Smartphone Technology Research Articles

Simple, accurate, and precise detection of total nitrogen in surface waters using a smartphone-based analytical method.

In this study, a simple, accurate, and precise analytical strategy was developed for the determination of total nitrogen (total dissolved nitrogen + particulate nitrogen) in surface waters using smartphone technology. The spectrophotometric method based on digestion with persulfate, commonly used in water analyses, was integrated with a digital image-based colorimetric detection system (DIC). To evaluate the accuracy of the total nitrogen-digital image-based colorimetric detection system (TN-DIC), results obtained from the Hach method and UV-VIS spectrophotometer were compared with the RGB values obtained from the smartphone imaging system. T-Test results indicated no statistically significant difference between the results obtained from the TN-DIC method and the standard methods. The linear range of the TN-DIC method was determined to be 0.25-5mg/L. The analytical quality indicators of the system were established as follows: limit of detection (LOD) 0.095ppm, limit of quantification (LOQ) 0.29ppm, %RSD 0.32, and R2 value 0.9982. Recovery percentages (99-112%) used to measure the analytical performance of the method were calculated through spike applications at concentrations of 1 and 2ppm in real water samples. Unlike traditional spectrophotometric methods, this smartphone-based approach is fast, simple, and cost-effective, requiring no complex equipment during analysis.

Reliability and concurrent validity of the iHandy level mobile application to measure thoracolumbar range of motion in low back pain patients

IntroductionThe use of smartphone technology and software (apps), in addition to an inclinometer and goniometer, has expanded the clinician’s assessment possibilities. However, the legitimacy and trustworthiness of these cell phones and applications are still questioned. This study aimed to determine the reliability and concurrent validity of the iHandy level mobile application to measure the thoracolumbar range of motion in people with low back pain.MethodsThis descriptive study included 40 participants, of which 20 were symptomatic, and 20 were asymptomatic for non-specific chronic pain in the lower back. Measurements were taken for extension, flexion, and left and right-side bending range of motion (ROM). Two investigators were involved. One measured ROM values with a Samsung iHandy level and inclinometer, and the second investigator registered the data. Two values were taken and the mean was used for further analysis. Measurements were taken at baseline and one-week follow-up.ResultsIn asymptomatic participants, the mobile application had ICC values of 0.81, 0.70, 0.79, and 0.85, while in symptomatic, regarding intra-rater reliability, these were 0.80, 0.78, 0.81Regarding the concurrent validity of mobile apps in asymptomatic patients, ICC values were 0.94, 0.93, 0.84, and 0.89, while in symptomatic patients, the ICC values were found to be 0.92, 0.89, 0.83, and 0.85 for flexion, extension, RLF, and LLF, respectively. These showed an excellent correlation between the inclinometer and mobile app regarding intra-rater reliability and concurrent validity.ConclusionsiHandy level smartphone applications might be considered reliable and valid for assessing thoracolumbar motion ranges in individuals with chronic pain in the lower back.

Smartphone scanning is a reliable and accurate alternative to contemporary residual limb measurement techniques.

Monitoring the volume and shape of residual limbs post-amputation is necessary to achieve optimal socket fit and determine overall limb health, yet contemporary clinical measurement techniques show high variance between measures. Three-dimensional scanning presents an opportunity for improved accuracy and reliability of residual limb measurements, however, three-dimensional scanners remain prohibitively expensive. A cost-effective alternative is the use of software that can utilise the photographs of modern smartphone cameras to create geometrically accurate scans. Whilst several studies have investigated the potential of privately developed photogrammetry algorithms for capturing residual limbs with clinical accuracy, none to the authors knowledge have explored commercially available software to do the same. Three applications were tested, namely Polycam, Luma, and Meshroom, to determine if they could produce clinically acceptable results. Scans of ten residual limbs were created using both smartphone technology and a reference structured-light scanner (Artec EVA), against which the validity and reliability of the resulting limb models were assessed using the Bland-Altman method and Intraclass Correlation Coefficient, respectively. Polycam and Luma achieved both Pearson Coefficients and Intraclass Correlation Coefficients of 0.999, and Coefficients of Variation of 1.1% and 1.4%, respectively. Volume reliability coefficients were 58.3 ml and 70.0 ml respectively for Polycam and Luma, whereas Meshroom failed to meet any of the criteria for clinical suitability, with a repeatability coefficient of 790.3 ml. Both Polycam and Luma exhibit sufficient accuracy and reliability to be considered for clinical volume measurements.

