
==== Front
NPJ Digit Med
NPJ Digit Med
NPJ Digital Medicine
2398-6352
Nature Publishing Group UK London

1246
10.1038/s41746-024-01246-x
Article
Can social media encourage diabetes self-screenings? A randomized controlled trial with Indonesian Facebook users
Fritz Manuela manuela.fritz@uni-passau.de
manuela.fritz@tum.de

12
Grimm Michael 134
http://orcid.org/0000-0003-4169-2579
Weber Ingmar 5
http://orcid.org/0000-0002-2380-4584
Yom-Tov Elad 6
Praditya Benedictus 7
1 https://ror.org/05ydjnb78 grid.11046.32 0000 0001 0656 5756 University of Passau, Department of Economics, Passau, Germany
2 https://ror.org/02kkvpp62 grid.6936.a 0000 0001 2322 2966 Technical University Munich, School of Social Science and Technology, Munich, Germany
3 https://ror.org/029s44460 grid.424879.4 0000 0001 1010 4418 IZA, Bonn, Germany
4 grid.437257.0 0000 0001 2160 3212 RWI Research Network, Essen, Germany
5 https://ror.org/01jdpyv68 grid.11749.3a 0000 0001 2167 7588 Saarland University, Department of Computer Science, Saarbruecken, Germany
6 https://ror.org/03kgsv495 grid.22098.31 0000 0004 1937 0503 Bar Ilan University, Department of Computer Science, Ramat Gan, Israel
7 Xiaomi Indonesia, DKI Jakarta, Indonesia
13 9 2024
13 9 2024
2024
7 24518 1 2024
31 8 2024
© The Author(s) 2024
2024
https://creativecommons.org/licenses/by/4.0/ Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
Nudging individuals without obvious symptoms of non-communicable diseases (NCDs) to undergo a health screening remains a challenge, especially in middle-income countries, where NCD awareness is low but the incidence is high. We assess whether an awareness campaign implemented on Facebook can encourage individuals in Indonesia to undergo an online diabetes self-screening. We use Facebook’s advertisement function to randomly distribute graphical ads related to the risk and consequences of diabetes. Depending on their risk score, participants receive a recommendation to undergo a professional screening. We were able to reach almost 300,000 individuals in only three weeks. More than 1400 individuals completed the screening, inducing costs of about US$0.75 per person. The two ads labeled “diabetes consequences” and “shock” outperform all other ads. A follow-up survey shows that many high-risk respondents have scheduled a professional screening. A cost-effectiveness analysis suggests that our campaign can diagnose an additional person with diabetes for about US$9.

Subject terms

Population screening
Risk factors
Health care economics
issue-copyright-statement© Springer Nature Limited 2024
==== Body
pmcIntroduction

Non-communicable diseases (NCDs), such as cardiovascular diseases, diabetes, and cancer, have overtaken infectious diseases as the leading cause of death worldwide1. Screening for metabolic NCD risk factors, such as high blood sugar and blood pressure, provides an effective tool to prevent more severe long-term health consequences. Also, behavioral risk factors, such as smoking, drinking, unhealthy diets, and a lack of physical activity, can be addressed once an individual is aware of its personal risk. Yet, nudging individuals to undergo such a screening in case of no apparent symptoms remains a challenge. This holds especially true in low- and middle-income countries (LMICs), where health literacy and the awareness of and screening for NCDs remain limited2–4. At the same time, NCDs are increasing at an unprecedented rate in many LMICs, requiring innovative solutions to increase NCD screening5–7.

To increase NCD awareness and screening in LMICs, the World Health Organization (WHO) promotes mass media awareness campaigns as a cost-effective instrument8,9. Yet, their focus is largely on traditional media such as TV, radio and print, whereas public health campaigns via social media advertising remain unmentioned. Social media public health campaigns and health advertisements have been shown to be promising to address a variety of health aspects and health behaviors. For example, social media public health campaigns have been used to address vaccination rates10–14, Covid-19 infections15, drinking during pregnancy16, smoking cessation17, sexual behaviors18, food choices and physical activity19,20.

Our study adds to this literature, but goes beyond these studies in multiple aspects. First, the major share of these campaigns is implemented and evaluated in high-income countries and addresses health topics of which the general public is broadly aware off. The question of whether such social media health campaigns work similarly well in LMIC contexts, especially if they address a disease for which there is little knowledge and awareness4,21,22, remains unanswered and we address this research gap. Thereby, we also directly speak to the literature that evaluates which other means and nudges (e.g., messages through community leaders or reminders) are effective in LMICs in encouraging better health-related outcomes and behavior23,24.

Second, most campaigns are limited to the pure provision of information and do not observe and engage viewers in concrete measurable actions other than those happening online (e.g., clicks or likes). Instead, users in our campaign were redirected to our campaign website, on which they could engage in an actual screening activity. Moreover, through a follow-up survey with part of the participants, we elicited behavior that happened (offline) after the campaign exposure. Notable exceptions to mention here are a study on Covid-19 infections15, which also expands the research design to offline measurements of user mobility and actual infection rates, and a study on HPV vaccination14 which measures actual vaccination rates.

Lastly, only a limited number of studies address the aspect of cost-effectiveness, despite the major advantage of online campaigns being cheap in comparison to other mass media campaigns. More specifically, while some studies evaluate the cost per person reached or the cost per person recruited with such campaigns25,26, they do not go as far as evaluating the cost per actual diagnosed case or prevented case. Hence, we conduct a cost-effectiveness analysis of our campaign to provide insights about the cost-saving potential of social media public health campaigns (beyond the cost per person reached), which is especially relevant in contexts of limited public health budgets as it is the case in Indonesia and in many other LMICs27.

We design, implement, and evaluate a diabetes health campaign and assess whether health advertisements (“ads”) distributed via Facebook can serve as a promising instrument to foster the individual decision to undergo a diabetes risk screening in Indonesia. Indonesia is a relevant setting for our campaign since diabetes is currently the third leading cause of death28. Moreover, the country ranks fifth in the list of absolute numbers of diabetes cases and third among the countries with the highest number of undiagnosed cases worldwide. In 2021, more than 19 million individuals were estimated to be living with the disease in Indonesia, with more than 70% of the cases remaining undiagnosed29. At the same time, the usage rate of Facebook is high, which lends itself as a perfect showcase to study whether social media campaigns are suitable to encourage people to engage in preventive health behavior such as diabetes screening. Given this setting, our results are relevant for many other middle-income countries with similar high rates of diabetes and large numbers of Facebook users, such as other countries in Southeast Asia, as well as for example India, Brazil, Mexico or Pakistan.

Facebook is becoming an increasingly relevant tool for scientific research, especially in terms of implementing randomized controlled trials (RCTs) with a large outreach12,13,15,30. Given the platform’s possibilities to specify concrete population targeting criteria and using Facebook’s A/B split test function, it allows us to target our campaign to Facebook users in the cities with the highest diabetes rates in Indonesia (Jakarta and Yogyakarta), and to provide causal evidence on the effectiveness of different ad designs. Specifically, we use an RCT on Facebook and distribute ads that differ in their framing, i.e., in their message and graphical design, but equally invite viewers to visit our campaign website and to complete a diabetes self-screening. We are especially interested in whether loss-framed, i.e., shocking, messages work better than more neutral ads. Theoretical work by Rothman et al.31,32 suggests that loss-framed or shocking messages should be more effective in inducing health behaviors that might be perceived as risky (i.e., have an uncertain outcome), such as disease detection activities. Following this argument, we hypothesize that a diabetes awareness campaign that encourages diabetes screening might be most effective if a shocking or loss-framed perspective is taken and investigate this proposition experimentally. Thereby, we also add to the empirical literature that explores what kind of information, framings or pictorial content drive health-related decisions33–39, in particular health screening activities40–42. Specifically, we provide evidence about which ads can effectively nudge individuals to learn about their risk of having or developing diabetes in a country where general disease awareness is low.

We then assess whether the most persuasive ad is good enough to design a cost-effective awareness campaign. Hence, in this second part of our analysis, we are interested in whether a campaign based on the cost and effectiveness parameters of the best-performing ad can be considered a cost-effective public health intervention. To this end, we follow-up with a subset of participants that completed the self-screening and investigate their compliance rate with the recommendation to schedule an appointment for a professional screening if they were found to be at high risk.

Results

Campaign outreach and engagement

From March 15 until April 5, 2022, we ran a diabetes health campaign entitled “Ada Gula, Ada Diabetes”. The title is related to the traditional Indonesian saying “Ada gula, ada semut”, which literally means “When there is sugar, there must be ants”. Figuratively, the saying means that for every action there is an equal and opposite reaction. Our adapted campaign name hence figuratively interprets diabetes as the reaction to too much sugar – also in relation to the fact that diabetes is known as “Sakit Gula” (“sugar disease” or “sugar sickness”) in Indonesia. We ran the campaign in Jakarta and Yogyakarta and used five different ads, two of which took on a loss-framed and rather disquieting perspective, with the remaining three referring to the family, religion, and the local diabetes prevalence rate (see Methods for a detailed description of the campaign and ads). After clicking on one of the ads, users were re-directed to our campaign website, where they were offered the opportunity to complete a diabetes risk screening questionnaire similar to the diabetes risk test of the American Diabetes Association and the diabetes FINDRISC (Finnish Diabetes Risk Score) screening test but adapted to the Indonesian population (see Methods section for details and Supplementary Tables 1 and 2 for the complete questionnaire). Based on the individual answers, a risk score between 0 and 16 points was calculated and participants received an assessment of their personal risk. Additionally, the assessment contained recommendations on how to keep the risk low, how the diabetes risk can be reduced and to visit a health center or a physician if the risk score was too high. Six weeks after the end of the campaign we sent a follow-up survey to (voluntarily left) e-mail addresses to elicit information about actual compliance with the recommendations received.

