Enhancing System Acceptance through User-Centred Design: Integrating Patient Generated Wellness Data.

Gestational diabetes mellitus (GDM) is a condition that appears during pregnancy and is expected to be a temporary one. While patients are encouraged to manage it themselves, research findings indicate that GDM may negatively affect the foetus; in addition, there is an increased risk of women with GDM subsequently developing Type 2 diabetes. To alleviate the risks, women with GDM are advised to maintain a record of their diet and blood glucose levels and to attend regular clinical reviews. Rather than using a paper diary, women with GDM can maintain a record of their blood glucose level readings and other relevant data using a wellness mobile application (app). However, such apps are developed for general use and may not meet the specific needs of clinical staff (physicians, dietitians, obstetricians and midwives) involved in managing GDM; for example, an app may record glucose readings but not the details of a meal taken before or after the glucose reading. Second, the apps do not permanently store the data generated by the patient and do not support the transfer of these data to a clinical system or information portal. The mobile health (mHealth) system designed and developed in this research allows one to integrate different types of user generated wellness data into a centralised database. A user-centered design (UCD) approach informed by the technology acceptance model (TAM) was adopted. This paper investigates and evaluates the effectiveness of the approach with regard to facilitating system acceptance and future adoption through an early focus on enhancing system usefulness and ease of use. The functional system requirements of the proposed system were refined through a series of interviews with the perspective of clinical users; ease-of-use and usability issues were resolved through 'think aloud' sessions with clinicians and GDM patients.

Disaster eHealth: Scoping Review.

BACKGROUND: Although both disaster management and disaster medicine have been used for decades, their efficiency and effectiveness have been far from perfect. One reason could be the lack of systematic utilization of modern technologies, such as eHealth, in their operations. To address this issue, researchers' efforts have led to the emergence of the disaster eHealth (DEH) field. DEH's main objective is to systematically integrate eHealth technologies for health care purposes within the disaster management cycle (DMC). OBJECTIVE: This study aims to identify, map, and define the scope of DEH as a new area of research at the intersection of disaster management, emergency medicine, and eHealth. METHODS: An extensive scoping review using published materials was carried out in the areas of disaster management, disaster medicine, and eHealth to identify the scope of DEH. This review procedure was iterative and conducted in multiple scientific databases in 2 rounds, one using controlled indexed terms and the other using similar uncontrolled terms. In both rounds, the publications ranged from 1990 to 2016, and all the appropriate research studies discovered were considered, regardless of their research design, methodology, and quality. Information extracted from both rounds was thematically analyzed to define the DEH scope, and the results were evaluated by the field experts through a Delphi method. RESULTS: In both rounds of the research, searching for eHealth applications within DMC yielded 404 relevant studies that showed eHealth applications in different disaster types and disaster phases. These applications varied with respect to the eHealth technology types, functions, services, and stakeholders. The results led to the identification of the scope of DEH, including eHealth technologies and their applications, services, and future developments that are applicable to disasters as well as to related stakeholders. Reference to the elements of the DEH scope indicates what, when, and how current eHealth technologies can be used in the DMC. CONCLUSIONS: Comprehensive data gathering from multiple databases offered a grounded method to define the DEH scope. This scope comprises concepts related to DEH and the boundaries that define it. The scope identifies the eHealth technologies relevant to DEH and the functions and services that can be provided by these technologies. In addition, the scope tells us which groups can use the provided services and functions and in which disaster types or phases. DEH approaches could potentially improve the response to health care demands before, during, and after disasters. DEH takes advantage of eHealth technologies to facilitate DMC tasks and activities, enhance their efficiency and effectiveness, and enhance health care delivery and provide more quality health care services to the wider population regardless of their geographical location or even disaster types and phases.

Maturation-Related Differences in Adaptations to Resistance Training in Young Male Swimmers.

