Protocol for the crowdsourced image-based morbidity hotspot surveillance for neglected tropical diseases (CIMS-NTDs).

INTRODUCTION: Efficient NTDs elimination strategies require effective surveillance and targeted interventions. Traditional methods are costly and time-consuming, often failing to cover entire populations in case of movement restrictions. To address these challenges, a morbidity image-based surveillance system is being developed. This innovative approach which leverages the smartphone technology aims at simultaneous surveillance of multiple NTDs, enhancing cost-efficiency, reliability, and community involvement, particularly in areas with movement constraints. Moreover, it holds promise for post-elimination surveillance. METHODOLOGY: The pilot of this method will be conducted across three states in southern Nigeria. It will target people affected by Neglected Tropical Diseases and members of their communities. The new surveillance method will be introduced to target communities in the selected states through community stakeholder's advocacy meetings and awareness campaigns. The pilot which is set to span eighteen months, entails sensitizing NTDs-affected individuals and community members using signposts, posters, and handbills, to capture photos of NTDs manifestations upon notice using smartphones. These images, along with pertinent demographic information, will be transmitted to a dedicated server through WhatsApp or Telegram accounts. The received images will be reviewed and organized at backend and then forwarded to a panel of experts for identification and annotation to specific NTDs. Data generated, along with geocoordinate information, will be used to create NTDs morbidity hotspot maps using ArcGIS. Accompanying metadata will be used to generate geographic and demographic distributions of various NTDs identified. To protect privacy, people will be encouraged to send manifestation photos of the affected body part only without any identifiable features. EVALUATION PROTOCOL: NTDs prevalence data obtained using conventional surveillance methods from both the pilot and selected control states during the pilot period will be compared with data from the CIMS-NTDs method to determine its effectiveness. EXPECTED RESULTS AND CONCLUSION: It is expected that an effective, privacy-conscious, population inclusive new method for NTDs surveillance, with the potential to yield real-time data for the identification of morbidity hotspots and distribution patterns of NTDs will be established. The results will provide insights into the effectiveness of the new surveillance method in comparison to traditional approaches, potentially advancing NTDs elimination strategies.

Rapid determination of seismic influence field based on mobile communication big data-A case study of the Luding Ms 6.8 earthquake in Sichuan, China.

Smartphone location data provide the most direct field disaster distribution data with low cost and high coverage. The large-scale continuous sampling of mobile device location data provides a new way to estimate the distribution of disasters with high temporal-spatial resolution. On September 5, 2022, a magnitude 6.8 earthquake struck Luding County, Sichuan Province, China. We quantitatively analyzed the Ms 6.8 earthquake from both temporal and geographic dimensions by combining 1,806,100 smartphone location records and 4,856 spatial grid locations collected through communication big data with the smartphone data under 24-hour continuous positioning. In this study, the deviation of multidimensional mobile terminal location data is estimated, and a methodology to estimate the distribution of out-of-service communication base stations in the disaster area by excluding micro error data users is explored. Finally, the mathematical relationship between the seismic intensity and the corresponding out-of-service rate of communication base stations is established, which provides a new technical concept and means for the rapid assessment of post-earthquake disaster distribution.

A feasibility study using motivational interviewing and a smartphone application to promote physical activity (+Stay-Active) for women with gestational diabetes.

