Human Factors in AI-Driven Digital Solutions for Increasing Physical Activity: Scoping Review.

BACKGROUND: Artificial intelligence (AI) has the potential to enhance physical activity (PA) interventions. However, human factors (HFs) play a pivotal role in the successful integration of AI into mobile health (mHealth) solutions for promoting PA. Understanding and optimizing the interaction between individuals and AI-driven mHealth apps is essential for achieving the desired outcomes. OBJECTIVE: This study aims to review and describe the current evidence on the HFs in AI-driven digital solutions for increasing PA. METHODS: We conducted a scoping review by searching for publications containing terms related to PA, HFs, and AI in the titles and abstracts across 3 databases-PubMed, Embase, and IEEE Xplore-and Google Scholar. Studies were included if they were primary studies describing an AI-based solution aimed at increasing PA, and results from testing the solution were reported. Studies that did not meet these criteria were excluded. Additionally, we searched the references in the included articles for relevant research. The following data were extracted from included studies and incorporated into a qualitative synthesis: bibliographic information, study characteristics, population, intervention, comparison, outcomes, and AI-related information. The certainty of the evidence in the included studies was evaluated using GRADE (Grading of Recommendations Assessment, Development, and Evaluation). RESULTS: A total of 15 studies published between 2015 and 2023 involving 899 participants aged approximately between 19 and 84 years, 60.7% (546/899) of whom were female participants, were included in this review. The interventions lasted between 2 and 26 weeks in the included studies. Recommender systems were the most commonly used AI technology in digital solutions for PA (10/15 studies), followed by conversational agents (4/15 studies). User acceptability and satisfaction were the HFs most frequently evaluated (5/15 studies each), followed by usability (4/15 studies). Regarding automated data collection for personalization and recommendation, most systems involved fitness trackers (5/15 studies). The certainty of the evidence analysis indicates moderate certainty of the effectiveness of AI-driven digital technologies in increasing PA (eg, number of steps, distance walked, or time spent on PA). Furthermore, AI-driven technology, particularly recommender systems, seems to positively influence changes in PA behavior, although with very low certainty evidence. CONCLUSIONS: Current research highlights the potential of AI-driven technologies to enhance PA, though the evidence remains limited. Longer-term studies are necessary to assess the sustained impact of AI-driven technologies on behavior change and habit formation. While AI-driven digital solutions for PA hold significant promise, further exploration into optimizing AI's impact on PA and effectively integrating AI and HFs is crucial for broader benefits. Thus, the implications for innovation management involve conducting long-term studies, prioritizing diversity, ensuring research quality, focusing on user experience, and understanding the evolving role of AI in PA promotion.

Effect of a Smartphone App (S-Check) on Actual and Intended Help-Seeking and Motivation to Change Methamphetamine Use Among Adult Consumers of Methamphetamine in Australia: Randomized Waitlist-Controlled Trial.

BACKGROUND: Interventions are required that address delays in treatment-seeking and low treatment coverage among people consuming methamphetamine. OBJECTIVE: We aim to determine whether a self-administered smartphone-based intervention, the "S-Check app" can increase help-seeking and motivation to change methamphetamine use, and determine factors associated with app engagement. METHODS: This study is a randomized, 28-day waitlist-controlled trial. Consenting adults residing in Australia who reported using methamphetamine at least once in the last month were eligible to download the app for free from Android or iOS app stores. Those randomized to the intervention group had immediate access to the S-Check app, the control group was wait-listed for 28 days before gaining access, and then all had access until day 56. Actual help-seeking and intention to seek help were assessed by the modified Actual Help Seeking Questionnaire (mAHSQ), modified General Help Seeking Questionnaire, and motivation to change methamphetamine use by the modified readiness ruler. χ2 comparisons of the proportion of positive responses to the mAHSQ, modified General Help Seeking Questionnaire, and modified readiness ruler were conducted between the 2 groups. Logistic regression models compared the odds of actual help-seeking, intention to seek help, and motivation to change at day 28 between the 2 groups. Secondary outcomes were the most commonly accessed features of the app, methamphetamine use, feasibility and acceptability of the app, and associations between S-Check app engagement and participant demographic and methamphetamine use characteristics. RESULTS: In total, 560 participants downloaded the app; 259 (46.3%) completed eConsent and baseline; and 84 (32.4%) provided data on day 28. Participants in the immediate access group were more likely to seek professional help (mAHSQ) at day 28 than those in the control group (n=15, 45.5% vs n=12, 23.5%; χ21=4.42, P=.04). There was no significant difference in the odds of actual help-seeking, intention to seek help, or motivation to change methamphetamine use between the 2 groups on the primary logistic regression analyses, while in the ancillary analyses, the imputed data set showed a significant difference in the odds of seeking professional help between participants in the immediate access group compared to the waitlist control group (adjusted odds ratio 2.64, 95% CI 1.19-5.83, P=.02). For participants not seeking help at baseline, each minute in the app increased the likelihood of seeking professional help by day 28 by 8% (ratio 1.08, 95% CI 1.02-1.22, P=.04). Among the intervention group, a 10-minute increase in app engagement time was associated with a decrease in days of methamphetamine use by 0.4 days (regression coefficient [ß] -0.04, P=.02). CONCLUSIONS: The S-Check app is a feasible low-resource self-administered intervention for adults in Australia who consume methamphetamine. Study attrition was high and, while common in mobile health interventions, warrants larger studies of the S-Check app. TRIAL REGISTRATION: Australian New Zealand Clinical Trials Registry ACTRN12619000534189; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=377288&isReview=true.

