Social network interventions to support cardiac rehabilitation and secondary prevention in the management of people with heart disease.

BACKGROUND: Globally, cardiovascular diseases (CVD, that is, coronary heart (CHD) and circulatory diseases combined) contribute to 31% of all deaths, more than any other cause. In line with guidance in the UK and globally, cardiac rehabilitation programmes are widely offered to people with heart disease, and include psychosocial, educational, health behaviour change, and risk management components. Social support and social network interventions have potential to improve outcomes of these programmes, but whether and how these interventions work is poorly understood.  OBJECTIVES: To assess the effectiveness of social network and social support interventions to support cardiac rehabilitation and secondary prevention in the management of people with heart disease. The comparator was usual care with no element of social support (i.e. secondary prevention alone or with cardiac rehabilitation).  SEARCH METHODS: We undertook a systematic search of the following databases on 9 August 2022: CENTRAL, MEDLINE, Embase, and the Web of Science. We also searched ClinicalTrials.gov and the WHO ICTRP. We reviewed the reference lists of relevant systematic reviews and included primary studies, and we contacted experts to identify additional studies.  SELECTION CRITERIA: We included randomised controlled trials (RCTs) of social network or social support interventions for people with heart disease. We included studies regardless of their duration of follow-up, and included those reported as full text, published as abstract only, and unpublished data. DATA COLLECTION AND ANALYSIS: Using Covidence, two review authors independently screened all identified titles. We retrieved full-text study reports and publications marked 'included', and two review authors independently screened these, and conducted data extraction. Two authors independently assessed risk of bias, and assessed the certainty of the evidence using GRADE. Primary outcomes were all-cause mortality, cardiovascular-related mortality, all-cause hospital admission, cardiovascular-related hospital admission, and health-related quality of life (HRQoL) measured at > 12 months follow-up.  MAIN RESULTS: We included 54 RCTs (126 publications) reporting data for a total of 11,445 people with heart disease. The median follow-up was seven months and median sample size was 96 participants. Of included study participants, 6414 (56%) were male, and the mean age ranged from 48.6 to 76.3 years. Studies included heart failure (41%), mixed cardiac disease (31%), post-myocardial infarction (13%), post-revascularisation (7%), CHD (7%), and cardiac X syndrome (1%) patients. The median intervention duration was 12 weeks. We identified notable diversity in social network and social support interventions, across what was delivered, how, and by whom.  We assessed risk of bias (RoB) in primary outcomes at > 12 months follow-up as either 'low' (2/15 studies), 'some concerns' (11/15), or 'high' (2/15). 'Some concerns' or 'high' RoB resulted from insufficient detail on blinding of outcome assessors, data missingness, and absence of pre-agreed statistical analysis plans. In particular, HRQoL outcomes were at high RoB. Using the GRADE method, we assessed the certainty of evidence as low or very low across outcomes. Social network or social support interventions had no clear effect on all-cause mortality (risk ratio (RR) 0.75, 95% confidence interval (CI) 0.49 to 1.13, I2 = 40%) or cardiovascular-related mortality (RR 0.85, 95% CI 0.66 to 1.10, I2 = 0%) at > 12 months follow-up. The evidence suggests that social network or social support interventions for heart disease may result in little to no difference in all-cause hospital admission (RR 1.03, 95% CI 0.86 to 1.22, I2 = 0%), or cardiovascular-related hospital admission (RR 0.92, 95% CI 0.77 to 1.10, I2 = 16%), with a low level of certainty. The evidence was very uncertain regarding the impact of social network interventions on HRQoL at > 12 months follow-up (SF-36 physical component score: mean difference (MD) 31.53, 95% CI -28.65 to 91.71, I2 = 100%, 2 trials/comparisons, 166 participants; mental component score MD 30.62, 95% CI -33.88 to 95.13, I2 = 100%, 2 trials/comparisons, 166 participants).  Regarding secondary outcomes, there may be a decrease in both systolic and diastolic blood pressure with social network or social support interventions. There was no evidence of impact found on psychological well-being, smoking, cholesterol, myocardial infarction, revascularisation, return to work/education, social isolation or connectedness, patient satisfaction, or adverse events.  Results of meta-regression did not suggest that the intervention effect was related to risk of bias, intervention type, duration, setting, and delivery mode, population type, study location, participant age, or percentage of male participants.  AUTHORS' CONCLUSIONS: We found no strong evidence for the effectiveness of such interventions, although modest effects were identified in relation to blood pressure. While the data presented in this review are indicative of potential for positive effects, the review also highlights the lack of sufficient evidence to conclusively support such interventions for people with heart disease. Further high-quality, well-reported RCTs are required to fully explore the potential of social support interventions in this context. Future reporting of social network and social support interventions for people with heart disease needs to be significantly clearer, and more effectively theorised, in order to ascertain causal pathways and effect on outcomes.

