JMIR FORMATIVE RESEARCH

Li et alOriginal Paper

A Promising Food-Coaching Intervention Program to Achieve
Optimal Gestational Weight Gain in Overweight and Obese
Pregnant Women: Pilot Randomized Controlled Trial of a
Smartphone App
Ling-Jun Li1, MD, PhD; Izzuddin M Aris2, PhD; Wee Meng Han3, MD; Kok Hian Tan1, MD
1Department of Obstetrics & Gynecology, KK Women's and Children's Hospital, Singapore, Singapore

2

Division of Chronic Disease Research Across the Lifecourse, Department of Population Medicine, Harvard Medical School and Harvard Pilgrim HealthCare Institute, Boston, MA, United States
3

Department of Nutrition, KK Women's and Children's Hospital, Singapore, Singapore

Corresponding Author:
Ling-Jun Li, MD, PhD
Department of Obstetrics & Gynecology
KK Women's and Children's Hospital
100 Bukit Timah Road, Children's Tower Level 3
Singapore, 229899
Singapore
Phone: 65 63941099
Email: queenie.li.l.j@gmail.com

Abstract
Background: Traditional dietary recommendations for achieving optimal gestational weight gain are ineffective for pregnant
women due to the lack of real-time communication and tedious consultation processes.
Objective: In this pilot study, we aimed to determine the feasibility of a novel food-coaching smartphone app for controlling
gestational weight gain and macronutrient intake among overweight and obese pregnant women.
Methods: We designed a randomized controlled trial and recruited 30 overweight and obese pregnant women (1:1 ratio) during
18-20 weeks of gestation and followed them up after 4 and 8 weeks, respectively. Both groups received standard pregnancy
dietary orientation at recruitment, while the intervention group received 8 weeks of real-time food coaching via a smartphoneapp. This food-coaching smartphone app (Glycoleap, Holmusk, Singapore) aimed to improve care and outcomes for people withdiabetes. Pregnant women using this app were able to upload food images (eg, a picture of a meal, a drink, or a dessert) andreceived real-time and detailed food-coaching comments and guidance provided by professional dietitians during the day (8 AM
to 8 PM). We recorded detailed characteristics during recruitment and examined anthropometry at all visits. We compared themean differences of the 8-week gestational weight gain and macronutrient intake between the two groups.
Results: Upon study completion, three subjects dropped out from the intervention, and one gave birth prematurely in the control
group. The acceptance rate of the smartphone app was 90%. More participants achieved optimal gestational weight gain per week
in the intervention group (8/12, 67%) than in the control group (5/14, 36%). After the 8-week intervention, women in theintervention group appeared to have lower gestational weight gain (mean difference=–0.08 kg; 95% CI –1.80 to 1.63) andcholesterol intake (mean difference=–31.73 mg; 95% CI –102.91 to 39.45) than those in the control group.
Conclusions: Our findings showed that this food-coaching smartphone app is feasible and favorable for weight gain control
and cholesterol intake control among overweight and obese pregnant women. Although our results were not significant (perhaps,
attributed to the small sample size), it provided proof of concept for the feasibility of applying such technology in future randomized
controlled trials with a larger sample size, an earlier intervention onset, and a longer follow-up for overweight and obese pregnant
women.
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Li et alKEYWORDS
overweight, obesity, pregnant women; gestational weight gain; food diary; randomized controlled trial; smartphone app; foodcoaching; dietary recommendation; feasibility

