Browsing by Subject "mobile health"

Now showing 1 - 8 of 8
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    Accuracy of Daily Fluid Intake Measurements Using a "Smart" Water Bottle
    (Springer, 2017) Borofsky, Michael S.; Dauw, Casey A.; York, Nadya; Terry, Colin; Lingeman, James E.; Urology, School of Medicine
    High fluid intake is an effective preventative strategy against recurrent kidney stones but is known to be challenging to achieve. Recently, a smart water bottle (Hidrate Spark™, Minneapolis, MN) was developed as a non-invasive fluid intake monitoring system. This device could help patients who form stones from low urine volume achieve sustainable improvements in hydration, but has yet to be validated in a clinical setting. Hidrate Spark™ uses capacitive touch sensing via an internal sensor. It calculates volume measurements by detecting changes in water level and sends data wirelessly to users’ smartphones through an application. A pilot study was conducted to assess accuracy of measured fluid intake over 24 h periods when used in a real life setting. Subjects were provided smart bottles and given short tutorials on their use. Accuracy was determined by comparing 24-h fluid intake measurements calculated through the smart bottle via sensor to standard volume measurements calculated by the patient from hand over the same 24 h period. Eight subjects performed sixty-two 24-h measurements (range 4–14). Mean hand measurement was 57.2 oz/1692 mL (21–96 oz/621–2839 mL). Corresponding mean smart bottle measurement underestimated true fluid intake by 0.5 ozs. (95% CI −1.9, 0.9). Percent difference between hand and smart bottle measurements was 0.0% (95% CI − 3%, 3%). Intraclass correlation coefficient (ICC), calculated to assess consistency between hand measures and bottle measures, was 0.97 (0.95, 0.98) indicating an extremely high consistency between measures. 24-h fluid intake measurements from a novel fluid monitoring system (Hidrate Spark™) are accurate to within 3%. Such technology may be useful as a behavioral aide and/or research tool particularly among recurrent stone formers with low urinary volume.
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    Addressing People and Place Microenvironments in Weight Loss Disparities (APP-Me): Design of a randomized controlled trial testing timely messages for weight loss behavior in low income black and white women
    (Elsevier, 2018) Clark, Daniel O.; Srinivas, Preethi; Bodke, Kunal; Keith, NiCole; Hood, Sula; Tu, Wanzhu; Medicine, School of Medicine
    Background Behavioral interventions for weight loss have been less effective in lower income and black women. These poorer outcomes may in part be related to these women having more frequent exposures to social and physical situations that are obesogenic, i.e., eating and sedentary cues or situations. Objectives Working with obese, lower income black and white women, Addressing People and Place Microenvironments (APP-Me) was designed to create awareness of self-behavior at times and places of frequent eating and sedentary behavior. Design APP-Me is being evaluated in a randomized controlled trial with 240 participants recruited from federally qualified health centers located in a single Midwestern city. All participants complete four weeks of ecological momentary assessments (EMA) of situations and behavior. At the end of the four weeks, participants are randomized to enhanced usual care (UC) or UC plus APPMe. Methods APP-Me is an automated short messaging system (SMS). Messages are text, image, audio, or a combination, and are delivered to participants’ mobile devices with the intent of creating awareness at the times and places of frequent eating or sedentary behavior. The EMA data inform the timing of message deliveries. Summary This project aims to create and test timely awareness messages in a subpopulation that has not responded well to traditional behavioral interventions for weight loss. Novel aspects of the study include the involvement of a low income population, the use of data on time and place of obesogenic behavior, and message delivery time tailored to an individual’s behavioral patterns.
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    Artificial Intelligence for Global Health: Learning From a Decade of Digital Transformation in Health Care
    (arXiv, 2020) Mathur, Varoon; Purkayastha, Saptarshi; Gichoya, Judy Wawira; BioHealth Informatics, School of Informatics and Computing
    The health needs of those living in resource-limited settings are a vastly overlooked and understudied area in the intersection of machine learning (ML) and health care. While the use of ML in health care is more recently popularized over the last few years from the advancement of deep learning, low-and-middle income countries (LMICs) have already been undergoing a digital transformation of their own in health care over the last decade, leapfrogging milestones due to the adoption of mobile health (mHealth). With the introduction of new technologies, it is common to start afresh with a top-down approach, and implement these technologies in isolation, leading to lack of use and a waste of resources. In this paper, we outline the necessary considerations both from the perspective of current gaps in research, as well as from the lived experiences of health care professionals in resource-limited settings. We also outline briefly several key components of successful implementation and deployment of technologies within health systems in LMICs, including technical and cultural considerations in the development process relevant to the building of machine learning solutions. We then draw on these experiences to address where key opportunities for impact exist in resource-limited settings, and where AI/ML can provide the most benefit.