Moran, J, Sandercock, GRH, Ramírez-Campillo, R, Wooller, J-J, Logothetis, S, Schoenmakers, PPJM, and Parry, DA. Maturation-related differences in adaptations to resistance training in young male swimmers. J Strength Cond Res 32(1): 139-149, 2018-This study examined the effects of resistance training on muscular strength and jump performances in young male swimmers. It was hypothesized that adaptations would be of a lower magnitude in less mature (prepeak height velocity [PHV]) than in more mature (post-PHV) subjects. Fourteen pre-PHV (-1.8 ± 1.0 years) and 8 post-PHV (1.6 ± 0.5 years) swimmers undertook a 30 minutes, twice-weekly resistance training program for 8 weeks. They were compared with matched control groups (pre-PHV: -2.0 ± 1.1, n = 15; post-PHV: 1.2 ± 1.0, n = 7). The effects on lower-body isometric strength (LBS), measured with midthigh pull, and vertical jump (VJ) height in the post-PHV group were large (effect size: 1.3 [0.4 to 2.2]) and small (0.4 [-0.4 to 1.2]), respectively. Effects on LBS and VJ height in the pre-PHV group were moderate (0.8 [0.1 to 1.4]) and trivial (0.2 [-0.5 to 0.8]), respectively. Estimates in the post-PHV control group (LBS: 0.7 [-0.2 to 1.6]; VJ: 0.2 [-0.7 to 1.0]) and the pre-PHV control group (LBS: 0.1 [-0.5 to 0.7]; VJ: -0.3 [-0.9 to 0.3]) may indicate the extent to which maturation could contribute to the performance changes seen in the respective training groups. Lower-body isometric strength and VJ are trainable, but to different magnitudes, in pre- and post-PHV swimmers. After appropriate foundational training to establish technical competency, twice-weekly resistance training sessions of 30 minutes duration, comprising 3 sets of 4 exercises can be effective in pre- and post-PHV youth.

Maturation-Related Effect of Low-Dose Plyometric Training on Performance in Youth Hockey Players.

PURPOSE: The purpose of this intervention study was to investigate if a low-dose of plyometric training (PT) could improve sprint and jump performance in groups of different maturity status. METHOD: Male youth field hockey players were divided into Pre-PHV (from -1 to -1.9 from PHV; Experimental: n = 9; Control = 12) and Mid-PHV (0 to +0.9 from PHV; Experimental: n = 8; Control = 9) groups. Participants in the experimental groups completed 60 foot contacts, twice-weekly for 6 weeks. RESULTS: PT exerted a positive effect (effect size: 0.4 [-0.4-1.2]) on 10 m sprint time in the experimental Mid-PHV group but this was less pronounced in the Pre-PHV group (0.1 [-0.6-0.9]). Sprint time over 30 m (Mid-PHV: 0.1 [-0.8-0.9]; Pre-PHV: 0.1 [-0.7-0.9]) and CMJ (Mid-PHV: 0.1 [-0.8-0.9]; Pre-PHV: 0.0 [-0.7-0.8]) was maintained across both experimental groups. Conversely, the control groups showed decreased performance in most tests at follow up. Between-group analysis showed positive effect sizes across all performance tests in the Mid-PHV group, contrasting with all negative effect sizes in the Pre-PHV group. CONCLUSION: These results indicate that more mature hockey players may benefit to a greater extent than less mature hockey players from a low-dose PT stimulus. Sixty foot contacts, twice per week, seems effective in improving short sprint performance in Mid-PHV hockey players.

A meta-analysis of maturation-related variation in adolescent boy athletes' adaptations to short-term resistance training.

