iOS Smartphone Research Articles

Fall Detection System based on iOS Smartphone Sensors

Activities of daily living (ADLs) are a crucial aspect of human life, especially in remote health monitoring and fall detection. As smartphones have become an integral part of our daily routines, with their ability to perform complex calculations, connect to the internet, and incorporate various sensors, researchers have been inspired to explore human activity recognition systems. This paper focuses on accelerometer and gyroscope data from iOS-based smartphones. We developed a data collection app to record fall types (e.g., Falling Right, Falling Left) and fall-like activities (e.g., Sitting Fast, Jumping). Volunteers carried smartphones naturally in their pockets during experiments, presenting a challenge of noise but enhancing user comfort. We applied different Machine Learning algorithms (Decision Trees, Random Forest, Logistic Regression, k-Nearest Neighbor, XGBoost, LightGBM, and Neural Networks) to analyze the collected dataset. In contrast to typical studies, our approach replicated real-world smartphone usage. The paper presents and analyzes promising results from the study. Furthermore, we implemented the trained model as a real-time mobile application for potential users. This research illustrates the potential of smartphones in fall detection and opens the way for user-friendly solutions in remote health monitoring.

Acceptability and Feasibility of a Smartphone-Based Real-Time Assessment of Suicide Among Black Men: Mixed Methods Pilot Study.

Suicide rates in the United States have increased recently among Black men. To address this public health crisis, smartphone-based ecological momentary assessment (EMA) platforms are a promising way to collect dynamic, real-time data that can help improve suicide prevention efforts. Despite the promise of this methodology, little is known about its suitability in detecting experiences related to suicidal thoughts and behavior (STB) among Black men. This study aims to clarify the acceptability and feasibility of using smartphone-based EMA through a pilot study that assesses the user experience among Black men. We recruited Black men aged 18 years and older using the MyChart patient portal messaging (the patient-facing side of the Epic electronic medical record system) or outpatient provider referrals. Eligible participants self-identified as Black men with a previous history of STB and ownership of an Android or iOS smartphone. Eligible participants completed a 7-day smartphone-based EMA study. They received a prompt 4 times per day to complete a brief survey detailing their STB, as well as proximal risk factors, such as depression, social isolation, and feeling like a burden to others. At the conclusion of each day, participants also received a daily diary survey detailing their sleep quality and their daily experiences of everyday discrimination. Participants completed a semistructured exit interview of 60-90 minutes at the study's conclusion. In total, 10 participants completed 166 EMA surveys and 39 daily diary entries. A total of 4 of the 10 participants completed 75% (21/28) or more of the EMA surveys, while 9 (90%) out of 10 completed 25% (7/28) or more. The average completion rate of all surveys was 58% (20.3/35), with a minimum of 17% (6/35) and maximum of 100% (35/35). A total of 4 (40%) out of 10 participants completed daily diary entries for the full pilot study. No safety-related incidents were reported. On average, participants took 2.08 minutes to complete EMA prompts and 2.72 minutes for daily diary surveys. Our qualitative results generally affirm the acceptability and feasibility of the study procedures, but the participants noted difficulties with the technology and the redundancy of the survey questions. Emerging themes also addressed issues such as reduced EMA survey compliance and diminished mood related to deficit-framed questions related to suicide. Findings from this study will be used to clarify the suitability of EMA for Black men. Overall, our EMA pilot study demonstrated mixed feasibility and acceptability when delivered through smartphone-based apps to Black men. Specific recommendations are provided for managing safety within these study designs and for refinements in future intervention and implementation science research. RR2-10.2196/31241.

In-Home Smartphone-Based Prediction of Obstructive Sleep Apnea in Conjunction With Level 2 Home Polysomnography

