Smart Technology for Aging and Reducing Disability (STAReD): Study protocol for a randomized pragmatic clinical trial.

BACKGROUND: There is a critical need to improve quality of life for community-dwelling older adults with disabilities. Prior research has demonstrated that a smart, in-home sensor system can facilitate aging in place for older adults living in independent living apartments with care coordination support by identifying early illness and injury detection. Self-management approaches have shown positive outcomes for many client populations. Pairing the smart, in-home sensor system with a self-management intervention for community-dwelling older adults with disabilities may lead to positive outcomes. METHODS: This study is a prospective, two-arm, randomized, pragmatic clinical trial to compare the effect of a technology-supported self-management intervention on disability and health-related quality of life to that of a health education control, for rural, community-dwelling older adults. Individuals randomized to the self-management study arm will receive a multidisciplinary (nursing, occupational therapist, and social work) self-management approach coupled with the smart-home sensor system. Individuals randomized to the health education study arm will receive standard health education coupled with the smart-home sensor system. The primary outcomes of disability and health-related quality of life will be assessed at baseline and post-intervention. Generalizable guidance to scale the technology-supported self-management intervention will be developed from qualitatively developed exemplar cases. CONCLUSION: This study has the potential to impact the health and well-being of rural, community-dwelling older adults with disabilities. We have overcome barriers including recruitment in a rural population and supply chain issues for the sensor system. Our team remains on track to meet our study aims.

Novel At-Home Mother's Milk Conductivity Sensing Technology as an Identification System of Delay in Milk Secretory Activation Progress and Early Breastfeeding Problems: Feasibility Assessment.

BACKGROUND: Prolonged exclusive breastfeeding is a public health priority and a personal desire by mothers; however, rates are low with milk supply challenges as a predominant cause. Early breastfeeding management at home is key. Milk electrolytes, mainly sodium ions, are accepted as biomarkers of secretory activation processes throughout the first weeks after birth and predictors for prolonged breastfeeding success, although they are not incorporated into routine care practice. OBJECTIVE: The aim of this study was to test the feasibility of a novel handheld smartphone-operated milk conductivity sensing system that was designed to compute a novel parameter, milk maturation percent (MM%), calculated from milk sample conductivity for tracking individual secretory activation progress in a real-world home setting. METHODS: System performance was initially evaluated in data collected from laboratory-based milk analysis, followed by a retrospective analysis of observational real-world data gathered with the system, on the spot in an at-home setting, implemented by lactation support providers or directly by mothers (N=592). Data collected included milk sample sensing data, baby age, and self-reported breastfeeding status and breastfeeding-related conditions. The data were retroactively classified in a day after birth-dependent manner. Results were compared between groups classified according to breastfeeding exclusivity and breastfeeding problems associated with ineffective breastfeeding and low milk supply. RESULTS: Laboratory analysis in a set of breast milk samples demonstrated a strong correlation between the system's results and sodium ion levels. In the real-world data set, a total of 1511 milk sensing records were obtained on the spot with over 592 real-world mothers. Data gathered with the system revealed a typical time-dependent increase in the milk maturation parameter (MM%), characterized by an initial steep increase, followed by a moderate increase, and reaching a plateau during the first weeks postpartum. Additionally, MM% levels captured by the system were found to be sensitive to breastfeeding status classifications of exclusive breastfeeding and breastfeeding problems, manifested by differences in group means in the several-day range after birth, predominantly during the first weeks postpartum. Differences could also be demonstrated for the per-case time after birth-dependent progress in individual mothers. CONCLUSIONS: This feasibility study demonstrates that the use of smart milk conductivity sensing technology can provide a robust, objective measure of individual breastfeeding efficiency, facilitating remote data collection within a home setting. This system holds considerable potential to augment both self-monitoring and remote breastfeeding management capabilities, as well as to refine clinical classifications. To further validate the clinical relevance and potential of this home milk monitoring tool, future controlled clinical studies are necessary, which will provide insights into its impact on user and care provider satisfaction and its potential to meet breastfeeding success goals.

Comparing Digital to Conventional Physical Therapy for Chronic Shoulder Pain: Randomized Controlled Trial.

