Interactive Messages Research Articles

Small Steps over time: A longitudinal usability test of an automated interactive text messaging intervention to support self-management of depression and anxiety symptoms

BackgroundDigital mental health interventions (DMHIs) offer potential solutions for addressing mental health care gaps, but often suffer from low engagement. Text messaging is one promising medium for increasing access and sustaining user engagement with DMHIs. This paper examines the Small Steps SMS program, an 8-week, automated, adaptive text message-based intervention for depression and anxiety. MethodsWe conducted an 8-week longitudinal usability test of the Small Steps SMS program, recruiting 20 participants who met criteria for major depressive disorder and/or generalized anxiety disorder. Participants used the automated intervention for 8 weeks and completed symptom severity and usability self-report surveys after 4 and 8 weeks of intervention use. Participants also completed individual interviews to provide feedback on the intervention. ResultsParticipants responded to automated messages on 70 % of study days and with 85 % of participants sending responses to messages in the 8th week of use. Usability surpassed established cutoffs for software that is considered acceptable. Depression symptom severity decreased significantly over the usability test, but reductions in anxiety symptoms were not significant. Participants noted key areas for improvement including addressing message volume, aligning message scheduling to individuals' availability, and increasing the customizability of content. LimitationsThis study does not contain a control group. ConclusionsAn 8-week automated interactive text messaging intervention, Small Steps SMS, demonstrates promise with regard to being a feasible, usable, and engaging method to deliver daily mental health support to individuals with symptoms of anxiety and depression.

Nifty method for prediction dynamic features of online social networks from users’ activity based on machine learning

Nowadays with the development of m4obile personal devices, the interaction of most people takes place through online social network more than ever. They rely on online applications to communicate, express their opinions, or react to others expressions instead of waiting the time to do that directly in a real life. Computationally, such interaction is modeled as a virtual network (or formally as graph) that is described with a set of features e.g. graph diameter, average-clustering coefficient etc. To compute these features, it is required to count or inspect all nodes or/and edges properties of the graph. When the graph is dynamic, i.e., the structure is change over time with each interaction, the computation of these features is a challenge and complex for time and space. Instead, AI based approaches are suggested to predict such features based on only few information of an interaction. This work trains the machine to learn computing the global features of online social networks through noticing the effect of users’ interaction on these features. Three datasets of different of two real online social networks are used in experiments. The obtained result shows an approximate accuracy of predication by RF 99% for Email interaction dataset and 82% by TreeNet for College message interaction dataset. The results of paper refer to: (a) The paper demonstrates the successful application of machine learning techniques to predict and analyze dynamic graph features in online social networks. (b) The Random Forest model emerges as the most suitable approach, providing accurate predictions and insights into the dynamics of dynamic graphs. (c) The study highlights the importance of the Graph Average Clustering Coefficient (GACC) as a significant feature in predicting global graph dynamics. (d) The research showcases the limitations of the KNN-Regression model and emphasizes the need for models that can handle the complexity and nonlinearity of dynamic graphs effectively.

A text messaging intervention to support the mental health of young adults: User engagement and feedback from a field trial of an intervention prototype

