Utilising a social-ecological approach to understand the barriers and facilitators of weight loss in behavioural weight management programmes

Background: Behavioural weight management programmes are efficacious in improving health and weight outcomes in adults living with obesity. Typically, a target of 5% weight loss is considered “successful” as this weight change has been associated with improvements in health. Despite the successes of these programmes, many participants fail to reach a 5% weight loss. Comparing barriers and facilitators during participation in programmes can highlight differences between those who are “successful” and “unsuccessful”. Research which aims to understand why participants are “unsuccessful” often focuses on programme or intrapersonal factors and does not consider wider contextual and environmental influences on experiences and outcomes. Where there is data on wider contextual influences, the data is often collected at follow-up (potentially introducing hindsight bias) or fails to compare commonalities and differences between “successful” and “unsuccessful” participants. Gathering data on what factors (i.e. internal, and external to the programme) influence success during participation can provide suggestions on how programmes can be improved. Therefore, this research aimed to explore the barriers and facilitators of weight loss for participants in behavioural weight management programmes, and to compare commonalities and differences between “successful” and “unsuccessful” participants, using a social-ecological approach. Methods: The study used a two-phase convergent parallel design mixed methods approach. This involved collecting qualitative and quantitative data concurrently, analysing them independently, and then merging them for interpretation. The first phase was a systematic review of the barriers and facilitators of weight loss and participation in behavioural weight management programmes. The review used a data-based convergent synthesis to combine qualitative and quantitative data for thematic analysis. Quality assessments were used to rank the trustworthiness of the themes identified in the data. The second phase involved a combination of surveys, interviews and personal network data collection with adults living with obesity participating in a 12-week online behavioural weight management programme. The content of the surveys and interviews was informed by the wider literature and systematic review and asked participants the degree to which and how different aspects described in the social-ecological model impacted their weight loss. Questions included intrapersonal, interpersonal, programme, environment, and also COVID-19 topics. Surveys were administered at baseline (n= 129) and the end of the programme (n= 102). Survey data were analysed using a sequential modelling approach to build an explanatory model of “successful” weight loss (i.e. ≥5%). Semi-structured interviews (n=48) were conducted midway through the programme. Data were analysed using a thematic framework approach. The data were coded before participants were grouped as “successful” or “unsuccessful” at achieving a ≥5% weight loss. Following the coding and grouping of participants, the themes were compared to identify commonalities and differences in the barriers and facilitators experienced between groups. Personal network data were collected at each timepoint as part of the surveys or interviews. Personal networks required participants to nominate people they spend time with (i.e. an alter) and answer questions on their attributes and connections to other alters. The personal network data explored the structure of participant networks (e.g. number of alters, density) and characteristics of the alters (e.g. their weight status, whether they offered social support) in the participants' lives and whether they affected success. Following individual analysis of each study, the results were combined into a conceptual map to reveal a comprehensive overview of influential factors of “successful” weight loss. Factors which were identified in each study were then extracted to highlight key contributors to success. Results: The systematic review identified 48 studies, including qualitative, randomised controlled trials and quasi-experimental methodologies. In total 39 barriers and 40 facilitators were extracted. Due to the generally high quality of the included studies, most themes were ranked as having high trustworthiness. Important factors included intrapersonal thoughts, feelings, behaviours and health, interpersonal dynamics, the programme materials, setting, and being mindful of participants and the facilities in the wider environment. The survey also identified a range of key influential factors across social-ecological domains. The explanatory model found lower baseline takeaway consumption, more dietary changes made at baseline and the end of the programme, lower levels of anxiety, and higher levels of social support from the household accounted for 29% of the variance in whether participants would successfully reach a 5% weight loss. The thematic framework analysis of the interview data revealed commonalities and distinctions between “successful” and “unsuccessful” participants. Commonalities largely reiterated the themes in the systematic review. Factors only reported by “successful” participants included being motivated by stressors, sourcing pragmatic solutions to barriers, being proactive in learning about risks associated with excess bodyweight and being aware of negative media and public health messaging concerning obesity. Factors only reported by “unsuccessful” participants included having challenging work patterns, disliking their weight target, having difficulty in managing stressors and overcoming barriers, being resistant to social support, and experiencing negative social reactions to their weight managements attempts. The personal network data collected as part of the surveys offered limited insights into the relationship between the network and weight loss due to issues with the data collection methods. The personal networks collected in the interviews did not find any significant relationships between “successful” weight loss and any of the tested variables. The integration of the results from the systematic review, surveys, and interviews highlighted intrapersonal and interpersonal factors as important contributors to “successful” weight loss. These included the adoption of more behavioural changes, receiving higher levels of social support, having higher levels of motivation, self-efficacy, and control, and lower levels of anxiety and depression. Conclusions: This research identified crucial barriers and facilitators for “successful” weight loss in adults living with obesity participating in behavioural weight management programmes. The findings show there are a variety of influential factors across the social-ecological model, and the importance and effect of these vary between participants. Although it’s not feasible to address all challenges, programmes can use these results to harness the best conditions for success within their control (e.g. adding in extra programme components, and considering how to address external challenges). Based on the findings from each study, suggestions for practice, policy and research are offered

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

Stakeholder Engagement Focus Groups on Digital Mental Health and Peer Support, 2022

Background: Digital mental health (DMH) delivered via peer support is of increasing interest following the pandemic. Such approaches have the potential to alleviate demand and increase access to support. However, little is known about the process of change while using these platforms from initial inputs to long-term impact. Purpose: This project used co-production to develop a Theory of Change (ToC) to understand the inputs, processes, outcomes, and impact of these platforms according to stakeholders. Methods: A series of semi-structured focus groups were held with stakeholders (n=23) in DMH peer support. Focus groups were guided by the ToC approach. The first focus group (stakeholder launch) involved a series 3 breakout rooms and participants were divided into 2 groups. The second set of focus groups aimed to expand on the findings and fill in identified gaps. The final focus group (stakeholder close) involved the researchers sharing the results with attendees and receiving their feedback on the ToC. Following this the ToC was updated. Data were analysed using a thematic framework approach to allow comparisons to be made between stakeholder groups. Results: The ToC generated 3 different pathways: platform, commissioners, and members. Each pathway supported member’s use of the platform through increasing engagement or maintaining resources. Stakeholders identified multifarious inputs, outcomes, and impact of the platform. These included increasing mental health literacy, improving self-management skills, and preventing worsening mental health. Insight into the processes of the platform was limited, although variations in member types and the role of user expectations were highlighted. Key risks, barriers, and how platforms fit into the wider mental health landscape were also reported. Conclusion: The ToC harnessed stakeholders understanding of DMH and peer support. Further research the active ingredients of a platform and how these effect members behaviour and mental health is needed

A qualitative exploration of weight management during COVID ‐19

Summary: 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

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

No abstract available