Determining containment policy impacts on public sentiment during the pandemic using social media data.

Stringent containment and closure policies have been widely implemented by governments to prevent the transmission of COVID-19. Yet, such policies have significant impacts on people's emotions and mental well-being. Here, we study the effects of pandemic containment policies on public sentiment in Singapore. We computed daily sentiment values scaled from −1 to 1, using high-frequency data of ∼240,000 posts from highly followed public Facebook groups during January to November 2020. The lockdown in April saw a 0.1 unit rise in daily average sentiment, followed by a 0.2 unit increase with partially lifting of lockdown in June, and a 0.15 unit fall after further easing of restrictions in August. Regarding the impacts of specific containment measures, a 0.13 unit fall in sentiment was associated with travel restrictions, whereas a 0.18 unit rise was related to introducing a facial covering policy at the start of the pandemic. A 0.15 unit fall in sentiment was linked to restrictions on public events, post lock-down. Virus infection, wearing masks, salary, and jobs were the chief concerns found in the posts. A 2 unit increase in these concerns occurred even when some restrictions were eased in August 2020. During pandemics, monitoring public sentiment and concerns through social media supports policymakers in multiple ways. First, the method given here is a near real-time scalable solution to study policy impacts. Second, it aids in data-driven and evidence-based revision of existing policies and implementation of similar policies in the future. Third, it identifies public concerns following policy changes, addressing which can increase trust in governments and improve public sentiment.

Personalization of Intervention Timing for Physical Activity: Scoping Review.

BACKGROUND: The use of sensors in smartphones, smartwatches, and wearable devices has facilitated the personalization of interventions to increase users' physical activity (PA). Recent research has focused on evaluating the effects of personalized interventions in improving PA among users. However, it is critical to deliver the intervention at an appropriate time to each user to increase the likelihood of adoption of the intervention. Earlier review studies have not focused on the personalization of intervention timing for increasing PA. OBJECTIVE: This review aims to examine studies of information technology-based PA interventions with personalized intervention timing (PIT); identify inputs (eg, user location) used by the system for generating the PIT, the techniques and methods used for generating the PIT, the content of the PA intervention, and delivery mode of the intervention; and identify gaps in existing literature and suggest future research directions. METHODS: A scoping review was undertaken using PsycINFO, PubMed, Scopus, and Web of Science databases based on a structured search query. The main inclusion criteria were as follows: the study aimed to promote PA, included some form of PIT, and used some form of information technology for delivery of the intervention to the user. If deemed relevant, articles were included in this review after removing duplicates and examining the title, abstract, and full text of the shortlisted articles. RESULTS: The literature search resulted in 18 eligible studies. In this review, 72% (13/18) of the studies focused on increasing PA as the primary objective, whereas it was the secondary focus in the remaining studies. The inputs used to generate the PIT were categorized as user preference, activity level, schedule, location, and predicted patterns. On the basis of the intervention technique, studies were classified as manual, semiautomated, or automated. Of these, the automated interventions were either knowledge based (based on rules or guidelines) or data driven. Of the 18 studies, only 6 (33%) evaluated the effectiveness of the intervention and reported positive outcomes. CONCLUSIONS: This work reviewed studies on PIT for PA interventions and identified several aspects of the interventions, that is, inputs, techniques, contents, and delivery mode. The reviewed studies evaluated PIT in conjunction with other personalization approaches such as activity recommendation, with no study evaluating the effectiveness of PIT alone. On the basis of the findings, several important directions for future research are also highlighted in this review.

Factors influencing user's adherence to physical activity applications: A scoping literature review and future directions.

