Evaluating the impact of physical activity apps and wearables: interdisciplinary review

Background: Although many smartphone apps and wearables have been designed to improve physical activity, their rapidly evolving nature and complexity present challenges for evaluating their impact. Traditional methodologies, such as randomized controlled trials (RCTs), can be slow. To keep pace with rapid technological development, evaluations of mobile health technologies must be efficient. Rapid alternative research designs have been proposed, and efficient in-app data collection methods, including in-device sensors and device-generated logs, are available. Along with effectiveness, it is important to measure engagement (ie, users’ interaction and usage behavior) and acceptability (ie, users’ subjective perceptions and experiences) to help explain how and why apps and wearables work. Objectives: This study aimed to (1) explore the extent to which evaluations of physical activity apps and wearables: employ rapid research designs; assess engagement, acceptability, as well as effectiveness; use efficient data collection methods; and (2) describe which dimensions of engagement and acceptability are assessed. Method: An interdisciplinary scoping review using 8 databases from health and computing sciences. Included studies measured physical activity, and evaluated physical activity apps or wearables that provided sensor-based feedback. Results were analyzed using descriptive numerical summaries, chi-square testing, and qualitative thematic analysis. Results: A total of 1829 abstracts were screened, and 858 articles read in full. Of 111 included studies, 61 (55.0%) were published between 2015 and 2017. Most (55.0%, 61/111) were RCTs, and only 2 studies (1.8%) used rapid research designs: 1 single-case design and 1 multiphase optimization strategy. Other research designs included 23 (22.5%) repeated measures designs, 11 (9.9%) nonrandomized group designs, 10 (9.0%) case studies, and 4 (3.6%) observational studies. Less than one-third of the studies (32.0%, 35/111) investigated effectiveness, engagement, and acceptability together. To measure physical activity, most studies (90.1%, 101/111) employed sensors (either in-device [67.6%, 75/111] or external [23.4%, 26/111]). RCTs were more likely to employ external sensors (accelerometers: P=.005). Studies that assessed engagement (52.3%, 58/111) mostly used device-generated logs (91%, 53/58) to measure the frequency, depth, and length of engagement. Studies that assessed acceptability (57.7%, 64/111) most often used questionnaires (64%, 42/64) and/or qualitative methods (53%, 34/64) to explore appreciation, perceived effectiveness and usefulness, satisfaction, intention to continue use, and social acceptability. Some studies (14.4%, 16/111) assessed dimensions more closely related to usability (ie, burden of sensor wear and use, interface complexity, and perceived technical performance). Conclusions: The rapid increase of research into the impact of physical activity apps and wearables means that evaluation guidelines are urgently needed to promote efficiency through the use of rapid research designs, in-device sensors and user-logs to assess effectiveness, engagement, and acceptability. Screening articles was time-consuming because reporting across health and computing sciences lacked standardization. Reporting guidelines are therefore needed to facilitate the synthesis of evidence across disciplines

The Neurocognitive Process of Digital Radicalization: A Theoretical Model and Analytical Framework

Recent studies suggest that empathy induced by narrative messages can effectively facilitate persuasion and reduce psychological reactance. Although limited, emerging research on the etiology of radical political behavior has begun to explore the role of narratives in shaping an individual’s beliefs, attitudes, and intentions that culminate in radicalization. The existing studies focus exclusively on the influence of narrative persuasion on an individual, but they overlook the necessity of empathy and that in the absence of empathy, persuasion is not salient. We argue that terrorist organizations are strategic in cultivating empathetic-persuasive messages using audiovisual materials, and disseminating their message within the digital medium. Therefore, in this paper we propose a theoretical model and analytical framework capable of helping us better understand the neurocognitive process of digital radicalization

eHealth interventions for people with chronic kidney disease

This is a protocol for a Cochrane Review (Intervention). The objectives are as follows: This review aims to look at the benefits and harms of using eHealth interventions in the CKD population