A Deep Learning-Based Rotten Food Recognition App for Older Adults: Development and Usability Study.

BACKGROUND: Older adults are at greater risk of eating rotten fruits and of getting food poisoning because cognitive function declines as they age, making it difficult to distinguish rotten fruits. To address this problem, researchers have developed and evaluated various tools to detect rotten food items in various ways. Nevertheless, little is known about how to create an app to detect rotten food items to support older adults at a risk of health problems from eating rotten food items. OBJECTIVE: This study aimed to (1) create a smartphone app that enables older adults to take a picture of food items with a camera and classifies the fruit as rotten or not rotten for older adults and (2) evaluate the usability of the app and the perceptions of older adults about the app. METHODS: We developed a smartphone app that supports older adults in determining whether the 3 fruits selected for this study (apple, banana, and orange) were fresh enough to eat. We used several residual deep networks to check whether the fruit photos collected were of fresh fruit. We recruited healthy older adults aged over 65 years (n=15, 57.7%, males and n=11, 42.3%, females) as participants. We evaluated the usability of the app and the participants' perceptions about the app through surveys and interviews. We analyzed the survey responses, including an after-scenario questionnaire, as evaluation indicators of the usability of the app and collected qualitative data from the interviewees for in-depth analysis of the survey responses. RESULTS: The participants were satisfied with using an app to determine whether a fruit is fresh by taking a picture of the fruit but are reluctant to use the paid version of the app. The survey results revealed that the participants tended to use the app efficiently to take pictures of fruits and determine their freshness. The qualitative data analysis on app usability and participants' perceptions about the app revealed that they found the app simple and easy to use, they had no difficulty taking pictures, and they found the app interface visually satisfactory. CONCLUSIONS: This study suggests the possibility of developing an app that supports older adults in identifying rotten food items effectively and efficiently. Future work to make the app distinguish the freshness of various food items other than the 3 fruits selected still remains.

Exploring the Functionality of Technology-driven CPR Training Methodologies Among Healthcare Practitioners: A Randomized Control Pilot Study.

Introduction: Cardiopulmonary resuscitation training in Malaysia has evolved from traditional to modern approaches, embracing technology for better outcomes. Smartphone-based training apps offer interactive learning with simulations and real-time feedback, improving cardiopulmonary resuscitation skills anytime, anywhere. This study evaluates the effectiveness of the smart-cardiopulmonary resuscitation application for healthcare practitioners. Methods: This randomized controlled pilot study was conducted with 30 healthcare practitioners at the University of Malaysia Sabah. Participants underwent a Cardiopulmonary Resuscitation Practical formal educational training program, and data were collected using a Basic Life Support questionnaire and skills assessment checklist sourced from the American Heart Association (2020). Data analysis was conducted utilizing repeated analysis of variance and the Cochran 'Q' test supported by Statistical Package for the Social Sciences statistical software. Result: The control and intervention groups showed improved knowledge and skills from pre-to post-cardiopulmonary resuscitation courses; a significant increase was observed in the intervention group compared to the control group. The F-test indicated a significant time-group effect (F-stat (df) = 16.14 (2), p = 0.01). Cochran's 'Q' test also revealed significant changes in the proportion of healthcare practitioners passing their skills assessments over time (2 = 14.90, control 01). Conclusion: The smart-cardiopulmonary resuscitation application is convenient for refreshing cardiopulmonary resuscitation skills and maintaining proficiency. While it doesn't replace formal cardiopulmonary resuscitation courses, it saves healthcare practitioners and the community time and money. Both groups showed improved cardiopulmonary resuscitation knowledge and skills, with the intervention group using the smart-cardiopulmonary resuscitation application showing higher success rates after two months. Adopting smartphone-based cardiopulmonary resuscitation training with comprehensive content is recommended.

