Objective: To assess the functionality and feasibility of the GROWIN app for promoting early detection of growth disorders in childhood, supporting early interventions, and improving children’s lifestyle by analyzingdata collected over 3 years (2018–2020).Methods: We retrospectively assessed the growth parameters (height, weight, body mass index [BMI], abdomi- nal circumference) entered by users (caregivers/parents) in the GROWIN app. We also analyzed the potential health problems detected and the messages/recommendations the app showed. Finally, we assessed the possi- ble impact/beneﬁt of the app on the growth of the children.Results: A total of 21 633 users (Spanish [65%], Latin American [30%], and others [5%]) entered 10.5 68.3 meas- urements (0–15 y old). 1200 recommendations were for low height and 550 for low weight. 1250 improved theirmeasurements. A specialist review was recommended in 500 patients due to low height. 2567 nutrition testswere run. All children with obesity ( n¼855, BMI: 27.8 kg/m 2[2.25 SD]) completed the initial test with a follow-up of/C211 year. Initial results (score: 8.1) showed poor eating habits (fast food, commercially baked goods, candy, etc.), with >90% not having breakfast. After 3–6 months, BMI decreased /C211 point, and test scores increased /C212 points. This beneﬁt was maintained beyond 1 year and was correlated with an improvement in BMI ( r¼/C0.65, P¼.01). Discussion/Conclusions: The GROWIN app represents an innovative automated solution for families to monitor growth. It allows the early detection of abnormal growth indicators during childhood and adolescence, promot-ing early interventions. Additionally, in children with obesity, an improvement in healthy nutritional habits anda decrease in BMI were observed. Key words: mHealth application, digital technologies, early diagnosis, growth monitoring, obesity, prevention VCThe Author(s) 2022. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For permissions, please email: journals.permissions@oup.com 1508Journal of the American Medical Informatics Association , 29(9), 2022, 1508–1517 https://doi.org/10.1093/jamia/ocac108 Advance Access Publication Date: 7 July 2022 Research and ApplicationsDownloaded from https://academic.oup.com/jamia/article/29/9/1508/6633588 by Biblioteca Universidad de Zaragoza user on 14 December 2023 BACKGROUND AND SIGNIFICANCE Growth disorders are health problems that prevent children from developing a normal height and weight. These problems include, among others, obesity (body mass index [BMI] >95th percentile), overweight (BMI >85th percentile), and short stature (height </C02 standard deviation [SD]). The diagnosis of growth disorders is frequently delayed despite the monitoring of linear growth to support their early detection and timely treatment.1Social barriers, difficulties recognizing the prob- lem, the wide range of underlying causes, and the lack of knowledge of the phenotypic spectrum of each condition make this early diag-nosis difficult for children referred to physicians, both due to short stature and childhood obesity. 2,3 Early intervention in growth disorders is critical due to the nega- tive consequences they can promote. For example, childhood obesity (mainly weight status from 2 to 6 y) is well-established as a strong predictor of adult obesity/overweight4that negatively impacts health status. Moreover, there are situations where late intervention is no longer as useful; for example, in short stature due to growth hor- mone deficiency, a delay in the intervention reduces the possibility of attaining their normal height potential.5Therefore, any tool that could aid diagnosis and facilitate early intervention would be very useful. The potential of digital technologies (eg, mobile health [mHealth]) to improve public health has already been recognized by the World Health Organization (WHO).6Their use can benefit pa- tient care, health strategies, and initiatives.7Moreover, it may be a promising approach due to the widespread use of mobile electronic devices (smart devices).8In the case of growth disorders, automated growth monitoring with algorithms integrated into electronic health records can enhance early detection, possibly achieving a higher rate of referrals to specialists and a greater diagnostic yield.1 New digital technologies in the pediatric endocrinology setting allow efficient and precise assessment performance.9Height meas- urements outside the clinic could increase the frequency of these evaluations and provide signs of growth failure to support earlier in-tervention. 10Moreover, in addition to early diagnosis, internet or telephone-based interventions have been reported to be effective inimproving BMI and weight loss or increasing physical activity inobese adults. 