Introduction

Diabetes mellitus – commonly known as diabetes – comprises a group of complex metabolic disorders mainly characterized by hyperglycemia. It is caused due to inadequate insulin secretion or impaired insulin action, or a combination of both [1–3]. There are four main types of diabetes: type 1 diabetes (T1DM), type 2 diabetes (T2DM), specific diabetes with known genetic and non-genetic etiologies, and gestational diabetes [1,4]. Of these, T1DM and T2DM account for the majority of total diabetes diagnoses [5]. T1DM is an autoimmune condition characterized by the progressive destruction of insulin-producing β-pancreatic cells [1,4]. Conversely, T2DM is mainly characterized by insulin resistance and hyperinsulinemia [3,4,6]. In chronic cases, diabetes is associated with long-term complications including but not limited to renal failure, blindness (retinopathy), diabetic foot disorders, and cardiovascular disease, which reduces life expectancy and causes death [2,7]. According to statistics provided by the 2017 International Diabetes Federation, diabetes is among the top 10 causes of death worldwide with a global prevalence of 8.8% among adults between 20 and 79 years of age [2,8–10]. These numbers are expected to increase to 10.9% in 2045 [10], posing a substantial socioeconomic burden [11].

Various metabolic, environmental, genetic, and behavioral risk factors individually and synergistically contribute to the progression from a prediabetic condition to an overt disease [9]. In the case of T2DM (the most common form of diabetes) [5], it includes age [12], gender [13], obesity [14,15], hypertension [16,17], smoking habits [18,19], sedentary lifestyle patterns [12], and polycystic ovary syndrome (PCOS) [20]. The multifactorial nature of this disease, its slow onset, and the presence of comorbidities necessitates careful and early diagnosis, personalized treatment regimens, and disciplined self-management programs to control disease progression and reduce the burden on the healthcare system [21–24]. However, these tailored solutions are not readily available to all due to limited healthcare resources and high associated costs.

Digital healthcare interventions can help individuals to overcome these myriad challenges by providing smart, scalable, and cost-effective technologies to prevent, manage and treat diabetes [25–27]. Waya is a simple and promising digital solution launched in May 2020 to prevent diabetes. It guides overweight, obese, or patients with prediabetes to monitor and manage their weight, diet, and lifestyle through simple exercises, valuable nutritional advice, useful challenges, and educational videos. The aim of this research was to characterize the demographic patterns of its user. Additionally, we study the prevalence of diabetes and its associated risk factors within the Waya cohort and compare its epidemiological pattern with that of the general German population in 2014–2015. This study potentially highlights variations in the prevalence pattern over time.

Methods Study design

Users of the digital prevention program Waya (Supplemental Figure 1, Supplemental Digital Content 1, https://links.lww.com/CAEN/A38) who registered between 1st May and 16th October 2020 and who provided their consent for the usage of their health data content were considered for this study. An app-based health survey was conducted using a multi-choice questionnaire. User response was collected and stored in a GDPR-compliant manner. Inclusion criteria were: age 18–80 years, height >150 cm, BMI ≤50 kg/m2, and responder of a specific parameter.

Parameters evaluated

Sociodemographic parameters such as gender, age, height, and BMI of the Waya population were analyzed. BMI, a measure of the nutritional status of an individual was calculated using the standard formula (27340299):

BMI=Bodyweight(kg)Height2(m2)

On the basis of their BMI, the nutritional status of an individual was divided into categories as shown in Table 1. This information was used to evaluate the percentage of overweight and obese individuals in the study population. Additionally, the prevalence of other risk factors of diabetes such as smoking status, hypertension, PCOS, and diabetes itself was analyzed.