Visual, ocular surface, and extraocular diagnostic criteria for determining the prevalence of computer vision syndrome: a cross-sectional smart-survey-based study.

Background: The American Optometric Association defines computer vision syndrome (CVS), also known as digital eye strain, as "a group of eye- and vision-related problems that result from prolonged computer, tablet, e-reader and cell phone use". We aimed to create a well-structured, valid, and reliable questionnaire to determine the prevalence of CVS, and to analyze the visual, ocular surface, and extraocular sequelae of CVS using a novel and smart self-assessment questionnaire. Methods: This multicenter, observational, cross-sectional, descriptive, survey-based, online study included 6853 complete online responses of medical students from 15 universities. All participants responded to the updated, online, fourth version of the CVS questionnaire (CVS-F4), which has high validity and reliability. CVS was diagnosed according to five basic diagnostic criteria (5DC) derived from the CVS-F4. Respondents who fulfilled the 5DC were considered CVS cases. The 5DC were then converted into a novel five-question self-assessment questionnaire designated as the CVS-Smart. Results: Of 10 000 invited medical students, 8006 responded to the CVS-F4 survey (80% response rate), while 6853 of the 8006 respondents provided complete online responses (85.6% completion rate). The overall CVS prevalence was 58.78% (n = 4028) among the study respondents; CVS prevalence was higher among women (65.87%) than among men (48.06%). Within the CVS group, the most common visual, ocular surface, and extraocular complaints were eye strain, dry eye, and neck/shoulder/back pain in 74.50% (n = 3001), 58.27% (n = 2347), and 80.52% (n = 3244) of CVS cases, respectively. Notably, 75.92% (3058/4028) of CVS cases were involved in the Mandated Computer System Use Program. Multivariate logistic regression analysis revealed that the two most statistically significant diagnostic criteria of the 5DC were ≥2 symptoms/attacks per month over the last 12 months (odds ratio [OR] = 204177.2; P <0.0001) and symptoms/attacks associated with screen use (OR = 16047.34; P <0.0001). The CVS-Smart demonstrated a Cronbach's alpha reliability coefficient of 0.860, Guttman split-half coefficient of 0.805, with perfect content and construct validity. A CVS-Smart score of 7-10 points indicated the presence of CVS. Conclusions: The visual, ocular surface, and extraocular diagnostic criteria for CVS constituted the basic components of CVS-Smart. CVS-Smart is a novel, valid, reliable, subjective instrument for determining CVS diagnosis and prevalence and may provide a tool for rapid periodic assessment and prognostication. Individuals with positive CVS-Smart results should consider modifying their lifestyles and screen styles and seeking the help of ophthalmologists and/or optometrists. Higher institutional authorities should consider revising the Mandated Computer System Use Program to avoid the long-term consequences of CVS among university students. Further research must compare CVS-Smart with other available metrics for CVS, such as the CVS questionnaire, to determine its test-retest reliability and to justify its widespread use.

Multisensory Fusion for Unsupervised Spatiotemporal Speaker Diarization.

