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Tooth shade determination methods for evaluating the effectiveness of whitening products at home are limited. In this study, an iPhone app for personalized tooth shade determination was developed. While capturing dental photographs in selfie mode before and after whitening, the app can maintain consistent illumination and tooth appearance conditions that affect tooth color measurement. An ambient light sensor was used to standardize the illumination conditions. To maintain consistent tooth appearance conditions determined by appropriately opening the mouth, facial landmark detection, an artificial intelligence technique that estimates key face parts and outlines, was used. The effectiveness of the app in ensuring uniform tooth appearance was investigated through color measurements of the upper incisors of seven participants via photographs captured in succession. The coefficients of variation for incisors L*, a*, and b* were less than 0.0256 (95% CI, 0.0173–0.0338), 0.2748 (0.1596–0.3899), and 0.1053 (0.0078–0.2028), respectively. To examine the feasibility of the app for tooth shade determination, gel whitening after pseudo-staining by coffee and grape juice was performed. Consequently, whitening results were evaluated by monitoring the ∆Eab color difference values (1.3 unit minimum). Although tooth shade determination remains a relative quantification method, the proposed method can support evidence-based selection of whitening products.

The Internet of Things (IoT) is a technology that can be employed to monitor several sectors, mostly precision agriculture, where environmental data can be used to better manage resources, reduce costs, and improve the use of natural resources. This work proposes the application of cloud-based IoT for monitoring irrigation water in rice fields. Southern Brazil was chosen as a case study because it suffers from water pollution problems due to urban activities, coal mining, and the inflow of saline water into the Araranguá River estuary. All these factors can compromise the water quality for irrigation. The presence of salt in irrigation water can compromise rice growth, making its monitoring essential for successful cultivation. Currently, the rice farmer needs to travel to the riverbank to assess the water quality using a manual sensor. This procedure demands time and resources and is not always efficient. The technology helps indicate whether the water is of quality to irrigate crops, helping the farmers decide whether to use that water to irrigate their fields. The solution is based on low-cost wireless sensor network devices, with subsequent transmission of data from the gateway using the network mobile phone cloud. Data collected were also accessed by the smartphone through a mobile application. This system was implemented for testing in the Araranguá River estuary, southern Santa Catarina, Brazil. Also, it appears as an option for farmers who need to monitor the quality of the water channel of the crop, thus avoiding the loss and reduction in crop productivity because the system can be continuously monitored and show notifications to the user. The highlights of this work were prototyping a complete solution to help the farmer using a smartphone application.

Background Health and fitness applications (apps) have gained popularity in interventions to improve diet, physical activity and sedentary behaviours but their efficacy is unclear. This systematic review examined the efficacy of interventions that use apps to improve diet, physical activity and sedentary behaviour in children and adults. Methods Systematic literature searches were conducted in five databases to identify papers published between 2006 and 2016. Studies were included if they used a smartphone app in an intervention to improve diet, physical activity and/or sedentary behaviour for prevention. Interventions could be stand-alone interventions using an app only, or multi-component interventions including an app as one of several intervention components. Outcomes measured were changes in the health behaviours and related health outcomes (i.e., fitness, body weight, blood pressure, glucose, cholesterol, quality of life). Study inclusion and methodological quality were independently assessed by two reviewers. Results Twenty-seven studies were included, most were randomised controlled trials ( n  = 19; 70%). Twenty-three studies targeted adults (17 showed significant health improvements) and four studies targeted children (two demonstrated significant health improvements). Twenty-one studies targeted physical activity (14 showed significant health improvements), 13 studies targeted diet (seven showed significant health improvements) and five studies targeted sedentary behaviour (two showed significant health improvements). More studies ( n  = 12; 63%) of those reporting significant effects detected between-group improvements in the health behaviour or related health outcomes, whilst fewer studies ( n  = 8; 42%) reported significant within-group improvements. A larger proportion of multi-component interventions (8 out of 13; 62%) showed significant between-group improvements compared to stand-alone app interventions (5 out of 14; 36%). Eleven studies reported app usage statistics, and three of them demonstrated that higher app usage was associated with improved health outcomes. Conclusions This review provided modest evidence that app-based interventions to improve diet, physical activity and sedentary behaviours can be effective. Multi-component interventions appear to be more effective than stand-alone app interventions, however, this remains to be confirmed in controlled trials. Future research is needed on the optimal number and combination of app features, behaviour change techniques, and level of participant contact needed to maximise user engagement and intervention efficacy.

