Comparing Human-Smartphone Interactions and Actigraphy Measurements for Circadian Rhythm Stability and Adiposity: Algorithm Development and Validation Study.

BACKGROUND: This study aimed to investigate the relationships between adiposity and circadian rhythm and compare the measurement of circadian rhythm using both actigraphy and a smartphone app that tracks human-smartphone interactions. OBJECTIVE: We hypothesized that the app-based measurement may provide more comprehensive information, including light-sensitive melatonin secretion and social rhythm, and have stronger correlations with adiposity indicators. METHODS: We enrolled a total of 78 participants (mean age 41.5, SD 9.9 years; 46/78, 59% women) from both an obesity outpatient clinic and a workplace health promotion program. All participants (n=29 with obesity, n=16 overweight, and n=33 controls) were required to wear a wrist actigraphy device and install the Rhythm app for a minimum of 4 weeks, contributing to a total of 2182 person-days of data collection. The Rhythm app estimates sleep and circadian rhythm indicators by tracking human-smartphone interactions, which correspond to actigraphy. We examined the correlations between adiposity indices and sleep and circadian rhythm indicators, including sleep time, chronotype, and regularity of circadian rhythm, while controlling for physical activity level, age, and gender. RESULTS: Sleep onset and wake time measurements did not differ significantly between the app and actigraphy; however, wake after sleep onset was longer (13.5, SD 19.5 minutes) with the app, resulting in a longer actigraphy-measured total sleep time (TST) of 20.2 (SD 66.7) minutes. The obesity group had a significantly longer TST with both methods. App-measured circadian rhythm indicators were significantly lower than their actigraphy-measured counterparts. The obesity group had significantly lower interdaily stability (IS) than the control group with both methods. The multivariable-adjusted model revealed a negative correlation between BMI and app-measured IS (P=.007). Body fat percentage (BF%) and visceral adipose tissue area (VAT) showed significant correlations with both app-measured IS and actigraphy-measured IS. The app-measured midpoint of sleep showed a positive correlation with both BF% and VAT. Actigraphy-measured TST exhibited a positive correlation with BMI, VAT, and BF%, while no significant correlation was found between app-measured TST and either BMI, VAT, or BF%. CONCLUSIONS: Our findings suggest that IS is strongly correlated with various adiposity indicators. Further exploration of the role of circadian rhythm, particularly measured through human-smartphone interactions, in obesity prevention could be warranted.

Urine multi-index intelligent detection based on polymer/paper hybrid microfluidic biochip for hyperuricemia monitoring.

Hyperuricemia (HUA) has gradually become a public health burden as an independent risk factor for a variety of chronic diseases. Herein, a user-friendly point-of-care (POC) detection system (namely "Smart-HUA-Monitor") based on smartphone-assisted paper-based microfluidic is proposed for colorimetric quantification of HUA urinary markers, including uric acid (UA), creatinine (CR) and pH. The detection limits of UA and CR were 0.0178 and 0.5983 mM, respectively, and the sensitivity of pH were 0.1. The method was successfully validated in artificial urine samples and 100 clinical samples. Bland-Altman plots showed a high consistency between µPAD and the testing instruments (HITACHI 7600 Automatic Analyzer, URIT-500B Urine Analyzer and AU5800B automatic biochemical analyzer) in hospital. Smart-HUA-Monitor provides an accurate quantitative, rapid, low-cost and reliable tool for the monitoring and early diagnosis of HUA urine indicators.

Smartphone based wearable sweat glucose sensing device correlated with machine learning for real-time diabetes screening.

BACKGROUND: Diabetes is a significant health threat, with its prevalence and burden increasing worldwide indicating its challenge for global healthcare management. To decrease the disease severity, the diabetic patients are recommended to regularly check their blood glucose levels. The conventional finger-pricking test possesses some drawbacks, including painfulness and infection risk. Nowadays, smartphone has become a part of our lives offering an important benefit in self-health monitoring. Thus, non-invasive wearable sweat glucose sensor connected with a smartphone readout is of interest for real-time glucose detection. RESULTS: Wearable sweat glucose sensing device is fabricated for self-monitoring of diabetes. This device is designed as a body strap consisting of a sensing strip and a portable potentiostat connected with a smartphone readout via Bluetooth. The sensing strip is modified by carbon nanotubes (CNTs)-cellulose nanofibers (CNFs), followed by electrodeposition of Prussian blue. To preserve the activity of glucose oxidase (GOx) immobilized on the modified sensing strip, chitosan is coated on the top layer of the electrode strip. Herein, machine learning is implemented to correlate between the electrochemical results and the nanomaterial content along with deposition cycle of prussian blue, which provide the highest current response signal. The optimized regression models provide an insight, establishing a robust framework for design of high-performance glucose sensor. SIGNIFICANCE: This wearable glucose sensing device connected with a smartphone readout offers a user-friendly platform for real-time sweat glucose monitoring. This device provides a linear range of 0.1-1.5 mM with a detection limit of 0.1 mM that is sufficient enough for distinguishing between normal and diabetes patient with a cut-off level of 0.3 mM. This platform might be an alternative tool for improving health management for diabetes patients.

