ORCID Profile
0000-0001-6153-2068
Current Organisations
Jena University Hospital
,
University of South Australia
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Publisher: Elsevier BV
Date: 2019
Publisher: Informa UK Limited
Date: 28-09-2020
Publisher: MDPI AG
Date: 14-10-2022
DOI: 10.20944/PREPRINTS202209.0084.V2
Abstract: Acute SARS-CoV-2 infections in children and adolescents are usually mild. However, they can suffer from ongoing symptoms generally referred as long COVID. Sleep disorders are one of the most frequent complaints in long COVID although precise data are missing. We assessed the sleep behavior of children and adolescents who presented at our outpatient clinic between January 2021 and May 2022 with the Children's Sleep Habits Questionnaire (CSHQ-DE). We compared sleep behavior at three different time points: pre-COVID-19, post-COVID-19 at initial presentation and post-COVID-19 at re-presentation. Data from 45 patients were analyzed. Of those, 64% were female and the median age was 10 years (range 0-18 years). Asymptomatic or mild COVID-19 disease was experienced in 89% of patients, whilst 11% experienced moderate disease. Initial presentation occurred at a median of 20.4 weeks (6 weeks - 14 months) after infection. The CSHQ-DE score increased significantly from pre-COVID-19 (45.82+8.7 points) to post-COVID-19 (49.40+8.3 points p=& .01). The score then normalized at re-presentation (46.98+7.8 p=0.1). The greatest changes were seen in the CSHQ-DE subscale score "daytime sleepiness". Our data show that children and adolescents with long COVID often suffer from sleep disturbance. For most children and adolescents these sleep disorders decreased over time without further medical intervention, aside from a basic sleep consultation.
Publisher: Elsevier BV
Date: 2010
DOI: 10.2139/SSRN.1717295
Publisher: MDPI AG
Date: 06-09-2022
DOI: 10.20944/PREPRINTS202209.0084.V1
Abstract: Acute SARS-CoV-2 infections in children and adolescents are usually mild. However, they can suffer from ongoing symptoms generally referred as long COVID. Sleep disorders are one of the most frequent complaints in long COVID although precise data are missing. We assessed the sleep behavior of children and adolescents who presented at our outpatient clinic between January 2021 and May 2022 with the Children's Sleep Habits Questionnaire (CSHQ-DE). We compared sleep behavior at three different time points: pre-COVID-19, post-COVID-19 at initial presentation and post-COVID-19 at re-presentation. Data from 45 patients were analyzed. Of those, 64% were female and the median age was 10 years (range 0-18 years). Asymptomatic or mild COVID-19 disease was experienced in 89% of patients, whilst 11% experienced moderate disease. Initial presentation occurred at a median of 20.4 weeks (6 weeks - 14 months) after infection. The CSHQ-DE score increased significantly from pre-COVID-19 (45.82+8.7 points) to post-COVID-19 (49.40+8.3 points p=& .01). The score then normalized at re-presentation (46.98+7.8 p=0.1). The greatest changes were seen in the CSHQ-DE subscale score "daytime sleepiness". Our data show that children and adolescents with long COVID often suffer from sleep disturbance. For most children and adolescents these sleep disorders decreased over time without further medical intervention, aside from a basic sleep consultation.
Publisher: Informa UK Limited
Date: 09-2013
Publisher: IGI Global
Date: 13-05-2022
DOI: 10.4018/978-1-6684-6291-1.CH043
Abstract: In this chapter, machine learning techniques are applied to examine consumer food choices, specifically purchasing patterns in relation to fresh fruit and vegetables. This product category contributes some of the highest profit margins for supermarkets, making understanding consumer choices in that category important not just for health but also economic reasons. Several unsupervised and supervised machine learning techniques, including hierarchical clustering, latent class analysis, linear regression, artificial neural networks, and deep learning neural networks, are illustrated using Nielsen Consumer Panel Dataset, a large and high-quality source of information on consumer purchases in the United States. The main finding from the clustering analysis is that households who buy less fresh produce are those with children – an important insight with significant public health implications. The main outcome from predictive modelling of spending on fresh fruit and vegetables is that contrary to expectations, neural networks failed to outperform a linear regression model.
Publisher: Wiley
Date: 08-2017
DOI: 10.1111/IMJ.13485
Abstract: Increasing demand for hospital services has resulted in more arrivals to emergency department (ED), increased admissions, and, quite often, access block and ED congestion, along with patients' dissatisfaction. Cost constraints limit an increase in the number of hospital beds, so alternative solutions need to be explored. To propose and test different discharge strategies, which, potentially, could reduce occupancy rates in the hospital, thereby improving patient flow and minimising frequency and duration of congestion episodes. We used a simulation approach using HESMAD (Hospital Event Simulation Model: Arrivals to Discharge) - a sophisticated simulation model capturing patient flow through a large Australian hospital from arrival at ED to discharge. A set of simulation experiments with a range of proposed discharge strategies was carried out. The results were tabulated, analysed and compared using common hospital occupancy indicators. Simulation results demonstrated that it is possible to reduce significantly the number of days when a hospital runs above its base bed capacity. In our case study, this reduction was from 281.5 to 22.8 days in the best scenario, and reductions within the above range under other scenarios considered. Some relatively simple strategies, such as 24-h discharge or discharge/relocation of long-staying patients, can significantly reduce overcrowding and improve hospital occupancy rates. Shortening administrative and/or some treatment processes have a smaller effect, although the latter could be easier to implement.