BACKGROUND: Although mobile app-delivered physical activity (PA) interventions have the potential to promote exercise, poor adherence to these apps is a common issue impeding their effectiveness. Gaining insights into the factors that influence PA app adherence is an important priority for app developers and intervention designers. OBJECTIVE: The objective of this study is to perform a literature review to synthesize the factors influencing PA app adherence and to identify directions for future research in this area. METHODS: A scoping review of prior research was conducted to uncover the factors influencing PA app adherence. Seven online journal databases were searched for relevant articles published from January 1, 2014, to December 31, 2018. The initial search identified 5,572 articles. After a screening and eligibility check based on inclusion criteria, 24 articles were finally selected. The definition of PA app adherence in this review could be categorized along four dimensions derived from previous studies: i.e., frequency of PA app usage, intention/motivation to sustain use of the PA app, degree of function use within the PA app, and the duration of PA app usage. RESULTS: Of the 24 included studies (both qualitative and quantitative), 12 studies were conducted in the U.S. The methods and study designs varied considerably, with the study durations ranging from 2 weeks to 24 months. The synthesized evidence indicates that 89 distinct factors influenced PA app adherence, and these could be classified into three categories: Personal Factors (n = 28), Technology Features (n = 53), and Contextual Factors (n = 8). Nine more detailed sub-categories were also compiled. Factors in sub-categories, such as psychological factors, health-related factors and predefined goals, are essential for physical activity behavior change experts to implement interventions. Factors in technology features, including PA tracking, PA goal setting and customization of exercise, are specifically useful for PA app developers or PA intervention designers. Overall, evidence of causal factors was limited. Only 5 of the 24 articles explored causal factors that affect PA app adherence. Furthermore, longitudinal studies with long durations were also limited. CONCLUSIONS: Uncovering the factors influencing PA app adherence is critical as it can expand our current knowledge and provide guidance for app-delivered PA interventions, as well as the design of PA apps. This scoping review identified and categorized factors that influence PA app adherence in prior studies. Based on the evidence synthesized, users are paying more attention to the "playfulness" and personalized features of PA apps, in addition to basic functional requirements. Also, app glitches are the most common factors found to negatively influence app adherence. Several important directions for future research are highlighted in this review, especially the design of studies to explore causality.

Combined interventions for physical activity, sleep, and diet using smartphone apps: A scoping literature review.

BACKGROUND: The use of smartphone apps to track and manage physical activity (PA), diet, and sleep is growing rapidly. Many apps aim to change individual behavior on these three key health dimensions (PA, sleep, diet) by using various interventions. Earlier reviews have examined interventions using smartphone apps for one or two of these dimensions. However, there is lack of reviews focusing on interventions for all three of these dimensions in combination with each other. This is important since the dimensions are often inter-related, and all are required for a healthy lifestyle. OBJECTIVE: The objective of this study is to conduct a review to: (1) map out the research done using smartphone app interventions targeting all three or any two of the three dimensions (PA, sleep, and diet), (2) examine if the studies consider the inter-relationships among the dimensions, and (3) identify the personalization methods implemented by the studies. METHODS: A literature search was conducted in electronic databases and libraries related to medical and informatics literature - PubMed, ScienceDirect, PsycINFO (ProQuest, Ovid) - using relevant selected keywords. Article selection and inclusion were done by removing duplicates, analyzing titles and abstracts, and then reviewing the full text of the articles. RESULTS: In the final analysis, 14 articles were selected - 2 articles focusing on PA and sleep, 8 on PA and diet, and 4 that examine or (at least) collect data of all three dimensions (PA, sleep, and diet). No research was found that focused on sleep and diet together. Of the 14 articles, only 4 build user profiles. Further, 3 of these 4 studies deliver personalized feedback based on the user's profile, with only 1 study providing automated, personalized recommendations for behavior change. Additionally, 6 of the included studies report all positive outcomes, while for 3 studies the primary outcomes are awaited. The remaining 5 studies do not report significant changes in all outcomes. In all, only 1 study examines the relationship between two (PA and diet) dimensions. No study was found to assess the relationships among the 3 dimensions.

Mobile-Based Interventions for Dietary Behavior Change and Health Outcomes: Scoping Review.