Table 1 presents the Facebook engagement statistics of our campaign by age, gender, and location (statistics by ad are presented in Supplementary Table 3). These descriptive statistics show that our Facebook campaign can be deemed effective in distributing diabetes-related ads and reaching the general public: Within only three weeks, we reached in total 286,776 individuals with our campaign, generated 758,977 impressions (distinct views of the ads) and 5274 link clicks. This amounts to a click rate of 1.84% (relative to the number of reached individuals), which is higher than the rates achieved in studies with a similar setup, for example in Tjaden et al.12 (1.7%), Choi et al.43 (1.4%) or Orazi30 (0.2%). Overall, we spent approximately US$1060 and the campaign resulted in 2052 started and 1469 completed screening questionnaires, implying a conversion-to-reach rate of 0.51% (1469/286,776) and a conversion-to-click rate of 27.85% (1469/5274). Moreover, this relates to a cost of around US$0.75 per person conducting such a self-screening. The age and gender patterns reflect the Indonesian Facebook user rates, with slightly more men than women using the platform and the elderly having the lowest user rates44,45.Table 1 Outreach of the Facebook campaign

	(1)	(2)	(3)	(4)	(5)	
	Reach	Impressions	Link clicks	Expenditure	Conversions	
	(# of persons)	(# of distinct views)	(# of ad clicks)	(in US$)	(compl. quest.)	
Total	286,776	758,977	5274	1066.04	1469	
By Gender						
Male	160,560 (56%)	433,677 (57%)	2725 (52%)	570.91 (54%)	754 (51%)	
Female	126,216 (44%)	325,300 (43%)	2549 (48%)	495.13 (46%)	715 (49%)	
By Age						
Below 45	136,500 (48%)	342,729 (45%)	1646 (31%)	372.01 (35%)	466 (32%)	
45–54	98,060 (34%)	271,914 (36%)	2091 (40%)	421.23 (40%)	686 (47%)	
55–64	32,820 (11%)	90,314 (12%)	978 (19%)	188.42 (18%)	238 (16%)	
65+	19,396 (7%)	54,020 (7%)	559 (11%)	83.37 (8%)	79 (5%)	
By Location1						
Jakarta	145,528 (51%)	321,154 (42%)	2834 (54%)	526.75 (49%)	876 (60%)	
Yogyakarta	141,248 (49%)	437,823 (58%)	2440 (46%)	539.29 (51%)	567 (40%)	
1The number of completed screening questionnaires by location does add up to 1443 and not to 1469 since for 26 completed questionnaires tracking was restricted and the referring ad and thus the location could not be determined.

Due to changes in Apple’s data policy, Facebook is unable to track users who opted out of tracking under iOS 14 or users who prohibit tracking in any other form and therefore relies on statistical modeling to estimate the total number of conversions46. Moreover, Facebook is unable to differentiate by age or gender once the users leave the platform and thus only provides aggregated data on conversions. Hence, for the results in terms of conversions (Column (5)), we rely on the more accurate data that was collected directly on our campaign website from which we could extract – without any loss or modeling – the absolute number of completed (and started) screening questionnaires by age, gender, and location.

Screening participation

Once redirected to our campaign website, participants could fill out the screening questionnaire. We used Facebook’s dynamic URL parameters47 to generate ad-specific referrer links containing information about the ad id, ad name, and ad placement. These URL parameters could then be read out whenever an individual started to fill out the screening questionnaire. For those individuals using an Apple device who opted out of tracking, the ad-specific URL parameters within the referrer link would not be displayed. However, given that a vast majority of smartphone users in Indonesia rely on an Android system, only 26 (out of 1469) completed screening questionnaires could not be linked to the ad from which users were redirected to our campaign website.

Respondents had the possibility to complete the screening questionnaire multiple times on our website, either for themselves or for other relatives and friends. This was to allow for possible spillover effects, for example, if a user, after completion of the screening questionnaire, re-did the screening for another person. This, however, also implies that the same person could fill out the screening questionnaire multiple times with different information, for example, to check for related changes in the obtained diabetes risk score. The individual link id together with the IP address and browser information, however, allowed us to identify repeated survey questionnaires that were completed from the same device. We therefore construct a data sample in which we drop the observations stemming from repeated questionnaires, i.e., for each link id × IP address combination we keep only the first completed observation in our sample. We use this first observation based on the assumption that a person filling out the questionnaire multiple times would do so first for him- or herself and only afterward for another person. Similarly, we assume that if it was filled out multiple times simply out of curiosity, the respondent would enter the true data the first time and hypothetical data only afterward. This procedure led to a reduction from 1533 completed questionnaires (with duplicates) to an individual sample containing the 1469 completed screening questionnaires presented in the summary statistics.

Table 2 presents the summary statistics of the completed screening questionnaires for the main sample. Summary statistics, including the information for all started questionnaires and for the sample of completed questionnaires including any duplicates are presented in Supplementary Tables 4 and 5.Table 2 Summary statistics of completed screening questionnaires

	(1)	(2)	(3)	(4)	
	Mean	SD	Min	Max	
Age distribution					
Below 45	0.32		0	1	
45–54	0.47		0	1	
55–64	0.16		0	1	
Above 65	0.05		0	1	
Female	0.49		0	1	
Ever had high blood glucose	0.50		0	1	
Ever diagnosed with high blood pressure	0.33		0	1	
Family member with diagnosed diabetes	0.54		0	1	
Weight	69.28	17.10	33	185	
Height	162.43	7.84	140	195	
BMI	26.15	5.61	11	70	
Daily physical activity	0.60		0	1	
Smoking					
Never smoked	0.66		0	1	
Stopped smoking	0.20		0	1	
Currently smoking	0.14		0	1	
Daily fruit consumption	0.45		0	1	
Daily sweet beverages consumption	0.30		0	1	
Risk score	6.37	2.57	0	14	
Low risk	0.14		0	1	
Medium risk	0.25		0	1	
High risk	0.61		0	1	
Provided e-mail address	0.14		0	1	
Number of observations	1469				
Table 2 displays the summary statistics of the completed screening questionnaires for the main sample without repeated answers.

The greatest proportion of users completing the risk screening questionnaire on our campaign website were in the 45–54 age group, the average BMI was about 26 and the users had on average a high diabetes risk with a risk score of 6.4. Sixty-one percent of them were found to be at high risk of diabetes, indicating that we were indeed able to reach out to persons who could benefit from such a self-screening. Men and women are almost equally represented. Half of the respondents report ever having been told that they have high blood sugar levels and one-third have ever been diagnosed with high blood pressure levels. In terms of smoking, 34% of participants report being ever-smokers, (i.e., either currently smoking or smoking previously but have now stopped). This average smoking rate, however, obscures a strong gender heterogeneity, with 8% of women and 57% of men in our sample being ever-smokers; a trend that is also well in line with the tobacco consumption pattern in Indonesia observed in the Indonesian Basic Health Research (RISKESDAS48, with 3.2% female and 65% male ever-smokers, respectively, for the total Indonesian population above the age of 10). Sixty percent of the respondents report doing at least 30 minutes of physical activity per day, while only 45% report consuming fruit or vegetables on a daily basis. Thirty percent of the respondents report consuming sugary beverages every day.

The summary statistics of the started screening questionnaires (Supplementary Table 4) reveal that a large share of survey starters dropped out after the first question (9%) and another large share before the question about participants’ weight and height (10%). Overall, 75% of started screening questionnaires were completed. Of all completers, 205 (14%) left their e-mail address to be contacted for further study activities. We sent a follow-up survey to this sub-sample six weeks after the end of the campaign. The full workflow and the number of observations at each step are presented in Fig. 1.Fig. 1 Workflow of the experiment.

The number 1533 in parentheses at Step 4 refers to the number of completed questionnaires when duplicated questionnaires are also counted.

Results from the follow-up survey

Of the 205 participants who left their e-mail addresses and agreed to be re-contacted for further research activities, 53 participated in the follow-up survey. The primary aim of the follow-up survey was to elicit whether individuals with a high risk of diabetes complied with the recommendation they received to schedule an appointment in a primary healthcare facility or with their physician to undergo a blood test for diabetes. Also, if they reported not planning to schedule an appointment, we were interested in the reasons. Of the 53 individuals participating in this survey, 32 (60%) had received a high-risk score in the screening, 15 (28)% a medium-risk score, and 6 (11%) a low-risk score. Obviously, we must assume that the group of respondents is not necessarily representative of the overall sample of 1469 individuals that participated in the screening, as survey participation was voluntary. However, when comparing their observable characteristics with those of the overall sample we did not find any statistically significant differences in their characteristics, as displayed in Supplementary Table 6. The power of these tests is of course limited, given the small sample size, but even the absolute size of the differences is in most cases surprisingly small. Moreover, we cannot detect any selection in terms of the ad the individual was exposed to (Supplementary Table 7), i.e., we do not find any significant effects of the different ads or the final risk score on the probability of participating in the follow-up survey.

We asked those individuals who either were at high risk according to their screening results or who mentioned remembering that they had a high risk about their plans for a professional appointment (n = 35). Of those individuals, 12 (34%) reported that they had already been aware that they had diabetes and hence no further professional test was needed, 13 (37%) reported that they did not plan to schedule a professional appointment, and 10 (28%) reported that they had already scheduled an appointment after participating in our screening or that they intended to do so in the next month (Supplementary Fig. 1). Hence, almost one-third of those deemed to be at high risk, corresponding to 43% of those who were unaware of their disease status, seem to comply with the recommendation to undergo a professional blood test for diabetes. If we extrapolate this share to the full sample, it amounts to 250 complying individuals at high risk. These numbers suggest that the campaign not only attracted individuals who were already aware that they had diabetes but that it also reached a substantial share of individuals at high risk of diabetes who were not aware of their status.

