Background: Multiple sclerosis often leads to fatigue and changes in physical behavior (PB). Changes in PB are often assumed as a consequence of fatigue, but effects of interventions that aim to reduce fatigue by improving PB are not sufficient. Since the heterogeneous nature of MS related symptoms, levels of PB of fatigued patients at the start of interventions might vary substantially. Better understanding of the variability by identification of PB subtypes in fatigued patients may help to develop more effective personalized rehabilitation programs in the future. This study aimed to identify PB subtypes in fatigued patients with multiple sclerosis based on multidimensional PB outcome measures. Methods: Baseline accelerometer (Actigraph) data, demographics and clinical characteristics of the TREFAMS-ACE participants (n = 212) were used for secondary analysis. All patients were ambulatory and diagnosed with severe fatigue based on a score of ≥35 on the fatigue subscale of the Checklist Individual Strength (CIS20r). Fifteen PB measures were used derived from 7 day measurements with an accelerometer. Principal component analysis was performed to define key outcome measures for PB and two-step cluster analysis was used to identify PB types. Results: Analysis revealed five key outcome measures: percentage sedentary behavior, total time in prolonged moderate-to-vigorous physical activity, number of sedentary bouts, and two types of change scores between day parts (morning, afternoon and evening). Based on these outcomes three valid PB clusters were derived. Conclusions: Patients with severe MS-related fatigue show three distinct and homogeneous PB subtypes. These PB subtypes, based on a unique set of PB outcome measures, may offer an opportunity to design more individually-tailored interventions in rehabilitation.
MULTIFILE
Using a multi-wave, multi-level design, this study unravels the impact of subjective (dis)similarities in teams on team effectiveness. Based on optimal distinctiveness theory and the social inclusion model, we assume combined effects of individual and shared perceptions of supplementary and complementary person–team fit on affective and performance-based outcomes. Furthermore, at the team level, we expect this relationship to be mediated by team cohesion. In a sample of 121 participants (across 30 teams), we found that teams in which members share perceptions of high supplementary as well as high complementary fit outperform those in which they do not. In addition, members of such teams report higher levels of team satisfaction and viability. Both of these occur through positive effects on the cohesion within the team. Thereby, our results support the central tenet of the social inclusion model. At the individual level, this enhancing effect of the interaction was not supported, providing additional evidence for considering perceived person–team fit as a collective construct.
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From the article: Globalization and technological innovation has led to an increasing competition between telecommunication service providers and has eroded traditional product- and service-based differentiation. One way to gain a competitive advantage is to create distinctiveness by improving customer experience in such a manner that it leads to higher customer satisfaction and loyalty. One of the drivers to improve the customer experience is the service interface. To improve this service interface, organizations must get insight into their customer interaction process. The amount of data about customers and the service provider processes is increasing and becoming more readily available for analysis. Process mining is a technique to provide insight into these processes. In this paper, a framework is presented to improve the customer satisfaction by alignment of the business service delivery process and the customer experience by applying process mining.
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