Prediction of Salicornia europaea L. biomass using a computer vision system to distinguish different salt-tolerant populations

BackgroundSalicornia europaea L. is emerging as a versatile crop halophyte, requiring a low-cost, non-destructive method for salt tolerance classification to aid selective breeding. We propose using a computer vision system (CVS) with multivariate analysis to classify S. europaea based on morphometric and colour traits to predict plant biomass and the salinity in their substrate.ResultsA trial and validation set of 96 and 24 plants from 2 populations confirmed the efficacy. CVS and multivariate analysis evaluated the plants by morphometric traits and CIELab colour variability. Through Pearson analysis, the strongest correlations were between biomass fresh weight (FW) vs. projected area (PA) (0.91) and anatomical cross-section (ACS) vs. shoot diameter (Sd) (0.94). The PA and FW correlation retrieved different equation fits between lower and higher salt-tolerant populations (R2 = 0.93 for linear and 0.90 for 2nd-degree polynomial), respectively. The higher salt-tolerant reached a maximum biomass PA at 400 mM NaCl, while the lower salt-tolerant produced less under 200 and 400 mM. A second Pearson correlation and PCA described sample variability with 80% reliability using only morphometric-colour parameters. Multivariate discriminant analysis (MDA) demonstrated that the method correctly classifies plants (90%) depending on their salinity level and tolerance, which was validated with 100% effectiveness. Through multiple linear regression, a predictive model successfully estimated biomass production by PA, and a second model predicted the salinity substrate (Sal.s.) where the plants thrive. Plants' Sd and height influenced PA prediction, while Sd and colour difference (ΔE1) influenced Sal.s. Models validation of actual vs. predicted values showed a R2 of 0.97 and 0.90 for PA, and 0.95 and 0.97 for Sal.s. for lower and higher salt-tolerant, respectively. This outcome confirms the method as a cost-effective tool for managing S. europaea breeding.ConclusionsThe CVS effectively extracted morphological and colour features from S. europaea cultivated at different salinity levels, enabling classification and plant sorting through image and multivariate analysis. Biomass and salinity substrate were accurately predicted by modelling non-destructive parameters. Enhanced by AI, machine learning and smartphone technology, this method shows great potential in ecology, bio-agriculture, and industry.

Comparison Between Ecological Momentary Assessment and Self-Report of Awake Bruxism Behaviours in a Group of Healthy Young Adults.

The aim of this investigation was to compare the self-reported frequency of awake bruxism (AB) behaviours by means of a validated single-observation point questionnaire (i.e., Oral Behaviour Checklist [OBC]) with the frequency reported in real-time with an Ecological Momentary Assessment (EMA) over 1 week. One hundred healthy young adults took part in the study. The frequency of some activities belonging to the spectrum of AB behaviours (i.e., teeth contact, mandible bracing, teeth clenching, teeth grinding) was evaluated using the EMA approach with smartphone technology support over 1 week. The OBC questionnaire was used to evaluate the self-reported frequency of the same AB behaviours. Spearman's rank correlation coefficient was adopted to test the correlation between the average score of each OBC question considered (i.e., teeth contact, mandible bracing, teeth clenching, teeth grinding) and the average frequency of the corresponding EMA item. Statistical analysis showed a weak-to-moderate level of correlation between the two different AB assessment methods. Mandible bracing showed the highest level of correlation, while teeth grinding had the lowest one. While the OBC represents a valid and easy-to-administer screening tool for evaluating bruxism behaviours, the EMA approach provides a more detailed real-time report on AB behaviours.

Smartphone tests quantify lower extremities dysfunction in multiple sclerosis.

