baseline systems on the three proposed tasks: state-of-mind recognition, depression assessment with AI, and cross-cultural affect sensing, respectively.
The INTERSPEECH 2020 Computational Paralinguistics Challenge addresses three different problems for the first time in a research competition under well-defined conditions: In the Elderly Emotion Sub-Challenge, arousal and valence in the speech of elderly individuals have to be modelled as a 3-class problem; in the Breathing Sub-Challenge, breathing has to be assessed as a regression problem; and in the Mask Sub-Challenge, speech without and with a surgical mask has to be told apart. We describe the Sub-Challenges, baseline feature extraction, and classifiers based on the 'usual' COMPARE and BoAW features as well as deep unsupervised representation learning using the AUDEEP toolkit, and deep feature extraction from pre-trained CNNs using the DEEP SPECTRUM toolkit; in addition, we partially add deep end-to-end sequential modelling, and, for the first time in the challenge, linguistic analysis.
Background Mobile health apps (MHA) have the potential to improve health care. The commercial MHA market is rapidly growing, but the content and quality of available MHA are unknown. Instruments for the assessment of the quality and content of MHA are highly needed. The Mobile Application Rating Scale (MARS) is one of the most widely used tools to evaluate the quality of MHA. Only few validation studies investigated its metric quality. No study has evaluated the construct validity and concurrent validity. Objective This study evaluates the construct validity, concurrent validity, reliability, and objectivity, of the MARS. Methods Data was pooled from 15 international app quality reviews to evaluate the metric properties of the MARS. The MARS measures app quality across four dimensions: engagement, functionality, aesthetics and information quality. Construct validity was evaluated by assessing related competing confirmatory models by confirmatory factor analysis (CFA). Non-centrality (RMSEA), incremental (CFI, TLI) and residual (SRMR) fit indices were used to evaluate the goodness of fit. As a measure of concurrent validity, the correlations to another quality assessment tool (ENLIGHT) were investigated. Reliability was determined using Omega. Objectivity was assessed by intra-class correlation. Results In total, MARS ratings from 1,299 MHA covering 15 different health domains were included. Confirmatory factor analysis confirmed a bifactor model with a general factor and a factor for each dimension (RMSEA = 0.074, TLI = 0.922, CFI = 0.940, SRMR = 0.059). Reliability was good to excellent (Omega 0.79 to 0.93). Objectivity was high (ICC = 0.82). MARS correlated with ENLIGHT (ps<.05). Conclusion The metric evaluation of the MARS demonstrated its suitability for the quality assessment. As such, the MARS could be used to make the quality of MHA transparent to health care stakeholders and patients. Future studies could extend the present findings by investigating the re-test reliability and predictive validity of the MARS.
Background: Eating problems are highly prevalent among young adults. Universities could be an optimal setting to prevent the onset of eating disorders through psychological intervention. As part of the World Mental Health-International College Student initiative, this systematic review and meta-analysis synthesizes data on the efficacy of eating disorder prevention programs targeting university students.Method: A systematic literature search of bibliographical databases (CENTRAL, MEDLINE, PsycINFO) for randomized trials comparing psychological preventive interventions for eating disorders targeting university students with psychoeducation or inactive controls was performed on October 22, 2019.Results: Twenty-seven studies were included. Thirteen (48.1%) were rated to have a low risk of bias. The relative risk of developing a subthreshold or full-blown eating disorder was incidence rate ratio = 0.62 (95% CI [0.44, 0.87], n c = 8, numbersneeded-to-treat [NNT] = 26.08; standardized clinical interviews only), indicating a 38% decrease in incidence in the intervention groups compared to controls. Small to moderate between-group effects at posttest were found on eating disorder symptoms (g = 0.35, 95% CI [0.24, 0.46], NNT = 5.10, n c = 26), dieting (g = 0.43, 95% CI [0.29, 0.57], NNT = 4.17, n c = 21), body dissatisfaction (g = 0.40, 95% CI [0.27, 0.53], NNT = 4.48, n c = 25), drive for thinness (g = 0.43, 95% CI [0.27, 0.59], NNT = 4.23, Int J Eat Disord. 2020;53:813-833.wileyonlinelibrary.com/journal/eat 813 n c = 12), weight concerns (g = 0.33, 95% CI [0.10, 0.57], NNT = 5.35, n c = 13), and affective symptoms (g = 0.27, 95% CI [0.15, 0.38], NNT = 6.70, n c = 18). The effects on bulimia nervosa symptoms were not significant. Heterogeneity was moderate across comparisons.Discussion: Eating disorder prevention on campus can have significant, small-tomoderate effects on eating disorder symptoms and risk factors. Results also suggest that the prevention of subthreshold and full-syndrome eating disorders is feasible using such interventions. More research is needed to identify ways to motivate students to use preventive eating disorder interventions. AbstractAntecedentes: Los trastornos de la conducta alimentaria son altamente prevalentes entre los adultos jóvenes. Las universidades podrían ser un entorno óptimo para prevenir la aparición de trastornos alimentarios a través de la intervención psicológica. Como parte de la iniciativa World Mental Health-International College Student, esta revisión sistemática y meta-análisis sintetiza datos sobre la eficacia de los programas de prevención de trastornos alimentarios dirigidos a estudiantes universitarios. Método: Una búsqueda bibliográfica sistemática de datos bibliográficas (CENTRAL, MEDLINE, PsycINFO) para ensayos aleatorios que comparaban intervenciones preventivas psicológicas para trastornos alimentarios dirigidos a estudiantes universitarios con psicoeducación o controles inactivos fue realizada hasta el 22 de octubre de 2019. Resultados: Se incluyeron 27 estudios. T...