The global impact of COVID-19 pandemic has led to a rapid development and utilization of mobile health applications. These are addressing the unmet needs of healthcare and public health system including contact tracing, health information dissemination, symptom checking and providing tools for training healthcare providers. Here we provide an overview of mobile applications being currently utilized for COVID-19 and their assessment using the Mobile Application Rating Scale. We performed a systematic review of the literature and mobile platforms to assess mobile applications currently utilized for COVID-19, and a quality assessment of these applications using the Mobile Application Rating Scale (MARS) for overall quality, Engagement, Functionality, Aesthetics, and Information. Finally, we provide an overview of the key salient features that should be included in mobile applications being developed for future use. Our search identified 63 apps that are currently being used for COVID-19. Of these, 25 were selected from the Google play store and Apple App store in India, and 19 each from the UK and US. 18 apps were developed for sharing up to date information on COVID-19, and 8 were used for contact tracing while 9 apps showed features of both. On MARS Scale, overall scores ranged from 2.4 to 4.8 with apps scoring high in areas of functionality and lower in Engagement. Future steps should involve developing and testing of mobile applications using assessment tools like the MARS scale and the study of their impact on health behaviours and outcomes.

Backgrounds The Smartphone Application-Based Addiction Scale (SABAS) is a validated 6-item measurement tool for assessing problematic smartphone use (PSU). However, the absence of established cutoff points for SABAS hinders its utilities. This study aimed to determine the optimal cutoff point for SABAS through latent profile analysis (LPA) and receiver operating characteristic curve (ROC) analyses among 63, 205. Chinese adolescents. Additionally, the study explored whether PSU screening with SABAS could effectively capture problematic social media use (PSMU) and internet gaming disorder (IGD). Method We recruited 63,205. adolescents using cluster sampling. Validated questionnaires were used to assess PSMU, IGD, and mental health (depression, anxiety, sleep disturbances, well-being, resilience, and externalizing and internalizing problems). Results LPA identified a 3-class model for PSU, including low-risk users (38.6%, n  = 24,388.), middle-risk users (42.5%, n  = 26,885.), and high-risk users (18.9%, n  = 11,932.). High-risk users were regarded as “PSU cases” in ROC analysis, which demonstrated an optimal cut-off point of 23 (sensitivity: 98.1%, specificity: 96.8%). According to the cutoff point, 21.1% ( n  = 13,317.) were identified as PSU. PSU adolescents displayed higher PSMU, IGD, and worse mental health. PSU screening effectively captured IGD (sensitivity: 86.8%, specificity: 84.5%) and PSMU (sensitivity: 84.5%, specificity: 80.2%). Conclusion A potential ideal threshold for utilizing SABAS to identify PSU could be 23 (out of 36). Employing SABAS as a screening tool for PSU holds the potential to reliably pinpoint both IGD and PSMU.

Background As smartphone becomes increasingly prevalent and affordable, more youths today can own a smartphone device and download applications in various application stores. Smartphone applications have been proven to be useful for youths in various aspects. However, there has been a paucity of data looking into the preferences of Vietnamese youths and adolescents with regards to health-related applications and their receptiveness towards smartphone apps. Therefore, this study aimed to determine the receptiveness and preferences of health-related smartphone applications (mHealth apps) among online Vietnamese youths and adolescents. Methods An online cross-sectional study was conducted between the periods of August till October 2015 in Vietnam. Respondent-driven sampling technique (RDS) was utilized to recruit participants. Participants were asked questions about their history of downloading and using health-related smartphone applications and their receptiveness when using these applications. Moreover, socio-demographic characteristics and health status were also self-reported. Multivariate logistic regression was employed to determine associated factors. Results Among 1028 participants, 57.4% owned a smartphone and only 14.1% of smartphone users have used a health-related smartphone application, and most of these individuals downloaded the applications for disease prevention (66.3%). 66.4% of the participants who owned these applications reported that health applications were useful and 92.8% reported being satisfied with the functionalities of the applications which they owned. Among smartphone users, people who were employed (OR = 15.46; 95%CI = 4.93–48.47) were more likely to download mHealth apps. Meanwhile, youths with higher EQ-5D index had a lower likelihood of downloading healthcare-related smartphone applications (OR = 0.17; 95%CI = 0.04–0.81). Conclusions This study highlighted a low rate of mHealth apps utilization among online Vietnamese youths and adolescents but a high acceptance of individuals who already used these apps. Developing mHealth apps or interventions towards the disease prevention and quality of life improvement could be feasible to proliferate the benefits of such applications in youths and adolescents in Vietnam. Further research should be conducted to optimize the contents and interfaces of mHealth apps that meet the needs of these populations.