Concurrent Validity and Reliability of a New Smartphone Application for Measuring Cervical Range of Motion in Healthy Individuals.

BACKGROUND: Range of motion (ROM) measurement is an important part of physical therapy assessment and patient progress. Smartphones are user-friendly instruments and if proven to be reliable and valid, clinicians can use them for a variety of tasks including ROM measurement. OBJECTIVES: To determine concurrent validity and intra- and inter-rater reliability of the PhysioMaster application in measuring cervical ROM in both Android and iOS operating systems. METHODS: Forty-five healthy individuals (age 31.75 ± 11.94 yrs; 18 men, 27 women) completed this study. Two raters measured cervical ROM, three times each, using an Android phone for intra-rater and inter-rater reliability. With an interval time of 1-7 days after the first session, measurements were repeated by one of the raters once to measure intersession reliability. Validity was estimated by one of the raters using iPhone and Android phones one at a time while 3D motion analysis (3DMA) recorded cervical movements simultaneously. For reliability, intraclass correlation coefficient (ICC), and for validity, Pearson correlation coefficient and Bland-Altman plots were used. RESULTS: ICC values of ≥0.76 and ≥0.84 demonstrated excellent intra-rater and inter-rater reliability, respectively. For concurrent validity, correlation between each phone and 3DMA was nearly perfect for all movements (0.93 ≤ r ≤ 0.97). CONCLUSION: PhysioMaster appears to be a valid and reliable application for measuring cervical ROM in healthy individuals.

Superior oxidase-mimetic activity of FeCo-NC dual-atom nanozyme for smartphone-based visually colorimetric assay of organophosphorus pesticides.

A colorimetric analysis platform has been successfully developed based on FeCo-NC dual-atom nanozyme (FeCo-NC DAzyme) for the detection of organophosphorus pesticides (OPPs). The FeCo-NC DAzyme exhibited exceptional oxidase-like activity (OXD), enabling the catalysis of colorless TMB to form blue oxidized TMB (oxTMB) without the need for H2O2 involvement. By combining acid phosphatase (ACP) hydrolase with FeCo-NC DAzyme, a "FeCo-NC DAzyme + TMB + ACP + SAP" colorimetric system was constructed, which facilitated the rapid detection of malathion. The chromogenic system was applied to detect malathion using a smartphone-based app and an auxiliary imaging interferogram device for colorimetric measurements, which have a linear range of 0.05-4.0 µM and a limit of detection (LOD) as low as 15 nM in real samples, comparable to UV-Vis and HPLC-DAD detection methods. Overall, these findings present a novel approach for convenient, rapid, and on-site monitoring of OPPs.

Gender differences in neck muscle activity during near-maximum forward head flexion while using smartphones with varied postures.

Women frequently express heightened neck discomfort even though they exhibit smaller neck flexion (NF) during smartphone use. Differences in natural posture while using smartphones may result in varying muscle activation patterns between genders. However, no study focused on this issue. This study investigated the influence of gender on neck muscle activity and NF when using smartphones, ranging from slight (20°) to nearly maximal forward head flexion, across different postures. We analyzed smartphone usage patterns in 16 men and 16 women and examined these behaviors across different scenarios: standing, supported sitting, and unsupported sitting, at 20°, 30°, 40°, and the maximum head angles. During data collection, muscle activity was measured, expressed as a percentage of the maximum voluntary contraction (%MVC), in the cervical erector spinae (CES) and upper trapezius (UTZ), along with NF. Results show significant influences of gender, head angle, and posture on all measures, with notable interactions among these variables. Women displayed higher muscle activities in CES and UTZ, yet exhibited lesser NF, while using smartphones in both standing (12.3%MVC, 10.7% MVC, and 69.0°, respectively) and unsupported sitting (10.8%MVC, 12.3%MVC, and 71.8°, respectively) compared to men (standing: 9.5%MVC, 8.8%MVC, and 76.1°; unsupported sitting: 9.7%MVC, 10.8%MVC, and 76.1°). This study provides a potential rationale for gender-related disparities in injury outcomes, emphasizing that women experience higher neck and shoulder discomfort level, despite their smaller NF during smartphone use, as found in previous research. Additionally, the cervical flexion-relaxation phenomenon may occur when the head angle exceeded 40°. The near-maximum head angle during smartphone use might induce the cervical flexion-relaxation phenomenon, potentially aggravating neck issues. We recommend limiting smartphone usage postures that exceed the near-maximum head angle, as they are commonly adopted by individuals in the daily smartphone activities.