Increasing shortage of neurologists compounded by the global aging of the population have translated into suboptimal care of patients with chronic neurological diseases. While some patients might benefit from expanding telemedicine, monitoring neurological disability via telemedicine is challenging. Smartphone technologies represent an attractive tool for remote, self-administered neurological assessment. To address this need, we have developed a suite of smartphone tests, called neurological functional test suite (NeuFun-TS), designed to replicate traditional neurological examination. The aim of this study was to assess the ability of two NeuFun-TS tests-short walk and foot tapping-to quantify motor functions of lower extremities as assessed by a neurologist. A cohort of 108 multiple sclerosis (MS) patients received a full neurological examination, imaging of the brain, and completed the NeuFun-TS smartphone tests. The neurological exam was digitalized using the NeurEx™ platform, providing calculation of traditional disability scales, as well as quantification of lower extremities-specific disability. We assessed unilateral correlations of 28 digital biomarkers generated from the NeuFun-TS tests with disability and MRI outcomes and developed machine learning models that predict physical disability. Model performance was tested in an independent validation cohort. NeuFun-TS-derived digital biomarkers correlated strongly with traditional outcomes related to gait and lower extremities functions (e.g., Spearman ρ > 0.8). As expected, the correlation with global disability outcomes was weaker, but still highly significant (e.g., ρ 0.46-0.65; p < 0.001 for EDSS). Digital biomarkers also correlated with semi-quantitative imaging outcomes capturing locations that can affect lower extremity functions (e.g., ρ ~ 0.4 for atrophy of medulla). Reliable digital outcomes with high test-retest values showed stronger correlation with disability outcomes. Combining strong, reliable digital features using machine learning resulted in models that outperformed predictive power of best individual digital biomarkers in an independent validation cohort. NeuFun-TS tests provide reliable digital biomarkers of lower extremity motor functions.

UV-curable polymer-treated polydimethylsiloxane substrate for ultrahigh-efficient flexible organic light-emitting diodes

Flexible organic light-emitting diodes (FOLEDs) have developed rapidly and have been successfully implemented in the technology of foldable smartphones. As one of the most promising flexible substrates, polydimethylsiloxane (PDMS) suffers from the drawbacks of low thermal stability and weak interfacial adhesion with the low-cost solution-processed poly(3,4-ethylenedioxythiophene):poly(styrene sulfonate) (PEDOT:PSS) film electrodes. In this work, a UV-curable polymer, Norland Optical Adhesive 63 (NOA63), is proposed to enhance the thermal stability and interfacial adhesion, as well as facilitating the light extraction of PDMS substrate. The PEDOT:PSS electrode film on the NOA63 modified PDMS substrate performs a uniform and smooth surface morphology with a low roughness of 3.1 nm, a high transmittance of 94.7 % at 550 nm wavelength, a low sheet resistance of 160 Ω/sq, and superior mechanical durability even after 18,000 bending cycles. The outstanding characteristics of the PDMS/NOA63 substrate enables the fabrication of an ultrahigh-efficient FOLED with a maximum current efficiency, power efficiency and external quantum efficiency of 230.6 cd/A, 144.9 lm/W and 65 %, respectively. This study paves the way to realize high-performance FOLEDs for wearable electronics in the future.

Smartphone Technology and Social Transformation of Older Adults: From Capability Approach Theory Perspective

Smartphone Technology and Social Transformation of Older Adults: From Capability Approach Theory Perspective

Electronic screening and brief interventions promoting healthy diet and physical activity among adult patients in medical settings: A systematic review

Background & AimsElectronic screening and brief interventions (eSBIs) are promising health promoting approaches but their utilization towards nutrition outcomes has been little documented so far. The aim of this systematic review is to examine the nature and findings of eSBIs targeting diet and/or physical activity (PA) among adult patients in healthcare settings since smartphone technologies emerged. MethodsThree systematic searches in PubMed, ScienceDirect and PsycINFO were conducted from January 2010 to May 2023. We include all studies assessing eSBI towards lifestyle habits (dietary habits and/or PA at least), implemented in healthcare settings and targeting adult participants. ResultsOf 1981 articles identified, 15 studies met inclusion criteria. Most of them documented eSBIs implemented in primary care (n=11), targeting PA (n=11) and diet (n=9) alone or combined (n=5). Some eSBIs targeted additional behaviors (e.g. smoking, drinking). Delivery modes included computer (n=8), printed documents (n=8) or phone (n=2) but only four eSBIs were interactive (or partially automated). In addition to personalized feedback, most common behavior change techniques were “information about social and environmental consequences” (n=9), “goal setting” (n=7), and “information about health consequences” (n=7). Most studies assessing behavior change had a control group (8 in 10), and eight studies found positive changes in behavior. ConclusionsAlthough implementing eSBIs towards PA/diet among adult patients in medical settings seems promising for primary, secondary and tertiary prevention, common conceptual framework, intervention and outcomes are severely lacking, and the extent to which they follow existing SBI guidelines remains unclear. Demonstrating their effectiveness requires fixing these issues before conducting prospective assessment studies in medical settings.