Table 1 - BMI classification according to WHO [28] Calculated BMI Nutritional Status <18.5 Underweight 18.5–24.9 Normal weight 25.0–29.9 Overweight >30 Obese (class I–III)
German population data

Population data from the ‘German Health Update’ (GEDA) study was used to compare features of the cohort of Waya users to those of the general German adult population (aged 18–65 years and above). The GEDA study was a cross-sectional health survey conducted on behalf of the German Federal Ministry of Health by the Robert Koch Institute (RKI) between November 2014 and July 2015 [29]. Only studies related to obesity [30], hypertension [31], smoking status [32], and diabetes [33] were considered for analysis. Table 2 briefly outlines the population size and gender of respondents for each GEDA 2014/2015-European Health Interview Survey (EHIS) population-based study. In case of hypertension and diabetes, the adjusted weight for gender was used.

Table 2 - Population size and gender of responders for German Health Update 2014/2015-European Health Interview Survey cross-sectional studies Study parameter Total no of responders No. females No. males Obesity 23 791 13 006 10 785 Smoking status 23 960 13 108 10 852 Hypertension 23 967 12 247a 11 720a Diabetes 23 345 11 929a 11 416a aCalculated numbers based on the reported weighted gender percentages [29].
Statistical analysis

Descriptive statistics were used to characterize the Waya population. The mean, SD, and proportions were calculated using Rstudio [R version 4.0.2 (2020-06-22)]. Test for equal proportions between the Waya population and the GEDA 2014/2015-EHIS population was evaluated using Pearson’s chi-square test [34–37]. A P value <0.05 indicated significant differences between the two populations.

Results Gender, age, and height distribution of Waya users

Among 903 Waya users, data from 837 users were available for analysis. We observed a higher percentage of female users (86.1%) compared to male users (13.9%). There were 721 females who subscribed to Waya. In contrast, only 116 males subscribed to and used the app. The average age of female users was 38 years of age while that of males was 39.5 years of age. The mean height of females was 167.1 cm and that of males was 181.5 cm (Table 3).

Table 3 - (Standard Deviation (SD)) to introduce the abbreviation Gender No. users Percentage users (%) Mean age (SD) in years Mean height (SD) in cm Female 721 86.1 38 (11.7) 167.1 (6.3) Male 116 13.9 39.5 (11.4) 181.5 (7.4)
BMI classification

On the basis of the self-reported weight and height input, BMI of each Waya user was calculated. Among the 721 female Waya users, 6 (0.83%) were underweight, 202 (28%) had a normal BMI, 229 (31.8%) were overweight, and 284 (39.4%) were obese (Fig. 1a). Among the 116 males, 2 (1.7%) were underweight, 27 (23.3%) had a normal BMI, 44 (37.9%) were overweight and 43 (37.1%) were obese.

Fig. 1:

(a) BMI distribution in women and men. The graph above depicts the percentage distribution of underweight, normal BMI, overweight and obese participants in the two populations. The ‘n’ value is the total number of men and women who participated in the study (*P < 0.05). (b) Smoking behavior in women and men. The graph above depicts the percentage distribution of active smokers, former smokers, and non-smokers within the Waya and GEDA cohorts. The ‘n’ value is the total number of men and women who participated in the current analysis (*P < 0.05 for the specific category). Among Waya users, one woman and one man did not answer the question related to their smoking status. (c) Prevalence of arterial hypertension among women and men. This graph depicts the percentage of women and men who suffer from arterial hypertension in the Waya and GEDA cohorts. For Waya users, the number of women = 720 (as one did not complete this question), and the number of men = 116. For the GEDA cohort, the number of women = 12 247 and that of men = 11 720 (*P < 0.05). (d) Prevalence of diabetes among women and men. This graph depicts the percentage of men with diabetes and women in the Waya and GEDA cohorts. For Waya users, the number of women = 720 (as one did not complete this question), and the number of men = 116. For the GEDA cohort, the number of women = 11 929 and that of men = 11 416 (*P < 0.05). GEDA, German Health Update.

