Mobile device screen time is associated with poorer language development among toddlers: results from a large-scale survey.

BACKGROUND: Despite young children's widespread use of mobile devices, little research exists on this use and its association with children's language development. The aim of this study was to examine the associations between mobile device screen time and language comprehension and expressive language skills. An additional aim was to examine whether three factors related to the domestic learning environment modify the associations. METHODS: The study uses data from the Danish large-scale survey TRACES among two- and three-year-old children (n = 31,125). Mobile device screen time was measured as time spent on mobile devices on a normal day. Measurement of language comprehension and expressive language skills was based on subscales from the Five to Fifteen Toddlers questionnaire. Multivariable linear regression was used to examine the association between child mobile device screen time and language development and logistic regression to examine the risk of experiencing significant language difficulties. Joint exposure analyses were used to examine the association between child mobile device screen time and language development difficulties in combination with three other factors related to the domestic learning environment: parental education, reading to the child and child TV/PC screen time. RESULTS: High mobile device screen time of one hour or more per day was significantly associated with poorer language development scores and higher odds for both language comprehension difficulties (1-2 h: AOR = 1.30; ≥ 2 h: AOR = 1.42) and expressive language skills difficulties (1-2 h: AOR = 1.19; ≥ 2 h: AOR = 1.46). The results suggest that reading frequently to the child partly buffers the negative effect of high mobile device screen time on language comprehension difficulties but not on expressive language skills difficulties. No modifying effect of parental education and time spent by the child on TV/PC was found. CONCLUSIONS: Mobile device screen time of one hour or more per day is associated with poorer language development among toddlers. Reading frequently to the child may have a buffering effect on language comprehension difficulties but not on expressive language skills difficulties.

Online Learning State Evaluation Method Based on Face Detection and Head Pose Estimation.

In this paper, we propose a learning state evaluation method based on face detection and head pose estimation. This method is suitable for mobile devices with weak computing power, so it is necessary to control the parameter quantity of the face detection and head pose estimation network. Firstly, we propose a ghost and attention module (GA) base face detection network (GA-Face). GA-Face reduces the number of parameters and computation in the feature extraction network through the ghost module, and focuses the network on important features through a parameter-free attention mechanism. We also propose a lightweight dual-branch (DB) head pose estimation network: DB-Net. Finally, we propose a student learning state evaluation algorithm. This algorithm can evaluate the learning status of students based on the distance between their faces and the screen, as well as their head posture. We validate the effectiveness of the proposed GA-Face and DB-Net on several standard face detection datasets and standard head pose estimation datasets. Finally, we validate, through practical cases, that the proposed online learning state assessment method can effectively assess the level of student attention and concentration, and, due to its low computational complexity, will not interfere with the student's learning process.

Designing and Implementation of a Digitalized Intersectoral Discharge Management System and Its Effect on Readmissions: Mixed Methods Approach.

