In Pursuit of Theoretical Ground in Behavior Change Support Systems: Analysis of Peer-to-Peer Communication in a Health-Related Online Community

Prevalence of technology and connectivity issues in general practices in rural New South Wales and their impact on staff capability to perform their job

OBJECTIVE

To identify the technology and connectivity issues in rural and remote general practices, and the factors independently associated with these issues that negatively impact staff's capability to perform their job.

METHODS

An annual cross-sectional survey of rural and remote general practice managers. Dependent variables included demographic data, practice size, geographic location, connection type and frequency of connectivity issues. Descriptive statistics are presented, and bivariate logistic regression was undertaken to determine factors independently associated with connectivity issues that negatively impact staff's capability to perform their job.

PARTICIPANTS

One hundred sixty-eight general practice managers from rural and remote New South Wales.

RESULTS

The majority of respondents (87%, n = 146) indicated that technology and connectivity issues had impacted staff's capability to perform their job. Internet problems were the most frequently reported issue (36%, n = 61). In bivariate analysis, practices that had a total clinical staff headcount between 5 and 7 (OR 0.27; 95% CI 0.10-0.67; p = 0.005) or between 8 and 11 (OR 0.39; 95% CI 0.16-0.95; p = 0.038) were significantly less likely to report technology and connectivity issues that negatively impact staff's capability to perform their job, compared with practices with a total clinical headcount of less than five.

CONCLUSIONS

Technology and connectivity issues persist in rural and remote general practices. This is the first study to demonstrate that technology and connectivity issues impact on rural staff's capability to perform their job. Furthermore, smaller practices face more technology and connectivity issues that negatively impact staff's capability to do their job than larger practices. Further research is required to find solutions to address these challenges.

How Do Adolescent Smoking Prevention Interventions Work in Different Contextual Settings? A Qualitative Comparative Study Between the UK and Colombia

BACKGROUND

Adolescent smoking is associated with significant health and social risks. Previous research has demonstrated the effectiveness of interventions based on behavior change theories in preventing adolescent smoking uptake. However, evidence from the theory-based perspective of evaluation is limited, especially for how such complex interventions work, and how they work when implemented in different contextual settings.

METHOD

A comparative qualitative analysis was conducted to explore various influences on behavior change among participants taking part in two smoking prevention interventions in Northern Ireland and Bogotá. Twenty-seven focus groups were conducted in 12 schools (6 in Northern Ireland and 6 in Bogota, n = 195 pupils participated; aged 11-15 years). The Theoretical Domains Framework guided a content analysis of the data.

RESULTS

We found similarities across settings in terms of knowledge, skills, and beliefs related to smoking or vaping behavior change, as well as differences in contextual resources and social influence. Different environmental resources included availability to purchase tobacco products in the neighborhoods and previous information about tobacco risk. Participants in both interventions perceived behavioral change outcomes related to personal skills and intention to not smoke or vape.

CONCLUSION

These findings have highlighted how both individual factors and contextual resources influence behavior change for smoking prevention in practice. Local contextual factors and social influences affecting pupils should be taken into account in the implementation and evaluation of health behavior change interventions. In particular, this study supports using social and contextual influence strategies in interventions to reduce the onset of adolescent smoking and vaping.

Words for the hearts: a corpus study of metaphors in online depression communities

Purpose/significance

Humans understand, think, and express themselves through metaphors. The current paper emphasizes the importance of identifying the metaphorical language used in online health communities (OHC) to understand how users frame and make sense of their experiences, which can boost the effectiveness of counseling and interventions for this population.

Methods/process

We used a web crawler to obtain a corpus of an online depression community. We introduced a three-stage procedure for metaphor identification in a Chinese Corpus: (1) combine MIPVU to identify metaphorical expressions (ME) bottom-up and formulate preliminary working hypotheses; (2) collect more ME top-down in the corpus by performing semantic domain analysis on identified ME; and (3) analyze ME and categorize conceptual metaphors using a reference list. In this way, we have gained a greater understanding of how depression sufferers conceptualize their experience metaphorically in an under-represented language in the literature (Chinese) of a new genre (online health community).

Results/conclusion

Main conceptual metaphors for depression are classified into PERSONAL LIFE, INTERPERSONAL RELATIONSHIP, TIME, and CYBERCULTURE metaphors. Identifying depression metaphors in the Chinese corpus pinpoints the sociocultural environment people with depression are experiencing: lack of offline support, social stigmatization, and substitutability of offline support with online support. We confirm a number of depression metaphors found in other languages, providing a theoretical basis for researching, identifying, and treating depression in multilingual settings. Our study also identifies new metaphors with source-target connections based on embodied, sociocultural, and idiosyncratic levels. From these three levels, we analyze metaphor research's theoretical and practical implications, finding ways to emphasize its inherent cross-disciplinarity meaningfully.

Discerning conversational context in online health communities for personalized digital behavior change solutions using Pragmatics to Reveal Intent in Social Media (PRISM) framework

BACKGROUND

Online health communities (OHCs) have emerged as prominent platforms for behavior modification, and the digitization of online peer interactions has afforded researchers with unique opportunities to model multilevel mechanisms that drive behavior change. Existing studies, however, have been limited by a lack of methods that allow the capture of conversational context and socio-behavioral dynamics at scale, as manifested in these digital platforms.