BACKGROUND: Chronic shoulder pain (CSP) is a common condition with various etiologies, including rotator cuff disorders, adhesive capsulitis, shoulder instability, and shoulder arthritis. It is associated with substantial disability and psychological distress, resulting in poor productivity and quality of life. Physical therapy constitutes the mainstay treatment for CSP, but several barriers exist in accessing care. In recent years, telerehabilitation has gained momentum as a potential solution to overcome such barriers. It has shown numerous benefits, including improving access and convenience, promoting patient adherence, and reducing costs. However, to date, no previous randomized controlled trial has compared fully remote digital physical therapy to in-person rehabilitation for nonoperative CSP. OBJECTIVE: The aim of this study is to compare clinical outcomes between digital physical therapy and conventional in-person physical therapy in patients with CSP. METHODS: We conducted a single-center, parallel-group, randomized controlled trial involving 82 patients with CSP referred for outpatient physical therapy. Participants were randomized into digital or conventional physical therapy (8-week interventions). The digital intervention consisted of home exercise, education, and cognitive behavioral therapy (CBT), using a device with movement digitalization for biofeedback and asynchronous physical therapist monitoring through a cloud-based portal. The conventional group received in-person physical therapy, including exercises, manual therapy, education, and CBT. The primary outcome was the change (baseline to 8 weeks) in function and symptoms using the short-form of Disabilities of the Arm, Shoulder, and Hand questionnaire. Secondary outcome measures included self-reported pain, surgery intent, analgesic intake, mental health, engagement, and satisfaction. All questionnaires were delivered electronically. RESULTS: A total of 90 participants were randomized into digital or conventional physical therapy, with 82 receiving the allocated intervention. Both groups experienced significant improvements in function measured by the short-form of the Disabilities of the Arm, Shoulder, and Hand questionnaire, with no differences between groups (-1.8, 95% CI -13.5 to 9.8; P=.75). For secondary outcomes, no differences were observed in surgery intent, analgesic intake, and mental health or worst pain. Higher reductions were observed in average and least pain in the conventional group, which, given the small effect sizes (least pain 0.15 and average pain 0.16), are unlikely to be clinically meaningful. High adherence and satisfaction were observed in both groups, with no adverse events. CONCLUSIONS: This study shows that fully remote digital programs can be viable care delivery models for CSP given their scalability and effectiveness, assessed through comparison with high-dosage in-person rehabilitation. TRIAL REGISTRATION: ClinicalTrials.gov (NCT04636528); https://clinicaltrials.gov/study/NCT04636528.

Open-source environmental data as an alternative to snail surveys to assess schistosomiasis risk in areas approaching elimination.

BACKGROUND: Although the presence of intermediate snails is a necessary condition for local schistosomiasis transmission to occur, using them as surveillance targets in areas approaching elimination is challenging because the patchy and dynamic quality of snail host habitats makes collecting and testing snails labor-intensive. Meanwhile, geospatial analyses that rely on remotely sensed data are becoming popular tools for identifying environmental conditions that contribute to pathogen emergence and persistence. METHODS: In this study, we assessed whether open-source environmental data can be used to predict the presence of human Schistosoma japonicum infections among households with a similar or improved degree of accuracy compared to prediction models developed using data from comprehensive snail surveys. To do this, we used infection data collected from rural communities in Southwestern China in 2016 to develop and compare the predictive performance of two Random Forest machine learning models: one built using snail survey data, and one using open-source environmental data. RESULTS: The environmental data models outperformed the snail data models in predicting household S. japonicum infection with an estimated accuracy and Cohen's kappa value of 0.89 and 0.49, respectively, in the environmental model, compared to an accuracy and kappa of 0.86 and 0.37 for the snail model. The Normalized Difference in Water Index (an indicator of surface water presence) within half to one kilometer of the home and the distance from the home to the nearest road were among the top performing predictors in our final model. Homes were more likely to have infected residents if they were further from roads, or nearer to waterways. CONCLUSION: Our results suggest that in low-transmission environments, leveraging open-source environmental data can yield more accurate identification of pockets of human infection than using snail surveys. Furthermore, the variable importance measures from our models point to aspects of the local environment that may indicate increased risk of schistosomiasis. For example, households were more likely to have infected residents if they were further from roads or were surrounded by more surface water, highlighting areas to target in future surveillance and control efforts.

Using Digital Technology to Quantify Habitual Physical Activity in Community Dwellers With Cognitive Impairment: Systematic Review.