BACKGROUND: Digital transformation offers new opportunities to improve the exchange of information between different health care providers, including inpatient, outpatient and care facilities. As information is especially at risk of being lost when a patient is discharged from a hospital, digital transformation offers great opportunities to improve intersectoral discharge management. However, most strategies for improvement have focused on structures within the hospital. OBJECTIVE: This study aims to evaluate the implementation of a digitalized discharge management system, the project "Optimizing instersectoral discharge management" (SEKMA, derived from the German Sektorübergreifende Optimierung des Entlassmanagements), and its impact on the readmission rate. METHODS: A mixed methods design was used to evaluate the implementation of a digitalized discharge management system and its impact on the readmission rate. After the implementation, the congruence between the planned (logic model) and the actual intervention was evaluated using a fidelity analysis. Finally, bivariate and multivariate analyses were used to evaluate the effectiveness of the implementation on the readmission rate. For this purpose, a difference-in-difference approach was adopted based on routine data of hospital admissions between April 2019 and August 2019 and between April 2022 and August 2022. The department of vascular surgery served as the intervention group, in which the optimized discharge management was implemented in April 2022. The departments of internal medicine and cardiology formed the control group. RESULTS: Overall, 26 interviews were conducted, and we explored 21 determinants, which can be categorized into 3 groups: "optimization potential," "barriers," and "enablers." On the basis of these results, 19 strategies were developed to address the determinants, including a lack of networking among health care providers, digital information transmission, and user-unfriendliness. On the basis of these strategies, which were prioritized by 11 hospital physicians, a logic model was formulated. Of the 19 strategies, 7 (37%; eg, electronic discharge letter, providing mobile devices to the hospital's social service, and generating individual medication plans in the format of the national medication plan) have been implemented in SEKMA. A survey on the fidelity of the application of the implemented strategies showed that 3 of these strategies were not yet widely applied. No significant effect of SEKMA on readmissions was observed in the routine data of 14,854 hospital admissions (P=.20). CONCLUSIONS: This study demonstrates the potential of optimizing intersectoral collaboration for patient care. Although a significant effect of SEKMA on readmissions has not yet been observed, creating a digital ecosystem that connects different health care providers seems to be a promising approach to ensure secure and fast networking of the sectors. The described intersectoral optimization of discharge management provides a structured template for the implementation of a similar local digital care networking infrastructure in other care regions in Germany and other countries with a similarly fragmented health care system.

Accuracy and tracing time of cephalometric analyses on a tablet or desktop computer : A prospective study.

BACKGROUND: This prospective study aimed to evaluate the influence of the computer type (tablet or desktop) on accuracy and tracing time of cephalometric analyses. METHODS: Dental students used a web-based application specifically developed for this purpose to perform cephalometric analyses on tablet and desktop computers. Landmark locations and timestamps were exported to measure the accuracy, successful detection rate and tracing time. Reference landmarks were established by six experienced orthodontists. Statistical analysis included reliability assessment, descriptive statistics, and linear mixed effect models. RESULTS: Over a period of 8 semesters a total of 277 cephalometric analyses by 161 students were included. The interrater reliability of the orthodontists establishing the reference coordinates was excellent (ICC > 0.9). For the students, the mean landmark deviation was 2.05 mm and the successful detection rate for the clinically acceptable threshold of 2 mm suggested in the literature was 68.6%, with large variations among landmarks. No effect of the computer type on accuracy and tracing time of the cephalometric analyses could be found. CONCLUSION: The use of tablet computers for cephalometric analyses can be recommended.

Association of mobile device proficiency and subjective cognitive complaints with financial management ability among community-dwelling older adults: a population-based cross-sectional study.

BACKGROUND: Mobile devices have been used by many older adults and have the potential to assist individuals with subjective cognitive complaints (SCCs) in daily living tasks. Financial management is one of the most complex daily activity for older adults, as it is easily impaired in the prodromal stage of dementia and cognitive impairment. AIM: To investigate financial management ability among older adults from SCCs and mobile device proficiency. METHODS: A self-administered questionnaire was sent to 529 participants who were ≥ 65 years and regularly use mobile devices. Participants were divided into four groups based on SCC prevalence and scores of the Mobile Device Proficiency Questionnaire (MDPQ-16). Financial management abilities were compared between groups using the Process Analysis of Daily Activities for Dementia subscale. Regression model and crosstabulation table were used to investigate associations in detail. RESULTS: A significant difference in financial management ability was observed among the four groups (p < 0.001), with the dual impairment group showed significantly lower than the robust and SCC groups (p < 0.001). Educational history, sex, and MDPQ-16 score were significantly associated with participants' financial management ability (p ≤ 0.01). The proportion of participants who could use ATMs and electronic money independently was significantly lower among those with low proficiency in mobile devices (LPM), regardless of SCC (p < 0.05). CONCLUSION: The LPM group showed an impaired ability to manage their finances, particularly in situations where they would use information devices. Therefore, healthcare professionals should assess not only the SCC but also their proficiency with mobile devices to predict their impairment in activities of daily living.

