Smartphone Technology Research Articles

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.

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 & 3 showing the highest number of contacts (175), and Devices 3 & 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.

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.

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 on the social media platform Instagram by combining neural network pose detection with an unsupervised learning approach. 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.

A fluorescence biosensor for organophosphorus pesticide detection with a portable fluorescence device-based smartphone

An innovative fluorescence biosensor was successfully developed to detect organophosphorus pesticide (OPs) by utilizing smartphone technology. The assay relied on the enzymatic activity of alkaline phosphatase (ALP), which facilitated the conversion of L-ascorbic acid 2-phosphate sesquimagnesium salt hydrate (AAP) into L-ascorbic acid (AA). The AA that generated was then reactedwith o-phenylenediamine (OPD) to yield a fluorescent marker identified as 3-(1,2-dihydroxyethyl)furo[3,4-b]quinoxalin-1(3H)-one (DFQ). A novel bandpass approach was specifically developed for a smartphone that was integrated with a customized portable fluorescence device to measure the fluorescence emission of DFQ. The device has a unique application that converts the fluorescence intensity into an RGB signal. In the presence of OPs, malathion was chosen as the representative of the OPs substance; the enzymatic activity of the ALP was inhibited, resulting in a decrease in fluorescence intensity, which was proportional to the concentration of malathion. Smartphones can be used to measure fluorescence emission, offering a calibration sensitivity more than 70 times higher than that of conventional spectrofluorometer. The recently developed methodology can be employed to identify malathion within the concentration range of 0.1–1 ppm, with a detection limit of 0.05 ppm. The practical applicability of the method was established using vegetable samples, and the acquired results were in good agreement with those obtained using the standard HPLC approach. This innovative method provides both portability and accuracy, while also exhibiting a notable degree of sensitivity in detecting traceamounts of OPs.

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.

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.

The rapid detection of pesticide residues in fruits and vegetables using electrochemical methods

To meet global demand, pesticides play a very important role in the control of pests that damage agricultural products. However, overuse of pesticides threatens ecosystems and human health. In recent years, gas chromatography, liquid chromatography, gas chromatography-mass spectrometry, liquid chromatography-mass spectrometry, capillary chromatography and capillary electrophoresis have been used in the determination of pesticides. Although these methods are highly specific, highly selective and sensitive, the high value of sophisticated laboratory equipment and tools is a complex and time-consuming process. Because of this, electrochemical sensors and biosensor platforms have become good analytical methods for detecting pesticides due to a number of advantages, including ease of detection of system manufacturing processes, high sensitivity and selectivity. This article describes the methods of manufacturing portable electrochemical sensors for detecting pesticide residues in fruits and vegetables on site, that is, the achievements in recent years in the field of cyclic voltammetry, square wave voltammetry, differential pulse voltammetry and electrochemical impedance spectroscopy. In addition, detailed information is provided on the basic mechanism and electrochemical sensitivity of these methods. The development of electrochemical methods and real-time monitoring of smartphone technologies, as well as their integration with detection platforms, are discussed. For readers with a scientific and technological focus, this article will provide additional valuable information for understanding the creation of portable electrochemical devices, rapid detection of pesticides, the role of electrochemical sensitive methods and contributing to their further development.

The Digital Lives of the Poor: Entertainment Traps and Information Isolation

Smartphones have enabled the delivery of life-improving information services to base-of-the-pyramid (BOP) consumers. However, little is known about how the poor interact with the digital world. Through a novel app we developed to investigate real-time smartphone usage, we identify an unnoticed barrier to digital information access by the poor—data shortages. By analyzing over 9.4 million minutes of smartphone usage data from 929 residents of a Mumbai settlement, we find that entertainment consumes 61% of their phone time. Our data reveal that under universally adopted monthly data plans, low-income individuals binge on YouTube and social media, resulting in data shortages and information isolation in the late-plan period. We offer a practical operational solution to this problem—shorter data-replenishment cycles—which serve as a commitment device to curb binge usage. We randomly assign participants to a “capped plan”—with daily data usage caps—or a standard (monthly) plan. Assignment to the capped plan increases late-plan access of invites to health camps sent via WhatsApp, increases attendance at these in-person camps by 27%, and reduces social media binge usage. Most participants (particularly those with low self-control and high fear of missing out) prefer the capped plan, even when costlier—clearly signaling demand. Because capped plans are inherently cheaper to provide, offering them could enable providers to increase BOP customer value and expand access. Our results suggest an opportunity to amplify the impact of life-improving services targeted at the poor by leveraging users’ interactions with smartphone technology. This paper was accepted by Victor Martínez de Albéniz, operations management. Funding: Financial support from The Wheeler Institute for Business & Development is gratefully acknowledged. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2024.4989 .

