The impact of an online personal health platform on lifestyle and anthropometric factors related to type 2 diabetes risk and management

Introduction

Type 2 diabetes mellitus (T2DM) stands as a persistent metabolic disorder characterized by elevated blood glucose levels, stemming from both insulin resistance and insufficient insulin production. Its debilitating consequences, including neuropathy, cardiovascular complications, vision impairment, and recurrent infections, are most pronounced in cases of uncontrolled or poorly managed T2DM. Notably, T2DM manifests as a systemic disease, impacting multiple bodily systems, such as the pancreas, liver, skeletal muscle, kidneys, small intestine, adipose tissue, and even the brain (1).

T2DM’s prevalence and impact is underscored by its classification as one of the most substantial epidemics in human history (2). Global statistics paint a stark picture, with both prevalence (49%) and mortality (10.8%) on an upward trajectory since 1990 (3). These figures not only signify an escalating prevalence of T2DM diagnoses but also point to a heightened toll on the quality of life for those affected. In 2017 alone, T2DM directly impacted approximately 462 million people, equating to roughly 6.28% of the world’s population. The economic burden of diabetes in adults, primarily comprising T2DM, surged to USD $1.3 trillion and is projected to reach USD $2.1–$2.5 trillion by 2030 (4).

Amid the array of interventions prescribed to manage T2DM and mitigate its risk factors, lifestyle adjustments centred around weight management through dietary and exercise regimens occupy a central role (5). Depending on the disease’s severity, pharmaceutical treatments may supplement these measures (6). In certain cases, healthcare professionals may recommend curtailing alcohol consumption or quitting smoking. However, research reveals that long-term adherence to these interventions often wanes, ranging from moderate to poor (7-9). Hence, attention to health literacy education for patients (10,11) and self-management education (12) assumes critical importance in sustaining adherence to health interventions among T2DM patients.

In recent years, the landscape of healthcare has witnessed the proliferation of mobile phone and computer applications (apps) and platforms designed to engage, educate, and promote healthier lifestyles. These digital tools vary widely in purpose and sophistication, ranging from public health campaigns to subscription-based or freely accessible commercial platforms that offer wellness guidance and track behaviours such as diet and physical activity. Many basic apps focus on simple metrics like step counts using global positioning system (GPS) or motion sensors, whereas more complex systems monitor additional parameters including sleep patterns, body weight, workout intensity, dietary habits, and aspects of psychological well-being. However, the body of evidence regarding the effectiveness of mobile and computer apps in improving overall lifestyles and health, including mental well-being, remains inconclusive. Earlier studies often concentrated on fitness and self-monitoring, yielding mixed results (13).

A more recent systematic review by Milne-Ives and colleagues (14) evaluated 52 randomized controlled trials, the majority of which addressed exercise and/or nutrition, while a smaller number examined interventions focused on mental health. The findings of this comprehensive review revealed limited evidence supporting significant changes resulting from the use of these apps. In contrast, a meta-analysis conducted by Wu and colleagues offered stronger support for the role of health apps in facilitating lifestyle changes in people living with diabetes (15). Similarly, a separate review assessing apps aimed at addressing obesity-related conditions—including T2DM—found encouraging effects on dietary behaviours and broader health markers (16). The authors of this review also underscored the need for future advancements to focus on strategies that encourage longer-term engagement, given that prolonged app usage correlated with reduced effectiveness.

Among the various online health platforms available, Shae is one example that functions as both an assessment and intervention tool. Shae is a digital platform designed to assess an individual’s phenotype and provide personalized lifestyle recommendations aimed at improving physical and mental well-being. It is available in over 140 countries and is used by more than 2,000 health and medical professionals, as well as in workplaces, schools, and gyms to enhance both performance and health outcomes. Users can access Shae by purchasing the app online or through their healthcare provider, employer, or gym. The platform analyzes an individual’s current health status, historical health factors, and potential future health risks through a detailed onboarding process, where users provide anthropometric data and complete medical and lifestyle questionnaires. The platform operates via both app and web interfaces, delivering static information and real-time push notifications related to nutrition, exercise, sleep, circadian rhythms, psychosocial tendencies, and environmental factors. By combining anthropometric measurements and medical questionnaire data, Shae creates a ‘digital twin’ that adapts its evidence-based recommendations as users update their information. While Shae is currently available in over 140 countries, further research is needed to evaluate its effectiveness relative to other digital health platforms in managing cardiometabolic risk factors.

