Most research into the health benefits of human-animal interaction has focused on species that interact physically with humans, such as dogs. This may be unsuitable for certain populations for reasons including accessibility and the risk of negative consequences to both the person and the animal. However, some research has associated viewing fish in aquariums with positive well-being outcomes; as there is no physical contact with the animal, this form of interaction carries less risk. At present, little is known about the specific benefits of human-fish interaction. To explore current evidence relating to the psychological and physiological benefits of interacting with fish in aquariums. Systematic searches were conducted to identify relevant primary research of any design. All forms of interaction were considered, including keeping fish as companion animals and fish aquarium-based interventions. "Non-live" alternatives, such as videos, were also considered. This review was conducted according to a registered protocol (PROSPERO ID: CRD42018090466). Nineteen studies were included. Two provided tentative evidence that keeping home aquaria is associated with relaxation. The remaining studies involved novel interactions with fish in home or public aquariums. Outcomes relating to anxiety, relaxation and/or physiological stress were commonly assessed; evidence was mixed with both positive and null findings. Preliminary support was found for effects on mood, pain, nutritional intake and body weight, but not loneliness. All studies had methodological issues and risk of bias was either high or unclear. Review findings suggest that interacting with fish in aquariums has the potential to benefit human well-being, although research on this topic is currently limited. Future research should aim to address gaps in the evidence, such as whether and how the type of human-fish interaction can influence well-being outcomes. Researchers should also aim to address the methodological concerns highlighted in this review.

To reduce the spread of COVID-19, countries worldwide placed limitations on social interaction, which is anticipated to have severe psychological consequences. Although findings are inconsistent, prior research has suggested that companion animals may positively influence human well-being and reduce loneliness. In the context of COVID-19, this has important implications, as companion animal guardians may be less negatively affected by the pandemic. The primary aim of this research was to investigate the influence of companion animals on mental well-being and loneliness during the pandemic, with specific interest in the role of ornamental fishes. A mixed-methods study was conducted, using an international sample. Quantitative data were collected via an online survey (n = 1199) and analysed using robust hierarchical multiple regression analyses; the influence of level of engagement with companion animals was examined for dogs, cats and ornamental fishes. There was no evidence that companion animal guardianship was associated with loneliness and mental well-being during the pandemic but spending more time engaging physically or socially with dogs (and to a lesser extent cats) was generally associated with poorer outcomes. Qualitative data were collected through open-ended survey responses (n = 757) and semi-structured interviews (n = 25) and analysed using reflexive thematic analysis. Two themes were developed—one related to companion animals as providers of social and emotional support, and the other to companion animals as providers of purpose and perspective. Concerns regarding the impact of the pandemic on animal welfare were also identified. Compared to other animal types, more participants expressed indifference regarding the impact of their fishes on their well-being during the pandemic, possibly because fishes cannot provide comfort via physical touch. The findings of this study reflect the wider field of human–animal interaction; although qualitative data suggest guardians believe their companion animals are a positive influence in their lives, there is little convincing quantitative data to support these beliefs. This highlights the need to refine theories regarding which aspects of companion animal guardianship may influence human well-being; the findings from this research may be useful in the refinement of such theories.

This paper was originally published under a standard licence. This has now been amended to a CC BY licence in the PDF and HTML.

This research addresses two specific knowledge gaps. The first regards the influence of domestic low-carbon technology (LCT) installation approaches and occupier status on user acceptance. The second is to demonstrate the role of machine learning techniques in producing an enhanced model-based understanding of domestic LCT acceptance. Together, these two approaches provide new insights into LCT acceptance through the theory of planned behaviour and demonstrate the value of machine learning for modelling such acceptance. Our aim is therefore to contribute to model-based knowledge about the acceptance of domestic LCTs. Specifically, we contribute new knowledge of the acceptance of LCTs according to the theory of planned behaviour and of the value of machine-learning techniques for modelling this acceptance. Through empirical research using an online quasi-experiment with 3813 English residents, we developed a model of low-carbon technology adoption and evaluated machine learning for model analysis. The design factors were the installation approach and occupier status, with main outcomes including adoption intention, willingness to accept, willingness to pay, attitude, subjective norm, and perceived behavioural control. To examine residents’ technology acceptance, we created two virtual reality models of technology implementation, differing in installation approach. For machine learning analysis, we employed nine techniques for model validation and predictor selection: linear regression, LASSO regression, ridge regression, support vector regression, regression tree (decision tree regression), random forest, XGBoost, k-NN, and neural network. LASSO regression emerged as the best technique in terms of predictor selection, with (near-)optimal model fit (R² and MSE). We found that attitude, subjective norm, and perceived behavioural control significantly predicted the intention to adopt low-carbon technologies. The installation approach influenced willingness to accept, with higher intention for new-build installations than retrofits. Homeownership positively predicted perceived behavioural control, while age negatively predicted several outcomes. This study concludes with implications for policy and future research, a specific emphasis upon contemporary UK policy towards Future Homes Standards, and public information campaigns targeted to specific demographic user groups. This research demonstrates the value of an extended theory of planned behaviour model to study the acceptance of LCTs and the value of machine learning analysis in acceptance modelling.

Background: Life expectancy is 10–20 years lower among people with a severe mental health disorder. Most of these early deaths are due to chronic conditions, including cardiovascular and respiratory diseases. Smoking is a major risk factor for these conditions and introducing smokefree policies has been recommended to mental health service providers in England by the National Institute for Health and Care Excellence (NICE), in their Public Health Guideline 48: Smoking: acute, maternity and mental health services. This paper reports a process evaluation of introducing these policy recommendations, which were updated in 2013. Method: Process data were collected through semi-structured interviews with staff (n = 51), members of partnering organisations (n = 5), service users (n = 7) and carers (n = 2) between November 2016 – April 2017. Normalization Process Theory (NPT) was used to design the data collection tools and analyse the data. A framework approach was taken with the analysis, using the four concepts of NPT: coherence, cognitive participation, collective action and reflexive monitoring. Results: The policy made sense to some staff, patients and carers (coherence) who ‘bought-into’ the idea (cognitive participation) but other participants disagreed. Although smokefree policies were operationalised (collective action), sometimes they were opposed. Progress was made, especially in some units, but continued to be resisted in others. Informal appraisal of progress (reflexive monitoring) presented a varied picture. Conclusion: Some progress has been made in terms of changing an entrenched, smoking culture into one that is smokefree on Trust sites across the region. Perseverance and resourcing over the long-term is required to establish a non-smoking culture in on-site provision of mental health services.