11The use of mHealth (mobile devices) for the treat- ment of overweight and obesity in children is still new, but, despitethe limited evidence available, it has shown to be promising, 8and it has piqued the interest of specialists.12 Considering all the above and that the published literature shows content of apps/digital technologies designed to record children’sgrowth data, 13,14mainly focused on short stature and the use of hu- man growth hormone therapy,15,16but without offering advice/rec- ommendations based on that data to improve it, we developed theGROWIN application (app). It was designed to help follow upchildren’s growth by offering personalized tips for healthy living.The objectives of this app are to support families during their child-ren’s growth and development, identify growth disorders and over-weight/obesity problems (before they manifest themselves), andprovide automatic recommendations adapted to the patient’s needs.In addition, it aims to empower families to make positive behavioralchanges (mainly in feeding practices and physical activity) and im-prove their lifestyles. To the best of our knowledge, this is the firstpublication that shows retrospective data analysis on outcomes afterusing an app designed to measure child’s growth and identify disor-ders, but also providing personalized recommendations.OBJECTIVES We describe the functionality of the GROWIN app and present ourexperience and outcomes over 3 years, focusing on the early detec-tion of abnormal growth and early interventions. This retrospective analysis aimed to test the utility and feasibility of the GROWIN app for promoting early detection of growth disor-ders in childhood, supporting earlier interventions, and improvingthe children’s lifestyle. MATERIALS AND METHODS Functionality of the GROWIN mHealth application The aim of the GROWIN app, a phone app, was to offer families anew way of monitoring children’s growth from 0 to 18 years old.Since its market launch in 2017, the GROWIN app has been free ofcharge. It was available in Spanish and English for both iOS and An-droid until 2020, and only available for Android afterward (al-though people with iOS who have already downloaded it cancontinue using it). It was presented in Spanish pediatric endocrinol-ogy meetings and to the Spanish Association of Pediatrics (AEP) sothat pediatricians could get to know it and present it to theirpatients. Additionally, it was advertised in the national press and ra-dio to the overall population. GROWIN was designed by the endocrinology service of Hospi- tal Miguel Servet (Spain) and developed by eHWin New Technolo-gies. It was supported by the AEP (by its Spanish acronym) and theSpanish Association of Primary Care Pediatrics (AEPap, by its Span-ish acronym) and approved by the Clinical Research Ethics Commit-tee of Arag /C19on (CEICA). It was funded by MERCK. After downloading the app, families should register each child and add some initial data: name; gender; data at birth (birth date,weeks of gestation, height [cm], weight [kg], country, and region),parents’ height (cm) in order to calculate the height potential andfind out the growth potential, and the use of concomitant treatmentsthat can influence growth and weight, such as corticosteroids ormethylphenidate. Depending on their geographical location, the app automatically selects a reference population to calculate percentiles, drawing datafrom the Spanish, 17WHO,18Centers for Disease Control and Pre- vention (CDC),19and Argentinian20studies that are entered in the app. However, users, pediatricians, or endocrinologists can alsomanually select another study. Families should enter their children’s growth parameters as they grow: height, weight, and abdominal circumference. BMI is calcu- lated by the app. The data entry was left entirely up to the discretionof each user. These 4 values are displayed in different graphicsshowing their percentiles and evolution. The illustrations make iteasier for families to understand the results ( Figure 1 ). Each time a user enters data, the app uses an automated growth monitoring strategy incorporating a scientific-mathematical vali-dated algorithm 21,22to predict the next measurement (weight, height, and BMI 2–4 mo later) if everything continues the same.These values are predicted based on the normality values of the dif-ferent studies concerning weight, height, BMI, and abdominal cir-cumference gain during the different stages of childhood. Additionally, the GROWIN app’s design includes a logistic re- gression study with auxological parameters and predictive models ofthe risk of developing obesity. 21,22Two validated logistical regres- sion models are used for the prediction; one model for the risk of de-veloping obesity in the future and other for the measurementsJournal of the American Medical Informatics Association , 2022, Vol. 29, No. 9 1509Downloaded from https://academic.oup.com/jamia/article/29/9/1508/6633588 by Biblioteca Universidad de Zaragoza user on 14 December 2023 (weight, height, BMI, and abdominal circumference) after 3–6 months of the current measurement. A complicated model to use(with many variables), and another simpler and more reproduciblemodel than those described by de Arriba Mu ~noz et al 21and Morlan Herrador et al,22which required multiple measurements, including unusual parameters such as fat folds. The app’s models use z-scores (number of SD away from a refer- ence median/mean) for the prediction; changes in 0.5 SD in any pa-rameter trigger a response. Responses are triggered by a valueoutside of a specific range (weight, BMI, or abdominal circumfer-ence>85th percentile, when weight or height are </C02 SD or when there is an increase or decrease in a value >0.5 SD; Table 1 ). This design enables the prediction of both disorders (obesity and over-weight). Each time data are entered, the app automatically displays 2 messages/recommendations based on the parameters’ SDs and theirpredicted values: one regarding the current status and the other oneconsidering its change since the last entered value. If any SD value is outside the recommended ranges, a recommen- dation is displayed, which was made by pediatric endocrinologistsand incorporated into the app. The