Consumer-level sleep analysis technologies have the potential to revolutionize the screening for obstructive sleep apnea (OSA). However, assessment of OSA prediction models based on in-home recording data is usually performed concurrently with level 1 in-laboratory polysomnography (PSG). Establishing the predictability of OSA using sound data recorded from smartphones based on level 2 PSG at home is important. To validate the performance of a prediction model for OSA using breathing sound recorded from smartphones in conjunction with level 2 PSG at home. This diagnostic study followed a prospective design, involving participants who underwent unattended level 2 home PSG. Breathing sounds were recorded during sleep using 2 smartphones, one with an iOS operating system and the other with an Android operating system, simultaneously with home PSG in participants' own home environment. Participants were 19 years and older, slept alone, and had either been diagnosed with OSA or had no previous diagnosis. The study was performed between February 2022 and February 2023. Sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of the predictive model based on the recorded breathing sounds. Of the 101 participants included during the study duration, the mean (SD) age was 48.3 (14.9) years, and 51 (50.5%) were female. For the iOS smartphone, the sensitivity values at apnea-hypopnea index (AHI) levels of 5, 15, and 30 per hour were 92.6%, 90.9%, and 93.3%, respectively, with specificities of 84.3%, 94.4%, and 94.4%, respectively. Similarly, for the Android smartphone, the sensitivity values at AHI levels of 5, 15, and 30 per hour were 92.2%, 90.0%, and 92.9%, respectively, with specificities of 84.0%, 94.4%, and 94.3%, respectively. The accuracy for the iOS smartphone was 88.6%, 93.3%, and 94.3%, respectively, and for the Android smartphone was 88.1%, 93.1%, and 94.1% at AHI levels of 5, 15, and 30 per hour, respectively. This diagnostic study demonstrated the feasibility of predicting OSA with a reasonable level of accuracy using breathing sounds obtained by smartphones during sleep at home.

Performance evaluation of image processing algorithms for eye blinking detection

The eye is considered a rich source for gathering information on our daily lives. In addition, the necessity to classify the eye as open or closed and detect eye blinks is increasing in various fields. For instance, it has been proven that eye blinks can be related to different ophthalmological conditions.Nevertheless, accurate, and robust blink estimation poses significant challenges to the efficacy of real-time applications due to the variability of eye images and light conditions.In this paper eye blinking has been monitored using a chin rest and an iOS smartphone attached to a cartesian machinery. Five video-based eye blink algorithms to classify frames have been analyzed and compared in terms of performance. To evaluate the results, different widely used statistical metrics have been employed. The results show that the proposed techniques successfully detect eye blinks, whereas the frame classifier shows different scores depending on the algorithm used.

IOS mobile malware analysis: a state-of-the-art

In earlier years, most malware attacks were against Android smartphones. Unfortunately, for the past few years, the trend has shifted towards attacks against the Apple iOS smartphone. Consequently, an in-depth analysis of the malware and iOS architecture is important to identify the best mitigation solution against malware exploitation. Hence, this paper presents a state-of-the-art deep analysis of malware against iOS smartphones. This includes comprehensive studies of malware architecture involving payload, propagation, operating algorithm, infection, and activation with underlying integration with a phylogenetic concept. Phylogenetic, borrowed from the biology field, can identify any evolution of the origin of the malware involved. To support this deep analysis of malware, a preliminary study was conducted using 12 malware samples, by focusing on social media and online banking. This took place in a controlled laboratory using hybrid analysis. The result showed that there is a way to identify the evolution of malware and as a result, a model has been developed. Based on the evaluation, 4% of mobile applications matched the patterns developed in this model. This proves that the model developed in this paper can detect any possible security exploitation related to social media and online banking for iOS mobile applications. This work can be used as guidance for other researchers working on similar interests in the future.

Automated Diet Capture Using Voice Alerts and Speech Recognition on Smartphones: Pilot Usability and Acceptability Study.

Effective monitoring of dietary habits is critical for promoting healthy lifestyles and preventing or delaying the onset and progression of diet-related diseases, such as type 2 diabetes. Recent advances in speech recognition technologies and natural language processing present new possibilities for automated diet capture; however, further exploration is necessary to assess the usability and acceptability of such technologies for diet logging. This study explores the usability and acceptability of speech recognition technologies and natural language processing for automated diet logging. We designed and developed base2Diet-an iOS smartphone application that prompts users to log their food intake using voice or text. To compare the effectiveness of the 2 diet logging modes, we conducted a 28-day pilot study with 2 arms and 2 phases. A total of 18 participants were included in the study, with 9 participants in each arm (text: n=9, voice: n=9). During phase I of the study, all 18 participants received reminders for breakfast, lunch, and dinner at preselected times. At the beginning of phase II, all participants were given the option to choose 3 times during the day to receive 3 times daily reminders to log their food intake for the remainder of the phase, with the ability to modify the selected times at any point before the end of the study. The total number of distinct diet logging events per participant was 1.7 times higher in the voice arm than in the text arm (P=.03, unpaired t test). Similarly, the total number of active days per participant was 1.5 times higher in the voice arm than in the text arm (P=.04, unpaired t test). Furthermore, the text arm had a higher attrition rate than the voice arm, with only 1 participant dropping out of the study in the voice arm, while 5 participants dropped out in the text arm. The results of this pilot study demonstrate the potential of voice technologies in automated diet capturing using smartphones. Our findings suggest that voice-based diet logging is more effective and better received by users compared to traditional text-based methods, underscoring the need for further research in this area. These insights carry significant implications for the development of more effective and accessible tools for monitoring dietary habits and promoting healthy lifestyle choices.