This meta-analysis investigated the maturation-related pattern of adaptations to resistance training in boy athletes. We included studies examining the effects of 4-16-week resistance training programmes in healthy boy athletes aged 10-18 years. Pooled estimates of effect size for change in strength across all studies (n = 19) were calculated using the inverse-variance random effects model for meta-analyses. Estimates were also calculated for groups based on likely biological maturity status ("before", "during" and "after" peak height velocity). Using the standardised mean difference, resistance training increased strength across all groups (effect size = 0.98, [CI: 0.70-1.27]). Strength gains were larger during (1.11 [0.67-1.54]) and after (1.01 [0.56-1.46]) peak height velocity than before (0.5 [-0.06-1.07]). Adaptations to resistance training are greater in adolescent boys during or after peak height velocity. These findings should help coaches to optimise the timing of training programmes that are designed to improve strength in boy athletes.

Age-Related Variation in Male Youth Athletes' Countermovement Jump After Plyometric Training: A Meta-Analysis of Controlled Trials.

Moran, J, Sandercock, GRH, Ramírez-Campillo, R, Meylan, CMP, Collison, J, and Parry, DA. Age-related variation in male youth athletes' countermovement jump after plyometric training: A meta-analysis of controlled trials. J Strength Cond Res 31(2): 552-565, 2017-Recent debate on the trainability of youths has focused on the existence of periods of accelerated adaptation to training. Accordingly, the purpose of this meta-analysis was to identify the age- and maturation-related pattern of adaptive responses to plyometric training in youth athletes. Thirty effect sizes were calculated from the data of 21 sources with studies qualifying based on the following criteria: (a) healthy male athletes who were engaged in organized sport; (b) groups of participants with a mean age between 10 and 18 years; and (c) plyometric-training intervention duration between 4 and 16 weeks. Standardized mean differences showed plyometric training to be moderately effective in increasing countermovement jump (CMJ) height (Effect size = 0.73 95% confidence interval: 0.47-0.99) across PRE-, MID-, and POST-peak height velocity groups. Adaptive responses were of greater magnitude between the mean ages of 10 and 12.99 years (PRE) (ES = 0.91 95% confidence interval: 0.47-1.36) and 16 and 18 years (POST) (ES = 1.02 [0.52-1.53]). The magnitude of adaptation to plyometric training between the mean ages of 13 and 15.99 years (MID) was lower (ES = 0.47 [0.16-0.77]), despite greater training exposure. Power performance as measured by CMJ may be mediated by biological maturation. Coaches could manipulate training volume and modality during periods of lowered response to maximize performance.

Pandemic Management: Health Data and Public Health Surveillance.

The inefficiency of the healthcare system in addressing pandemics is highlighted after COVID-19 which is mostly rooted in data availability and accuracy. As it is believed we might witness more pandemics in future, our research's main objective is to propose an integrated health system to support healthcare preparedness for future infectious outbreaks and pandemics. The system could support managers and authorities in healthcare and disaster management, and policymakers through data collection, sharing, and analysis.

Mental Health Monitoring: Design Concept of a Smartphone Application.

Mental health (MH) has become a global issue. Digital phenotyping in mental healthcare provides a highly effective, scaled, cost-effective approach to handling global MH problems. We propose an MH monitoring application. The application monitors overall MH based on mood, stress, behavior, and personality. Further, it proposes objective MH assessment from smartphone data and subjective screening of MH via periodic, short, self-report standardized questionnaires.

Machine learning with a snapshot of data: Spiking neural network 'predicts' reinforcement histories of pigeons' choice behavior.

An accumulated body of choice research has demonstrated that choice behavior can be understood within the context of its history of reinforcement by measuring response patterns. Traditionally, work on predicting choice behaviors has been based on the relationship between the history of reinforcement-the reinforcer arrangement used in training conditions-and choice behavior. We suggest an alternative method that treats the reinforcement history as unknown and focuses only on operant choices to accurately predict (more precisely, retrodict) reinforcement histories. We trained machine learning models known as artificial spiking neural networks (SNNs) on previously published pigeon datasets to detect patterns in choices with specific reinforcement histories-seven arranged concurrent variable-interval schedules in effect for nine reinforcers. Notably, SNN extracted information from a small 'window' of observational data to predict reinforcer arrangements. The models' generalization ability was then tested with new choices of the same pigeons to predict the type of schedule used in training. We examined whether the amount of the data provided affected the prediction accuracy and our results demonstrated that choices made by the pigeons immediately after the delivery of reinforcers provided sufficient information for the model to determine the reinforcement history. These results support the idea that SNNs can process small sets of behavioral data for pattern detection, when the reinforcement history is unknown. This novel approach can influence our decisions to determine appropriate interventions; it can be a valuable addition to our toolbox, for both therapy design and research.

