Purpose: The purpose of this paper is to develop an in-depth understanding of the effectiveness, evolution and dynamism of the current health communication media used in Ghana. Design/methodology/approach: This paper uses a multi-method approach which utilizes a combination of qualitative and quantitative approaches. In-depth interviews are conducted with health promotion professionals in Ghana and 150 members of the general public were surveyed. Findings: The paper finds that the general public sees social media as an effective place for health professionals to share health-related messages. They also view health-related messages on social media seriously. Health professionals see social media as an effective tool for sending health-related messages to the public and are concerned about the fact that traditional media are losing their effectiveness as means to share health-related messages. Research limitations/implications: This study was conducted in Ghana. Future studies should be conducted across multiple countries to examine the conclusions developed in this paper and the possibility of multiple perspectives regarding the use of social media for sending health messages. Practical implications: The results inform public health officials on developments in health communication and suggest prescriptions on how to adjust to the new media. Originality/value: Health messaging is an area that has been relatively ignored in the literature and almost no research focusses on the effectiveness of social media and other health messaging technologies, particularly in developing countries. Although the study was conducted in Ghana, health professionals worldwide can use the findings to help improve their health messaging strategies.

Background: Digital behavioral weight loss programs are scalable and effective, and they provide an opportunity to personalize intervention components. However, more research is needed to test the acceptability and efficacy of personalized digital behavioral weight loss interventions. Objective: In a 6-month single-arm trial, we examined weight loss, acceptability, and secondary outcomes of a digital commercial weight loss program (WeightWatchers). This digital program included a personalized weight loss program based on sex, age, height, weight, and personal food preferences, as well as synchronous (eg, virtual workshops and individual weekly check-ins) and asynchronous (eg, mobile app and virtual group) elements. In addition to a personalized daily and weekly PersonalPoints target, the program provided users with personalized lists of ≥300 ZeroPoint foods, which are foods that do not need to be weighed, measured, or tracked. Methods: We conducted a pre-post evaluation of this 6-month, digitally delivered, and personalized WeightWatchers weight management program on weight loss at 3 and 6 months in adults with overweight and obesity. The secondary outcomes included participation, satisfaction, fruit and vegetable intake, physical activity, sleep quality, hunger, food cravings, quality of life, self-compassion, well-being, and behavioral automaticity. Results: Of the 153 participants, 107 (69.9%) were female, and 65 (42.5%) identified as being from a minoritized racial or ethnic group. Participants’ mean age was 41.09 (SD 13.78) years, and their mean BMI was 31.8 (SD 5.0) kg/m2. Participants had an average weight change of −4.25% (SD 3.93%) from baseline to 3 months and −5.05% (SD 5.59%) from baseline to 6 months. At 6 months, the percentages of participants who experienced ≥3%, ≥5%, and ≥10% weight loss were 63.4% (97/153), 51% (78/153), and 14.4% (22/153), respectively. The mean percentage of weeks in which participants engaged in ≥1 aspects of the program was 87.53% (SD 23.40%) at 3 months and 77.67% (SD 28.69%) at 6 months. Retention was high (132/153, 86.3%), and more than two-thirds (94/140, 67.1%) of the participants reported that the program helped them lose weight. Significant improvements were observed in fruit and vegetable intake, physical activity, sleep quality, hunger, food cravings, quality of life, and well-being (all P values <.01). Conclusions: This personalized, digital, and scalable behavioral weight management program resulted in clinically significant weight loss in half (78/153, 51%) of the participants as well as improvements in behavioral and psychosocial outcomes. Future research should compare personalized digital weight loss programs with generic programs on weight loss, participation, and acceptability.

Background: Behavioral weight loss programs typically enroll 12-40 people into groups that then suffer from declining engagement over time. Web-based patient communities, on the other hand, typically offer no limits on capacity and membership is fluid. This model may be useful for boosting engagement in behavioral weight loss interventions, which could lead to better outcomes. Objective: In this study, we aimed to examine the feasibility and acceptability of continuously enrolling participants into a Facebook-delivered weight loss intervention for the first 8 of 16 weeks relative to the same intervention where no new participants were enrolled after randomization. Methods: We conducted a randomized pilot trial to compare a Facebook weight loss group that used open enrollment with a group that used closed enrollment on feasibility and acceptability in adults with BMI 27-45 kg/m2. The feasibility outcomes included retention, engagement, and diet tracking adherence. We described the percentage loss of ≥5% weight in both groups as an exploratory outcome. We also explored the relationship between total volume of activity in the group and weight loss. The participants provided feedback via web-based surveys and focus groups. Results: Randomized participants (68/80, 85% women) were on average, aged 40.2 (SD 11.2) years with a mean BMI of 34.4 (SD 4.98) kg/m2. We enrolled an additional 54 participants (50/54, 93% female) in the open enrollment condition between weeks 1 and 8, resulting in a total group size of 94. Retention was 88% and 98% under the open and closed conditions, respectively. Randomized participants across conditions did not differ in engagement (P=.72), or diet tracking adherence (P=.42). Participant feedback in both conditions revealed that sense of community was what they liked most about the program and not enough individualized feedback was what they liked the least. Weight loss of ≥5% was achieved by 30% (12/40) of the participants randomized to the open enrollment condition and 18% (7/40) of the participants in the closed enrollment condition. Exploratory analyses revealed that the open condition (median 385, IQR 228-536.5) had a greater volume of engagement than the closed condition (median 215, IQR 145.5-292; P=.007). Furthermore, an increase of 100 in the total volume of engagement in the Facebook group each week was associated with an additional 0.1% weekly weight loss among the randomized participants (P=.02), which was independent of time, individual participant engagement, and sociodemographic characteristics. Conclusions: Open enrollment was as feasible and acceptable as closed enrollment. A greater volume of engagement in the Facebook group was associated with weight loss, suggesting that larger groups that produce more engagement overall may be beneficial. Future research should examine the efficacy of the open enrollment approach for weight loss in a fully powered randomized trial. Clinical Trial: ClinicalTrials.gov NCT02656680; https://clinicaltrials.gov/ct2/show/NCT02656680

