Smartphone-based App Research Articles

Design and development of reverse osmosis (RO) plant status monitoring system for early fault prediction and predictive maintenance

Automation and reliability are the crucial elements of any advance reverse osmosis plant to meet the environmental and economic demands. Early fault indication, diagnosis and regular maintenance are the key challenges with most of the reverse osmosis plants in the Indian scenario. The present work introduces a modern reverse osmosis (RO) plant status monitoring unit to monitor different plant parameters in real time and early prediction for faults and maintenance. Developed RO plant status monitoring unit consists of a touch screen-based embedded monitoring unit, water quality sensors (pH, TDS), sampling chamber for controlled water flow, flow sensors, pressure and level sensors. The present system has been developed in a modular fashion so that it could be integrated with any capacity of RO plant units. Developed embedded system monitors various parameters of the plant such as input power, efficiency of the plant, level of input and output water tank and also guides operator with instructions for plant operation. Other than this, a dedicated smartphone app interface has been developed for the operator to acquire data from status monitoring unit, storage on smartphone, and transfer it to the cloud. The developed smartphone-based app also provides facility to integrate plant data with Google map with location information for easy understanding and quick action. The system has also a backup facility to transfer data to the server using 2G GSM module during the unavailability of the operator. A dedicated centralized Web server has been developed for real-time visualization of all installed RO plant status monitoring units. Different machine learning techniques have been implemented on acquired sensors data to predict early warnings related to power failure, membrane fouling and scaling, input water shortage, pipe, tank leakage, water quality sensors damage, non-operation or wrong operation of the plant along with different maintenance actions such as membrane water and chemical wash. Developed RO status monitoring unit has been tested with various RO plants having capacity from 500 LPH to 2000 LPH and deployed at various nearby villages of Rajasthan.

First Experience with Feasibility and Implementation of the Smartphone Habits Heart® App: A Proof-of-Concept Study for Patient Engagement in Heart Failure Management

Background Active patient engagement in heart failure (HF) management beyond the clinical setting is limited. Alternatively, using a smartphone app to target areas such as patient education and self-management may be useful and easily implemented. Hypothesis We hypothesized that a HF-specific smartphone-based app would enhance patient engagement in HF. Methods Ambulatory cardiology clinic patients were enrolled, randomized to control (paper HF education material) or Habits Heart® App and followed for ≥6 weeks. Patients in the intervention arm were provided with a smartphone with the app installed. The Atlanta HF Knowledge Test (AHFKT-V2) was performed at baseline and at final visit. The App includes short HF education videos and interactive lessons, weekly symptoms surveys, a messaging platform, and tracking for weight, sodium intake, and activity. Depending on symptom severity, patients were prompted to contact the study team, the study cardiologist, or call 911. Through an online dashboard, the study team tracked patient weight and symptoms and responded to patient messages sent from the app. Continuous variables were compared using Mann-Whitney U test. Results Of 28 patients (53.6% HF with reduced ejection fraction, 71.4% male, 59.5 mean age) enrolled, 11/13 in the control arm and 12/15 in the App arm completed both study visits. During the 6 weeks, the 12 patients averaged 403 screen views across 56 distinct sessions, with an average session duration of 5 minutes. On average, those patients recorded their weight 22 times, sodium intake 16 times, and exercise 23 times (Figure). AHFKT-V2 score was 80.0% at both baseline and final visit for the control arm while the score was 83.3% at baseline and 85.0% at final visit for the App arm (0% vs. 1.7% change respectively, p=0.70). Patients in the control arm gained 2.5 lbs bodyweight during the study while patients in the App arm lost 3.8 lbs bodyweight (p=0.05). The App was rated a 3.8 out of 5 on user satisfaction. The patients described the App's educational lessons and videos and the recording of weight and exercise as the most enjoyable aspects. Areas for improvement included the food input layout, internet connectivity issues, and the need to carry two smartphones. Conclusions The Habits Heart® App is a feasible way to engage patients in HF management. Future iterations of the App will feature more streamlined exercise, diet, and weight tracking, as well as direct download of the App onto the patient's personal phone.