To account for a potential desirability bias in our survey, i.e., individuals simply reporting complying with the received recommendation because they expected this to be the socially desirable answer, we randomized two different framings of the same question. One highlighted the importance of scheduling a professional appointment given the possible severe health consequences of diabetes, the other implied that the time that had passed since the screening was probably too short to already have scheduled a meeting (the exact framings are shown in Supplementary Material 3). Whereas the first framing should increase the psychological cost of admitting to not having made an appointment, the second framing makes it psychologically rather easy to admit to not having made an appointment. If both framings lead to a comparable share of respondents who report having made an appointment, we can interpret this as evidence that a desirability bias is not at work. Indeed, we do not find any significant differences in the response pattern to the questions, which increases our trust in the reported answers (Supplementary Table 8).

Individuals reporting not intending to schedule an appointment for a professional blood test were further asked for the main reasons keeping them from doing so (Supplementary Fig. 2). More than half of the respondents answered being afraid of the possible costs of such a test. Given the small sample size for this question, the results have to be interpreted carefully. Yet, since preventive health care visits, including tests for chronic diseases, are free of charge for those covered by the JKN national health insurance scheme (which around 80% in our sample are), a potentially promising strategy to increase screening rates could be to distribute detailed information about the services covered in the scheme.

Ad performance

Next to the assessment of the outreach and engagement with our campaign, we were interested in which ad design and framing would be most effective in creating clicks and conversions (completed screening questionnaires). In particular, we were interested in whether the two loss-framed ads would outperform the more neutrally framed ads (see the Methods section for the different designs). To assess ad performance, we estimate the following logistic regression models:1 P(Linkclicki=1∣Adij,Zi)=λβ0+∑j=14βjAdij+βZi+ui

and2 P(Conversioni=1∣Adij,Zi)=λδ0+∑j=14δjAdij+δZi+ei,

where λ is the logistic function, ∑j=14Adij is a set of four dummy variables that are equal to one whenever person i saw ad j (the ad “family” serves as the reference group), Zi is a vector of control variables (age, gender, region), and ui (ei) is the error term. Note that the coefficients βj and δj can be interpreted as causal effects since the ads were randomly assigned to Facebook users. Additionally, we investigate the effects separately by gender, since previous empirical evidence suggests that the effects of framing and information differ significantly for men and women49–52.

Figures 2 and 3 together with Supplementary Tables 9 and 10 in Supplementary Material 4 show the results for link clicks and conversions for the total sample and separately for men and women. Figures 2 and 3 show the relative increases in comparison to the “family” ad, which implies a reference click-to-reach-ratio of 1.7% and a reference conversion-to-reach-ratio of 0.4%. Supplementary Tables 9 and 10 display the regression coefficients and marginal effects (with and without controls and by gender) from the logit model, together with the p-values of pairwise Wald tests for the different coefficients.Fig. 2 Ad effectiveness for link clicks.

Figure 2 shows the effectiveness of the different ads in terms of link clicks for a the full sample and b by gender. The effects are presented as relative effect to the “family ad'', which serves as a reference category. Black whiskers present the 95% confidence intervals.

Fig. 3 Ad effectiveness for conversions.

Figure 3 shows the effectiveness of the different ads in terms of conversions for a the full sample and b by gender. The effects are presented as relative effects to the “family ad'', which serves as reference category. Black whiskers present the 95% confidence intervals.

Graph (a) for the full sample in Fig. 2 shows that we can establish a clear hierarchy in terms of ad effectiveness for generating link clicks, with the two loss-framed ads clearly outperforming the ads “family” and “geography”. Only the effect of the “religion” ad is not statistically different from that of the “shock” ad. The performance of the “consequences” ad is somewhat larger than that of the “shock” ad, yet this difference is only significant at the 10% level (see also Supplementary Table 9).

In terms of the effect size, a user seeing one of the two loss-framed ads “shock” or “consequences” was 15% and 23%, respectively, more likely to click on the ad compared to someone who saw the least performing “family” ad. In absolute terms, this implies an increase to a click-to-reach-ratio of 1.9% and 2.1%. Those seeing the “shock” or “consequences” ads were also 3% and 11% more likely to click on the ads in comparison to the “religion” ad, though the differential effect between the “shock” and “religion” ads is not statistically significant. The magnitudes of these effects are comparable to those found in a study with a similar set-up, also based on Facebook’s A/B split function: Tjaden et al.12 test several ads to increase Covid-19 vaccination rates in Germany and vary the pictured messenger (doctor, governmental representative, religious leader). They report an increase between 20% and 40% in clicks of the best-performing versus other ads.

Differentiating the ads’ effectiveness by gender (Graph (b)), however, shows that the effectiveness of the “consequences” and “shock” ads in terms of link clicks seems to be driven by women, whereas men reacted to all ads in a rather similar manner. In fact, while the effect is still the largest for the two loss-framed ads in qualitative terms, we cannot reject the hypothesis of equal performance of all five ads for the male audience.

Turning to conversions, Fig. 3, Graph (a) shows a slightly different picture. While the “consequences” ad is again the best-performing ad in generating conversions (significantly different from all but the “shock” ad), the performance of the “religion” ad, which was the one that came closest to the performance of the loss-framed ads in terms of creating link clicks, is no longer significantly different from the least performing “family” ad. This might be a sign that the “religion” ad did not sufficiently relate to the topic of diabetes and viewers of the ad did not proceed to the screening once they realized that the website did not contain religious content.

In contrast, the “geography” ad is significantly more effective than the “family” and “religion” ads and equally effective as the “shock” ad in generating finalized risk screening tests. Differentiating by gender (Graph (b)) reveals, however, that the effectiveness of the “geography” ad is again solely due to female users. For men, responsiveness to the “consequences” ad was greatest and the ad performed significantly better compared to all other ads with the exception of the “shock” ad (p-value 0.188).

The effect magnitudes are somewhat larger than those for link clicks when comparing the best-performing ad against the others: an individual exposed to the consequences ad was 57%, 48%, 19% and 10% more likely to complete the self-screening than someone seeing the family, religion, geography or shocking ad, respectively.

Women were also more likely overall (+25%) to complete a screening questionnaire conditional on seeing any of the ads compared to their male counterparts. Yet, given that the number of women seeing an ad on Facebook was lower in absolute terms (since there are generally fewer female Facebook users than male users in Indonesia45), the sample of completed questionnaires is balanced in the gender distribution. Although the oldest age group (65+) was more likely to click on the ads than users below the age of 45, they are about equally likely to complete the questionnaire as the youngest age group, which is driven by a higher attrition rate in the oldest age group. Specifically, when we regress the probability of attrition on participants’ characteristics (conditional on having started the screening questionnaire), we find that elderly respondents above the age of 65 were 34 percentage points more likely to drop out in the course of the questionnaire compared to the youngest age groups. This effect is larger for older men, though not statistically different from the effect for older women (results shown in Supplementary Table 11).

Overall, we can confirm the hypothesis that an ad with a loss-framed perspective, i.e., highlighting the adverse health consequences of diabetes, performs significantly better than ads referring to the family, religion, or local prevalence rates. Only the second loss-framed and “shocking” ad comes close to the performance of the “consequences” ad in our health awareness campaign. Hence, an online diabetes awareness campaign focusing on the health consequences of diabetes can be an effective tool to induce diabetes self-screenings. When we assess whether the diabetes risk level of the screening completers differs in relation to the ad they saw, we find that those who saw one of the loss-framed ads had a risk score that was on average higher by 0.28 (p-value 0.039) than the score of those who saw one of the other three ads. This supports the hypothesis by Rothman et al.31,32 by showing that those who do indeed have a higher diabetes risk, and might also perceive it as such, were more responsive to the loss-framing ads than someone with a lower risk.

Our campaign also shows that the content and framing of the ads is particularly important when targeting women. Women reacted more differentially to the different ads, whereas men responded to the ads more equally, especially for the outcome of link clicks. Yet, also for men, the “consequences” ad performed significantly better than the “family”, “geography” and “religion” ads for the conversion outcome, indicating that the loss-perspective was successful in engaging men in the actual self-screening activity.

While such gender-heterogeneous responses are in line with previous research highlighting the moderating effect of gender in loss- versus gain-framing experiments e.g.,49–52, we must refrain from a more extensive analysis of the drivers of this effect, simply due to data limitations. We did not collect any information on underlying characteristics that could explain such differential behavior. Yet, the literature suggests that gender-differences in risk perceptions49, avoidance orientation50 or trust41 can shape these gender-specific responses. Also, we did not explicitly test loss- versus gain-framing but rather loss-focused versus differently focused ads, which limits the comparability of our results with more precise gain- versus loss-framed campaigns. Nevertheless, our results provide important insights into the question of what type of ads can effectively be used to enhance preventive health behavior and how responsiveness differs between men and women.

Comparison of the sample and benchmark populations

A valid concern that might arise at this point is that we were only able to reach out to a particular population group with our Facebook campaign. While the distribution of the ads was random conditional on being in the pre-specified target group, the actual selection into completing the screening questionnaire is endogenous, and hence the results concerning the effectiveness of our campaign might not to be generalized to other population groups. To investigate the importance of such selection effects, we compare our sample of participants who completed the screening questionnaire with the universe of people who met our eligibility criteria in Jakarta and Yogyakarta. This comparison is presented in detail in Supplementary Material 5 and Supplementary Table 12. It suggests that the sample generated by our experiment is slightly skewed toward the 45-55 age group and to those who seem to be significantly more at risk of having or developing diabetes compared to the total population above the age of 35 in Jakarta and Yogyakarta. We interpret this self-selection as an indication that our campaign was very effective in reaching out to people at high risk who could potentially benefit from such online screening. Since we also showed above in the results of the follow-up survey that only one-third of the individuals who were found to have a high risk and that self-selected into the follow-up survey had already been aware that they have diabetes, we deem this as evidence that our campaign was indeed able to reach out to a large number of individuals who were unaware of their high risk and that our campaign was able to effectively engage them in the diabetes self-screening.