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    Categorizing Health Outcomes and Efficacy of mHealth Apps for Persons With Cognitive Impairment: A Systematic Review
    (JMIR, 2017-08-20) Bateman, Daniel R; Srinivas, Bhavana; Emmett, Thomas W; Schleyer, Titus K; Holden, Richard J; Hendrie, Hugh C; Callahan, Christopher M; Psychiatry, School of Medicine
    Background Use of mobile health (mHealth) apps is growing at an exponential rate in the United States and around the world. Mild cognitive impairment (MCI), Alzheimer disease, and related dementias are a global health problem. Numerous mHealth interventions exist for this population, yet the effect of these interventions on health has not been systematically described. Objective The aim of this study is to catalog the types of health outcomes used to measure effectiveness of mHealth interventions and assess which mHealth interventions have been shown to improve the health of persons with MCI, Alzheimer disease, and dementia. Methods We searched 13 databases, including Ovid MEDLINE, PubMed, EMBASE, the full Cochrane Library, CINAHL, PsycINFO, Ei Compendex, IEEE Xplore, Applied Science & Technology Source, Scopus, Web of Science, ClinicalTrials.gov, and Google Scholar from inception through May 2017 for mHealth studies involving persons with cognitive impairment that were evaluated using at least one quantitative health outcome. Proceedings of the Annual ACM Conferences on Human Factors in Computing Systems, the ACM User Interface Software and Technology Symposium, and the IEEE International Symposium on Wearable Computers were searched in the ACM Digital Library from 2012 to 2016. A hand search of JMIR Publications journals was also completed in July 2017. Results After removal of duplicates, our initial search returned 3955 records. Of these articles, 24 met final inclusion criteria as studies involving mHealth interventions that measured at least one quantitative health outcome for persons with MCI, Alzheimer disease, and dementia. Common quantitative health outcomes included cognition, function, mood, and quality of life. We found that 21.2% (101/476) of the fully reviewed articles were excluded because of a lack of health outcomes. The health outcomes selected were observed to be inconsistent between studies. For those studies with quantitative health outcomes, more than half (58%) reported postintervention improvements in outcomes. Conclusions Results showed that many mHealth app interventions targeting those with cognitive impairment lack quantitative health outcomes as a part of their evaluation process and that there is a lack of consensus as to which outcomes to use. The majority of mHealth app interventions that incorporated health outcomes into their evaluation noted improvements in the health of persons with MCI, Alzheimer disease, and dementia. However, these studies were of low quality, leading to a grade C level of evidence. Clarification of the benefits of mHealth interventions for people with cognitive impairment requires more randomized controlled trials, larger numbers of participants, and trial designs that minimize bias. Trial Registration PROSPERO Registration: PROSPERO 2016:CRD42016033846; http://www.crd.york.ac.uk/PROSPERO/ display_record.asp?ID=CRD42016033846 (Archived by WebCite at http://www.webcitation.org/6sjjwnv1M)
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    Design Implementation and Evaluation of a Mobile Continuous Blood Oxygen Saturation Monitoring System
    (MDPI, 2020-11) Zhang, Qingxue; Arney, David; Goldman, Julian M.; Isselbacher, Eric M.; Armoundas, Antonis A.; Electrical and Computer Engineering, School of Engineering and Technology
    Objective: In this study, we built a mobile continuous Blood Oxygen Saturation (SpO2) monitor, and for the first time, explored key design principles towards daily applications. Methods: We firstly built a customized wearable computer that can sense two-channel photoplethysmogram (PPG) signals, and transmit the signals wirelessly to smartphone. Afterwards, we explored many SpO2 model building principles, focusing on linear/nonlinear models, different PPG parameter calculation methods, and different finger types. Moreover, we further compared PPG sensor placement principles by comparing different hand configurations and different finger configurations. Finally, a dataset collected from eleven human subjects was used to evaluate the mobile health monitor and explore all of the above design principles. Results: The experimental results show that the root mean square error of the SpO2 estimation is only 1.8, indicating the effectiveness of the system. Conclusion: These results indicate the effectiveness of the customized mobile SpO2 monitor and the selected design principles. Significance: This research is expected to facilitate the continuous SpO2 monitoring of patients with clinical indications.
