Engage! a pilot study of a brief behavioural activation program to promote engagement and well-being in older adults.

Previous research has indicated the suitability of behavioural activation (BA) as an intervention for reducing depression in older adults. However, little research has investigated the potential of BA to increase active engagement and well-being in older adults. The current pilot study sought to investigate the usefulness and acceptability of BA to promote well-being in a group of non-clinical older adults. Participants (N = 18) aged between 65 and 86 (M = 77.82, SD = 5.59) who were retired and living independently in the community were provided a 6-week BA program predominantly delivered online. Treatment retention, self-ratings, and participants' compliance to treatment principles indicate preliminary feasibility for the use of BA as an approach for increasing active engagement in older adult populations. Participants also provided feedback on their experiences with the program post-intervention via individual structured interviews. Thematic analysis of these data revealed that participants found the program to be beneficial in terms of increased self-awareness and social engagement, and provided several recommendations for improving acceptability of the program and workbook. The unexpected events relating to the first wave of the novel coronavirus (COVID-19) led to necessary adaptations to delivery modalities, and provided the researchers with an opportunity to investigate the use of a structured well-being program on a high-risk population during a pandemic. Our findings support the proposition that BA is a suitable intervention for increasing engagement and well-being in older adults, provide insight into adapting programs for older adults, and suggest next steps for testing intervention efficacy.

Young drivers' perceptions of risk and difficulty: Day versus night.

Inexperienced drivers have a higher crash risk than others, particularly at night when drivers of all ages are at increased risk, but there has been little if any research on day-night differences in drivers' perceptions of risk and difficulty. Also, previous research on determinants of risk and difficulty ratings has focused largely on effects of vehicle speed, and researchers have identified a need to look more broadly at how ratings are affected by the complexity of depicted road-traffic situations. This study addressed those issues, using an exploratory approach entailing both quantitative and qualitative analyses. The 62 participants (28 females, 34 males) were grouped according to level of driving experience and age (16-30 years). Determinants of ratings were investigated using 6 day-night pairs of video clips at 6 different locations, plus 2 night-only clips at 2 other locations (14 clips in all). Depicted driving situations varied naturalistically in complexity as well as speed. They were presented in random orders twice to each participant, who used a slider to rate each clip continuously, once for risk and once for difficulty in balanced order. Consistent with previous evidence, there were no significant differences in ratings between participant groups. Ratings of risk and difficulty were highly correlated, as expected, but Rasch analysis confirmed that they reflected empirically different constructs. Ratings were significantly higher for night than day. They also differed significantly between locations, with vehicle speed not being their main determinant. Further, there were significant differences between locations in the relationship between risk and difficulty ratings, suggesting that participants' interpretations of 'risk' and 'difficulty' varied between locations. Qualitative analysis of rating patterns in relation to video clip content utilised constructs drawn from information theory and research on human information processing. It was concluded that more systematic research is required to investigate effects on ratings of variables determining the amount of information drivers must process, such as numbers and types of other road users and the predictability of their actions, road types and configurations, visibility conditions, particularly day versus night - as well as effects of vehicle speed which determines the required rate of processing.