BACKGROUND: Participating in habitual physical activity (HPA) can support people with dementia and mild cognitive impairment (MCI) to maintain functional independence. Digital technology can continuously measure HPA objectively, capturing nuanced measures relating to its volume, intensity, pattern, and variability. OBJECTIVE: To understand HPA participation in people with cognitive impairment, this systematic review aims to (1) identify digital methods and protocols; (2) identify metrics used to assess HPA; (3) describe differences in HPA between people with dementia, MCI, and controls; and (4) make recommendations for measuring and reporting HPA in people with cognitive impairment. METHODS: Key search terms were input into 6 databases: Scopus, Web of Science, Psych Articles, PsychInfo, MEDLINE, and Embase. Articles were included if they included community dwellers with dementia or MCI, reported HPA metrics derived from digital technology, were published in English, and were peer reviewed. Articles were excluded if they considered populations without dementia or MCI diagnoses, were based in aged care settings, did not concern digitally derived HPA metrics, or were only concerned with physical activity interventions. Key outcomes extracted included the methods and metrics used to assess HPA and differences in HPA outcomes across the cognitive spectrum. Data were synthesized narratively. An adapted version of the National Institute of Health Quality Assessment Tool for Observational Cohort and Cross-sectional Studies was used to assess the quality of articles. Due to significant heterogeneity, a meta-analysis was not feasible. RESULTS: A total of 3394 titles were identified, with 33 articles included following the systematic review. The quality assessment suggested that studies were moderate-to-good quality. Accelerometers worn on the wrist or lower back were the most prevalent methods, while metrics relating to volume (eg, daily steps) were most common for measuring HPA. People with dementia had lower volumes, intensities, and variability with different daytime patterns of HPA than controls. Findings in people with MCI varied, but they demonstrated different patterns of HPA compared to controls. CONCLUSIONS: This review highlights limitations in the current literature, including lack of standardization in methods, protocols, and metrics; limited information on validity and acceptability of methods; lack of longitudinal research; and limited associations between HPA metrics and clinically meaningful outcomes. Limitations of this review include the exclusion of functional physical activity metrics (eg, sitting/standing) and non-English articles. Recommendations from this review include suggestions for measuring and reporting HPA in people with cognitive impairment and for future research including validation of methods, development of a core set of clinically meaningful HPA outcomes, and further investigation of socioecological factors that may influence HPA participation. TRIAL REGISTRATION: PROSPERO CRD42020216744; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=216744 .

Recommendations for the Quality Management of Patient-Generated Health Data in Remote Patient Monitoring: Mixed Methods Study.

BACKGROUND: Patient-generated health data (PGHD) collected from innovative wearables are enabling health care to shift to outside clinical settings through remote patient monitoring (RPM) initiatives. However, PGHD are collected continuously under the patient's responsibility in rapidly changing circumstances during the patient's daily life. This poses risks to the quality of PGHD and, in turn, reduces their trustworthiness and fitness for use in clinical practice. OBJECTIVE: Using a sociotechnical health informatics lens, we developed a data quality management (DQM) guideline for PGHD captured from wearable devices used in RPM with the objective of investigating how DQM principles can be applied to ensure that PGHD can reliably inform clinical decision-making in RPM. METHODS: First, clinicians, health information specialists, and MedTech industry representatives with experience in RPM were interviewed to identify DQM challenges. Second, these stakeholder groups were joined by patient representatives in a workshop to co-design potential solutions to meet the expectations of all the stakeholders. Third, the findings, along with the literature and policy review results, were interpreted to construct a guideline. Finally, we validated the guideline through a Delphi survey of international health informatics and health information management experts. RESULTS: The guideline constructed in this study comprised 19 recommendations across 7 aspects of DQM. It explicitly addressed the needs of patients and clinicians but implied that there must be collaboration among all stakeholders to meet these needs. CONCLUSIONS: The increasing proliferation of PGHD from wearables in RPM requires a systematic approach to DQM so that these data can be reliably used in clinical care. The developed guideline is an important next step toward safe RPM.

Smartphones and Threshold-Based Monitoring Methods Effectively Detect Falls Remotely: A Systematic Review.