BACKGROUND: Physical activity (PA) interventions have an encouraging role in gestational diabetes mellitus (GDM) management. Digital technologies can potentially be used at scale to support PA. The aim of this study was to assess the feasibility and acceptability of + Stay-Active: a complex intervention which combines motivational interviewing with a smartphone application to promote PA levels in women with GDM. METHODS: This non-randomised feasibility study used a mixed methods approach. Participants were recruited from the GDM antenatal clinic at Oxford University Hospitals. Following baseline assessments (visit 1) including self-reported and device determined PA measurements (wrist worn accelerometer), women participated in an online motivational interview, and then downloaded (visit 2) and used the Stay-Active app (Android or iOS). Women had access to Stay-Active until 36 weeks' gestation, when acceptability and PA levels were reassessed (visit 3). The primary outcome measures were recruitment and retention rates, participant engagement, and acceptability and fidelity of the intervention. Secondary outcome measures included PA levels, app usage, blood glucose and perinatal outcomes. Descriptive statistics were performed for assessments at study visits. Statistics software package Stata 14 and R were used. RESULTS: Over the recruitment period (46 weeks), 114 of 285 women met inclusion criteria and 67 (58%) enrolled in the study. Mean recruitment rate of 1.5 participants/clinic with 2.5 women/clinic meeting inclusion criteria. Fifty-six (83%) received the intervention at visit 2 and 53 (79%) completed the study. Compliance to accelerometer measurement protocols were sufficient in 78% of participants (52/67); wearing the device for more than 10 h on 5 or more days at baseline and 61% (41/67) at 36 weeks. There was high engagement with Stay-Active; 82% (55/67) of participants set goals on Stay-Active. Sustained engagement was evident, participants regularly accessed and logged multiples activities on Stay-Active. The intervention was deemed acceptable; 85% of women rated their care was satisfactory or above, supported by written feedback. CONCLUSIONS: This combined intervention was feasible and accepted. Recruitment rates were lower than expected. However, retention rates remained satisfactory and participant compliance with PA measurements and engagement was a high. Future work will explore the intervention's efficacy to increase PA and impact on clinical outcomes. TRIAL REGISTRATION: The study has received a favourable opinion from South Central-Hampshire B Research Ethics Committee; REC reference: 20/SC/0342. ISRCTN11366562.

Effectiveness of Applying Green Heart, a Smartphone-Based Self-management Intervention to Control Smoking: A Randomized Clinical Trial.

BACKGROUND: Cardiovascular diseases (CVDs) pose a significant global health concern and are the most common cause of death and disability, necessitating preventive interventions targeting modifiable risk factors. Recently, mobile-health technology has been developed to improve the delivery of cardiovascular prevention by risk factor modification. The "Green Heart" mobile application (app) was designed to aid in risk factor control among coronary artery disease (CAD) patients. METHODS: This parallel-group, single-blinded randomized controlled trial enrolled 1590 CAD patients, including 668 current smokers, randomly assigned to control (paper-based education) and intervention (application-based) groups. The app encompassed three modules targeting smoking cessation, dyslipidemia control, and blood pressure management. This study evaluated the impact of the smoking cessation module on behavioral change among current smokers. Green Heart assesses nicotine dependence, offering personalized quit plans, educational content, motivational messages, and automated progress tracking. The odds of smoking behavior changes during the 24-week follow-up underwent assessment. RESULTS: The intention-to-treat analysis highlighted significantly elevated rates of smoking cessation and reductions in the intervention group versus the control group. Adherence to the app (per-treatment analysis) also demonstrated significantly more favorable smoking behavior changes among the application users. Logistic regression emphasized higher odds of quitting and reduction in smoking in the application group, showing an odds ratio of 2.14 (95% CI: 1.16-3.97) compared to those not using the app (P=0.015). CONCLUSION: Our results confirmed that complete adherence to the app for at least 24 weeks was linked to alterations in cigarette smoking behavior among CAD patients. Trial Registration Number: IRCT20221016056204N1.

A randomized controlled trial of an app-based intervention on physical activity and glycemic control in people with type 2 diabetes.

