The current set of research methods on ictresearchmethods.nl contains only one research method that refers to machine learning: the “Data analytics” method in the “Lab” strategy. This does not reflect the way of working in ML projects, where Data Analytics is not a method to answer one question but the main goal of the project. For ML projects, the Data Analytics method should be divided in several smaller steps, each becoming a method of its own. In other words, we should treat the Data Analytics (or more appropriate ML engineering) process in the same way the software engineering process is treated in the framework. In the remainder of this post I will briefly discuss each of the existing research methods and how they apply to ML projects. The methods are organized by strategy. In the discussion I will give pointers to relevant tools or literature for ML projects.
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Background: Although physical activity (PA) has positive effects on health and well-being, physical inactivity is a worldwide problem. Mobile health interventions have been shown to be effective in promoting PA. Personalizing persuasive strategies improves intervention success and can be conducted using machine learning (ML). For PA, several studies have addressed personalized persuasive strategies without ML, whereas others have included personalization using ML without focusing on persuasive strategies. An overview of studies discussing ML to personalize persuasive strategies in PA-promoting interventions and corresponding categorizations could be helpful for such interventions to be designed in the future but is still missing. Objective: First, we aimed to provide an overview of implemented ML techniques to personalize persuasive strategies in mobile health interventions promoting PA. Moreover, we aimed to present a categorization overview as a starting point for applying ML techniques in this field. Methods: A scoping review was conducted based on the framework by Arksey and O’Malley and the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) criteria. Scopus, Web of Science, and PubMed were searched for studies that included ML to personalize persuasive strategies in interventions promoting PA. Papers were screened using the ASReview software. From the included papers, categorized by the research project they belonged to, we extracted data regarding general study information, target group, PA intervention, implemented technology, and study details. On the basis of the analysis of these data, a categorization overview was given. Results: In total, 40 papers belonging to 27 different projects were included. These papers could be categorized in 4 groups based on their dimension of personalization. Then, for each dimension, 1 or 2 persuasive strategy categories were found together with a type of ML. The overview resulted in a categorization consisting of 3 levels: dimension of personalization, persuasive strategy, and type of ML. When personalizing the timing of the messages, most projects implemented reinforcement learning to personalize the timing of reminders and supervised learning (SL) to personalize the timing of feedback, monitoring, and goal-setting messages. Regarding the content of the messages, most projects implemented SL to personalize PA suggestions and feedback or educational messages. For personalizing PA suggestions, SL can be implemented either alone or combined with a recommender system. Finally, reinforcement learning was mostly used to personalize the type of feedback messages. Conclusions: The overview of all implemented persuasive strategies and their corresponding ML methods is insightful for this interdisciplinary field. Moreover, it led to a categorization overview that provides insights into the design and development of personalized persuasive strategies to promote PA. In future papers, the categorization overview might be expanded with additional layers to specify ML methods or additional dimensions of personalization and persuasive strategies.
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Background: To avoid overexertion in critically ill patients, information on the physical demand, i.e., metabolic load, of daily care and active exercises is warranted. Objective: The objective of this study was toassess the metabolic load during morning care activities and active bed exercises in mechanically ventilated critically ill patients. Methods: This study incorporated an explorative observational study executed in a university hospital intensive care unit. Oxygen consumption (VO2) was measured in mechanically ventilated (≥48 h) critically ill patients during rest, routine morning care, and active bed exercises. We aimed to describe and compare VO2 in terms of absolute VO2 (mL) defined as the VO2 attributable to the activity and relative VO2 in mL per kilogram bodyweight, per minute (mL/kg/min). Additional outcomes achieved during the activity were perceived exertion, respiratory variables, and the highest VO2 values. Changes in VO2 and activity duration were tested using paired tests. Results: Twenty-one patients were included with a mean (standard deviation) age of 59 y (12). Median (interquartile range [IQR]) durations of morning care and active bed exercises were 26 min (21–29) and 7 min (5–12), respectively. Absolute VO2 of morning care was significantly higher than that of active bed exercises (p = 0,009). Median (IQR) relative VO2 was 2.9 (2.6–3.8) mL/kg/min during rest; 3.1 (2.8–3.7) mL/kg/min during morning care; and 3.2 (2.7–4) mL/kg/min during active bed exercises. The highest VO2 value was 4.9 (4.2–5.7) mL/kg/min during morning care and 3.7 (3.2–5.3) mL/kg/min during active bed exercises. Median (IQR) perceived exertion on the 6–20 Borg scale was 12 (10.3–14.5) during morning care (n = 8) and 13.5 (11–15) during active bed exercises (n = 6). Conclusion: Absolute VO2 in mechanically ventilated patients may be higher during morning care than during active bed exercises due to the longer duration of the activity. Intensive care unit clinicians should be aware that daily-care activities may cause intervals of high metabolic load and high ratings of perceived exertion.
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Veel ouderen ervaren tijdens en na ziekenhuisopname functieverlies. ‘Function Focused Care in Hospital’, ook wel bekend als bewegingsgerichte zorg, is een interventie gericht op het voorkomen en verminderen van functieverlies bij ouderen tijdens een ziekenhuisopname. Verpleegkundigen moedigen patiënten aan tot actieve betrokkenheid in de dagelijkse zorgmomenten.
In het project werken onderzoekers van het Lectoraat samen met publieke organisaties toe naar een tool waarmee onderstromen in het publieke debat rondom issues eerder kunnen worden opgemerkt. We exploreren met welk algoritme we patronen in geruchtvorming en mobilisatie kunnen opsporen, en tevens hoe we de interactie tussen newsroom-analisten en de output van een monitoring tool het beste kunnen vormgeven.
Heb je wel eens gemerkt dat de premie voor je autoverzekering verandert als je in een andere wijk gaat wonen? Verzekeraars berekenen dit met een algoritme, wat kan leiden tot indirecte discriminatie. Dit project onderzoekt hoe zulke digitale differentiatie (DD) zowel eerlijk als rendabel kan.