OBJECTIVE

We develop, evaluate, and apply a novel methodological framework, Pragmatics to Reveal Intent in Social Media (PRISM), to facilitate granular characterization of peer interactions by combining multidimensional facets of human communication.

METHODS

We developed and applied PRISM to analyze peer interactions (N = 2.23 million) in QuitNet, an OHC for tobacco cessation. First, we generated a labeled set of peer interactions (n = 2,005) through manual annotation along three dimensions: communication themes (CTs), behavior change techniques (BCTs), and speech acts (SAs). Second, we used deep learning models to apply our qualitative codes at scale. Third, we applied our validated model to perform a retrospective analysis. Finally, using social network analysis (SNA), we portrayed large-scale patterns and relationships among the aforementioned communication dimensions embedded in peer interactions in QuitNet.

RESULTS

Qualitative analysis showed that the themes of social support and behavioral progress were common. The most used BCTs were feedback and monitoring and comparison of behavior, and users most commonly expressed their intentions using SAs-expressive and emotion. With additional in-domain pre-training, bidirectional encoder representations from Transformers (BERT) outperformed other deep learning models on the classification tasks. Content-specific SNA revealed that users' engagement or abstinence status is associated with the prevalence of various categories of BCTs and SAs, which also was evident from the visualization of network structures.

CONCLUSIONS

Our study describes the interplay of multilevel characteristics of online communication and their association with individual health behaviors.

Online health community for change: Analysis of self-disclosure and social networks of users with depression

Background

A key research question with theoretical and practical implications is to investigate the various conditions by which social network sites (SNS) may either enhance or interfere with mental well-being, given the omnipresence of SNS and their dual effects on well-being.

Method/process

We study SNS’ effects on well-being by accounting for users’ personal (i.e., self-disclosure) and situational (i.e., social networks) attributes, using a mixed design of content analysis and social network analysis.

Result/conclusion

We compare users’ within-person changes in self-disclosure and social networks in two phases (over half a year), drawing on Weibo Depression SuperTalk, an online community for depression, and find: ① Several network attributes strengthen social support, including network connectivity, global efficiency, degree centralization, hubs of communities, and reciprocal interactions. ② Users’ self-disclosure attributes reflect positive changes in mental well-being and increased attachment to the community. ③ Correlations exist between users’ topological and self-disclosure attributes. ④ A Poisson regression model extracts self-disclosure attributes that may affect users’ received social support, including the writing length, number of active days, informal words, adverbs, negative emotion words, biological process words, and first-person singular forms.

Innovation

We combine social network analysis with content analysis, highlighting the need to understand SNS’ effects on well-being by accounting for users’ self-disclosure (content) and communication partners (social networks).

Implication/contribution

Authentic user data helps to avoid recall bias commonly found in self-reported data. A longitudinal within-person analysis of SNS’ effects on well-being is helpful for policymakers in public health intervention, community managers for group organizations, and users in online community engagement.

Digital Phenotyping in Health Using Machine Learning Approaches: Scoping Review

BACKGROUND

Digital phenotyping is the real-time collection of individual-level active and passive data from users in naturalistic and free-living settings via personal digital devices, such as mobile phones and wearable devices. Given the novelty of research in this field, there is heterogeneity in the clinical use cases, types of data collected, modes of data collection, data analysis methods, and outcomes measured.

OBJECTIVE

The primary aim of this scoping review was to map the published research on digital phenotyping and to outline study characteristics, data collection and analysis methods, machine learning approaches, and future implications.

METHODS

We utilized an a priori approach for the literature search and data extraction and charting process, guided by the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-analyses Extension for Scoping Reviews). We identified relevant studies published in 2020, 2021, and 2022 on PubMed and Google Scholar using search terms related to digital phenotyping. The titles, abstracts, and keywords were screened during the first stage of the screening process, and the second stage involved screening the full texts of the shortlisted articles. We extracted and charted the descriptive characteristics of the final studies, which were countries of origin, study design, clinical areas, active and/or passive data collected, modes of data collection, data analysis approaches, and limitations.

RESULTS

A total of 454 articles on PubMed and Google Scholar were identified through search terms associated with digital phenotyping, and 46 articles were deemed eligible for inclusion in this scoping review. Most studies evaluated wearable data and originated from North America. The most dominant study design was observational, followed by randomized trials, and most studies focused on psychiatric disorders, mental health disorders, and neurological diseases. A total of 7 studies used machine learning approaches for data analysis, with random forest, logistic regression, and support vector machines being the most common.

CONCLUSIONS

Our review provides foundational as well as application-oriented approaches toward digital phenotyping in health. Future work should focus on more prospective, longitudinal studies that include larger data sets from diverse populations, address privacy and ethical concerns around data collection from consumer technologies, and build "digital phenotypes" to personalize digital health interventions and treatment plans.