Cocreating an Automated mHealth Apps Systematic Review Process With Generative AI: Design Science Research Approach.

BACKGROUND: The use of mobile devices for delivering health-related services (mobile health [mHealth]) has rapidly increased, leading to a demand for summarizing the state of the art and practice through systematic reviews. However, the systematic review process is a resource-intensive and time-consuming process. Generative artificial intelligence (AI) has emerged as a potential solution to automate tedious tasks. OBJECTIVE: This study aimed to explore the feasibility of using generative AI tools to automate time-consuming and resource-intensive tasks in a systematic review process and assess the scope and limitations of using such tools. METHODS: We used the design science research methodology. The solution proposed is to use cocreation with a generative AI, such as ChatGPT, to produce software code that automates the process of conducting systematic reviews. RESULTS: A triggering prompt was generated, and assistance from the generative AI was used to guide the steps toward developing, executing, and debugging a Python script. Errors in code were solved through conversational exchange with ChatGPT, and a tentative script was created. The code pulled the mHealth solutions from the Google Play Store and searched their descriptions for keywords that hinted toward evidence base. The results were exported to a CSV file, which was compared to the initial outputs of other similar systematic review processes. CONCLUSIONS: This study demonstrates the potential of using generative AI to automate the time-consuming process of conducting systematic reviews of mHealth apps. This approach could be particularly useful for researchers with limited coding skills. However, the study has limitations related to the design science research methodology, subjectivity bias, and the quality of the search results used to train the language model.

Remote Multi-Person Heart Rate Monitoring with Smart Speakers: Overcoming Separation Constraint.

Heart rate is a key vital sign that can be used to understand an individual's health condition. Recently, remote sensing techniques, especially acoustic-based sensing, have received increasing attention for their ability to non-invasively detect heart rate via commercial mobile devices such as smartphones and smart speakers. However, due to signal interference, existing methods have primarily focused on monitoring a single user and required a large separation between them when monitoring multiple people. These limitations hinder many common use cases such as couples sharing the same bed or two or more people located in close proximity. In this paper, we present an approach that can minimize interference and thereby enable simultaneous heart rate monitoring of multiple individuals in close proximity using a commonly available smart speaker prototype. Our user study, conducted under various real-life scenarios, demonstrates the system's accuracy in sensing two users' heart rates when they are seated next to each other with a median error of 0.66 beats per minute (bpm). Moreover, the system can successfully monitor up to four people in close proximity.

Mobile Crowdsensing in Ecological Momentary Assessment mHealth Studies: A Systematic Review and Analysis.

As mobile devices have become a central part of our daily lives, they are also becoming increasingly important in research. In the medical context, for example, smartphones are used to collect ecologically valid and longitudinal data using Ecological Momentary Assessment (EMA), which is mostly implemented through questionnaires delivered via smart notifications. This type of data collection is intended to capture a patient's condition on a moment-to-moment and longer-term basis. To collect more objective and contextual data and to understand patients even better, researchers can not only use patients' input via EMA, but also use sensors as part of the Mobile Crowdsensing (MCS) approach. In this paper, we examine how researchers have embraced the topic of MCS in the context of EMA through a systematic literature review. This PRISMA-guided review is based on the databases PubMed, Web of Science, and EBSCOhost. It is shown through the results that both EMA research in general and the use of sensors in EMA research are steadily increasing. In addition, most of the studies reviewed used mobile apps to deliver EMA to participants, used a fixed-time prompting strategy, and used signal-contingent or interval-contingent self-assessment as sampling/assessment strategies. The most commonly used sensors in EMA studies are the accelerometer and GPS. In most studies, these sensors are used for simple data collection, but sensor data are also commonly used to verify study participant responses and, less commonly, to trigger EMA prompts. Security and privacy aspects are addressed in only a subset of mHealth EMA publications. Moreover, we found that EMA adherence was negatively correlated with the total number of prompts and was higher in studies using a microinteraction-based EMA (µEMA) approach as well as in studies utilizing sensors. Overall, we envision that the potential of the technological capabilities of smartphones and sensors could be better exploited in future, more automated approaches.

