinic.
View this figure
Engagement and Evaluation

Overall, 83% (33/40) of participants completed at least 1 health coaching session. Participants completed a median of 7 (IQR 2-10) weekly sessions. Initial participant engagement across all Kurbo components was initially high but decreased over time (Figure 3). During the 12-week program, participants logged mean of 4.1 (SD 2.1) weight measurements. On average, participants tracked mean green foods 3.8 (SD 3.9) times and red foods 4.6 (SD 3.7) times and recorded physical activity 3.2 (SD 2.6) times each week. In addition, 18% (7/40) of participants did not engage in any health coaching session, weight, meals, or physical activity tracking.

A total of 23 participants completed the evaluation of Kurbo and individual components, as shown in Figures 4 and 5. Participants rated communication with the health coach as the most user-friendly and useful component. A total of 21 participants reported following the health coaches’ advice to a moderate-to-large extent. The participants did not report difficulties in using the app. Moreover, 18 participants rated the app as good to excellent, with 16 participants stating that they would recommend it to others.

Figure 3. Number of participants who logged in at least one weight, food, or physical activity by week.
View this figure
Figure 4. User-friendliness of the various components of Kurbo where 1=very difficult and 5=very easy.
View this figure
Figure 5. Usefulness of the various component of Kurbo where 1=not useful and 5=very useful.
View this figure

Effectiveness (3-Month Outcome) and Maintenance (6-Month Outcome)

Anthropometric and Blood Pressure Outcomes

There were no significant changes in BMI z-score (primary outcome) at either 3 or 6 months. However, there was a significant reduction in body fat percentage at both 3 months (−1.3%; 95% CI −2.5% to −0.2%; P=.03) and 6 months (−2%; 95% CI −3.6% to −0.4%; P=.02; Table 2). After adjusting for multidisciplinary weight management visits during engagement with Kurbo P, there was no significant difference in BMI z-scores at 3 months (F27=0.406; P=.53). No significant differences were detected at 6 months for blood pressure, waist circumference, or waist-to-height ratio (Table 2).

Table 2. Changes in adolescents’ anthropometric and blood pressure (N=40).
VariableBaseline to 3 months (n=21)Baseline to 6 months (n=20)

Value, mean (SD; 95% CI)P valueValue, mean (SD; 95% CI)P value
Body mass (kg)2.7 (4.74; 0.5 to 4.8).023.59 (4.55; 1.45 to 5.71).002
BMI z-score0.045 (0.15; −0.024 to 0.114).190.035 (0.14; −0.028 to 0.098).27
Waist circumference (cm)1.1 (6.74; −1.9 to 4.2).450.3 (6.21; −2.6 to 3.2).84
Waist-to-height ratio−0.003 (0.040; −0.20 to 0.015).750.004 (0.038; −0.014 to 0.022).67
Body fat (%)−1.31 (2.54; −2.47 to −0.15).03−2.0 (3.46; −3.6 to −0.38).02
Systolic BPa (mm Hg)−5.5 (9.21; −9.8 to −1.2).02−2.1 (9.62; −6.8 to 2.5).35
Diastolic BP (mm Hg)−4.2 (8.50; −8.1 to −0.2).04−3.2 (8.47; −7.3 to 0.85).11

aBP: blood pressure.

Eating and Physical Activity Behaviors

The 3-day food diary revealed significant reductions in caloric intake at 3 months (mean −300, SD 456; 95% CI −576 to −24; P=.04) and 6 months (mean −332, SD 517; 95% CI 598 to −66; P=.02; Table 3) [5]. Moreover, the time spent in MVPA (minutes) at 6 months increased (mean 5.3, SD 4.8; 95% CI 0.88-9.75; P=.03; Table 3).

