Can written disclosure reduce psychological distress and increase objectively measured injury mobility of student-athletes? A randomized controlled trial

Injured students-athletes took part in a randomized controlled trial to test whether written disclosure could reduce psychological distress and improve injury mobility. Writing took place alongside prescribed physical rehabilitation and consisted of three 20- minute writing sessions, once a week for three consecutive weeks. Participants in the experimental injury-writing group (푛 = 25) followed a structured form of written disclosure, called the guided disclosure protocol (GDP). They firstly, wrote about the onset of their injury in a chronological manner, secondly, they explicitly labelled their emotions and described the impact of the injury, finally they wrote about future coping and psychological growth. Controls(푛 = 21)wrote about nonemotional and noninjury related topics. In addition to self-report measures, a physiotherapist, blind to experimental condition, assessed mobility at the injury site. Although self-report indices remained unchanged, the GDP group evidenced a significant improvement in injury mobility compared to controls

Big data and smart cities: a public sector organizational learning perspective

Public sector organizations (city authorities) have begun to explore ways to exploit big data to provide smarter solutions for cities. The way organizations learn to use new forms of technology has been widely researched. However, many public sector organisations have found themselves in new territory in trying to deploy and integrate this new form of technology (big data) to another fast moving and relatively new concept (smart city). This paper is a cross-sectional scoping study—from two UK smart city initiatives—on the learning processes experienced by elite (top management) stakeholders in the advent and adoption of these two novel concepts. The findings are an experiential narrative account on learning to exploit big data to address issues by developing solutions through smart city initiatives. The findings revealed a set of moves in relation to the exploration and exploitation of big data through smart city initiatives: (a) knowledge finding; (b) knowledge reframing; (c) inter-organization collaborations and (d) ex-post evaluations. Even though this is a time-sensitive scoping study it gives an account on a current state-of-play on the use of big data in public sector organizations for creating smarter cities. This study has implications for practitioners in the smart city domain and contributes to academia by operationalizing and adapting Crossan et al’s (Acad Manag Rev 24(3): 522–537, 1999) 4I model on organizational learning

Pointwise characteristic factors for Wiener Wintner double recurrence theorem

In this paper, we extend Bourgain's double recurrence result to the Wiener-Wintner averages. Let $(X, \mathcal{F}, \mu, T)$ be a standard ergodic system. We will show that for any $f_1, f_2 \in L^\infty(X)$, the double recurrence Wiener-Wintner average $$\frac{1}{N} \sum_{n=1}^N f_1(T^{an}x)f_2(T^{bn}x) e^{2\pi i n t}$$ converges off a single null set of $X$ independent of $t$ as $N \to \infty$. Furthermore, we will show a uniform Wiener-Wintner double recurrence result: If either $f_1$ or $f_2$ belongs to the orthogonal complement of the Conze-Lesigne factor, then there exists a set of full measure such that the supremum on $t$ of the absolute value of the averages above converges to $0$.Comment: revised version includes referee suggestion