Patient Input for Design of a Type 1 Diabetes (T1D) Decision Support Smartphone Application

The complexity of T1D results in inadequate glucose control for patients which can lead to acute and chronic complications. We are developing a decision support smartphone application (app) for patients using multiple daily injection (MDI) therapy and continuous glucose monitoring (CGM). This app will use glucose trends, insulin dosing, and physical activity to provide recommendations for titration of insulin dosing to improve glycemic control. We performed an online survey of patients age 18-80 with T1D from a diabetes specialty clinic, targeting MDI users, to determine current needs and preferences of candidates for decision support tools. Twenty-seven respondents are included in the analysis: mean age of 39.4 years, 54% female, 17.5 years mean duration of T1D, 89% use MDI. A total of 41% of respondents used CGM 50-100% of the time with all checking their CGM >6 times per day. Over 90% use a smartphone, 88% have the phone with them 75-100% of the time. Only 19% used an app to calculate insulin doses, while 81% were interested in using an app to manage diabetes. Participants wanted the following features in a decision support app: hypoglycemia avoidance (89%), insulin dose calculation (78%), behavioral change suggestions (74%), predicted glucose trends (70%), and insulin-on-board (70%). Many expressed low confidence in their ability to manage glucose during exercise (51%). Most respondents were interested in viewing predicted glucose trends prior to exercise (85%). Preferences about alerts to improve glucose control were varied with 44% saying they would be comfortable with 1-3 alerts per day. Preferences about alerts for predicted hypoglycemia were also varied; 11% would want to be alerted 5 minutes prior, 19% 15 minutes, 44% 30 minutes, and 26% >1 hour. These survey results confirm the need for and interest in a smartphone-based decision support app that will assist with management of T1D. The results will enable intelligent app design to better meet the needs of end users. Disclosure L.M. Wilson: None. V. Gabo: None. N.S. Tyler: None. R. Reddy: None. P.G. Jacobs: Stock/Shareholder; Self; Pacific Diabetes Technologies. J.R. Castle: Consultant; Self; Zealand Pharma A/S. Advisory Panel; Self; Novo Nordisk Inc..

Outcomes of a Drug Dosage Calculation Training Smartphone App on Learning Achievement, Metacognition, and Flow State According to Prior Knowledge

The purpose of this study was to define the effectiveness of a smartphone-based dosage calculation training app on nursing students’ learning achievement, metacognition, and flow based on prior knowledge. The study used a quasi-experimental design with a pre- and post-test. Participants were 157 nursing students from 3 universities with baccalaureate programs in South Korea. After recruiting the experimental and control groups, they were also categorized by prior knowledge of drug dosage calculation ability into above and below-mean clusters. The experimental groups were provided with the smartphone-based app. In the above-mean cluster, changes in total learning achievement (Z=3.16, p=.002), drop rate calculation (Z=2.76, p=.006), metacognition (Z=2.50, p=.012), and flow score (Z=2.42, p=.016) in the experimental group were significantly higher than those in the control group. A smartphone-based calculation training app improves calculation achievement, metacognition, and flow among students with higher prior knowledge. For learners with lower prior knowledge, additional instructional design or implementation strategies may be needed.

PD40-03 UTILIZATION OF MEDMEASURE!, A SMARTPHONE-BASED APP, AS A RELIABLE METHOD FOR FLUOROSCOPIC AND ENDOSCOPIC MEASUREMENTS