Targeted and personalized health apps that emphasize user education and health literacy offer a promising strategy to address the growing global burden of T2DM. Given the complex interplay of modifiable lifestyle factors and genetic predispositions in T2DM, scalable digital platforms can deliver tailored interventions across a broad spectrum—from individuals without diabetes to those with pre-diabetes and established T2DM. In this study, we evaluate the impact of engagement with the online platform, Shae, on T2DM risk and management—quantified using the Australian Type 2 Diabetes Risk Assessment Tool (AUSDRISK) score—and on associated lifestyle and anthropometric factors. This research contributes to the field by providing insights into how mHealth interventions may improve health outcomes and inform future strategies for chronic disease prevention and management.

Methods

Participants

Participant data were sourced from individuals who engaged with the Shae digital health platform between May 2014 and March 2018. The raw dataset was provided to the research team by Precision Health Alliance. Individuals included in the study were drawn from the platform’s active user base and had provided informed consent during the initial registration process. A total of 2,072 participants accessed the platform during this timeframe. Data were removed prior to analysis if entries were incomplete, if users were under 18 years of age, if their engagement duration was less than 3 months, or if there were notable health disruptions such as serious illness, pregnancy, or extended hospitalization. After applying these criteria, data from 1,690 participants were retained for analysis.

Shae platform

Shae is a digital health app designed to serve both as a tool for health assessment and as a personalized intervention platform. It evaluates a user’s current health profile, relevant medical history, and potential risk trajectories for future health conditions. During the onboarding process, users provide detailed anthropometric information and complete medical and lifestyle questionnaires. This information is used to generate a tailored health profile—or “digital twin”—which forms the basis for individualized, evidence-informed recommendations. The platform delivers guidance across multiple lifestyle domains, including diet, physical activity, sleep hygiene, emotional and psychological wellbeing, environmental interactions, biological rhythms, and behavioural tendencies.

Measures

To gather the required data, participants were initially asked to provide their date of birth and gender. They then submitted five anthropometric measurements and answered six survey questions during the data collection phase. The anthropometric measurements collected were height (cm), weight (kg), neck circumference (cm), waist circumference (cm), and hip circumference (cm). The survey included four closed-ended questions and two Likert scale questions. Participants could enter measurements in either metric or imperial units, with imperial values automatically converted to metric in the database.

The closed-ended questions were: (I) “Have you been informed by a medical professional that you have high cholesterol?”; (II) “Have you been informed by a medical professional that you have high blood pressure?”; (III) “Have you been diagnosed as pre-diabetic or diabetic by a medical professional?”; and (IV) “Have you been informed by a medical professional that you have elevated or high blood sugar?”. The Likert scale questions were: (V) “What is your current level of physical activity?”, with options ranging from “sedentary lifestyle” to “bodybuilding with the use of hormones”, and (VI) “Do you smoke?”, with responses ranging from “yes, more than 1 pack a day” to “no, I don’t smoke at all”.

Procedures

Participant information—including anthropometric data and survey responses—collected between May 2014 and March 2018 was extracted from the Shae platform and formatted for analysis. Records that did not meet eligibility requirements were excluded. For each eligible participant, risk of developing T2DM was estimated using the AUSDRISK, calculated via a modified version of the publicly available AUSDRISK score calculator (https://www.mdcalc.com/calc/3931/australian-type-2-diabetes-risk-ausdrisk-assessment-tool#evidence). The AUSDRISK tool consists of 10 items (age, gender, country of birth, family history, hypertension history, elevated blood glucose history, smoking status, fruit and vegetable intake, physical activity, and waist circumference) and aims to identifies user risk of future T2DM diagnosis. Potential scores range from 0–38, with 0–5 being low future risk, 6–11 being moderate future risk, and 12+ being high future risk of T2DM or undiagnosed T2DM.

Participants updated their data via check-ins and self-report. The check-ins consisted of logging their own anthropometric measurements (height, weight, neck circumference, waist circumference, hip circumference). Imperial measurements (feet, inches, pounds) were converted into metric (centimetres and kilograms).

In preparing the data for analysis, additional new variables were computed, and these include:

• Waist-to-height ratio (WHt) = waist/height.
• Body mass index (BMI) = weight/(height/100)².
• Body fat index (BFI) for men =86.010 × log₁₀(waist-neck) − 70.041 × log₁₀(height) + 36.76.
• BFI for women =163.205 × log₁₀(waist + hip + neck) − 97.684 × log₁₀(height) − 78.387.
• Days: number of days in the program (date of last observation – baseline date).
• AUSDRISK = this tool estimates an individual’s likelihood of developing type 2 diabetes within five years, based on self-reported and anthropometric data collected from participants using the Shae platform. As specific responses regarding ethnicity and family history of diabetes were unavailable or incomplete, a score of zero was applied to those items by default—reflecting a conservative assumption of no added risk from these factors. Additionally, any participant who reported smoking—whether occasionally, daily, using tobacco products, or e-cigarettes—was classified as a smoker for the purposes of risk calculation.