Regression Equations for Weight Estimation in Paediatric Resuscitation.

BACKGROUND: Weight estimation is critical in paediatric resuscitation, as stopping to weigh a child could influence their survival. Weight estimation methods used in New Zealand (NZ) are not accurate for the population, increasing the complexity of prescribing medication and selecting equipment. AIM: Develop regression equations (RE) to predict the weight of NZ children based on height, sex, age and ethnicity to be deployed in a mobile application (Weight Estimation Without Waiting). METHODS: The RE was derived from retrospective regression modelling of a large existing dataset. Data were presented using descriptive statistics and calculation of means, limits of agreement and the proportion of weight estimates within a percentage of actual weight. CONCLUSION: The RE developed in this study outperformed existing age-based weight estimation methods while providing a method to ensure that weight estimation techniques evolve with NZ children.

TADAA: towards automated detection of anaesthetic activity.

Task analysis is a valuable research method for better understanding the activity of anaesthetists in the operating room (OR), providing evidence for designing and evaluating improvements to systems and processes. It may also assist in identifying potential error paths to adverse events, ultimately improving patient safety. Human observers are the current 'gold standard' for capturing task data, but they are expensive and have cognitive limitations. Our current research - Towards Automated Detection of Anaesthetic Activity (TADAA) - aims to produce an automated task analysis system, employing Radio Frequency Identification (RFID) technology to capture anaesthetists' location, orientation and stance (LOS), and machine learning techniques to translate that data into low-level and high-level activity labels. In this paper we present details of the system design and promising results from LOS sensing testing in laboratory and high fidelity OR simulator settings.

IoT, Cloud Computing and Big Data: Integrated Framework for Healthcare in Disasters.

Currently, healthcare in disaster management context faces a number of challenges mostly due to the lack of availability of reliable data from diverse sources required to be accessible by appropriate authorities. Therefore, the main objective of this study is the introduction of a framework based on the integration of three technologies, Internet of Things (IoT), cloud computing and big data to solve this issue in all disaster phases and provide precise and effective healthcare. This framework supports healthcare managers by enabling data sharing among them and assists them in performing analytical calculations to discover meaningful, logical and accurate trend(s) required for strategic planning and better preparedness in the face of disasters. Also, the outcome of the framework may help decision makers to identify and predict the health consequences of the disasters for any specific geographical location in any country based on its geographical properties and disaster background.

mHealth and big-data integration: promises for healthcare system in India.

BACKGROUND: The use of mobile devices in health (mobile health/mHealth) coupled with related technologies promises to transform global health delivery by creating new delivery models that can be integrated with existing health services. These delivery models could facilitate healthcare delivery into rural areas where there is limited access to high-quality access care. Mobile technologies, Internet of Things and 5G connectivity may hold the key to supporting increased velocity, variety and volume of healthcare data. OBJECTIVE: The purpose of this study is to identify and analyse challenges related to the current status of India's healthcare system-with a specific focus on mHealth and big-data analytics technologies. To address these challenges, a framework is proposed for integrating the generated mHealth big-data and applying the results in India's healthcare. METHOD: A critical review was conducted using electronic sources between December 2018 and February 2019, limited to English language articles and reports published from 2010 onwards. MAIN OUTCOME: This paper describes trending relationships in mHealth with big-data as well as the accessibility of national opportunities when specific barriers and constraints are overcome. The paper concentrates on the healthcare delivery problems faced by rural and low-income communities in India to illustrate more general aspects and identify key issues. A model is proposed that utilises generated data from mHealth devices for big-data analysis that could result in providing insights into the India population health status. The insights could be important for public health planning by the government towards reaching the Universal Health Coverage. CONCLUSION: Biomedical, behavioural and lifestyle data from individuals may enable customised and improved healthcare services to be delivered. The analysis of data from mHealth devices can reveal new knowledge to effectively and efficiently support national healthcare demands in less developed nations, without fully accessible healthcare systems.

