We investigate the feasibility of applying standard text categorisation methods to patient text in order to predict treatment outcome in Internet-based cognitive behavioural therapy. The data set is unique in its detail and size for regular care for depression, social anxiety, and panic disorder. Our results indicate that there is a signal in the depression data, albeit a weak one. We also perform terminological and sentiment analysis, which confirm those results. 

As people age, they tend to spend more time indoors, and the colours in their surroundings may significantly impact their mood and overall well-being. However, there is a lack of empirical evidence to provide informed guidance on colour choices, irrespective of age group. To work towards informed choices, we investigated whether the associations between colours and emotions observed in younger individuals also apply to older adults. We recruited 7393 participants, aged between 16 and 88 years and coming from 31 countries. Each participant associated 12 colour terms with 20 emotion concepts and rated the intensity of each associated emotion. Different age groups exhibited highly similar patterns of colour–emotion associations (average similarity coefficient of.97), with subtle yet meaningful age-related differences. Adolescents associated the greatest number but the least positively biased emotions with colours. Older participants associated a smaller number but more intense and more positive emotions with all colour terms, displaying a positivity effect. Age also predicted arousal and power biases, varying by colour. Findings suggest parallels in colour–emotion associations between younger and older adults, with subtle but significant age-related variations. Future studies should next assess whether colour–emotion associations reflect what people actually feel when exposed to colour. 

Distracted driving is known to be one of the leading causes of vehicle accidents. With the increase in the number of sensors available within vehicles, there exists an abundance of data for monitoring driver behaviour, which, however, have so far only been comparable across vehicle manufacturers to a limited extent due to proprietary solutions. A special role in distraction is played by the smartphone, which is repeatedly a source of distraction for drivers through calls and messages. However, the smartphone can be used for driver behaviour monitoring (like driver distraction detection) too, as current developments show. As vehicle manufacturer-independent device, which is usually equipped with adequate sensor technology, smartphones can provide significant advantages, however, an overview of such approaches is missing so far. Thus, this work carries out an author-centric literature review of 16 research papers to illustrate the opportunities in using smartphones to detect driver distraction.