Artificial Intelligence Biosensing System on Hand Gesture Recognition for the Hearing Impaired

AI technologies have the potential to help deaf individuals communicate. Due to the complexity of sign fragmentation and the inadequacy of capturing hand gestures, the authors present a sign language recognition (SLR) system and wearable surface electromyography (sEMG) biosensing device based on a Deep SLR that converts sign language into printed message or speech, allowing people to better understand sign language and hand motions. On the forearms, two armbands containing a biosensor and multi-channel sEMG sensors are mounted to capture quite well arm and finger actions. Deep SLR was tested on an Android and iOS smartphone, and its usefulness was determined by comprehensive testing. Sign Speaker has a considerable limitation in terms of recognising two-handed signs with smartphone and smartwatch. To solve these issues, this research proposes a new real-time end-to-end SLR method. The average word error rate of continuous sentence recognition is 9.6%, and detecting signals and recognising a sentence with six sign words takes less than 0.9 s, demonstrating Deep SLR's recognition.

An interactive smartphone application for trainees in Radiation Oncology: "The Rad Onc Handbook"

1The app is well structured and easy to use. 2The app content is relevant to radiation oncology. 3The audio lessons enhanced my learning. 4The quizzes and cases enhanced my learning. 5My overall knowledge in radiation oncology has increased. 6I would recommend this application to a colleague. 25 participants completed the survey on the advanced learning module. 70% of all survey participants ""agree"" or ""strongly agree"" to the five statements listed below: 1The module was well structured. 2The module is relevant to radiation oncology. 3The module was an appropriate level of complexity. 4My overall knowledge about breast cancers has increased. 5I would recommend this module to a colleague. Overall, the application and advanced learning modules were well received and the trainees express a desire for other advanced learning modules in the free text responses. This free smartphone application provides an easily accessible resource for self-directed learning in radiation oncology. Initial feedback was very favourable and recommended use of this innovative learning resource among a wider group of trainees. In response to trainee feedback, future work will focus on the development of additional modules, incorporating interactive virtual patient cases, in other tumour sites. As web-based learning continues to become increasingly popular, this application can be a useful educational resource for medical trainees." Background Radiation oncology (RO) textbooks and primary references are often complex, technically advanced, and inaccessible to medical trainees. To address this, we designed a free, interactive smartphone application, to introduce trainees to the basics of RO. Methods We created and implemented an Apple iOS smartphone application with RO content, targeted for medical trainees. The beta version is free for download via the TestFlight application (https://testflight.apple.com/join/4A6qXjq2). The application incorporates both introductory topics relevant to radiation oncology and advanced, site-specific modules, that focus on the different tumour sites and their management. Content includes written text, audio lectures, interactive quizzes, and clinical cases. Two questionnaires were implemented to elicit feedback; one for application design and the other a knowledge-quiz for an advanced learning module on breast cancers. Both contain quantitative 5-point Likert-scale questions and one qualitative freeform text question seeking suggestions for improvement. Results There have been over 70 unique downloads of the application. 40 participants completed the survey on application design. Over 80% have selected ""agree"" or ""strongly agree"" to the six statements listed below: Radiation oncology (RO) textbooks and primary references are often complex, technically advanced, and inaccessible to medical trainees. To address this, we designed a free, interactive smartphone application, to introduce trainees to the basics of RO. We created and implemented an Apple iOS smartphone application with RO content, targeted for medical trainees. The beta version is free for download via the TestFlight application (https://testflight.apple.com/join/4A6qXjq2). The application incorporates both introductory topics relevant to radiation oncology and advanced, site-specific modules, that focus on the different tumour sites and their management. Content includes written text, audio lectures, interactive quizzes, and clinical cases. Two questionnaires were implemented to elicit feedback; one for application design and the other a knowledge-quiz for an advanced learning module on breast cancers. Both contain quantitative 5-point Likert-scale questions and one qualitative freeform text question seeking suggestions for improvement. There have been over 70 unique downloads of the application. 40 participants completed the survey on application design. Over 80% have selected ""agree"" or ""strongly agree"" to the six statements listed below:

"Re-Materialized" Medical Data: Paper-Based Transmission of Structured Medical Data Using QR-Code, for Medical Imaging Reports.