app incorporates 6000 potentialrecommendations. Some recommendations are based on the child’sage, because the growth at different ages (0–18 y) is completely dif-ferent, and there are also recommendations based on the birthweight because the growth of adequate or small for gestational age(SGA) children is different. Moreover, there are messages/recom-mendations for children with no growth problems to send goodhabit reminders, with new customized tips and advice about nutri-tion, fitness, and healthy living reported in a very simple and famil-iar manner. Table 1 shows an example of how recommendations are selected based on the children’s age and the SD of weight value. The aim of these messages/recommendations is to monitor the child’s evolution closely and prevent health problems related tosmall weight at birth (SGA children), 23obesity and overweight, low weight, or growth disorders. When the app analyzes the data included by the users and the results deviate from those predicted and it interprets that a healthproblem is objectively likely to occur (major growth disorders, lowweight, obesity, or overweight), then, 3 options appear at the bot-tom of the screen ( Figure 1C ). One option (“Share”) is to download the data into a spread- sheet and share them with anyone via social networks, email, orthe physician. This possibility is available at any time, allowing theuser to share the information with their pediatrician and resolve any doubts that may arise. The second option (“Read more”) includes additional and more extensive recommendations adjustedto the children’s age. Finally, the third option (“Test”) leads to aneasy-to-complete questionnaire to assess their eating habits (if atendency to gain weight dangerously or overweight/obesity isdetected) or physical activity. The questionnaires data were col-lected 3 timepoints. When the health problem is predicted (base-line), after 3–6 months and after year (12 mo). According to theresponses provided, customized/personalized recommendations areautomatically sent to improve the items accordingly (eating habitsor physical activity, as required). The nutrition questionnaire used is the Quality Test of the Medi- terranean Diet (MD) in Childhood and Adolescence. 24,25It meas- ures the adherence of the child’s diet to the MD (considerednutritionally correct). The questionnaire consists of 16 questionsthat are answered/selected only if the statement corresponds to thechild’s behavior ( Table 3 ). The first 12 questions (related to good habits) score positively ( þ1), and the remaining 4 questions (related to bad habits) score negatively ( /C01). Therefore, the best possible score is 12. The score determines the child’s diet quality: low ( <3), fair (4–7; needs adjustment), and optimal ( >8). A higher score indicates a bet- Figure 1. Appearance of the different app screens. (A) New way of representing percentiles. (B) Usual representation of percentiles. (C) Personal recommenda tions.1510 Journal of the American Medical Informatics Association , 2022, Vol. 29, No. 9Downloaded from https://academic.oup.com/jamia/article/29/9/1508/6633588 by Biblioteca Universidad de Zaragoza user on 14 December 2023 Table 1. Organization of the messages/recommendations launched by the app based on the children’s age and SD value of weight measurements Age ranges (y) SD of weight value0–0.5 0.5–1 1–2 2–3 3–4 4–5 5–6 6–7 7–8 8–9 9–10 10–11 11–12 /C21þ2 . 5 6666111 2 . 1 2 . 1 2 . 1 2 . 1 2 . 1 2 . 1 (þ2.0,þ2.5) 6666111 2 . 1 2 . 1 2 . 1 2 . 1 2 . 1 2 . 1 (þ1.5,þ2.0) 6666666666666 (/C01.5,þ1.5) 6666666666666 (/C02.0,/C01.5) 6666666666666 (/C02.5,/C02.0) 10 10 13 10 8 8 8 9.1 9.1 9.1 9.1 9.1 9.1 /C20/C02.5 10 10 13 10 13 10 10 9.1 9.1 9.1 9.1 9.1 9.1 ID Message/Recommendation 1 The weight is above what is desired Send appendixaþQuestionnairebþrecommendations according to questionnairebþweight and measure again in 3 mo, if weight >2DS and BMI >þ2DS we recommend assessment 2.1 Be careful with the weight, it is above what is desired Send appendixaþquestionnairebþrecommendations according to questionnairebþwe recommend assessment by your pediatrician 4.16 During this period, it has gained more weight than desired We recommend send appendixaand reweigh in 1 mo. If weight >þ2 SD, questionnaire þrecommendations according to questionnaire results and reweight and measure in 2 mo. If weight >þ2 SD and BMI >þ2 SD we recommend assessment by Pediatrician 8 The weight is below what we would like Increase the number of feedings per day or the amount per feeding, reweight in 1 wk 9.1 The weight is below what we would like Add cereals to the milk or increase the amount, add olive oil and potato to the purees and reweight in 1 wk 10 The weight is below what we would like We recommend an evaluation by your pediatrician 11.1 In this period, he/she has gained less weight than expected5.1 The weight in this period has changed normally, but it is necessary to monitor it, try to reduce the amount of food, eat only 4 main meals, do not exceed 500–600 mL of dairy products per day, do not offer sweetened beverages 5.2 In this period of time, he has gained a little more weight than desired. Watch the amount of food, cut down on portions, and sugary drinks 6 Ok: your weight is perfect, good situation, keep it up!!!6.1 The weight gain was adequate, congratulations! Abbreviations : MD: Mediterranean diet; SD: standard deviation. aAppendix: more complex recommendations sent based on the speciﬁc situation. bQuestionnaire: MD Quality