Table 3. Changes in adolescents’ health behavior and psychosocial parameters (N=40).
VariableBaseline to 3 months (n=21)Baseline to 6 months (n=20)

Value, mean (SD; 95% CI)P valueValue, mean (SD; 95% CI)P value
Total (kcal/day)−300 (457; −576 to −24).04−332 (518; −598 to −66).02
Servings of vegetables per day−0.17 (0.50; −0.5 to 0.1).24−0.0 (0.79; −0.4 to 0.4).99
Average moderate-to-vigorous physical activity per day (minutes)1.47 (10.03; −4.3 to 7.2).595.3 (4.78; 0.88 to 9.75).03
AdolescentsPediatric Quality of Life Inventory

Total2.4 (13.11; −3.1 to 7.9).381.4 (11.67; −4.2 to 7.0).61

Physical4.0 (13.56; −1.7 to 9.8).161.0 (11.60; −4.6 to 6.6).72

Emotional6.5 (23.34; −3.4 to 16.3).199.7 (20.58; −0.2 to 19.7).05

School8.3 (18.28; 0.6 to 16.1).046.9 (13.52; 0.2 to 13.7).04

Psychosocial6.3 (14.52; 0.2 to 12.5).046.5 (12.98; 0.2 to 12.7).04
Eating Pattern Inventory for Children

Dietary restraint0.033 (0.42; −0.15 to 0.21).720.00 (0.48; 0.23 to −0.23).99

External eating−0.087 (0.82; −0.44 to 0.27).62−0.19 (0.65; −0.50 to 0.12).22

Parental pressure to eat0.116 (0.54; −0.12 to 0.35).310.018 (0.66; −0.30 to 0.33).91

Emotional eating0.00 (0.79; −0.34 to 0.34).990.171 (0.67; −0.15 to 0.49).28
Psychosocial Outcomes

At 3 months, adolescents’ self-reported quality of life improved in the school (mean 8.3; 95% CI 0.6-16.1; P=.04) and psychosocial (mean 6.3; 95% CI 0.19-12.5; P=.04) domains (Table 2). These improvements persisted for 6 months. There were no significant changes in eating patterns, including dietary restraint, external eating, parental pressure to eat, and emotional eating subscales.

Principal Findings

This pilot study is one of the few studies to evaluate the implementation of a multicomponent mobile app as an early intervention before enrollment in an adolescent WMC. The Kurbo pilot was successful in reaching a low-income and racially diverse population. Although there was no significant reduction in BMI z-scores, there were significant improvements in fat percentage, total caloric intake, and quality of life, suggesting potential benefits of enrolment and the need for a more formal randomized trial.

Obtaining a reach of 58% is comparable with that of other pediatric obesity studies [27]. Moreover, the high percentage of minority, lower education, and low-income enrollees suggests that Kurbo is more likely to reach disadvantaged populations than traditional in-person programs. Given the high rates of obesity among children of lower socioeconomic status, this is a significant advantage of Kurbo.

Kurbo participants completed a median of 7 (IQR 2-10) coaching sessions. This level of engagement is considered very low (<10 hours of intervention time) based on the US Preventive Special Task Force criteria [28]. This may account for the lack of BMI changes among our participants. However, this lack of engagement is not unique to Kurbo, as other programs, including clinic-based programs, have shown similar levels of engagement [29-31].

Our results show that the maximal period of engagement with Kurbo occurred in the first 7 weeks, which corresponded to the period between the initial WMC referral and the first WMC clinic visit. This suggests that Kurbo may be helpful in engaging participants as an early intervention before the first WMC visit. Early engagement may account for the increase in WMC attendance at 6 months (20/40, 50% attendance) compared with our historical rate of 42.1% (51/121). The use of a mobile app as an early intervention also provided mutual benefits to both the health care provider team and Kurbo health coaches. The administrator platform allowed the multidisciplinary team to gain a better understanding of patient progress in health behaviors and weight before presenting to the clinic. This allowed for more targeted discussions about barriers that adolescents faced in the management of obesity and more efficient care. Kurbo health coaches were able to highlight any concerns that they faced during the health coaching sessions of the health care team.

A challenge with Kurbo as an early intervention was the high attrition rate. Although the study’s dropout rate of 50% (20/40) is less than the in-clinic rate of 57.8% (70/121), it suggests that strategies need to be crafted to reduce attrition if Kurbo is to be successfully used as an early-stage intervention. Further research on the reasons for attrition is recommended.