In the US, at least one fall occurs in at least 28.7% of community-dwelling seniors 65 and older each year. Falls had medical costs of USD 51 billion in 2015 and are projected to reach USD 100 billion by 2030. This review aims to discuss the extent of smartphone (SP) usage in fall detection and prevention across a range of care settings. A computerized search was conducted on six electronic databases to investigate the use of remote sensing technology, wireless technology, and other related MeSH terms for detecting and preventing falls. After applying inclusion and exclusion criteria, 44 studies were included. Most of the studies targeted detecting falls, two focused on detecting and preventing falls, and one only looked at preventing falls. Accelerometers were employed in all the experiments for the detection and/or prevention of falls. The most frequent course of action following a fall event was an alarm to the guardian. Numerous studies investigated in this research used accelerometer data analysis, machine learning, and data from previous falls to devise a boundary and increase detection accuracy. SP was found to have potential as a fall detection system but is not widely implemented. Technology-based applications are being developed to protect at-risk individuals from falls, with the objective of providing more effective and efficient interventions than traditional means. Successful healthcare technology implementation requires cooperation between engineers, clinicians, and administrators.

Performing a shortened version of the Action Research Arm Test in immersive virtual reality to assess post-stroke upper limb activity.

BACKGROUND: To plan treatment and measure post-stroke recovery, frequent and time-bounded functional assessments are recommended. With increasing needs for neurorehabilitation advances, new technology based methods, such as virtual reality (VR) have emerged. Here, we developed an immersive VR version of the Action Research Arm Test (ARAT-VR) to complement neurorehabilitation. OBJECTIVE: This study aimed to assess the validity, usability and test-retest reliability of the ARAT-VR among individuals with stroke, healthcare professionals and healthy control subjects (HCS). METHODS: Among the 19 items of the ARAT, 13 items were selected and developed in immersive VR. 11 healthcare professionals, 30 individuals with stroke, and 25 HCS were recruited. Content validity was assessed by asking healthcare professionals to rate the difficulty of performing each item of the ARAT-VR in comparison to the classical Action Research Arm Test (ARAT-19). Concurrent validity was first measured using correlation (Spearman tests) between the ARAT-VR and ARAT-19 scores for the individuals with stroke, and second through correlation and comparison between the scores of the ARAT-VR and the reduced version of the ARAT (ARAT-13) for both individuals with stroke and HCS (Wilcoxon signed rank tests and Bland-Altman plots). Usability was measured using the System Usability Scale. A part of individuals with stroke and HCS were re-tested following a convenient delay to measure test-retest reliability (Intra-class correlation and Wilcoxon tests). RESULTS: Regarding the content validity, median difficulty of the 13 ARAT-VR items (0[0 to - 1] to 0[0-1]) evaluated by healthcare professionals was rated as equivalent to the classical ARAT for all tasks except those involving the marbles. For these, the difficulty was rated as superior to the real tasks (1[0-1] when pinching with the thumb-index and thumb-middle fingers, and 1[0-2] when pinching with thumb-ring finger). Regarding the concurrent validity, for paretic hand scores, there were strong correlations between the ARAT-VR and ARAT-13 (r = 0.84), and between the ARAT-VR and ARAT-19 (r = 0.83). Usability (SUS = 82.5[75-90]) and test-retest reliability (ICC = 0.99; p < 0.001) were excellent. CONCLUSION: The ARAT-VR is a valid, usable and reliable tool that can be used to assess upper limb activity among individuals with stroke, providing potential to increase assessment frequency, remote evaluation, and improve neurorehabilitation. Trial registration https://clinicaltrials.gov/ct2/show/NCT04694833 ; Unique identifier: NCT04694833, Date of registration: 11/24/2020.

Understanding how people with Parkinson's disease turn in gait from a real-world in-home dataset.

INTRODUCTION: Turning in gait digital parameters may be useful in measuring disease progression in Parkinson's disease (PD), however challenges remain over algorithm validation in real-world settings. The influence of clinician observation on turning outcomes is poorly understood. Our objective is to describe a unique in-home video dataset and explore the use of turning parameters as biomarkers in PD. METHODS: 11 participants with PD, 11 control participants stayed in a home-like setting living freely for 5 days (with two sessions of clinical assessment), during which high-resolution video was captured. Clinicians watched the videos, identified turns and documented turning parameters. RESULTS: From 85 hours of video 3869 turns were evaluated, averaging at 22.7 turns per hour per person. 6 participants had significantly different numbers of turning steps and/or turn duration between "ON" and "OFF" medication states. Positive Spearman correlations were seen between the Movement Disorders Society-sponsored revision of the Unified Parkinson's Disease Rating Scale III score with a) number of turning steps (rho = 0.893, p < 0.001), and b) duration of turn (rho = 0.744, p = 0.009) "OFF" medications. A positive correlation was seen "ON" medications between number of turning steps and clinical rating scale score (rho = 0.618, p = 0.048). Both cohorts took more steps and shorter durations of turn during observed clinical assessments than when free-living. CONCLUSION: This study shows proof of concept that real-world free-living turn duration and number of turning steps recorded can distinguish between PD medication states and correlate with gold-standard clinical rating scale scores. It illustrates a methodology for ecological validation of real-world digital outcomes.