Questionnaire.Journal of the American Medical Informatics Association , 2022, Vol. 29, No. 9 1511Downloaded from https://academic.oup.com/jamia/article/29/9/1508/6633588 by Biblioteca Universidad de Zaragoza user on 14 December 2023 ter diet. The app displays the following messages/recommendations based on the score of the Quality Test of the MD in Childhood andAdolescence; >8: “CONGRATULATIONS! Your diet is optimal”; 4–7: “We think you need to improve your nutrition”; <3: “YOU NEED to improve your nutrition.” In failed questions, a recommendation was launched aimed at improving the habit it addresses. Moreover, physical activity waspromoted (personalized recommendation based on the age of the children): climb the stairs, play sports, avoid spending more than 1 hour in front of a screen, and NO more than 2 h a day watchingtelevision or playing video games. There is no specific questionnaire if the potential problem is re- lated to physical activity, although there are individual questionsand recommendations. According to the evolution of the risk of be-coming overweight/obese, users are asked about the frequency (d/wk and h/d) and intensity (light–moderate–strong) of the physicalexercise children do as well as the number of hours of screen/tech-nology use per week. According to their answers, they are sent rec-ommendations advising them to increase the number and intensityof daily exercise and to decrease the number of hours of screen use. When the recommendations do not bring about improvements and/or there is evidence of growth, underweight, or obesity prob-lems, the app recommends a visit to a specialist doctor or pediatri-cian. The company EHWIN NEW TECHNOLOGIES SL is the holder, data controller, and data processor of the data that the userenters into the mobile app. After the data are entered, they areencrypted and transferred to the central database (located in thecloud and protected in compliance with the entire Data ProtectionAct for storage purposes). The names and identifiers of the users andtheir children are coded (pseudo-anonymized) according to Euro-pean Directive 95/46/EC. Data analysis A general description of the total sample (users/children) was madebased on the region to which they belong and their birth weight forgestational age (first objective: early detection of growth disorders).A study of the normality and homoscedasticity of the data was car-ried out using the Kolmogorov-Smirnov and Levene tests, respec-tively. Categorical variables (sex, adequate for gestational age(AGA), or SGA, country, percentage of affirmative [YES] and nega-tive [NO] responses of the 16 items of the Quality Questionnaire ofthe MD in Childhood and Adolescence) were described as the num-ber of cases in each category and percentages referred to the wholestudy population. Continuous variables (age, BMI, and mean of the results of the Quality Questionnaire of the MD in Childhood and Adolescence) were described as a mean 6SD. For all statistical studies, the app’s model use z-scores; the values of the parameters (weight, height, and BMI) have been used in SDinstead of absolute values that would prevent us from making com-parisons between subjects of different ages or sexes. The large differ-ence between the absolute values of ages and sexes prevents theseparameters from being analyzed together. In order to be able tomake these comparisons, regardless the age or gender, absolute val-ues are converted into the value of the SD (value referenced accord-ing to the population mean). Variables were compared with the Student’s ttest (second objec- tive: supporting earlier interventions). Paired data were analyzedwith paired Student’s ttest to compare quantitative variables (third objective: improving the children’s lifestyle): (1) the responses of the16 items evaluated in the nutrition questionnaire (initial, at 3–6, and 12 mo) and assess whether the recommendations launched by theapp promote an improvement in the eating habits and (2) the BMI(SD) to evaluate if the improvement in the eating habits was associ-ated with an improvement in BMI values. Paired Student’s ttest was used to analyze differences in the same subject over time: the changein the results for each subject, thus assessing whether there is im-provement. The statistical analysis was performed using SPSS software, ver- sion 21.0 for Windows. Two-sided Pvalues were obtained, and sta- tistically significant results were declared if P<.05. The 95% CI for the mean difference was based on an asymptotic method. Ethics and consent process The app includes a privacy policy that explains how sensitive userand device data are treated. Users must accept this policy before us-ing the app. By entering their data, users give their consent to the holder/data controller to process the data for the purpose, among others, of con-ducting academic studies based on such contents. RESULTS App users This app was downloaded by 21 633 users from January 1, 2018, toDecember 31, 2020. Most were from Spain (14 061), although 6490users were from Latin America (LATAM), and 1052 users fromother countries also downloaded it, among which the United King-dom stands out. Figure 2 shows the geographical distribution of the users. Overall, 90% of the users used the app regularly and frequently entered data during the 3-year period analyzed (95%, 86%, and83% in the children’s age subgroups of 0–5, 6–10, and 11–15 y, re-spectively). Only 10% of those who downloaded the app were not“active” users (5%, 14%, and 17% in the