Overcoming fatigue: An app for greater quality of life

Cancer patients often suffer from fatigue syndrome as a side effect. This makes it difficult to cope with everyday life and is usually treated with physiotherapy and behavioral therapy. In the future, an anti-fatigue app should enable treatment to be individually tailored to the person.

The app is being developed as part of the FAITH project, supported by funding from the state of North Rhine-Westphalia. Priv.-Doz. Dr. Mitra Tewes and Tim Fellerhoff explain in an interview with MEDICA-tradefair.com how the app can use AI to help cancer patients deal with fatigue syndrome.

Why an app for fatigue syndrome in cancer patients?

Priv.-Doz. Dr. Mitra Tewes: Many cancer patients suffer from fatigue, also known as exhaustion syndrome. This is often underestimated. Depending on the type of cancer and therapy, fatigue occurs in 30 to 85 percent of patients. Some kind of therapies even affect almost 100 percent. Accordingly, fatigue syndrome affects many people with cancer in everyday life and in work situations.

During oncological treatment, the fatigue of those affected is quite variable. For example, shortly after chemotherapy, patients are often very weak and have little motivation to exercise. The motivation to exercise is heavily dependent on two factors: Were those affected already very active beforehand and to what extent are they educated about the effect of exercise therapy?

This is precisely where the app comes in as a low-threshold tool for support and as an information platform.

How can the app improve the care and treatment of those affected?

Tewes: The conditions of patients is very dynamic. It is therefore important that an app reacts in a way that is adapted to that state so that no one is under-challenged, but also not over-challenged. Otherwise, this quickly leads to frustration and must be adapted and ideally also discussed with the therapist.

Tim Fellerhoff: An app gives us the opportunity to respond precisely to the individual needs of those affected. In a first step, we can collect vital and behavioral data via sensors in the smartphone or wearables and thus make a statement about the fatigue condition of the user.

In the next step, we can then digitally provide therapeutic content, for example from cognitive behavioral therapy or exercise therapy, precisely adapted to the fatigue condition.

In this way, we also provide therapeutic incentives outside of the clinic, in patients' everyday lives, which can complement outpatient treatment very well and promote the proactive management of symptoms.

How does the app work and based on what does the AI decide the recommended level of exercise therapy?

Fellerhoff: That's exactly what we want to find out in our research project. The main research question we are asking ourselves is: How do we make fatigue objectively measurable? We think that there is important information to be found in the recording of objective parameters such as heart rate, sleep data and behavioral patterns.

Another assumption is that multifactoriality is the key. In other words, there is not just one parameter that explains the condition of those affected. So we want to bring together information from different sources. AI, and machine learning in particular, offers us the appropriate methodology for linking this data and understanding correlations. Based on these analyses, we can then specifically adapt the movement therapy content of the app to the patient's current condition.

Is it possible to link the app with other wearables and, if so, what advantages does this offer?

Fellerhoff: We are currently combining the app specifically with smartwatches and fitness trackers. These devices continuously provide us with a large proportion of the necessary data. Our requirement for the connected wearable is that it should ideally provide data over a 24-hour period, which works best with a smartwatch or fitness tracker.

What are the next steps and goals in the project?

Tewes: Step one is about data collection. We need to find out and record exactly which objective and subjective parameters are decisive for the severity of fatigue. To do this, we collect data from patients with fatigue, but also from patients without fatigue syndrome.

The next step is to expand the existing app with exercise therapy. Here we are working together with sports scientists who are integrating movement elements into the app. The aim is to offer a gradation between intensive exercise and less intensive exercise for those affected.

Fellerhoff: We hope to be able to make an initial statement about the data towards the end of the year. That depends on how well the data collection goes. It is important to use the feedback from patients to readjust the AI if we encounter problems or if the data and subjective perceptions are very far apart.