Validation of the Munich Actimetry Sleep Detection Algorithm for estimating sleep-wake patterns from activity recordings

© 2021 The Authors. Journal of Sleep Research published by John Wiley & Sons Ltd on behalf of European Sleep Research Society. This is an open access article under the terms of the Creative Commons Attribution‐NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.Periods of sleep and wakefulness can be estimated from wrist-locomotor activity recordings via algorithms that identify periods of relative activity and inactivity. Here, we evaluated the performance of our Munich Actimetry Sleep Detection Algorithm. The Munich Actimetry Sleep Detection Algorithm uses a moving 24-h threshold and correlation procedure estimating relatively consolidated periods of sleep and wake. The Munich Actimetry Sleep Detection Algorithm was validated against sleep logs and polysomnography. Sleep-log validation was performed on two field samples collected over 54 and 34 days (median) in 34 adolescents and 28 young adults. Polysomnographic validation was performed on a clinical sample of 23 individuals undergoing one night of polysomnography. Epoch-by-epoch analyses were conducted and comparisons of sleep measures carried out via Bland-Altman plots and correlations. Compared with sleep logs, the Munich Actimetry Sleep Detection Algorithm classified sleep with a median sensitivity of 80% (interquartile range [IQR] = 75%-86%) and specificity of 91% (87%-92%). Mean onset and offset times were highly correlated (r = .86-.91). Compared with polysomnography, the Munich Actimetry Sleep Detection Algorithm reached a median sensitivity of 92% (85%-100%) but low specificity of 33% (10%-98%), owing to the low frequency of wake episodes in the night-time polysomnographic recordings. The Munich Actimetry Sleep Detection Algorithm overestimated sleep onset (~21 min) and underestimated wake after sleep onset (~26 min), while not performing systematically differently from polysomnography in other sleep parameters. These results demonstrate the validity of the Munich Actimetry Sleep Detection Algorithm in faithfully estimating sleep-wake patterns in field studies. With its good performance across daytime and night-time, it enables analyses of sleep-wake patterns in long recordings performed to assess circadian and sleep regularity and is therefore an excellent objective alternative to sleep logs in field settings.ASL received a stipend from the Max‐Weber‐Programm (Studienstiftung), AMB received funding from the Graduate School of Systemic Neurosciences Munich, CR received funding from the Fundação para a Ciência e Tecnologia (FCT) PhD research grants (PDE/BDE/114584/2016), LKP received a fellowship from the Coordenação de Aperfeiçoamento Pessoal de Nível Superior (CAPES, Finance Code 001), and NG received research funding from the FoeFoLe program at LMU (registration No. 37/2013).info:eu-repo/semantics/publishedVersio

Talking about talk: tutor and student expectations of oracy skills in higher education

Although participation in academic speaking events is a key to developing disciplinary understanding, students for whom English is a second language may have limited access to these learning events due to an increasingly dialogic and active higher education pedagogy which places considerable demands on their oracy skills. Drawing on the Oracy Skills Framework we explore disciplinary tutors' and students' expectations of oracy skills required for disciplinary study. An analysis of both quantitative and qualitative data found that disciplinary tutors placed importance on the cognitive dimension of oracy skills such as argumentation and asking questions, whilst students placed importance on linguistic accuracy. The findings also suggest that tutors and students lack a shared metalanguage to talk about oracy skills. We argue that a divergence of expectations and lack of shared terminology can result in compromising students' access to valuable classroom dialogue. The paper concludes with a number of practical suggestions through which both tutors and students can increase their understanding of oracy skills

Talking about talk: tutor and student expectations of oracy skills in higher education

Although participation in academic speaking events is a key to developing disciplinary understanding, students for whom English is a second language may have limited access to these learning events due to an increasingly dialogic and active higher education pedagogy which places considerable demands on their oracy skills. Drawing on the Oracy Skills Framework we explore disciplinary tutors’ and students’ expectations of oracy skills required for disciplinary study. An analysis of both quantitative and qualitative data found that disciplinary tutors placed importance on the cognitive dimension of oracy skills such as argumentation and asking questions, whilst students placed importance on linguistic accuracy. The findings also suggest that tutors and students lack a shared metalanguage to talk about oracy skills. We argue that a divergence of expectations and lack of shared terminology can result in compromising students’ access to valuable classroom dialogue. The paper concludes with a number of practical suggestions through which both tutors and students can increase their understanding of oracy skills