Making Digital Health Equitable.

Most agree that the current healthcare system is broken. Fortunately, technology is increasing at an exponential rate and provides a solution for the future. Digital Health is an integrator concept that has the potential to take advantage of technological advantages. Digital Health converges health, healthcare, research, and everyday life. It includes technologies, platforms, and systems that engage consumers in all aspects of life. It makes health and healthcare be people-centered and personalized. Digital health requires total interoperability - standards, common data elements, and the integration of data from all sources. It demands data sharing. Digital Health brings together a wide range of stakeholders for similar goals using the same resources. Digital Health uses mobile devices and wearable sensors and uses Artificial Intelligence and Machine Learning to handle the vast amount of data Digital Health engages. Finally, Digital Health has the potential to open the gap between the different social and economic classes that must be addressed.

Fingerprint Recognition in Forensic Scenarios.

Fingerprints are unique patterns used as biometric keys because they allow an individual to be unambiguously identified, making their application in the forensic field a common practice. The design of a system that can match the details of different images is still an open problem, especially when applied to large databases or, to real-time applications in forensic scenarios using mobile devices. Fingerprints collected at a crime scene are often manually processed to find those that are relevant to solving the crime. This work proposes an efficient methodology that can be applied in real time to reduce the manual work in crime scene investigations that consumes time and human resources. The proposed methodology includes four steps: (i) image pre-processing using oriented Gabor filters; (ii) the extraction of minutiae using a variant of the Crossing Numbers method which include a novel ROI definition through convex hull and erosion followed by replacing two or more very close minutiae with an average minutiae; (iii) the creation of a model that represents each minutia through the characteristics of a set of polygons including neighboring minutiae; (iv) the individual search of a match for each minutia in different images using metrics on the absolute and relative errors. While in the literature most methodologies look to validate the entire fingerprint model, connecting the minutiae or using minutiae triplets, we validate each minutia individually using n-vertex polygons whose vertices are neighbor minutiae that surround the reference. Our method also reveals robustness against false minutiae since several polygons are used to represent the same minutia, there is a possibility that even if there are false minutia, the true polygon is present and identified; in addition, our method is immune to rotations and translations. The results show that the proposed methodology can be applied in real time in standard hardware implementation, with images of arbitrary orientations.

AMDDLmodel: Android smartphones malware detection using deep learning model.

Android is the most popular operating system of the latest mobile smart devices. With this operating system, many Android applications have been developed and become an essential part of our daily lives. Unfortunately, different kinds of Android malware have also been generated with these applications' endless stream and somehow installed during the API calls, permission granted and extra packages installation and badly affected the system security rules to harm the system. Therefore, it is compulsory to detect and classify the android malware to save the user's privacy to avoid maximum damages. Many research has already been developed on the different techniques related to android malware detection and classification. In this work, we present AMDDLmodel a deep learning technique that consists of a convolutional neural network. This model works based on different parameters, filter sizes, number of epochs, learning rates, and layers to detect and classify the android malware. The Drebin dataset consisting of 215 features was used for this model evaluation. The model shows an accuracy value of 99.92%. The other statistical values are precision, recall, and F1-score. AMDDLmodel introduces innovative deep learning for Android malware detection, enhancing accuracy and practical user security through inventive feature engineering and comprehensive performance evaluation. The AMDDLmodel shows the highest accuracy values as compared to the existing techniques.

Using mobile technology to influence alcohol-impaired driving risk perceptions and decisions.