Introduction
Overweight and obese pregnant women are often at an increased
risk of a series of maternal and offspring adverse outcomes[1-3]. In developed countries, overweight and obese womenaccounted for 30%-50% of fertile women [4-6]. Therefore, thehigh prevalence of overweight and obesity among pregnant
women signifies a substantial burden to public health welfare
worldwide. Current clinical care management of overweight
and obese women during pregnancy consists of information on
healthy eating practices according to standard dietary guidelines
both worldwide [7-10] and in Singapore [11,12], in terms ofrestricting daily energy intake; balancing the proportion ofcomplex carbohydrate (33%-40%), protein (20%), and fat(40%); and lowering cholesterol intake However, the reported
effects were equivocal due to limitations such as low
compliance, delayed feedback, late initiation, and inefficientdelivery of intervention [13,14].
In recent years, the use of mobile technology (such as
smartphone apps) for patient care has been increasing. The
feasibility and efficacy of such technology has been proven forweight management among pediatric obese patients [15] andfor glucose control in mothers with gestational diabetes mellitus
during pregnancy [16]. Furthermore, a systematic review
summarized 12 studies using phone-based reportinginterventions such as video call, phone calls, short messaging
service (SMS) and smartphone apps and showed consistentevidence that although such approaches could help pregnant
women control their gestational weight gain, they were noteffective in preventing other pregnancy outcomes such as
gestational diabetes mellitus [17]. Another systematic review
of four randomized controlled trials (RCTs) did not draw any
firm conclusions on the effects of mobile app interventions
during pregnancy on maternal knowledge, behavior change,and perinatal health outcomes due to heterogeneity ofinterventions, comparators, and outcome measures across all
RCTs [18]. In addition, based on the intervention evaluation
alone, none of these mobile technologies provide a real-timecommunication between end users and medical workers or
dietitians, thus likely affecting the compliance and efficacy ofthe phone-based intervention. Given the high prevalence of

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smartphone app usage among pregnant women, more rigorousstudies are needed to optimize the study design andimplementation of these technologies in improving maternal
health outcomes. In this pilot randomized controlled trial study,
we tested a food-coaching intervention program delivered
through a smartphone app in overweight and obese pregnant
subjects without gestational diabetes mellitus and examined its
feasibility, acceptance, and preliminary utility during an 8-weekfollow-up in second trimester.

Methods
Study Design and Population
We conducted a prospective, two-arm, unblinded RCT in a
subsidized clinic within a tertiary government hospital in
Singapore (KK Women’s and Children’s Hospital [KKH])
between March and July 2018. We recruited pregnant women
if they were Singapore citizens or permanent residents,
overweight or obese (ie, prepregnancy or booking body massindex≥25 kg/m2), age≥21 years, between 18 and 20 weeks’gestation at the time of recruitment, planning to deliver in KKH,
capable of reading and writing in English, and able to download
and use the smartphone app. We excluded women with special
dietary restrictions due to medical conditions such as type 1 ortype 2 diabetes, gestational diabetes mellitus, hypertension, andchronic kidney disease.
We conducted the study according to the tenets of the
Declaration of Helsinki and obtained approval by the SingHealthCentralized Institutional Review Board and the National HealthGroup’s Domain Specific Review Board. We obtained written
informed consent from all pregnant women at baseline
recruitment. As the primary aim of this study is to test thefeasibility of the smartphone app in the pilot phase, we only
obtained the local institutional review board approval (CIRB
2017/2132), but did not register for an online RCT number.

Randomization and Procedures
Research coordinators randomly assigned pregnant women to
the intervention (food-coaching smartphone app) or control
group (standard pregnancy dietary orientation) in a 1:1 allocation
ratio, using the envelope randomization method (Figure 1).

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Li et alFigure 1. Study flow of the randomized controlled trial.

Standard Pregnancy Dietary Orientation
The same research coordinators administered the standard
dietary orientation to women in the intervention and control
groups using standardized materials, including basic nutrition
principles of choosing a wide variety of nutrient-dense foods,
limiting intake of high-fat and high-sugar food and beverages,
and guiding food portion sizes, with reference to the local
gestational dietary guidelines by the Health Promotion Board[19]. We informed participants in both groups about therecommended gestational weight gain, together with some
simple physical activity pointers.

A Food-Coaching Smartphone App
The intervention group participants could download and use
the food-coaching smartphone app (Glycoleap, Holmusk,
Singapore) for free for up to 8 weeks during the RCT. The app
aimed to improve care and outcomes of people with type 2
diabetes in terms of diet control. Pregnant women using thisapp were able to upload food images and received real-time anddetailed food-coaching comments and guidance fromprofessional dietitians during the day (8 AM to 8 PM).
According to the local pregnancy guideline [20], food dietitians
rated the food image from 1 to 5 (1=the least recommended
score, 5=the most recommended scored) and provided feedbackin terms of degree and balance of the food items andcomposition (Multimedia Appendix 1). Although thissmartphone app was available on both Apple and Google store
platforms, we did not think there was a high chance for thecontrols to obtain access to this app for two reasons: (1) This
smartphone app requires an in-store purchase of up to SG $30(US $22) per month, which is not subsidized by any local
Singaporean medical insurance. All participants were recruitedfrom the government tertiary hospital’s subsidized clinic;
therefore, the chances for them to pay from their own pocket
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to afford additional medical care are low. (2) Only pregnant
women randomized into the intervention group were informedof the name and company of the smartphone app, while thosein the control group were not provided any information
regarding the app. In addition, subjects in the intervention andcontrol groups did not know of each other.
We recruited all subjects at baseline (18-20 weeks’ gestation)and followed them up after 4 weeks (22-24 weeks’ gestation)and 8 weeks (26-28 weeks’ gestation).

