Smart Pain Assessment tool for critically ill patients unable to communicate: Early stage development of a medical device

Critically ill patients often experience pain during their treatment but due to patients’ lowered ability to communicate, pain assessment may be challenging. The aim of the study was to develop the concept of the Smart Pain Assessment tool based on the Internet of Things technology for critically ill patients who are unable to communicate their pain. The study describes two phases of the early stage development of the Smart Pain Assessment tool in a medical device development framework. The initiation Phase I consists of a scoping review, conducted to explore the potentiality of the Internet of Things technology in basic nursing care. In the formulation Phase II, the prototype of the Smart Pain Assessment tool was tested and the concept was evaluated for feasibility. The prototype was tested with healthy participants (n=31) during experimental pain, measuring pain-related physiological variables and activity of five facial muscles. The variables were combined using machine learning to create a model for pain prediction. The feasibility of the concept was evaluated in focus group interviews with critical care nurses (n=20) as potential users of the device. The literature review suggests that the development of Internet of Things -based innovations in basic nursing care is diverse but still in its early stages. The prototype was able to identify experimental pain and classify its intensity as mild or moderate/severe with 83% accuracy. In addition, three of the five facial muscles tested were recognised to provide the most pain-related information. According to critical care nurses, the Smart Pain Assessment tool could be used to ensure pain assessment, but it needs to be integrated into an existing patient monitoring and information system, and the reliability of the data provided by the device needs to be assessable for nurses. Based on the results of this study, detecting and classifying experimental pain's intensity automatically using an Internet of Things -based device is possible. The prototype of the device should be further developed and tested in clinical trials, involving the users at each stage of the development to ensure clinical relevance and a user-centric design.Älykäs kipumittari kommunikoimaan kykenemättömille kriittisesti sairaille potilaille: Lääkinnällisen laitteen varhainen kehittäminen Kriittisesti sairaat potilaat kokevat usein kipua hoidon aikana, mutta potilaiden kivun arviointi on haastavaa tilanteissa, joissa potilaan kyky kommunikoida on alentunut. Tutkimuksen tavoitteena oli kehittää toimintakonsepti esineiden internet -teknologiaan perustuvalle Älykkäälle kipumittarille, joka on suunniteltu kriittisesti sairaille potilaille, jotka eivät kykene kommunikoimaan kivustaan. Tutkimuksessa kuvataan Älykkään kipumittarin varhaisia kehitysvaiheita lääkinnällisen laitteen kehitysprosessin mukaisesti. Aloitusvaiheessa I toteutettiin kartoittava kirjallisuuskatsaus, jossa selvitettiin esineiden internet -teknologian mahdollisuuksia perushoidossa. Muotoiluvaiheessa II testattiin laitteen prototyyppiä ja arvioitiin laitteen toimintakonseptin toteutettavuutta. Prototyypin testaukseen osallistui terveitä koehenkilöitä (n=31), joille tuotettiin kipua. Kipualtistuksen aikana mitattiin kipuun liittyviä fysiologisia muuttujia ja viiden kasvolihaksen aktivoitumista. Muuttujat yhdistettiin koneoppimismenetelmällä kivun ennustemalliksi. Lisäksi teho-osastolla työskentelevät sairaanhoitajat (n=20) arvioivat fokusryhmähaastatteluissa laitteen toimintakonseptin toteutettavuutta. Kirjallisuuskatsauksen tuloksista käy ilmi, että esineiden internetiin perustuvien innovaatioiden kehittäminen perushoidon tukemiseen on monipuolista mutta se on vielä alkuvaiheessa. Älykkään kipumittarin prototyyppi osoittautui lupaavaksi kokeellisen kivun tunnistamisessa ja sen voimakkuuden luokittelussa, saavuttaen 83 %:n tarkkuuden kivun luokittelussa lievään tai kohtalaiseen/voimakkaaseen. Lisäksi todettiin, että viidestä mitatusta kasvolihaksesta kolme antoi merkittävintä tietoa kivun tunnistamiseen ja voimakkuuteen liittyen. Sairaanhoitajat näkivät potentiaalia Älykkään kipumittarin käytössä potilaiden kivun arvioinnissa teho-osastolla. Laite tulisi kuitenkin integroida käytössä olevaan potilastietojärjestelmään, ja laitteen tuottamien tietojen luotettavuus tulisi olla hoitajien arvioitavissa. Tulosten perusteella esineiden internet -teknologiaan perustuvan laitteen avulla on mahdollista tunnistaa ja luokitella kokeellisen kivun voimakkuutta automaattisesti. Laitteen prototyyppiä tulee jatkokehittää ja testata kliinisissä tutkimuksissa. Tulevat käyttäjät tulee ottaa mukaan jokaiseen kehitysvaiheeseen laitteen kliinisen merkityksen ja käyttäjälähtöisen muotoilun varmistamiseksi

No Longer Without a Reward: Do Digital Rewards Crowd Out Intrinsic Motivation of Young Children?

In the learning journey of young children, rewards are ubiquitous. Yet, psychologists and behavioral economists question the success of rewards and even claim that they displace intrinsic motivation, a phenomenon referred to as motivation crowding out. While information systems can help children learn everyday tasks, it is unclear if and when digital rewards produce motivation crowding out. Theoretically sound, empirical field studies on this topic are lacking and existing information system research on motivation crowding is limited to specific domains, not covering children’s behavior. Therefore, we aim to elicit how digital rewards influence an everyday health behavior that children learn in kindergarten – handwashing – and the underlying intrinsic motivation. We conduct a randomized controlled trial that is conceptualized in this paper. Our results will extend motivation crowding theory in the context of young children and inform the design of digital behavior change interventions

Hand hygiene of kindergarten children-Understanding the effect of live feedback on handwashing behaviour, self-efficacy, and motivation of young children: Protocol for a multi-arm cluster randomized controlled trial.

Early implementation of interventions at a young age fosters behaviour changes and helps to adopt behaviours that promote health. Digital technologies may help to promote the hand hygiene behaviour of children. However, there is a lack of digital feedback interventions focusing on the hand hygiene behaviour of preschool children in childhood education and care settings. This study protocol aims to describe a study that evaluates the effectiveness of a gamified live feedback intervention and explores underlying behavioural theories in achieving better hand hygiene behaviour of preschool children in early childhood education and care settings. This study will be a four-arm cluster randomized controlled trial with three phases and a twelve-month follow-up by country stratification. The sample size is 106 children of which one cluster will have a minimum number of 40 children. During the baseline phase, all groups will have automated monitoring systems installed. In the intervention phase, the control group will have no screen activity. The intervention groups will have feedback displays during the handwashing activity. Intervention A will receive instructions, and intervention B and C groups will receive instructions and a reward. In the post-intervention phase, all the groups will have no screen activity except intervention C which will receive instructions from the screen but no reward. The outcome measures will be hand hygiene behaviour, self-efficacy, and intrinsic motivation. Outcome measures will be collected at baseline, intervention, and post-intervention phases and a 12-month follow-up. The data will be analysed with quantitative and qualitative methods. The findings of the planned study will provide whether this gamified live feedback intervention can be recommended to be used in educational settings to improve the hand hygiene behaviour of preschool children to promote health. The trial is registered with ClinicalTrials.gov (registration number NCT05395988 https://clinicaltrials.gov/ct2/show/NCT05395988?term=NCT05395988&draw=2&rank=1)