BACKGROUND: We investigated the effects of a physical activity encouragement intervention based on a smartphone personal health record (PHR) application (app) on step count increases, glycemic control, and body weight in patients with type 2 diabetes (T2D). METHODS: In this 12-week, single-center, randomized controlled, 12-week extension study, patients with T2D who were overweight or obese were randomized using ratio 1:2 to a group using a smartphone PHR app (control group) or group using the app and received individualized motivational text messages (intervention group) for 12 weeks. During the extension period, the sending of the encouraging text messages to the intervention group was discontinued. The primary outcome was a change in daily step count after 12 weeks and analyzed by independent t-test. The secondary outcomes included HbA1c, fasting glucose, and body weight analyzed by paired or independent t-test. RESULTS: Of 200 participants, 62 (93.9%) and 118 (88.1%) in the control and intervention group, respectively, completed the 12-week main study. The change in daily step count from baseline to week 12 was not significantly different between the two groups (P = 0.365). Among participants with baseline step counts < 7,500 steps per day, the change in the mean daily step count at week 12 in the intervention group (1,319 ± 3,020) was significantly larger than that in control group (-139 ± 2,309) (P = 0.009). At week 12, HbA1c in the intervention group (6.7 ± 0.5%) was significantly lower than that in control group (6.9 ± 0.6%, P = 0.041) and at week 24, changes in HbA1c from baseline were significant in both groups but, comparable between groups. Decrease in HbA1c from baseline to week 12 of intervention group was greater in participants with baseline HbA1c ≥ 7.5% (-0.81 ± 0.84%) compared with those with baseline HbA1c < 7.5% (-0.22 ± 0.39%) (P for interaction = 0.014). A significant reduction in body weight from baseline to week 24 was observed in both groups without significant between-group differences (P = 0.370). CONCLUSIONS: App-based individualized motivational intervention for physical activity did not increase daily step count from baseline to week 12, and the changes in HbA1c levels from baseline to week 12 were comparable. TRIAL REGISTRATION: ClinicalTrials.gov (NCT03407222).

Parental perspectives on the impact of screen time on the language skills of typically developing Indian children.

PURPOSE: The overuse of screen-based devices results in developmental problems in children. Parents are an integral part of the children's language development. The present study explores the parental perspectives on the impact of screen time on the language skills of typically developing school-going children using a developed questionnaire. METHODS: 192 parents of typically developing children between 6 and 10 years of age participated in the study. Phase 1 of the study included the development of a questionnaire targeting the impact of screen devices on language development. The questionnaire was converted into an online survey and was circulated among the parents in Phase 2. Descriptive statistics were performed on the retrieved data and a chi-square test was done to determine the association between the use of screen devices across all language parameters. RESULTS: Parents reported television and smartphones to be the most used type of device, with a large proportion of children using screen-based devices for 1-2 hours per day. Most parents reported children prefer watching screens mainly for entertainment purposes, occasionally under supervision, without depending on them as potential rewards. The impact of screen-based devices on language skills has been discussed under the semantics, syntax, and pragmatic aspects of language. CONCLUSION: The findings of this study will help identify the existing trends in the usage of screen-based devices by children, thereby identifying potential contributing factors towards language delays. This information will also benefit in parental counselling during the interventional planning of children with language delays.

Quantitative analysis of CL-20 explosive by smartphone-based chemiluminescence method.

A new smartphone-based chemiluminescence method has been introduced for the quantitative analysis of CL-20 (Hexanitroazaisowuertzitan) explosive. The solvent mixture, oxidizer agent, and concentration of the reactants were optimized using statistical procedures. CL-20 explosive showed a quenching effect on the chemiluminescence intensity of the luminol-NaClO reaction in the solvent mixture of DMSO/H2O. A smartphone was used as a detector to record the light intensity of chemiluminescence reaction as a video file. The recorded video file was converted to an analytical signal as intensity luminescence-time curve by a written code in MATLAB software. Dynamic range and limit of detection of the proposed method were obtained 2.0-240.0 and 1.1 mg⋅L-1, respectively, in optimized concentrations 1.5 × 10-3 mol⋅L-1 luminol and 1.0 × 10-2 mol⋅L-1 NaClO. Precursors TADB, HBIW, and TADNIW in CL-20 explosive synthesis did not show interference in measurement the CL-20 purity. The analysis of CL-20 spiked samples of soil and water indicated the satisfactory ability of the method in the analysis of real samples. The interaction of CL-20 molecules and OCl- ions is due to quench of chemiluminescence reaction of the luminol-NaClO.

Optimizing Outcomes in Psychotherapy for Anxiety Disorders Using Smartphone-Based and Passive Sensing Features: Protocol for a Randomized Controlled Trial.