You have accessJournal of UrologySurgical Technology & Simulation: Instrumentation & Technology III1 Apr 2018PD40-03 UTILIZATION OF MEDMEASURE!, A SMARTPHONE-BASED APP, AS A RELIABLE METHOD FOR FLUOROSCOPIC AND ENDOSCOPIC MEASUREMENTS Samuel Washington, III and Maurice Garcia Samuel Washington, IIISamuel Washington, III More articles by this author and Maurice GarciaMaurice Garcia More articles by this author View All Author Informationhttps://doi.org/10.1016/j.juro.2018.02.1928AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookTwitterLinked InEmail INTRODUCTION AND OBJECTIVES Endoscopic procedures are ubiquitous amongst urology practices across the world. A reliable evaluation of the size of the stone or tumor evaluated during an endoscopic procedure can significantly alter management. To our knowledge a standardized, reliable method for measurement in endoscopic cases has yet to be developed. We describe utilization of a smartphone-based application, MedMeasure! for objective measurements during endoscopy. METHODS We conducted an ex-vivo experiment using an Olympus cystoscope with a 30 degree lens to simulate a standard endoscopic procedure. We then visualized a reference item of known diameter (quarter and nickel) and a target object (ruler) in the same field of view. We describe the reference and target objects in several configurations based upon their position to each other and relative distance to the cystoscope. Nomenclature used as follows: A-P, both objects are perpendicular to the cystoscope; Angled, objects parallel to one another and tilted away from scope; Askew, objects at different angles relative to scope; Equidistant, same distance from cystoscope; Offset, objects at unequal distances from scope; Foreground, one object closer to scope than the other. For each configuration a picture was taken of the image on the monitor using an iPhone camera. MedMeasure! was used to then measure the width of the target object. We calculated the difference between the measured and known diameter of the target object in each configuration. We then derived the percentage distortion between measured and known values by dividing the previously calculated differences by the known diameter. Box plot graphs was used to illustrate the inter-observer variation as well as the extent of distortion of the target image. RESULTS The reference objects used were items whose diameters are well-known and standardized, a quarter, 24.3mm, and a nickel, 21.21mm. The target object of interest was a ruler with a width of 4cm. Distortion was the least when both objects were equidistant, either A-P (12-14% distortion) or askew (6-30%) (p>0.05 for both). The distortion increased significantly when offset or at different distances from the scope (p<0.001 for all). CONCLUSIONS Using a smartphone-based app, MedMeasure!, we demonstrate that accurate measurements can be obtained during fluoroscopic and endoscopic procedures using a reference object with the target at the same distance from the cystoscope. Distortion increases significantly when objects are askew or at different distances from the cystoscope. © 2018FiguresReferencesRelatedDetails Volume 199Issue 4SApril 2018Page: e803-e804 Advertisement Copyright & Permissions© 2018MetricsAuthor Information Samuel Washington, III More articles by this author Maurice Garcia More articles by this author Expand All Advertisement Advertisement PDF downloadLoading ...

Improving Internal Medicine Residents' Colorectal Cancer Screening Knowledge Using a Smartphone App: Pilot Study.

BackgroundColorectal cancer (CRC) is the third most common type of cancer and the second leading cause of cancer death in the United States. About one in three adults in the United States is not getting the CRC screening as recommended. Internal medicine residents are deficient in CRC screening knowledge.ObjectiveThe objective of our study was to assess the improvement in internal medicine residents’ CRC screening knowledge via a pilot approach using a smartphone app.MethodsWe designed a questionnaire based on the CRC screening guidelines of the American Cancer Society, American College of Gastroenterology, and US Preventive Services Task Force. We emailed the questionnaire via a SurveyMonkey link to all the residents of an internal medicine department to assess their knowledge of CRC screening guidelines. Then we designed an educational intervention in the form of a smartphone app containing all the knowledge about the CRC screening guidelines. The residents were introduced to the app and asked to download it onto their smartphones. We repeated the survey to test for changes in the residents’ knowledge after publication of the smartphone app and compared the responses with the previous survey. We applied the Pearson chi-square test and the Fisher exact test to look for statistical significance.ResultsA total of 50 residents completed the first survey and 41 completed the second survey after publication of the app. Areas of CRC screening that showed statistically significant improvement (P<.05) were age at which CRC screening was started in African Americans, preventive tests being ordered first, identification of CRC screening tests, identification of preventive and detection methods, following up positive tests with colonoscopy, follow-up after colonoscopy findings, and CRC surveillance in diseases.ConclusionsIn this modern era of smartphones and gadgets, developing a smartphone-based app or educational tool is a novel idea and can help improve residents’ knowledge about CRC screening.

Digital Health Innovation: A Toolkit to Navigate From Concept to Clinical Testing.