Cost-effectiveness

Having identified that ads focusing on the detrimental health consequences of diabetes can be a particularly well-suited approach to encourage diabetes risk screening among those with a comparably high diabetes risk, we are now interested in the cost-effectiveness of such an online campaign. We analyze the cost-effectiveness of our Facebook health campaign under the assumption that it would be scaled-up to a one-year health campaign across the whole island of Java. This implies a target population of about 25 million Facebook users above the age of 35. We perform a simple cost-effectiveness calculation based on the cost and effectiveness parameters derived from our study and enrich them with a repeated decision-tree model. The final cost parameter of interest is the cost per newly diagnosed person.

The assumptions, results, and sensitivity analysis of the cost-effectiveness analysis are presented in Supplementary Material 6 (Supplementary Tables 13 and 14 and Supplementary Fig. 3). We show that the hypothetical up-scaling of the campaign to the whole of Java over the period of one year could lead to about 1.7 million users participating in the online screening, of whom about 250,000 would continue with the professional follow-up screening, and finally to the diagnosis of almost 170,000 previously undetected diabetes cases. This corresponds to an increase from 25% to 29% of diagnosed cases relative to all cases, i.e., an increase of 16%. While the share might still seem small, the absolute number is large, especially in light of the low cost and low effort needed to implement an online health campaign. This low cost is further confirmed when we look at the total cost of the proposed intervention (including the professional follow-up screening), which is slightly higher than US$1.5 million. Dividing the total cost by the 170,000 newly diagnosed cases, the cost of detecting one more previously undiagnosed person amounts to approximately US$9 (with a lower bound of US$5.20 in a best-case scenario and an upper bound of US$37 in a worst-case scenario).

Contrasting these amounts to the cost of long-term diabetes care in Indonesia suggests a large cost-saving potential. Hidayat et al.53 estimate the direct medical costs for a patient in the Indonesian healthcare system with severe diabetes health consequences at US$930 per person per year, whereas a patient without severe diabetes consequences costs the healthcare system only US$420. Under the premise that early diagnosis reduces the probability of severe diabetic health consequences, an online diabetes health campaign offers the possibility of reducing healthcare expenditures in the long term. Further, the cost per detected case is lower in comparison to other screening strategies, for example, screening with a similar diabetes risk questionnaire during annual health check-ups in Thailand (~US$30 per detected case, counting only direct medical cost)54.

Discussion

NCDs are the leading cause of death worldwide. In LMICs, the health and economic burden due to NCDs is rising rapidly and innovative solutions to increase screening activities and encourage healthy lifestyles could counteract this problem. Public health campaigns can help to increase awareness of NCDs and encourage populations at risk to change unhealthy lifestyles, inform them about important preventive health measures such as screening, ensure adequate treatment in the event of a positive diagnosis, and thereby reduce health care costs and productivity losses in the long run.

We show that using social media platforms, such as Facebook, for such health campaigns sets out new opportunities to increase awareness and screening for diabetes in LMICs. Such campaigns can generate high exposure and engagement rates at very low cost. With our campaign, we were able to reach out to almost 300,000 individuals in only three weeks and with a budget of less than US$1100. More than 1400 individuals completed the offered online diabetes risk screening on our campaign website, implying a cost of less than US$0.75 per person screened in that way. We also relied on insights from psychology and assessed whether such a campaign should rely on ads with a focus on a loss-framed or shocking perspective to effectively induce preventive health screenings. Our randomized experiment shows that this is indeed a promising approach and that ads focusing on the adverse health consequences of diabetes are most effective in nudging viewers to click on the ads and to carry out a diabetes self-screening. In particular, we find that an ad highlighting the risk of losing eyesight or developing heart- and kidney diseases as a consequence of diabetes outperformed all other ads in the number of link clicks and completed screening questionnaires. Only the second loss-framed ad, which focused on the fact that diabetes can result in death, came near the performance of the “consequences” ad. Yet, this framing effect was more pronounced for the female sample in our study. Men responded more equally also to other ads. These gender differences should be considered by policymakers aiming to design an effective public health campaign.

We also find that such a campaign is especially well-suited for reaching out to the population in the 45–55 age range. This is an encouraging finding, given that the risk of diabetes increases after the age of 45 and a diagnosis of elevated blood sugar at this age offers the opportunity for early treatment to prevent further adverse health consequences.

However, while we can establish that loss-framed or more shocking ads are more effective in terms of creating link clicks and completed self-screenings, it is beyond the scope of our study to assess whether such negatively framed ads could have longer-term negative consequences. A potential adverse effect could for example arise if individuals exposed to the loss-framed ads would engage in information avoidance. In the context of our study, we can show that those individuals being exposed to the loss-framed ads were more likely to participate in the self-screening and equally likely to participate in the follow-up survey, indicating that they did not engage in information avoidance in the short term. Yet, we cannot rule out that long-term health behavior after having received a high-risk result in the self-screening could be adversely affected by the prospect of negative health consequences. Moreover, while shocking contents work well in social media networks to go viral, such content could also induce anxiety or trigger mental health consequences. A recent study in the context of Covid-1955, for example, shows that loss-framed ads increased anxiety levels. Together with the fact that a diabetes diagnosis can lead to diabetes distress56 and affected individuals are at increased risk for mental health disorders57, our results call for further research in terms of longer-term consequences of using loss-framed ads in public health campaigns, especially when implemented at scale.

A remaining limitation of our study is that our measure of compliance with the received recommendation to visit a physician or the report of an existing diagnosis is self-reported. Even though we control for social desirability bias, we are limited in our ability to measure whether individuals claiming to have scheduled an appointment indeed follow through with the professional screening, or whether an individual indeed was already diagnosed with diabetes before. This leaves ample room for future studies in which actual compliance rates are being measured. This could be done, for example, by cooperating directly with local health centers that verify whether a person was referred via an online campaign (e.g., via a referral voucher). Moreover, to confirm the self-reported diabetes diagnoses, it would be interesting to set up a study aiming to verify existing diagnoses through medical records. Yet, privacy concerns and data protection rules pose a substantial hurdle for such a study design.

While we run our campaign in Indonesia, many other middle-income countries are equally experiencing a rapidly increasing diabetes burden and have high social media usage rates. This suggests that the insights from our campaign and study should not only be transferable to other countries in Southeast Asia but also to countries such as India, Brazil, Mexico, and Pakistan.

Overall, our study suggests that a health awareness campaign implemented on the social media platform Facebook is a useful tool to increase awareness of and (self-)screenings for diabetes, and loss-framed ads work particularly well. Policymakers in Indonesia and comparable countries should consider using such social media health campaigns as an innovative tool to address the increasing diabetes burden.

Methods

Campaign and ad design

From March 15 until April 5, 2022, we ran a diabetes health campaign on Facebook, targeting Indonesian Facebook users in Jakarta and Yogyakarta – the two cities with the highest diabetes rates in Indonesia48. In Indonesia’s urban areas, which also have higher diabetes prevalence rates than rural areas, internet penetration rates and usage of social media platforms are high. As of January 2022, the internet penetration rate in Indonesia stood at 74%, with 94% of all users accessing the internet via smartphones. Around 190 million Indonesians are active social media users, of which 130-135 million are active Facebook users, according to the audience size to be reached with Facebook’s advertising tool58,59.

We implemented the campaign via Facebook’s advertisement function which permits the distribution of self-designed ads to Facebook users while using specific demographic and geographic targeting criteria. This advertisement tool was originally developed for businesses to boost their customer base and increase sales, but it is also increasingly used by scientific researchers to recruit survey participants43,60–63. While using the tool for the recruitment of survey participants is indisputably practical, it also offers an even more sophisticated and scientifically valuable function that allows researchers to implement randomized controlled trials. Facebook’s A/B split test allows for a random distribution of two or more ads to evenly split and statistically comparable audiences to test which ad performs best in terms of a pre-specified campaign target64. The ads can thus differ in their design or placement, depending on which variable is being tested. This A/B test design also ensures that the same budget is allocated to each ad and hence avoids Facebook’s algorithm determining the budget allocation, something which could generate unbalanced Facebook user exposure rates across ads.

We designed five different ads, two of which took on a loss-framed and rather disquieting perspective, with the remaining three referring to the family, religion, and the local diabetes prevalence rate. The two loss-framed ads were entitled “diabetes consequences” and “shock”. The non-loss-framed ads were entitled “family”, “religion” and “geography”. These non-loss-framed ads were inspired by different strands of the literature that link religion and health65, family and health66, and information about local health conditions and health behavior67. While this design does not allow us to infer the effects of loss- versus gain-framing (since we do not include a specifically gain-framed ad), it allows us to compare the effect of loss-framed ads with ads that rely on different psychological channels that have been shown to affect health-related behavior. The ads and their displayed message are described in more detail below and presented in Fig. 4.Consequences: The consequences ad contained a statement about the possible health consequences of diabetes, including blindness, kidney- and heart diseases. The graphic showed a wooden mannequin on which the body parts that can be affected by diabetes were marked with a black cross.

Shock: The shocking ad pictured a man in front of a coffin and contained the message that diabetes can have deadly consequences.

Family: The family ad pictured three generations of an Indonesian family and contained the message that every family can be affected by diabetes.

Geography: One geography ad was designed for each of the two regions in our study (Jakarta and Yogyakarta). The graphics showed a landmark of each of the two cities (the National Monument in Jakarta and the Yogyakarta Monument in Yogyakarta, respectively) covered in sweets. The message referred to the local prevalence rate of diabetes in each of the regions.