Outcomes
Based on the usage of the food-coaching smartphone app, we
assessed the feasibility of the smartphone app by collecting
participant feedback in the intervention group, using anevaluation form at the 8-week follow-up visit (Multimedia
Appendix 2). In addition, we assessed the compliance in theintervention group via the times of log in for each participant
in the first half and second half of the follow-up. We calculated
the proportion of participants with optimal second trimester
gestational weight gain based on the Institute of Medicine
guidelines for overweight (0.23-0.33 kg/week) and obese women(0.17-0.27 kg/week) [9,21]. We assisted all pregnant subjects
in quantifying their food and beverage intake. Such methods ofassessing dietary data have been widely published [22-24]. We
obtained data on 24-hour food recall at recruitment and collected
self-administered 3-day food diary data (Multimedia Appendix
3) in both groups at two follow-ups. We analyzed the dietary
records using an online nutrient analysis software [25], which
was derived from locally available foods [20,26,27]. The
Singapore Health Promotion Board online guidelines provided
specific dietary instructions upon completion of the 3-day fooddiary and provided a picture compendium to each subject tofacilitate identification of each type and quantity of foodconsumed. The compendium of food pictures consisted of
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photographs of individual food items coded to reflect the portion
size of food items and standardize the estimated amounts eaten.
The guidelines also used standard bowls, glasses, and spoonsof varying sizes to estimate the volume of fluids or the amountof food consumed. Research coordinators entered each recorded
food item into an online nutrient analysis database (FoodInformation and Nutrient Database system, Health Promotion
Board, Singapore) based on a food composition database of
locally available foods [25]. The daily food intake data werethen summed up, and the total daily energy, macronutrients(carbohydrate, fiber, fat, and protein), calcium, cholesterol, andsodium intakes were averaged over each 3-day period and listed
in kilocalories, grams, and percentages in the data summary
form. The intake was compared between the intervention andcontrol groups at the two follow-up visits.

Statistical Analysis
We applied the Fisher exact test and Student t test for categoricaland continuous variables, respectively, to compare
characteristics between the intervention and control groups. We
used linear regression to examine the effect of the food-coachingsmartphone app on weight gain control and macronutrient intake
between two groups. Mean differences as an estimate
referencing the control group were shown in linear regression.
We performed statistical analysis using STATA (Version 14.0.
STATA Corp, College Station, Texas), set a two-tailed P value
for significance at .05, and provided the 95% CIs for all
estimates.

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Li et alResults
Among 30 pregnant women recruited at baseline with 1:1
allocation, 26 (12 in the intervention group and 14 in the control
group) completed the 8-week RCT. The detailed RCT flow
chart is shown in Figure 1. Based on the log-in records, theuptake of this smartphone app among our intervention subjects
was up to 90% at the beginning (n=15) and 70% at the end of
the study (n=12), respectively. Based on the 12 returned user
evaluation forms, 75% (n=9) of the app users found that the app
was easy to operate, more than 80% (n=10) thought the
food-coaching guidance was acceptably fast, and 90% (n=11)
were satisfied and reported that the app had somewhat or greatly
improved their own diet. Therefore, they would recommend thesmartphone app to family and friends. Furthermore, according
to the backlog records from the smartphone app provider, we
assessed compliance by calculating the average log-in frequency
in the first and second half of the 8-week follow-up. Each of
the 12 subjects in the intervention group logged into thesmartphone app 8 times per week in the first 4 weeks, on anaverage, but this number reduced to 2 times per week in thesubsequent 4 weeks.
Tables 1-3 show the baseline and follow-up characteristics
between the intervention and control groups. More participants
met the Institute of Medicine recommendation for optimalgestational weight gain per week in the intervention group(4-week follow-up: 7/12, 58%; 8-week follow-up: 8/12, 67%)
than in the control group (4-week follow-up: 8/15, 53%; 8-weekfollow-up: 5/14, 36%).