A Randomized Trial of 21 Days of Loving Kindness Meditation for Stress Reduction and Emotional Well-being Within an Online Health Community for Patients, Family, and Friends Experiencing a Cancer Health Journey

Objectives: CaringBridge (CB) is an online health community for people undergoing challenging health journeys. Loving Kindness Meditation (LKM) is a systemized mind-body approach developed to increase loving acceptance and has previously been reported to increase resilience in the face of adversity. Materials and Methods: Results of a randomized controlled trial of immediate compared with deferred 21-day LKM intervention in an online community are reported. The deferred group received LKM intervention after a waiting period of 3 weeks. Inclusion criteria were >18 years old, ability to understand English, willingness to participate in a mind-body practice, and use of CB for a cancer journey. Change in perceived stress, self-compassion, social connectedness and assurance, and compassionate love scales from baseline to 21 days was assessed. Results: Of the 979 participants included in the study, 649 (66%) provided 3-week follow-up data and 330 (49%) self-reported engaging in the LKM practice 5 or more days/week. Participants in the immediate LKM group reported medium effect size improvement in stress (0.4), self-compassion (0.5), and social connectedness (0.4) compared with the deferred LKM group. Changes in perceived stress and self-compassion were larger in magnitude and increased with more frequent engagement in LKM. Conclusions: The immediate LKM group showed improvements in stress, self-compassion, and social connectedness compared with the deferred control group. Differential study retention rates by treatment arm and self-reported engagement in LKM subject the results to selection bias. Future research of similar interventions within online health communities might pay greater attention to promoting intervention adherence and engaging a more diverse economic and racial/ethnic population. ClinicalTrials.gov (NCT05002842).

Development of a framework to promote rural health workforce capability through digital solutions: A qualitative study of user perspectives

A high-quality, sustained, health workforce contributes to a healthy population. However, a global reality is that rural health services, and the workforces that provide those services, are under unprecedented pressure. It is posited that improving a rural health practitioners' capability could help to retain them working rurally for longer. Capability refers to skills and experience and the extent to which individuals can adapt to change, generate new knowledge and continue to improve their performance. With rapidly increasing access to, and use of, digital technology worldwide, there are new opportunities to build capability and leverage personal and professional support for those who are working rurally. In 2021, semi-structured interviews were conducted in rural Australia with thirteen General Practitioners and allied health professionals. Thematic analysis was adopted to analyse the data and map it to the Health Information Technology Acceptance Model. Whilst it could be assumed that low technology literacy would act as a barrier to the use of digital tools, the study demonstrated that this was not a significant impediment to participants' willingness to adopt digital tools when social and professional networks weren't available face to face to address their capability challenges. The findings provide insight into the concept of health workforce capability and important considerations when designing capability support. This includes key features of health apps or digital tools to support the capability of the rural health workforce. Understanding the factors that make up a health professionals' capability and the motivations or cues to act to build or maintain their capability may have a positive effect on their retention in a rural location.

Pragmatics to Reveal Intent in Social Media Peer Interactions: Mixed Methods Study

Background

Online health communities (OHCs) have emerged as the leading venues for behavior change and health-related information seeking. The soul and success of these digital platforms lie in their ability to foster social togetherness and a sense of community by providing personalized support. However, we have a minimal understanding of how conversational posts in these settings lead to collaborative societies and ultimately result in positive health changes through social influence.

Objective

Our objective is to develop a content-specific and intent-sensitive methodological framework for analyzing peer interactions in OHCs.

Methods

We developed and applied a mixed-methods approach to understand the manifestation of expressions in peer interactions in OHCs. We applied our approach to describe online social dialogue in the context of two online communities, QuitNet (QN) and the American Diabetes Association (ADA) support community. A total of 3011 randomly selected peer interactions (n=2005 from QN, n=1006 from ADA) were analyzed. Specifically, we conducted thematic analysis to characterize communication content and linguistic expressions (speech acts) embedded within the two data sets. We also developed an empirical user persona based on their engagement levels and behavior profiles. Further, we examined the association between speech acts and communication themes across observed tiers of user engagement and self-reported behavior profiles using the chi-square test or the Fisher test.

Results

Although social support, the most prevalent communication theme in both communities, was expressed in several subtle manners, the prevalence of emotions was higher in the tobacco cessation community and assertions were higher in the diabetes self-management (DSM) community. Specific communication theme-speech act relationships were revealed, such as the social support theme was significantly associated (P<.05) with 9 speech acts from a total of 10 speech acts (ie, assertion, commissive, declarative, desire, directive, expressive, question, stance, and statement) within the QN community. Only four speech acts (ie, commissive, emotion, expressive, and stance) were significantly associated (P<.05) with the social support theme in the ADA community. The speech acts were also significantly associated with the users’ abstinence status within the QN community and with the users’ lifestyle status within the ADA community (P<.05).

Conclusions

Such an overlay of communication intent implicit in online peer interactions alongside content-specific theory-linked characterizations of social media discourse can inform the development of effective digital health technologies in the field of health promotion and behavior change. Our analysis revealed a rich gradient of expressions across a standardized thematic vocabulary, with a distinct variation in emotional and informational needs, depending on the behavioral and disease management profiles within and across the communities. This signifies the need and opportunities for coupling pragmatic messaging in digital therapeutics and care management pathways for personalized support.