Religion: The religion ad presented an Indonesian woman in hijab cooking and contained a statement from the Quran that conveyed the message that one should not live a potentially self-harming life.

Fig. 4 Ad design.

a Diabetes consequences – Diabetes can cause blindness, heart diseases, and kidney failure. Learn about your diabetes risk now! b Shock – Diabetes can have deadly consequences. Diabetes can be prevented and controlled. Learn about your diabetes risk now! c Family – Diabetes can affect every family. Diabetes can be prevented and controlled. Learn about your diabetes risk now! d Geography (Jakarta) – Jakarta is the city with the highest diabetes prevalence rate in Indonesia. Learn about your diabetes risk now! (e) Geography (Yogyakarta) – Yogyakarta is one of the cities with the highest diabetes prevalence rates. Learn about your diabetes risk now! f Religion – “and do not throw [yourselves] with your [own] hands into destruction” (Q.S. Al-Baqarah, 2:195). Diabetes can be prevented and controlled. Learn about your diabetes risk now!

In addition to the messages outlined above, each ad carried the statement “Learn about your diabetes risk now” (“Pelajari tentang risiko diabetes Anda sekarang”) to encourage the ad viewers to click on the ad and visit the campaign website on which they could conduct the risk screening test. Technically, we ran two different campaigns, one for each of the targeted regions, and then pooled the data for the analysis. Each ad received an equal budget of US$5 per day, summing to a total daily budget of US$50 for both cities. In terms of the target population, we restricted the audience demographically to Facebook users above the age of 35 and geographically to users living in either Jakarta or Yogyakarta.

The campaign objective was chosen to optimize “conversions”, with conversion programmed to be equal to completion of the screening questionnaire. Setting “conversions” as the campaign objective (instead of the other two possibilities “awareness” or “consideration”) allowed us to focus on possible screening questionnaire completers who would thus gain from the campaign, while simultaneously preventing showing the ads to seemingly uninterested users. This conversion objective required the generation of a so-called Facebook Pixel code, which had to be embedded in the code of the website to which the ad viewers were redirected. Facebook could then use this Pixel to track user actions taking place on our website and optimize accordingly. This implies that after a learning phase, Facebook’s algorithm aimed to show the ads to individuals more likely to click on the ads and to complete the screening questionnaire, based on the characteristics of earlier completers. The success of the algorithm is confirmed by the positive trend in the number of daily clicks and conversions over time as presented in Fig. 5. After a first peak in link clicks, most likely driven by immediate reactions from viewers always responding to such ads, the learning phase sets in and translates into a positive trend in clicks and conversions. While this internal algorithm exaggerates a selection bias per ad if the conversion objective is used in regular campaigns68, the use of the A/B split test ensured that, conditional on being in the target audience, the ad version the user saw was random. This randomization procedure allowed us to compare the different ads based on their effectiveness in generating clicks and conversions, i.e., completed screening questionnaires.Fig. 5 Link clicks and conversions over time.

Figure 5 displays the time trend in a daily link clicks and b daily conversions (i.e., completed screening questionnaires). Vertical gray dashed lines indicate Sundays.

Campaign website

After clicking on one of the ads in Facebook, individuals were redirected to the landing page of the campaign website. Before being able to browse further on the website, the participants were informed about our privacy policy and that data generated on the website were used for an academic study. For both, they had to indicate their informed consent. Individuals were then offered the opportunity to complete a diabetes risk screening questionnaire on this website similar to the diabetes risk test of the American Diabetes Association and the diabetes FINDRISC (Finnish Diabetes Risk Score) screening test. The questionnaire version we used is an adapted and translated version specifically for the Indonesian population. The original FINDRISC questionnaire was developed to identify individuals at risk of diabetes using a Finnish population sample69. Since then, the questionnaire has been evaluated and validated many times and has been adjusted to different populations and country samples70–73. The original diabetes risk test of the American Diabetes Association dates back to 1993 and has likewise been adapted multiple times74. The version we used is based on the diabetes risk test of the American Diabetes Association75,76, the ModAsian FINDRISC for Asia, the FINDRISC Bahasa Indonesia77, and the Malay version of the American Diabetes Association diabetes risk test74. It consisted of eleven questions which could be answered in approximately 90 seconds. Based on the individual answers, a risk score between 0 and 16 points was calculated and participants received an assessment of their personal risk rated as low risk (0-3 points), medium risk (4-5 points), or high risk (6 or more points). Additionally, the assessment contained recommendations on how to keep the risk low, how the diabetes risk can be reduced and to visit a health center or a physician if the risk score was too high.

The website also included a page with factual information on diabetes in Indonesia, including the distribution of prevalence rates across the country, behavioral risk factors, as well as information about how diabetes can be diagnosed and how it can be treated. Furthermore, we provided detailed information about the institutions involved in the research activities, the aim of the campaign, and the notification that the campaign was purely educational and could not replace a professional health visit or screening. We also asked participants to leave their e-mail addresses so that they could get follow-up information and continue to be involved in the study.

Follow-up survey

Six weeks after the end of the campaign we sent a follow-up survey to all these addresses to elicit information about actual compliance with the recommendations received. Since providing the e-mail address was voluntary and hence the sub-sample of respondents was subject to a potential self-selection bias, we provide a description of the sample that completed this follow-up survey and contrast it with the profile of the entire sample (see Results section). In this follow-up survey, we asked the respondents about their plans to comply with the received recommendations. Specifically, we asked whether they plan to schedule a professional medical screening (or have already done so), if yes, when and where they planned to go and if no, what their reasons were for not doing so. We also asked several questions about diabetes risk factors, symptoms, and health consequences, whether the respondent had health insurance, whether this was the first time they had conducted a diabetes risk test, whether they had already been diagnosed with diabetes, and whether they were currently on medication.

IRB approval and RCT registration

This study received ethical approval from the University of Passau Research Ethics Committee (15.03.2022, IRB Approval Number I-07.5090/2022). Informed consent was obtained from all participants who browsed our website. Informed consent for the experiment on Facebook is covered by Facebook’s data use policy. Identifiable images relating to persons in our ads are no patients and no written consent was required since ads were designed by ourselves with pictures taken from openly accessible stocks with license-free images. The study was pre-registered at the AEA RCT Registry (0008781, 10.1257/rct.8781). In the final manuscript/study, we deviated in some features from our initial analysis plan, partly for technical reasons, and marginally adjusted our hypotheses after the pilot study. These changes are explained in detail in an appendix to our pre-analysis plan (downloadable under the same registration number). The study was conducted without any support from or connection to Facebook (Meta group) and Facebook had no access to the responses that were generated on our website or during the follow-up survey.

Supplementary information

Supplemental material

Supplementary information

The online version contains supplementary material available at 10.1038/s41746-024-01246-x.

Acknowledgements

We thank Ferdyani Yulia Atikaputri, Ayu Paramudita and Mardha Tilla Septiani for their research assistance. We also thank Gerard van den Berg, Annegret Kuhn, Robert Lensink and participants at seminars at the University of Groningen and the University of Passau as well as at the NCDE 2023 in Gothenburg, GDEC 2023 in Dresden and the Web Conference 2023 in Austin, Texas for very useful comments and suggestions. Part of this work was done while IW was at the Qatar Computing Research Institute, HBKU, Doha, Qatar, EYT was at Microsoft Research, Herzliya, Israel and at the Technion Israel Institute of Technology, Faculty of Industrial Engineering and Management, Haifa, Israel, and MF was at the University of Groningen, Department for Economics, Econometrics and Finance, Groningen, The Netherlands. We acknowledge financial support by the Open Access Publication Fund of the University Library Passau.

Author contributions

MF: Conceptualization, Methodology, Software, Data Curation, Formal analysis, Writing - Original Draft, Writing - Review & Editing. MG: Conceptualization, Methodology, Writing - Original Draft, Writing - Review & Editing, Supervision. IW: Conceptualization, Writing - Review & Editing. EYT: Conceptualization, Writing - Review & Editing. BP: Conceptualization, Visualization. All authors have read and approved the final manuscript.

Funding

Open Access funding enabled and organized by Projekt DEAL.

Data availability

All data underlying this study are available from the authors upon request.

Code availability

All codes underlying this study are available from the authors upon request.

Competing interests

The authors declare no competing interests.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
==== Refs
References