Multimodal Content Analysis for Effective Advertisements on YouTube

The rapid advances in e-commerce and Web 2.0 technologies have greatly increased the impact of commercial advertisements on the general public. As a key enabling technology, a multitude of recommender systems exists which analyzes user features and browsing patterns to recommend appealing advertisements to users. In this work, we seek to study the characteristics or attributes that characterize an effective advertisement and recommend a useful set of features to aid the designing and production processes of commercial advertisements. We analyze the temporal patterns from multimedia content of advertisement videos including auditory, visual and textual components, and study their individual roles and synergies in the success of an advertisement. The objective of this work is then to measure the effectiveness of an advertisement, and to recommend a useful set of features to advertisement designers to make it more successful and approachable to users. Our proposed framework employs the signal processing technique of cross modality feature learning where data streams from different components are employed to train separate neural network models and are then fused together to learn a shared representation. Subsequently, a neural network model trained on this joint feature embedding representation is utilized as a classifier to predict advertisement effectiveness. We validate our approach using subjective ratings from a dedicated user study, the sentiment strength of online viewer comments, and a viewer opinion metric of the ratio of the Likes and Views received by each advertisement from an online platform.Comment: 11 pages, 5 figures, ICDM 201

Leveraging Genomic Associations in Precision Digital Care for Weight Loss: Cohort Study

Background: The COVID-19 pandemic has highlighted the urgency of addressing an epidemic of obesity and associated inflammatory illnesses. Previous studies have demonstrated that interactions between single-nucleotide polymorphisms (SNPs) and lifestyle interventions such as food and exercise may vary metabolic outcomes, contributing to obesity. However, there is a paucity of research relating outcomes from digital therapeutics to the inclusion of genetic data in care interventions. Objective: This study aims to describe and model the weight loss of participants enrolled in a precision digital weight loss program informed by the machine learning analysis of their data, including genomic data. It was hypothesized that weight loss models would exhibit a better fit when incorporating genomic data versus demographic and engagement variables alone. Methods: A cohort of 393 participants enrolled in Digbi Health’s personalized digital care program for 120 days was analyzed retrospectively. The care protocol used participant data to inform precision coaching by mobile app and personal coach. Linear regression models were fit of weight loss (pounds lost and percentage lost) as a function of demographic and behavioral engagement variables. Genomic-enhanced models were built by adding 197 SNPs from participant genomic data as predictors and refitted using Lasso regression on SNPs for variable selection. Success or failure logistic regression models were also fit with and without genomic data. Results: Overall, 72.0% (n=283) of the 393 participants in this cohort lost weight, whereas 17.3% (n=68) maintained stable weight. A total of 142 participants lost 5% bodyweight within 120 days. Models described the impact of demographic and clinical factors, behavioral engagement, and genomic risk on weight loss. Incorporating genomic predictors improved the mean squared error of weight loss models (pounds lost and percent) from 70 to 60 and 16 to 13, respectively. The logistic model improved the pseudo R 2 value from 0.193 to 0.285. Gender, engagement, and specific SNPs were significantly associated with weight loss. SNPs within genes involved in metabolic pathways processing food and regulating fat storage were associated with weight loss in this cohort: rs17300539_G (insulin resistance and monounsaturated fat metabolism), rs2016520_C (BMI, waist circumference, and cholesterol metabolism), and rs4074995_A (calcium-potassium transport and serum calcium levels). The models described greater average weight loss for participants with more risk alleles. Notably, coaching for dietary modification was personalized to these genetic risks. Conclusions: Including genomic information when modeling outcomes of a digital precision weight loss program greatly enhanced the model accuracy. Interpretable weight loss models indicated the efficacy of coaching informed by participants’ genomic risk, accompanied by active engagement of participants in their own success. Although large-scale validation is needed, our study preliminarily supports precision dietary interventions for weight loss using genetic risk, with digitally delivered recommendations alongside health coaching to improve intervention efficac

Intention to Use Recommendation Agents for Online Shopping: The Role of Cognitive Age and Agent Complexity

Online recommendation agents (RAs) are increasingly being made available to consumers to facilitate their online shopping decision making. However, some customers may perceive difficulty in using online RAs if they are too complex, particularly older adults who experience limitations in their cognitive abilities. Grounded in the theory of reasoned action, and the aging and information systems adoption literatures, this study proposes a theoretical model to explore the effects of cognitive age and RA complexity on consumers’ intentions to use RAs. An experimental design and research methodology are outlined to validate the proposed model and identify differences between the experiences of younger and older adults in using RAs based on their cognitive age