Exploring features of social support in a Chinese online smoking cessation community: A multidimensional content analysis of user interaction data

Due to the rapid development of information technology, an increasing number of smokers choose online smoking cessation communities to interact with other individuals to help themselves quit smoking. Though it is well known that social support plays a key role in the process of smoking cessation, the features of social support that one can get from online smoking cessation communities remain unclear. We collected user interaction data from the largest Chinese online smoking cessation community, the quit smoking forum of Baidu Tieba. We selected 2758 replies from 29 active repliers and 408 correlated posts as our data set. Multidimensional content analysis is carried out from three aspects: posting scenarios, user quitting behavior stages, and types of social support. This article also explores the co-occurrence relationships of different types of social support by social network analysis. Results showed that users receive different compositions of social support in various posting scenarios and behavior stages. In most cases, emotional support is the most typical support the community provides. The community will provide more informational support when needed. Besides, informational support, especially personal experience and perceptual knowledge, has more diverse combination patterns with other types of social support. "Gratitude-Mutual assistance" and "Encouragement-Mutual assistance" are the most frequent co-occurrence relationships. The online smoking cessation community brings people who quit smoking together, and users provide rich types of social support for each other. Users can effectively obtain expected social support in different posting scenarios and smoking cessation stages. Smoking cessation projects should be designed to promote user communication and interaction, which positively affects achieving users' smoking cessation goals.

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.

The role of digital technology in providing education, training, continuing professional development and support to the rural health workforce

Purpose

Education, training and continuing professional development are amongst the evidence-based initiatives for attracting and retaining rural and remote health professionals. With rapidly increasing access to and use of digital technology worldwide, there are new opportunities to leverage training and support for those who are working in rural and remote areas. In this paper we determine the key elements associated with the utility of digital technologies to provide education, training, professional learning and support for rural health workforce outside the University and tertiary sector.

Design/methodology/approach

A scoping review of peer-reviewed literature from Australia, Canada, US and New Zealand was conducted in four bibliographic databases – Medline complete, CINAHL, Academic Search complete and Education Complete. Relevant studies published between January 2010 and September 2020 were identified. The Levac et al. (2010) enhanced methodology of the Arksey and O'Malley (2005) framework was used to analyse the literature.

Findings

The literature suggests there is mounting evidence demonstrating the potential for online platforms to address the challenges of rural health professional practice and the tyranny of distance. After analysing 22 publications, seven main themes were found – Knowledge and skills (n = 13), access (n = 10), information technology (n = 7), translation of knowledge into practice (n = 6), empowerment and confidence (n = 5), engagement (n = 5) and the need for support (n = 5). Ongoing evaluation will be critical to explore new opportunities for digital technology to demonstrate enhanced capability and retention of rural health professionals.

Originality/value

To date there has been limited examination of research that addresses the value of digital platforms on continuing professional development, education and support for rural health professionals outside the university and tertiary training sectors.

'Just Drive': An Employee-Based Intervention to Reduce Distracted Driving

Distracted driving is a major danger on today's roadways. Employers play a critical role in developing distracted driving policies and promoting a culture of workplace driving safety. The purpose of this study was to evaluate the effectiveness of an in-person work-based class to reduce distracted driving in participating employees. The "Just Drive-Take Action Against Distraction" class was designed by the UC San Diego Training, Research and Education for Driving Safety (TREDS) program to increase awareness of the dangers of distracted driving and to encourage employees to be safe and responsible drivers, both on and off the job. Participants completed pre- and post-anonymous surveys and, in a subset of attendees, volunteers were contacted via email 3 months post-intervention to complete a driving-behavior survey on Surveymonkey.com. 115 classes for 6896 employees were delivered at 54 agencies in Southern California. A total of 4928 participants completed the pre- and post-survey; 2014 n = 2263 and 2015 n = 2665. The course was found useful (85%) and engaging (85.6%). For non-commercial drivers, 55.6% of participants reported an increase of 80-100% in awareness of the dangers of distracted driving, and 67.2% reported an increase of 80-100% in their motivation to change. For commercial drivers, 71.3% reported a motivation increase of 80-100%. There were significant increases in knowledge for both groups. In the three-month follow-up survey, participants identified multiple positive changes in distracted driving behavior. This 1-h employer-supported intervention demonstrated positive changes in short-term intention and medium-term behaviors.

Digital technology to support lifestyle and health behaviour changes in surgical patients: systematic review

BACKGROUND

Digital technologies (such as smartphone applications, activity trackers, and e-learning platforms) have supported patients with long-term conditions to change their lifestyle health behaviours. The aim of this study was to examine the effectiveness of digital technologies in supporting patients undergoing elective surgery to change their health behaviours.

METHODS

A systematic review was conducted of articles reporting a digital intervention supporting behaviour change in adult patients who underwent elective bariatric, oncological or orthopaedic surgery. MEDLINE, Embase, CINAHL, PsycINFO, Web of Science, and Scopus were searched from inception to March 2019 for quantitative intervention studies with a specific focus on physical activity, dietary intake, and weight loss in patients before and after surgery (PROSPERO: CRD42019127972). The Joanna Briggs Institute critical appraisal checklist was used to assess study quality.