Factors Affecting Usage of a Digital Asthma Monitoring Application by Old-Age Asthmatics Living in Inner Central Portugal.

Purpose: To analyse factors affecting the ability to use the digital asthma monitoring application Mask-Air® in old-age individuals living in inland Portugal. Patients and Methods: In this observational study, patients with medically confirmed asthma who agreed to participate were interviewed and subdivided into Non-users Group: those who could not use the application and Users Group: those who could. Sociodemographic and psychological data, comorbidities, and asthma status were compared between groups. Assessment of reasons for refusal was based on a 6-item questionnaire. Results: Among the 72 sequentially recruited patients (mean age±SD 73.26±5.43 yrs; 61 women; 11 men), 44 (61.1%; mean age±SD 74.64±5.68 yrs; 38 women; 6 men)) were included in Non-users Group and 28 (38.9%; mean age±SD 71.11±4.26 yrs; 23 women; 5 men) in Users Group. Non-users Group patients were significantly older, had lower socioeconomic level, and more frequently had severe asthma (25% vs 3.6%; Odds ratio=0.08 (95% CI=0.01-0.81; p=0.033)) and diabetes (32.6% vs 7.4%; Odds ratio=0.17 (95% CI=0.03-0.80; p=0.025)) than Users Group. The main reasons for not using the App were "Lack of required hardware" (n=35) and "Digital illiteracy" (n=26), but lack of interest to use the App among those who had conditions to use it was uncommon. Conclusion: Most old-age asthmatics living in Beira Interior either lack a smartphone or digital skills, which are significant obstacles to implementing app-based monitoring studies.

This study was done to see whether it was possible to use a mobile phone application (App) to help old-age asthmatics living in inner Central Portugal better monitor and self-manage their disease. The researchers interviewed a group of 72 patients with proven asthma who agreed to participate in the study. This group was subdivided into two subgroups: Non-users Group (44 patients) included those who could not use the App because they did not have a smartphone; Users Group (28 patients) included those who had all the conditions to use the App. Patients were helped to download the App (called MASK-Air), were given a thorough explanation about it, and about how it should be used on a daily basis to monitor their asthma symptoms. The researchers found that patients in Non-users Group were significantly older, had worse socioeconomic conditions, and more often had severe asthma and diabetes. They also discovered that the main reasons for not using the App were lack of a smartphone and not knowing how to use a smartphone. These results show that lacking a smartphone and not knowing how to use digital tools are frequent situations in old-age asthmatics living in inner Central Portugal, and these may be obstacles for patients in monitoring their own asthma symptoms.

Effects of frequent assessments on the severity of suicidal thoughts: an ecological momentary assessment study.

Objective: In recent years, there has been a significant increase in research using ecological momentary assessment (EMA) to explore suicidal thoughts and behaviors (STBs). Meanwhile, concerns have been raised regarding the potential impacts of frequent and intense STBs assessments on the study participants. Methods: From November 2021 to June 2023, a total of 83 adolescent and young adult outpatients (Mage = 21.0, SDage = 6.3, 71.1% female), who were diagnosed with mood disorders, were recruited from three psychiatric clinics in China. Smartphone-based EMA was used to measure suicidal thoughts three times per day at randomly selected times. We examined the change of suicidal thoughts in each measurement and within 1 day to evaluate potential adverse effects using Bayesian multilevel models. Results: The 3,105 effective surveys were nested in 83 participants (median follow-up days: 14 days). The results of two-level models indicated that suicidal thoughts decreased during the monitoring period. However, this effect varied among different individuals in the two-level model. Conclusion: Our findings did not support the notion that repeated assessment of suicidal thoughts is iatrogenic, but future research should continue to investigate the impact of frequent assessment on suicidal thoughts, taking into account individual differences and utilizing larger sample sizes.