Speaker diarization consists of answering the question of "who spoke when" in audio recordings. In meeting scenarios, the task of labeling audio with the corresponding speaker identities can be further assisted by the exploitation of spatial features. This work proposes a framework designed to assess the effectiveness of combining speaker embeddings with Time Difference of Arrival (TDOA) values from available microphone sensor arrays in meetings. We extract speaker embeddings using two popular and robust pre-trained models, ECAPA-TDNN and X-vectors, and calculate the TDOA values via the Generalized Cross-Correlation (GCC) method with Phase Transform (PHAT) weighting. Although ECAPA-TDNN outperforms the Xvectors model, we utilize both speaker embedding models to explore the potential of employing a computationally lighter model when spatial information is exploited. Various techniques for combining the spatial-temporal information are examined in order to determine the best clustering method. The proposed framework is evaluated on two multichannel datasets: the AVLab Speaker Localization dataset and a multichannel dataset (SpeaD-M3C) enriched in the context of the present work with supplementary information from smartphone recordings. Our results strongly indicate that the integration of spatial information can significantly improve the performance of state-of-the-art deep learning diarization models, presenting a 2-3% reduction in DER compared to the baseline approach on the evaluated datasets.

Identifying Person-Specific Drivers of Depression in Adolescents: Protocol for a Smartphone-Based Ecological Momentary Assessment and Passive Sensing Study.

BACKGROUND: Adolescence is marked by an increasing risk of depression and is an optimal window for prevention and early intervention. Personalizing interventions may be one way to maximize therapeutic benefit, especially given the marked heterogeneity in depressive presentations. However, empirical evidence that can guide personalized intervention for youth is lacking. Identifying person-specific symptom drivers during adolescence could improve outcomes by accounting for both developmental and individual differences. OBJECTIVE: This study leverages adolescents' everyday smartphone use to investigate person-specific drivers of depression and validate smartphone-based mobile sensing data against established ambulatory methods. We describe the methods of this study and provide an update on its status. After data collection is completed, we will address three specific aims: (1) identify idiographic drivers of dynamic variability in depressive symptoms, (2) test the validity of mobile sensing against ecological momentary assessment (EMA) and actigraphy for identifying these drivers, and (3) explore adolescent baseline characteristics as predictors of these drivers. METHODS: A total of 50 adolescents with elevated symptoms of depression will participate in 28 days of (1) smartphone-based EMA assessing depressive symptoms, processes, affect, and sleep; (2) mobile sensing of mobility, physical activity, sleep, natural language use in typed interpersonal communication, screen-on time, and call frequency and duration using the Effortless Assessment of Risk States smartphone app; and (3) wrist actigraphy of physical activity and sleep. Adolescents and caregivers will complete developmental and clinical measures at baseline, as well as user feedback interviews at follow-up. Idiographic, within-subject networks of EMA symptoms will be modeled to identify each adolescent's person-specific drivers of depression. Correlations among EMA, mobile sensor, and actigraph measures of sleep, physical, and social activity will be used to assess the validity of mobile sensing for identifying person-specific drivers. Data-driven analyses of mobile sensor variables predicting core depressive symptoms (self-reported mood and anhedonia) will also be used to assess the validity of mobile sensing for identifying drivers. Finally, between-subject baseline characteristics will be explored as predictors of person-specific drivers. RESULTS: As of October 2023, 84 families were screened as eligible, of whom 70% (n=59) provided informed consent and 46% (n=39) met all inclusion criteria after completing baseline assessment. Of the 39 included families, 85% (n=33) completed the 28-day smartphone and actigraph data collection period and follow-up study visit. CONCLUSIONS: This study leverages depressed adolescents' everyday smartphone use to identify person-specific drivers of adolescent depression and to assess the validity of mobile sensing for identifying these drivers. The findings are expected to offer novel insights into the structure and dynamics of depressive symptomatology during a sensitive period of development and to inform future development of a scalable, low-burden smartphone-based tool that can guide personalized treatment decisions for depressed adolescents. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/43931.

A Multicomponent Intervention (POSSIBLE) to Improve Perceived Risk for HIV Among Black Sexual Minority Men: Feasibility and Preliminary Effectiveness Pilot Study.