RESULTS

Of 3021 citations screened, 17 studies were included comprising 4923 surgical patients; these included experimental (pre-post design, feasibility studies, and RCTs) and observational studies. Three factors were identified as effective for supporting health behaviour change in elective surgical populations: digital technology delivery, implementation, and theoretical underpinning. Six of eight studies that referred to behaviour change theories observed significant improvements in health behaviour relating to reduced weight regain, and improved lifestyle choices for physical activity and diet. Meta-analysis was not possible because of heterogeneous outcome measures.

CONCLUSION

Digital technologies may effectively support behavioural change in patients undergoing elective surgery.

Social Media as a Research Tool (SMaaRT) for Risky Behavior Analytics: Methodological Review

BACKGROUND

Modifiable risky health behaviors, such as tobacco use, excessive alcohol use, being overweight, lack of physical activity, and unhealthy eating habits, are some of the major factors for developing chronic health conditions. Social media platforms have become indispensable means of communication in the digital era. They provide an opportunity for individuals to express themselves, as well as share their health-related concerns with peers and health care providers, with respect to risky behaviors. Such peer interactions can be utilized as valuable data sources to better understand inter-and intrapersonal psychosocial mediators and the mechanisms of social influence that drive behavior change.

OBJECTIVE

The objective of this review is to summarize computational and quantitative techniques facilitating the analysis of data generated through peer interactions pertaining to risky health behaviors on social media platforms.

METHODS

We performed a systematic review of the literature in September 2020 by searching three databases-PubMed, Web of Science, and Scopus-using relevant keywords, such as "social media," "online health communities," "machine learning," "data mining," etc. The reporting of the studies was directed by the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Two reviewers independently assessed the eligibility of studies based on the inclusion and exclusion criteria. We extracted the required information from the selected studies.

RESULTS

The initial search returned a total of 1554 studies, and after careful analysis of titles, abstracts, and full texts, a total of 64 studies were included in this review. We extracted the following key characteristics from all of the studies: social media platform used for conducting the study, risky health behavior studied, the number of posts analyzed, study focus, key methodological functions and tools used for data analysis, evaluation metrics used, and summary of the key findings. The most commonly used social media platform was Twitter, followed by Facebook, QuitNet, and Reddit. The most commonly studied risky health behavior was nicotine use, followed by drug or substance abuse and alcohol use. Various supervised and unsupervised machine learning approaches were used for analyzing textual data generated from online peer interactions. Few studies utilized deep learning methods for analyzing textual data as well as image or video data. Social network analysis was also performed, as reported in some studies.

CONCLUSIONS

Our review consolidates the methodological underpinnings for analyzing risky health behaviors and has enhanced our understanding of how social media can be leveraged for nuanced behavioral modeling and representation. The knowledge gained from our review can serve as a foundational component for the development of persuasive health communication and effective behavior modification technologies aimed at the individual and population levels.

Diabetes Self-Management in the Age of Social Media: Large-Scale Analysis of Peer Interactions Using Semiautomated Methods

BACKGROUND

Online communities have been gaining popularity as support venues for chronic disease management. User engagement, information exposure, and social influence mechanisms can play a significant role in the utility of these platforms.

OBJECTIVE

In this paper, we characterize peer interactions in an online community for chronic disease management. Our objective is to identify key communications and study their prevalence in online social interactions.

METHODS

The American Diabetes Association Online community is an online social network for diabetes self-management. We analyzed 80,481 randomly selected deidentified peer-to-peer messages from 1212 members, posted between June 1, 2012, and May 30, 2019. Our mixed methods approach comprised qualitative coding and automated text analysis to identify, visualize, and analyze content-specific communication patterns underlying diabetes self-management.

RESULTS

Qualitative analysis revealed that "social support" was the most prevalent theme (84.9%), followed by "readiness to change" (18.8%), "teachable moments" (14.7%), "pharmacotherapy" (13.7%), and "progress" (13.3%). The support vector machine classifier resulted in reasonable accuracy with a recall of 0.76 and precision 0.78 and allowed us to extend our thematic codes to the entire data set.

CONCLUSIONS

Modeling health-related communication through high throughput methods can enable the identification of specific content related to sustainable chronic disease management, which facilitates targeted health promotion.

Improving cancer survivors' e-health literacy via online health communities (OHCs): a social support perspective

PURPOSE

Cancer survivors should have adequate e-health literacy to help them better use online health information. Online health communities (OHCs) can offer cancer survivors different types of social support that can represent another resource to improve health outcomes. However, there is little knowledge of how these OHC are directly related to a cancer survivors' e-health literacy. This study explores how different types of social support in OHCs are associated with cancer survivors' e-health literacy.

METHODS

A questionnaire was developed to collect data from two Chinese OHCs used by cancer survivors. The questionnaire is composed of two parts: six sociodemographic variables (i.e., gender, age, city, education, tenure, and prior Internet experience), two scales for informational support behaviors (i.e., health knowledge seeking and provision of health knowledge), a measure of emotional support within such a setting, and a measure of e-health literacy. Based on 162 complete samples, we determined the measurement properties of the scales used, provided descriptive statistics on major sociodemographic variables and conducted bivariate and multivariable hierarchical regression.