Although paper-based transmission of medical information might seem outdated, it has proven efficient, and remains structurally safe from massive data leaks. As part of the ICIPEMIR project for improving medical imaging report, we explored the idea of structured data storage within a medical report, by embedding the data themselves in a QR-Code (and no URL-to-the-data). Three different datasets from ICIPEMIR were serialized, then encoded in a QR-Code. We compared 4 compression algorithms to reduce file size before QR-Encoding. YAML was the most concise format (character sparing), and allowed for embedding of a 2633-character serialized file within a QR-Code. The best compression rate was obtained with gzip, with a compression ratio of 2.32 in 15.7ms. Data were easily extracted and decompressed from a digital QR-Code using a simple command line. YAML file was also successfully recovered from the printed QR-Code with both Android and iOS smartphone. Minimal detected size was 3*3cm.

Access and Experiences of Arabic Native Speakers With Disabilities on Social Media During and After the World Pandemic

Since the start of the coronavirus 2019 (COVID-19) outbreak, governments across the world have mobilized to inform citizens on the virus spread details, nation-level processes, and best health measures and practices to be taken. A large percentage of the media posted through the COVID-19 crisis has been graphical, which raised the question of whether Arabic-speaking blind and deaf persons were able to independently access reliable information. This article presents the results of two studies. The first study involves a content analysis of official social media posts about COVID-19 during critical phases of the outbreak via heuristic evaluation of WCAG2.1 on an iOS smartphone and an iPad. The second study explores the experiences of native Arabic-speaking blind and deaf persons on social media during the pandemic and curfew or lockdown periods in the State of Kuwait using a semi-structured interview (11 people who are blind/low vision and 7 people who are deaf). Overall, our findings highlight the accessibility gaps in the current government social media information content and its dissemination practices and barriers in providing information and services. Also, it gives insights into how people who are blind and people who are deaf are able to manage their lifestyle within and beyond the COVID-19 pandemic.

The Effects of Using the Sun Safe App on Sun Health Knowledge and Behaviors of Young Teenagers: Results of Pilot Intervention Studies.

A balanced approach toward sun exposure and protection is needed by young people. Excessive sun exposure increases their risk for skin cancers such as melanoma, whereas some exposure is necessary for vitamin D and healthy bones. We have developed a new iOS smartphone app-Sun Safe-through a co-design process, which aims to support healthy and balanced decision-making by young teenagers (aged 12-13 years). The aim of this study was to test the capacity of Sun Safe to improve sun health knowledge and behaviors of young teenagers in 3 pilot intervention studies completed in 2020. Young teenagers (aged 12-13 years; N=57) were recruited through the web or through a local school via an open-access website and given access to Sun Safe (29/57, 51%) or a placebo (SunDial) app (28/57, 49%). Participants completed sun health questionnaires and knowledge quizzes before and after the 6-week intervention (either on the web or in class) and rated the quality of the app they used via a survey. Of the 57 participants, 51 (89%) participants (26, 51% for placebo arm and 25, 49% for the Sun Safe arm) completed these studies, with most (>50%) reporting that they used a smartphone to access their designated app either "once a fortnight" or "once/twice in total." Improved sun health knowledge-particularly about the UV Index-was observed in participants who were given access to Sun Safe compared with those who used the placebo (-6.2 [percentage correct] difference in predicted means, 95% CI -12.4 to -0.03; P=.049; 2-way ANOVA). Unexpectedly, there were significantly more sunburn events in the Sun Safe group (relative risk 1.7, 95% CI 1.1-1.8; P=.02; Fisher exact test), although no differences in time spent outdoors or sun-protective behaviors were reported. COVID-19 pandemic-related community-wide shutdowns during April 2020 (when schools were closed) reduced the time spent outdoors by >100 minutes per day (-105 minutes per day difference in predicted means, 95% CI -150 to -59 minutes per day; P=.002; paired 2-tailed Student t test). Sun Safe was well-rated by participants, particularly for information (mean 4.2, SD 0.6 out of 5). Access to the Sun Safe app increased sun health knowledge among young teenagers in these pilot intervention studies. Further investigations with larger sample sizes are required to confirm these observations and further test the effects of Sun Safe on sun-protective behaviors.