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Li et alTable 1. Comparison of baseline characteristics between the intervention and control groups (baseline recruitment at 18-20 weeks’ gestation).
Characteristics

Intervention group (n=15)

Control group (n=15)

P valuea

Age (years), mean (SD)29.3 (4.4)

30.7 (5).45

Ethnicity, n (%).15

Chinese2 (13)

6 (40)Malay

12 (80)8 (53)

Indian

1 (7)

0 (0)

Others

0 (0)

1 (6)

4 (27)1 (6)Smoking history (yes), n (%).33

Parity, n (%).60

0

7 (47)

5 (33)

1

4 (27)

7 (47)≥2

4 (27)

3 (20)

Maternal college degree (yes), n (%)

3 (20)

5 (33).68

Paternal college degree (yes), n (%)

1 (7)

4 (27).33

Household income ≥US $6000/month, n (%)

3 (20)

6 (40).43

Past history of pregnancy outcomes
Gestational diabetes mellitus, n (%)

0 (0)3 (20)

Hypertensive disorders during pregnancy, n (%)

0 (0)

1 (7)

Prepregnancy weight (kg), mean (SD)78.5 (15.8)

73.9 (6.8).31Prepregnancy body mass index (kg/m2), mean (SD)

31.0 (4.7)

29.4 (2.2).27

19.2 (2.1)

17.6 (2.8).08Baseline body mass index (kg/m ), mean (SD)34.2 (4.3)

31.3 (2.8).04Baseline systolic blood pressure (mm Hg), mean (SD)118.6 (8.9)

116.8 (9.4).59

Baseline diastolic blood pressure (mm Hg), mean (SD)

69.4 (4.6)

64.1 (7.9).03

Gestational age at recruitment (weeks), mean (SD)
2

a

.16

Student t test or Fisher exact test.

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Li et alTable 2. Comparison of follow-up measures between the intervention and control groups (4-week follow-up at 22-24 weeks’ gestation).
Intervention group (n=12)

Control group (n=15)

P valuea

First follow-up body mass index (kg/m2)

36.4 (4.9)

31.9 (2.6).003First follow-up systolic blood pressure (mm Hg)

121.9 (6.8)

120.9 (6.4).73First follow-up diastolic blood pressure (mm Hg)

70.9 (7.5)

66.5 (6.7).15Weight gain from baseline (kg)1.3 (1.5)

1.5 (1.6).83

7 (58)

8 (53).67

Energy intake (kcal)

1370.5 (359.4)

1514.0 (362.6).32

Carbohydrate (g)177.5 (55.3)

189.5 (49.3).56

Protein (g)52.6 (15.0)

65.1 (17.5).06

Total fat (g)49.4 (15.9)

54.3 (17.3).46

Cholesterol (mg)182.0 (90.6)

246.9 (109.7).11

Calcium (g)513.4 (320.8)

513.3 (286.9).99

Dietary fiber (g)12.9 (4.3)

12.9 (3.5).95

Sodium (g)2.4 (0.8)

2.8 (0.8).18

Clinical measures
Anthropometric and blood pressure measures, mean (SD)Patients obtaining optimal weight gain per week, n (%)
Dietary measures, mean (SD)aStudent t test, Fisher exact test, or Wilcoxon signed rank test.

Table 3. Comparison of follow-up measures between the intervention and control groups (8-week follow-up at 26-28 weeks’ gestation).
Intervention group (n=12)

Control group (n=14)

P valuea

Second follow-up body mass index (kg/m2)

36.0 (4.6)

32.4 (2.8).02Second follow-up systolic blood pressure (mm Hg)

120.2 (7.5)

117.3 (7.3).33

Second follow-up diastolic blood pressure (mm Hg)

69.6 (9.1)

67.4 (5.8).47Weight gain from baseline (kg)2.9 (1.9)

3.0 (2.24).92

8 (67)

5 (36).43

Energy intake (kcal)

1370.5 (359.4)

1514.0 (362.6).44

Carbohydrate (g)1815.0)170.6 (49.4).61

Protein (g)56.7 (19.8)

58.6 (15.8).79

Total fat (g)54.3 (20.7)

43.5 (17.5).15Cholesterol (mg)196.2 (89.3)

220.6 (87.9).48

Calcium (g)461.0 (246.0)

489.1 (222.9).76

Dietary fiber (g)13.0 (5.3)

12.9 (4.5).99

Sodium (g)2.2 (0.7)

2.4 (0.8).55

Clinical measures
Anthropometric and blood pressure measures, mean (SD)Patients obtaining optimal weight gain per week, n (%)
Dietary measures, mean (SD)aStudent t test, Fisher exact test, or Wilcoxon signed rank test.