RESULTS

For cancer survivors, females demonstrate higher levels of e-health literacy. Higher education level was related to higher e-health literacy. Health knowledge seeking, contributing to health knowledge, and emotional support were all positively associated with e-health literacy. The interaction effect between health knowledge and emotional support is positively associated with e-health literacy.

CONCLUSIONS

Informational support and emotional support, as two major subtypes of social support within resources available in OHCs, are positively associated with e-health literacy among cancer survivors.

IMPLICATIONS FOR CANCER SURVIVORS

Cancer survivors might benefit from an active strategy for improving personal e-health literacy that includes more active informational involvement and emotional support rather than a passive lurking through e-health information and seeking and reading postings in OHCs.

Utilizing Digital Health Technologies for Patient Education in Lifestyle Medicine

Technology has redefined the way patients and providers communicate and obtain health information. The realm of digital health encompasses a diverse set of technologies, including mobile health, health information technology, wearable devices, telehealth and telemedicine, and personalized medicine. These technologies have begun to improve care delivery without the traditional constraints of distance, location, and time. A growing body of evidence supports the use of digital health technology for improving patient education and implementation of skills and behaviors integral to lifestyle medicine. Patient education can now be delivered in standard formats (eg, articles, written messages) as well a wide array of multimedia (video, audio, interactive games, etc), which may be more appropriate for certain topics and learning styles. In addition, patient engagement in their care plays an important role in improving health outcomes. Despite digital health technology development often outpacing its research, there is sufficient evidence to support the use of many current technologies in clinical practice. Digital health tools will continue to grow in their ability to cost-effectively monitor and encourage healthy behaviors at scale, and better methods of evaluation will likely increase clinician confidence in their use.

Characterization of Behavioral Transitions Through Social Media Analysis: A Mixed-Methods Approach

Unhealthy behaviors are a socioeconomic burden and lead to the development of chronic diseases. Relapse is a common issue that most individuals deal with as they adopt and sustain a positive healthy lifestyle. Proper identification of behavioral transitions can help design agile, adaptive, and just-in-time interventions. In this paper, we present a methodology that integrates qualitative coding, machine learning, and formal data analysis using stage transition probabilities and linguistics-based text analysis to track shifts in stages of behavior change as embedded in journal entries recorded by users in an online community for tobacco cessation. Results indicate that our semi-automated stage identification method has an accuracy of 90%. Further analysis revealed stage-specific language features and transition probabilities. Implications for targeted social interventions are discussed.

Exposure to positive peer sentiment about nicotine replacement therapy in an online smoking cessation community is associated with NRT use

BACKGROUND

Little is known about the influence of online peer interactions on health behavior change. This study examined the relationship between exposure to peer sentiment about nicotine replacement therapy (NRT) in an online social network for smoking cessation and NRT use.

METHODS

Participants were 3297 current smokers who enrolled in an Internet smoking cessation program, participated in a randomized trial, and completed a 3-month follow-up. Half received free NRT as part of the trial. Automated text classification identified 27,038 posts about NRT that one or more participants were exposed to in the social network. Sentiment towards NRT was rated on Amazon Mechanical Turk. Participants' exposure to peer sentiment about NRT was determined by analysis of clickstream data. Modified Poisson regression examined self-reported use of NRT at 3-months as a function of exposure to NRT sentiment, controlling for study arm and post exposure.

RESULTS

One in five participants (19.3%, n = 639) were exposed to any NRT-related posts (mean exposure = 6.5 ± 14.7, mean sentiment = 5.4 ± 0.8). The association between sentiment exposure and NRT use varied by receipt of free NRT. Greater exposure to positive NRT sentiment was associated with an increased likelihood of NRT use among participants who did not receive free NRT (adjusted rate ratio 1.22, 95% CI 1.01, 1.47; p = .043), whereas no such relationship was observed among participants who did receive free NRT (p = .48).

CONCLUSIONS

Exposure to positive sentiment about NRT was associated with increased NRT use when smokers obtained it on their own. Highlighting user-generated content containing positive NRT sentiment may increase NRT use among treatment-seeking smokers.

The State of Digital Interventions for Demand Generation in Low- and Middle-Income Countries: Considerations, Emerging Approaches, and Research Gaps

The recent introduction of digital health into generating demand for health commodities and services has provided practitioners with an expanded universe of potential tools to strengthen demand and ensure service delivery receipt. However, considerable gaps remain in our understanding of which interventions are effective, which characteristics mediate their benefit for different target populations and health domains, and what is necessary to ensure effective deployment. This paper first provides an overview of the types of digital health interventions for demand generation, including untargeted client communication, client-to-client communication, on-demand information services, personal health tracking, client financial transactions, and targeted client communication. It then provides a general overview of 118 studies published between January 1, 2010, and October 3, 2017, that used digital interventions to generate demand for health interventions. The majority (61%) of these studies used targeted client communication to provide health education or reminders to improve treatment adherence, and the most frequently (27%) studied health condition was HIV/AIDS. Intervention characteristics that have been found to have some effect on gains in demand generation include modality, directionality, tailoring, phrasing, and schedule. The paper also explores new emergent digital approaches that expand the potential effect of traditional demand generation in terms of personalization of content and services, continuity of care, and accountability tracking. Applying existing frameworks for monitoring and evaluation and reporting, research on emerging approaches will need to consider not only their feasibility but also their effectiveness in achieving demand generation outcomes. We propose a research agenda to help guide the field of digital demand generation studies and programs within a broader health systems strengthening agenda, including establishing and documenting the influence of intervention characteristics within different populations and health domains and examining the long-term effects and cost-effectiveness of digital demand generation interventions, as well as equity in access to such interventions.