1. Global Burden of Disease Collaborative Network. Global Burden of Disease Study 2019 (GBD 2019) Reference Life Table. https://ghdx.healthdata.org/record/ihme-data/global-burden-disease-study-2019-gbd-2019-reference-life-table (2021).
2. Geldsetzer P The state of hypertension care in 44 low-income and middle-income countries: A cross-sectional study of nationally representative individual-level data from 1.1 million adults Lancet 2019 394 652 662 10.1016/S0140-6736(19)30955-9 31327566
Geldsetzer, P. et al. The state of hypertension care in 44 low-income and middle-income countries: A cross-sectional study of nationally representative individual-level data from 1.1 million adults. Lancet 394, 652–662 (2019).31327566 10.1016/S0140-6736(19)30955-9
3. Manne-Goehler J Health system performance for people with diabetes in 28 low-and middle-income countries: A cross-sectional study of nationally representative surveys Plos Med. 2019 16 e1002751 10.1371/journal.pmed.1002751 30822339
Manne-Goehler, J. et al. Health system performance for people with diabetes in 28 low-and middle-income countries: A cross-sectional study of nationally representative surveys. Plos Med. 16, e1002751 (2019).30822339 10.1371/journal.pmed.1002751
4. Widyaningsih V Missed opportunities in hypertension risk factors screening in Indonesia: A mixed-methods evaluation of integrated health post (Posbindu) implementation BMJ Open 2022 12 e051315 10.1136/bmjopen-2021-051315 35190419
Widyaningsih, V. et al. Missed opportunities in hypertension risk factors screening in Indonesia: A mixed-methods evaluation of integrated health post (Posbindu) implementation. BMJ Open 12, e051315 (2022).35190419 10.1136/bmjopen-2021-051315
5. Lin X Global, regional, and national burden and trend of diabetes in 195 countries and territories: an analysis from 1990 to 2025 Sci. Rep. 2020 10 14790 10.1038/s41598-020-71908-9 32901098
Lin, X. et al. Global, regional, and national burden and trend of diabetes in 195 countries and territories: an analysis from 1990 to 2025. Sci. Rep. 10, 14790 (2020).32901098 10.1038/s41598-020-71908-9
6. Abegunde DO Mathers CD Adam T Ortegon M Strong K The burden and costs of chronic diseases in low-income and middle-income countries Lancet 2007 370 1929 1938 10.1016/S0140-6736(07)61696-1 18063029
Abegunde, D. O., Mathers, C. D., Adam, T., Ortegon, M. & Strong, K. The burden and costs of chronic diseases in low-income and middle-income countries. Lancet 370, 1929–1938 (2007).18063029 10.1016/S0140-6736(07)61696-1
7. Tabassum R Untapped aspects of mass media campaigns for changing health behaviour towards non-communicable diseases in Bangladesh Glob. Health 2018 14 1 4 10.1186/s12992-018-0325-1
Tabassum, R. et al. Untapped aspects of mass media campaigns for changing health behaviour towards non-communicable diseases in Bangladesh. Glob. Health 14, 1–4 (2018).10.1186/s12992-018-0325-1
8. World Health Organization. Tackling NCDs: “Best buys” and other recommended interventions for the prevention and control of noncommunicable diseases. https://apps.who.int/iris/bitstream/handle/10665/259232/WHO-NMH-NVI-17.9-eng.pdf?sequence=1&isAllowed=y (2017).
9. World Health Organization. Package of Essential Noncommunicable (PEN) disease interventions for primary health care in low-resource settings. https://www.who.int/publications/i/item/9789240009226 (2020).
10. Pereira da Veiga CR Semprebon E da Silva JL Lins Ferreira V Pereira da Veiga C Facebook HPV vaccine campaign: Insights from Brazil Hum. Vaccines Immunother. 2020 16 1824 1834 10.1080/21645515.2019.1698244
Pereira da Veiga, C. R., Semprebon, E., da Silva, J. L., Lins Ferreira, V. & Pereira da Veiga, C. Facebook HPV vaccine campaign: Insights from Brazil. Hum. Vaccines Immunother. 16, 1824–1834 (2020).10.1080/21645515.2019.1698244
11. Krupenkin M Yom-Tov E Rothschild D Vaccine advertising: Preach to the converted or to the unaware? NPJ Digit. Med. 2021 4 23 10.1038/s41746-021-00395-7 33574473
Krupenkin, M., Yom-Tov, E. & Rothschild, D. Vaccine advertising: Preach to the converted or to the unaware? NPJ Digit. Med. 4, 23 (2021).33574473 10.1038/s41746-021-00395-7
12. Tjaden J Haarmann E Savaskan N Experimental evidence on improving COVID-19 vaccine outreach among migrant communities on social media Sci. Rep. 2022 12 16256 10.1038/s41598-022-20340-2 36171245
Tjaden, J., Haarmann, E. & Savaskan, N. Experimental evidence on improving COVID-19 vaccine outreach among migrant communities on social media. Sci. Rep. 12, 16256 (2022).36171245 10.1038/s41598-022-20340-2
13. Ho L The impact of large-scale social media advertising campaigns on COVID-19 vaccination: Evidence from two randomized controlled trials AEA Pap. Proc. 2023 113 653 658 10.1257/pandp.20231112 38665387
Ho, L. et al. The impact of large-scale social media advertising campaigns on COVID-19 vaccination: Evidence from two randomized controlled trials. AEA Pap. Proc. 113, 653–658 (2023).38665387 10.1257/pandp.20231112
14. Mohanty S Leader AE Gibeau E Johnson C Using Facebook to reach adolescents for human papillomavirus (HPV) vaccination Vaccine 2018 36 5955 5961 10.1016/j.vaccine.2018.08.060 30172634
Mohanty, S., Leader, A. E., Gibeau, E. & Johnson, C. Using Facebook to reach adolescents for human papillomavirus (HPV) vaccination. Vaccine 36, 5955–5961 (2018).30172634 10.1016/j.vaccine.2018.08.060
15. Breza E Effects of a large-scale social media advertising campaign on holiday travel and Covid-19 infections: A cluster randomized controlled trial Nat. Med. 2021 27 1622 1628 10.1038/s41591-021-01487-3 34413518
Breza, E. et al. Effects of a large-scale social media advertising campaign on holiday travel and Covid-19 infections: A cluster randomized controlled trial. Nat. Med. 27, 1622–1628 (2021).34413518 10.1038/s41591-021-01487-3
16. Parackal M Parackal S Eusebius S Mather D The use of Facebook advertising for communicating public health messages: A campaign against drinking during pregnancy in New Zealand J. Med. Internet Res.: Public Health Surveill. 2017 3 e7032
Parackal, M., Parackal, S., Eusebius, S. & Mather, D. The use of Facebook advertising for communicating public health messages: A campaign against drinking during pregnancy in New Zealand. J. Med. Internet Res.: Public Health Surveill. 3, e7032 (2017).
17. Thrul J Klein AB Ramo DE Smoking cessation intervention on Facebook: which content generates the best engagement? J. Med. Internet Res. 2015 17 e244 10.2196/jmir.4575 26561529
Thrul, J., Klein, A. B. & Ramo, D. E. Smoking cessation intervention on Facebook: which content generates the best engagement? J. Med. Internet Res. 17, e244 (2015).26561529 10.2196/jmir.4575
18. Bull SS Levine DK Black SR Schmiege SJ Santelli J Social media–delivered sexual health intervention: A cluster randomized controlled trial Am. J. Prev. Med. 2012 43 467 474 10.1016/j.amepre.2012.07.022 23079168
Bull, S. S., Levine, D. K., Black, S. R., Schmiege, S. J. & Santelli, J. Social media–delivered sexual health intervention: A cluster randomized controlled trial. Am. J. Prev. Med. 43, 467–474 (2012).23079168 10.1016/j.amepre.2012.07.022
19. Yom-Tov E Shembekar J Barclay S Muennig P The effectiveness of public health advertisements to promote health: A randomized-controlled trial on 794,000 participants NPJ Digit. Med. 2018 1 24 10.1038/s41746-018-0031-7 31304306
Yom-Tov, E., Shembekar, J., Barclay, S. & Muennig, P. The effectiveness of public health advertisements to promote health: A randomized-controlled trial on 794,000 participants. NPJ Digit. Med. 1, 24 (2018).31304306 10.1038/s41746-018-0031-7
20. Northcott C Evaluating the effectiveness of a physical activity social media advertising campaign using Facebook, Facebook Messenger, and Instagram Transl. Behav. Med. 2021 11 870 881 10.1093/tbm/ibaa139 33484152
Northcott, C. et al. Evaluating the effectiveness of a physical activity social media advertising campaign using Facebook, Facebook Messenger, and Instagram. Transl. Behav. Med. 11, 870–881 (2021).33484152 10.1093/tbm/ibaa139
21. Widyahening I Van Der Graaf Y Soewondo P Glasziou P Van Der Heijden G Awareness, agreement, adoption and adherence to type 2 diabetes mellitus guidelines: A survey of Indonesian primary care physicians BMC Fam. Pract. 2014 15 72 10.1186/1471-2296-15-72 24755412
Widyahening, I., Van Der Graaf, Y., Soewondo, P., Glasziou, P. & Van Der Heijden, G. Awareness, agreement, adoption and adherence to type 2 diabetes mellitus guidelines: A survey of Indonesian primary care physicians. BMC Fam. Pract. 15, 72 (2014).24755412 10.1186/1471-2296-15-72
22. Bakti IGMY Sumardjo S Fatchiya A Syukri AF Public knowledge of diabetes and hypertension in metropolitan cities, Indonesia Public Health Sci. J. 2021 13 1 13
Bakti, I. G. M. Y., Sumardjo, S., Fatchiya, A. & Syukri, A. F. Public knowledge of diabetes and hypertension in metropolitan cities, Indonesia. Public Health Sci. J. 13, 1–13 (2021).
23. Banerjee, A. et al. Messages on COVID-19 prevention in India increased symptoms reporting and adherence to preventive behaviors among 25 million recipients with similar effects on non-recipient members of their communities. National Bureau of Economic Research. Preprint available at. https://www.nber.org/system/files/working_papers/w27496/w27496.pdf (2020).