As this study utilized retrospectively collected data from an existing database [2014–2018], a priori sample size calculations were not conducted, as the sample size was predetermined and could not be influenced. However, post hoc power analyses were performed using G*Power (Version 3.1.7.9) to assess whether the sample sizes for both male and female subgroups were sufficient for within-group comparisons over time. Power analyses were based on a paired-samples t-test for the difference between two dependent means, with an alpha level of 0.05 and power (1 − β) of 0.80. The calculation determined that a sample of at least 156 participants per group would be sufficiently powered. These analyses indicated that the study was adequately powered to detect meaningful changes in anthropometric measures and AUSDRISK scores within each subgroup.

Statistical analysis

Data was screened and unrealistic responses or spurious data were removed. Descriptive statistics were obtained for sex, age, waist, weight, height, BMI, BFI, total time in program, and the AUSDRISK score and variables, and means and standard deviations were reported across all participants (Table 1). To evaluate changes in diabetes risk, mean AUSDRISK scores were calculated at both the initial and final data entry points for each participant, with adjustments made for age category, sex, and duration of engagement with the program. Differences between baseline and follow-up scores were examined using paired-sample t-tests. Similar analyses were performed for key anthropometric variables to identify any significant shifts over time. Additionally, a generalized additive model was employed to explore changes in diabetes risk, incorporating random intercepts to account for individual variability. The model also adjusted for participant age, sex, and total follow-up duration (in days). A categorical time variable was included to distinguish whether each AUSDRISK score corresponded to a participant’s baseline, final, or interim data entry.

Ethical statement

The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was approved by the institutional ethics committee of Queensland University of Technology Human Research Ethics Committee (No. EC00171) and individual consent for this retrospective analysis was waived.

Results

Descriptive statistics

Table 1 presents the descriptive statistics of participants’ anthropometric measures, health conditions, and lifestyle factors. It includes the mean and standard error for each anthropometric measure and AUSDRISK scores, along with the number (and percentage) of participants with conditions such as hypertension and hypoglycemia. These data are categorized by participants’ gender and timepoints (baseline and follow-up). Notably, the sample was predominantly female, with 1,468 women (86.9%) compared to 222 men (13.1%).

The average AUSDRISK score for female participants decreased from 8.95 at baseline to 7.87 at follow-up, while for male participants, it decreased from 10.82 to 9.79. Similar improvements were observed in other anthropometric measures, including BMI, BFI, and WHt. Importantly, the WHt ratio was reduced below the 0.51 threshold for diabetes risk in both genders. Additionally, women tended to stay in the program longer than men, with an average duration of 11.70 months compared to 10.27 months for men.

Data are presented as mean and (standard error) for age, anthropometric measures and indices, total time in program, and AUSDRISK. Data for hypertension, diabetes, and hypoglycemia are presented as total number of participants meeting diagnostic thresholds for the values and percentage of total number of female and male participants.

Changes of AUSDRISK scores of participants

Figure 1 illustrates the trend in AUSDRISK score improvement over time for all participants. A generalized additive model was used to capture this trend, allowing for flexibility without assuming a strict linear relationship. The data revealed a gradual and steady decline in AUSDRISK scores among men, whereas women exhibited a more rapid and variable decrease in their scores.

Figure 1 Changes in AUSDRISK scores with 95% CI. AUSDRISK, Australian Type 2 Diabetes Risk Assessment Tool; CI, confidence interval.

Changes in anthropometric measurements of participants

Beyond the improvements in AUSDRISK scores, it was important to examine changes in anthropometric measures such as BMI, BFI, and WHt. Overall, there were reductions (indicating improvements) in these measures, particularly among women. Figure 2 illustrates the trends in these anthropometric changes throughout the program. The linear trend indicates a steady improvement over time in BMI, BFI, and WHt, with female participants showing faster progress.

Figure 2 Changes in BMI, BFI and WHt with 95% CI. BFI, body fat index; BMI, body mass index; CI, confidence interval; WHt, waist-to-height ratio.

Discussion

The aim of this study was to determine the association between the use of a targeted and personalised online health platform and T2DM risk via the AUSDRISK tool. The observed gender-specific differences in response to the lifestyle intervention program corroborate findings from previous studies highlighting the complex interplay between gender, lifestyle factors, and health outcomes (17-19). Women, exhibited greater improvements in diabetes risk scores and anthropometric measures compared to men, suggesting potential biological and sociocultural determinants influencing health behaviours and outcomes (18,19).

