The Covid-19 pandemic raises questions about the role that relationships and interactions between humans and animals play in the context of widespread social distancing and isolation measures. We aimed to investigate links between mental health and loneliness, companion animal ownership, the human-animal bond, and human-animal interactions; and to explore animal owners’ perceptions related to the role of their animals during lockdown. A cross-sectional online survey of UK residents over 18 years of age was conducted between April and June 2020. The questionnaire included validated and bespoke items measuring demographics; exposures and outcomes related to mental health, wellbeing and loneliness; the human-animal bond and human-animal interactions. Of 5,926 participants, 5,323 (89.8%) had at least one companion animal. Most perceived their animals to be a source of considerable support, but concerns were reported related to various practical aspects of providing care during lockdown. Strength of the human-animal bond did not differ significantly between species. Poorer mental health pre-lockdown was associated with a stronger reported human-animal bond (b = -.014, 95% CI [-.023 - -.005], p = .002). Animal ownership compared with non-ownership was associated with smaller decreases in mental health (b = .267, 95% CI [.079 - .455], p = .005) and smaller increases in loneliness (b = -.302, 95% CI [-.461 - -.144], p = .001) since lockdown. The human-animal bond is a construct that may be linked to mental health vulnerability in animal owners. Strength of the human-animal bond in terms of emotional closeness or intimacy dimensions appears to be independent of animal species. Animal ownership seemed to mitigate some of the detrimental psychological effects of Covid-19 lockdown. Further targeted investigation of the role of human-animal relationships and interactions for human health, including testing of the social buffering hypothesis and the development of instruments suited for use across animal species, is required.

The coronavirus disease 2019 (COVID-19) pandemic presents an opportunity to explore the role of animals as sources of emotional and physical support during a period when most of the population is experiencing social and environmental challenges. We investigated how companion animal owners perceived the influence of human–animal interaction on their physical and mental health during the first COVID-19 lockdown phase in the U.K., and what concerns they had regarding their animals at this time. We also explored the impact of participants’ interaction with non-companion animals during this phase. A cross-sectional online survey of U.K. residents aged over 18 was conducted between April and June 2020. The final item of the survey invited open-ended free-text responses, allowing participants to describe any experiences and/or perceptions of their human–animal relationships during the COVID-19 lockdown phase. A qualitative thematic analysis of responses was undertaken. Four main themes related to the following aspects of human–animal interactions during the COVID-19 lockdown phase were identified: the positive impact of animal ownership during the COVID-19 lockdown (e.g., amelioration of wellbeing and mental health), concerns relating to animal ownership during the COVID-19 lockdown (e.g., concerns over animals carrying the COVID-19 virus), grief and loss of an animal during the COVID-19 lockdown and the impact of engaging with non-companion animals during the COVID-19 lockdown. The findings complement and extend previous insights into the impact of human–animal interaction with both companion and non-companion animals. They also highlight the challenges of caring for an animal during the lockdown phase and indicate the need to consider the development of further targeted support strategies, such as “day care” for the companion animals of key workers in this context.

Introduction: Evidence-based smoking cessation and temporary abstinence interventions to address smoking in mental health settings are available, but the impact of these interventions is limited. Aims and Methods: We aimed to identify and synthesize the perceived barriers and enablers to supporting smoking cessation in mental health settings. Six databases were searched for articles reporting the investigation of perceived barriers and enablers to supporting smoking cessation in mental health settings. Data were extracted and coded using a mixed inductive/deductive method to the theoretical domains framework, key barriers and enablers were identified through the combining of coding frequency, elaboration, and expressed importance. Results: Of 31 included articles, 56 barriers/enablers were reported from the perspectives of mental healthcare professionals (MHPs), 48 from patient perspectives, 21 from mixed perspectives, and 0 from relatives/carers. Barriers to supporting smoking cessation or temporary abstinence in mental health settings mainly fell within the domains: environmental context and resources (eg, MHPs lack of time); knowledge (eg, interactions around smoking that did occur were ill informed); social influences (eg, smoking norms within social network); and intentions (eg, MHPs lack positive intentions to deliver support). Enablers mainly fell within the domains: environmental context and resources (eg, use of appropriate support materials) and social influences (eg, pro-quitting social norms). Conclusions: The importance of overcoming competing demands on staff time and resources, the inclusion of tailored, personalized support, the exploitation of patients wider social support networks, and enhancing knowledge and awareness around the benefits smoking cessation is highlighted. Implications: Identified barriers and enablers represent targets for future interventions to improve the support of smoking cessation in mental health settings. Future research needs to examine the perceptions of the carers and family/friends of patients in relation to the smoking behavior change support delivered to patients.

Psychosocial person-centred interventions are considered best practice for addressing complex behaviours and care needs such as agitation and anxiety, and for improving the quality of life of people with dementia in care homes. Dementia Care Mapping (DCM™) is an established practice development tool and process aimed to help care home staff deliver more person-centred care. To date, few studies have evaluated the efficacy of DCM™ and have found mixed results. These results are suggested to be the outcome of intervention implementation, which may be impacted by a range of factors. This study reports the barriers and facilitators to DCM™ implementation in care homes found during the process evaluation conducted as part of a randomized controlled trial. Eighteen of the 31 DCM™ intervention care homes were recruited to participate in the embedded process evaluation. Semi-structured interviews were conducted with 83 participants, comprising care home managers, trained DCM™ users (mappers), expert external mappers, staff members, relatives, and residents. Barriers and facilitators to DCM™ implementation were found at the mapper level (e.g. motivation and confidence), the DCM™ intervention level (e.g. understanding of DCM™) and the care home level (e.g. staffing issues, manager support). Further barriers caused by the burden of trial participation were also identified (e.g. additional paperwork). Implementing DCM™ is complex and a greater consideration of potential barriers and facilitators in planning future studies and in practice could help improve implementation. Current Controlled Trials ISRCTN82288852 , registered 16/01/2014.