24. Marcus, M. E., Reuter, A., Rogge, L. & Vollmer, S. The effect of SMS reminders on health screening uptake: A randomized experiment in Indonesia. J. Econ. Behav. Organ. (forthcoming)
25. Athey S Grabarz K Luca M Wernerfelt N Digital public health interventions at scale: The impact of social media advertising on beliefs and outcomes related to covid vaccines Proc. Natl Acad. Sci. 2023 120 e2208110120 10.1073/pnas.2208110120 36701366
Athey, S., Grabarz, K., Luca, M. & Wernerfelt, N. Digital public health interventions at scale: The impact of social media advertising on beliefs and outcomes related to covid vaccines. Proc. Natl Acad. Sci. 120, e2208110120 (2023).36701366 10.1073/pnas.2208110120
26. Tunkl C Are digital social media campaigns the key to raise stroke awareness in low-and middle-income countries? A study of feasibility and cost-effectiveness in Nepal Plos One 2023 18 e0291392 10.1371/journal.pone.0291392 37682967
Tunkl, C. et al. Are digital social media campaigns the key to raise stroke awareness in low-and middle-income countries? A study of feasibility and cost-effectiveness in Nepal. Plos One 18, e0291392 (2023).37682967 10.1371/journal.pone.0291392
27. World Bank. Current health expenditure (% of GDP). World Development Indicators. [Dataset]. Washington D.C.: World Bank. https://data.worldbank.org/indicator/SH.XPD.CHEX.GD.ZS (2022).
28. Centers for Disease Control and Prevention. CDC in Indonesia. Factsheet Indonesia. https://www.cdc.gov/globalhealth/countries/indonesia/pdf/indonesia-fs.pdf (2020).
29. International Diabetes Federation. IDF Diabetes Atlas 2021 Brussels International Diabetes Federation
International Diabetes Federation. IDF Diabetes Atlas. (International Diabetes Federation, Brussels, 2021).
30. Orazi DC Johnston AC Running field experiments using Facebook split test J. Bus. Res. 2020 118 189 198 10.1016/j.jbusres.2020.06.053 32834210
Orazi, D. C. & Johnston, A. C. Running field experiments using Facebook split test. J. Bus. Res. 118, 189–198 (2020).32834210 10.1016/j.jbusres.2020.06.053
31. Rothman A Salovey P Shaping perceptions to motivate healthy behavior: The role of message framing Psychol. Bull. 1997 121 3 19 10.1037/0033-2909.121.1.3 9000890
Rothman, A. & Salovey, P. Shaping perceptions to motivate healthy behavior: The role of message framing. Psychol. Bull. 121, 3–19 (1997).9000890 10.1037/0033-2909.121.1.3
32. Rothman AJ Bartels RD Wlaschin J Salovey P The strategic use of gain-and loss-framed messages to promote healthy behavior: How theory can inform practice J. Commun. 2006 56 S202 S220 10.1111/j.1460-2466.2006.00290.x
Rothman, A. J., Bartels, R. D., Wlaschin, J. & Salovey, P. The strategic use of gain-and loss-framed messages to promote healthy behavior: How theory can inform practice. J. Commun. 56, S202–S220 (2006).10.1111/j.1460-2466.2006.00290.x
33. Cherry TL James AG Murphy J The impact of public health messaging and personal experience on the acceptance of mask wearing during the COVID-19 pandemic J. Econ. Behav. Organ. 2021 187 415 430 10.1016/j.jebo.2021.04.006 33994606
Cherry, T. L., James, A. G. & Murphy, J. The impact of public health messaging and personal experience on the acceptance of mask wearing during the COVID-19 pandemic. J. Econ. Behav. Organ. 187, 415–430 (2021).33994606 10.1016/j.jebo.2021.04.006
34. Seah SSY Impact of tax and subsidy framed messages on high-and lower-sugar beverages sold in vending machines: A randomized crossover trial Int. J. Behav. Nutr. Phys. Act. 2018 15 1 9 10.1186/s12966-018-0711-3 29291739
Seah, S. S. Y. et al. Impact of tax and subsidy framed messages on high-and lower-sugar beverages sold in vending machines: A randomized crossover trial. Int. J. Behav. Nutr. Phys. Act. 15, 1–9 (2018).29291739 10.1186/s12966-018-0711-3
35. Kuehnle D How effective are pictorial warnings on tobacco products? New evidence on smoking behaviour using Australian panel data J. Health Econ. 2019 67 102215 10.1016/j.jhealeco.2019.06.002 31319336
Kuehnle, D. How effective are pictorial warnings on tobacco products? New evidence on smoking behaviour using Australian panel data. J. Health Econ. 67, 102215 (2019).31319336 10.1016/j.jhealeco.2019.06.002
36. Cil G Effects of posted point-of-sale warnings on alcohol consumption during pregnancy and on birth outcomes J. Health Econ. 2017 53 131 155 10.1016/j.jhealeco.2017.03.004 28343094
Cil, G. Effects of posted point-of-sale warnings on alcohol consumption during pregnancy and on birth outcomes. J. Health Econ. 53, 131–155 (2017).28343094 10.1016/j.jhealeco.2017.03.004
37. Hall MG The impact of pictorial health warnings on purchases of sugary drinks for children: A randomized controlled trial Plos Med. 2022 19 e1003885 10.1371/journal.pmed.1003885 35104297
Hall, M. G. et al. The impact of pictorial health warnings on purchases of sugary drinks for children: A randomized controlled trial. Plos Med. 19, e1003885 (2022).35104297 10.1371/journal.pmed.1003885
38. de Vries Mecheva M Rieger M Sparrow R Prafiantini E Agustina R Snacks, nudges and asymmetric peer influence: Evidence from food choice experiments with children in Indonesia J. Health Econ. 2021 79 102508 10.1016/j.jhealeco.2021.102508 34333202
de Vries Mecheva, M., Rieger, M., Sparrow, R., Prafiantini, E. & Agustina, R. Snacks, nudges and asymmetric peer influence: Evidence from food choice experiments with children in Indonesia. J. Health Econ. 79, 102508 (2021).34333202 10.1016/j.jhealeco.2021.102508
39. Maclean JC Buckell J Information and sin goods: Experimental evidence on cigarettes Health Econ. 2021 30 289 310 10.1002/hec.4189 33220157
Maclean, J. C. & Buckell, J. Information and sin goods: Experimental evidence on cigarettes. Health Econ. 30, 289–310 (2021).33220157 10.1002/hec.4189
40. Eibich P Goldzahl L Health information provision, health knowledge and health behaviours: Evidence from breast cancer screening Soc. Sci. Med. 2020 265 113505 10.1016/j.socscimed.2020.113505 33218891
Eibich, P. & Goldzahl, L. Health information provision, health knowledge and health behaviours: Evidence from breast cancer screening. Soc. Sci. Med. 265, 113505 (2020).33218891 10.1016/j.socscimed.2020.113505
41. Beam EA Masatioglu Y Watson T Yang D Loss aversion or lack of trust: Why does loss framing work to encourage preventive health behaviors? J. Behav. Exp. Econ. 2023 104 102022 10.1016/j.socec.2023.102022
Beam, E. A., Masatioglu, Y., Watson, T. & Yang, D. Loss aversion or lack of trust: Why does loss framing work to encourage preventive health behaviors? J. Behav. Exp. Econ. 104, 102022 (2023).10.1016/j.socec.2023.102022
42. Bertoni M Corazzini L Robone S The good outcome of bad news: A field experiment on formatting breast cancer screening invitation letters Am. J. Health Econ. 2020 6 372 409 10.1086/708930
Bertoni, M., Corazzini, L. & Robone, S. The good outcome of bad news: A field experiment on formatting breast cancer screening invitation letters. Am. J. Health Econ. 6, 372–409 (2020).10.1086/708930
43. Choi I Using different Facebook advertisements to recruit men for an online mental health study: Engagement and selection bias Internet Interv. 2017 8 27 34 10.1016/j.invent.2017.02.002 30135825
Choi, I. et al. Using different Facebook advertisements to recruit men for an online mental health study: Engagement and selection bias. Internet Interv. 8, 27–34 (2017).30135825 10.1016/j.invent.2017.02.002
44. Statista. Share of Facebook users in Indonesia as of April 2021, by age group. https://www.statista.com/statistics/1235773/indonesia-share-of-facebook-users-by-age/ (2024).
45. Statista. Share of Facebook users in Indonesia as of April 2021, by gender. https://www.statista.com/statistics/997045/share-of-facebook-users-by-gender-indonesia/ (2024).
46. Meta. Understand how results are sometimes calculated differently. https://en-gb.facebook.com/business/help/1329822420714248 (2024).
47. Meta. How to add URL parameters to Meta ads. https://en-gb.facebook.com/business/help/1016122818401732 (2024).
48. Kementerian Kesehatan Republik Indonesia. RISKESDAS 2018. Laporan Nasional Riskesdas. https://repository.badankebijakan.kemkes.go.id/id/eprint/3514/ (2018).
49. Toll BA Message framing for smoking cessation: The interaction of risk perceptions and gender Nicotine Tob. Res. 2008 10 195 200 10.1080/14622200701767803 18188760
Toll, B. A. et al. Message framing for smoking cessation: The interaction of risk perceptions and gender. Nicotine Tob. Res. 10, 195–200 (2008).18188760 10.1080/14622200701767803
50. Nan X Communicating to young adults about HPV vaccination: Consideration of message framing, motivation, and gender Health Commun. 2012 27 10 18 10.1080/10410236.2011.567447 22276999
Nan, X. Communicating to young adults about HPV vaccination: Consideration of message framing, motivation, and gender. Health Commun. 27, 10–18 (2012).22276999 10.1080/10410236.2011.567447
51. Hasseldine J Hite PA Framing, gender and tax compliance J. Econ. Psychol. 2003 24 517 533 10.1016/S0167-4870(02)00209-X
Hasseldine, J. & Hite, P. A. Framing, gender and tax compliance. J. Econ. Psychol. 24, 517–533 (2003).10.1016/S0167-4870(02)00209-X
52. Kim HJ The effects of gender and gain versus loss frame on processing breast cancer screening messages Commun. Res. 2012 39 385 412 10.1177/0093650211427557