Outcomes of Vital Sign Monitoring of an Acute Surgical Cohort With Wearable Sensors and Digital Alerting Systems: A Pragmatically Designed Cohort Study and Propensity-Matched Analysis.

Background: The implementation and efficacy of wearable sensors and alerting systems in acute secondary care have been poorly described. Objectives: to pragmatically test one such system and its influence on clinical outcomes in an acute surgical cohort. Methods: In this pragmatically designed, pre-post implementation trial, participants admitted to the acute surgical unit at our institution were recruited. In the pre-implementation phase (September 2017 to May 2019), the SensiumVitals™ monitoring system, which continuously measures temperature, heart, and respiratory rates, was used for monitoring alongside usual care (intermittent monitoring in accordance with the National Early Warning Score 2 [NEWS 2] protocol) without alerts being generated. In the post-implementation phase (May 2019 to March 2020), alerts were generated when pre-established thresholds for vital parameters were breached, requiring acknowledgement from healthcare staff on provided mobile devices. Hospital length of stay, intensive care use, and 28-days mortality were measured. Balanced cohorts were created with 1:1 'optimal' propensity score logistic regression models. Results: The 1:1 matching method matched the post-implementation group (n = 141) with the same number of subjects from the pre-implementation group (n = 141). The median age of the entire cohort was 52 (range: 18-95) years and the median duration of wearing the sensor was 1.3 (interquartile range: 0.7-2.0) days. The median alert acknowledgement time was 111 (range: 1-2,146) minutes. There were no significant differences in critical care admission (planned or unplanned), hospital length of stay, or mortality. Conclusion: This study offered insight into the implementation of digital health technologies within our institution. Further work is required for optimisation of digital workflows, particularly given their more favourable acceptability in the post pandemic era. Clinical trials registration information: ClinicalTrials.gov Identifier: NCT04638738.

Research on Cyanobacterial-Bloom Detection Based on Multispectral Imaging and Deep-Learning Method.

Frequent outbreaks of cyanobacterial blooms have become one of the most challenging water ecosystem issues and a critical concern in environmental protection. To overcome the poor stability of traditional detection algorithms, this paper proposes a method for detecting cyanobacterial blooms based on a deep-learning algorithm. An improved vegetation-index method based on a multispectral image taken by an Unmanned Aerial Vehicle (UAV) was adopted to extract inconspicuous spectral features of cyanobacterial blooms. To enhance the recognition accuracy of cyanobacterial blooms in complex scenes with noise such as reflections and shadows, an improved transformer model based on a feature-enhancement module and pixel-correction fusion was employed. The algorithm proposed in this paper was implemented in several rivers in China, achieving a detection accuracy of cyanobacterial blooms of more than 85%. The estimate of the proportion of the algae bloom contamination area and the severity of pollution were basically accurate. This paper can lay a foundation for ecological and environmental departments for the effective prevention and control of cyanobacterial blooms.

Physical Activity Behavior of Patients at a Skilled Nursing Facility: Longitudinal Cohort Study.