Digital health technologies such as smartphone apps, Web-based platforms, and wearable devices are rapidly emerging as promising interventions for acute and chronic disease management, particularly in the field of cardiovascular medicine. However, there is limited guidance on how to effectively develop and rigorously test digital health interventions (DHIs). Through our experience with innovating Corrie, a smartphone-based app paired with a smartwatch and blood pressure monitor for myocardial infarction recovery in the acute setting, we aim to provide a toolkit for navigating the digital health technology development and clinical testing processes. The toolkit consists of 6 steps: step one emphasizes concept generation by defining a specific clinical problem and the existing solutions aimed to address it; step two aims to recruit a multidisciplinary team within an academic institution; step three leverages technology accelerators and industry partnerships; step four develops the digital health technology with continuous feedback from patient and family end-users; step five solicits feedback from a diverse array of stakeholders; and step six performs a clinical study at a single site that, if successful, rapidly scales to multiple sites. DHI development is often a complex and vastly uncharted territory. By exploring the steps we took from concept to clinical testing with the first cardiology CareKit app, we hope to provide useful insights to teams that are starting out on their path to digital health innovation. We emphasize the central importance of embracing transdisciplinary work to move from silos to synergy.

A parent–science partnership to improve postsurgical pain management in young children: Co-development and usability testing of the Achy Penguin smartphone-based app

ABSTRACT Background Young children are at risk for poorly managed pain after surgery, with significant negative consequence to their quality of life and health outcomes. Mobile applications offer a highly accessible, engaging, and interactive medium to improve pain assessment and management; however, they generally lack scientific foundation or support. Aims The aims of this study were to describe a successful parent–science partnership in the development and testing of Achy Penguin, a parent-developed iOS app to help assess and manage acute pain in young children, and to evaluate and refine the usability of Achy Penguin in young children with acute postoperative pain. Methods Three cycles of iterative usability testing were conducted with 20 4- to 7-year-old children (M = 5.8 years) in hospital who had recently undergone surgery (n = 6–7 children/cycle). Semistructured qualitative interviews were analyzed using simple content analysis. Results Feedback from children and further integration of evidence-based pediatric pain knowledge led to refinements in app pain assessment and management content, as well as app flow and functionality. Changes improved children’s ease of use and understanding and satisfaction by simplifying language in app instructions and content, adding audio and pictorial instructions, and increasing the engagement, interactiveness, immersiveness, and general appeal of pain management strategies. Conclusions This article showcases the value of collaborative partnerships between various stakeholders (parents, app developers, and researcher/health care providers) to address gaps in pediatric pain care. The Achy Penguin app shows promise for improving pain assessment and management in young children, although further evaluation of app effectiveness and implementation is warranted.

EMG-Based Mobile Assessment System for Neck and Shoulder Fatigue

The neck and shoulders are the key channels for blood supply to the head. Bad blood circulation cannot only cause dizziness and headache but can also affect the vitality of brain cells and cognitive function. The neck muscles also provide all support for the head, and incorrect posture can put stress on the lower cervical vertebrae, accelerating joint wear and poor blood circulation, leading to hypoxia of neck muscle and tissue, and resulting in muscle fatigue and stiffness. Keeping the neck and shoulders rigid over an extended period, can produce neck and shoulder pain, dizziness and headache, and possibly even memory loss and short-term cognitive impairment, which can thus negatively impact learning ability and work efficiency. In this article, electromyography (EMG) characteristics were extracted through EMG analysis. Test subjects wore sensors while engaged in work or studying. The sensors retrieved EMG data which was then uploaded to a cloud-based platform for computation. A smartphone-based app then allowed users to monitor their own neck and shoulder fatigue in real time. Moreover, based on users' personal basic information, the system recommends personalized exercises to promote neck and shoulder relief, thus promoting comfort and reducing stress.

Development of Decision Support Formulas for the Prediction of Bladder Outlet Obstruction and Prostatic Surgery in Patients With Lower Urinary Tract Symptom/Benign Prostatic Hyperplasia: Part II, External Validation and Usability Testing of a Smartphone App.

PurposeWe aimed to externally validate the prediction model we developed for having bladder outlet obstruction (BOO) and requiring prostatic surgery using 2 independent data sets from tertiary referral centers, and also aimed to validate a mobile app for using this model through usability testing.MethodsFormulas and nomograms predicting whether a subject has BOO and needs prostatic surgery were validated with an external validation cohort from Seoul National University Bundang Hospital and Seoul Metropolitan Government-Seoul National University Boramae Medical Center between January 2004 and April 2015. A smartphone-based app was developed, and 8 young urologists were enrolled for usability testing to identify any human factor issues of the app.ResultsA total of 642 patients were included in the external validation cohort. No significant differences were found in the baseline characteristics of major parameters between the original (n=1,179) and the external validation cohort, except for the maximal flow rate. Predictions of requiring prostatic surgery in the validation cohort showed a sensitivity of 80.6%, a specificity of 73.2%, a positive predictive value of 49.7%, and a negative predictive value of 92.0%, and area under receiver operating curve of 0.84. The calibration plot indicated that the predictions have good correspondence. The decision curve showed also a high net benefit. Similar evaluation results using the external validation cohort were seen in the predictions of having BOO. Overall results of the usability test demonstrated that the app was user-friendly with no major human factor issues.ConclusionsExternal validation of these newly developed a prediction model demonstrated a moderate level of discrimination, adequate calibration, and high net benefit gains for predicting both having BOO and requiring prostatic surgery. Also a smartphone app implementing the prediction model was user-friendly with no major human factor issue.