Background. Eating problems are highly prevalent among young adults. Universities could be an optimal setting to prevent eating disorders through psychological intervention. As part of the World Mental Health-International College Student initiative, this systematic review and meta-analysis synthesizes data on the efficacy of eating disorder prevention programs targeting university students.Method. A systematic literature search of bibliographical databases (CENTRAL, MEDLINE, PsycINFO) for randomized trials comparing psychological preventive interventions for eating disorders in university students to psychoeducation or inactive controls was performed through October 8th, 2018.Results. Twenty-two studies were included. Eight (36.4%) were rated to have a low risk of bias. The relative risk of developing a subthreshold or full-blown eating disorder was IRR=0.62 (95%CI: 0.44-0.87, n=8; standardized clinical interviews only), indicating a 38% decrease in incidence in the intervention groups compared to controls. Small to moderate between- group effects at post-test were found on self-reported global eating disorder symptoms (g=0.36, 95%CI: 0.25-0.47, n=20), dieting (g=0.47, 95%CI: 0.30- 0.64, n=18), body dissatisfaction (g=0.50, 95%CI: 0.33-0.67, n=14), drive for thinness (g=0.43, 95%CI: 0.27-0.59, n=12), weight concerns (g=0.33, 95%CI: 0.10-0.57, n=13) and affective symptoms (g=0.28, 95%CI: 0.16-0.40, n=14). Effects on bulimia were not significant. Heterogeneity was low to moderate across comparisons.Discussion. Eating disorder prevention on campus can have significant, small-to-moderate effects on eating disorder symptoms and risk factors. Results also suggest that the prevention of subthreshold and full-syndrome eating disorders is feasible using such interventions. More research is needed to identify effects on academic functioning, as well as ways to motivate students to use preventive eating disorder interventions.
With the advent of the World Wide Web, the smartphone and the Internet of Things, not only society but also the sciences are rapidly changing. In particular, the social sciences can profit from these digital developments, because now scientists have the power to study real-life human behavior via smartphones and other devices connected to the Internet of Things on a large-scale level. Although this sounds easy, scientists often face the problem that no practicable solution exists to participate in such a new scientific movement, due to a lack of an interdisciplinary network. If so, the development time of a new product, such as a smartphone application to get insights into human behavior takes an enormous amount of time and resources. Given this problem, the present work presents an easy way to use a smartphone application, which can be applied by social scientists to study a large range of scientific questions. The application provides measurements of variables via tracking smartphone–use patterns, such as call behavior, application use (e.g., social media), GPS and many others. In addition, the presented Android-based smartphone application, called Insights, can also be used to administer self-report questionnaires for conducting experience sampling and to search for co-variations between smartphone usage/smartphone data and self-report data. Of importance, the present work gives a detailed overview on how to conduct a study using an application such as Insights, starting from designing the study, installing the application to analyzing the data. In the present work, server requirements and privacy issues are also discussed. Furthermore, first validation data from personality psychology are presented. Such validation data are important in establishing trust in the applied technology to track behavior. In sum, the aim of the present work is (i) to provide interested scientists a short overview on how to conduct a study with smartphone app tracking technology, (ii) to present the features of the designed smartphone application and (iii) to demonstrate its validity with a proof of concept study, hence correlating smartphone usage with personality measures.
The INTERSPEECH 2020 Computational Paralinguistics Challenge addresses three different problems for the first time in a research competition under well-defined conditions: In the Elderly Emotion Sub-Challenge, arousal and valence in the speech of elderly individuals have to be modelled as a 3-class problem; in the Breathing Sub-Challenge, breathing has to be assessed as a regression problem; and in the Mask Sub-Challenge, speech without and with a surgical mask has to be told apart. We describe the Sub-Challenges, baseline feature extraction, and classifiers based on the 'usual' COMPARE and BoAW features as well as deep unsupervised representation learning using the AUDEEP toolkit, and deep feature extraction from pre-trained CNNs using the DEEP SPECTRUM toolkit; in addition, we partially add deep end-to-end sequential modelling, and, for the first time in the challenge, linguistic analysis.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.