Maintenance Notice

Due to necessary scheduled maintenance, the JMIR Publications website will be unavailable from Monday, March 11, 2019 at 4:00 PM to 4:30 PM EST. We apologize in advance for any inconvenience this may cause you.

Who will be affected?

Advertisement

Journal Description

JMIR mHealth and uHealth (JMU, ISSN 2291-5222; Impact Factor 4.301) is a sister journal of JMIR, the leading eHealth journal. JMIR mHealth and uHealth is indexed in PubMed, PubMed Central, Scopus, MEDLINE and Science Citation Index Expanded (SCIE), and in June 2019 received an Impact Factor of 4.301, which ranks the journal #2 (behind JMIR) in the medical informatics category indexed by the Science Citation Index Expanded (SCIE) by Thomson Reuters/Clarivate

The journal focusses on health and biomedical applications in mobile and tablet computing, pervasive and ubiquitous computing, wearable computing and domotics.

JMIR mHealth and uHealth publishes since 2013 and was the first mhealth journal in Pubmed. It publishes even faster and has a broader scope with including papers which are more technical or more formative/developmental than what would be published in the Journal of Medical Internet Research

JMIR mHealth and uHealth features a rapid and thorough peer-review process, professional copyediting, professional production of PDF, XHTML, and XML proofs.

JMIR mHealth and uHealth adheres to the same quality standards as JMIR and all articles published here are also cross-listed in the Table of Contents of JMIR, the worlds' leading medical journal in health sciences / health services research and health informatics.

 

Recent Articles:

  • Source: freepik; Copyright: katemangostar; URL: https://www.freepik.com/free-photo/cropped-view-man-texting-smartphone_1196364.htm#page=4&query=hand+and+phone&position=33; License: Licensed by JMIR.

    Validation of an mHealth App for Depression Screening and Monitoring (Psychologist in a Pocket): Correlational Study and Concurrence Analysis

    Abstract:

    Background: Mobile health (mHealth) is a fast-growing professional sector. As of 2016, there were more than 259,000 mHealth apps available internationally. Although mHealth apps are growing in acceptance, relatively little attention and limited efforts have been invested to establish their scientific integrity through statistical validation. This paper presents the external validation of Psychologist in a Pocket (PiaP), an Android-based mental mHealth app which supports traditional approaches in depression screening and monitoring through the analysis of electronic text inputs in communication apps. Objective: The main objectives of the study were (1) to externally validate the construct of the depression lexicon of PiaP with standardized psychological paper-and-pencil tools and (2) to determine the comparability of PiaP, a new depression measure, with a psychological gold standard in identifying depression. Methods: College participants downloaded PiaP for a 2-week administration. Afterward, they were asked to complete 4 psychological depression instruments. Furthermore, 1-week and 2-week PiaP total scores (PTS) were correlated with (1) Beck Depression Index (BDI)-II and Center for Epidemiological Studies–Depression (CES-D) Scale for congruent construct validation, (2) Affect Balance Scale (ABS)–Negative Affect for convergent construct validation, and (3) Satisfaction With Life Scale (SWLS) and ABS–Positive Affect for divergent construct validation. In addition, concordance analysis between PiaP and BDI-II was performed. Results: On the basis of the Pearson product-moment correlation, significant positive correlations exist between (1) 1-week PTS and CES-D Scale, (2) 2-week PTS and BDI-II, and (3) PiaP 2-week PTS and SWLS. Concordance analysis (Bland-Altman plot and analysis) suggested that PiaP’s approach to depression screening is comparable with the gold standard (BDI-II). Conclusions: The evaluation of mental health has historically relied on subjective measurements. With the integration of novel approaches using mobile technology (and, by extension, mHealth apps) in mental health care, the validation process becomes more compelling to ensure their accuracy and credibility. This study suggests that PiaP’s approach to depression screening by analyzing electronic data is comparable with traditional and well-established depression instruments and can be used to augment the process of measuring depression symptoms.

