Background: People living with HIV (PLWH) have high rates of depressive symptoms. However, only a few effective mental health interventions exist for this vulnerable population. Objective: The aim of this study was to assess the efficacy of a WeChat-based intervention, Run4Love, with a randomized controlled trial among 300 people living with HIV and depression (PLWHD) in China. Methods: We recruited PLWH from the HIV outpatient clinic in South China. Participants were screened based on the Center for Epidemiologic Studies-Depression (CES-D) scale. Those who scored 16 or higher were eligible to participate. A total of 300 eligible patients were enrolled. After obtaining informed consent from the participants, completion of a baseline survey, and collection of participants’ hair samples for measuring cortisol, the participants were randomly assigned to an intervention or a control group in a 1:1 ratio. The intervention group received the Run4Love program, delivered via the popular social media app WeChat. Cognitive behavioral stress management courses and weekly reminders of exercise were delivered in a multimedia format. Participants’ progress was monitored with timely and tailored feedback. The control group received usual care and a brochure on nutrition for PLWH. Data were collected at 3, 6, and 9 months. The primary outcome was depression, which was measured by a validated instrument. Results: Participants in the intervention and control groups were comparable at baseline; about 91.3% (139/150), 88.3% (132/150), and 86.7% (130/150) participants completed the 3-, 6-, and 9-month follow-ups, respectively. At the 3-month follow-up, a significant reduction in CES-D score was observed in the intervention group (from 23.9 to 17.7 vs from 24.3 to 23.8; mean difference=−5.77, 95% CI −7.82 to −3.71; P<.001; standard effect size d=0.66). The mean changes in CES-D score from baseline to the 6- and 9-month follow-ups between the two groups remained statistically significant. No adverse events were reported. Conclusions: The WeChat-based mobile health (mHealth) intervention Run4Love significantly reduced depressive symptoms among PLWHD, and the effect was sustained. An app-based mHealth intervention could provide a feasible therapeutic option for many PLWHD in resource-limited settings. Further research is needed to assess generalizability and cost-effectiveness of this intervention. Trial Registration: Chinese Clinical Trial Registry ChiCTR-IPR-17012606; http://www.chictr.org.cn/showproj.aspx?proj=21019 (Archived by WebCite at https://www.webcitation.org/78Bw2vouF)

Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet #Preprint #PeerReviewMe: Warning: This is a unreviewed preprint. Readers are warned that the document has not been peer-reviewed by expert/patient reviewers or an academic editor, may contain misleading claims, and is likely to undergo changes before final publication, if accepted, or may have been rejected/withdrawn. Readers with interest and expertise are encouraged to sign up as peer-reviewer, if the paper is within an open peer-review period. Please cite this preprint only for review purposes or for grant applications and CVs (if you are the author). Background: Most people living with HIV (PLWH) reside in middle- and low-income countries with limited access to health services. Thus, cost-effective interventions that can reach a large number of PLWH are urgently needed. Objective: The objective of our study was to assess the feasibility and acceptability of an mHealth intervention among PLWH in China. Methods: Based on previous formative research, we designed an mHealth intervention program that included sending weekly reminders to participants via text messages (short message service, SMS) and articles on HIV self-management three times a week via a popular social media app WeChat. A total of 62 PLWH recruited from an HIV outpatient clinic were randomly assigned to intervention or control group. The intervention lasted for 3 months, and all participants were assessed for their medication adherence, presence of depression, quality of life (QoL), and CD4 (cluster of differentiation 4) counts. Upon completing the intervention, we interviewed 31 participants to further assess the feasibility and acceptability of the study. Results: At baseline, the intervention and control groups did not differ in terms of demographic characteristics or any of the major outcome measures. About 85% (53/62) of the participants completed the intervention, and they provided valuable feedback on the design and content of the intervention. Participants preferred WeChat as the platform for receiving information and interactive communication for ease of access. Furthermore, they made specific recommendations about building trust, interactive features, and personalized feedback. In the follow-up assessment, the intervention and control groups did not differ in terms of major outcome measures. Conclusions: This pilot study represents one of the first efforts to develop a text messaging (SMS)- and WeChat-based intervention that focused on improving the medication adherence and QoL of PLWH in China. Our data indicates that an mHealth intervention is feasible and acceptable to this population. The data collected through this pilot study will inform the future designs and implementations of mHealth interventions in this vulnerable population. We recommend more innovative mHealth interventions with rigorous designs for the PLWH in middle- and low-income countries. Trial Registration: N/A