Kim, H. J. The effects of gender and gain versus loss frame on processing breast cancer screening messages. Commun. Res. 39, 385–412 (2012).10.1177/0093650211427557
53. Hidayat B Direct medical cost of type 2 diabetes mellitus and its associated complications in Indonesia Value Health Reg. Issues 2022 28 82 89 10.1016/j.vhri.2021.04.006 34839111
Hidayat, B. et al. Direct medical cost of type 2 diabetes mellitus and its associated complications in Indonesia. Value Health Reg. Issues 28, 82–89 (2022).34839111 10.1016/j.vhri.2021.04.006
54. Srichang N Jiamjarasrangsi W Aekplakorn W Supakankunti S Cost and effectiveness of screening methods for abnormal fasting plasma glucose among Thai adults participating in the annual health check-up at King Chulalongkorn Memorial Hospital J. Med. Assoc. Thail. 2011 94 833 41
Srichang, N., Jiamjarasrangsi, W., Aekplakorn, W. & Supakankunti, S. Cost and effectiveness of screening methods for abnormal fasting plasma glucose among Thai adults participating in the annual health check-up at King Chulalongkorn Memorial Hospital. J. Med. Assoc. Thail. 94, 833–41 (2011).
55. Dorison CA In COVID-19 health messaging, loss framing increases anxiety with little-to-no concomitant benefits: Experimental evidence from 84 countries Affect. Sci. 2022 3 577 602 10.1007/s42761-022-00128-3 36185503
Dorison, C. A. et al. In COVID-19 health messaging, loss framing increases anxiety with little-to-no concomitant benefits: Experimental evidence from 84 countries. Affect. Sci. 3, 577–602 (2022).36185503 10.1007/s42761-022-00128-3
56. Sofyan H The state of diabetes care and obstacles to better care in Aceh, Indonesia: a mixed-methods study BMC Health Serv. Res. 2023 23 271 10.1186/s12913-023-09288-9 36941640
Sofyan, H. et al. The state of diabetes care and obstacles to better care in Aceh, Indonesia: a mixed-methods study. BMC Health Serv. Res. 23, 271 (2023).36941640 10.1186/s12913-023-09288-9
57. Ducat L Philipson LH Anderson BJ The mental health comorbidities of diabetes JAMA 2014 312 691 692 10.1001/jama.2014.8040 25010529
Ducat, L., Philipson, L. H. & Anderson, B. J. The mental health comorbidities of diabetes. JAMA 312, 691–692 (2014).25010529 10.1001/jama.2014.8040
58. Kepios. Digital 2022: Indonesia.https://datareportal.com/reports/digital-2022-indonesia (2022).
59. Kepios. Facebook Users, Stats, Data & Trends. https://datareportal.com/essential-facebook-stats (2023).
60. Kosinski M Matz SC Gosling SD Popov V Stillwell D Facebook as a research tool for the social sciences: Opportunities, challenges, ethical considerations, and practical guidelines Am. Psychol. 2015 70 543 556 10.1037/a0039210 26348336
Kosinski, M., Matz, S. C., Gosling, S. D., Popov, V. & Stillwell, D. Facebook as a research tool for the social sciences: Opportunities, challenges, ethical considerations, and practical guidelines. Am. Psychol. 70, 543–556 (2015).26348336 10.1037/a0039210
61. Thornton L Recruiting for health, medical or psychosocial research using Facebook: Systematic review Internet Interv. 2016 4 72 81 10.1016/j.invent.2016.02.001 30135792
Thornton, L. et al. Recruiting for health, medical or psychosocial research using Facebook: Systematic review. Internet Interv. 4, 72–81 (2016).30135792 10.1016/j.invent.2016.02.001
62. Ananda A Bol D Does knowing democracy affect answers to democratic support questions? A survey experiment in Indonesia Int. J. Public Opin. Res. 2021 33 433 443 10.1093/ijpor/edaa012
Ananda, A. & Bol, D. Does knowing democracy affect answers to democratic support questions? A survey experiment in Indonesia. Int. J. Public Opin. Res. 33, 433–443 (2021).10.1093/ijpor/edaa012
63. Grow A Addressing public health emergencies via Facebook surveys: Advantages, challenges, and practical considerations J. Med. Internet Res. 2020 22 e20653 10.2196/20653 33284782
Grow, A. et al. Addressing public health emergencies via Facebook surveys: Advantages, challenges, and practical considerations. J. Med. Internet Res. 22, e20653 (2020).33284782 10.2196/20653
64. Meta. About A/B testing. https://en-gb.facebook.com/business/help/1738164643098669 (2024).
65. Alfano M Islamic law and investments in children: Evidence from the Sharia introduction in Nigeria J. Health Econ. 2022 85 102660 10.1016/j.jhealeco.2022.102660 35926253
Alfano, M. Islamic law and investments in children: Evidence from the Sharia introduction in Nigeria. J. Health Econ. 85, 102660 (2022).35926253 10.1016/j.jhealeco.2022.102660
66. Fadlon I Nielsen TH Family health behaviors Am. Econ. Rev. 2019 109 3162 3191 10.1257/aer.20171993
Fadlon, I. & Nielsen, T. H. Family health behaviors. Am. Econ. Rev. 109, 3162–3191 (2019).10.1257/aer.20171993
67. Haglin K Chapman D Motta M Kahan D How localized outbreaks and changes in media coverage affect Zika attitudes in national and local contexts Health Commun. 2020 35 1686 1697 10.1080/10410236.2019.1662556 31475575
Haglin, K., Chapman, D., Motta, M. & Kahan, D. How localized outbreaks and changes in media coverage affect Zika attitudes in national and local contexts. Health Commun. 35, 1686–1697 (2020).31475575 10.1080/10410236.2019.1662556
68. Neundorf A Öztürk A How to improve representativeness and cost-effectiveness in samples recruited through meta: A comparison of advertisement tools Plos One 2023 18 e0281243 10.1371/journal.pone.0281243 36753470
Neundorf, A. & Öztürk, A. How to improve representativeness and cost-effectiveness in samples recruited through meta: A comparison of advertisement tools. Plos One 18, e0281243 (2023).36753470 10.1371/journal.pone.0281243
69. Lindstrom J Tuomilehto J The diabetes risk score: A practical tool to predict type 2 diabetes risk Diab. Care 2003 26 725 731 10.2337/diacare.26.3.725
Lindstrom, J. & Tuomilehto, J. The diabetes risk score: A practical tool to predict type 2 diabetes risk. Diab. Care 26, 725–731 (2003).10.2337/diacare.26.3.725
70. Nieto-Martínez R González-Rivas JP Aschner P Barengo NC Mechanick JI Transculturalizing diabetes prevention in Latin America Ann. Glob. Health 2017 83 432 443 10.1016/j.aogh.2017.07.001 29221516
Nieto-Martínez, R., González-Rivas, J. P., Aschner, P., Barengo, N. C. & Mechanick, J. I. Transculturalizing diabetes prevention in Latin America. Ann. Glob. Health 83, 432–443 (2017).29221516 10.1016/j.aogh.2017.07.001
71. Muñoz-González MC FINDRISC modified for Latin America as a screening tool for persons with impaired glucose metabolism in Ciudad Bolívar, Venezuela Med. Princ. Pract. 2019 28 324 332 10.1159/000499468 30852570
Muñoz-González, M. C. et al. FINDRISC modified for Latin America as a screening tool for persons with impaired glucose metabolism in Ciudad Bolívar, Venezuela. Med. Princ. Pract. 28, 324–332 (2019).30852570 10.1159/000499468
72. Ku GM Kegels G The performance of the Finnish Diabetes Risk Score, a modified Finnish Diabetes Risk Score and a simplified Finnish Diabetes Risk Score in community-based cross-sectional screening of undiagnosed type 2 diabetes in the Philippines Prim. Care Diab. 2013 7 249 259 10.1016/j.pcd.2013.07.004
Ku, G. M. & Kegels, G. The performance of the Finnish Diabetes Risk Score, a modified Finnish Diabetes Risk Score and a simplified Finnish Diabetes Risk Score in community-based cross-sectional screening of undiagnosed type 2 diabetes in the Philippines. Prim. Care Diab. 7, 249–259 (2013).10.1016/j.pcd.2013.07.004
73. Lim HM Chia YC Koay ZL Performance of the Finnish Diabetes Risk Score (FINDRISC) and Modified Asian FINDRISC (ModAsian FINDRISC) for screening of undiagnosed type 2 diabetes mellitus and dysglycaemia in primary care Prim. Care Diab. 2020 14 494 500 10.1016/j.pcd.2020.02.008
Lim, H. M., Chia, Y. C. & Koay, Z. L. Performance of the Finnish Diabetes Risk Score (FINDRISC) and Modified Asian FINDRISC (ModAsian FINDRISC) for screening of undiagnosed type 2 diabetes mellitus and dysglycaemia in primary care. Prim. Care Diab. 14, 494–500 (2020).10.1016/j.pcd.2020.02.008
74. Fauzi NFM Wafa SW Ibrahim AM Raj NB Nurulhuda MH Translation and validation of American Diabetes Association diabetes risk test: The Malay version Malays. J. Med. Sci. 2022 29 113 125 10.21315/mjms2022.29.1.11 35283673
Fauzi, N. F. M., Wafa, S. W., Ibrahim, A. M., Raj, N. B. & Nurulhuda, M. H. Translation and validation of American Diabetes Association diabetes risk test: The Malay version. Malays. J. Med. Sci. 29, 113–125 (2022).35283673 10.21315/mjms2022.29.1.11
75. American Diabetes Association. American diabetes alert Diab. Forecast 1993 46 54 55
American Diabetes Association. American diabetes alert. Diab. Forecast 46, 54–55 (1993).
76. American Diabetes Association. Good to know: Diabetes risk test Clin. Diab. 2019 37 291 10.2337/CD19-0036
American Diabetes Association. Good to know: Diabetes risk test. Clin. Diab. 37, 291 (2019).10.2337/CD19-0036
77. Rokhman M Translation and performance of the Finnish Diabetes Risk Score for detecting undiagnosed diabetes and dysglycaemia in the Indonesian population Plos One 2022 17 e0269853 10.1371/journal.pone.0269853 35862370
Rokhman, M. et al. Translation and performance of the Finnish Diabetes Risk Score for detecting undiagnosed diabetes and dysglycaemia in the Indonesian population. Plos One 17, e0269853 (2022).35862370 10.1371/journal.pone.0269853