BackgroundYoung adults have high rates of mental health conditions, but most do not want or cannot access treatment. By leveraging a medium that young adults routinely use, text messaging programs have potential to keep young adults engaged with content supporting self-management of mental health issues and can be delivered inexpensively at scale. We designed an intervention that imparts strategies for self-managing mental health symptoms through interactive text messaging dialogues and engages users through novelty and variety in strategies (from cognitive behavioral therapy, acceptance and commitment therapy, and positive psychology) and styles of interaction (e.g., prompts, peer stories, writing tasks). MethodsThe aim of this mixed-methods study was to pilot 1- and 2-week versions of an interactive text messaging intervention among young adults (ages 18–25), and to obtain feedback to guide intervention refinements. Young adults were recruited via a mental health advocacy website and snowball sampling at a North American University. We used Wizard-of-Oz methods in which study staff sent messages based on a detailed script. Transcripts of interviews were subject to qualitative analysis to identify aspects of the program that need improvements, and to gather participant perspectives on possible solutions. ResultsForty-eight individuals ages 18–25 participated in the study (mean age: 22.0). 85 % responded to the program at least once. Among those who ever responded, they replied to messages on 85 % of days, and with engagement sustained over the study period. Participants endorsed the convenience of text messaging, the types of interactive dialogues, and the variety of content. They also identified needed improvements to message volume, scheduling, and content. ConclusionsYoung adults showed high levels of engagement and satisfaction with a texting program supporting mental health self-management. The program may be improved through refining personalization, timing, and message volume, and extending content to support use over a longer timeframe. If shown to be effective in randomized trials, this program has potential to help address a substantial treatment gap in young adults' mental health.

Knowledge Management in Multimedia Communication Using Software Agents

This paper presents the problem of knowledge management related to software agents and agent systems (i.e. systems composed of communicating and cooperating software agents). The author presents basic theoretical issues, but the main focus is on the areas of application of software agents in multimedia communication - both existing and prospective applications. Particular attention has been paid to the applications of software agents for: supporting customer service processes through the Contact Centre system; supporting the distance learning system; creating virtual assistants in the form of conversation bots; searching for information in the Semantic Web, and assisting in setting up a video conference call. Software agents are often referred to as 'intelligent' - due to their intelligent behaviour. At their core, they involve: perception, reasoning, learning, communication and operation in complex environments, using all multimedia techniques and procedures. Software agents and agent systems, as presented in this article, represent a new approach to the analysis, design and implementation of complex (usually decentralised and distributed) computer systems, offering a whole range of concepts, techniques and tools to significantly improve the software development process. The ability of software agents to make autonomous decisions have been a subject of considerations. Among other things, they are based on the example of cognitive sciences, which deal with modelling real processes of information processing in humans and building programmes that imitate these processes. Research on the common understanding of shared knowledge and knowledge management in agent-based systems focuses on ontologies, understood as a description of the relationship between the way knowledge is represented and the concept represented by that knowledge. In contrast, research on knowledge sharing has focused on communication languages and their associated - interaction protocol and message transport protocol.

BIP-Tree: Tree Variant With Behavioral Intention Perception for Heterogeneous Trajectory Prediction

An insightful understanding and relational reasoning of motion behavior are typical components for trajectory prediction to achieve safe planning when navigating in complex scenarios. Due to the differences in behavioral responses of heterogeneous agents and the existence of chain effect in message passing, an effective prediction method is desired to better acquire potential behavioral intention and model motion behavior. In this paper, we construct a trajectory prediction method to represent and encode the behavioral interactions among heterogeneous agents, called as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Tree variant with Behavioral Intention Perception (BIP-Tree)</i> . Specifically, a dual-behavior interaction module is presented to deeply understand behavioral intention by simultaneously considering the behavioral perception and behavioral response in spatial interaction. The behavioral perception means that individual acquires behavioral features from interactive objects located in its perception range, while the behavioral response means that each agent makes distinctive reactions to different categories of agents (for example, due to different collision risks caused by pedestrian and vehicle, a pedestrian will respond differently to the interactive agents at the same distance). Meanwhile, we also introduce one new tree variant in message passing stage to enhance the acquisition of potential motion feature, denoting traffic agents as nodes and the interactions among them as tree trunks. The interaction message can be delivered along tree trunks from leaf nodes to root node, to further achieves the chain effect of high-order interactions beyond adjacent entities. Our method is evaluated on several public datasets, such as Apolloscape, nuScenes, Argoverse, SDD, INTERACTION, inD, and Waymo. The extensive experimental results demonstrate that our method can predict more plausible and realistic trajectories with multi-modality. Among them, the best performance is achieved on three datasets. More remarkably, compared with state-of-the-arts, our method achieves significant performance and decreases by at least <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$13.04\%$</tex-math> </inline-formula> on average ADE and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$19.42\%$</tex-math> </inline-formula> on average FDE on inD dataset with four intersections. The dataset and code are available at: htpps://github.com/VTP-TL/BIP-Tree.