OBJECTIVE: Despite significant individual and societal risk, alcohol-impaired driving (AID) remains prevalent in the United States. Our aim was to determine whether breathalyzer-cued warning messages administered via mobile devices in the natural drinking environment could influence real-world AID cognitions and behaviors. METHOD: One hundred twenty young adults (53% women; mean age = 24.7) completed 6 weeks of ecological momentary assessment (EMA) and provided breathalyzer samples using a BACtrack Mobile Pro linked to their mobile device. On mornings after drinking episodes, participants reported their driving activities from the previous evening (787 episodes). Participants were randomly assigned to receive warning messages if they reached a breath alcohol concentration (BrAC) ≥ .05, or no messages. Participants in the warnings condition reported their willingness to drive and perceived danger of driving at EMA prompts (1,541 reports). RESULTS: We observed a significant effect of condition, such that the association between cumulative AID engagement and driving after reaching a BrAC of .05 was dampened among individuals in the warnings condition, compared to those in the no warnings condition. Receiving a warning message was associated with increased momentary perceived danger of driving and decreased willingness to drive. CONCLUSIONS: We found that BrAC-cued warning messages reduced the probability of AID and willingness to drive while impaired, and increased the perceived danger of driving after drinking. These results serve as proof-of-concept for the use of mobile technology to deliver an adaptive just-in-time intervention to reduce the probability of AID. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Freeing the Animal Model: A Modular, Wirelessly Powered, Implantable Electronic Platform.

SUMMARY: Fully implantable electronic devices in freely roaming animal models are useful in biomedical research, but their development is prohibitively resource intensive for many laboratories. The advent of miniaturized microcontrollers with onboard wireless data exchange capabilities has enabled cost-efficient development of myriad do-it-yourself electronic devices that are easily customizable with open-source software ( https://www.arduino.cc/ ). Likewise, the global proliferation of mobile devices has led to the development of low-cost miniaturized wireless power technology. The authors present a low-cost, rechargeable, and fully implantable electronic device comprising a commercially available, open-source, wirelessly powered microcontroller that is readily customizable with myriad readily available miniature sensors and actuators. The authors demonstrate the utility of this platform for chronic nerve stimulation in the freely roaming rat with intermittent wireless charging over 4 weeks. Device assembly was achieved within 2 hours and necessitated only basic soldering equipment. Component costs totaled $115 per device. Wireless data transfer and wireless recharging of device batteries was achieved within 30 minutes, and no harmful heat generation occurred during charging or discharging cycles, as measured by external thermography and internal device temperature monitoring. Wireless communication enabled triggered cathodic pulse stimulation of the facial nerve at various user-selected programmed frequencies (1, 5, and 10 Hz) for periods of 4 weeks or longer. This implantable electronic platform could be further miniaturized and expanded to study a vast array of biomedical research questions in live animal models. CLINICAL RELEVANCE STATEMENT: The clinical relevance of electrical stimulation in neural recovery remains controversial, and long-term neural stimulation in small animal models is challenging. We have developed a low-cost, fully implantable, wirelessly powered nerve stimulation device to facilitate further research in nerve stimulation in animal models.

Digital Mental Health and Its Discontents: Assumptions About Technology That Create Barriers to Equitable Access.

Despite the potential of digital mental health interventions to aid recovery for people with serious mental illness, access to these digital tools remains a key barrier. In this column, the authors discuss three key assumptions that shape the integration of digital mental health tools into community health settings: clinical context, digital literacy, and financial burden. Clinical contexts have shifted with the increased use of telehealth, altering intervention environments; access to a mobile device is not the same as digital literacy; and digital mental health care is not necessarily affordable. Context-centered study design through ethnography will facilitate transfer of digital resources to real-world settings.

Use of Telemedicine Among Hand Surgeons and Their Patients.