BACKGROUND: Psychotherapies, such as cognitive behavioral therapy (CBT), currently have the strongest evidence of durable symptom changes for most psychological disorders, such as anxiety disorders. Nevertheless, only about half of individuals treated with CBT benefit from it. Predictive algorithms, including digital assessments and passive sensing features, could better identify patients who would benefit from CBT, and thus, improve treatment choices. OBJECTIVE: This study aims to establish predictive features that forecast responses to transdiagnostic CBT in anxiety disorders and to investigate key mechanisms underlying treatment responses. METHODS: This study is a 2-armed randomized controlled clinical trial. We include patients with anxiety disorders who are randomized to either a transdiagnostic CBT group or a waitlist (referred to as WAIT). We index key features to predict responses prior to starting treatment using subjective self-report questionnaires, experimental tasks, biological samples, ecological momentary assessments, activity tracking, and smartphone-based passive sensing to derive a multimodal feature set for predictive modeling. Additional assessments take place weekly at mid- and posttreatment and at 6- and 12-month follow-ups to index anxiety and depression symptom severity. We aim to include 150 patients, randomized to CBT versus WAIT at a 3:1 ratio. The data set will be subject to full feature and important features selected by minimal redundancy and maximal relevance feature selection and then fed into machine leaning models, including eXtreme gradient boosting, pattern recognition network, and k-nearest neighbors to forecast treatment response. The performance of the developed models will be evaluated. In addition to predictive modeling, we will test specific mechanistic hypotheses (eg, association between self-efficacy, daily symptoms obtained using ecological momentary assessments, and treatment response) to elucidate mechanisms underlying treatment response. RESULTS: The trial is now completed. It was approved by the Cantonal Ethics Committee, Zurich. The results will be disseminated through publications in scientific peer-reviewed journals and conference presentations. CONCLUSIONS: The aim of this trial is to improve current CBT treatment by precise forecasting of treatment response and by understanding and potentially augmenting underpinning mechanisms and personalizing treatment. TRIAL REGISTRATION: ClinicalTrials.gov NCT03945617; https://clinicaltrials.gov/ct2/show/results/NCT03945617. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/42547.

Competition of multiplatform firms: Implications for the Internet of Things.

The Internet of Things (IoT) technology trend is transforming business and society. This creates a need to understand strategic behavior in the consumer IoT, where firms tend to offer multiple platform devices, and new generations of devices are introduced frequently. We propose a novel analytical model that formalizes the concept of a multiplatform firm that offers a system of platforms, such as a smartphone, and a new platform device, such as a smartwatch, and orchestrates a multiplatform ecosystem. The analysis shows how a platform design decision, like offering a new standalone device, affects consumer choices and market outcomes. We identify two classes of new devices that matter, and show when a new platform device may disrupt the smartphone market. Moreover, we characterize conditions under which it is profitable for a vendor to make its new platform device look and feel more like its smartphone. Overall, we provide insights into how multiplatform firms differ from platform firms. We identify future research opportunities on the economics of consumer IoT and multiplatform ecosystems.

Adapting an artificial intelligence sexually transmitted diseases symptom checker tool for Mpox detection: the HeHealth experience.

Artificial Intelligence (AI) applications have shown promise in the management of pandemics. In response to the global Monkeypox (Mpox) outbreak, the HeHealth.ai team leveraged an existing tool to screen for sexually transmitted diseases (STD) to develop a digital screening test for symptomatic Mpox using AI. Before the global Mpox outbreak, the team developed a smartphone app (HeHealth) where app users can use a smartphone to photograph their own penises to screen for symptomatic STD. The AI model initially used 5000 cases and a modified convolutional neural network to output prediction scores across visually diagnosable penis pathologies including syphilis, herpes simplex virus, and human papillomavirus. A total of about 22,000 users had downloaded the HeHealth app, and ~21,000 images were analysed using HeHealth AI technology. We then used formative research, stakeholder engagement, rapid consolidation images, a validation study, and implementation of the tool. A total of 1000 Mpox-related images had been used to train the Mpox symptom checker tool. Based on an internal validation, our digital symptom checker tool showed specificity of 87% and sensitivity of 90% for symptomatic Mpox. Several hurdles identified included issues of data privacy and security for app users, initial lack of data to train the AI tool, and the potential generalisability of input data. We offer several suggestions to help others get started on similar projects in emergency situations, including engaging a wide range of stakeholders, having a multidisciplinary team, prioritising pragmatism, as well as the concept that 'big data' in fact is made up of 'small data'.