Parasocial Interaction Message Elements and Disclosure Timing in Nano- and Microinfluencers’ Sponsored Content As Alternative Explanations for Follower Count’s Influence on Engagement

Micro- and nanoinfluencers are typically segmented based on number of followers, yet follower count cannot adequately explain the different persuasion outcomes between micro- and nanoinfluencers. Based on the persuasion knowledge model (PKM), a content analysis and two experiments were conducted to examine (1) the relationship between parasocial attributes, influencer type, and engagement rates and (2) whether influencer type and parasocial attributes moderate the impact of sponsorship disclosure placement on perceived authenticity and purchase intentions. Results suggested that instead of follower count, the inclusion of parasocial attributes in influencer videos was the main driving factor of perceived authenticity. Sponsorship disclosure placed at the ends of videos increased perceived authenticity more than disclosure placed at the beginning of the video, but it did not affect purchase intentions; this effect was observed only for the nanoinfluencer’s video. Parasocial attributes reduced persuasion knowledge activation and thus minimized the effect of disclosure placement on perceived authenticity.

Backdoor-Resistant Public Data Integrity Verification Scheme Based on Smart Contracts

This paper analyzes existing smart contract-based public data integrity verification schemes and identifies certain weaknesses. First, the fair arbitration mechanism deployed in these schemes fails to meet the users’ requirements as it may not promptly notify users of data corruption or loss. Second, to ensure outsourced data confidentiality, existing data integrity schemes use a conventional encrypted method, where each user randomly selects a key to encrypt the outsourced data. Such a method results in varying ciphertexts for the same data by different users, leading to additional storage costs for the cloud server. Third, users’ devices, if poorly designed or even intentionally backdoored, can potentially exfiltrate secrets and compromise the security of schemes. To address these issues, we propose the first backdoor-resistant public data integrity verification scheme based on smart contracts (ASSIST). The key idea is to introduce a new entity (a whistleblower) to periodically monitor the state of verification results recorded in the blockchain. This allows for timely notification of data corruption to users. ASSIST requires users to encrypt their data with a cryptographic primitive called message-locked encryption (MLE), which motivates different users to produce the same ciphertext for the same data and reduces storage costs for cloud servers. We also deploy a cryptographic reverse firewall between users’ devices and the external to re-randomize interactive messages, making the exfiltration impossible. We provide rigorous security proofs to demonstrate the security of ASSIST. The performance evaluation shows that ASSIST is efficient regarding computation and communication costs.

Effect of AI Chatbot’s Interactivity on Consumers’ Negative Word-of-Mouth Intention: Mediating Role of Perceived Empathy and Anger

Although much research has examined chatbot interactivity as one of the most human-like qualities, replicating a conversation norm in human-to-human dialogue, far less has examined the "native state" of two-way communication between humans and chatbots. This study fills the gap by substantiating a synchronous two-way conversation between users and chatbots and testing how chatbot interactivity shapes the users’ perceived empathy. Guided by the computers-are-social-actors (CASA) paradigm, like skilled customer service representatives interacting with customers during product failures, this study designs a between-subject experiment (N = 300) to embody the warm and competent types of human conversation and suggests two versions of chatbot interactivities: social interactivity (high/low) and message interactivity (high/low). We further investigate whether a chatbot’s warm or competent persona manifested in its interaction with consumers elicits empathic responses from consumers, such as calming down their anger in line with social response theory. As a result, both social and message interactivity in chatbots effectively elicit users’ perception of empathy, alleviating customer anger and reducing adverse word-of-mouth (WOM) intentions. The findings provide empirical evidence that supports the explanatory framework of social response theory. This research demonstrates how technological features, particularly interactivity, contribute to creating experiential value for customers. Specifically, it elucidates how customers perceive empathy displayed by chatbots, which subsequently influences their level of anger and inclination to engage in negative word-of-mouth communication. Notably, these results offer novel insights into the roles of chatbot interactivity and empathy as psychological mechanisms that enable consumers to express their dissatisfaction by interacting with chatbot service agents, particularly in situations involving product failures. The implications of this study extend to the practical realm, providing valuable guidance on the design and implementation of empathetic chatbots in frontline consumer service contexts.