Background: The dose–response relationship between patient engagement and long-term intervention effects in mobile health (mHealth) interventions are understudied. Studies exploring long-term and potentially changing relationships between patient engagement and health outcomes in mHealth interventions are needed. Objective: This study aims to examine dose–response relationships between patient engagement and 3 psychosocial outcomes in an mHealth intervention, Run4Love, using repeated measurements of outcomes at baseline and 3, 6, and 9 months. Methods: This study is a secondary analysis using longitudinal data from the Run4Love trial, a randomized controlled trial with 300 people living with HIV and elevated depressive symptoms to examine the effects of a 3-month mHealth intervention on reducing depressive symptoms and improving quality of life (QOL). We examined the relationships between patient engagement and depressive symptoms, QOL, and perceived stress in the intervention group (N=150) using 4–time-point outcome measurements. Patient engagement was assessed using the completion rate of course assignments and frequency of items completed. Cluster analysis was used to categorize patients into high- and low-engagement groups. Generalized linear mixed effects models were conducted to investigate the dose–response relationships between patient engagement and outcomes. Results: The cluster analysis identified 2 clusters that were distinctively different from each other. The first cluster comprised 72 participants with good compliance to the intervention, completing an average of 74% (53/72) of intervention items (IQR 0.22). The second cluster comprised 78 participants with low compliance to the intervention, completing an average of 15% (11/72) of intervention items (IQR 0.23). Results of the generalized linear mixed effects models showed that, compared with the low-engagement group, the high-engagement group had a significant reduction in more depressive symptoms (β=−1.93; P=.008) and perceived stress (β=−1.72; P<.001) and an improved QOL (β=2.41; P=.01) over 9 months. From baseline to 3, 6, and 9 months, the differences in depressive symptoms between the 2 engagement groups were 0.8, 1.6, 2.3, and 3.7 points, respectively, indicating widening between-group differences over time. Similarly, between-group differences in QOL and perceived stress increased over time (group differences in QOL: 0.9, 1.9, 4.7, and 5.1 points, respectively; group differences in the Perceived Stress Scale: 0.9, 1.4, 2.3, and 3.0 points, respectively). Conclusions: This study revealed a positive long-term dose–response relationship between patient engagement and 3 psychosocial outcomes among people living with HIV and elevated depressive symptoms in an mHealth intervention over 9 months using 4 time-point repeat measurement data. The high- and low-engagement groups showed significant and widening differences in depressive symptoms, QOL, and perceived stress at the 3-, 6-, and 9-month follow-ups. Future mHealth interventions should improve patient engagement to achieve long-term and sustained intervention effects. Trial Registration: Chinese Clinical Trial Registry ChiCTR-IPR-17012606; https://www.chictr.org.cn/showproj.aspx?proj=21019

Background To control the COVID-19 pandemic, governments need to ensure a successful large-scale administration of COVID-19 vaccines when safe and efficacious vaccines become available. Vaccine acceptance could be a critical factor influencing vaccine uptake. Health information has been associated with vaccine acceptance. For college students who are embracing a digital era and being exposed to multimedia, the sources of COVID-19 vaccine information and their trust in these sources may play an important role in shaping their acceptance of vaccine uptake. Methods In September 2020, we conducted an online survey among 1062 college students in South Carolina to understand their perceptions and attitudes toward COVID-19 vaccination. Descriptive analysis and linear regression analysis were used to investigate vaccine information sources among college students and examine how COVID-19 vaccine acceptance was associated with information source and trust level in each source. Results The top three sources of COVID-19 vaccine information were health agencies (57.7%), mass media (49.5%), and personal social networks (40.5%). About 83.1% of the participants largely or always trusted scientists, 73.9% trusted healthcare providers, and 70.2% trusted health agencies. After controlling for key demographics, vaccine acceptance was positively associated with scientists as information sources but negatively associated with pharmaceutical companies as sources. Higher trust levels in mass media, health agencies, scientists, and pharmaceutical companies was significantly associated with higher COVID-19 vaccine acceptance. However, trust in social media was negatively associated with vaccine acceptance. Discussion College students use multiple sources to learn about upcoming COVID-19 vaccines including health agencies, personal networks, and social media. The level of trust in these information sources play a critical role in predicting vaccine acceptance. Trust in health authorities and scientists rather than social media is related to higher level vaccine acceptance. Our findings echo the call for restoring trust in government, healthcare system, scientists, and pharmaceutical industries in the COVID-19 era and highlight the urgency to dispel misinformation in social media. Effective strategies are needed to disseminate accurate information about COVID-19 vaccine from health authorities and scientific research to improve vaccine communication to the public and promote COVID-19 vaccine uptake.

For college students who are exposed to multimedia, the sources of COVID-19 vaccine information and their trust in these sources may play a role in shaping the vaccine acceptance spectrum (refusal,...

Background: Population mobility is closely associated with COVID-19 transmission, and it could be used as a proximal indicator to predict future outbreaks, which could inform proactive nonpharmaceutical interventions for disease control. South Carolina is one of the US states that reopened early, following which it experienced a sharp increase in COVID-19 cases. Objective: The aims of this study are to examine the spatial-temporal relationship between population mobility and COVID-19 outbreaks and use population mobility data to predict daily new cases at both the state and county level in South Carolina. Methods: This longitudinal study used disease surveillance data and Twitter-based population mobility data from March 6 to November 11, 2020, in South Carolina and its five counties with the largest number of cumulative confirmed COVID-19 cases. Population mobility was assessed based on the number of Twitter users with a travel distance greater than 0.5 miles. A Poisson count time series model was employed for COVID-19 forecasting. Results: Population mobility was positively associated with state-level daily COVID-19 incidence as well as incidence in the top five counties (ie, Charleston, Greenville, Horry, Spartanburg, and Richland). At the state level, the final model with a time window within the last 7 days had the smallest prediction error, and the prediction accuracy was as high as 98.7%, 90.9%, and 81.6% for the next 3, 7, and 14 days, respectively. Among Charleston, Greenville, Horry, Spartanburg, and Richland counties, the best predictive models were established based on their observations in the last 9, 14, 28, 20, and 9 days, respectively. The 14-day prediction accuracy ranged from 60.3%-74.5%. Conclusions: Using Twitter-based population mobility data could provide acceptable predictions of COVID-19 daily new cases at both the state and county level in South Carolina. Population mobility measured via social media data could inform proactive measures and resource relocations to curb disease outbreaks and their negative influences.