In comparison to the Waya users, substantial differences could be observed in the BMI status of both women and men in the GEDA 2014/2015 population (Fig. 1a). A higher proportion of women in the GEDA cohort were underweight (2.9% vs. 0.83% in the Waya cohort, P < 0.05) or had a normal BMI (50.4% vs. 28% in the Waya cohort, P < 0.05). A relatively lower proportion of women was obese (18% vs. 39.4% in the Waya cohort, P < 0.05), and the proportion of overweight women was similar (28.8% vs. 31.8%, P = 0.096). Among men, the proportion of underweight (0.8% vs. 1.7% in the Waya cohort, P = 0.56), as well as overweight individuals (43.3% vs. 37.9% in the Waya cohort, P = 0.29), was similar between the two populations, a larger proportion had a normal BMI (37.6% vs. 23.3% in Waya, P < 0.05) and a smaller proportion was obese (18.3% vs. 37.1% in Waya, P < 0.05).

Smoking status

Smoking behavior of users was recorded in three different categories: active smokers, former smokers, and non-smokers. Among Waya users, 119 women (16.5%) and 14 men (12.2%) were active smokers, 190 women (26.4%) and 45 men (39.1%) were former smokers, and 411 women (57.1%) and 56 men (48.7%) were non-smokers. As shown in Fig. 1b, the proportion of smokers in the GEDA cohort was significantly larger (women: 20.8% vs. 16.5% in Waya, P < 0.05; men: 27% vs. 12.2% in Waya, P < 0.05) in comparison to Waya users. As a result, the proportion of non-smokers was lower (women: 52.6% vs. 57.1% in the Waya cohort, P < 0.05; men: 38% vs. 48.8% in the Waya cohort, P < 0.05). The percentage of former smokers between the two groups remained the same (women: 26.6% vs. 26.4% in the Waya cohort, P = 0.93; men: 35% vs. 39.1% in the Waya cohort, P = 0.405).

Hypertension

Within the Waya population, 87 women (12.1%) and 26 men (22.4%) suffered from arterial hypertension. On comparing these proportions with the 12-month prevalence of arterial hypertension in the GEDA population, a significantly higher proportion of affected individuals was observed (women: 30.9% vs. 12.1% in the Waya cohort, P < 0.05; men: 32.8% vs. 22.4% in the Waya cohort, P < 0.05, Fig. 1c).

Polycystic ovary syndrome

Among the 721 women in the Waya cohort, we observed 13 women (1.8%) to be affected with PCOS. As there were no available population studies specifically targeting German women for the prevalence of PCOS at the time of writing, we could not further compare these results with any existing surveys.

Diabetes

Among Waya users, 28 women (3.9%) and 6 men (5.2%) were diabetic. Similar to the trend observed in hypertension, a higher proportion of women (7% vs. 3.9%, P < 0.05) were diabetic in the GEDA cohort. However, the proportion of diabetic men was similar between the two groups (5.2% vs. 8.6%, P = 0.26) (Fig. 1d).

Discussion

In this study, we characterized the demographic features such as gender, age, height, and weight of users of a digital prevention course based on their responses to patient-reported outcome questionnaires. We observed a higher number of female users in comparison to male users. On further studying the prevalence of diabetes and its associated risk factors, we noted significant differences in the real-world population data collected in 2014–2015 by Robert Koch Institute and in the data collected in 2020 by the Waya app. These findings highlight the importance of Waya in collecting valuable real-world data, which might be a very useful asset in terms of studying the prevalence of diabetes and its risk factors over time. Monitoring disease progression and re-evaluating incidence rates at regular intervals is crucial on both national and international levels as it guides healthcare officials – as well as the government – to take the relevant measures required to improve the quality of life in patients.