BACKGROUND: Increased pre-exposure prophylaxis (PrEP) use is urgently needed to substantially decrease HIV incidence among Black sexual minority men. Low perceived risk for HIV (PRH) is a key unaddressed PrEP barrier for Black sexual minority men. Peers and smartphone apps are popular intervention tools to promote community health behaviors, but few studies have used these together in a multicomponent strategy. Therefore, we designed a multicomponent intervention called POSSIBLE that used an existing smartphone app called PrEPme (Emocha Mobile Health, Inc) and a peer change agent (PCA) to increase PRH as a gateway to PrEP. OBJECTIVE: This paper aims to describe the feasibility and preliminary impact of POSSIBLE on PRH and willingness to accept a PrEP referral among Black sexual minority men. METHODS: POSSIBLE was a theoretically guided, single-group, 2-session pilot study conducted among Black sexual minority men from Baltimore, Maryland between 2019 and 2021 (N=69). POSSIBLE integrated a PCA and the PrEPme app that allows users to self-monitor sexual risk behaviors and chat with the in-app community health worker to obtain PrEP service information. PRH was assessed using the 8-item PRH scale before and after baseline and follow-up study visits. At the end of each study visit, the PCA referred interested individuals to the community health worker to learn more about PrEP service options. RESULTS: The average age of participants was 32.5 (SD 8.1, range 19-62) years. In total, 55 (80%) participants were retained for follow-up at month 1. After baseline sessions, 29 (42%) participants were willing to be referred to PrEP services, 20 (69%) of those confirmed scheduled appointments with PrEP care teams. There were no statistically significant differences in PRH between baseline and follow-up visits (t122=-1.36; P=.17). CONCLUSIONS: We observed no statistically significant improvement in PRH between baseline and month 1. However, given the high retention rate and acceptability, POSSIBLE may be feasible to implement. Future research should test a statistically powered peer-based approach on PrEP initiation among Black sexual minority men. TRIAL REGISTRATION: ClinicalTrials.gov NCT04533386; https://clinicaltrials.gov/study/NCT04533386.

Accuracy of Smartphone-Based Three-Dimensional Facial Scanning System: A Systematic Review.

BACKGROUND: Recently, the integration of 3D face scanning into smartphones has raised vast interest in plastic surgery. With the release of smartphones featuring 3D face scanning technology, users now can capture detailed 3D models of their faces using their smartphones. However, trueness and precision of this system is less well established. METHODS: PubMed, Cochrane Library, Embase, ScienceDirect, Scopus, and Web of Science databases were searched for studies evaluating 3D scanning of smartphone devices and conventional 3D imaging systems from January 1, 2017, to June 1, 2023. A qualitative systematic review was conducted by two review authors after independently selecting studies, extracting data, and assessing the risk of bias of included studies. RESULTS: A total of 11 studies were included, all focusing on the accuracy of smartphone 3D facial scanning. The results show that although smartphones perform poorly on deep and irregular surfaces, they are accurate enough for clinical applications and have the advantage of being economical and portable. CONCLUSIONS: Smartphone-based 3D facial scanning has been basically validated for clinical application, showing broad clinical application prospects in plastic surgery. LEVEL OF EVIDENCE II: This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors   www.springer.com/00266 .

Development and Evaluation of a Prototype Mobile Application for Intravenous Drug Dose Calculation in Overweight and Obese Thai Children: Precision Dosing in Practice.

Background: Medication dosing in overweight and obese children often involves complex weight-based calculations, leading to higher dosing errors, particularly with intravenous drugs. Currently, tools to aid in dosage calculations are lacking for these patients, especially in Thai population. Objective: This study aimed to develop a mobile application with the intent of utilizing it as a tool to enhance the efficiency and accuracy of dosing calculations required for obese and overweight Thai children. Methods: The performance of the application was assessed in 3 key aspects using a sample of 30 healthcare professionals. These key aspects included: 1) the accuracy of dosage calculations, assessed through pre- and posttests comparing manual calculations to app-based calculations using a 10-item questionnaire, 2) the time taken for calculations before and after app usage, 3) user satisfaction, which was measured through a questionnaire. Results: The integration of applications into the calculation demonstrated a significant improvement when compared to the manual calculation in both accuracy (6.10 vs 9.33 out of 10, P < .001) and efficiency (10.40 vs 8.53 minutes per 10 questions, P = .008). Also, the application elicited high levels of satisfaction among users, as reflected by an overall mean satisfaction score of 4.57 on a 5-point scale. Conclusion: The integration of this application to assist in dosage calculations for overweight and obese pediatric Thai patients has yielded favorable outcomes concerning accuracy, efficiency, and user satisfaction. Further development should be pursued within a larger cohort, with an emphasis on real-world implementation in clinical settings.