The Google Health Digital Well-Being Study: Protocol for a Digital Device Use and Well-Being Study.

BACKGROUND: The impact of digital device use on health and well-being is a pressing question. However, the scientific literature on this topic, to date, is marred by small and unrepresentative samples, poor measurement of core constructs, and a limited ability to address the psychological and behavioral mechanisms that may underlie the relationships between device use and well-being. Recent authoritative reviews have made urgent calls for future research projects to address these limitations. The critical role of research is to identify which patterns of use are associated with benefits versus risks and who is more vulnerable to harmful versus beneficial outcomes, so that we can pursue evidence-based product design, education, and regulation aimed at maximizing benefits and minimizing the risks of smartphones and other digital devices. OBJECTIVE: The objective of this study is to provide normative data on objective patterns of smartphone use. We aim to (1) identify how patterns of smartphone use impact well-being and identify groups of individuals who show similar patterns of covariation between smartphone use and well-being measures across time; (2) examine sociodemographic and personality or mental health predictors and which patterns of smartphone use and well-being are associated with pre-post changes in mental health and functioning; (3) discover which nondevice behavior patterns mediate the association between device use and well-being; (4) identify and explore recruitment strategies to increase and improve the representation of traditionally underrepresented populations; and (5) provide a real-world baseline of observed stress, mood, insomnia, physical activity, and sleep across a representative population. METHODS: This is a prospective, nonrandomized study to investigate the patterns and relationships among digital device use, sensor-based measures (including both behavioral and physiological signals), and self-reported measures of mental health and well-being. The study duration is 4 weeks per participant and includes passive sensing based on smartphone sensors, and optionally a wearable (Fitbit), for the complete study period. The smartphone device will provide activity, location, phone unlocks and app usage, and battery status information. RESULTS: At the time of submission, the study infrastructure and app have been designed and built, the institutional review board of the University of Oregon has approved the study protocol, and data collection is underway. Data from 4182 enrolled and consented participants have been collected as of March 27, 2023. We have made many efforts to sample a study population that matches the general population, and the demographic breakdown we have been able to achieve, to date, is not a perfect match. CONCLUSIONS: The impact of digital devices on mental health and well-being raises important questions. The Digital Well-Being Study is designed to help answer questions about the association between patterns of smartphone use and well-being. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/49189.

Prediction of anemia in real-time using a smartphone camera processing conjunctival images.

Anemia is defined as a low hemoglobin (Hb) concentration and is highly prevalent worldwide. We report on the performance of a smartphone application (app) that records images in RAW format of the palpebral conjunctivae and estimates Hb concentration by relying upon computation of the tissue surface high hue ratio. Images of bilateral conjunctivae were obtained prospectively from a convenience sample of 435 Emergency Department patients using a dedicated smartphone. A previous computer-based and validated derivation data set associating estimated conjunctival Hb (HBc) and the actual laboratory-determined Hb (HBl) was used in deriving Hb estimations using a self-contained mobile app. Accuracy of HBc was 75.4% (95% CI 71.3, 79.4%) for all categories of anemia, and Bland-Altman plot analysis showed a bias of 0.10 and limits of agreement (LOA) of (-4.73, 4.93 g/dL). Analysis of HBc estimation accuracy around different anemia thresholds showed that AUC was maximized at transfusion thresholds of 7 and 9 g/dL which showed AUC values of 0.92 and 0.90 respectively. We found that the app is sufficiently accurate for detecting severe anemia and shows promise as a population-sourced screening platform or as a non-invasive point-of-care anemia classifier.