Content-Sensitive Characterization of Peer Interactions of Highly Engaged Users in an Online Community for Smoking Cessation: Mixed-Methods Approach for Modeling User Engagement in Health Promotion Interventions

Background

Online communities provide affordable venues for behavior change. However, active user engagement holds the key to the success of these platforms. In order to enhance user engagement and in turn, health outcomes, it is essential to offer targeted interventional and informational support.

Objective

In this paper, we describe a content plus frequency framework to enable the characterization of highly engaged users in online communities and study theoretical techniques employed by these users through analysis of exchanged communication.

Methods

We applied the proposed methodology for analysis of peer interactions within QuitNet, an online community for smoking cessation. Firstly, we identified 144 highly engaged users based on communication frequency within QuitNet over a period of 16 years. Secondly, we used the taxonomy of behavior change techniques, text analysis methods from distributional semantics, machine learning, and sentiment analysis to assign theory-driven labels to content. Finally, we extracted content-specific insights from peer interactions (n=159,483 messages) among highly engaged QuitNet users.

Results

Studying user engagement using our proposed framework led to the definition of 3 user categories—conversation initiators, conversation attractors, and frequent posters. Specific behavior change techniques employed by top tier users (threshold set at top 3) within these 3 user groups were found to be goal setting, social support, rewards and threat, and comparison of outcomes. Engagement-specific trends within sentiment manifestations were also identified.

Conclusions

Use of content-inclusive analytics has offered deep insight into specific behavior change techniques employed by highly engaged users within QuitNet. Implications for personalization and active user engagement are discussed.

Patient Continued Use of Online Health Care Communities: Web Mining of Patient-Doctor Communication

BACKGROUND

In practice, online health communities have passed the adoption stage and reached the diffusion phase of development. In this phase, patients equipped with knowledge regarding the issues involved in health care are capable of switching between different communities to maximize their online health community activities. Online health communities employ doctors to answer patient questions, and high quality online health communities are more likely to be acknowledged by patients. Therefore, the factors that motivate patients to maintain ongoing relationships with online health communities must be addressed. However, this has received limited scholarly attention.

OBJECTIVE

The purpose of this study was to identify the factors that drive patients to continue their use of online health communities where doctor-patient communication occurs. This was achieved by integrating the information system success model with online health community features.

METHODS

A Web spider was used to download and extract data from one of the most authoritative Chinese online health communities in which communication occurs between doctors and patients. The time span analyzed in this study was from January 2017 to March 2017. A sample of 469 valid anonymous patients with 9667 posts was obtained (the equivalent of 469 respondents in survey research). A combination of Web mining and structural equation modeling was then conducted to test the research hypotheses.

RESULTS

The results show that the research framework for integrating the information system success model and online health community features contributes to our understanding of the factors that drive patients' relationships with online health communities. The primary findings are as follows: (1) perceived usefulness is found to be significantly determined by three exogenous variables (ie, social support, information quality, and service quality; R²=0.88). These variables explain 87.6% of the variance in perceived usefulness of online health communities; (2) similarly, patient satisfaction was found to be significantly determined by the three variables listed above (R²=0.69). These variables explain 69.3% of the variance seen in patient satisfaction; (3) continuance use (dependent variable) is significantly influenced by perceived usefulness and patient satisfaction (R²=0.93). That is, the combined effects of perceived usefulness and patient satisfaction explain 93.4% of the variance seen in continuance use; and (4) unexpectedly, individual literacy had no influence on perceived usefulness and satisfaction of patients using online health communities.

CONCLUSIONS

First, this study contributes to the existing literature on the continuance use of online health communities using an empirical approach. Second, an appropriate metric was developed to assess constructs related to the proposed research model. Additionally, a Web spider enabled us to acquire objective data relatively easily and frequently, thereby overcoming a major limitation of survey techniques.

From loquacious to reticent: understanding patient health information communication to guide consumer health IT design

Background and significance

It is increasingly recognized that some patients self-manage in the context of social networks rather than alone. Consumer health information technology (IT) designed to support socially embedded self-management must be responsive to patients' everyday communication practices. There is an opportunity to improve consumer health IT design by explicating how patients currently leverage social media to support health information communication.

Objective

The objective of this study was to determine types of health information communication patterns that typify Facebook users with chronic health conditions to guide consumer health IT design.

Materials and methods

Seven hundred participants with type 2 diabetes were recruited through a commercial survey access panel. Cluster analysis was used to identify distinct approaches to health information communication both on and off Facebook. Analysis of variance (ANOVA) methods were used to identify demographic and behavioral differences among profiles. Secondary analysis of qualitative interviews ( n  = 25) and analysis of open-ended survey questions were conducted to understand participant rationales for each profile.