BACKGROUND: Mobile apps are being widely used for delivering health interventions, with their ubiquitous access and sensing capabilities. One such use is the delivery of interventions for healthy eating behavior. OBJECTIVE: The aim of this study was to provide a comprehensive view of the literature on the use of mobile interventions for eating behavior change. We synthesized the studies with such interventions and mapped out their input methods, interventions, and outcomes. METHODS: We conducted a scoping literature search in PubMed/MEDLINE, Association for Computing Machinery Digital Library, and PsycINFO databases to identify relevant papers published between January 2013 and April 2018. We also hand-searched relevant themes of journals in the Journal of Medical Internet Research and registered protocols. Studies were included if they provided and assessed mobile-based interventions for dietary behavior changes and/or health outcomes. RESULTS: The search resulted in 30 studies that we classified by 3 main aspects: input methods, mobile-based interventions, and dietary behavior changes and health outcomes. First, regarding input methods, 5 studies allowed photo/voice/video inputs of diet information, whereas text input methods were used in the remaining studies. Other than diet information, the content of the input data in the mobile apps included user's demographics, medication, health behaviors, and goals. Second, we identified 6 categories of intervention contents, that is, self-monitoring, feedback, gamification, goal reviews, social support, and educational information. Although all 30 studies included self-monitoring as a key component of their intervention, personalized feedback was a component in 18 studies, gamification was used in 10 studies, goal reviews in 5 studies, social support in 3 studies, and educational information in 2 studies. Finally, we found that 13 studies directly examined the effects of interventions on health outcomes and 12 studies examined the effects on dietary behavior changes, whereas only 5 studies observed the effects both on dietary behavior changes and health outcomes. Regarding the type of studies, although two-thirds of the included studies conducted diverse forms of randomized control trials, the other 10 studies used field studies, surveys, protocols, qualitative interviews, propensity score matching method, and test and reference method. CONCLUSIONS: This scoping review identified and classified studies on mobile-based interventions for dietary behavior change as per the input methods, nature of intervention, and outcomes examined. Our findings indicated that dietary behavior changes, although playing a mediating role in improving health outcomes, have not been adequately examined in the literature. Dietary behavior change as a mechanism for the relationship between mobile-based intervention and health outcomes needs to be further investigated. Our review provides guidance for future research in this promising mobile health area.

User Models for Personalized Physical Activity Interventions: Scoping Review.

BACKGROUND: Fitness devices have spurred the development of apps that aim to motivate users, through interventions, to increase their physical activity (PA). Personalization in the interventions is essential as the target users are diverse with respect to their activity levels, requirements, preferences, and behavior. OBJECTIVE: This review aimed to (1) identify different kinds of personalization in interventions for promoting PA among any type of user group, (2) identify user models used for providing personalization, and (3) identify gaps in the current literature and suggest future research directions. METHODS: A scoping review was undertaken by searching the databases PsycINFO, PubMed, Scopus, and Web of Science. The main inclusion criteria were (1) studies that aimed to promote PA; (2) studies that had personalization, with the intention of promoting PA through technology-based interventions; and (3) studies that described user models for personalization. RESULTS: The literature search resulted in 49 eligible studies. Of these, 67% (33/49) studies focused solely on increasing PA, whereas the remaining studies had other objectives, such as maintaining healthy lifestyle (8 studies), weight loss management (6 studies), and rehabilitation (2 studies). The reviewed studies provide personalization in 6 categories: goal recommendation, activity recommendation, fitness partner recommendation, educational content, motivational content, and intervention timing. With respect to the mode of generation, interventions were found to be semiautomated or automatic. Of these, the automatic interventions were either knowledge-based or data-driven or both. User models in the studies were constructed with parameters from 5 categories: PA profile, demographics, medical data, behavior change technique (BCT) parameters, and contextual information. Only 27 of the eligible studies evaluated the interventions for improvement in PA, and 16 of these concluded that the interventions to increase PA are more effective when they are personalized. CONCLUSIONS: This review investigates personalization in the form of recommendations or feedback for increasing PA. On the basis of the review and gaps identified, research directions for improving the efficacy of personalized interventions are proposed. First, data-driven prediction techniques can facilitate effective personalization. Second, use of BCTs in automated interventions, and in combination with PA guidelines, are yet to be explored, and preliminary studies in this direction are promising. Third, systems with automated interventions also need to be suitably adapted to serve specific needs of patients with clinical conditions. Fourth, previous user models focus on single metric evaluations of PA instead of a potentially more effective, holistic, and multidimensional view. Fifth, with the widespread adoption of activity monitoring devices and mobile phones, personalized and dynamic user models can be created using available user data, including users' social profile. Finally, the long-term effects of such interventions as well as the technology medium used for the interventions need to be evaluated rigorously.