A pyrene-based hydrogen-bonded framework with excimer induced ratiometric emission for discriminative luminescence detection of metal ions

Discriminatory fluorescence detection of various metal ions is of significant importance in environmental and health-related applications. A luminescent hydrogen-bonded organic framework (HOF) material, PFC-1 was synthesized using 1,3,6,8-tetra(4-carboxylbenzene) pyrene (H4TBAPy) as the organic building block. The fluorescence behavior of PFC-1 is influenced by the concentration of the suspension, demonstrating different emissions characteristic of monomer and excimer fluorescence, which are highly sensitive to the surrounding environment. This allows for the potential differentiation and sensing of different target analytes. PFC-1 showed discriminatory fluorescence sensing performance towards metal ions such as Al3+, Sc3+, Cr3+, and Cu2+, with changes in fluorescence intensity, emission peak shifts, and intensity ratio changes between monomer and excimer emissions. Furthermore, a smartphone-based detection strategy was proposed, leveraging color recognition capabilities of smartphones for onsite and real-time sensing. The work demonstrate that PFC-1 is a promising material for the development of portable, cost-effective fluorescent sensors for onsite and real-time detection of metal ions. The integration of PFC-1 with smartphone technology paves the way for practical applications in environmental monitoring, industrial processes, and healthcare diagnostics.

Business Strategy and Enterprise Modeling: Nokia Case Study

This study aims to analyze the main causes of Nokia's market share decline and formulate recovery strategies using the PESTEL framework, Porter’s Five Forces analysis, and SWOT. The research adopts a case study method, collecting data from Nokia’s financial reports, industry reports, and relevant academic literature. Key findings reveal that Nokia’s delayed adoption of smartphone technology and internal management conflicts significantly contributed to the company’s downfall. Additionally, the PESTEL analysis highlights external challenges such as shifting consumer preferences and intense technological competition. Based on the analysis, strategic recommendations include adopting a Blue Ocean strategy, internal restructuring, and enhancing technological innovation to regain Nokia's competitive advantage. This research provides valuable insights into how technology companies can avoid decline in rapidly changing industries.

Authentication of indigenous Brazilian specialty canephora coffees using smartphone image analysis

The prevention of coffee fraud through the use of digital and intelligence-based technologies is an analytical challenge because depending on the adulterant, visual inspection is unreliable in roasted and ground coffee due to the similarity in color and texture of the materials used. In this work, a 3D-printed apparatus for smartphone image acquisiton is proposed. The digital images are used to authenticate the geographical origin of indigenous canephora coffees produced at Amazon region, Brazil, against canephora coffees from Espírito Santo, Brazil, and to capture the adulteration of indigenous samples. The results evidenced that the technology is favorable to identify the geographical origin and adulteration with multiple substances using smartphone technology. Pure coffees were adulterated with arabica coffee, spent coffee ground, low-quality Canephora coffee, coffee husks, açaí, corn, and soybean in increasing proportions of 10, 20, 30, 40, 50, 60, and 70 %. These adulterants were roasted and grounded similarly to Canephora coffees to mimetize a highly-sophisticated fraud. The images were converted into Red-Green-Blue (RGB) fingerprinting and used as analytical response to construct Data-Driven Soft Independent Modeling of Class Analogy (DD-SIMCA) models. A total of 95 % of all target and non-target samples in the test set were correctely identified, aiding producers and consumers in ensuring accurate labeling and supporting traditional communities economically and culturally. Smartphone-based method demonstrated potential to innovate the coffee safety control representing a new analytical tecnology.

Highly sensitive and selective L-lactate monitoring in complex matrices with a ratiometric fluorescent sensor RhB@Zn-MOF

L-lactate is an essential biomarker in clinical diagnostics and food quality assessment. This study introduces a novel ratiometric fluorescence sensor, RhB@Zn-MOF, which was specifically designed for the sensitive and selective detection of L-lactate. Through the strategic incorporation of Rhodamine B (RhB) into Zn-MOF, RhB@Zn-MOF was synthesized, exhibiting dual-emission properties and could effectively distinguish L-lactate in complex biological and food matrices such as milk and sweat based on the competitive absorption mechanism. Notably, the sensor achieves a low detection limit of 0.091 μM and demonstrates excellent stability and reproducibility in varied conditions. Furthermore, the integration of the sensor with smartphone technology enables rapid, real-time analysis, showcasing potential applications in sports medicine, clinical environments, and the food industry.