EFarm: A Tool for Better Observing Agricultural Land Systems.

Currently, observations of an agricultural land system (ALS) largely depend on remotely-sensed images, focusing on its biophysical features. While social surveys capture the socioeconomic features, the information was inadequately integrated with the biophysical features of an ALS and the applications are limited due to the issues of cost and efficiency to carry out such detailed and comparable social surveys at a large spatial coverage. In this paper, we introduce a smartphone-based app, called eFarm: a crowdsourcing and human sensing tool to collect the geotagged ALS information at the land parcel level, based on the high resolution remotely-sensed images. We illustrate its main functionalities, including map visualization, data management, and data sensing. Results of the trial test suggest the system works well. We believe the tool is able to acquire the human–land integrated information which is broadly-covered and timely-updated, thus presenting great potential for improving sensing, mapping, and modeling of ALS studies.

Novel Mobile App Allows for Fast and Validated Intraoperative Assessment of Otolaryngology Residents.

Evaluation of resident operative skills is challenging in the fast-paced operating room environment and limited by lack of validated assessment metrics. We describe a smartphone-based app that enables rapid assessment of operative skills. Accreditation Council for Graduate Medical Education (ACGME) otolaryngology taxonomy surgical procedures (n = 593) were uploaded to the software platform. The app was piloted over 1 month. Outcomes included (1) completion of evaluation, (2) time spent completing the evaluation, and (3) quantification of case complexity, operative autonomy, and performance. During the study, 12 of 12 procedures, corresponding to 3 paired evaluated by the resident/attending dyad. Mean ± SD time of evaluation completion was 98.0 ± 24.2 and 123.0 ± 14.0 seconds for the resident and attending, respectively. Mean time between resident and attending evaluation completion was 27.9 ± 26.8 seconds. Resident and attending scores for case complexity, operative autonomy, and performance were strongly correlated (P < .0001). Rapid evaluation of resident intraoperative performance is feasible using smartphone-based technology.

A smartphone application to educate undergraduate nursing students about providing care for infant airway obstruction

PurposeThis study had two aims: (a) to develop a smartphone-based application and (b) to evaluate the effectiveness of the application by measuring nursing students' knowledge, skills, and confidence in simulated performance when providing that care. DesignWe conducted a randomized trial using a pre- and post-test design at a university in Korea. Seventy-three junior nursing students participated. MethodsA smartphone-based app using a video was developed for the experimental group and one time lecture-based education was designed for the control group. We provided the app and information about its use to the experimental group, and we encouraged its use. We provided classroom instruction to the control group. Then, learning outcomes were evaluated. Results: The smartphone-based education group showed significantly higher scores on skills (t=4.774, p<0.001) and confidence in performance (t=2.888, p=0.005) than the control group. The scores on knowledge (t=0.886, p=0.379) and satisfaction with the learning method (t=0.168, p=0.867) for the experimental group were higher than for the control group, but the differences were not statistically significant. ConclusionThis study suggests that smartphone-based education may be an effective method to use in nursing education related to teaching infant airway obstruction.

Development of a novel smartphone-based application for accurate and sensitive on-field hemoglobin measurement

A smartphone-based app for the accurate and sensitive measurement of hemoglobin was developed.