Social presence and dynamics of group communication: An analysis of a health professionals WhatsApp group chats.

WhatsApp has become a medium of communication with the potential of promoting collaborative environment with peers, patients and general population. Till date, no analysis of professional WhatsApp groups' activities exists in physiotherapy. The official WhatsApp group chats of the Association of Clinical and Academic Physiotherapists of Nigeria (ACAPN) was analyzed. A total of 20760 chats were gleaned from July 2020 to June 2021. Videos, audios and emoticons were excluded in the analysis. Administrative permission to conduct was obtained from ACAPN leadership. Two consenting physiotherapists who had never deleted their ACAPN group chats shared and exported all chats to a Gmail. The social presence theory for group communication was used as the framework of analysis. Thematic content analysis was used to analyze qualitative data. Descriptive statistics of frequency and percentages were used to summarize data. Based on social presence theory classifications, interactive messages (64.5%) followed by cohesive messages (30%) were predominant. Members used the platform more for expression of emotions affectively (100%), referring explicitly to others' messages interactively (56.6%) and for greetings (phatic and salutation) cohesively (61.8%). Qualitative themes indicate that all three categories of social presence theory communications were present sufficiently with interactive category being the most common, as members used the WhatsApp platform to interact, construct and share knowledge. Group WhatsApp platform is a veritable means of communication and an indicator of level of social presence among Nigerian physiotherapists. Communication among Nigerian physiotherapists is mostly interactive, then cohesive and affective in terms of dynamics.

BDRA: Blockchain and Decentralized Identifiers Assisted Secure Registration and Authentication for VANETs

In vehicular ad-hoc networks (VANETs), road safety and road traffic efficiency can be improved through message interaction between vehicle users, which inevitably relies on secure identity authentication and message credibility verification. Existing authentication and message verification mechanisms are prone to severe single points of failure and low authentication efficiency due to their reliance on the trusted third party, especially during the user registration phase. This paper proposes a double-layer blockchain and decentralized identifiers assisted secure registration and authentication (BDRA) mechanism for decentralized VANETs, which can achieve the following advantages: 1) realizing a secure and decentralized user registration phase by using decentralized identifier (DID) technology; 2) accomplishing efficient authentication and message verification by combining double-layer blockchain, DIDs and a reputation feedback strategy; 3) enabling a more efficient cross-section re-registration that reduces the communication time by 30%. The security features and efficiency of the BDRA mechanism are demonstrated by carrying out security analysis and performance evaluation, which is based on the Hyperledger Fabric platform.

Comedic convergence: Humor responses to verbal irony in text messages

Mode adoption is a term used primarily in pragmatics and linguistics which refers to responses to verbal irony that: a.) converge with (or ‘adopt’) the humor frame and, b.) add a similar element of ironic humor. This paper argues that mode adoption is a form of high-level accommodation. In order to mode adopt, respondents must converge with the speaker's verbal content, their humorous intent, as well as the form this intent is taking. Respondents can, therefore, display convergence/divergence along each of these dimensions with each response to verbal irony falling along a scale of humor convergence. Scholars have suggested that differences in mode adoption may be based on familiarity between the speakers, however, this hypothesis has not been tested experimentally, and CAT has not been directly applied in this context. Over two studies, (N = 430) participants were asked to think of a specific person from their life and type exactly how they would respond to instances of ironic overstatement from this person in a hypothetical text message interaction. Regression analyses revealed that perceived relational closeness predicted humor convergence in study two but not study one. Awareness of the irony was significant predictor of convergence in study one, a finding not replicated in study two. In line with previous CAT research, liking and relative power were also found to predict convergence in humor. These studies provide support for CAT within the domain of humor in texting, clarify important predictors for humor convergence, and argue that definitions of accommodation should explicitly include both behavioral and cognitive elements of communication.