Estimation of isoniazid by digital image colorimetry: an integration of green chemistry and smartphone technology

BackgroundThis study proposes a simple digital image colorimetric method using a smartphone for the quantitative analysis of isoniazid in pharmaceutical medications. The analytical methodology employed in this study involved the utilization of the reaction between isoniazid and FC reagent under alkaline conditions. The chemical reaction led to the creation of a complex with a blue-gray color, exhibiting a peak absorption at a wavelength of 760 nm. An Android application was employed to perform a smartphone-based determination based on separating the captured color into different color channels such as red, blue, green, etc. The experimental procedure involved the utilization of three smartphones for the determination, which was subsequently followed by a comparative analysis of the results obtained from these devices using spectrophotometric measurements.ResultsThe B channel had the highest level of optimization in terms of analytical parameters. The limits of detection (LOD) were 0.586, 2.478, and 1.396 µg/ml for S1, S2, and S3, respectively, and for the spectrophotometer, it was found to be 0.416 µg/ml. Similarly, LOQs were determined as 1.673, 7.511, 4.232, and 1.302 µg/ml. A %RSD of 1.3 for precision and an LOD of 0.013 g dm−3 were obtained for the spectroscopic method. The % recovery in the accuracy study was found to be 99.69, 101.804, 109.28, and 100.13 for S1, S2, S3, and spectrophotometer, respectively.ConclusionThe colorimetric results from smartphone application are similar to those from spectrophotometric analyses.

SP1.8 - Reducing the burden to deliver remote postoperative surveillance using machine learning for surgical-site infection (SSI)

Abstract Aims Remote postoperative wound (RPW) surveillance has been shown to improve the delivery of care and minimise the harm to patients from surgical-site infection (SSI). The burden for healthcare staff to deliver is a major barrier to implementation, therefore we aimed to develop novel methods for automated assessment of RPW surveillance to reduce this. Methods This was a secondary analysis of two interventional studies on RPW surveillance: “Tracking wound infection with smartphone technology” (TWIST) and “ImplementatioN of Remote Surgical wOund Assessment During the coviD-19 pandEmic” (INROADE). Adult general surgery patients could submit images of their surgical wound(s), and patient-reported outcome measures (PROMs) of SSI for 30-days postoperatively. A multi-input neural network (MNN) was developed to predict a clinical diagnosis of SSI within 48h based on symptoms and wound images. Performance was evaluated using area under the curve (AUC) and externally validated. Results There were 1540 submissions containing PROMs (48h SSI rate = 3.7%, n=57) and 2615 images (48h SSI rate = 3.1%, n=82). The MNN exceeded performance of the component models within the external validation subset (AUC: 0.94, 95% CI: 0.89-0.99) and remained equivalent to clinician triage (AUC: 0.92, 95% CI: 0.90-0.94). Usage to screen out “low-risk” responses prior to clinical triage was estimated to reduce the staff-time required to deliver (6.3h vs 25.5h). Conclusion Automated assessment can be successfully deployed within RPW surveillance pathways to reduce the burden on staff to deliver without compromising care, and allow resources to be appropriately directed to those at greatest risk of SSI.

Evaluation of Smartphone Technology on Spatiotemporal Gait in Older and Diseased Adult Populations.

Objective: Advancements in smartphone technology provide availability to evaluate movement in a more practical and feasible manner, improving clinicians' ability to diagnose and treat adults at risk for mobility loss. The purpose of this study was to evaluate the validity and reliability of a smartphone application to measure spatiotemporal outcomes during level (primary) and uphill/downhill (secondary) walking with and without an assistive device for older adults (OAs), Parkinson's Disease (PD) and cerebrovascular accident (CVA) populations. Methods: A total of 50 adults (OA = 20; PD = 15; CVA = 15) underwent gait analysis at self-selected gait speeds under 0-degree, 5-degree uphill and 5-degree downhill environments. The validity and reliability of the smartphone outcomes were compared to a motion-capture laboratory. Bland-Altman analysis was used to evaluate limits of agreement between the two systems. Intraclass correlation coefficients (ICCs) were used to determine absolute agreement, and Pearson correlation coefficients (r) were used to assess the strength of the association between the two systems. Results: For level walking, Bland-Altman analysis revealed relatively equal estimations of spatiotemporal outcomes between systems for OAs without an assistive device and slight to mild under- and overestimations of outcomes between systems for PD and CVA with and without an assistive device. Moderate to very high correlations between systems (without an assistive device: OA r-range, 0.72-0.99; PD r-range, 0.87-0.97; CVA r-range, 0.56-0.99; with an assistive device: PD r-range, 0.35-0.98; CVA r-range, 0.50-0.99) were also observed. Poor to excellent ICCs for reliability between systems (without an assistive device: OA ICC range, 0.71-0.99; PD ICC range, 0.73-0.97; CVA ICC range, 0.56-0.99; with an assistive device: PD ICC range, 0.22-0.98; CVA ICC range, 0.44-0.99) were observed across all outcomes. Conclusions: This smartphone application can be clinically useful in detecting most spatiotemporal outcomes in various walking environments for older and diseased adults at risk for mobility loss.