Publisher: SAGE Publications
Date: 19-08-2020
Abstract: When purchasing packaged products within a supermarket, consumers choose between proprietary or private label brands. However, when purchasing fresh fruits and vegetables, non-branded produce is the dominant option—with proprietary and private label brands only recently becoming available. Previous fast-moving consumer goods (FMCG) research finds that proprietary and private label brands affect consumer loyalty—however, no research exists for fresh categories. This research is the first to determine the effect of emerging brands in fresh categories on consumer buying behavior. Our research examines consumers’ loyalty toward proprietary, private label, or non-branded fresh fruits and vegetables and the level of customer sharing between these options, using analytical approaches applicable to FMCG categories. The panel data contains nearly 46,000 households making over 8 million purchases in the United States during 2015. Results show that proprietary, private label, and now non-branded fresh produce have expected loyalty levels, for their size, and consumers share their purchases across the three options (i.e., consumers are not loyal to just one option). The study analyzes and interprets purchase data in fresh categories offering marketing academics and practitioners actionable advice for working with fresh produce purchase data.
Publisher: Emerald
Date: 25-01-2021
Abstract: This research describes and evaluates the co-creation of a programme called “A Healthy Choice”. Underpinned by design thinking (DT), this study aims to improve the healthfulness of food choices in supermarkets among consumers to promote their well-being. The research features two studies. Study 1 included five co-design workshops with consumers and staff ( n = 32) to develop a consumer-centred programme. The findings supported the design and implementation of a programme evaluated in Study 2 (an ecological trial). The programme modified a supermarket environment to increase the prominence of healthier products (shelf-talkers and no discount), ran positive food experiences (cooking and label reading workshops) and was supported by a community-wide information c aign in social and local print media. A total of 15 new strategies were developed by consumers and staff to support health and well-being in supermarkets. Feasibility discussions and staff voting contributed to the development and storewide implementation of the programme. Evaluation showed that the programme was effective in increasing consumer knowledge of healthier food choices (measured via public survey). Sales analysis showed mixed results sales increased for promoted products in some categories, but there was no effect in others. Given the real-world setting in which this programme and its evaluation were conducted, there were several innate limitations. The co-design process generated many more ideas than could be implemented, thus creating a healthy “pipe line” for the next iterations of the programme. The key contribution of this work to supermarket intervention literature is the recommendation to change the paradigm of engagement between the key stakeholders who are typically involved in supermarket programs. Using the co-design and DT frameworks, the authors offer an ex le of stakeholders working together in close partnership to co-design and collaboratively implement a programme that promotes healthier choices. This project contributes to the emerging body of empirical work using DT principles in the area of healthy food choices in supermarkets. A rigorously designed evaluation of a co-designed supermarket programme contributes to scholarly evidence on food well-being programs in supermarkets.
Publisher: Elsevier BV
Date: 03-2020
Publisher: Springer Science and Business Media LLC
Date: 04-2013
DOI: 10.1057/JAM.2013.7
Publisher: IGI Global
Date: 2020
DOI: 10.4018/978-1-7998-0106-1.CH018
Abstract: In this chapter, machine learning techniques are applied to examine consumer food choices, specifically purchasing patterns in relation to fresh fruit and vegetables. This product category contributes some of the highest profit margins for supermarkets, making understanding consumer choices in that category important not just for health but also economic reasons. Several unsupervised and supervised machine learning techniques, including hierarchical clustering, latent class analysis, linear regression, artificial neural networks, and deep learning neural networks, are illustrated using Nielsen Consumer Panel Dataset, a large and high-quality source of information on consumer purchases in the United States. The main finding from the clustering analysis is that households who buy less fresh produce are those with children – an important insight with significant public health implications. The main outcome from predictive modelling of spending on fresh fruit and vegetables is that contrary to expectations, neural networks failed to outperform a linear regression model.
Publisher: Wiley
Date: 09-1986
Publisher: SAGE Publications
Date: 05-2016
DOI: 10.1016/J.AUSMJ.2016.01.002
Abstract: Time is one of the resources shoppers bring to a store (along with money). Enabling shoppers to complete their grocery shopping more efficiently, that is to spend less time to buy the desired number of items, could result in higher shopper satisfaction and continued patronage. This research proposes a novel way of measuring shopper efficiency by distinguishing the “fixed” vs “per item” times for a grocery trip. We then analyse the differences in shopping efficiency across different sub-groups offering insights into shopper efficiency heterogeneity and benchmarks. We collected data from 1176 shoppers across three Australian supermarkets in 2014 using systematic s ling for entry/exit interviews and objectively recorded time using supermarket receipts and entry time st s. We used linear regression to model the “fixed” and “per item” times, while ANCOVA analysis provided statistical confirmation of observed differences across the sub-groups. The results revealed females were more efficient than males on a “per item” basis, while males had shorter “fixed” times associated with entry, navigation and checking out. Older shoppers were less efficient than younger shoppers. Unemployed respondents tended to spend more time in-store and were less efficient than employed shoppers. There was also a difference between part- and full-time employees. Shopping efficiency in peak and off peak periods was not significantly different. Contrary to the assumption in popular media that weekend shopping is more time consuming and hence inefficient, we found that weekend shopping is no less efficient than weekday trips. Our approach assumes that shopper efficiency stays constant across the trip. The data did not allow testing of interactions between factors. Future research should also consider other attributes such as shopping list use, presence of others, including children, and familiarity with the store. We present a novel approach in measuring shopper efficiency that splits the time in-store across “fixed” and “per item” times, associated with different shopper tasks (navigating and checking out vs choosing and buying). This split allows for a deeper understanding of where and how retailers can make shopping more efficient for their consumers, thus improving the overall in-store experience and outcomes. The identified differences in efficiencies across sub-groups have important implications for benchmarking and comparison of the performance of different stores, as these will be influenced not only by different times of the day and days of the week, but also by differences in the make-up of the customer base.
No related grants have been discovered for Michael Lorenz.