Better App: (Further-)Development and Evaluation of a Digital Lifestyle Programme Based on Subtypes for Overweight or Obese People

Backgroud: In the Netherlands, the number of people with chronic conditions has increased significantly in recent years and is expected to continue to grow (2). Treating chronic conditions is expensive and requires a lot of care and attention. This leads to high healthcare costs for both the patient and the government. The Dutch state spent 108 billion euros on medical and long-term care in 2021, the largest part of the 125 billion euros spent on Care and Welfare in total at that time (3). Chronic conditions have a major impact on people's quality of life. They can cause symptoms and limitations in daily life and also affect people's work participation.

Overweight and obesity are related to an increased risk of many chronic conditions, such as cardiovascular disease, type 2 diabetes and certain cancers. Lifestyle plays an important role in the development of overweight and obesity. It is known that an unhealthy diet and lack of physical activity are the most common causes of overweight and obesity. Therefore, it is important to take action and offer programmes to reverse these trends and promote healthy lifestyles (4,5). In contrast to a generalised approach, more personalised interventions match a person's specific needs, characteristics and situations and can therefore potentially lead to better results when it comes to sustainable changes in healthy lifestyles (6-8).

Personalised lifestyle interventions, the BETTER programmes, have been developed and evaluated by Zuyd University of Applied Sciences' lectorate of Nutrition, Lifestyle and Exercise in recent years. BETTER stands (BETER in Dutch) for Move, Eat, Change and the programmes focus on exercise, nutrition and behavioural change. Knowledge and insights from systems biological fundamental research provide the basis for these programmes to make lifestyle recommendations more personalised. There is increasing evidence, that people with the same condition, may have different underlying causes, which also makes lifestyle recommendations and interventions different (9). In the BETTER programmes, this knowledge is practically applied by working with subtypes.

The face-to-face BETTER lifestyle programme has been successfully applied to overweight and obese individuals. After completing the programme, participants lost weight, felt fitter and indicated they felt more in control of their lifestyle (10).

Within the BETTER programme, individual subtyping of participants took place by one or more experts. This is time-consuming and (relatively) expensive, especially for repeated measurements. In a pilot study, we investigated to what extent the subtype determined by means of a digital symptom questionnaire, including algorithm, corresponds to the subtype determined by an expert (criterion validity). First preliminary results showed that the outcomes from the questionnaire corresponded moderately to reasonably well with the expert classification. Based on these data, a machine learning algorithm was developed, capable of increasing the validity of the questionnaire (optimising weightings and dependencies of answers). This algorithm can be trained by adding new data (completed questionnaire and subtyping by expert). The algorithm works on the basis of the Semi-Supervised Classification principle (11).