Support after return to alcohol use: a mixed-methods study on how abstinence motivation and app use change after return to alcohol use in an app-based aftercare intervention for individuals with alcohol use disorder.

BACKGROUND: As the return to alcohol use in individuals with alcohol use disorder (AUD) is common during treatment and recovery, it is important that abstinence motivation is maintained after such critical incidences. Our study aims to explore how individuals with AUD participating in an app-based intervention with telephone coaching after inpatient treatment perceived their abstinence motivation after the return to alcohol use, whether their app use behavior was affected and to identify helpful factors to maintain abstinence motivation. METHODS: Using a mixed-methods approach, ten participants from the intervention group of the randomized controlled trial SmartAssistEntz who returned to alcohol use and recorded this in the app Appstinence, a smartphone application with telephone coaching designed for individuals with AUD, were interviewed about their experiences. The interviews were recorded, transcribed and coded using qualitative content analysis. App use behavior was additionally examined by using log data. RESULTS: Of the ten interviewees, seven reported their abstinence motivation increased after the return to alcohol use. Reasons included the reminder of negative consequences of drinking, the desire to regain control of their situation as well as the perceived support provided by the app. App data showed that app use remained stable after the return to alcohol use with an average of 58.70 days of active app use (SD = 25.96, Mdn = 58.50, range = 24-96, IQR = 44.25) after the return to alcohol use which was also indicated by the participants' reported use behavior. CONCLUSIONS: The findings of the study tentatively suggest that the app can provide support to individuals after the return to alcohol use to maintain and increase motivation after the incidence. Future research should (1) focus on specifically enhancing identification of high risk situations and reach during such critical incidences, (2) actively integrate the experience of the return to alcohol use into app-based interventions to better support individuals in achieving their personal AUD behavior change goals, and (3) investigate what type of support individuals might need who drop out of the study and intervention and discontinue app use altogether. TRIAL REGISTRATION: The primary evaluation study is registered in the German Clinical Trials Register (DRKS, registration number DRKS00017700) and received approval of the ethical committee of the Friedrich-Alexander University Erlangen-Nuremberg (193_19 B).

Self-Selected Versus Assigned Target to Reduce Smartphone Use and Improve Mental Health: Protocol for a Randomized Controlled Trial.

BACKGROUND: Smartphones have become integral to people's lives, with a noticeable increase in the average screen time, both on a global scale and, notably, in India. Existing research links mobile consumption to sleep problems, poor physical and mental health, and lower subjective well-being. The comparative effectiveness of monetary incentives given for self-selected versus assigned targets on reducing screen time and thereby improving mental health remains unanswered. OBJECTIVE: This study aims to assess the impact of monetary incentives and target selection on mobile screen time reduction and mental health. METHODS: We designed a 3-armed randomized controlled trial conducted with employees and students at an educational institution in India. The study is conducted digitally over 12 weeks, including baseline (2 weeks), randomization (1 week), intervention (5 weeks), and postintervention (4 week) periods. We emailed the employees and students to inquire about their interest in participation. Those who expressed interest received detailed study information and consent forms. After securing consent, participants were asked to complete the initial survey and provide their mobile screen time during the baseline period. At the beginning of the intervention period, the participants were randomly allocated into 1 of 3 study groups in a 2:2:1 ratio (self-selected vs assigned vs control). Participants in the self-selected group were presented with 3 target options: 10%, 20%, and 30%, and they were asked to self-select a target to reduce their mobile screen time from their baseline average mobile screen time. Participants in the assigned group were given a target to reduce their mobile screen time from their baseline average mobile screen time. The assigned target was set as the average of the targets selected by participants in the self-selected group. During the intervention period, participants in the self-selected and assigned group were eligible to receive a monetary incentive of INR (Indian Rupee) 50 (US $0.61) per day for successfully attaining their target. Participants in the control group neither received nor selected a target for reducing their mobile screen time and did not receive any monetary incentives during the intervention period. All participants received information regarding the advantages of reducing mobile screen time. As an incentive, all participants would receive INR 500 (US $6.06) upon completion of the study and a chance to win 1 of 2 lotteries valued at INR 5000 (US $60.55) for consistently sharing their mobile screen time data. RESULTS: Currently, the study intervention is being rolled out. Enrollment occurred between August 21, 2023, and September 2, 2023; data collection concluded in November 2023. We expect that results will be available by early 2024. CONCLUSIONS: The monetary incentives and self-selected versus assigned targets might be effective interventions in reducing mobile screen time among working professionals and students. TRIAL REGISTRATION: AsPredicted 142497; https://aspredicted.org/hr3nn.pdf. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/53756.