BACKGROUND: On-body wearable sensors have been used to predict adverse outcomes such as hospitalizations or fall, thereby enabling clinicians to develop better intervention guidelines and personalized models of care to prevent harmful outcomes. In our previous work, we introduced a generic remote patient monitoring framework (Sensing At-Risk Population) that draws on the classification of human movements using a 3-axial accelerometer and the extraction of indoor localization using Bluetooth low energy beacons, in concert. Using the same framework, this paper addresses the longitudinal analyses of a group of patients in a skilled nursing facility. We try to investigate if the metrics derived from a remote patient monitoring system comprised of physical activity and indoor localization sensors, as well as their association with therapist assessments, provide additional insight into the recovery process of patients receiving rehabilitation. OBJECTIVE: The aim of this paper is twofold: (1) to observe longitudinal changes of sensor-based physical activity and indoor localization features of patients receiving rehabilitation at a skilled nursing facility and (2) to investigate if the sensor-based longitudinal changes can complement patients' changes captured by therapist assessments over the course of rehabilitation in the skilled nursing facility. METHODS: From June 2016 to November 2017, patients were recruited after admission to a subacute rehabilitation center in Los Angeles, CA. Longitudinal cohort study of patients at a skilled nursing facility was followed over the course of 21 days. At the time of discharge from the skilled nursing facility, the patients were either readmitted to the hospital for continued care or discharged to a community setting. A longitudinal study of the physical therapy, occupational therapy, and sensor-based data assessments was performed. A generalized linear mixed model was used to find associations between functional measures with sensor-based features. Occupational therapy and physical therapy assessments were performed at the time of admission and once a week during the skilled nursing facility admission. RESULTS: Of the 110 individuals in the analytic sample with mean age of 79.4 (SD 5.9) years, 79 (72%) were female and 31 (28%) were male participants. The energy intensity of an individual while in the therapy area was positively associated with transfer activities (ß=.22; SE 0.08; P=.02). Sitting energy intensity showed positive association with transfer activities (ß=.16; SE 0.07; P=.02). Lying down energy intensity was negatively associated with hygiene activities (ß=-.27; SE 0.14; P=.04). The interaction of sitting energy intensity with time (ß=-.13; SE 0.06; P=.04) was associated with toileting activities. CONCLUSIONS: This study demonstrates that a combination of indoor localization and physical activity tracking produces a series of features, a subset of which can provide crucial information to the story line of daily and longitudinal activity patterns of patients receiving rehabilitation at a skilled nursing facility. The findings suggest that detecting physical activity changes within locations may offer some insight into better characterizing patients' progress or decline.

Technologies for Medication Adherence Monitoring and Technology Assessment Criteria: Narrative Review.

BACKGROUND: Accurate measurement and monitoring of patient medication adherence is a global challenge because of the absence of gold standard methods for adherence measurement. Recent attention has been directed toward the adoption of technologies for medication adherence monitoring, as they provide the opportunity for continuous tracking of individual medication adherence behavior. However, current medication adherence monitoring technologies vary according to their technical features and data capture methods, leading to differences in their respective advantages and limitations. Overall, appropriate criteria to guide the assessment of medication adherence monitoring technologies for optimal adoption and use are lacking. OBJECTIVE: This study aims to provide a narrative review of current medication adherence monitoring technologies and propose a set of technology assessment criteria to support technology development and adoption. METHODS: A literature search was conducted on PubMed, Scopus, CINAHL, and ProQuest Technology Collection (2010-present) using the combination of keywords medication adherence, measurement technology, and monitoring technology. The selection focused on studies related to medication adherence monitoring technology and its development and use. The technological features, data capture methods, and potential advantages and limitations of the identified technology applications were extracted. Methods for using data for adherence monitoring were also identified. Common recurring elements were synthesized as potential technology assessment criteria. RESULTS: Of the 3865 articles retrieved, 98 (2.54%) were included in the final review, which reported a variety of technology applications for monitoring medication adherence, including electronic pill bottles or boxes, ingestible sensors, electronic medication management systems, blister pack technology, patient self-report technology, video-based technology, and motion sensor technology. Technical features varied by technology type, with common expectations for using these technologies to accurately monitor medication adherence and increase adoption in patients' daily lives owing to their unobtrusiveness and convenience of use. Most technologies were able to provide real-time monitoring of medication-taking behaviors but relied on proxy measures of medication adherence. Successful implementation of these technologies in clinical settings has rarely been reported. In all, 28 technology assessment criteria were identified and organized into the following five categories: development information, technology features, adherence to data collection and management, feasibility and implementation, and acceptability and usability. CONCLUSIONS: This narrative review summarizes the technical features, data capture methods, and various advantages and limitations of medication adherence monitoring technology reported in the literature and the proposed criteria for assessing medication adherence monitoring technologies. This collection of assessment criteria can be a useful tool to guide the development and selection of relevant technologies, facilitating the optimal adoption and effective use of technology to improve medication adherence outcomes. Future studies are needed to further validate the medication adherence monitoring technology assessment criteria and construct an appropriate technology assessment framework.