PULSE‐SMART: Pulse‐Based Arrhythmia Discrimination Using a Novel Smartphone Application

Atrial fibrillation (AF) is a common and dangerous rhythm abnormality. Smartphones are increasingly used for mobile health applications by older patients at risk for AF and may be useful for AF screening. To test whether an enhanced smartphone app for AF detection can discriminate between sinus rhythm(SR), AF, premature atrial contractions (PACs), and premature ventricular contractions (PVCs). We analyzed two hundred and nineteen 2-minute pulse recordings from 121 participants with AF (n = 98), PACs (n = 15), or PVCs (n = 15) using an iPhone 4S. We obtained pulsatile time series recordings in 91 participants after successful cardioversion to sinus rhythm from preexisting AF. The PULSE-SMART app conducted pulse analysis using 3 methods (Root Mean Square of Successive RR Differences; Shannon Entropy; Poincare plot). We examined the sensitivity, specificity, and predictive accuracy of the app for AF, PAC, and PVC discrimination from sinus rhythm using the 12-lead EKG or 3-lead telemetry as the gold standard. We also administered a brief usability questionnaire to a subgroup (n = 65) of app users. The smartphone-based app demonstrated excellent sensitivity (0.970), specificity (0.935), and accuracy (0.951) for real-time identification of an irregular pulse during AF. The app also showed good accuracy for PAC (0.955) and PVC discrimination (0.960). The vast majority of surveyed app users (83%) reported that it was "useful" and "not complex" to use. A smartphone app can accurately discriminate pulse recordings during AF from sinus rhythm, PACs, and PVCs.

Abstract 11586: Pulse-based Arrhythmia Discrimination Using a Novel Smartphone Application

Introduction: Screening for atrial fibrillation (AF) normally involves the use of expensive and cumbersome outpatient monitoring equipment. Currently, smartphone based detection allows for assessment of heart rate alone or requires additional hardware which has limited its widespread use in screening for AF. We describe the use of the first smartphone based monitor that has the ability to discriminate AF from ectopy (including PACs and PVCs) without the need of additional hardware. Methods: We analyzed 219 2-minute pulse recordings from 121 study participants with AF (n=98), PACs (n=15), or PVCs (n=15) using an iPhone 4S. We obtained pulsatile time series recordings in 91 participants after successful cardioversion to sinus rhythm from pre-existing AF. The PULSESMART app conducted pulse analysis using 3 methods [Root Mean Square of Successive RR Differences; Shannon Entropy; Poincare plot]. We examined the sensitivity, specificity, and predictive accuracy of the app for AF, PAC, and PVC discrimination from sinus rhythm using the 12-lead EKG or 3-lead telemetry as the gold standard. We also administered a brief usability questionnaire to a subgroup (n=41) of app users. Results: The smartphone-based app demonstrated excellent sensitivity (0.970), specificity (0.935), and accuracy (0.951) for real-time identification of an irregular pulse during AF. The app also showed good accuracy for PAC (0.955) and PVC discrimination (0.960). The vast majority of surveyed app users (83%) reported that it was “useful” and “not complex” to use. Conclusion: A smartphone app can accurately discriminate pulse recordings during AF from sinus rhythm, PACs, and PVCs without the need of additional hardware.

A Smart Work Performance Measurement System for Police Officers

With rapid economic growth, the management of employees working outdoors gradually becomes more intensive. The traditional attendance systems typically focus on collecting employee attendance information and are not suited to the quantification of work performance. As a remedy to this issue, a smart work performance measurement system is proposed in this paper. The proposed system consists of three components: 1) a smartphone-based APP to collect employee attendance, work, and location information; 2) a data warehouse to preprocess and store the data; and 3) a smart data analysis center to make a comprehensive and systematic evaluation of employee work performance. Management can obtain the key information quickly and adjust work assignments based on performance. New solutions and algorithms for indoor and outdoor location, GPS deviation improvement, and work performance measurement are put forward. This system is being used by over 1100 police officers in one city traffic management bureau, and the observed results are encouraging, demonstrating the efficiency and accuracy of our system, and helping governments to better regulate the traffic operation and reduce associated costs.