Mobile phone apps for behavioral interventions for at-risk drinkers in Australia: literature Review

Background: The mobile technology era has ushered in the use of mobile phone apps for behavioral intervention for at-risk drinkers. Objective: Our objective was to review recent research relevant to mobile phone apps that can be used for behavioral intervention for at-risk drinkers in Australia. Methods: The inclusion criteria for this review were articles published in peer-reviewed journals from 2001 to 2017 with use of the search terms "smartphone application," "alcohol," "substance," "behavioural intervention," "electronic health," and "mobile health." Results: In total, we identified 103 abstracts, screened 90 articles, and assessed 50 full-text articles that fit the inclusion criteria for eligibility. We included 19 articles in this review. Conclusions: This review highlighted the paucity of evidence-based and empirically validated research into effective mobile phone apps that can be used for behavioral interventions with at-risk drinkers in Australia

The Association Between Willingness of Frontline Care Providers’ to Adaptively Use of Telehealth Technology and Virtual Service Performance in Provider-to-Provider Communication: Quantitative Study

Background: Telehealth technology can create a disruptive communication environment for frontline care providers who mediate virtual communication with specialists in electronic consultations. As providers are dealing with various technology features when communicating with specialists, their flexible attitude and behaviors to use various telehealth-related technology features can change the outcome of virtual care service. Objective: The objective of this study is to examine frontline care providers’ technology adaptation behaviors in the electronic consultation context. From the perspective of frontline care providers, we reapply and retest a theoretical model, reflecting a mechanism through which technology users’ personal characteristics and technology adaptation behavior enhance virtual service performance, which is an important performance enabler in this online meeting context. In provider-to-provider communication, particularly, we explore the association among providers’ information technology (IT)–related personal characteristics, adaptive telehealth technology use, and virtual service performance. Methods: An online survey was administered to collect individual providers’ personal traits, IT adaptation, and perception on virtual service performance. Partial least squares-structural equation modeling was used to estimate our predictive model of personal traits—IT adaptation, such as exploitative use (use the telehealth technology in a standard way), and exploratory use (use the telehealth technology as innovative way)—and virtual service performance. Results: We collected 147 responses from graduate nursing students who were training to be nurse practitioners in their master’s program, resulting in 121 valid responses from the cross-section online survey. Our theoretical model explained 60.0% of the variance in exploitative use of telehealth technology, 44% of the variance in exploratory use of telehealth technology, and 66% of the variance in virtual service performance. We found that exploitative IT use is an important driver to increase virtual service performance (β=0.762, P\u3c.001), and personal characteristics such as habit are positively associated with both exploitative (β=0.293, P=.008) and exploratory use behaviors (β=0.414, P=.006), while computer self-efficacy is positively associated with exploitative use of telehealth technology (β=0.311, P=.047). Conclusions: This study discusses the unique role of frontline care providers in a virtual care service context and highlights the importance of their telehealth adaptation behavior in provider-to-provider communication. We showed that providers perceive that telehealth technologies should function as intended, otherwise it may create frustration or avoidance of the telehealth technology. Moreover, providers’ habitual use of various technologies in daily lives also motivates them to adaptively use telehealth technology for improving virtual care service. Understanding providers’ technology habit and adaptation can inform health care policy and further provide a better view of the design of telehealth technology for online communication

Improved DASH Architecture for Quality Cloud Video Streaming in Automated Systems

In modern times, multimedia streaming systems that transmit video across a channel primarily use HTTP services as a delivery component. Encoding the video for all quality levels is avoided thanks to fuzzy based encoders' ability to react to network changes. Additionally, the system frequently uses packet priority assignment utilising a linear error model to enhance the dynamic nature of DASH without buffering. Based on a fuzzy encoder, the decision of video quality is made in consideration of the bandwidth available. This is a component of the MPEG DASH encoder. The Fuzzy DASH system seeks to increase the scalability of online video streaming, making it suitable for live video broadcasts through mobile and other devices