A Randomized Controlled Trial Comparing the Effect of Tracheostomy Training using Mannequin-Based Simulation and Smartphone Application on Self-Efficacy and Anxiety of Caregivers.

Inefficient education is a cause of anxiety and low self-efficacy among caregivers, especially for those caring for patients with tracheostomy. This randomized controlled trial aimed to compare the outcomes of tracheostomy care education by mannequin-based simulation and smartphone application. The participants were 126 primary caregivers of tracheostomy patients being discharged home from hospitals affiliated with Tehran University of Medical Sciences. The control group received routine care. Caregiver self-efficacy was assessed using the Caregiver Inventory and the Hamilton Anxiety Rating Scale prior to the education and 1 month after. There were significant differences among the three groups regarding the mean scores of self-efficacy and anxiety. There was a significant increase in self-efficacy (P ≤ .0001) and a significant decrease in anxiety (P ≤ .0001) scores after the intervention. The intergroup comparison showed a significant difference between the intervention groups and the control group in terms of changes in the anxiety and self-efficacy scores of caregivers (P < .001).

Tele-dentistry in COVID-19 era: A conduit for dental care in uncertain times.

Tele-dentistry encompasses all sorts of digital technologies that involve the exchange of patient's clinical data from a distant site for the provision of dental health care. Tele-dentistry has emerged from the concept of telemedicine, which has been in practice since the 19th century. In recent times, an upsurge in the digital technologies was noted, which has made the possibility of remote access to dental care. The outbreak of COVID- 19 pandemic has restricted the normal routine ways of clinical practice. In these challenging times, tele-dentistry serves as effective platform for providing dental health care. Tele-dentistry has vast applications across various disciplines of dentistry, including preventive dentistry, paediatric dentistry, oral medicine, and oral pathology etc. In these pandemic times, tele-dentistry can be efficiently used for identification of dental emergencies, allowing effective triage and subsequent management. There are different communication platforms available for tele-dentistry. The most common technologies used are web-based video conferencing and smart phone-based applications. As the clinicians are not aware of these digital technologies utilised in tele-dentistry, there are certain challenges associated with its use. In conclusion, tele-dentistry serves as an effective tool in providing health care in challenging times, but it has been underutilised by the dental fraternity. The legislative authorities should establish proper standard protocols to ensure the safety and confidentiality of patient information while using these digital platforms.

Technology-based Cognitive Behavioral Therapy Interventions.

Technology-delivered cognitive behavioral therapy (CBT) has enabled more people to access effective, affordable mental health care. This study provides an overview of the most common types of technology-delivered CBT, including Internet-delivered, smartphone app, and telehealth CBT, as well as their evidence for the treatment of a range of mental health conditions. We discuss gaps in the existing evidence and future directions in the field for the use of technology CBT interventions.

Computer vision digitization of smartphone images of anesthesia paper health records from low-middle income countries.