“There’s an App for That”: An Interview with Dr. Jennifer Stinson, an M-Health Expert

Increased adoption of smartphone technology by the general public has opened up an exciting new means by which healthcare professionals can interact with their patients [1]. The smartphone’s unique ability to combine mobile communication and computation offers a novel modality by which physicians can deliver healthcare interventions to their patients. Thus, it is no wonder that the use of smartphones in healthcare settings (so called “m-health”) has become the focus of widespread interest amongst healthcare professionals, with many smartphone-based medical applications already in widespread use amongst physicians and patients [2]. Leading the charge in this m-health revolution is Dr. Jennifer Stinson, a nurse clinician scientist based at The Hospital for Sick Children in Toronto, who aims to capitalize on the popularity of smartphones among adolescents [3]. Dr. Stinson is a pioneer in the field of m-health, creating one of the first electronic pain diaries using the Palm Tungsten PDA to help adolescents with juvenile idiopathic arthritis (JIA) related pain [4]. More recently, she has created the “Pain Squad” smartphone-based app, a multiple award-winning pain measurement tool for children and adolescents with cancer [5].I was able to speak with Dr. Stinson about her experience with m-health, her views about the future of m-health, and her advice for interested healthcare professionals and trainees who want to integrate mobile technology into their own patient care. The following is an edited version of that conversation.

Effectiveness of a smart phone app on improving immunization of children in rural Sichuan Province, China: study protocol for a paired cluster randomized controlled trial

BackgroundAlthough good progress has been achieved in expanding immunization of children in China, disparities exist across different provinces. Information gaps both from the service supply and demand sides hinder timely vaccination of children in rural areas. The rapid development of mobile health technology (mHealth) provides unprecedented opportunities for improving health services and reaching underserved populations. However, there is a lack of literature that rigorously evaluates the impact of mHealth interventions on immunization coverage as well as the usability and feasibility of smart phone applications (apps). This study aims to assess the effectiveness of a smart phone-based app (Expanded Program on Immunization app, or EPI app) on improving the coverage of children’s immunization.Methods/DesignThis cluster randomized trial will take place in Xuanhan County, Sichuan Province, China. Functionalities of the app include the following: to make appointments automatically, record and update children’s immunization information, generate a list of children who missed their vaccination appointments, and send health education information to village doctors. After pairing, 36 villages will be randomly allocated to the intervention arm (n = 18) and control arm (n = 18). The village doctors in the intervention arm will use the app while the village doctors in the control arm will record and manage immunization in the usual way in their catchment areas. A household survey will be used at baseline and at endline (8 months of implementation). The primary outcome is full-dose coverage and the secondary outcome is immunization coverage of the five vaccines that are included in the national Expanded Program on Immunization program as well as Hib vaccine, Rotavirus vaccine and Pneumococcal conjugate vaccine. Multidimensional evaluation of the app will also be conducted to assess usability and feasibility.DiscussionThis study is the first to evaluate the effectiveness of a smart phone app for child immunization in rural China. This study will contribute to the knowledge about the usability and feasibility of a smart phone app for managing immunization in rural China and to similar populations in different settings.Trial registrationChinese Clinical Trials Registry (ChiCTR): ChiCTR-TRC-13003960

ICanCope with Pain™: User-centred design of a web- and mobile-based self-management program for youth with chronic pain based on identified health care needs.

While there are emerging web-based self-management programs for children and adolescents with chronic pain, there is currently not an integrated web- and smartphone-based app that specifically addresses the needs of adolescents with chronic pain. To conduct a needs assessment to inform the development of an online chronic pain self-management program for adolescents, called iCanCope with Pain™. A purposive sample of adolescents (n=23; 14 to 18 years of age) was recruited from two pediatric chronic pain clinics in Ontario. Interdisciplinary health care providers were also recruited from these sites. Three focus groups were conducted with adolescents (n=16) and one with pediatric health care providers (n=7). Individual adolescent interviews were also conducted (n=7). Qualitative analysis uncovered four major themes: pain impact; barriers to care; pain management strategies; and transition to adult care. Pain impacted social, emotional, physical and role functioning, as well as future goals. Barriers to care were revealed at the health care system, patient and societal levels. Pain management strategies included support systems, and pharmacological, physical and psychological approaches. Transition subthemes were: disconnect between pediatric and adult systems; skills development; parental role; and fear⁄anxiety. Based on these identified needs, the iCanCope with Pain™ architecture will include the core theory-based functionalities of: symptom self-monitoring; personalized goal setting; pain coping skills training; peer-based social support; and chronic pain education. The proposed iCanCope with Pain™ program aims to address the self-management needs of adolescents with chronic pain by improving access to disease information, strategies to manage symptoms and social support.