Convolution neural networks for pothole detection of critical road infrastructure

A well developed and maintained highway infrastructure is essential for the economic and social prosperity of modern societies. Highway maintenance poses significant challenges pertaining to the ever-increasing ongoing traffic, insufficient budget allocations and lack of resources. Road potholes detection and timely repair is a major contributing factor to sustaining a safe and resilient critical road infrastructure. Current pothole detection methods require laborious manual inspection of roads and lack in terms of accuracy and inference speed. This paper proposes a novel application of Convolutional Neural Networks on accelerometer data for pothole detection. Data is collected using an iOS smartphone installed on the dashboard of a car, running a dedicated application. The experimental results show that the proposed CNN approach has a significant advantage over the existing solutions, with respect to accuracy and computational complexity in pothole detection.

Reproducible Analysis Pipeline for Data Streams: Open-Source Software to Process Data Collected With Mobile Devices.

Smartphone and wearable devices are widely used in behavioral and clinical research to collect longitudinal data that, along with ground truth data, are used to create models of human behavior. Mobile sensing researchers often program data processing and analysis code from scratch even though many research teams collect data from similar mobile sensors, platforms, and devices. This leads to significant inefficiency in not being able to replicate and build on others' work, inconsistency in quality of code and results, and lack of transparency when code is not shared alongside publications. We provide an overview of Reproducible Analysis Pipeline for Data Streams (RAPIDS), a reproducible pipeline to standardize the preprocessing, feature extraction, analysis, visualization, and reporting of data streams coming from mobile sensors. RAPIDS is formed by a group of R and Python scripts that are executed on top of reproducible virtual environments, orchestrated by a workflow management system, and organized following a consistent file structure for data science projects. We share open source, documented, extensible and tested code to preprocess, extract, and visualize behavioral features from data collected with any Android or iOS smartphone sensing app as well as Fitbit and Empatica wearable devices. RAPIDS allows researchers to process mobile sensor data in a rigorous and reproducible way. This saves time and effort during the data analysis phase of a project and facilitates sharing analysis workflows alongside publications.

A Review of Mortality Risk Prediction Models in Smartphone Applications.

Healthcare professionals in healthcare systems need access to freely available, real-time, evidence-based mortality risk prediction smartphone applications to facilitate resource allocation. The objective of this study is to evaluate the quality of smartphone mobile health applications that include mortality prediction models, and corresponding information quality. We conducted a systematic review of commercially available smartphone applications in Google Play for Android, and iTunes for iOS smartphone applications. We performed initial screening, data extraction, and rated smartphone application quality using the Mobile Application Rating Scale: user version (uMARS). The information quality of smartphone applications was evaluated using two patient vignettes, representing low and high risk of mortality, based on critical care data from the Medical Information Mart for Intensive Care (MIMIC) III database. Out of 3051 evaluated smartphone applications, 33 met our final inclusion criteria. We identified 21 discrete mortality risk prediction models in smartphone applications. The most common mortality predicting models were Sequential Organ Failure Assessment (SOFA) (n = 15) and Acute Physiology and Clinical Health Assessment II (n = 13). The smartphone applications with the highest quality uMARS scores were Observation—NEWS 2 (4.64) for iOS smartphones, and MDCalc Medical Calculator (4.75) for Android smartphones. All SOFA-based smartphone applications provided consistent information quality with the original SOFA model for both the low and high-risk patient vignettes. We identified freely available, high-quality mortality risk prediction smartphone applications that can be used by healthcare professionals to make evidence-based decisions in critical care environments.

Developing an Online Tool to Promote Safe Sun Behaviors With Young Teenagers as Co-researchers.