In Australia men diagnosed 1.4 times more likely to be diagnosed with T2DM at a lower age and BMI compared to women (20,21). However, women experience more complications and comorbidities, with evidence suggesting they have smaller reductions in HbA1c levels and are less likely to achieve glycaemic targets (20,22). Whilst our results showed a greater reduction in diabetes risk scores, a meta-analysis of twelve randomized controlled trials examining sex differences in individuals with prediabetes found that lifestyle interventions were equally effective in reducing diabetes risk and promoting weight loss in both men and women, with benefits lasting up to 3 years post-intervention (23). Women, compared to men, are more likely to access health related apps (24), however, they engage significantly less with the content except for exercise based content (25,26). Additionally, there is a prevailing perception that nutrition and fitness apps are more commonly associated with female users (27). This may in part explain the results our study where we observed women staying longer in the program and having thus having greater reductions in diabetes risk scores.

Our study provides insights into changes in anthropometric measures, including BMI, WHt, and body composition when engaging with a mobile e-health platform. These parameters serve as important indicators of overall health and are closely linked to the risk of developing chronic diseases, including T2DM and cardiovascular disorders (28-30). Reductions in BMI observed among participants reflect positive changes in body composition and weight management following the lifestyle intervention program. Women exhibited significant improvements in BMI across all age groups, highlighting the effectiveness of targeted lifestyle modifications in promoting healthy weight management (31,32). Elevated BMI is a well-established risk factor for metabolic disorders (33,34) and interventions aimed at reducing BMI can contribute to lowering the overall burden of chronic diseases in the population (28). Similarly, reductions in WHt ratio signify improvements in central adiposity and distribution of body fat, which are closely associated with cardiometabolic risk (35,36). Our findings reveal a steady decline in WHt among both genders, with women demonstrating more pronounced improvements over time. By targeting reductions in WHt through lifestyle interventions, such as dietary modifications and physical activity promotion, individuals can mitigate their risk of developing obesity-related complications such as diabetes.

The integration of mobile health (mHealth) platforms into T2DM prevention and management efforts can significantly enhance the impact of interventions, as evidenced by the findings of our study. By leveraging technology to deliver personalized interventions, monitor health parameters, and promote behaviour change, mHealth platforms can play a crucial role in reducing the risk of diabetes development and improving long-term health outcomes (37). Our study demonstrated the effectiveness of lifestyle interventions in reducing T2DM risk factors, including anthropometric measures and body composition. mHeath apps that address psychosocial factors, diet and physical activity in young people (18–29 years old) who are prediabetic have been shown to reduce HBA_1C through a reduction in sedentary behaviour, improved diet and health literacy (38). By incorporating these findings into mHealth platforms, individuals at risk of developing T2DM can receive tailored interventions that address their specific needs and preferences. mHealth apps can deliver interactive tools, educational resources, and behavioural coaching to promote healthy behaviours, such as increasing physical activity, improving dietary habits, and reducing sedentary time in people with T2DM (39). Real-time monitoring of key health parameters, such as blood glucose levels and physical activity, enables individuals to track their progress and make informed decisions about their health.

Limitations

This study is not without limitations, whereby participants were asked to self-report their anthropometric measurements, sedentary time, diabetes risk factors e.g., dietary intake, cholesterol, smoking status, and medication. Participants were not restricted to engaging in other forms of support, information, education, and physical activity programs. Therefore, future research should like incorporating the Shae mHealth app as an adjunct to behavioural lifestyle interventions to determine the effectiveness of the app.

Conclusions

Our study underscores the potential of mHealth platforms to complement traditional interventions in T2DM prevention and management. By delivering personalized, scalable, and accessible interventions, these platforms empower individuals to take control of their health, adopt healthy behaviours, and reduce their risk of developing T2DM. Future research and implementation efforts should focus on optimizing the effectiveness, usability, and scalability of mHealth solutions to maximize their impact on public health.

Acknowledgments

The authors would like to acknowledge data analysis undertaken by Oluwasegun Ojo, Grupo Ruiz, Spain. The authors would like to acknowledge Cameron McDonald, Precision Health Alliance, for participant data requisition and descriptions of the Shae Health App.

Footnote

Peer Review File: Available at https://mhealth.amegroups.com/article/view/10.21037/mhealth-24-87/prf

Funding: None.

Conflicts of Interest: Both authors have completed the ICMJE uniform disclosure form (available at https://mhealth.amegroups.com/article/view/10.21037/mhealth-24-87/coif). C.M. and J.H. report that Precision Health Alliance (PHA) provided study materials in the form of user data (with consent) to the research team for analysis and writing of manuscript. PHA remained entirely independent of the analysis and writing, with no input to the final product, apart from information provided relating specifically to the functionality of the app. As per initial agreement between PHA and the research team, PHA financed the article processing fee for publication. The authors have no other conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was approved by the institutional ethics committee of Queensland University of Technology Human Research Ethics Committee (No. EC00171) and individual consent for this retrospective analysis was waived.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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