A portable gas sensor based on In2O3@CuO P-N heterojunction connected via Wi-Fi to a smartphone for real-time carbon monoxide determination.

This research presents a compact portable electronic gas sensor that can be monitored through a smartphone application. The smart sensor utilizes three state-of-the-art sensors. The sensors integrate an ESP8266 microcontroller within the same device. This facilitates their integration with the electronics and enhances their performance. Herein, primarily focuses on utilizing the sensor to detect carbon monoxide. This article outlines the fabrication process of a gas sensor utilizing a P-N heterojunction, eliminating the need for a binder. The sensor consists of CuO/copper foam nanowires and hierarchical In2O3. In order to verify the system's functionality, it underwent testing with various levels of CO concentrations (10-900 ppm), including particular tests designed to examine the device's performance in different humidity and temperature circumstances. A mobile application for the provision of monitoring services has been developed at last. To process the information obtained from the gas sensor, an algorithm has been constructed, trained, and integrated into a smartphone for this purpose. This research demonstrated that a smartphone-coupled gas sensor is a viable system for real-time monitoring and the detection of CO gas.

Smartphone Prospects in Bridge Structural Health Monitoring, a Literature Review.

Bridges are critical components of transportation networks, and their conditions have effects on societal well-being, the economy, and the environment. Automation needs in inspections and maintenance have made structural health monitoring (SHM) systems a key research pillar to assess bridge safety/health. The last decade brought a boom in innovative bridge SHM applications with the rise in next-generation smart and mobile technologies. A key advancement within this direction is smartphones with their sensory usage as SHM devices. This focused review reports recent advances in bridge SHM backed by smartphone sensor technologies and provides case studies on bridge SHM applications. The review includes model-based and data-driven SHM prospects utilizing smartphones as the sensing and acquisition portal and conveys three distinct messages in terms of the technological domain and level of mobility: (i) vibration-based dynamic identification and damage-detection approaches; (ii) deformation and condition monitoring empowered by computer vision-based measurement capabilities; (iii) drive-by or pedestrianized bridge monitoring approaches, and miscellaneous SHM applications with unconventional/emerging technological features and new research domains. The review is intended to bring together bridge engineering, SHM, and sensor technology audiences with decade-long multidisciplinary experience observed within the smartphone-based SHM theme and presents exemplary cases referring to a variety of levels of mobility.

Feasibility and Acceptability Evaluation of a Digital Therapeutic Program for Improving Cancer Prevention: A Quasi-experimental Pre-post Interventional Pilot Study.

Previous studies have proved that healthy behaviors hinder the onset and progression of tumors. Digital therapeutics (DTx), playing a pivotal role in facilitating behavioral adjustments through educational interventions, lifestyle support, and symptom monitoring, contribute to the goal of tumor prevention. We aim to optimize the evaluation of the feasibility and acceptability of DTx for cancer prevention. This involves assessing AITI's daily activity rates and user feedback, and comparing changes in behavioral habits and differences in SF-36 before and after the intervention. In a 4-week trial with 57 participants engaging actively, we found both the average daily activity rate and 4-week retention rate at 35 (61.4%). The USE Questionnaire scores (validity, ease of use, acquisition, and satisfaction) ranged from 68.06 to 83.10, indicating AITI's user-friendliness and acceptability. Furthermore, positive habit changes were noted among participants in exercise and diet (p < 0.0001), suggesting the effectiveness of the DTx approach in modifying behavioral habits related to physical activity and nutrition. This pilot study underscores the potential of DTx in advancing cancer prevention. However, larger and longer studies are needed to comprehensively assess its impact.

Daily links between objective smartphone use and sleep among adolescents.

INTRODUCTION: Concerns abound on how digital technology such as smartphone use may impair adolescent sleep. Although these linkages are supported in cross-sectional studies, research involving intensive longitudinal assessments and objective measures has called into question the robustness of associations. METHODS: In this study, a sample of ethnically diverse U.S. adolescents (N = 71; Mage = 16.49; 56% girls) wore Fitbit devices and submitted screenshots of their smartphone screen time, pickups, and notifications over a 14-day period in 2021. The Fitbits recorded nightly sleep quality and sleep onset. Adolescents also completed daily diaries reporting the previous night's sleep onset time and sleep quality. RESULTS: On days when adolescents engaged in greater nighttime screen time and, to some extent, pickups relative to their own average, they also had poorer sleep outcomes that night. Greater screen time was associated with later self-reported and Fitbit-recorded sleep onset and poorer self-reported sleep quality. Greater pickups was associated with later self-reported and Fitbit-recorded sleep onset. Smartphone use during the day did not relate to sleep outcomes, indicating the importance of distinguishing nighttime from daytime use. CONCLUSIONS: Parents and clinicians should help adolescents develop healthy digital skills to avoid exacerbating sleep problems that are known to occur during this developmental period.

