In-home estimation and Prediction of Sarcopenia

In-home estimation and Prediction of Sarcopenia

Sarcopenia is an age-related or secondary to comorbid conditions, generalized and progressive disorder of the skeletal muscles. Sarcopenia is diagnosed when low muscle strength occurs in combination with low muscle quantity or quality, while when the above findings are associated with low physical performance, sarcopenia is considered severe. Muscle wasting is an important finding in cancer patients, since it is a hallmark finding in cancer cachexia, a multifactorial clinical condition with deleterious effects in the patients’ Quality of Life and disease prognosis. The loss of muscle mass is a common feature in sarcopenia and cachexia, while sarcopenia in cancer patients can manifest in isolation or as a partial component of cancer cachexia. Muscle wasting throughout the course of a neoplastic disease is generally attributed to poor nutrition, physical inactivity and cytokine-mediated inflammation and this mechanism is further exacerbated in older patients.

The early identification of muscle loss in cancer patients could be a key measure for early enough intervention. Current state-of-the-art defines muscle loss (sarcopenia) using measurements of muscle thickness, muscle quantity and physical performance. All measurements are usually performed in the hospital environment. Until now, to assess muscle quantity,imaging modalities are being used, such as MRI, CT or Xrays.

Caption : Y Minorou et al. “Differential Characteristics of Skeletal Muscle in Community-Dwelling Older Adults” JAMDA September 2017.

AINIGMA develops an innovative solution for remote monitoring of muscle mass and the early detection of sarcopenia, with the aim to provide timely intervention for the prevention of cachexia. The proposed solution will be based on the combination of measurements from a handheld dynamometer, triaxial accelerometers and ultrasound measurements. The resulting signals, combined with the dynamometer measurements will be automatically processed by signal processing and machine learning algorithms These measurements will be monitored at a weekly level, and will allow clinicians and caregivers to measure the rate of muscle mass loss and adapt the nutrition and exercise schedule of patients to prevent the onset of cachexia and associated comorbidities. The timely detection/prediction of sarcopenia will lead to early nutritional and psychological interventions, hence improving the palliative care in this frail patient group.