The effects of a 24-week interactive text message-based mobile health intervention for enhancing self-care behaviours of patients with heart failure: A quasi-experimental study.

The aim of this study was to identify the effects of a 24-week interactive text message-based mobile health intervention (called) on enhancing the self-care behaviours of patients with heart failure. Whether text message-based mobile health intervention can be used to improve long-term adherence to self-care behaviours among heart failure patients remains unclear. A quasi-experimental study with a pretest-post-test design and repeated measures. Data from 100 patients (mean age, 58.78 years; 83.0% men) were analysed. The intervention group (n = 50) used the program over 24 weeks, which consisted of weekly goal setting and interactive text messaging, while the control group (n = 50) received usual care. Trained research assistants collected data using self-reported Likert questionnaires. Primary (self-care behaviours) and secondary (health literacy, eHealth literacy, and disease knowledge) outcome variables were measured at baseline and at 1, 3 and 6 months after intervention for follow-up. The findings showed that the intervention group demonstrated significantly better self-care behaviours than the control group during the 6 months. Notably, the trajectory of self-care behaviours of the patients in the intervention group showed a steep rise between the first- and third-month follow-up, followed by high stability between the third- and sixth-month follow-up. In addition, the intervention group had significantly higher disease knowledge than the control group at the first- and sixth-month follow-up. We found that the program, as an interactive text messaging service, may be an optimal strategy for improving long-term adherence to self-care behaviours through motivating and providing social support. The WithUs program can help nurses and other healthcare professionals to track patients' health indicators such as symptom severity, diet and physical activity. In addition, nurses can take an important role in evaluating the efficacy of the app in relation to patients' health outcome. Patients have completed a self-reported questionnaire after providing informed consent.

Interactive Advertising Typology

The purpose of the article is to establish the most probable determinants for the classification of interactive advertising and to identify and systematise, on their basis, the variable forms of implementation of advertising messages, as well as to establish the possibility of their interaction. Research methodology: the method of complex analysis was applied. The scientific novelty is the systematisation of scholars’ views on the typology of interactive advertising, its updating, and supplementation. It is established that the classifications of interactive advertising presented so far have already lost their relevance since they were limited by the platforms of placement of advertising messages or forms of broadcasting and did not take into account the conditions of the interaction of various technologies in the process of implementing interactive advertising messages. This study, based on a synthesis of such determinants as creation and distribution technologies, as well as forms of content and the nature of interaction with consumers, identifies the following types of interactive advertising: advertising via mobile communications and television; advertising on the Internet; environmental installations (with and without gadgets); events and activities. It is found that mobile communication technologies are currently a connecting link between most forms of interactive communications and remain effective both as a separate form of advertising and in combination with television and Internet advertising. It has also been established that innovative technologies of augmented reality (AR) and virtual reality (VR) can be classified as differentiated types of advertising messages, depending on the presence of programmed or spontaneous transformations under the influence of the interaction of advertising messages based on these technologies with consumers. Transformations absence allows them to be classified as environmental installations, while the presence of a variable plot and scenario leads to the classification of AR and VR as events and activities. Conclusions: interactive advertising design involves the deliberate use of variable forms of its implementation, the choice and justification of which should be facilitated by the typology presented by us. The possibility of differentiating between different types of interactive advertising and taking into account their features is confirmed by a thorough analysis of each type of interactive advertising. Thus, we get a holistic picture based on which we can build advertising strategies, and offer the most effective ways to communicate with consumers and use one or more of the above types of advertising messages.