Digital mental health and peer support: Building a Theory of Change informed by stakeholders' perspectives.

Digital Mental Health and Peer support has the potential to bridge gaps in support through its scalability and accessibility. Despite the increasing use of these platforms, there remains a lack of understanding of how they operate in real life, from initial engagement to longer-term impact. We aimed to explore the key inputs, processes, user interactions, assumptions, barriers, facilitators, outcomes, and impacts associated with the use of DMH and peer support platforms by developing a Theory of Change with stakeholders. Stakeholders (n = 77) contributed to the formulation of the Theory of Change through a series of online workshops, focus groups, interviews, and open-ended survey feedback. Workshops were structured to capture information related to aspects of the Theory of Change and to allow stakeholders to provide feedback to improve the diagram. A thematic framework approach was used to analyze transcripts to enable comparisons of factors reported by members, commissioners, and platform staff. Stakeholders identified a variety of factors contributing to initial inputs, processes, outcomes, and impact. Engagement emerged as the most significant barrier to the use of platforms. Motivations for use included filling in gaps in available support, connecting with others and upskilling. Different member types determined how users would interact with the platform which could influence the social response of others. Outcomes were largely positive including provision of a safe online space, improvement in wellbeing, and feeling connected to others. Stakeholders noted impact was harder to identify due to the preventative nature of these platforms but suggested this related to the knowledge of available support, reduction in waiting for support and in referrals, and increasing engagement and uptake of the platforms. Stakeholders identified assumptions regarding internet access as a significant barrier. The Theory of Change illustrated three distinct pathways in digital mental health and peer support. Further research is needed to improve engagement and factors influencing engagement, the member experience and how impact is measured.

A qualitative exploration of weight management during COVID-19.

COVID-19 has been associated with worse outcomes in people living with obesity and has altered how people can engage with weight management. However, the impact of risk perceptions and changes to daily life on weight loss has not been explored. This study aimed to examine how COVID-19 and perception of risk interacted with weight loss attempts in adults participating in a behavioural weight management programme. Forty-eight participants completed a semi-structured interview exploring the impact of COVID-19 on their weight management experience. Interviews were completed via telephone and analysed using a thematic approach. Reaction to perceived risk varied, but most participants reported the knowledge of increased risk promoted anxiety and avoidance behaviours. Despite this, many reported it as a motivating factor for weight loss. Restrictions both helped (e.g., reduced temptation) and hindered their weight loss (e.g., less support). However, there was consensus that the changes to everyday life meant participants had more time to engage with and take control of their weight loss. To the authors' knowledge, this is the first study to explore the impact of COVID-19 on participation in a weight management programme started during the pandemic in the United Kingdom. Restrictions had varying impacts on participant's weight loss. How risk is perceived and reported to participants is an important factor influencing engagement with weight management. The framing of health information needs to be considered carefully to encourage engagement with weight management to mitigate risk. Additionally, the impact of restrictions and personal well-being are key considerations for weight management programmes.

Barriers and facilitators of successful weight loss during participation in behavioural weight management programmes: a protocol for a systematic review.

BACKGROUND: Behavioural weight management programmes are effective in assisting people with overweight or obesity to lose excess body weight. Yet, many still struggle to attain their weight loss goals in such programmes. Little is understood about the factors which impact success in these programmes. Synthesising this data will allow for theory to be developed on how to improve success in such programmes. The main aim of this review will be to extract and synthesise the barriers and facilitators of successful weight loss during participation of behavioural weight loss programmes in adults living with overweight and obesity. METHODS: A systematic search of MEDLINE, Embase, PsycINFO, The Cochrane Library and CINAHL will be performed from inception onwards. Studies will also be sought by contacting experts in the field, reference and website searching. Studies will be eligible if the participants are adults living with obesity (population) undertaking or recently completed behavioural weight loss programmes (intervention) with the primary focus of weight loss (outcome). The primary outcomes will be amount of weight lost and information on barriers and/or facilitators to success. The secondary outcomes will be reasons or factors related to attrition and adherence and behaviour change techniques used in programmes. Two reviewers will screen citations and full-text data. Reviewer 1 will screen all, and reviewer 2 will screen a random 50% of articles. Data extraction will be completed by reviewer 1, and 10% will be checked by the research team. Potential conflicts will be resolved through discussion. Data will be synthesised and described narratively to show the characteristics of each study, levels of success and barriers and facilitators during programme participation. A thematic approach will be taken, and themes will be coded against the levels of the socioecological model. Quantitative data will be extracted and categorised according to these themes and presented alongside the qualitative data. DISCUSSION: Our findings can be used to inform how weight loss programmes can be improved to facilitate success in those at risk of failure. Results will be published in a peer-reviewed journal. SYSTEMATIC REVIEW REGISTRATION: ( PROSPERO CRD42019148158 ).