Gender differences among Waya app users

On grouping 837 Waya users based on gender, we observed a large number of female users (86.1%) in comparison to male users (13.8%). One of the main reasons for this observation is that women are more proactive in exploring and using nutrition and self-care apps compared to men [38]. An online survey conducted on 958 Germans in 2012 showed that women experience a higher social drive and joy in searching for health-related information on the internet than men. Furthermore, women had higher health and nutrition awareness on an aggregate level than men even though they were reluctant to make use of medical support in comparison to their male counterparts [39]. Waya is an interactive app that benefits users through a balanced diet plan, physical activities, challenges, and chat functions. This kind of interactive setup might explain the higher number of women using Waya in comparison to men. In the future, we plan on integrating more gamification features to also make the app more attractive for men to use. Additionally, the risk of an unhealthy lifestyle and being overweight should be made aware of during physical check-ups. Especially since medical applications like Waya are (partially) covered by health insurance in Germany, physicians could raise awareness of these easily accessible applications.

BMI distribution

To evaluate the prevalence of obesity – a known predictor of diabetes [14,15] – we studied the BMI distribution pattern in the Waya population. We observed a larger proportion of overweight and obese users compared to those who had a normal BMI or were underweight. This trend was expected as Waya is mainly intended to guide overweight and obese individuals through a structured weight loss program. Additionally, over 20% of users with a normal BMI highlight that such an app can also be used to maintain a healthy lifestyle with a balanced diet and routine exercises.

On further assessing the similarity of proportions between Waya users and the GEDA cohort, we observed a relatively higher percentage of individuals (women and men) with a normal BMI and a corresponding decrease in the percentage of obese individuals within the GEDA population compared to the population of Waya users. However, the proportion of overweight participants remained similar across the two groups. In the case of the underweight category, only the proportion of women was significantly larger in the GEDA cohort while the proportions of men remained unaltered. These variations are expected considering the purpose of the app, as explained above.

Obesity contributes directly to incident cardiovascular risk factors, including dyslipidemia, T2DM, hypertension, and sleep disorders [40]. The prevalence of obesity has been increasing in most countries since the 1980s [40]. Also in Germany, there has been a significant rise in the prevalence of obesity in recent years [1,30]. The GEDA 2014/2015-EHIS data showed a 2% increase in the prevalence of overweight and obese individuals compared to the 2010 study [30]. This alarming rise impacts health outcomes and increases the economic burden [14,41–43]. The recent collation of five different population surveys associated with the KORA project showed a direct correlation between medical expenses with BMI. On the basis of the grade of obesity, participants were expected to spend an excess of 300–1800€ per annum on medical expenses compared to normal individuals [41]. This burden makes obese individuals more eager to find easily accessible weight loss remedies and self-management programs compared to overweight participants. As a result, the BMI distribution observed in our population is more specific to our target group and may not entirely reflect the prevalence of the general German population.

Increased risk of T2DM [44], cardiovascular disease, and certain types of cancer [45,46], are associated with physical inactivity. On the contrary, physical activity reduces the risk of cardiovascular diseases and mortality [47] and has a positive long-term effect on weight gain, obesity, coronary heart disease, T2DM, as well as dementia, and Alzheimer’s disease [48]. In our previous study [49], we could observe an increased level of physical activity independent of age and gender. Waya focuses on guiding its users to monitor and manage their weight, diet, lifestyle, and physical activity through simple exercises, nutritional advice, and educational articles and videos, thereby contributing to an affordable, intervention to reduce weight and increase physical activity, in turn reducing the risk of cardiovascular diseases.

Smoking status

Smoking is one of the top preventable risk factors associated with diabetes [18], cardiovascular disease [50,51], respiratory disease [52], and cancer [53]. This prompted us to aim to understand the smoking behavior of Waya app users and compare their prevalence with previously reported observations. Our results clearly showed a higher proportion of non-smokers (>50% women and 48% men) compared to former smokers and current smokers. Furthermore, there were significant differences in the proportions of smokers and non-smokers between the Waya and GEDA populations. The proportions of smokers were much lower and those of non-smokers were much higher among Waya users compared to the GEDA population.

