A mixed-methods assessment of community-engaged learning in a Master of Public Health program.

Objective: Community-engaged learning is used in Master of Public Health programs to enhance student training, connect with communities, help solve societal issues, develop competencies, and build partnerships. However, it is unclear how much community-engaged learning components supplement existing Master of Public Health programs and prepare students in developing these competencies. Thus, the aim of this study was to apply an explanatory mixed-methods study design to evaluate a Canadian Master of Public Health program's community-engaged learning activities and propose recommendations to strengthen public health training and course delivery. Methods: We conducted a questionnaire among Master of Public Health students (n = 25), focus group discussion with a subset of these students (n = 7), and one-on-one semi-structured telephone interviews with community partners who had previously hosted Master of Public Health students for practicum placements (n = 11). Results: Community-engagement enhanced learning among Master of Public Health students, with the practicum placement, and program development capstone resulting in the largest self-reported development. Students in the focus group indicated community engagement provided skill and professional development, but also identified wanting additional curriculum coverage on various statistical software and qualitative research methods. Interviews with community partners revealed benefits of practicum placements such as mutual knowledge transfer, increased organizational capacity, and strengthened academic-community partnerships. Community partners also commented on challenges with recruitment, training, and aligning student-organization goals. Conclusion: The findings from this study suggest that an update to the Master of Public Health program curriculum, its core competencies, a combination of community-engagement activities, and future evaluations will be needed to advance education delivery.

Behavioural interventions delivered through interactive social media for health behaviour change, health outcomes, and health equity in the adult population.