Underwater near-infrared spectroscopy can measure training adaptations in adolescent swimmers.

The development of an underwater near-infrared spectroscopy (uNIRS) device has enabled previously unattainable measurements of peripheral muscle hemodynamics and oxygenation to be taken within the natural aquatic environment. The purposes of this study were (i) to trial the use of uNIRS, in a real world training study, and (ii) to monitor the effects of a swim training program upon muscle oxygenation status in short distance swimming. A total of 14 junior club level swimmers completed a repeated swim sprint test before and after an eight week endurance training program. A waterproof, portable Near-Infrared Spectroscopy device was attached to the vastus lateralis. uNIRS successfully measured changes in muscle oxygenation and blood volume in all individuals; rapid sub-second time resolution of the device was able to demonstrate muscle oxygenation changes during the characteristic swim movements. Post training heart rate recovery and swim performance time were significantly improved. uNIRS data also showed significant changes. A larger rise in deoxyhemoglobin during individual sprints suggested training induced an increase in muscle oxygen extraction; a faster recovery time for muscle oxygenation suggested positive training induced changes and significant changes in muscle blood flow also occur. As a strong correlation was seen between an increased reoxygenation rate and an improved swim performance time, these findings support the use of uNIRS as a new performance analysis tool in swimming.

Maturation-related adaptations in running speed in response to sprint training in youth soccer players.

OBJECTIVES: This study investigated the effects of a previously recommended dose of sprint training (ST) in young male soccer players of differing maturity status. DESIGN: Quasi-experimental design. METHODS: Male soccer players from two professional academies were divided into Pre-PHV (Training: n=12; Control: n=13) and Mid-PHV (Training: n=7; Control=10) groups. The training groups completed 16 sprints of 20m with 90s recovery, once per week for 8weeks. RESULTS: Between-group effect sizes (ES) were substantially larger in Pre-PHV (10m [1.54, CI: 0.74-2.23]; 20m [1.49, CI: 0.75-2.23]; 5-10-5 [0.92, CI: 0.23-1.61]) than in Mid-PHV (10m [-0.00, CI: -0.81 to 0.81]; 20m [-0.12, CI: -0.93 to 0.69]; 5-10-5 [-0.41, CI: -1.22 to 0.41]). Within-group effects demonstrated a similar, though less accentuated, trend which revealed ST to be effective in both Pre-PHV (10m [0.44, CI: -0.24 to 1.12]; 20m [0.45, CI: -0.23 to 1.13]; 5-10-5 [0.69, CI: 0.00-1.38]) and Mid-PHV (10m [0.51, CI: -0.38 to 1.40]; 20m [0.33, CI: -0.56 to 1.21]; 5-10-5 [0.43, CI: -0.46 to 1.32]). CONCLUSIONS: ST, in the amount of 16 sprints over 20m with a 90s rest, may be more effective in Pre-PHV youths than in Mid-PHV youths.

KQA: A Knowledge Quality Assessment Model for Clinical Decision Support Systems.

Informatics researchers have developed many methods for using computers to utilize knowledge in decision making in the form of clinical decision support systems (CDSSs). These systems can enhance human decision making in the healthcare domain. The knowledge acquisition bottleneck is one of the well-known issues in developing knowledge-based systems such as CDSS. It can be considered as a flow of knowledge from different knowledge sources to the main system. Most existing methods for extracting knowledge from knowledge resources suffer from the lack of a proper mechanism for extracting high-quality knowledge. In this paper, we propose a framework to discover high-quality knowledge by utilizing Semantic Web technologies.