children’s age subgroupsof 0–5, 6–10, and 11–15 y, respectively). An active user is defined asa user who has entered at least 4 data per year during the first 2years of life and at least 2 data per year after the age of 2 years. Most data were entered outside the period of usual child’s rou- tine doctor’s appointment. Data entered in the app Among the users who downloaded the app during this 3-year period(2018–2020), 20% did so when their child was born and 80%(17 307 patients) after the birth of the child. Of the latter, 15 141 patients (70% of the total users), entered retrospectively the data from birth until downloading the app (at a mean age of 6 y; range0.5–15 y) and then those obtained thereafter. The remainingpatients (10% of the total users) entered data since the unloading (ata mean age of 3 y; range 0.1–12 y), but did not add any previous in-formation. Thirty-four point four percent of users ( n¼7442) entered the data of 2 children. Therefore, data were collected from 29 075 chil-dren. Table 2 shows the basal characteristics of the children whose data were entered in the app by the users. The age of the children for whom users entered data in the app ranged from 0 to 15 years old ( Table 2 ). At birth, 7.3% of the chil- dren were SGA, 89.5% were AGA, and 3.2% were large for gesta-tional age (LGA; Table 2 ). No differences between regions in the percentage of children AGA, LGA, and SGA were observed.1512 Journal of the American Medical Informatics Association , 2022, Vol. 29, No. 9Downloaded from https://academic.oup.com/jamia/article/29/9/1508/6633588 by Biblioteca Universidad de Zaragoza user on 14 December 2023 Users entered a mean of 10.5 68.3 measurements (11.9 66.3, 10.365.4, and 6.7 63.1 in the children’s age subgroups of 0–5, 6– 10, and 11–15 y, respectively; range of 1–47). The mean of measure- ments in case of active users was (14 65.2 [range of 10–47]). Most measurements belong to young children (first year of life). Data ofolder children were entered mainly when they had weight or heightproblems. Abdominal circumference (not measured at routine pedia-tricians’ examinations) was the least included measurement in theapp, only added 15% of the times that users enter data. App interventions Most (91%) of the messages/recommendation displayed by the appwere positive (ie, recommendations for maintaining a healthy life-style). They reinforced users’ habits and encouraged them to con-tinue along the same path. The app displayed recommendations for improving dietary and physical exercise habits due to low weight or low height for 1750children (1200 [69%] due to low height and 550 [31%] due to low weight). We observed an improvement in the next children’s meas-urements 3–6 months after most of the recommendations ( n¼1250 for 1250 subjects; 1200 in terms of height, P¼.03 and 50 in terms of weight, P¼.01). In the remaining 500 recommendations (for 500 subjects; all due to low height) measurements were not improvedand then the app recommended a visit to the pediatrician or pediat-ric endocrinologist. The app ran a total of 2567 initial nutrition questionnaires. When a growth problem was identified (baseline situation), 855 (33%) were due to obesity, 1239 (48%) due to overweight, and 473 (18%) due to an observed tendency to develop overweight and thenobesity if the children continued along the same path. These initialnutrition questionnaires were completed by all the children withobesity (855/855; 100%); however, they were not completed by112/473 (23.6%) children with a tendency to develop overweightand obesity, and by 163/1,239 (13.2%) overweight children. How-ever, the children who did not complete the questionnaires kept us-ing the app. Table 3 shows the results of the initial tests (baseline situation) completed by the children with obesity and the ones completed dur-ing the follow-up (3–6 and 12 mo later). Among the 855 childrenwith obesity at baseline (41.7% male; age [mean (SD), 11.8 (3.1)y]), 410 were from Spain (3% of the Spanish children in the app;40.2% male; age [mean (SD), 12.9 (2.8) y]), 415 were from LATAM(6.5% of the Latin American children; 42.2% male; age [mean (SD),10.1 (3.7) y]) and 30 were from other countries/regions (2.5% ofthese population; 53.3% male; age [mean (SD), 11.8 (3.1) y]). The average BMI of the children for whom tests were completed was 27.8 kg/m 2(2.25 SD), with a test score of 8.1. Table 4 shows the data according to the distribution of children. Three to sixmonths after receiving advice from the app, an improvement in BMI(a decrease /C211 point) and in the Quality Test of the MD Score (an increase /C212 points) were observed ( Table 4 ). The items that im- proved the most after the intervention (recommendations) werethose related to the intake of fruit every day, vegetables, and dairyproducts, as well as the items on breakfast habits and the daily in-take of candy, commercially baked goods, and fast-foods. The bene- Figure 2. Geographic distribution of the users of GROWIN app (2018–2020) and characteristics of the children for whom they entered data in the app. Table 2. Main characteristics of the total sample (children for whom users entered data in the app; n¼29 075) Basal characteristics Sex;n(%) Male 12 793 (44) Female 16 282 (56) Mean gestational age, wk (SD) 38.3 (3.7) Mean weight at birth, g (SD) 3275.3 (267) Mean length at birth, cm (SD) 49.2 (3.4)LGA, n(%) 930 (3.2) AGA, n(%) 26 022 (89.5) SGA, n(%) 2123 (7.3) Children’s age groups; n(%) 0–5 y 14 101 (48.5) 6–10 y 10 