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The European Society for Clinical Nutrition and Metabolism (ESPEN) and the European Association for the Study of Obesity (EASO) launched the Sarcopenic Obesity Global Leadership Initiative (SOGLI) to reach expert consensus on a definition and diagnostic criteria for Sarcopenic Obesity (SO).The present paper describes the proceeding of the Sarcopenic Obesity Global Leadership Initiative (SOGLI) meeting that was held on November 25th and 26th, 2022 in Rome, Italy. This consortium involved the participation of 50 researchers from different geographic regions and countries.The document outlines an agenda advocated by the SOGLI expert panel regarding the pathophysiology, screening, diagnosis, staging and treatment of SO that needs to be prioritized for future research in the field.

Using picture description speech for dementia detection has been studied for 30 years. Despite the long history, previous models focus on identifying the differences in speech patterns between healthy subjects and patients with dementia but do not utilize the picture information directly. In this paper, we propose the first dementia detection models that take both the picture and the description texts as inputs and incorporate knowledge from large pre-trained image-text alignment models. We observe the difference between dementia and healthy samples in terms of the text's relevance to the picture and the focused area of the picture. We thus consider such a difference could be used to enhance dementia detection accuracy. Specifically, we use the text's relevance to the picture to rank and filter the sentences of the samples. We also identified focused areas of the picture as topics and categorized the sentences according to the focused areas. We propose three advanced models that pre-processed the samples based on their relevance to the picture, sub-image, and focused areas. The evaluation results show that our advanced models, with knowledge of the picture and large image-text alignment models, achieve state-of-the-art performance with the best detection accuracy at 83.44%, which is higher than the text-only baseline model at 79.91%. Lastly, we visualize the sample and picture results to explain the advantages of our models.

Introduction: Loss of skeletal muscle mass and function (sarcopenia) is common in individuals with obesity due to metabolic changes associated with a sedentary lifestyle, adipose tissue derangements, comorbidities (acute and chronic diseases), and during the ageing process. Co-existence of excess adiposity and low muscle mass/function is referred to as sarcopenic obesity (SO), a condition increasingly recognized for its clinical and functional features that negatively influence important patient-centred outcomes. Effective prevention and treatment strategies for SO are urgently needed, but efforts are hampered by the lack of an universally established SO Definition and diagnostic criteria. Resulting inconsistencies in the literature also negatively affect the ability to define prevalence as well as clinical relevance of SO for negative health outcomes. Aims and methods: The European Society for Clinical Nutrition and Metabolism (ESPEN) and the European Association for the Study of Obesity (EASO) launched an initiative to reach expert consensus on a Definition and diagnostic criteria for SO. The jointly appointed international expert panel proposes that SO is defined as the co-existence of excess adiposity and low muscle mass/function. The diagnosis of SO should be considered in at-risk individuals who screen positive for a co-occurring elevated body mass index or waist circumference, and markers of low skeletal muscle mass and function (risk factors, clinical symptoms, or validated questionnaires). Diagnostic procedures should initially include assessment of skeletal muscle function, followed by assessment of body composition where presence of excess adiposity and low skeletal muscle mass or related body compartments confirm the diagnosis of SO. Individuals with SO should be further stratified into Stage I in the absence of clinical complications, or Stage II if cases are associated with complications linked to altered body composition or skeletal muscle dysfunction

Speech pause is an effective biomarker in dementia detection. Recent deep learning models have exploited speech pauses to achieve highly accurate dementia detection, but have not exploited the interpretability of speech pauses, i.e., what and how positions and lengths of speech pauses affect the result of dementia detection. In this paper, we will study the positions and lengths of dementia-sensitive pauses using adversarial learning approaches. Specifically, we first utilize an adversarial attack approach by adding the perturbation to the speech pauses of the testing samples, aiming to reduce the confidence levels of the detection model. Then, we apply an adversarial training approach to evaluate the impact of the perturbation in training samples on the detection model. We examine the interpretability from the perspectives of model accuracy, pause context, and pause length. We found that some pauses are more sensitive to dementia than other pauses from the model's perspective, e.g., speech pauses near to the verb "is". Increasing lengths of sensitive pauses or adding sensitive pauses leads the model inference to Alzheimer's Disease, while decreasing the lengths of sensitive pauses or deleting sensitive pauses leads to non-AD.

Sarcopenia is the age-related loss of muscle mass and strength that is associated with adverse health outcomes. Resistance-based exercises are effective for mitigation and enhancement of strength; however, adherence is low and challenging to measure when patients are at home. In a single-arm, pilot study of seven older adults, we conducted a field-based usability study evaluating the feasibility and acceptability of using a system consisting of a Bluetooth-connected resistance exercise band and tablet-based app which together we call BandPass in completing four different home-based exercises. The system measured a total of 147 exercises by participants with a mean duration of 94±66 seconds, completing an average of 30±20 repetitions. Though not all patients completed each exercise type, patients were positive about use: patient activation measure: 80.7±14; system usability scale: 6.9±2.9; and confidence in use: 7.7±2.7. The BandPass system demonstrated its ability to collect data on exercise type, force during an exercise, and duration of exercise when older adults use it for monitoring exercise at home.