Acceptance of Using an Ecosystem of Mobile Apps for Use in Diabetes Clinic for Self-Management of Gestational Diabetes Mellitus.

Mobile applications (apps) for self-management of diseases such as diabetes and for general well-being, including keeping track of food, diet, and exercise, are widely available. However, consumers face a flood of new mobile apps in the app stores and have no guidance from clinicians about choosing the appropriate app. As much as clinicians would like to support a patient-centered approach and promote health and wellness mobile apps, they may be unable to provide advice due to the lack of comprehensive and reliable app reviews. This research reviewed a selection of health and wellness mobile apps suitable for the self-management of gestational diabetes mellitus (GDM). A prototype of an ecosystem that integrated the data generated by the apps was built and its usefulness and ease of use were evaluated. The results show that the ecosystem can provide support for GDM self-management by sharing health and wellness data across the diabetes clinic.

Optic flow influences perceived exertion and distance estimation but not running pace.

PURPOSE: This study aimed to examine the effect of variations in optic flow on perceived and actual running pace and RPE. METHODS: Twelve male moderately trained competitive runners performed a self-paced 5-km running reference time trial followed by a three 5-km running self-paced trials under different optic flow conditions (RNORM = actual running speed, RSLOW = actual speed - 25%, and RFAST = actual speed + 25%). Participants were asked to self-report RPE when they thought they had reached each kilometer. RESULTS: There was no difference in the 5-km completion times (P > 0.05) or actual pace (P > 0.05) between the optic flow conditions. A greater distance was covered between the perceived kilometer points during RSLOW compared with RNORM (P < 0.01) and RFAST (P < 0.01). RPE (normalized for running distance) was lower during RSLOW compared with RNORM (P < 0.01) and RFAST (P < 0.01). CONCLUSIONS: Different rates of optic flow lead to an apparent compression or elongation of perceived distance during running, with slow optic flow resulting in an increased ratio of perceived to actual running distance. Pacing was not different in the slow optic flow condition, despite lower perceived exertion, which is contrary to the predictions of the RPE template model, hazard scores, and estimated time limits.

Errors in the administration of intravenous medications in hospital and the role of correct procedures and nurse experience.

BACKGROUND: Intravenous medication administrations have a high incidence of error but there is limited evidence of associated factors or error severity. OBJECTIVE: To measure the frequency, type and severity of intravenous administration errors in hospitals and the associations between errors, procedural failures and nurse experience. METHODS: Prospective observational study of 107 nurses preparing and administering 568 intravenous medications on six wards across two teaching hospitals. Procedural failures (eg, checking patient identification) and clinical intravenous errors (eg, wrong intravenous administration rate) were identified and categorised by severity. RESULTS: Of 568 intravenous administrations, 69.7% (n = 396; 95% CI 65.9 to 73.5) had at least one clinical error and 25.5% (95% CI 21.2 to 29.8) of these were serious. Four error types (wrong intravenous rate, mixture, volume, and drug incompatibility) accounted for 91.7% of errors. Wrong rate was the most frequent and accounted for 95 of 101 serious errors. Error rates and severity decreased with clinical experience. Each year of experience, up to 6 years, reduced the risk of error by 10.9% and serious error by 18.5%. Administration by bolus was associated with a 312% increased risk of error. Patient identification was only checked in 47.9% of administrations but was associated with a 56% reduction in intravenous error risk. CONCLUSIONS: Intravenous administrations have a higher risk and severity of error than other medication administrations. A significant proportion of errors suggest skill and knowledge deficiencies, with errors and severity reducing as clinical experience increases. A proportion of errors are also associated with routine violations which are likely to be learnt workplace behaviours. Both areas suggest specific targets for intervention.