554 (36.3) 11–15 y 4420 (15.2) Abbreviations : AGA: adequate for gestational age; LGA: large for gesta- tional age; SGA: small for gestational age; SD: standard deviation.Journal of the American Medical Informatics Association , 2022, Vol. 29, No. 9 1513Downloaded from https://academic.oup.com/jamia/article/29/9/1508/6633588 by Biblioteca Universidad de Zaragoza user on 14 December 2023 fit in the scores was maintained beyond 1 year of follow-up ( Table 4 ) and correlated with an improvement in the BMI ( r¼/C0.65,P¼.01). DISCUSSION Main findings We present our experience and analysis of the data collected with the mHealth app GROWIN during a 3-year period (2018–2020). This app facilitates automated growth monitoring in children and screening of growth disorders by using a scientific-mathematical al-gorithm and creating predictive models of the risk of developingobesity. The users of this app ( n¼21 633) were mainly from Spain (65%), likely due to the app being only advertised in Spain. Theusers were parents or caregivers of children with ages ranging from0 to 15 years old. The fact that the data belong to children up to 15years of age would suggest that parents are less concerned whentheir children are older and have finished or are about to finish theirgrowth. In fact, it has been previously reported that caregivers ofchildren <6 months are more likely to regularly use a mobile app for growth monitoring than those with children >6 months. 26 Parents’ concern for their children’s development could also be reflected by the frequency they enter data. Most (90%) were activeusers entering data, with only 10% of the users’ missing data from before they downloaded the app. Furthermore, users entered a meanof 10 measurements during the 3 years under study, showing contin-ued use after installation, contrary to previous publications regard-ing the short time that most mHealth apps are used. 27This continued use by caregivers, even without reminders (eg, with short message service) as reported in other mHealth technologies on up-take of routine growth monitoring, 28and the fact that most of the data entry were outside the period of standard well-child checks(children’s routine doctor’s appointments) also reflect the ease of useand the utility of the GROWIN app as a source for additional data.All this can contribute to the early detection of growth and nutri-tional disorders by providing additional measurements, as the lineargrowth monitoring of apparently healthy children as a screeningstrategy can detect early indications of serious conditions, establish-ing weight status (underweight, normal weight, overweight, andobesity), and identifying short stature. 9,29 In line with observations on the overall population concerning weight for gestational age (in European countries, there is a 10%prevalence of children who are born SGA or LGA), 30,31most of the data (89.5%) entered in the GROWIN app belong to children whowere AGA. In addition, data included belonged mainly to childrenwith an adequate growth (height and weight), with a much lowerpercentage of children with low height/weight or overweight/obe-Table 3. Percentage of questions of the nutrition assessment answered in the afﬁrmative by children with obesity ( n¼855) at the initial questionnaire completion (baseline situation) and 3–6 and 12 months later Questionnaire of the MD in Childhood and AdolescenceAnswers (%) Baseline (n¼855)3–6 mo (n¼855)Change from baseline12 mo (n¼855)Change from baseline-Pavalue Good habits 1 Do you eat a piece of fruit or drink fruit juice every day?81.2 83.0 1.8 82.5 1.3 NS 2 Do you eat a second piece of fruit every day? 40.6 75.2 34.6 74.0 33.4 .00 3 Do you eat fresh (salads) or cooked vegetables once a day?60.4 69 8.6 70.2 9.8 .01 4 Do you eat fresh or cooked vegetables more than once a day?25.0 62.3 37.3 60.7 35.7 .01 5 Do you eat fresh ﬁsh regularly (at least 2–3 times per week)?53.1 61.0 7.9 63.2 10.1 .03 6 Do you like pulses and eat them more than once a week?71.0 70.5 /C00.5 70.1 /C00.9 NS 7 Do you eat pasta or rice almost daily (at least 5 days a week)?36.4 50.8 14.4 50.5 14.1 .02 8 Do you eat cereals or related products (bread, toast, etc.)?63.0 61.8 /C01.2 61.2 /C01.8 NS 9 Do you eat nuts regularly (at least to 3 times a week)? “Nuts are not recommended in children under 6 y old”24.0 35.2 11.2 32.6 8.6 .04 10 Do you use olive oil at home? 87.5 88.0 0.5 89.6 2.1 NS 11 Do you consume dairy products for breakfast (milk, yogurt, etc.)?52.1 67.2 15.1 65.6 13.5 .03 12 Do you consume 2 yogurts and/or 40 g of cheese every day?75.0 87.4 12.4 87.0 12 .04 Bad habits 13 You eat in fast-food restaurants (hamburgers, pizza ...) at least once a week.87.0 85.0 /C02 88.2 1.2 NS 14 You don’t have breakfast 91.6 51.7 /C039.9 53.5 /C038.1 .01 15 You usually eat commercially baked goods at breakfast84.4 62.3 /C022.1 65.0 /C019.4 .02 16 You eat candy every day 94.8 44.8 /C050 48.5 /C046.3 .01 Abbreviation : NS: not signiﬁcant. aBetween baseline values and 12 months1514 Journal of the American Medical Informatics Association , 2022, Vol. 29, No. 9Downloaded from https://academic.oup.com/jamia/article/29/9/1508/6633588 by Biblioteca Universidad de Zaragoza user on 14 December 2023 sity. This finding can also be justified by the previously reported low prevalence of growth disorders, 5.9%–13.4% in children with auto-mated growth monitoring. 32 With data from at least 29 075 children, the GROWIN app iden- tified 1750 children with low weight or low height and provided rec-ommendations for them. A potential reported reason for a delay ingrowth and development is a very low nutrient intake. 