BACKGROUND: Social networking platforms offer a wide reach for public health interventions allowing communication with broad audiences using tools that are generally free and straightforward to use and may be combined with other components, such as public health policies. We define interactive social media as activities, practices, or behaviours among communities of people who have gathered online to interactively share information, knowledge, and opinions. OBJECTIVES: We aimed to assess the effectiveness of interactive social media interventions, in which adults are able to communicate directly with each other, on changing health behaviours, body functions, psychological health, well-being, and adverse effects. Our secondary objective was to assess the effects of these interventions on the health of populations who experience health inequity as defined by PROGRESS-Plus. We assessed whether there is evidence about PROGRESS-Plus populations being included in studies and whether results are analysed across any of these characteristics. SEARCH METHODS: We searched CENTRAL, CINAHL, Embase, MEDLINE (including trial registries) and PsycINFO. We used Google, Web of Science, and relevant web sites to identify additional studies and searched reference lists of included studies. We searched for published and unpublished studies from 2001 until June 1, 2020. We did not limit results by language. SELECTION CRITERIA: We included randomised controlled trials (RCTs), controlled before-and-after (CBAs) and interrupted time series studies (ITSs). We included studies in which the intervention website, app, or social media platform described a goal of changing a health behaviour, or included a behaviour change technique. The social media intervention had to be delivered to adults via a commonly-used social media platform or one that mimicked a commonly-used platform. We included studies comparing an interactive social media intervention alone or as a component of a multi-component intervention with either a non-interactive social media control or an active but less-interactive social media comparator (e.g. a moderated versus an unmoderated discussion group). Our main outcomes were health behaviours (e.g. physical activity), body function outcomes (e.g. blood glucose), psychological health outcomes (e.g. depression), well-being, and adverse events. Our secondary outcomes were process outcomes important for behaviour change and included knowledge, attitudes, intention and motivation, perceived susceptibility, self-efficacy, and social support. DATA COLLECTION AND ANALYSIS: We used a pre-tested data extraction form and collected data independently, in duplicate. Because we aimed to assess broad outcomes, we extracted only one outcome per main and secondary outcome categories prioritised by those that were the primary outcome as reported by the study authors, used in a sample size calculation, and patient-important. MAIN RESULTS: We included 88 studies (871,378 participants), of which 84 were RCTs, three were CBAs and one was an ITS. The majority of the studies were conducted in the USA (54%). In total, 86% were conducted in high-income countries and the remaining 14% in upper middle-income countries. The most commonly used social media platform was Facebook (39%) with few studies utilising other platforms such as WeChat, Twitter, WhatsApp, and Google Hangouts. Many studies (48%) used web-based communities or apps that mimic functions of these well-known social media platforms. We compared studies assessing interactive social media interventions with non-interactive social media interventions, which included paper-based or in-person interventions or no intervention. We only reported the RCT results in our 'Summary of findings' table. We found a range of effects on health behaviours, such as breastfeeding, condom use, diet quality, medication adherence, medical screening and testing, physical activity, tobacco use, and vaccination. For example, these interventions may increase physical activity and medical screening tests but there was little to no effect for other health behaviours, such as improved diet or reduced tobacco use (20,139 participants in 54 RCTs). For body function outcomes, interactive social media interventions may result in small but important positive effects, such as a small but important positive effect on weight loss and a small but important reduction in resting heart rate (4521 participants in 30 RCTs). Interactive social media may improve overall well-being (standardised mean difference (SMD) 0.46, 95% confidence interval (CI) 0.14 to 0.79, moderate effect, low-certainty evidence) demonstrated by an increase of 3.77 points on a general well-being scale (from 1.15 to 6.48 points higher) where scores range from 14 to 70 (3792 participants in 16 studies). We found no difference in effect on psychological outcomes (depression and distress) representing a difference of 0.1 points on a standard scale in which scores range from 0 to 63 points (SMD -0.01, 95% CI -0.14 to 0.12, low-certainty evidence, 2070 participants in 12 RCTs). We also compared studies assessing interactive social media interventions with those with an active but less interactive social media control (11 studies). Four RCTs (1523 participants) that reported on physical activity found an improvement demonstrated by an increase of 28 minutes of moderate-to-vigorous physical activity per week (from 10 to 47 minutes more, SMD 0.35, 95% CI 0.12 to 0.59, small effect, very low-certainty evidence). Two studies found little to no difference in well-being for those in the intervention and control groups (SMD 0.02, 95% CI -0.08 to 0.13, small effect, low-certainty evidence), demonstrated by a mean change of 0.4 points on a scale with a range of 0 to 100. Adverse events related to the social media component of the interventions, such as privacy issues, were not reported in any of our included studies. We were unable to conduct planned subgroup analyses related to health equity as only four studies reported relevant data. AUTHORS' CONCLUSIONS: This review combined data for a variety of outcomes and found that social media interventions that aim to increase physical activity may be effective and social media interventions may improve well-being. While we assessed many other outcomes, there were too few studies to compare or, where there were studies, the evidence was uncertain. None of our included studies reported adverse effects related to the social media component of the intervention. Future studies should assess adverse events related to the interactive social media component and should report on population characteristics to increase our understanding of the potential effect of these interventions on reducing health inequities.

Behavior Change Techniques Included in Reports of Social Media Interventions for Promoting Health Behaviors in Adults: Content Analysis Within a Systematic Review.