The Effectiveness of Interactive Text Messaging and Structured Psychosocial Support Groups on Developmental Milestones of Children From Adolescent Pregnancies in Kenya: Quasi-Experimental Study

In sub-Saharan Africa, one-quarter of all pregnancies occur in adolescents. Children born to adolescent mothers have poorer physical and socio-cognitive development. One reason may be inadequate knowledge on childcare and psychosocial support during pregnancy and post partum, since adolescent mothers have less antenatal care attendance and overall interaction with the health care system. Mobile health technology has been used to relay health information to special groups; however, psychosocial support commonly requires physical interaction. We aimed to assess the efficacy of an interactive mobile text messaging platform and support groups in improving adolescent mothers' knowledge and practices as well as infant growth and development. This was a quasi-experimental study, conducted among adolescent mothers with infants younger than 3 months, in Homa Bay County, Kenya. Five of the 8 subcounties in Homa Bay County were purposively selected as study clusters. Four subcounties were assigned as intervention clusters and 1 as a control cluster. Adolescent mothers from 2 intervention subcounties received interactive text messaging only (limited package), whereas those from the other 2 subcounties received text messaging and weekly support groups, moderated by a community health extension worker and a counselor (full package); the control cluster only received the end-line evaluation (posttest-only control). The follow-up period was 9 months. Key outcomes were maternal knowledge on childcare and infant development milestones assessed using the Developmental Milestones Checklist (DMC III). Knowledge and DMC III scores were compared between the intervention and control groups, as well as between the 2 intervention groups. We recruited 791 mother-infant pairs into the intervention groups (full package: n=375; limited package: n=416) at baseline and 220 controls at end line. Attrition from the intervention groups was 15.8% (125/791). Compared with the control group, adolescent mothers receiving the full package had a higher knowledge score on infant care and development (9.02 vs 8.01; P<.001) and higher exclusive breastfeeding rates (238/375, 63.5% vs 112/220, 50.9%; P=.004), and their infants had higher average DMC III scores (53.09 vs 48.59; P=.01). The limited package group also had higher knowledge score than the control group (8.73 vs 8.01; P<.001); this group performed better than the full package group on exclusive breastfeeding (297/416, 71.4% vs 112/220, 50.9%; P<.001) and DMC III scores (58.29 vs 48.59; P<.001) when compared with the control group. We found a marginal difference in knowledge scores between full and limited package groups (9.02 vs 8.73; P=.048) but no difference in DMC III scores between the 2 groups (53.09 vs 58.29; P>.99). An interactive text messaging platform improved adolescent mothers' knowledge on nurturing infant care and the development of their children, even without physical support groups. Such platforms offer a convenient avenue for providing reproductive health information to adolescents. Pan African Clinical Trials Registry PACTR201806003369302; https://tinyurl.com/kkxvzjse.

PTAP: A novel secure privacy-preserving &amp; traceable authentication protocol in VANETs

In vehicular ad-hoc networks (VANETs), road traffic efficiency and road safety can be improved through message interaction and sharing between vehicle users, which inevitably depends on secure identity authentication and message credibility verification. However, security and privacy of message are the critical factors restricting VANETs’ development. Most existing protocols rely on the trusted third-party to achieve identity authentication and message, which are prone to system single points of failure and low efficiency. To address these challenges, in 2021, Vasudev and Das proposed a privacy preserving secure hash based authentication and revelation protocol. They claimed that their protocol can resist various known attacks, e.g. modification, man-in-the-middle, TPD stolen attack, et al. Unfortunately, in this paper, we found that their protocol does not address above-mentioned problems and has other devastating security bugs. Attacker can easily obtain vehicle’s original/real identity, pseudo-identity and password, which are the cornerstone of their protocol security. In other words, the attacker successfully can launch any attacks including those mentioned above to destroy the whole system. Then, a novel secure privacy-preserving traceable authentication protocol (abbreviated to PTAP) is proposed. The PTAP not only mitigates the weaknesses, but has other advantages. First, the PTAP uses semi-honest RSUs to realize interaction between stakeholders without the help of trusted third-party in extremely low computational cost (reducing 32.75% in vehicle side). Second, the PTAP can enable vehicles to enjoy the remote services with identity anonymity protection and location privacy (traceability when needed). Finally, the PTAP is proven to be safe against passive and active attacks under CDHP assumption and CL-eCK model. Hence, these features make the PTAP very suitable for high safety coefficient and computation-limited mobile devices (such as TPD) compared with other related existing protocols.