Despite education about the risks of excessive sun exposure, teenagers in Australia are sun-seeking, with sunburn common in summer. Conversely, some regular (time-limited) exposure to sunlight (that avoids sunburn) is necessary for vitamin D and healthy bones and other molecules important for immune and metabolic health. New interventions are thus required to better support teenagers to make healthy and balanced decisions about their sun behaviors. This paper describes the development of a prototype online tool—a smartphone app—that aimed to foster safe sun practices in teenagers. We recruited young adolescents (aged 12–13 years, n = 24) as “co-researchers” to provide ongoing input into the nature and design of the online tool. This age group was selected, as it is a critical time when young people transition from primary education, where “SunSmart” behaviors are entrenched in Australian schools, to high school, where risky behaviors emerge. Through a series of interviews and workshops, we codesigned an Apple iOS smartphone app with the co-researchers, leading health promotion professionals, researchers, and app designers. The developed app, Sun Safe, contains educational content relevant to teenagers about safe sun behaviors, complemented by other features requested by co-researchers and stakeholders to help engage young people, including gamified quizzes to test their sun health knowledge, real-time weather data on the UV Index and temperature, a sunscreen application timer, and reminders to check the UV Index. The developed prototype app was rated well by co-researchers, suggesting it is suitable for further feasibility and efficacy testing as an intervention tool to improve knowledge and promote safe sun behaviors by young adolescents.

Music to Our Ears: Are Dancers at Risk for High Sound Level Exposure?

Enduring exposure to high sound pressure levels (SPLs) can lead to noise-induced hearing loss (NIHL). In the performing arts population, NIHL has been studied primarily in the context of sound exposure experienced by musicians and less so by dancers. This research aimed to identify sound exposure that dancers may experience in some dance classes. Decibel levels were recorded in 12 dance classes (6 ballet, 4 modern, and 1 soft and 1 hard shoe Irish dance) at 8 different studios using the NIOSH SLM app on an iOS smartphone with external microphone. A minimum of five recordings of each class was measured, each collected on a different day, yielding a total of 114 measurements. Results showed that 20.2% of all recordings exceeded the recommended NIOSH sound exposure limits of both 100% projected daily dose and 85 LAeq. Analysis between styles of dance demonstrated significantly lower LAeq (p≤0.05) in soft shoe Irish dance compared to ballet (p=0.023), modern (p=0.035), and Irish hard shoe dance (p=0.009). Irish soft shoe dance demonstrated minimal to no risk of high sound exposure. Conversely, 53.25% of ballet, 90.9% of Irish hard shoe dance, and 68.24% of modern recordings exhibited minimal to moderate risk of high sound exposure. Furthermore, we found wide ranges of projected daily noise doses within classes taught by the same teacher. It is recommended that multiple recordings of dance environments be obtained, as a single sound recording may not accurately represent potential exposure. These findings indicate that dancers of Irish hard shoe, modern, and ballet may benefit from noise intervention such as audiometric testing, noise controls, and hearing protection.

Evaluation of the MoMba Live Long Remote Smoking Detection System During and After Pregnancy: Development and Usability Study.

BackgroundThe smoking relapse rate during the first 12 months after pregnancy is around 80% in the United States. Delivering remote smoking cessation interventions to women in the postpartum period can reduce the burden associated with frequent office visits and can enable remote communication and support. Developing reliable, remote, smoking measuring instruments is a crucial step in achieving this vision.ObjectiveThe study presents the evaluation of the MoMba Live Long system, a smartphone-based breath carbon monoxide (CO) meter and a custom iOS smartphone app. We report on how our smoking detection system worked in a controlled office environment and in an out-of-office environment to examine its potential to deliver a remote contingency management intervention.MethodsIn-office breath tests were completed using both the MoMba Live Long system and a commercial monitor, the piCO+ Smokerlyzer. In addition, each participant provided a urine test for smoking status validation through cotinine. We used in-office test data to verify the validity of the MoMba Live Long smoking detection system. We also collected out-of-office tests to assess how the system worked remotely and enabled user verification. Pregnant adult women in their second or third trimester participated in the study for a period of 12 weeks. This study was carried out in the United States.ResultsAnalyses of in-office tests included 143 breath tests contributed from 10 participants. CO readings between the MoMba Live Long system and the piCO+ were highly correlated (r=.94). In addition, the MoMba Live Long system accurately distinguished smokers from nonsmokers with a sensitivity of 0.91 and a specificity of 0.94 when the piCO+ was used as a gold standard, and a sensitivity of 0.81 and specificity of 1.0 when cotinine in urine was used to confirm smoking status. All participants indicated that the system was easy to use.ConclusionsRelatively inexpensive portable and internet-connected CO monitors can enable remote smoking status detection in a wide variety of nonclinical settings with reliable and valid measures comparable to a commercially available CO monitor.Trial RegistrationClinicalTrials.gov NCT02237898; https://clinicaltrials.gov/ct2/show/NCT02237898

Abstract 2033: Reducing cancer caregiver burden: A user-centered design approach for an mHealth app