Despite the lack of changes in BMI z-scores, a significant improvement in quality of life is an important finding. Quality of life among adolescents with overweight is lower than that among normal-weight peers [25] and youth with other chronic conditions [32]. Improvement in quality of life has been reported with participation in other obesity treatment programs, even in children who do not achieve significant weight loss [33]. Given the prevalence of weight-based victimization experienced by adolescents with obesity in school [34], the improvement in the school dimension of quality of life is promising. Improvement in quality of life is critical, as it has been associated with improved long-term health indicators, with lower use of health care resources and greater long-term weight reduction [35]. To the best of our knowledge, improvement in self-reported quality of life has not previously been reported with the use of a mobile app to address adolescent obesity, which should be validated in future research.

Reassuringly, our study found no measured increase in disordered eating behaviors, as measured by the EPI-C. On the basis of these results, the integrated model of a mobile app with multidisciplinary adolescent obesity management is unlikely to increase disordered eating behaviors despite concerns for the development of disordered eating habits with the use of mobile apps [36].


This study had several limitations. The study had a small sample size and a high attrition rate. Second, as this was a feasibility study, the study did not include a control group.


In this pilot study, the use of the Kurbo mobile app as an early intervention before a multidisciplinary clinical care for adolescent obesity treatment is feasible in a low-income and ethnically diverse Asian population. Although there was no significant change in the BMI z-score, Kurbo showed promise in improving quality of life and reducing body fat percentage and total caloric intake. Given the promising outcomes in several dimensions, further research using more rigorous trial designs should be conducted to evaluate the effects of Kurbo as part of an early, stepped care intervention for adolescents with obesity.


The authors thank Kurbo for providing the data for this study. Kurbo was not involved in the design of the study, the analysis and interpretation of the data, or the preparation and submission of this manuscript. The authors wish to thank the patients and their families for their participation in the study and members of the pediatric weight management team for their contribution to the study. The study was funded by the Pediatrics Academic Clinical Programme/Tan Cheng Lim Fund grant PAEDACP-TCL/2017/CLIN/006.

Conflicts of Interest

EAF is on the scientific advisory board for WW Int (formerly Weight Watchers), the company that now owns Kurbo.