Adapting Reach Up and Learn in Crisis and Conflict Settings: An Exploratory Multiple Case Study.

In 2019, >71 million children aged <5 had spent their entire lives in conflict-affected settings. Compounding adversities including violence, poverty, and displacement have immediate and long-term effects on early childhood development, health, behavior, and well-being. In response, adaptations of Reach Up have been implemented in conflict and crisis settings. This article uses exploratory multiple case study methodology, drawing from implementation and qualitative data from 3 interventions: a mobile phone-based intervention promoting nurturing care among Rohingya and crisis-affected host communities in Bangladesh; Reach Up amid acute violence and displacement in Northeast Syria; and Reach Up group sessions and home visits integrated with health services for an indigenous population in Venezuela. In Bangladesh, tailoring interactive voice response messages improved responsiveness to the developmental needs of young children, yet complementary in-person services were identified as a key program enhancement. In Syria, rapid adaptations of Reach Up addressed the needs of families in acute crisis, including social-emotional learning games for school-aged children. In Venezuela, Reach Up, coupled with complementary lactation counseling, yielded high rates of uptake and satisfaction, and children's language development was highlighted as a key area of growth. Recommendations to promote early childhood development in crisis and conflict settings include: (1) cultural adaptation based on a holistic understanding of children and caregivers' needs; (2) the integration of child and family safety and linkages with complementary services on the basis of community needs and priorities, and (3) the importance of designing for scale through blended models and costing analyses.

Parent-emerging adult text interactions and emerging adult perceived parental support of autonomy

Parents play an important role in scaffolding autonomy and independence as their children transition to adulthood. In the digital age, mobile phones allow for increased connection at this important developmental transition, but we know little about the extent to which digital connection may help (i.e., through developmentally appropriate support) or hinder (i.e., through intrusiveness or helicopter parenting) emerging adult (EA) autonomy development. We tested whether digital parent-EA interactions tapping engagement, monitoring, and responsiveness were associated with EA perceptions of parental autonomy support in a sample of 238 college students ( M age = 19.85) who contributed all text messages exchanged with their parents over 2 weeks. Results indicate that many dimensions of parent-EA text message interactions are unrelated to perceived parental autonomy support, but those that did emerge point towards a potentially maladaptive role of overparenting in associations with less perceived parental autonomy support. Results underscore that, for most EAs, parental text messaging is not likely to be perceived as autonomy inhibiting, but that for a small minority of parent-EA dyads, intense levels of digital connection with parents may be associated with perceived autonomy inhibition.

Implementation and Evaluation of an Automated Text Message-Based Diabetes Prevention Program for Adults With Pre-diabetes.

Despite the efficacy of diabetes prevention programs, only an estimated 5% of people with pre-diabetes actually participate. Mobile health (mHealth) holds promise to engage patients with pre-diabetes into lifestyle modification programs by decreasing the referral burden, centralizing remote enrollment, removing the physical requirement of a brick-and-mortar location, lowering operating costs through automation, and reducing time and transportation barriers. Non-randomized implementation study enrolling patients with pre-diabetes from a large health care organization. Patients were exposed to a text message-based program combining live human coaching guidance and support with automated scheduled, interactive, data-driven, and on-demand messages. The primary analysis examined predicted weight outcomes at 6 and 12 months. Secondary outcomes included predicted changes in HbA1c and minutes of exercise at 6 and 12 months. Of the 163 participants included in the primary analysis, participants had a mean predicted weight loss of 5.5% at six months (P < .001) and of 4.3% at 12 months (P < .001). We observed a decrease in predicted HbA1c from 6.1 at baseline to 5.8 at 6 and 12 months (P < .001). Activity minutes were statistically similar from a baseline of 155.5 minutes to 146.0 minutes (P = .567) and 142.1 minutes (P = .522) at 6 and 12 months, respectively, for the overall cohort. In this real-world implementation of the myAgileLife Diabetes Prevention Program among patients with pre-diabetes, we observed significant decreases in weight and HbA1c at 6 and 12 months. mHealth may represent an effective and easily scalable potential solution to deliver impactful diabetes prevention curricula to large numbers of patients.