The study examined the relationship between food swamps and self-reported food shopping frequency and perceived food access, while considering transportation mode and travel time. This Community-Based Participatory Research study surveyed residents from six neighborhoods in Hartford. Individual-level food swamp exposure (the ratio of unhealthy to healthy food stores within a 0.5-mile radius of participants’ homes) was measured both objectively (using GIS-based methods) and subjectively (through self-reporting). Poisson regression models assessed the associations between food swamps and outcomes (shopping frequency by store types and perceived access to food), with transportation mode and travel time as moderators. Of 304 participants, 51% lived in subjective (n = 153) and 71% in objective (n = 198) food swamps. Food swamp exposure was associated with greater shopping frequency at unhealthy outlets (β = 0.12, p < 0.001), less access to healthier food (β = −0.13, p < 0.001), and increased access to unhealthy food (β = 0.08, p < 0.001). Transportation significantly moderates these relationships; bus riders reported the highest rates of unhealthy food purchasing (β = 0.17, p < 0.001). Longer travel times increased both healthy and unhealthy food access (β = 0.01, p < 0.001 for each). Food swamps interact with public transportation to contribute to food shopping and access, underscoring the need for integrated food and transportation policies to address structural barriers and promote health equity in underserved urban communities.

Long Lasting Insecticidal Net (LLIN) is an effective malaria prevention mechanism. However, ownership of LLIN does not imply its use among households. The availability of enough sleeping space is a natural prerequisite to install and use LLINs. The objective of this study was to explore the effect of sleeping space and other socio-demographic factors of households’ heads on LLINs usage among households. A cross-sectional household-based study was conducted using a quantitative approach. Data was collected exclusively from households that received LLINs at no direct financial cost to them in a mass malaria campaign conducted in the study area using a structured questionnaire. A total of 383 households sampled for the study received 1,181 LLINs with a range of 1 to 15 LLINs per household. Less than 16% of households that received more than 2 LLINs installed all the LLINs they received during the distribution. Among households that received LLINs, 45% of them did not use them at all and 36% of them used them every night including the night before data collection. The number of bedrooms, children and members per household, and the number of occupants per bedroom were also found statistically associated with the use of LLINs among households. The study used a quantitative approach to investigate sleeping space in relation to LLINs usage and malaria control, an area and topic that has not been adequately covered in the literature.

Background: Social media–delivered lifestyle interventions have shown promising outcomes, often generating modest but significant weight loss. Participant engagement appears to be an important predictor of weight loss outcomes; however, engagement generally declines over time and is highly variable both within and across studies. Research on factors that influence participant engagement remains scant in the context of social media–delivered lifestyle interventions. Objective: This study aimed to identify predictors of participant engagement from the content generated during a social media–delivered lifestyle intervention, including characteristics of the posts, the conversation that followed the post, and participants’ previous engagement patterns. Methods: We performed secondary analyses using data from a pilot randomized trial that delivered 2 lifestyle interventions via Facebook. We analyzed 80 participants’ engagement data over a 16-week intervention period and linked them to predictors, including characteristics of the posts, conversations that followed the post, and participants’ previous engagement, using a mixed-effects model. We also performed machine learning–based classification to confirm the importance of the significant predictors previously identified and explore how well these measures can predict whether participants will engage with a specific post. Results: The probability of participants’ engagement with each post decreased by 0.28% each week (P<.001; 95% CI 0.16%-0.4%). The probability of participants engaging with posts generated by interventionists was 6.3% (P<.001; 95% CI 5.1%-7.5%) higher than posts generated by other participants. Participants also had a 6.5% (P<.001; 95% CI 4.9%-8.1%) and 6.1% (P<.001; 95% CI 4.1%-8.1%) higher probability of engaging with posts that directly mentioned weight and goals, respectively, than other types of posts. Participants were 44.8% (P<.001; 95% CI 42.8%-46.9%) and 46% (P<.001; 95% CI 44.1%-48.0%) more likely to engage with a post when they were replied to by other participants and by interventionists, respectively. A 1 SD decrease in the sentiment of the conversation on a specific post was associated with a 5.4% (P<.001; 95% CI 4.9%-5.9%) increase in the probability of participants’ subsequent engagement with the post. Participants’ engagement in previous posts was also a predictor of engagement in subsequent posts (P<.001; 95% CI 0.74%-0.79%). Moreover, using a machine learning approach, we confirmed the importance of the predictors previously identified and achieved an accuracy of 90.9% in terms of predicting participants’ engagement using a balanced testing sample with 1600 observations. Conclusions: Findings revealed several predictors of engagement derived from the content generated by interventionists and other participants. Results have implications for increasing engagement in asynchronous, remotely delivered lifestyle interventions, which could improve outcomes. Our results also point to the potential of data science and natural language processing to analyze microlevel conversational data and identify factors influencing participant engagement. Future studies should validate these results in larger trials. Trial Registration: ClinicalTrials.gov NCT02656680; https://clinicaltrials.gov/ct2/show/NCT02656680