33When this situation is detected, nutritional rehabilitation (increase in both ca-loric and protein intake) can enable catch-up growth. 34In this sense, the recommendations provided by the GROWIN app on nutritionalissues could help to improve the follow-up of overweight/obesepatients. Eventually, only 500 recommendations promoted a visit toa physician (all due to low height) for the review of pathologicalconditions associated with the growth failure. The early diagnosis ofshort stature can be established during these visits, avoiding a delaythat is extremely frequent in growth disorders (eg, Turner syndrome,celiac disease, and growth hormone deficiency). 32 Early diagnosis leads to early and appropriate therapy, which has an optimal effect on growth and final height and prevents or diminishes complications, and could, therefore, result in a betterquality of life. 29,35The findings obtained with the GROWIN app and its recommendations show the potential of the new digital tech-nologies to get greater standardization and automatization of heightmonitoring in children, improving the accuracy of referrals to spe-cialists. 9This potential benefit can justify the design of similar app, such as the presented by Thaventhiran et al36in the Society for En- docrinology BES 2021, that enables families to measure the UKchild’s height including alerts to facilitate early referral/diagnosis ofgrowth disorders This reflects the trend to use this type of apps, be-ing GROWIN more complete (not only height monitorization andwith recommendations for improving the measures). With the avail-ability of this kind of app (e-growth monitoring) for electronic devi-ces, height measurements can be performed outside the clinic andwith increased frequency, even in places with limited access topediatricians; this can promote the detection of signs of short statureand support earlier intervention. 10This can be also applied to weight monitoring. Regarding excess weight in children, the app ran a total of 2567 nutrition tests: 855 due to obesity, 1239 due to overweight, and 473due to an observed trend toward overweight and potential obesity.Currently, social changes have modified nutritional and physical ac-tivity habits, promoting an energy imbalance that leads to over-weight and obesity. 37Globally more people are obese than underweight, with 39 million children (5 y old) with obesity in2020. 38This supports the GROWIN app’s findings of more frequent(1.5 times higher) excess weight than underweight/short stature in children. Our analysis focused on children with obesity ( n¼855) because it was the subgroup with more questionnaires answered, probablybecause families show more interest when their children are moreaffected. Our data showed a higher percentage of children withobesity in LATAM (6.5%) versus Spain (3%) or other locations(3%). However, this result must be interpreted cautiously and can-not be generalized due to the small number of users per region. Infact, this finding is not in concordance with the estimated propor-tion of children with obesity recently published by Di Cesare et al. 39In this publication, the proportion of children with obesity aged between 2 and 4 years old is similar between high-incomecountries and LATAM and the Caribbean; on the contrary, thisproportion is higher in high-income Western countries in the agerange from 5 to 19. 39 The initial mean score of the Quality Test of the MD in Child- hood and Adolescence filled in by children with obesity was 8.1, just on the lower end of the threshold to require diet adjustments. Responses showed low compliance with some of the characteristicsof the MD, 40specifically the high intake of vegetables and fruits, the moderate consumption of fish, and the low intake of candy. Allthe items related to poor dietary habits showed the highest percent-age of answers, with more than 90% of the children not eatingbreakfast. The MD encourages healthy eating, 41and adherence to its prin- ciples is recommended to combat obesity (among other chronic dis-eases). 37Therefore, one of the aims of the recommendations provided by the GROWIN app was to improve nutritional habits.The 2-point increase in the test score reflects the positive impact ofthe recommendations, with the most improved items the intake ofvegetables, fruits, and dairy products. Moreover, improvementswere also observed in breakfast habits and in the reduction of the in-take of candy, baked goods, and fast food. A decrease in BMI wassignificantly correlated with the improvement of test scores. Theseachievements corroborate the previously reported usefulness of tele-medicine interventions to improve BMI and control weight inadults 11and teenagers.37 There are publications on the promising results of smartphone- based mHealth interventions to promote physical activity inadults. 