BACKGROUND: Social media are an increasingly commonly used platform for delivering health promotion interventions. Although recent research has focused on the effectiveness of social media interventions for health promotion, very little is known about the optimal content within such interventions, and the active ingredients to promote health behavior change using social media are not clear. Identifying which behavior change techniques (BCTs) are reported may help to clarify the content of interventions using a generalizable terminology that may facilitate future intervention development. OBJECTIVE: This study aimed to identify which BCTs are reported in social media interventions for promoting health behavior change in adults. METHODS: We included 71 studies conducted with adult participants (aged ≥18 years) and for which social media intervention was considered interactive in a Cochrane review of the effectiveness of such interventions. We developed a coding manual informed by the Behavior Change Technique Taxonomy version 1 (BCTTv1) to identify BCTs in the included studies. We identified BCTs in all study arms (including control) and described BCTs in the group and self-directed components of studies. We characterized the dose of delivery for each BCT by low and high intensity. We used descriptive analyses to characterize the reported BCTs. RESULTS: Our data consisted of 71 studies published from 2001 to 2017, mainly conducted in high-income countries (n=65). Most studies (n=31) used tailored, interactive websites to deliver the intervention; Facebook was the most used mainstream platform. In developing our coding manual, we adapted some BCTTv1 instructions to better capture unique nuances of how BCTs were operationalized in social media with respect to likes, retweets, smiles, congratulations, and badges. Social support (unspecified), instruction on how to perform the behavior, and credible source were most frequently identified BCTs in intervention arms of studies and group-delivery settings, whereas instruction on how to perform the behavior was most commonly applied in self-directed components of studies, control arms, and individual participant settings. Instruction on how to perform the behavior was also the most frequently reported BCT in both intervention and control arms simultaneously. Instruction on how to perform the behavior, social support (unspecified), self-monitoring of behavior, information about health consequences, and credible source were identified in the top 5 BCTs delivered with the highest intensity. CONCLUSIONS: This study within a review provides a detailed description of the BCTs and their dose to promote behavior change in web-based, interactive social media interventions. Clarifying active ingredients in social media interventions and the intensity of their delivery may help to develop future interventions that can more clearly build upon the existing evidence.

Potential harms associated with routine collection of patient sociodemographic information: A rapid review.

BACKGROUND: Health systems are recommended to capture routine patient sociodemographic data as a key step in providing equitable person-centred care. However, collection of this information has the potential to cause harm, especially for vulnerable or potentially disadvantaged patients. OBJECTIVE: To identify harms perceived or experienced by patients, their families, or health-care providers from collection of sociodemographic information during routine health-care visits and to identify best practices for when, by whom and how to collect this information. SEARCH STRATEGY: We searched OVID MEDLINE, PubMed "related articles" via NLM and healthevidence.org to the end of January 2018 and assessed reference lists and related citations of included studies. INCLUSION CRITERIA: We included studies reporting on harms of collecting patient sociodemographic information in health-care settings. DATA EXTRACTION AND SYNTHESIS: Data on study characteristics and types of harms were extracted and summarized narratively. MAIN RESULTS: Eighteen studies were included; 13 provided patient perceptions or experiences with the collection of these data and seven studies reported on provider perceptions. Five reported on patient recommendations for collecting sociodemographic information. Patients and providers reported similar potential harms which were grouped into the following themes: altered behaviour which may affect care-seeking, data misuse or privacy concerns, discomfort, discrimination, offence or negative reactions, and quality of care. Patients suggested that sociodemographic information be collected face to face by a physician. DISCUSSION AND CONCLUSIONS: Overall, patients support the collection of sociodemographic information. However, harms are possible, especially for some population subgroups. Harms may be mitigated by providing a rationale for the collection of this information.

Assessment of variation in bulbar conjunctival redness, temperature, and blood flow.

PURPOSE: To assess the diurnal variation in bulbar conjunctival redness, conjunctival temperature, and conjunctival blood flow. METHODS: Bulbar redness was quantified by CIE u' chromaticity using a SpectraScan PR650 spectrophotometer. Conjunctival temperature was measured using a Tasco-Thi 500 infrared thermometer. Measurements of conjunctival blood flow were obtained using a modified Heidelberg Retinal Flowmeter (HRF). Measurements on 10 subjects were made on a periodic basis over the day and on waking. RESULTS: For each factor measured a cyclical pattern was observed, with highest values on waking, a reduction in values towards mid-day, and then a gradual increase over the remainder of the day. There was a significant effect of time for redness, temperature, and conjunctival blood flow (p < 0.001 for all three variables), with no significant difference in the cyclical pattern between eyes being observed (p = NS). CONCLUSIONS: Diurnal bulbar redness, temperature, and conjunctival blood flow variation may be objectively quantified and all three are lowest during the middle of the day and maximal at the start of the day. This information should be considered when undertaking studies in which redness, temperature, and ocular surface blood flow are important outcome variables and time of day is a potential confounding factor.