PURPOSE: Initially designed to address geographic obstacles to patient care, reliance on telemedicine rapidly increased during the coronavirus pandemic. The purpose of this study was to analyze the proficiency of computer and mobile device usage among a cohort of surgeons and their patients who either used telemedicine or had in-person visits. METHODS: We retrospectively identified patients who had an outpatient telemedicine visit (T group), or in-person visit (NT group) with a hand and wrist orthopedic surgeon, between March 2020 and July 2020. These patients and their surgeons were sent the Computer Proficiency Questionnaire (CPQ-12) and the Mobile Device Questionnaire (MDPQ-16) via email. A total of 602 survey responses were collected, 279 of which belonged to patients in the T group and 323 to patients in the NT group. RESULTS: The two groups were similar in demographics, including age and sex. Scores on the CPQ-12 and MDPQ-16 did not significantly differ between the two groups. In the patient sample, there was no correlation between CPQ-12 and MDPQ-16 scores and the proportion of telehealth visits. The orthopedic surgeon group also had no observed correlation between the CPQ-12 and MDPQ-16 scores and number or proportion of telemedicine visits. CONCLUSIONS: Overall proficiency with computer and mobile devices was not correlated with the likelihood of patients or orthopedic surgeons using telemedicine visits. Patient selection appears to be driven by other factors, which could include limitations in transportation, convenience, and time constraints. CLINICAL RELEVANCE: Orthopedic surgeons should continue to offer telehealth visits to their patients regardless of estimated capabilities with electronic devices of both the patient and the surgeon.

Mobile Health Intervention Contents and Their Effects on the Healthcare of Patients with Left Ventricular Assist Devices: An Integrative Review.

Self-care in daily life is important for patients with a left ventricular assist device. Mobile health interventions that use an application, the cloud, or telemonitoring enable active health management. This study reviewed the literature on the contents of mobile health interventions for patients with left ventricular assist devices and their effects. We searched four electronic databases (CINAHL, Cochrane Library, EMBASE, and MEDLINE) and reference lists in May 2022. The search terms consisted of "heart-assist devices" and "residence characteristics," combined with "mobile applications," "telemonitoring," "medical informatics applications," "cell*," "app*," "smartphone," and "cloud." In total, seven studies were included in the review. Mobile health interventions included (1) self-management contents and (2) interactions between patient and healthcare providers. The mobile health device and patient's health management were evaluated as a measure of the effect. This review provides a unique understanding for leveraging mobile health interventions as an effective approach to improve healthcare among patients with left ventricular assist devices. Future mobile health intervention strategies targeting these patients should fully consider the patient's perspective. Furthermore, they should be designed and applied to help with long-term health management, accompanied by an evaluation of their effectiveness on self-care improvement.

Test-retest evaluation of a notched-noise test using consumer-grade mobile audio equipment.

OBJECTIVE: The aim of this study was to investigate whether consumer-grade mobile audio equipment can be reliably used as a platform for the notched-noise test, including when the test is conducted outside the laboratory. DESIGN: Two studies were conducted: Study 1 was a notched-noise masking experiment with three different setups: in a psychoacoustic test booth with a standard laboratory PC; in a psychoacoustic test booth with a mobile device; and in a quiet office room with a mobile device. Study 2 employed the same task as Study 1, but compared circumaural headphones to insert earphones. STUDY SAMPLE: Nine and ten young, normal-hearing participants completed studies 1 and 2, respectively. RESULTS: The test-retest accuracy of the notched-noise test on the mobile implementation did not differ from that for the laboratory setup. A possible effect of the earphone design was identified in Study 1, which was corroborated by Study 2, where test-retest variability was smallest when comparing results from experiments conducted using identical acoustic transducers. CONCLUSIONS: Results and test-retest repeatability comparable to standard laboratory settings for the notched-noise test can be obtained with mobile equipment outside the laboratory.

Development of the Digital Inclusion Questionnaire (DIQUEST) in Parkinson's Disease.

BACKGROUND: No tool is currently able to measure digital inclusion in clinical populations suitable for telemedicine. We developed the "Digital Inclusion Questionnaire" (DIQUEST) to estimate access and skills in Parkinson's Disease (PD) patients and verified its properties with a pilot study. METHODS: Thirty PD patients completed the initial version of the DIQUEST along with the Mobile Device Proficiency Questionnaire (MDPQ) and a practical computer task. A Principal Components Analysis (PCA) was conducted to define the DIQUEST factor structure and remove less informative items. We used Cronbach's α to measure internal reliability and Spearman's correlation test to determine the convergent and predictive validity with the MDPQ and the practical task, respectively. RESULTS: The final version of the DIQUEST consisted of 20 items clustering in five components: "advanced skills," "navigation skills," "basic skills/knowledge," "physical access," and "economical access." All components showed high reliability (α > 0.75) as did the entire questionnaire (α = 0.94). Correlation analysis demonstrated high convergent (rho: 0.911; p<0.001) and predictive (rho: 0.807; p<0.001) validity. CONCLUSIONS: We have here presented the development of the DIQUEST as a screening tool to assess the level of digital inclusion, particularly addressing the access and skills domains. Future studies are needed for its validation beyond PD.