Measuring the effects of listening for leisure on outcome after stroke (MELLO): A pilot randomized controlled trial of mindful music listening.

BACKGROUND: Cognitive deficits and low mood are common post-stroke. Music listening is suggested to have beneficial effects on cognition, while mindfulness may improve mood. Combining these approaches may enhance cognitive recovery and improve mood early post-stroke. AIMS: To assess the feasibility and acceptability of a novel mindful music listening intervention. METHODS: A parallel group randomized controlled feasibility trial with ischemic stroke patients, comparing three groups; mindful music listening, music listening and audiobook listening (control group), eight weeks intervention. Feasibility was measured using adherence to protocol and questionnaires. Cognition (including measures of verbal memory and attention) and mood (Hospital Anxiety and Depression Scale) were assessed at baseline, end of intervention and at six-months post-stroke. RESULTS: Seventy-two participants were randomized to mindful music listening (n = 23), music listening (n = 24), or audiobook listening (n = 25). Feasibility and acceptability measures were encouraging: 94% fully consistent with protocol; 68.1% completing ≥6/8 treatment visits; 80-107% listening adherence; 83% retention to six-month endpoint. Treatment effect sizes for cognition at six month follow-up ranged from d = 0.00 ([-0.64,0.64], music alone), d = 0.31, ([0.36,0.97], mindful music) for list learning; to d = 0.58 ([0.06,1.11], music alone), d = 0.51 ([-0.07,1.09], mindful music) for immediate story recall; and d = 0.67 ([0.12,1.22], music alone), d = 0.77 ([0.16,1.38]mindful music) for attentional switching compared to audiobooks. No signal of change was seen for mood. A definitive study would require 306 participants to detect a clinically substantial difference in improvement (z-score difference = 0.66, p = 0.017, 80% power) in verbal memory (delayed story recall). CONCLUSIONS: Mindful music listening is feasible and acceptable post-stroke. Music listening interventions appear to be a promising approach to improving recovery from stroke.

A smartphone-based test for the assessment of attention deficits in delirium: A case-control diagnostic test accuracy study in older hospitalised patients.

BACKGROUND: Delirium is a common and serious acute neuropsychiatric syndrome which is often missed in routine clinical care. Inattention is the core cognitive feature. Diagnostic test accuracy (including cut-points) of a smartphone Delirium App (DelApp) for assessing attention deficits was assessed in older hospital inpatients. METHODS: This was a case-control study of hospitalised patients aged ≥65 years with delirium (with or without pre-existing cognitive impairment), who were compared to patients with dementia without delirium, and patients without cognitive impairment. Reference standard delirium assessment, which included a neuropsychological test battery, was based on Diagnostic and Statistical Manual of Mental Disorders-5 criteria. A separate blinded assessor administered the DelApp arousal assessment (score 0-4) and attention task (0-6) yielding an overall score of 0 to 10 (lower scores indicate poorer performance). Analyses included receiver operating characteristic curves and sensitivity and specificity. Optimal cut-points for delirium detection were determined using Youden's index. RESULTS: A total of 187 patients were recruited, mean age 83.8 (range 67-98) years, 152 (81%) women; n = 61 with delirium; n = 61 with dementia without delirium; and n = 65 without cognitive impairment. Patients with delirium performed poorly on the DelApp (median score = 4/10; inter-quartile range 3.0, 5.5) compared to patients with dementia (9.0; 5.5, 10.0) and those without cognitive impairment (10.0; 10.0, 10.0). Area under the curve for detecting delirium was 0.89 (95% Confidence Interval 0.84, 0.94). At an optimal cut-point of ≤8, sensitivity was 91.7% (84.7%, 98.7%) and specificity 74.2% (66.5%, 81.9%) for discriminating delirium from the other groups. Specificity was 68.3% (56.6%, 80.1%) for discriminating delirium from dementia (cut-point ≤6). CONCLUSION: Patients with delirium (with or without pre-existing cognitive impairment) perform poorly on the DelApp compared to patients with dementia and those without cognitive impairment. A cut-point of ≤8/10 is suggested as having optimal sensitivity and specificity. The DelApp is a promising tool for assessment of attention deficits associated with delirium in older hospitalised adults, many of whom have prior cognitive impairment, and should be further validated in representative patient cohorts.