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    Mobile Health Assessing the Barriers
    (2015-05) Terry, Nicolas P.
    Mobile health (mHealth) combines the decentralization of health care with patient centeredness. Mature mHealth applications (apps) and services could provide actionable information, coaching, or alerts at a fraction of the cost of conventional health care. Different categories of apps attract diverse safety and privacy regulation. It is too early to tell whether these apps can overcome questions about their use cases, business models, and regulation.
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    Untold Stories in User-Centered Design of Mobile Health: Practical Challenges and Strategies Learned From the Design and Evaluation of an App for Older Adults With Heart Failure
    (JMIR Publications, 2020-07-21) Cornet, Victor Philip; Toscos, Tammy; Bolchini, Davide; Ghahari, Romisa Rohani; Ahmed, Ryan; Daley, Carly; Mirro, Michael J.; Holden, Richard J.; Medicine, School of Medicine
    Background User-centered design (UCD) is a powerful framework for creating useful, easy-to-use, and satisfying mobile health (mHealth) apps. However, the literature seldom reports the practical challenges of implementing UCD, particularly in the field of mHealth. Objective This study aims to characterize the practical challenges encountered and propose strategies when implementing UCD for mHealth. Methods Our multidisciplinary team implemented a UCD process to design and evaluate a mobile app for older adults with heart failure. During and after this process, we documented the challenges the team encountered and the strategies they used or considered using to address those challenges. Results We identified 12 challenges, 3 about UCD as a whole and 9 across the UCD stages of formative research, design, and evaluation. Challenges included the timing of stakeholder involvement, overcoming designers’ assumptions, adapting methods to end users, and managing heterogeneity among stakeholders. To address these challenges, practical recommendations are provided to UCD researchers and practitioners. Conclusions UCD is a gold standard approach that is increasingly adopted for mHealth projects. Although UCD methods are well-described and easily accessible, practical challenges and strategies for implementing them are underreported. To improve the implementation of UCD for mHealth, we must tell and learn from these traditionally untold stories.
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    When Your Wearables Become Your Fitness Mate
    (Elsevier, 2020-05) Guo, Xiaonan; Liu, Jian; Chen, Yingying; Computer Information and Graphics Technology, School of Engineering and Technology
    Acknowledging the powerful sensors on wearables and smartphones enabling various applications to improve users' life styles and qualities (e.g., sleep monitoring and running rhythm tracking), this paper takes one step forward developing FitCoach, a virtual fitness coach leveraging users' wearable mobile devices (including wrist-worn wearables and arm-mounted smartphones) to assess dynamic postures (movement patterns & positions) in workouts. FitCoach aims to help the user to achieve effective workout and prevent injury by dynamically depicting the short-term and long-term picture of a user's workout based on various sensors in wearable mobile devices. In particular, FitCoach recognizes different types of exercises and interprets fine-grained fitness data (i.e., motion strength and speed) to an easy-to-understand exercise review score, which provides a comprehensive workout performance evaluation and recommendation. Our system further enables contactless device control during workouts (e.g., gesture to pick up an incoming call) through distinguishing customized gestures from regular exercise movement. In addition, FitCoach has the ability to align the sensor readings from wearable devices to the human coordinate system, ensuring the accuracy and robustness of the system. Extensive experiments with over 5000 repetitions of 12 types of exercises involve 12 participants doing both anaerobic and aerobic exercises in indoors as well as outdoors. Our results demonstrate that FitCoach can provide meaningful review and recommendations to users by accurately measure their workout performance and achieve and accuracy for workout analysis and customized control gesture recognition, respectively.