Results

Our analysis yielded 7 distinct health information communication profiles. Five of 7 profiles had consistent patterns both on and off Facebook, while the remaining 2 demonstrated distinct practices, with no health information communication on Facebook but some off Facebook. One profile was distinct from all others in both health information communication practices and demographic composition. Rationales for following specific health information communication practices were categorized under 6 themes: altruism, instrumental support, social support, privacy and stigma, convenience, and Facebook knowledge.

Conclusion

Facebook has been widely adopted for health information communication; This study demonstrates that Facebook has been widely adopted for health information communication. It also shows that the ways in which patients communicate health information on and off Facebook are diverse.

Preliminary Evidence for the Emergence of a Health Care Online Community of Practice: Using a Netnographic Framework for Twitter Hashtag Analytics

Background

Online communities of practice (oCoPs) may emerge from interactions on social media. These communities offer an open digital space and flat role hierarchy for information sharing and provide a strong group identity, rapid flow of information, content curation, and knowledge translation. To date, there is only a small body of evidence in medicine or health care to verify the existence of an oCoP.

Objective

We aimed to examine the emergence of an oCoP through the study of social media interactions of the free open access medical education (FOAM) movement.

Methods

We examined social media activity in Twitter by analyzing the network centrality metrics of tweets with the #FOAMed hashtag and compared them with previously validated criteria of a community of practice (CoP).

Results

The centrality analytics of the FOAM community showed concordance with aspects of a general CoP (in terms of community, domain, and practice), as well as some specific traits of a health care community, including social control, common purpose, flat hierarchy, and network-based and concrete achievement.

Conclusions

This study demonstrated preliminary evidence of an oCoP focused on education and based on social media interactions. Further examination of the topology of the network is needed to definitely prove the existence of an oCoP. Given that these communities result in significant knowledge translation and practice change, further research in this area appears warranted.

Demographic and Indication-Specific Characteristics Have Limited Association With Social Network Engagement: Evidence From 24,954 Members of Four Health Care Support Groups

Background

Digital health social networks (DHSNs) are widespread, and the consensus is that they contribute to wellness by offering social support and knowledge sharing. The success of a DHSN is based on the number of participants and their consistent creation of externalities through the generation of new content. To promote network growth, it would be helpful to identify characteristics of superusers or actors who create value by generating positive network externalities.

Objective

The aim of the study was to investigate the feasibility of developing predictive models that identify potential superusers in real time. This study examined associations between posting behavior, 4 demographic variables, and 20 indication-specific variables.

Methods

Data were extracted from the custom structured query language (SQL) databases of 4 digital health behavior change interventions with DHSNs. Of these, 2 were designed to assist in the treatment of addictions (problem drinking and smoking cessation), and 2 for mental health (depressive disorder, panic disorder). To analyze posting behavior, 10 models were developed, and negative binomial regressions were conducted to examine associations between number of posts, and demographic and indication-specific variables.

Results

The DHSNs varied in number of days active (3658-5210), number of registrants (5049-52,396), number of actors (1085-8452), and number of posts (16,231-521,997). In the sample, all 10 models had low R² values (.013-.086) with limited statistically significant demographic and indication-specific variables.

Conclusions

Very few variables were associated with social network engagement. Although some variables were statistically significant, they did not appear to be practically significant. Based on the large number of study participants, variation in DHSN theme, and extensive time-period, we did not find strong evidence that demographic characteristics or indication severity sufficiently explain the variability in number of posts per actor. Researchers should investigate alternative models that identify superusers or other individuals who create social network externalities.

Content-specific network analysis of peer-to-peer communication in an online community for smoking cessation

Analysis of user interactions in online communities could improve our understanding of health-related behaviors and inform the design of technological solutions that support behavior change. However, to achieve this we would need methods that provide granular perspective, yet are scalable. In this paper, we present a methodology for high-throughput semantic and network analysis of large social media datasets, combining semi-automated text categorization with social network analytics. We apply this method to derive content-specific network visualizations of 16,492 user interactions in an online community for smoking cessation. Performance of the categorization system was reasonable (average F-measure of 0.74, with system-rater reliability approaching rater-rater reliability). The resulting semantically specific network analysis of user interactions reveals content- and behavior-specific network topologies. Implications for socio-behavioral health and wellness platforms are also discussed.

Characterization of Temporal Semantic Shifts of Peer-to-Peer Communication in a Health-Related Online Community: Implications for Data-driven Health Promotion

With online social platforms gaining popularity as venues of behavior change, it is important to understand the ways in which these platforms facilitate peer interactions. In this paper, we characterize temporal trends in user communication through mapping of theoretically-linked semantic content. We used qualitative coding and automated text analysis to assign theoretical techniques to peer interactions in an online community for smoking cessation, subsequently facilitating temporal visualization of the observed techniques. Results indicate manifestation of several behavior change techniques such as feedback and monitoring' and 'rewards'. Automated methods yielded reasonable results (F-measure=0.77). Temporal trends among relapsers revealed reduction in communication after a relapse event. This social withdrawal may be attributed to failure guilt after the relapse. Results indicate significant change in thematic categories such as 'social support', 'natural consequences', and 'comparison of outcomes' pre and post relapse. Implications for development of behavioral support technologies that promote long-term abstinence are discussed.