Abstract Background: Informal cancer caregivers are being asked to provide more extensive home care for cancer patients. It is well documented that caregivers frequently experience psychological and behavioral effects that impact their overall well-being. Digital solutions can support informal caregivers by providing key information on the patient's care and nudging them to focus on their own mental and physical health. Objective: In this paper, we describe a user-centered design approach to build a digital solution by engaging with stakeholders that include clinical staff, health care decision makers, cancer patients and survivors, and informal caregivers. The NIH funded digital solution, TOGETHERCareTM, (Track Outcomes & Guidance, Technology for Health & Effective Resources for Care) is a smart phone mobile app to provide support and resources to informal caregivers while enabling them to monitor the cancer survivor's health for potential adverse events, thereby preventing unnecessary emergency care and reducing daily caregiver worries. Methods: An iterative information gathering process was conducted that included a) key-informant (n=139) and semi-structured interviews (n=11) with stakeholders to assess health care value propositions and corresponding modules of benefit; b) prototype review, evaluation and feedback by a Patient Advisory Council and Clinical Advisory Team and; c) a 28-day beta user-testing with feasibility and acceptability feedback from 15 stakeholders in two geographically diverse academic cancer centers (Duke and Stanford). This study was registered on clinicaltrials.gov (NCT04018677). Results: The interviews conducted prior to developing the mHealth app prototype identified consistency in responses between different stakeholder groups in terms of how the mobile app should work, and areas of difference. Clinical teams focused on app usage efficiency and features that would improve information sharing and follow-up visits with survivors. Survivors and caregivers were more focused on features that would provide assistance with at-home medical tasks and activities of daily living. The interviews and data analysis following the Beta test of the prototype indicated satisfaction with the app's usability, that caregivers preferred to focus primarily on the patient's health and not their own, and that regular surveys on the patient's symptoms educated and reduced anxiety among caregivers. Conclusions: This study describes the user-centered design process and demonstrates the feasibility and acceptability of TOGETHERCareTM, an iOS smart phone app for informal cancer caregivers. Larger studies, in various oncology populations, are needed to further evaluate efficacy of the app across health metrics that matter to the different stakeholders. Citation Format: Ingrid Oakley-Girvan, Vasu Divi, Oxana Palesh, Jena Daniels, Lisa G. Rosas, Dale O'Brien, Sharon W. Davis, Arif H. Kamal, Allison W. Kurian, Michelle R. Longmire. Reducing cancer caregiver burden: A user-centered design approach for an mHealth app [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 2033.

VIDE - Vault App Identification and Extraction System for iOS Devices

Content hiding (or vault) apps are a class of applications that allow users to hide photos, videos, documents and other content securely. A subclass of these applications called decoy apps further supports secret hiding by having a mode which mimics standard apps such as calculators but can turn into a vault app through entering a specific input. In this work we focus on iOS devices and first describe how to identify content hiding applications from the App Store. We consider not only the US Store but also give results for App Stores in Russia, India and China. We show an effective and very fast identification of content hiding apps through a two-phase process: initial categorization using keywords followed by more precise binary classification. We next turn to understanding the behavior and features of these vault apps and how to extract the hidden information from artifacts of the app's stored data. Based on this work, we have designed and built a fully automated vault app identification and extraction system that first identifies and then extracts the hidden data from the apps on an iOS smartphone. Using our vault identification and data extraction system (VIDE), law enforcement investigators can more easily identify and extract data from such apps as needed. Although vault apps are removed regularly from the App Store, VIDE can still identify removed apps as our system continues to maintain information on such apps in our vault database.

Prototype of Smart Lock Based on Internet Of Things (IOT) With ESP8266

This study aims to design a prototype house that has an automatic safety system installed that has two inputs for its control. The developed system can be controlled via an Android or IOS smartphone that has the Blynk application installed, or by using a PIR sensor installed in the circuit. The output in this series of systems is solenoid lock which is used as a door lock for the house later. This research refers to the ADDIE development model with details: (1) Analyze, (2) Design, (3) Development, (4) Implementation and (5) Evaluate. Based on the testing of each component used, the results are obtained in the form of PIR sensor readings with a radius of 90cm with an angle of 120o with a voltage of 5V, a solenoid lock that uses a 12V voltage, a pin from the Wemos D1 R1 board that has errors on its 2 input / output pins, and a battery that can be used as a backup power source when the main power source goes out for more than 5 hours.