Voice Search Technology and Consumer Buying Behaviour of Smart Phones in Port Harcourt

This study investigated the relationship between voice search technology and consumer buying behaviour of smartphones in Port Harcourt. Specifically, the objectives of the study were to determine how voice recognition accuracy and user experience relates to routine and impulse purchase behaviour of smartphones in Port Harcourt. The population of this study comprised of twenty-two registered mobile phone dealers in Port Harcourt. The census approach was adopted to choose the respondents for this study. To generate data for the study, researcher purposively distributed the copies of questionnaire in the frame of three (3) copies to each mobile phone firm. A total of sixty-six (66) respondents were used as the study subjects, however only fifty-eight (58) respondents provided data for the analysis. From results of the analysis it was revealed that voice recognition accuracy and user experience which are the dimensions of voice search technology positively and significantly relate with routine buying behaviour and impulse buying behaviour (measures of consumer buying behaviour). Therefore, the study concludes that the impact of voice search technology on consumer buying behavior of smartphones in Port Harcourt reveals significant correlations between the dimensions of voice search technology—voice recognition accuracy and user experience—and different buying behaviors. Furthermore, the study recommended that amongst others that Smartphone dealers should invest in advanced voice recognition technology to ensure high accuracy in voice commands. This will enhance consumer satisfaction and potentially increase both routine and impulse purchases.

Chromatic covalent organic frameworks enabling in-vivo chemical tomography

Covalent organic frameworks designed as chromatic sensors offer opportunities to probe biological interfaces, particularly when combined with biocompatible matrices. Particularly compelling is the prospect of chemical tomography – or the 3D spatial mapping of chemical detail within the complex environment of living systems. Herein, we demonstrate a chromic Covalent Organic Framework (COF) integrated within silk fibroin (SF) microneedles that probe plant vasculature, sense the alkalization of vascular fluid as a biomarker for drought stress, and provide a 3D in-vivo mapping of chemical gradients using smartphone technology. A series of Schiff base COFs with tunable pKa ranging from 5.6 to 7.6 enable conical, optically transparent SF microneedles with COF coatings of 120 to 950 nm to probe vascular fluid and the surrounding tissues of tobacco and tomato plants. The conical design allows for 3D mapping of the chemical environment (such as pH) at standoff distances from the plant, enabling in-vivo chemical tomography. Chromatic COF sensors of this type will enable multidimensional chemical mapping of previously inaccessible and complex environments.

Smartphone Technology to Facilitate Remote Postural Balance Assessment in Acute Concussion Management: Pilot Study.

Impaired balance is a key symptom following acute concussion. Unfortunately, the recommended clinical balance assessment lacks sensitivity and discriminative ability, relying on the experience of the clinician for interpretation. The aim of this pilot study is to explore smartphone technology to remotely assess balance impairment in people with acute concussion. A smartphone app was developed to allow the clinician to connect remotely using their personal web browser to the participant's smartphone and collect motion data while instructing the participant to perform the following balance tests: standing on firm and foam surface with eyes opened and closed (FIRMEO, FIRMEC, FOAMEO, and FOAMEC). Outcome measures were processed from the raw acceleration to calculate the average acceleration magnitude from the mean and the root-mean square, with greater values indicating more sway. Eleven healthy controls (HCs) and 11 people with concussion (CON) participated. In all sway measurements, the CON group had significantly (p < 0.05) greater values when standing on a firm surface. In the FOAMEC condition, the CON group had significantly (p < 0.05) greater sway measures only in the AP direction, while significantly greater sway in all directions were found in the CON group in the FOAMEO condition. This study shows that remote balance assessment using a smartphone can discriminate between healthy controls and people with acute concussion. This technology could play an important role in concussion management to assist with determining recovery from concussions and the optimal timing for return to sport.