Do digital technologies reduce racially biased reporting? Evidence from NYPD administrative data.

Recent work has emphasized the disproportionate bias faced by minorities when interacting with law enforcement. However, research on the topic has been hampered by biased sampling in administrative data, namely that records of police interactions with citizens only reflect information on the civilians that police elect to investigate, and not civilians that police observe but do not investigate. In this work, we address a related bias in administrative police data which has received less empirical attention, namely reporting biases around investigations that have taken place. Further, we investigate whether digital monitoring tools help mitigate this reporting bias. To do so, we examine changes in reports of interactions between law enforcement and citizens in the wake of the New York City Police Department's replacement of analog memo books with mobile smartphones. Results from a staggered difference in differences estimation indicate a significant increase in reports of citizen stops once the new smartphones are deployed. Importantly, we observe that the rise is driven by increased reports of "unproductive" stops, stops involving non-White citizens, and stops occurring in areas characterized by a greater concentration of crime and non-White residents. These results reinforce the recent observation that prior work has likely underestimated the extent of racial bias in policing. Further, they highlight that the implementation of digital monitoring tools can mitigate the issue to some extent.

Tuberculosis Treatment Compliance Under Smartphone-Based Video-Observed Therapy Versus Community-Based Directly Observed Therapy: Cluster Randomized Controlled Trial.

BACKGROUND: There are no recent studies comparing the compliance rates of both patients and observers in tuberculosis treatment between the video-observed therapy (VOT) and directly observed therapy (DOT) programs. OBJECTIVE: This study aims to compare the average number of days that patients with pulmonary tuberculosis and their observers were compliant under VOT and DOT. In addition, this study aims to compare the sputum conversion rate of patients under VOT with that of patients under DOT. METHODS: Patient and observer compliance with tuberculosis treatment between the VOT and DOT programs were compared based on the average number of VOT and DOT compliance days and sputum conversion rates in a 60-day cluster randomized controlled trial with patients with pulmonary tuberculosis (VOT: n=63 and DOT: n=65) with positive sputum acid-fast bacilli smears and 38 observers equally randomized into the VOT and DOT groups (19 observers per group and n=1-5 patients per observer). The VOT group submitted videos to observers via smartphones; the DOT group followed standard procedures. An intention-to-treat analysis assessed the compliance of both the patients and the observers. RESULTS: The VOT group had higher average compliance than the DOT group (patients: mean difference 15.2 days, 95% CI 4.8-25.6; P=.005 and observers: mean difference 21.2 days, 95% CI 13.5-28.9; P<.001). The sputum conversion rates in the VOT and DOT groups were 73% and 61.5%, respectively (P=.17). CONCLUSIONS: Smartphone-based VOT significantly outperformed community-based DOT in ensuring compliance with tuberculosis treatment among observers. However, the study was underpowered to confirm improved compliance among patients with pulmonary tuberculosis and to detect differences in sputum conversion rates. TRIAL REGISTRATION: Thai Clinical Trials Registry (TCTR) TCTR20210624002; https://tinyurl.com/3bc2ycrh. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/38796.

Designing a Smartphone-Based Pulse Oximeter for Children in South Africa (Phefumla Project): Qualitative Analysis of Human-Centered Design Workshops With Health Care Workers.

Background: Pulse oximeters noninvasively measure blood oxygen levels, but these devices have rarely been designed for low-resource settings and are inconsistently available at outpatient clinics. Objective: The Phefumla project aims to develop and validate a pediatric smartphone-based pulse oximeter designed specifically for this context. We present the process of human-centered oximeter design with health care workers in South Africa. Methods: We purposively sampled 19 health care workers from 5 clinics in Khayelitsha, Cape Town. Using a human-centered design approach, we conducted participatory workshops with four activities with health care workers: (1) they received 3D-printed prototypes of potential oximeter designs to provide feedback; (2) we demonstrated on dolls how they would use the novel oximeter; (3) they used pile sorting to rank design features and suggest additional features they desired; and (4) they designed their preferred user interface using a whiteboard, marker, and magnetized features that could be repositioned. We audio recorded the workshops, photographed outputs, and took detailed field notes. Analysis involved iterative review of these data to describe preferences, identify key design updates, and provide modifications. Results: Participants expressed a positive sentiment toward the idea of a smartphone pulse oximeter and suggested that a pediatric device would address an important gap in outpatient care. Specifically, participants expressed a preference for the prototype that they felt enabled more diversity in the way it could be used. There was a strong tendency to prioritize pragmatic design features, such as robustness, which was largely dictated by health care worker context. They also added features that would allow the oximeter device to serve other clinical functions in addition to oxygen saturation measurement, such as temperature and respiratory rate measurements. Conclusions: Our end user-centered rapid participatory approach led to tangible design changes and prompted design discussions that the team had not previously considered. Overall, health care workers prioritized pragmatism for pediatric pulse oximeter device design.

