Due to a lack of transparency in both algorithm and validation methodology, it is diffcult for researchers and clinicians to select the appropriate tracker for their application. The aim of this work is to transparently present an adjustable physical activity classification algorithm that discriminates between dynamic, standing, and sedentary behavior. By means of easily adjustable parameters, the algorithm performance can be optimized for applications using different target populations and locations for tracker wear. Concerning an elderly target population with a tracker worn on the upper leg, the algorithm is optimized and validated under simulated free-living conditions. The fixed activity protocol (FAP) is performed by 20 participants; the simulated free-living protocol (SFP) involves another 20. Data segmentation window size and amount of physical activity threshold are optimized. The sensor orientation threshold does not vary. The validation of the algorithm is performed on 10 participants who perform the FAP and on 10 participants who perform the SFP. Percentage error (PE) and absolute percentage error (APE) are used to assess the algorithm performance. Standing and sedentary behavior are classified within acceptable limits (+/- 10% error) both under fixed and simulated free-living conditions. Dynamic behavior is within acceptable limits under fixed conditions but has some limitations under simulated free-living conditions. We propose that this approach should be adopted by developers of activity trackers to facilitate the activity tracker selection process for researchers and clinicians. Furthermore, we are convinced that the adjustable algorithm potentially could contribute to the fast realization of new applications.
If you, as a municipality or waste collector, you want to learn more abouthow residents view to the separation of organic waste and at the same timeencourage them to separate their organic waste (better), then the infosurveymay be an interesting solution. This is a survey and behavioural interventionin one. The infosurvey can be used in a neighborhood where organic wastecan be separated.
Many citizens experience ambivalence – having simultaneously positive and negative evaluations – about changing their behaviour towards a more environmentally friendly lifestyle. Based on 36 studies, this study identifies and synthesises the current evidence on how ambivalence impacts environmental behaviours. In most studies, ambivalence is shown to be directly and negatively associated with environmental behaviours, i.e., higher levels of ambivalence are linked to lower levels of environmentally friendly and unfriendly behaviours. This applies to both types of ambivalence: objective (OA) and subjective (SA). Mediator analyses show, in line with the theory, that SA, not OA, drives behavioural change. In addition, results indicate that ambivalence moderates the relationship between independent–dependent variables mainly negatively, for example, by weakening attitude–behaviour relationships. This review shows the potential of ambivalence to facilitate behaviour change: SA about environmentally friendly behaviour can hinder, whereas SA about environmentally unfriendly behaviour can motivate, behaviour change. In addition, this review highlights some significant knowledge gaps in this body of research. A lack of validated standardised measurements of ambivalence makes it challenging to compare studies and reach conclusions about underlying theoretical constructs. Methods, research designs, and theoretical underpinnings need improvement to fully understand ambivalence and progress towards the transition of environmentally friendly behaviours.
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