A process was proposed through which individuals regulate their motivation to perform necessary but uninteresting activities over time. If committed to continuing, individuals may engage in interest-enhancing strategies that can change the activity into something more positive to perform. In Study 1 Ss performed novel tasks and generated strategies to make regular performance interesting. In Study 2 Ss actually used these strategies primarily in conditions indicating a self-regulatory attempt: The task was currently boring, there was a perceived reason to continue (alleged health benefit), and a relevant strategy was available. Strategy use was associated with a change in activity definition and greater likelihood of subsequently performing the activity. In Study 3 Ss beliefs about how to maintain motivation to perform more everyday activities emphasized the importance of regulating interest relative to other self-regulatory strategies.

The participants (107 preadolescents, 124 college students, 118 middle-aged adults, and 131 older adults) described 2 everyday problems (1 unconstrained, the other constrained to 1 of 6 domains) that they experienced and their goals and strategies. Problem definitions reflected interpersonal or competence components or both; strategies reflected altering cognitions, actions, or regulating and including others. Age differences in problem definitions were found. For unconstrained-domain problems, age and problem definition were related to strategies; for unconstrained-domain problems age differences in strategies were not found. For constrained-domain problems, strategies related to problem domain and problem definition, with problem definition the better predictor of strategies. The results illustrate the value of individuals' problem definitions for addressing age and context effects on strategies used.

Objective The amount of information for clinicians and clinical researchers is growing exponentially. Text summarization reduces information as an attempt to enable users to find and understand relevant source texts more quickly and effortlessly. In recent years, substantial research has been conducted to develop and evaluate various summarization techniques in the biomedical domain. The goal of this study was to systematically review recent published research on summarization of textual documents in the biomedical domain. Materials and methods MEDLINE (2000 to October 2013), IEEE Digital Library, and the ACM Digital library were searched. Investigators independently screened and abstracted studies that examined text summarization techniques in the biomedical domain. Information is derived from selected articles on five dimensions: input, purpose, output, method and evaluation. Results Of 10,786 studies retrieved, 34 (0.3%) met the inclusion criteria. Natural Language processing (17; 50%) and a Hybrid technique comprising of statistical, Natural language processing and machine learning (15; 44%) were the most common summarization approaches. Most studies (28; 82%) conducted an intrinsic evaluation. Discussion This is the first systematic review of text summarization in the biomedical domain. The study identified research gaps and provides recommendations for guiding future research on biomedical text summarization. conclusion Recent research has focused on a Hybrid technique comprising statistical, language processing and machine learning techniques. Further research is needed on the application and evaluation of text summarization in real research or patient care settings.

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