Although not significant, we found a trend among women in
the intervention group who tended to have less weight gain thanthose in the control group at the 4-week follow-up (meandifference=–0.15 kg; 95% CI –1.51 to 1.21) and 8-weekfollow-up (mean difference=–0.08 kg; –1.80 to 1.63; Table 4).

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In addition, women in the intervention group tended to consumeless cholesterol than those in the control group at the 4-weekfollow-up (mean difference=–64.87 mg; 95% CI –146.04 to
16.31) and 8-week follow-up (mean difference=–31.73 mg;
95% CI –102.91 to 39.45).

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Li et alTable 4. Weight gain from baseline and dietary intake between the intervention and control groups.
Dietary componentsaMean difference between intervention and control (ref) groups
4-week follow-up (22-24 weeks’ gestation)a8-week follow-up (26-28 weeks’ gestation)bβ (95% CI)

P valueβ (95% CI)

P value

Weight gain from baseline (kg)–0.15 (–1.51 to 1.21).83–0.08 (–1.80 to 1.63).92

Energy intake (kcal)–143.55 (–431.66 to 144.56).32

123.99 (–222.74 to 470.72).47

Carbo (g)–12.05 (–53.54 to 29.44).56

9.35 (–33.62 to 52.31).66

Protein (g)–12.58 (–25.69 to 0.53).06–2.08 (–16.73 to 12.57).77

Total fat (g)–4.91 (–18.24 to 8.42).46

11.11 (–4.60 to 26.81).16

Cholesterol (mg)–64.87 (–146.04 to 16.31).11–31.73 (–102.91 to 39.45).37

Calcium (g)0.04 (–241.08 to 241.16)>.99–19.16 (–211.31 to 172.99).84

Dietary fiber (g)0.10 (–2.97 to 3.17).95

0.20 (–3.82 to 4.22).92

Sodium (g)–413.16 (–1032.09 to 205.76).18–98.83 (–692.65 to 494.98).73

Intervention group: n=12; control group: n=15.

bIntervention group: n=12; control group: n=14.

Discussion
In this in a small sample RCT, we used a smartphone app toguide overweight and obese pregnant women to eat heathier inorder to obtain optimal weight gain. We noted a high uptake ofthe smartphone app, as described above, and found evidence
that pregnant women in the intervention group were more likely
to have optimal gestational weight gain and consume lesscholesterol compared with women in the control group.
Although our pilot results were not significant, it provided proof
of concept for the feasibility of applying such technology infuture RCTs with a larger sample size, an earlier intervention
onset, and a longer follow-up for overweight and obese pregnant
women.
Although the dietary intervention has been proven to be effective
in terms of weight gain control among overweight and obese
pregnant women [28,29], there are huge variations in thedelivery of dietary recommendation such as timing ofintervention initiation, intensity of intervention, and feedback
availability [13,28-31]. In a recently published systematic review
summarizing 12 phone-based intervention studies on gestational
weight gain control outcomes, most of the studies used phone
calls or SMS to provide weight control guidelines, encouraged
physical activity, and provided educational information about
healthy nutrition. Several studies suggested that telephone
communication is one of the most cost-effective tools to keep
track of pregnant women’s health [32]. However, phone-based
interventions are typically initiated by health care providers andmight not effectively motivate pregnant women to self-manage
their behaviors. Given the high prevalence of smartphone app
usage among pregnant women to improve their healthy
behaviors, no firm conclusion has been drawn in terms ofimproving perinatal outcomes [18]. One of the major drawbacks
is the lack of rigorous studies examining self-managing andself-regulatory behaviors in pregnant women. Our food-coaching
app is more flexible and interactive and has greater variety ofcommunication modalities, thus overcoming all aforementioned
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limitations in current technology. Given the high smartphone
usage rate (up to 80%) among Singaporeans [33] and highprevalence (up to 30%) of overweight and obesity among
pregnant women in tertiary hospital settings [4], use of a mobile
app to promote healthy dietary intake is feasible and likely apromising intervention strategy. Our findings showed feasibility
and acceptability of such a food-coaching smartphone app. For
example, 75% of the app users found that the app was easy tooperate, and more than 80% thought the food-coaching guidance
was acceptably fast. Furthermore, 90% reported that the app
had somewhat or greatly improved their own diet.
Interestingly, we did observe a trend of lower energy,
carbohydrate, protein, total fat, and cholesterol intake in thefirst 4 weeks of the intervention, while most of themacronutrients did not maintain the same trend at 8 weeks offollow-up, except for cholesterol. This may be because pregnant
subjects were more inclined to remember food items that arehigh in cholesterol (ie, saturated fat, red meat, full-fat dairy
products) and were able to make an effort to avoid such fooditems compared with others. However, further studies are needed
to verify our findings, as such findings with a small sample
might be biased. Although the effect estimates are in the desired
direction and supported the proof of concept as the primary
focus in this pilot RCT, we still need to further verify the utilityof such a smartphone app in a larger targeted population withan earlier phase intervention during pregnancy and a longer
follow-up.
The strength of our study included a prospective RCT study
design and the use of standard protocols in anthropometricmeasures and dietary assessments. However, our study has
significant limitations regarding the loss to follow-up and asmall sample for analysis.
In conclusion, our study provides proof of concept thatsmartphone technology is feasible and acceptable in clinicaldietary guidance among overweight and obese pregnant women.
In the future, we will adopt this food-coaching smartphone app