  1. Singh AS, Mulder C, Twisk JW, van MW, Chinapaw MJ. Tracking of childhood overweight into adulthood: a systematic review of the literature. Obes Rev 2008 Sep;9(5):474-488. [CrossRef] [Medline]
  2. Barlow SE, Expert C. Expert committee recommendations regarding the prevention, assessment, and treatment of child and adolescent overweight and obesity: summary report. Pediatrics 2007 Dec;120 Suppl 4:S164-S192. [CrossRef] [Medline]
  3. Teo TS. Demographic and motivation variables associated with internet usage activities. Internet Res 2001 May;11(2):125-137. [CrossRef]
  4. Langlet B, Maramis C, Diou C, Maglaveras N, Fagerberg P, Heimeier R, et al. Formative evaluation of a smartphone app for monitoring daily meal distribution and food selection in adolescents: acceptability and usability study. JMIR Mhealth Uhealth 2020 Jul 21;8(7):e14778 [FREE Full text] [CrossRef] [Medline]
  5. Nikolaou CK, Tay Z, Leu J, Rebello SA, Te Morenga L, Van Dam RM, et al. Young people's attitudes and motivations toward social media and mobile apps for weight control: mixed methods study. JMIR Mhealth Uhealth 2019 Oct 10;7(10):e11205 [FREE Full text] [CrossRef] [Medline]
  6. Almandoz JP, Xie L, Schellinger JN, Mathew MS, Gazda C, Ofori A, et al. Impact of COVID-19 stay-at-home orders on weight-related behaviours among patients with obesity. Clin Obes 2020 Oct;10(5):e12386 [FREE Full text] [CrossRef] [Medline]
  7. Elnaggar RK, Alqahtani BA, Mahmoud WS, Elfakharany MS. Physical activity in adolescents during the social distancing policies of the COVID-19 pandemic. Asia Pac J Public Health 2020 Nov;32(8):491-494. [CrossRef] [Medline]
  8. Pietrobelli A, Pecoraro L, Ferruzzi A, Heo M, Faith M, Zoller T, et al. Effects of COVID-19 lockdown on lifestyle behaviors in children with obesity living in Verona, Italy: a longitudinal study. Obesity (Silver Spring) 2020 Aug;28(8):1382-1385 [FREE Full text] [CrossRef] [Medline]
  9. Kurbo homepage. Kurbo.   URL: [accessed 2021-09-03]
  10. Cueto V, Wang CJ, Sanders LM. Impact of a mobile app-based health coaching and behavior change program on participant engagement and weight status of overweight and obese children: retrospective cohort study. JMIR Mhealth Uhealth 2019 Nov 15;7(11):e14458 [FREE Full text] [CrossRef] [Medline]
  11. Glasgow RE, Vogt TM, Boles SM. Evaluating the public health impact of health promotion interventions: the RE-AIM framework. Am J Public Health 1999 Sep;89(9):1322-1327. [CrossRef] [Medline]
  12. Nicholas Hong WJ, Huang HL, Rajasegaran K, Oh JY, Kelly S, Saffari SE, et al. Presence of obesity related comorbidities associated with lower attrition rate in pediatric weight management program. J Child Obes 2017 May 29;2(2):2-10. [CrossRef]
  13. Kuczmarski RJ, Ogden CL, Guo SS, Grummer-Strawn LM, Flegal KM, Mei Z, et al. 2000 CDC Growth Charts for the United States: methods and development. Vital Health Stat 11 2002 May(246):1-190 [FREE Full text] [Medline]
  14. Mohr DC, Cuijpers P, Lehman K. Supportive accountability: a model for providing human support to enhance adherence to eHealth interventions. J Med Internet Res 2011 Mar 10;13(1):e30 [FREE Full text] [CrossRef] [Medline]
  15. Epstein LH, Gordy CC, Raynor HA, Beddome M, Kilanowski CK, Paluch R. Increasing fruit and vegetable intake and decreasing fat and sugar intake in families at risk for childhood obesity. Obes Res 2001 Mar;9(3):171-178. [CrossRef] [Medline]
  16. Mukherjee S, Leong HF, Wong XX. Waist circumference percentiles for Singaporean children and adolescents aged 6-17 years. Obes Res Clin Pract 2016 Sep;10 Suppl 1:S17-S25. [CrossRef] [Medline]
  17. Talma H, Chinapaw MJ, Bakker B, HiraSing RA, Terwee CB, Altenburg TM. Bioelectrical impedance analysis to estimate body composition in children and adolescents: a systematic review and evidence appraisal of validity, responsiveness, reliability and measurement error. Obes Rev 2013 Nov;14(11):895-905. [CrossRef] [Medline]
  18. Chew CS, Oh JY, Rajasegaran K, Saffari SE, Lim CM, Lim SC, et al. Evaluation of a group family-based intervention programme for adolescent obesity: the LITE randomised controlled pilot trial. Singapore Med J 2019 Oct 08;62(1):39-47 [FREE Full text] [CrossRef] [Medline]
  19. Ling Ang K, Foo S. An exploratory study of eating patterns of Singapore children and teenagers. Health Educ 2002 Oct 1;102(5):239-248. [CrossRef]