Smartwatch-Based Interventions for People With Dementia: User-Centered Design Approach.

BACKGROUND: Assistive technologies can help people living with dementia maintain their everyday activities. Nevertheless, there is a gap between the potential and use of these materials. Involving future users may help close this gap, but the impact on people with dementia is unclear. OBJECTIVE: We aimed to determine if user-centered development of smartwatch-based interventions together with people with dementia is feasible. In addition, we evaluated the extent to which user feedback is plausible and therefore helpful for technological improvements. METHODS: We examined the interactions between smartwatches and people with dementia or people with mild cognitive impairment. All participants were prompted to complete 2 tasks (drinking water and a specific cognitive task). Prompts were triggered using a smartphone as a remote control and were repeated up to 3 times if participants failed to complete a task. Overall, 50% (20/40) of the participants received regular prompts, and 50% (20/40) received intensive audiovisual prompts to perform everyday tasks. Participants' reactions were observed remotely via cameras. User feedback was captured via questionnaires, which included topics like usability, design, usefulness, and concerns. The internal consistency of the subscales was calculated. Plausibility was also checked using qualitative approaches. RESULTS: Participants noted their preferences for particular functions and improvements. Patients struggled with rating using the Likert scale; therefore, we assisted them with completing the questionnaire. Usability (mean 78 out of 100, SD 15.22) and usefulness (mean 9 out of 12) were rated high. The smartwatch design was appealing to most participants (31/40, 76%). Only a few participants (6/40, 15%) were concerned about using the watch. Better usability was associated with better cognition. The observed success and self-rated task comprehension were in agreement for most participants (32/40, 80%). In different qualitative analyses, participants' responses were, in most cases, plausible. Only 8% (3/40) of the participants were completely unaware of their irregular task performance. CONCLUSIONS: People with dementia can have positive experiences with smartwatches. Most people with dementia provided valuable information. Developing assistive technologies together with people with dementia can help to prioritize the future development of functional and nonfunctional features.

Technology-based Cognitive Behavioral Therapy Interventions.

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

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

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

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

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

Dual-mode fluorescence and colorimetric smartphone-based sensing platform with oxidation-induced self-assembled nanoflowers for sarcosine detection.

BACKGROUND: Early prostatic cancer (PCa) diagnosis significantly improves the chances of successful treatment and enhances patient survival rates. Traditional enzyme cascade-based early cancer detection methods offer efficiency and signal amplification but are limited by cost, complexity, and enzyme dependency, affecting stability and practicality. Meanwhile, sarcosine (Sar) is commonly considered a biomarker for PCa development. It is essential to develop a Sar detection method based on cascade reactions, which should be efficient, low skill requirement, and suitable for on-site testing. RESULTS: To address this, our study introduces the synthesis of organic-inorganic self-assembled nanoflowers to optimize existing detection methods. The Sar oxidase (SOX)-inorganic hybrid nanoflowers (Cu3(PO4)2:Ce@SOX) possess inherent fluorescent properties and excellent peroxidase activity, coupled with efficient enzyme loading. Based on this, we have developed a dual-mode multi-enzyme cascade nanoplatform combining fluorescence and colorimetric methods for the detection of Sar. The encapsulation yield of Cu3(PO4)2:Ce@SOX reaches 84.5 %, exhibiting a remarkable enhancement in catalytic activity by 1.26-1.29 fold compared to free SOX. The present study employing a dual-signal mechanism encompasses 'turn-off' fluorescence signals ranging from 0.5 µM to 60 µM, with a detection limit of 0.226 µM, and 'turn-on' colorimetric signals ranging from 0.18 µM to 60 µM, with a detection limit of 0.120 µM. SIGNIFICANCE: Furthermore, our study developed an intelligent smartphone sensor system utilizing cotton swabs for real-time analysis of Sar without additional instruments. The nano-platform exhibits exceptional repeatability and stability, rendering it well-suited for detecting Sar in authentic human urine samples. This innovation allows for immediate analysis, offering valuable insights for portable and efficient biosensors applicable to Sar and other analytes.

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

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

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

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

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

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

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

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