BACKGROUND: In low-middle income countries, healthcare providers primarily use paper health records for capturing data. Paper health records are utilized predominately due to the prohibitive cost of acquisition and maintenance of automated data capture devices and electronic medical records. Data recorded on paper health records is not easily accessible in a digital format to healthcare providers. The lack of real time accessible digital data limits healthcare providers, researchers, and quality improvement champions to leverage data to improve patient outcomes. In this project, we demonstrate the novel use of computer vision software to digitize handwritten intraoperative data elements from smartphone photographs of paper anesthesia charts from the University Teaching Hospital of Kigali. We specifically report our approach to digitize checkbox data, symbol-denoted systolic and diastolic blood pressure, and physiological data. METHODS: We implemented approaches for removing perspective distortions from smartphone photographs, removing shadows, and improving image readability through morphological operations. YOLOv8 models were used to deconstruct the anesthesia paper chart into specific data sections. Handwritten blood pressure symbols and physiological data were identified, and values were assigned using deep neural networks. Our work builds upon the contributions of previous research by improving upon their methods, updating the deep learning models to newer architectures, as well as consolidating them into a single piece of software. RESULTS: The model for extracting the sections of the anesthesia paper chart achieved an average box precision of 0.99, an average box recall of 0.99, and an mAP0.5-95 of 0.97. Our software digitizes checkbox data with greater than 99% accuracy and digitizes blood pressure data with a mean average error of 1.0 and 1.36 mmHg for systolic and diastolic blood pressure respectively. Overall accuracy for physiological data which includes oxygen saturation, inspired oxygen concentration and end tidal carbon dioxide concentration was 85.2%. CONCLUSIONS: We demonstrate that under normal photography conditions we can digitize checkbox, blood pressure and physiological data to within human accuracy when provided legible handwriting. Our contributions provide improved access to digital data to healthcare practitioners in low-middle income countries.

Multi-level prediction of substance use: Interaction of white matter integrity, resting-state connectivity and inhibitory control measured repeatedly in every-day life.

Substance use disorders are characterized by inhibition deficits related to disrupted connectivity in white matter pathways, leading via interaction to difficulties in resisting substance use. By combining neuroimaging with smartphone-based ecological momentary assessment (EMA), we questioned how biomarkers moderate inhibition deficits to predict use. Thus, we aimed to assess white matter integrity interaction with everyday inhibition deficits and related resting-state network connectivity to identify multi-dimensional predictors of substance use. Thirty-eight patients treated for alcohol, cannabis or tobacco use disorder completed 1 week of EMA to report substance use five times and complete Stroop inhibition testing twice daily. Before EMA tracking, participants underwent resting state functional MRI and diffusion tensor imaging (DTI) scanning. Regression analyses were conducted between mean Stroop performances and whole-brain fractional anisotropy (FA) in white matter. Moderation testing was conducted between mean FA within significant clusters as moderator and the link between momentary Stroop performance and use as outcome. Predictions between FA and resting-state connectivity strength in known inhibition-related networks were assessed using mixed modelling. Higher FA values in the anterior corpus callosum and bilateral anterior corona radiata predicted higher mean Stroop performance during the EMA week and stronger functional connectivity in occipital-frontal-cerebellar regions. Integrity in these regions moderated the link between inhibitory control and substance use, whereby stronger inhibition was predictive of the lowest probability of use for the highest FA values. In conclusion, compromised white matter structural integrity in anterior brain systems appears to underlie impairment in inhibitory control functional networks and compromised ability to refrain from substance use.

Cognitive Training with Older Adults Using Smartphone and Web-Based Applications: A Scoping Review.

INTRODUCTION: The present scoping review focused on: i) which apps were previously studied; ii) what is the most common frequency for implementing cognitive training; and iii) what cognitive functions the interventions most focus on. METHODS: PRISMA guidelines were followed, and the search was conducted on Web of Science, PsycInfo, Cochrane, and Pubmed. From 1733 studies found, 34 were included. RESULTS: it was highlighted the necessity for forthcoming investigations to tackle the methodical restrictions and disparities in the domain. DISCUSSION: great diversity in intervention protocols was found. Incorporating evaluations of physical fitness in conjunction with cognitive evaluations can offer a more all-encompassing comprehension of the impacts of combined interventions. Furthermore, exploring the efficacy of cognitive training applications requires additional scrutiny, considering individual variances and practical outcomes in real-life settings.