A rumor heat prediction model based on rumor and anti-rumor multiple messages and knowledge representation

In the field of online social networks, the effective prediction of group behavior is the key to fitting the trajectory of rumor topic propagation. Considering the heterogeneity and complexity of a rumor topic propagation network, this paper proposes a rumor heat prediction model based on multiple rumor and anti-rumor messages and knowledge representation. Firstly, aiming at the dynamic interaction of multiple messages under the rumor topic network, this paper introduces evolutionary game theory to quantify the influence of multiple messages on group behavior to truly reflect the relationship structure of the rumor topic spreading network. Secondly, to solve the difficulties in mining the complex features of multisource entities, knowledge representation is considered the theoretical basis to map the heterogeneous topic network to the feature vector space and learn the low-rank vector representation of the complex features. Finally, considering the time-sensitive nature of rumor propagation and the advantages of graph neural networks in processing non-Euclidean structured data, the dynamic rumor topic heat prediction model called K-GraphSAGE, which is based on group behavior, is proposed. The experiment results show that this method can not only effectively predict the group behavior and the heat of the rumor topics, but also unearth the mechanism behind the spreading law of the rumor topics.

Reduced Diarrhea Prevalence and Improvements in Handwashing with Soap and Stored Drinking Water Quality Associated with Diarrheal Disease Awareness Measured by Interactive Voice Response Messages in the CHoBI7 Mobile Health Program.

The Cholera-Hospital-Based-Intervention-for-7-Days (CHoBI7) mobile health program promotes water, sanitation, and hygiene (WASH) behaviors through interactive voice response (IVR), voice, and text messages to reduce diarrheal diseases in Bangladesh. The objective of this study was to investigate the relationship between responses to CHoBI7 WASH IVR quiz messages and subsequent diarrhea and WASH behaviors. Fourteen CHoBI7 IVR quiz messages on handwashing with soap and treatment of stored water were sent to 517 households with 1,777 participants during the 12-month program period. IVR message responses were classified as correct answer, incorrect answer, no response (did not press 1 or 2), and failed (did not answer the phone). Diarrhea prevalence was assessed through self-reported monthly clinical surveillance visits. Handwashing with soap was assessed by a 5-hour structured observation, and stored water quality was defined by Escherichia coli concentration. Households that responded correctly to a CHoBI7 IVR quiz message had significantly lower odds of diarrhea for all age groups (adults and children) at the subsequent visit 1 month later (odds ratio [OR], 0.73; 95% CI, 0.54-0.98), and significantly greater odds of handwashing with soap after stool-related events (OR, 2.48; 95% CI, 1.12-5.49) and E. coli levels < 100 colony forming units (CFU)/100 mL (World Health Organization high-risk cutoff) in the stored household water (OR, 2.04; 95% CI, 1.25-3.33) compared with households that did not answer CHoBI7 IVR quiz calls. Correct responses to CHoBI7 IVR quizzes were associated with decreased diarrhea prevalence and improved stored drinking water quality and handwashing with soap behaviors at the subsequent visits. These findings suggest engagement in the CHoBI7 mobile health (mHealth) program and awareness of diarrheal disease prevention can reduce diarrhea and facilitate changes in WASH behaviors.