Evaluation of different color measurement methods for determining tooth color.

AIM: The purpose of this study is to develop software at a low cost that enables the detection of tooth colors by capturing photographs using various devices, and to compare its effectiveness with existing expensive methods. MATERIAL AND METHODS: A total of 60 anterior central incisor teeth from 30 individuals were included in the study. The CIELAB values (L,a,b) of each tooth were measured using a spectrophotometer, which is considered the gold standard. Subsequently, photographs of the teeth were taken using four different smartphones (iPhone- Xiaomi) and one digital camera (Canon). These images were then subjected to image processing techniques and compared with measurements obtained through computer-based analysis in order to assess the correlation. Data with three or more groups, the Kruskal-Wallis H test was utilized, and multiple comparisons were conducted using the Dunn test. A significance level of p<0.05 was considered. RESULTS: Upon examining the results of multiple comparisons, a statistically significant difference was observed (p<0.001) between the DeltaE values obtained from the camera of the iPhone and those obtained from the Canon DSLR and Xiaomi cameras. The iPhone cameras yielded result values ranging from 2.68 to 2.90 for DeltaE. CONCLUSIONS: It is reported that color determination methods based on image processing of photographs taken with iPhone mobile phones could potentially gain an advantageous position in routine clinical practice, as compared to spectrophotometry.

Evaluating the Energy Efficiency of Popular US Smartphone Health Care Apps: Comparative Analysis Study Toward Sustainable Health and Nutrition Apps Practices.

BACKGROUND: The emergence of smartphones has sparked a transformation across multiple fields, with health care being one of the most notable due to the advent of mobile health (mHealth) apps. As mHealth apps have gained popularity, there is a need to understand their energy consumption patterns as an integral part of the evolving landscape of health care technologies. OBJECTIVE: This study aims to identify the key contributors to elevated energy consumption in mHealth apps and suggest methods for their optimization, addressing a significant void in our comprehension of the energy dynamics at play within mHealth apps. METHODS: Through quantitative comparative analysis of 10 prominent mHealth apps available on Android platforms within the United States, this study examined factors contributing to high energy consumption. The analysis included descriptive statistics, comparative analysis using ANOVA, and regression analysis to examine how certain factors impact energy use and consumption. RESULTS: Observed energy use variances in mHealth apps stemmed from user interactions, features, and underlying technology. Descriptive analysis revealed variability in app energy consumption (150-310 milliwatt-hours), highlighting the influence of user interaction and app complexity. ANOVA verified these findings, indicating the critical role of engagement and functionality. Regression modeling (energy consumption = ß0 + ß1 × notification frequency + ß2 × GPS use + ß3 × app complexity + ε), with statistically significant P values (notification frequency with a P value of .01, GPS use with a P value of .05, and app complexity with a P value of .03), further quantified these bases' effects on energy use. CONCLUSIONS: The observed differences in the energy consumption of dietary apps reaffirm the need for a multidisciplinary approach to bring together app developers, end users, and health care experts to foster improved energy conservation practice while achieving a balance between sustainable practice and user experience. More research is needed to better understand how to scale-up consumer engagement to achieve sustainable development goal 12 on responsible consumption and production.

Digital phenotyping data and anomaly detection methods to assess changes in mood and anxiety symptoms across a transdiagnostic clinical sample.