Reliability of an AI-Powered Application Across Different Mobile Devices for Assessment of Chronic Wounds.

Objective: Evaluate the inter- and intrarater reliability of a wound assessment tool in iPhone 12 and 13 mini modalities against a validated iPad mini/Structure Sensor configuration. Approach: We assessed a wound measurement application (eKare inSight®) for result consistency in patients presenting with wounds. Assessments were analyzed using a two-way analysis of variance. Intraclass correlation coefficient (ICC) was computed for intrarater (ICC1,1) and inter-rater (ICC2,1) analysis using a two-way random effects model. Paired t-test assessed the statistical difference between measurement methods. Results: Forty-two lesions were analyzed with surface areas ranging from 0.2 to 23 cm2 (average 4.33 ± 5.44 cm2). A high level of reliability was observed for repeat wound area measurements by the same examiner (ICC1,1 = 0.997) and between examiners with iPhone 13 mini (ICC2,1 = 0.998). There was no significant difference between iPhone 12 and iPad mini/Structure Sensor (p = 0.78) or between iPhone 13 mini and iPhone 12 (p = 0.22). Minimal difference existed between iPhone 13 mini and iPad mini/Structure Sensor (p = 0.049, Cohen's d = 0.01). Innovation: Increased pervasiveness of smartphones in clinical care, coupled with advances in smartphone imaging and machine learning, allows for a potential solution to the problem of fast and accurate wound measurements. The application investigated produces wound measurement results quickly and with demonstrated accuracy. It does not require a calibration sticker or reference marker and allows for automatic wound boundary delineation. Conclusion: The results of this study suggest that a digital planimetry mobile application may offer high levels of reliability across devices and users.

Mobile Technology-Based Interventions for Stroke Self-Management Support: Scoping Review.

BACKGROUND: There is growing interest in enhancing stroke self-management support using mobile health (mHealth) technology (eg, smartphones and apps). Despite this growing interest, "self-management support" is inconsistently defined and applied in the poststroke mHealth intervention literature, which limits efforts to synthesize and compare evidence. To address this gap in conceptual clarity, a scoping review was conducted. OBJECTIVE: The objectives were to (1) identify and describe the types of poststroke mHealth interventions evaluated using a randomized controlled trial design, (2) determine whether (and how) such interventions align with well-accepted conceptualizations of self-management support (the theory by Lorig and Holman and the Practical Reviews in Self-Management Support [PRISMS] taxonomy by Pearce and colleagues), and (3) identify the mHealth functions that facilitate self-management. METHODS: A scoping review was conducted according to the methodology by Arksey and O'Malley and Levac et al. In total, 7 databases were searched. Article screening and data extraction were performed by 2 reviewers. The data were analyzed using descriptive statistics and content analysis. RESULTS: A total of 29 studies (26 interventions) were included. The interventions addressed 7 focal areas (physical exercise, risk factor management, linguistic exercise, activities of daily living training, medication adherence, stroke education, and weight management), 5 types of mobile devices (mobile phones or smartphones, tablets, wearable sensors, wireless monitoring devices, and laptops), and 7 mHealth functions (educating, communicating, goal setting, monitoring, providing feedback, reminding, and motivating). Collectively, the interventions aligned well with the concept of self-management support. However, on an individual basis (per intervention), the alignment was less strong. CONCLUSIONS: On the basis of the results, it is recommended that future research on poststroke mHealth interventions be more theoretically driven, more multidisciplinary, and larger in scale.