A novel integrated approach for quantifying the convergence of disruptive technologies from science to technology

With rapid developments in science and technology, knowledge transfer from science to technology and technology convergence from different fields are accelerating. Technology convergence has become a main source of disruptive technologies (DTs). To facilitate enterprise R&D strategic decision-making and government innovation policies formulation, it is necessary to quantify the convergence processes of DTs and understand the DTs' emergence characteristics from science to technology. Existing research on technology convergence measurement mainly used patent citation information, patent co-classification analysis, and text mining. However, since these studies have limited analysis of the sources and causes of technology convergence from the perspective of knowledge memes, resulting in insufficient revelation of the processes and characteristics of DTs' emergence. Knowledge meme theory helps to reveal the relationships between knowledge diffusion, knowledge convergence, and technology convergence. Therefore, in this paper, we proposed a research framework for quantifying the convergence of DTs from science to technology. In this framework, we analyzed the knowledge diffusion and technology convergence of DTs from science to technology based on knowledge meme theory. We also integrated patent citation analysis, text mining, and cascade network models to quantitatively measure knowledge diffusion and technology convergence characteristics. We tried to understand the generation mechanisms of DTs from the perspective of technology convergence. We took smartphones as a case study to verify the framework's validity and flexibility. This paper provides a novel approach for quantifying the convergence of DTs from science to technology, which can help us to understand the emergence and development trends of DTs. This paper will also be of interest to smartphone technology R&D experts.

Intention to participate in smartphone-based data collection: the case of smallholder farmers in Uganda

ABSTRACT Smartphone technology is increasingly being used to conduct surveys, including in low-income countries where the timely collection of socio-economic data is of great importance. This study investigated the intention of Ugandan smallholder farmers to participate in smartphone-based data collection (SPDC). The data was collected through a cross-sectional survey of 306 smallholder farmers. The results show that the willingness of farmers to participate in SPDC is high. The intention to participate in SPDC is directly influenced by attitude, subjective norms and perceived enjoyment. Indirect influences are perceived usefulness, perceived trustworthiness and perceived cost through their effects on attitude. Similarly, perceived ease of use indirectly influences intention through its effects on perceived usefulness and perceived enjoyment. Statistically significant differences were found in terms of age, gender, mobile phone ownership and education. These results show the great potential of SPDC for data collection in Uganda for researchers, policy makers, agribusinesses and stakeholders.

Developing a Novel Contact Tracing Mobile Application for Coronavirus Disease 2019 (COVID-19) for Improving Testing Capacity and Controlling the Spread of COVID-19 in Nigeria

In this study, a novel contact tracing model that leverages smartphone technology to enhance efficiency, reduce costs, and extend the duration of contact tracing efforts is developed. This model utilizes smartphones as identification systems, collecting data on the proximity of other smartphone users through integrated Bluetooth and GPS technology. The study examines the frequency, duration, and proximity of interactions between smartphone devices in a clinical setting, highlighting potential implications for infectious disease transmission to pilot the mobile application developed. Contact data from six pairs of devices were analyzed, focusing on metrics such as total contacts, total contact time, average contact time, average distance, and the percentage of contacts occurring within 1.5 meters. The results showed varying levels of interaction across device pairs, with Devices 1 &amp; 3 showing the highest number of contacts (175), and Devices 3 &amp; 4 displaying the longest average contact time (20,133,193.01 seconds). Correlation analysis revealed weak and statistically insignificant relationships between total contacts and average distance (r = 0.13, p = 0.81), contact time and the percentage of close contacts (r = -0.15, p = 0.78). These findings suggest that while there are observable trends in contact patterns, the statistical insignificance highlights the need for further investigation to establish stronger associations that could inform infection control practices in healthcare settings.

Use of Digital Smartphone Applications for Blood Pressure Monitoring: An Integrative Review

With the purpose of identifying in the literature and describing the scientific evidence for the use of smartphone applications for blood pressure monitoring, this study is an integrative literature review, employing a qualitative approach conducted with studies from 2014 to 2024, accessed via the Virtual Health Library (VHL) portal. The research resulted in 10 articles addressing the use of digital technology in smartphones and answering the following guiding question: What is the effectiveness of this technology for diagnostic purposes and blood pressure control? This study showed that digital smartphone applications are effective for blood pressure monitoring, providing important information for both patients and healthcare professionals. The evidence found indicates that digital applications are effective in monitoring blood pressure variations; however, they are not effective for diagnostic purposes but can be used to complement existing data.