Development stage of novel digital health interventions for postoperative monitoring: protocol of a systematic review.

Introduction: The postoperative period represents a time where patients are at a high-risk of morbidity, which warrants effective surveillance. While digital health interventions (DHIs) for postoperative monitoring are promising, a coordinated, standardized and evidence-based approach regarding their implementation and evaluation is currently lacking. This study aimed to identify DHIs implemented and evaluated in postoperative care to highlight research gaps and assess the readiness for routine implementation. Methods: A systematic review will be conducted in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines to identify studies describing the implementation and evaluation of DHIs for postoperative monitoring published since 2000 (PROSPERO ID: CRD42021264289). This will encompass the Embase, Cumulative Index to Nursing and Allied Health Literature, Cochrane Library, Web of Science and ClinicalTrials.gov databases, and manual search of bibliographies for relevant studies and gray literature. Methodological reporting quality will be evaluated using the Idea, Development, Exploration, Assessment and Long-term Follow-up (IDEAL) reporting guideline relevant to the IDEAL stage of the study, and risk of bias will be assessed using the Grading of Recommendations, Assessment, Development and Evaluations (GRADE) framework. Data will be extracted according to the WHO framework for monitoring and evaluating DHIs, and a narrative synthesis will be performed. Discussion: This review will assess the readiness for implementation of DHIs for routine postoperative monitoring and will include studies describing best practice from service changes already being piloted out of necessity during the COVID-19 pandemic. This will identify interventions with sufficient evidence to progress to the next IDEAL stage, and promote standardized and comprehensive evaluation of future implementational studies.

A scoping review of mHealth monitoring of pediatric bronchial asthma before and during COVID-19 pandemic.

Mobile (m) Health technology is well-suited for Remote Patient Monitoring (RPM) in a patient's habitual environment. In recent years there have been fast-paced developments in mHealth-enabled pediatric RPM, especially during the COVID-19 pandemic, necessitating evidence synthesis. To this end, we conducted a scoping review of clinical trials that had utilized mHealth-enabled RPM of pediatric asthma. MEDLINE, Embase and Web of Science were searched from September 1, 2016 through August 31, 2021. Our scoping review identified 25 publications that utilized synchronous and asynchronous mHealth-enabled RPM in pediatric asthma, either involving mobile applications or via individual devices. The last three years has seen the development of evidence-based, multidisciplinary, and participatory mHealth interventions. The quality of the studies has been improving, such that 40% of included study reports were randomized controlled trials. In conclusion, there exists high-quality evidence on mHealth-enabled RPM in pediatric asthma, warranting future systematic reviews and/or meta-analyses of the benefits of such RPM.

Analysis on ecological status and spatial-temporal variation of Tamarix chinensis forest based on spectral characteristics and remote sensing vegetation indices.

The reserve of Tamarix forest, located in Changyi, China, is the only national marine special reserve taking Tamarix as the main object of protection. Compared with conventional monitoring technology, remote sensing technology can more comprehensively reflect the ecological environment status and spatial-temporal variation of monitoring objects. Based on spectral characteristics and remote sensing vegetation indices, the ecological status and spatial-temporal variation of Tamarix chinensis forest in the reserve deserve further exploration. Therefore, spectral characteristic, typical vegetation indices, comprehensive health index, VFC, and REP were analyzed based on Sentinel-2A images. Spatial-temporal variation analysis during 2014 to 2018 was analyzed based on GF-1 images. The research result indicated that ecological quality of protection zone showed an overall growth trend with the help of artificial ecological restoration, and it is possible to continuously implement ecological recovery towards the protection zone.

[Application of remote sensing technology in medicinal plant resources].