Background and Aims: Admission to a smoke‐free setting presents a unique opportunity to encourage smokers to quit. However, risk of relapse post‐discharge is high, and little is known about effective strategies to support smoking cessation following discharge. We aimed to identify interventions that maintain abstinence following a smoke‐free stay and determine their effectiveness, as well as the probable effectiveness of behaviour change techniques (BCTs) used in these interventions. Methods: Systematic review and meta‐analyses of studies of adult smokers aged ≥ 18 years who were temporarily or fully abstinent from smoking to comply with institutional smoke‐free policies. Institutions included prison, inpatient mental health, substance misuse or acute hospital settings. A Mantel–Haenszel random‐effects meta‐analysis of randomized controlled trials (RCTs) was conducted using biochemically verified abstinence (7‐day point prevalence or continuous abstinence). BCTs were defined as ‘promising’ in terms of probable effectiveness (if BCT was present in two or more long‐term effective interventions) and feasibility (if BCT was also delivered in ≥ 25% of all interventions). Results: Thirty‐seven studies (intervention n = 9041, control n = 6195) were included: 23 RCTs (intervention n = 6593, control n = 5801); three non‐randomized trials (intervention n = 845, control n = 394) and 11 cohort studies ( n = 1603). Meta‐analysis of biochemically verified abstinence at longest follow‐up (4 weeks–18 months) found an overall effect in favour of intervention [risk ratio (RR) = 1.27, 95% confidence interval (CI) = 1.08–1.49, I ² = 42%]. Nine BCTs (including ‘pharmacological support’, ‘goal‐setting (behaviour)’ and ‘social support’) were characterized as ‘promising’ in terms of probable effectiveness and feasibility. Conclusions: A systematic review and meta‐analyses indicate that behavioural and pharmacological support is effective in maintaining smoking abstinence following a stay in a smoke‐free institution. Several behaviour change techniques may help to maintain smoking abstinence up to 18 months post‐discharge.

Background: The quality of care for people with dementia in care homes is of concern. Interventions that can improve care outcomes are required. Objective: To investigate the clinical effectiveness and cost-effectiveness of Dementia Care Mapping™ (DCM) for reducing agitation and improving care outcomes for people living with dementia in care homes, versus usual care. Design: A pragmatic, cluster randomised controlled trial with an open-cohort design, follow-up at 6 and 16 months, integrated cost-effectiveness analysis and process evaluation. Clusters were not blinded to allocation. The primary end point was completed by staff proxy and independent assessors. Setting: Stratified randomisation of 50 care homes to the intervention and control groups on a 3 : 2 ratio by type, size, staff exposure to dementia training and recruiting hub. Participants: Fifty care homes were randomised (intervention, n = 31; control, n = 19), with 726 residents recruited at baseline and a further 261 recruited after 16 months. Care homes were eligible if they recruited a minimum of 10 residents, were not subject to improvement notices, had not used DCM in the previous 18 months and were not participating in conflicting research. Residents were eligible if they lived there permanently, had a formal diagnosis of dementia or a score of 4+ on the Functional Assessment Staging Test of Alzheimer’s Disease, were proficient in English and were not terminally ill or permanently cared for in bed. All homes were audited on the delivery of dementia and person-centred care awareness training. Those not reaching a minimum standard were provided training ahead of randomisation. Eighteen homes took part in the process evaluation. Intervention: Two staff members from each intervention home were trained to use DCM and were asked to carry out three DCM cycles; the first was supported by an external expert. Main outcome measures: The primary outcome was agitation (Cohen-Mansfield Agitation Inventory), measured at 16 months. Secondary outcomes included resident behaviours and quality of life. Results: There were 675 residents in the final analysis (intervention, n = 388; control, n = 287). There was no evidence of a difference in agitation levels between the treatment arms. The adjusted mean difference in Cohen-Mansfield Agitation Inventory score was –2.11 points, being lower in the intervention group than in the control (95% confidence interval –4.66 to 0.44; p = 0.104; adjusted intracluster correlation coefficient: control = 0, intervention = 0.001). The sensitivity analyses results supported the primary analysis. No differences were detected in any of the secondary outcomes. The health economic analyses indicated that DCM was not cost-effective. Intervention adherence was problematic; only 26% of homes completed more than their first DCM cycle. Impacts, barriers to and facilitators of DCM implementation were identified. Limitations: The primary completion of resident outcomes was by staff proxy, owing to self-report difficulties for residents with advanced dementia. Clusters were not blinded to allocation, although supportive analyses suggested that any reporting bias was not clinically important. Conclusions: There was no benefit of DCM over control for any outcomes. The implementation of DCM by care home staff was suboptimal compared with the protocol in the majority of homes. Future work: Alternative models of DCM implementation should be considered that do not rely solely on leadership by care home staff. Trial registration: Current Controlled Trials ISRCTN82288852. Funding: This project was funded by the National Institute for Health Research (NIHR) Health Technology Assessment programme and will be published in full in Health Technology Assessment; Vol. 24, No. 16. See the NIHR Journals Library website for further project information.