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and test its utility in a larger setting with a targeted population,

Li et alearlier intervention, and longer follow-up throughout pregnancy.

Acknowledgments
This study is supported by Singapore National Medical Research Council Centre Grant (NMRC/CG/C008A/2017_KKH). L-JL
is funded by Singapore National Medical Research Council Transition Award (NMRC TA/0027/2014) and(NMRC/CG/C008A/2017_KKH).

Conflicts of Interest
None declared.
Editorial notice: This randomized study was not prospectively registered. The editor granted an exception of ICMJE rules forprospective registration of randomized trials because the risk of bias appears low and the study was considered formative. However,readers are advised to carefully assess the validity of any potential explicit or implicit claims related to primary outcomes or
effectiveness.

Multimedia Appendix 1
The electronic interface layout of the food-coaching smartphone app (Glycoleap).
[PDF File (Adobe PDF File), 50 KB-Multimedia Appendix 1]

Multimedia Appendix 2
Self-evaluation of acceptability on the GlycoLeap application during pregnancy.
[PDF File (Adobe PDF File), 161 KB-Multimedia Appendix 2]

Multimedia Appendix 3
Food diary form.
[PDF File (Adobe PDF File), 60 KB-Multimedia Appendix 3]

Multimedia Appendix 4
CONSORT‐EHEALTH checklist (V 1.6.1).
[PDF File (Adobe PDF File), 2801 KB-Multimedia Appendix 4]

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Abbreviations
KKH: KK Women’s and Children’s Hospital
RCT: randomized controlled trial
SMS: short messaging service

Edited by G Eysenbach; submitted 03.12.18; peer-reviewed by L Garnweidner-Holme, J Most, R Ciptaningtyas; comments to author
17.04.19; revised version received 22.05.19; accepted 21.07.19; published 24.10.19
Please cite as:
Li LJ, Aris IM, Han WM, Tan KH
A Promising Food-Coaching Intervention Program to Achieve Optimal Gestational Weight Gain in Overweight and Obese Pregnant
Women: Pilot Randomized Controlled Trial of a Smartphone App
JMIR Form Res 2019;3(4):e13013
URL: http://formative.jmir.org/2019/4/e13013/
doi: 10.2196/13013
PMID: 31651407©Ling-Jun Li, Izzuddin M Aris, Wee Meng Han, Kok Hian Tan. Originally published in JMIR Formative Research(http://formative.jmir.org), 24.10.2019. This is an open-access article distributed under the terms of the Creative Commons
Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction
in any medium, provided the original work, first published in JMIR Formative Research, is properly cited. The complete
bibliographic information, a link to the original publication on http://formative.jmir.org, as well as this copyright and license
information must be included.

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