The sustainable use of medicinal plants is the foundation of the inheritance of traditional Chinese medicine(TCM) and the acquisition of information on medicinal plants is the basis for the development of TCM. The traditional methods of investigating medicinal plant resources are disadvantageous in strong subjectivity and poor timeliness, making it difficult to real-time monitor medicinal plant resources. In recent years, remote sensing technology has become an important means of obtaining information on medicinal plants. The application of this technology has made up for the shortcomings of traditional methods. The open-access remote sensing data with medium spatial resolution satellites provide an opportunity for extracting information on medicinal plant resources. This study firstly introduced the principles of remote sensing technology, summarized the satellites and the parameters commonly used in the field of medicinal plant resources, and compared the survey methods of remote sensing technology with traditional methods. Secondly, it reviewed the applications of remote sensing technology in the extraction of information on the cultivation of medicinal plants and the common methods for extracting the planting structure information of medicinal plants based on remote sensing technology. Thirdly, the applications of remote sensing technology in the investigation and monitoring of medicinal plants were further analyzed with the research objects divided into wild and cultivated medicinal plants according to the characteristics of the habitats. Finally, it pointed out the key unsolved technical problems in the remote sensing monitoring of medicinal plant resources, and proposed solutions for the intelligent information processing of medicinal plants based on remote sensing big data, which is expected to provide references for the development of remote sensing technology in derivative application in medicinal plant resources.

Feasibility, Efficacy, and Efficiency of eHealth-Supported Pediatric Asthma Care: Six-Month Quasi-Experimental Single-Arm Pretest-Posttest Study.

BACKGROUND: Early detection of loss of asthma control can effectively reduce the burden of the disease. However, broad implementation in clinical practice has not been accomplished so far. We are in need of research investigating the operationalization of eHealth pediatric asthma care in practice, which can provide the most potential benefits in terms of adoption, efficiency, and effectiveness. OBJECTIVE: The aim of this study was to investigate the technical and clinical feasibility, including an exploration of the efficacy and cost-efficiency, of an eHealth program implemented in daily clinical pediatric asthma practice. METHODS: We designed an eHealth-supported pediatric asthma program facilitating early detection of loss of asthma control while increasing symptom awareness and self-management. In the 6-month program, asthma control was monitored by 4 health care professionals (HCPs) by using objective home measurements and the web-based Puffer app to allow timely medical anticipation and prevent treatment delay. Technical feasibility was assessed by technology use, system usability, and technology acceptance. Clinical feasibility was assessed by participation and patient-reported health and care outcomes and via a focus group with HCPs regarding their experiences of implementing eHealth in daily practice. The efficacy and cost-efficiency were explored by comparing pretest-posttest program differences in asthma outcomes (asthma control, lung function, and therapy adherence) and medical consumption. RESULTS: Of 41 children, 35 children with moderate-to-severe asthma volunteered for participation. With regard to technical feasibility, the Puffer app scored a good usability score of 78 on the System Usability Scale and a score of 70 for technology acceptance on a scale of 1 to 100. Approximately 75% (18/24) of the children indicated that eHealth helped them to control their asthma during the program. HCPs indicated that home measurements and real time communication enabled them to make safe and substantiated medical decisions during symptom manifestations. With an average time commitment of 15 minutes by patients, eHealth care led to a 80% gross reduction (from €71,784 to €14,018, US $1=€0.85) in health care utilization, 8.6% increase (from 18.6 to 20.2, P=.40) in asthma control, 25.0% increase (from 2.8 to 3.5, P=.04) in the self-management level, and 20.4% improved (from 71.2 to 76.8, P=.02) therapy adherence. CONCLUSIONS: eHealth asthma care seems to be technically and clinically feasible, enables safe remote care, and seems to be beneficial for pediatric asthma care in terms of health outcomes and health care utilization. Follow-up research should focus on targeted effectiveness studies with the lessons learned, while also enabling individualization of eHealth for personalized health care.

Continuous monitoring in COVID-19 care: a retrospective study in time of crisis.

BACKGROUND: A new monitoring system was implemented to support nursing staff and physicians on the COVID-19 ward. This system was designed to remotely monitor vital signs, to calculate an automated Early Warning Score, and to help identify patients at risk of deterioration. METHODS: Hospitalized patients who tested positive for SARS-CoV-2 were connected to 2 wireless sensors measuring vital signs. Patients were divided into 2 groups based on the occurrence of adverse events during hospitalization. Heart and respiratory rate were monitored continuously and an automated EWS was calculated every 5 minutes. Data were compared between groups. RESULTS: Prior to the occurrence of adverse events, significantly higher median heart and respiration rate and significantly lower median SPO2 values were observed. Mean and median automated EWS were significantly higher in patients with an adverse event. CONCLUSION: Continuous monitoring systems might help to detect clinical deterioration in COVID-19 patients at an earlier stage.