42The GROWIN app already includes customized tips and advice about fitness, but, in the future, it could be improved withthe addition of remote monitoring of the child’s physical activityusing wearables (body-worn devices, such as smartbands andsmartwatches). Physical activity monitoring would enable the esti-Table 4. Scores and BMI of children with obesity for whom a nutrition questionnaire was completed ( n¼855) Baseline 3–6 mo Pvalueb12 mo Pvalueb Overall ( n¼855) BMI, mean (SD) 27.8 (2.25) 26.9 (2.05) .01 26.2 (1.90) .01 Questionnairea8.1 10.2 .03 10.1 .02 Spain ( n¼410) BMI, mean (SD) 27.5 (2.2) 26.7 (1.98) .01 26 (1.87) .01 Questionnairea8.6 11.1 .03 10.5 .02 LATAM ( n¼415) BMI, mean (SD) 28.2 (2.33) 27 (2.1) .01 26.4 (1.95) .01 Questionnairea7.5 9.3 .03 9.8 .02 Others ( n¼30) BMI, mean (SD) 27.9 (2.27) 27 (2.1) .01 26.3 (1.93) .01 Questionnairea8 9.5 .03 10 .02 Notes : Child’s diet quality based on test scores: low ( <3), fair (4–7; needs adjustment), and optimal ( >8). A higher score indicates a better diet. Abbreviations : BMI: body mass index; LATAM: Latin America; MD: Mediterranean diet. aQuality Questionnaire of the MD in Childhood and Adolescence. bChange from baseline.Journal of the American Medical Informatics Association , 2022, Vol. 29, No. 9 1515Downloaded from https://academic.oup.com/jamia/article/29/9/1508/6633588 by Biblioteca Universidad de Zaragoza user on 14 December 2023 mation of calories burned by each physical activity recorded and enable personalized recommendations to be displayed in the app.The appropriate validation method/study for wearables assessingphysical behavior has not yet been established. 43However, this strategy has already been used to remotely monitor patients’ physi-cal activity remotely in adult patients pre and post bariatric surgeryvia the Moves app. 44,45Moreover, this type of physical activity monitoring device has shown good tolerability in children and led to high compliance.46 In this study, with retrospective data analysis on outcomes, we demonstrate the potential of digital technologies (mHealth) to im-prove public health—a potential already recognized by theWHO 6—but, this time, focusing on growth monitoring in children. Additionally, our data show that growth monitoring can provideprimary overweight/obesity prevention and healthy behaviorcounseling opportunities. These early interventions have been de-scribed as more effective than weight management interventions af-ter established weight problems. 29An additional benefit of the GROWIN app is facilitating communication between parents/care-givers and doctors/health providers. With the “Share” option, datacan be shared with the physician (by email). With this communica-tion channel, parents can share the findings (concerns), and physi-cians can promote an intervention adapted to the needs of thechildren. This could imply a further step toward achieving growthmonitoring apps that could provide an excellent channel for poten-tial future intervention in childhood health. 14 Limitations The distribution of users was uneven, and the sample was small ineach area, meaning no zone-specific conclusions could be drawn.However, the overall conclusion shows the app’s usefulness, regard-less of the area to which the user belongs. Another limitation that should be considered is possible mea- surement errors as measurements were carried out by the parentsand/or caregivers at home, and it is not possible to ensure data qual-ity. Height is more difficult to measure than weight, which can bemeasured using a scale. Nevertheless, this possible error is mitigatedby the large amount of data available, and the parents entered an av-erage of 10 measurements for each child, making these measure-ments more reliable. Moreover, if an erroneous measure that entailsa very abrupt change is entered, the app would send many recom-mendations that make users aware of the error, and they can deleteit and reenter the correct data. Another possible limitation of the data analysis is the lack of a control arm to assess the impact of GROWIN app use. Finally, de-spite the app displaying more than 500 recommendations to visit apediatrician, we cannot determine whether these children actuallywent to the consultations. CONCLUSIONS The use of the GROWIN app allows the early detection of indica-tions of abnormal growth (low weight/height or overweight/obesity)in children and adolescents, promoting early interventions. Amongchildren with obesity, after receiving recommendations from theapp, an improvement in healthy nutritional habits and a decrease inBMI were observed. For this reason, GROWIN represents an inno-vative solution for families to monitor growth and supports the pro-vision of personalized medical care with individualized advice,which can lead to positive changes and healthy living habits.Moreover, it can provide a new collaborative mHealth network between parents/caregivers and doctors. FUNDING MERCK provided administrative support for the development of the GRO- WIN app without intervening in the development of the GROWIN app or the recommendations displayed. AUTHOR CONTRIBUTIONS AAM and IMR designed and directed the Project and developed the scientific-mathematical algorithm. AAM, MDC, IMR, and JIL designed and performed the app. AAM, IMR, MTGC, and ABO have developed the design of the app.AAM, MDC, MTGC, and ABO have carried out data analysis. AAM, MTGC, MDC, ABO, IMR, and JIL contributed to the writing of the article. All authors reviewed and approved the submitted article and have agreed to be accountable for its contents. ACKNOWLEDGMENTS The authors wish to thank all the families who use the app and their children for making it possible for the GROWIN app to remain on the market and to be of help. CONFLICTS OF INTEREST STATEMENT None declared. DATA AVAILABILITY The data underlying this article cannot be shared publicly due to the privacyof individuals that participated in the app because they are sensible data of children. The data will be shared on reasonable request to the correspondingauthor.