INTRODUCTION: Clinical assessment of mood and anxiety change often relies on clinical assessment or self-reported scales. Using smartphone digital phenotyping data and resulting markers of behavior (e.g., sleep) to augment clinical symptom scores offers a scalable and potentially more valid method to understand changes in patients' state. This paper explores the potential of using a combination of active and passive sensors in the context of smartphone-based digital phenotyping to assess mood and anxiety changes in two distinct cohorts of patients to assess the preliminary reliability and validity of this digital phenotyping method. METHODS: Participants from two different cohorts, each n = 76, one with diagnoses of depression/anxiety and the other schizophrenia, utilized mindLAMP to collect active data (e.g., surveys on mood/anxiety), along with passive data consisting of smartphone digital phenotyping data (geolocation, accelerometer, and screen state) for at least 1 month. Using anomaly detection algorithms, we assessed if statistical anomalies in the combination of active and passive data could predict changes in mood/anxiety scores as measured via smartphone surveys. RESULTS: The anomaly detection model was reliably able to predict symptom change of 4 points or greater for depression as measured by the PHQ-9 and anxiety as measured for the GAD-8 for both patient populations, with an area under the ROC curve of 0.65 and 0.80 for each respectively. For both PHQ-9 and GAD-7, these AUCs were maintained when predicting significant symptom change at least 7 days in advance. Active data alone predicted around 52% and 75% of the symptom variability for the depression/anxiety and schizophrenia populations respectively. CONCLUSION: These results indicate the feasibility of anomaly detection for predicting symptom change in transdiagnostic cohorts. These results across different patient groups, different countries, and different sites (India and the US) suggest anomaly detection of smartphone digital phenotyping data may offer a reliable and valid approach to predicting symptom change. Future work should emphasize prospective application of these statistical methods.

Impact of smartphone on mental health among medical undergraduates: A cross-sectional study.

BACKGROUND: Smartphones have become an indispensable part of almost everyone's life. India has now become the leading and second-largest Smartphone market in the world. It has been noted that the purpose of Smartphone usage has exceptionally changed over the past few years, especially among students, professionals, and the common man. Smartphones have now become essential and the need of the hour, and medical students are no exception. Smartphones can make "smart students smarter"! There are now 10,275 unique applications labeled under the "Medical" and "Healthcare and Fitness" categories. The common medical applications used are MedCalc, Drug Infusion, Flashcards, Encyclopedia, Merck Manual, Medscape, PubMed, Epocrates, MedlinePlus, Lab test applications, Medical Dictionary, Eponyms etc. Despite the advantages and needs of smartphones, they have proven to be a source of potential hazard to human health, not only physical but also mental, social, and emotional well-being. There is consistent evidence for co-morbidity (such as obesity, heart diseases, neck and back pain, etc.) between excessive smartphone use and other psychiatric disorders, such as depression, anxiety, obsessive and compulsive disorder (OCD), and attention deficit hyperactivity disorder (ADHD) similar to internet addiction. The significant association of this addiction with poorer sleep quality and higher perceived stress has been a cause for concern. Hence, further investigation to explore the association between smartphone addiction and mental health, this study was undertaken. MATERIALS AND METHODS: Our study was undertaken in Dr VMGMC, Solapur, from June to August 2022, after obtaining approval from the ethical committee, approval number 172/22. Total voluntary participation for the study was 600 (from first to final year), and accordingly convenient sample size was taken. RESULTS: We found that out of the total participants, 42% of the participants had an average screen time of 4-6 h daily. A very small percentage of participants (4%) spent less than two hours in front of a screen. Alarmingly, 65% of the participants had an average screen time of more than 4 hours, which puts them at risk for the negative health impacts of prolonged screen time. Around 12% of them had symptoms of mild stress, 10.3% for mild anxiety, and 15.6% for mild depression. 10.6% had symptoms of moderate stress, 23.3% for moderate anxiety, and 16% for moderate depression. A small proportion of undergraduates, that is, 5%, 16%, and 11.6%, had symptoms of severe and extremely severe stress, anxiety, and depression, respectively. CONCLUSION: The study participants did not feel a lot of stress, anxiety, or depression symptoms when smartphones were used judiciously and mostly for non-social purposes (such as studying, listening to music, or watching videos). This investigation led us to the conclusion that there are some positive effects of smartphones on mental health. However, those who spent an excessive amount of time on their smartphones for social contact, with an average screen time of 5 h, showed signs of mild to moderate sadness, moderate anxiety, and tension, demonstrating that social media had a negative impact on the mental health of medical undergraduates. Therefore, efforts should be made to inform medical students about how using a smartphone is harming their mental health.