Specific and enzyme-free monitoring of propiconazole pesticide residues in vegetables with a portable nanozyme-based paper sensor

The nanozyme-based colorimetric sensor shows promise for rapid pesticide detection but struggles with non-specific enzyme inhibition. This study developed a portable paper-based sensor for detecting the propiconazole (PC) pesticide using Fe@PCN-224 nanocubes (NCs). Characterization confirmed the successful synthesis of Fe@PCN-224 NCs, which displayed peroxidase-like activity. The specific interaction between PC's triazole ring and the Fe active site inhibited their activity, enabling selective detection with a limit of 8 × 10−9 mol L−1 and a linear range of 0.03 × 10−6 to 0.90 × 10−6 mol L−1. Kinetic studies revealed a Michaelis-Menten constants (Km) of 0.68 × 10−3 mol L−1 for TMB, indicating higher affinity in Fe@PCN-224 NCs. The electron paramagnetic resonance (EPR) analysis revealed the production of •OH, 1O2, O2•− during the catalytic reactions. By integrating smartphone technology, this portable sensor achieved recoveries from vegetable samples between 94.6 % and 109.2 %, demonstrating its potential as an accurate, cost-effective analytical tool for food safety and advancing nanozyme applications.

Helping Patients to Predict and Confirm Ovulation with the Use of Combined Urinary Hormonal and Smartphone Technology: A Proof-of-Concept Retrospective Descriptive Case Series.

Smartphone-based fertility awareness methods with home-based urinary hormonal testing are gaining popularity for fertility tracking. In our university-affiliated family practice, we integrated a previously developed ovulation tracking application into a protocol for monitoring urinary sex hormones and cervical secretions. Serum progesterone was used to confirm the luteal phase, with levels ≥ 15.9 nmol/L ensuring confirmation. Data from 110 women seen for infertility treatment (n = 95) or family planning advice (n = 15) and using our ovulation prediction protocol showed that most opted for a combination of cervical mucus and luteinizing hormone testing (n = 86). Among those using it for family planning, the median usage among women spanned 56 cycles, and 13 cycles per woman required progesterone testing for confirmation. Thirteen patients are still using the method without unintended pregnancies. No unintended pregnancies occurred. Confidence in tests based on serum progesterone was high (93%). For infertility, the method helped in the identification of anovulation, evaluating treatment response, and in diagnosing subfertility causes. This proof-of-concept retrospective descriptive case series suggests the potential for smartphone-based monitoring in fertility management, urging further studies for application enhancements and prospective validation.

Unveiling digital mirrors: Decoding gendered body poses in instagram imagery

Social media platforms have had a significant impact on people's everyday lives worldwide and imagery on social media has become a vital means of practicing views of the self and communicating them to others. Albeit a significant proportion of people frequently upload self-representations on social media, this source of information on individuals, groups, and societies remains under-examined within the social sciences and neighboring disciplines. In our study, we focus on gender-stereotypical body-posing in self-portraits on social media. While sociology has examined gender stereotypes for decades, research lacks empirical evidence on representations in digital contexts as well as novel forms of stereotyping. We present a scalable and transferable methodology for analyzing body poses in images by combining neural network pose detection with an unsupervised learning approach and applying this methodology to data from the social media platform Instagram. Based on a clustering algorithm applied to gender-annotated imagery, we identify 150 body posing clusters. Our results reveal significant gender differences in 20 percent of clusters, many of which represent gender-stereotypical body poses addressed in sociological literature. Moreover, we can identify new stereotypical poses related to smartphone technology and social media trends. This study represents a novel approach to utilizing large-scale image data for social science research and contributes to a better understanding of the consolidation and reproduction of gender stereotypes in digital realms.

Impact of Digital Health Interventions on Birth Equity: A Review.

The rise in smartphone utilization and technology uptake has popularized digital health interventions as a means of supporting healthy pregnancies and optimizing maternal and child health. Digital health interventions include several modalities, such as telemedicine, remote patient monitoring, smartphone applications, web-based interventions, wearables, and health information technology. However, the impact of these interventions on improving maternal and infant health outcomes by race and socioeconomic status to achieve birth equity is unknown. This review summarizes current literature on the impact of digital health interventions on the outcomes of communities of color and lower socioeconomic status in the United States. We demonstrate there is emerging evidence of the impact of digital health interventions on maternal health outcomes, particularly for telemedicine, but evidence specifically focused on assessing outcomes by race and ethnicity and for other modalities, like mHealth apps or wearables, is limited. Digital health interventions may play a part in birth equity initiatives, but should not be considered a standalone solution, and instead should be integrated into other existing efforts to achieve birth equity, like diversifying the clinician workforce, expanding access to high-quality prenatal and postpartum care, or delivering respectful maternity care.