A previous health survey conducted by the RKI revealed a considerable 8- to 11-point decline in the percentage of adult smokers since 2003 [54]. Similar trends were also observed in the Epidemiological Survey of Substance Abuse [55] and the studies conducted by the Federal Centre for Health Education [56]. Our results are in line with recent trends observed in Germany. Over the course of the last 20 years, the German government has taken out numerous measures to curb the growth of the smoking population. These include age restrictions, higher taxes, and increased awareness of the health risks caused by smoking [57]. Owing to these strict measures and awareness, we believe that over time many have taken an informed decision to stop smoking (or not to start at all), explaining the rise in non-smokers and the growing prevalence of former smokers. Although the numbers look promising, there is still room for improvement when considering the premature death rate caused by smoking [32,58].

Hypertension and diabetes

In this study, we assessed the prevalence of arterial hypertension among Waya users given its high risk in the development of diabetes [16,17]. Our analysis showed a lower prevalence of arterial hypertension within the Waya population compared to the 12-month prevalence observed in the GEDA study. One of the main reasons for this difference is that we report point prevalence (i.e. the prevalence of arterial hypertension at that specific time point), whereas the GEDA study reports a 12-month prevalence [31]. Considering the fact that arterial hypertension can be controlled through medication and regular physical activity [59,60], users with controlled blood pressure would ideally not report arterial hypertension.

In the case of diabetes, we observed similar trends (i.e. lower prevalence in the Waya population (women and men) compared to the GEDA study). However, the change in proportion in the case of men is not significant. Owing to the strong association between diabetes and arterial hypertension and their frequency of co-occurrence [61,62], this observation was expected. We also believe that the reason for non-significance in the case of men is due to the low sample size (n = 116) compared to that of women (n = 720).

The German Health Interview and Examination Survey for Adults (DEGS1 study) conducted between 2008 and 2011 reported that age and socioeconomic status play a substantial role in the rising prevalence of diabetes [63]. The relatively low average age of users (38–39.5 years) could possibly explain the lower prevalence rate of diabetes. Additionally, Waya is mainly intended to prevent or control the development of diabetes. These factors cumulatively explain the low prevalence of diabetes among Waya app users.

Polycystic ovary syndrome

PCOS is a complex, partly heterogeneous endocrine disorder affecting women of reproductive age [20,64]. It is most commonly associated with obesity [65,66], hypertension [67], and diabetes [68]. We are among the first to study the prevalence of PCOS specifically in German women. Our results showed a PCOS prevalence of 1.8% among Waya users compared to the commonly accepted worldwide prevalence of 6–10% [69]. Within the confines of our research, the prevalence varied from fewer than 10 to 20% among respondents [70]. Our study provides valuable first insights and steps toward better understanding the prevalence of PCOS in Germany (n = 721). However, given the high dependency of PCOS prevalence on the diagnostic criteria and geographic distribution [69,70], further studies may be required to strongly suggest that the results observed in our cohort reflect the general prevalence of PCOS in Germany.

Limitations of the study

The current study has a number of limitations with respect to data analysis and interpretation. Because the study mainly relies on self-reported data, the probability of a small percentage of false data inputs by users cannot be ignored. We assessed the prevalence of every risk factor independently and have not considered the interplay and association between co-existing factors. Due to the short time frame of the study, we collected health-related information from users only at the time of onboarding. This limits the scope of the study and prevents us from reaching a conclusion on the long-term effects of each risk factor for diabetes. In addition to the above, the low representation of males in our study population does not allow us to draw meaningful conclusions on gender-based prevalence patterns, instead providing initial hints towards probable trends.

Conclusion and outlook

Our study provides a number of interesting insights into the sociodemographic patterns and prevalence of diabetes and its risk factors among users of a digital prevention course in Germany. We aim to further study the impact of the digital prevention course Waya on the quality of life of users and its influence on weight management and nutrition.

Acknowledgements

We confirm that this article has not been submitted nor published elsewhere in part or in whole.

Conflicts of interest

There are no conflicts of interest.

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