Background Worldwide physical activity levels of adults are declining, which is associated with increased chronic disease risk. Wearables and smartphone applications offer new opportunities to change physical activity behaviour. This systematic review summarizes the evidence regarding the effect of wearables and smartphone applications on promoting physical activity. Methods PubMed, EMBASE and Cochrane databases were searched for RCTs, published since January 2008, on wearables and smartphone applications to promote physical activity. Studies were excluded when the study population consisted of children or adolescents, the intervention did not promote physical activity or comprised a minor part of the intervention, or the intervention was Internet-based and not accessible by smartphone. Risk of bias was assessed by the Cochrane collaboration tool. The primary outcome was changed in physical activity level. Meta-analyses were performed to assess the pooled effect on (moderate-to-vigorous) physical activity in minutes per day and daily step count. Results Eighteen RCTs were included. Use of wearables and smartphone applications led to a small to moderate increase in physical activity in minutes per day (SMD = 0.43, 95% CI = 0.03 to 0.82; I 2  = 85%) and a moderate increase in daily step count (SMD = 0.51, 95% CI = 0.12 to 0.91; I 2  = 90%). When removing studies with an unclear or high risk of bias, intervention effects improved and statistical heterogeneity was removed. Conclusions This meta-analysis showed a small to moderate effect of physical activity interventions comprising wearables and smartphone applications on physical activity. Hence, wearables and smartphone applications are likely to bring new opportunities in delivering tailored interventions to increase levels of physical activity.

Purpose: There is limited information in literature about the scope and usability of Smartphone Applications (Apps) in ophthalmology. Eye care professionals are therefore skeptical about the benefits of smartphone Apps and are reluctant to adopt it in their day to day practice. The purpose of this study was to provide an updated overview of all Apps exclusive to ophthalmology. Methods: This study was a quantitative analysis of Smartphone Apps exclusively designed for ophthalmic care. The Apple iPhone and Google play store were searched for eye care themed Apps. Any App related to eye treatment and management such as visual acuity screening, eye education, calculators, eBooks, Low Vision Aids were included in the study. Data on the purpose of the Apps, target end-users, validation, App usage, user ratings, and App developer qualifications were documented. Results: As of March 2020, a total of 131 Apps exclusively dedicated for eye care were identified. 53% (69/131) were available in iPhones, 44% (58/131) in Android smartphones, and 3% in both. 32% (41/131) Apps were designed for visual acuity (VA) screening, 13% (17/131) for eye relaxation exercises, 12% (15/131) for professional education, and the remaining for Apps detecting color blindness, low vision aids, and assistance and patient education. Among the 131 Apps, 6 (4.5%) Apps are claimed by the developers as validated. Conclusion: This study unveils a wide range of smartphone Apps exclusive to ophthalmology and suggests guidelines to choose an appropriate App. The study also highlights the importance of interdisciplinary collaboration in the design, development, and validation of such Apps.

Tobacco use is the leading cause of preventable disease and death in the USA. However, limited data exists regarding smoking cessation mobile app quality and intervention effectiveness. Innovative and scalable interventions are needed to further alleviate the public health implications of tobacco addiction. The proliferation of the smartphone and the advent of mobile phone health interventions have made treatment more accessible than ever. The purpose of this review was to examine the relation between published scientific literature and available commercial smartphone health apps for smoking cessation to identify the percentage of scientifically supported apps that were commercially available to consumers and to determine how many of the top commercially available apps for smoking cessation were supported by the published scientific literature. Adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, apps were reviewed in four phases: (1) identified apps from the scientific literature, (2) searched app stores for apps identified in the literature, (3) identified top apps available in leading app stores, and (4) determined which top apps available in stores had scientific support. Seven articles identified six apps with some level of scientific support, three (50%) were available in at least one app store. Conversely, among the top 50 apps suggested by each of the leading app stores, only two (4%) had any scientific support. While half of the scientifically vetted apps remain available to consumers, they are difficult to find among the many apps that are identified through app store searches.

Background Carers looking after someone with cancer often experience negative impacts on their own health. M-health interventions have been designed to provide information and support to patients and their carers. However, the effectiveness of technology-based interventions for carers is less well understood. The objectives were to assess the feasibility, useability and acceptability of technology-based interventions among carers of people living with cancer. Methods A systematic search of the CINAHL, MEDLINE and PSYCINFO databases was performed using terms related to web-based interventions and smartphone applications, carers and cancer. Studies were included if a randomised controlled trial or pilot study was conducted, focused on adult carers looking after another adult with cancer and were published between January 2007-June 2017. Articles were excluded if they reported qualitative results only or were evaluating existing websites and applications. Feasibility was measured by attrition, recruitment rates and frequency of intervention use; useability was measured by the ease of intervention use and the role of features to minimise errors in use. Acceptability was measured by carers’ perception of the appropriateness of the content and their ability to incorporate the intervention into their daily routines. Results Of the 729 articles, six articles met the inclusion criteria. Attrition ranged from 14% - 77%, recruitment rates from 20% - 66% and intervention useability varied across studies. Half of the studies implemented measures to improve useability. Overall, carers rated the content of the interventions as appropriate and reported improved knowledge and communication. Acceptability was further demonstrated as carers preferred the flexibility available with web-based interventions. Conclusions Technology-based interventions are suitable for use among carers of people with cancer. Further research is required to fully assess the impact of technology as an information and support mechanism for carers.