  20. Trost SG, Loprinzi PD, Moore R, Pfeiffer KA. Comparison of accelerometer cut points for predicting activity intensity in youth. Med Sci Sports Exerc 2011 Jul;43(7):1360-1368. [CrossRef] [Medline]
  21. Puyau MR, Adolph AL, Vohra FA, Zakeri I, Butte NF. Prediction of activity energy expenditure using accelerometers in children. Med Sci Sports Exerc 2004 Sep;36(9):1625-1631. [Medline]
  22. Ching Ting JL, Mukherjee S, Yong Hwa MC. Physical activity and sedentary behavior patterns of Singaporean adolescents. J Phys Act Health 2015 Sep;12(9):1213-1220. [CrossRef] [Medline]
  23. Schacht M, Richter-Appelt H, Schulte-Markwort M, Hebebrand J, Schimmelmann BG. Eating pattern inventory for children: a new self-rating questionnaire for preadolescents. J Clin Psychol 2006 Oct;62(10):1259-1273. [CrossRef] [Medline]
  24. Gow ML, Baur LA, Ho M, Chisholm K, Noakes M, Cowell CT, et al. Can early weight loss, eating behaviors and socioeconomic factors predict successful weight loss at 12- and 24-months in adolescents with obesity and insulin resistance participating in a randomised controlled trial? Int J Behav Nutr Phys Act 2016 Apr 01;13(1):43 [FREE Full text] [CrossRef] [Medline]
  25. Østbye T, Malhotra R, Wong H, Tan S, Saw S. The effect of body mass on health-related quality of life among Singaporean adolescents: results from the SCORM study. Qual Life Res 2010 Mar;19(2):167-176. [CrossRef] [Medline]
  26. NG PJ. Foreword. Int J Water Resour Dev 2020 Nov 05;36(6):871-873. [CrossRef]
  27. Lew MS, L'Allemand D, Meli D, Frey P, Maire M, Isenschmid B, et al. Evaluating a childhood obesity program with the Reach, Effectiveness, Adoption, Implementation, Maintenance (RE-AIM) framework. Prev Med Rep 2019 Jan 24;13:321-326 [FREE Full text] [CrossRef] [Medline]
  28. Whitlock EP, O'Connor EA, Williams SB, Beil TL, Lutz KW. Effectiveness of weight management interventions in children: a targeted systematic review for the USPSTF. Pediatrics 2010 Feb;125(2):e396-e418. [CrossRef] [Medline]
  29. Andrews M, Sawyer C, Frerichs L, Skinner AC, Hoffman J, Gaskin K, et al. Feasibility of a clinic-community partnership to treat childhood obesity. Clin Pediatr (Phila) 2018 Jun;57(7):783-791. [CrossRef] [Medline]
  30. Hoffman J, Frerichs L, Story M, Jones J, Gaskin K, Apple A, et al. An integrated clinic-community partnership for child obesity treatment: a randomized pilot trial. Pediatrics 2018 Jan 01;141(1):783-791 [FREE Full text] [CrossRef] [Medline]
  31. Serra-Paya N, Ensenyat A, Castro-Viñuales I, Real J, Sinfreu-Bergués X, Zapata A, et al. Effectiveness of a multi-component intervention for overweight and obese children (Nereu Program): a randomized controlled trial. PLoS One 2015 Dec 14;10(12):e0144502 [FREE Full text] [CrossRef] [Medline]
  32. Ingerski LM, Modi AC, Hood KK, Pai AL, Zeller M, Piazza-Waggoner C, et al. Health-related quality of life across pediatric chronic conditions. J Pediatr 2010 Apr;156(4):639-644 [FREE Full text] [CrossRef] [Medline]
  33. Mollerup PM, Nielsen TR, Bøjsøe C, Kloppenborg JT, Baker JL, Holm J. Quality of life improves in children and adolescents during a community-based overweight and obesity treatment. Qual Life Res 2017 Jun;26(6):1597-1608. [CrossRef] [Medline]
  34. Puhl RM, Peterson JL, Luedicke J. Weight-based victimization: bullying experiences of weight loss treatment-seeking youth. Pediatrics 2013 Jan;131(1):e1-e9. [CrossRef] [Medline]
  35. Griffiths LJ, Parsons TJ, Hill AJ. Self-esteem and quality of life in obese children and adolescents: a systematic review. Int J Pediatr Obes 2010 Aug;5(4):282-304. [CrossRef] [Medline]
  36. Weight loss for kids? Thanks to WW, there's an app for that. Time. 2019 Aug.   URL: [accessed 2021-09-08]

EPI-C: Eating Pattern Inventory for Children
KKH: KK Women’s and Children’s Hospital
mHealth: mobile health
MVPA: moderate-to-vigorous physical activity
WMC: weight management clinic

Edited by R Kukafka; submitted 28.12.20; peer-reviewed by J Alvarez Pitti, A Videira-Silva; comments to author 16.03.21; revised version received 07.05.21; accepted 12.08.21; published 28.09.21


©Chu Shan Elaine Chew, Courtney Davis, Jie Kai Ethel Lim, Chee Meng Micheal Lim, Yi Zhen Henny Tan, Jean Yin Oh, Kumudhini Rajasegaran, Yong Hwa Michael Chia, Eric Andrew Finkelstein. Originally published in the Journal of Medical Internet Research (, 28.09.2021.

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