  • A user is demonstrating the use of our app. Source: Image created by the Authors; Copyright: The Authors; URL: http://mhealth.jmir.org/2019/9/e14172/; License: Creative Commons Attribution (CC-BY).

    A Tablet-Based App for Carpal Tunnel Syndrome Screening: Diagnostic Case-Control Study

    Abstract:

    Background: Carpal tunnel syndrome (CTS), the most common neuropathy, is caused by a compression of the median nerve in the carpal tunnel and is related to aging. The initial symptom is numbness and pain of the median nerve distributed in the hand area, while thenar muscle atrophy occurs in advanced stages. This atrophy causes failure of thumb motion and results in clumsiness; even after surgery, thenar atrophy does not recover for an extended period. Medical examination and electrophysiological testing are useful to diagnose CTS; however, visits to the doctor tend to be delayed because patients neglect the symptom of numbness in the hand. To avoid thenar atrophy-related clumsiness, early detection of CTS is important. Objective: To establish a CTS screening system without medical examination, we have developed a tablet-based CTS detection system, focusing on movement of the thumb in CTS patients; we examined the accuracy of this screening system. Methods: A total of 22 female CTS patients, involving 29 hands, and 11 female non-CTS participants were recruited. The diagnosis of CTS was made by hand surgeons based on electrophysiological testing. We developed an iPad-based app that recorded the speed and timing of thumb movements while playing a short game. A support vector machine (SVM) learning algorithm was then used by comparing the thumb movements in each direction among CTS and non-CTS groups with leave-one-out cross-validation; with this, we conducted screening for CTS in real time. Results: The maximum speed of thumb movements between CTS and non-CTS groups in each direction did not show any statistically significant difference. The CTS group showed significantly slower average thumb movement speed in the 3 and 6 o’clock directions (P=.03 and P=.005, respectively). The CTS group also took a significantly longer time to reach the points in the 2, 3, 4, 5, 6, 8, 9, and 11 o’clock directions (P<.05). Cross-validation revealed that 27 of 29 CTS hands (93%) were classified as having CTS, while 2 of 29 CTS hands (7%) did not have CTS. CTS and non-CTS were classified with 93% sensitivity and 73% specificity. Conclusions: Our newly developed app could classify disturbance of thumb opposition movement and could be useful as a screening test for CTS patients. Outside of the clinic, this app might be able to detect middle-to-severe-stage CTS and prompt these patients to visit a hand surgery specialist; this may also lead to medical cost-savings.

  • Mobile attention bias modification iintervention. Source: Image created by the Authors; Copyright: The Authors; URL: https://mhealth.jmir.org/2019/8/e15465/; License: Public Domain (CC0).

    A Smartphone Attention Bias App for Individuals With Addictive Disorders: Feasibility and Acceptability Study

    Abstract:

    Background: Conventional psychology therapies are unable to address automatic biases that result in individuals relapsing into their substance use disorder. Advances in experimental psychology have led to a better understanding of attention and approach biases and methods to modify these biases. Several studies have demonstrated the effectiveness of bias modification among clinical cohorts. The advances in mobile health technologies have allowed remote delivery of these interventions. To date, there is a lack of studies examining bias modification in a substance-using non-Western sample. Objective: This study was designed to determine the feasibility of an attention bias modification intervention and an attention bias modification smartphone app for the reduction of attention biases among treatment-seeking individuals. The secondary aim is to determine the acceptability of the intervention. Methods: A feasibility study was conducted among inpatients who were in their rehabilitation phase at the National Addictions Management Service. Participants were to complete a set of baseline questionnaires, and on each day that they are in the study, undertake an attention bias assessment and modification task while completing a visual analogue scale to assess their craving. Feasibility was determined by the acceptance rate of participation and participants’ adherence to the interventions. Acceptability was assessed by a perception questionnaire. Descriptive statistical analyses were performed using SPSS version 22. A thematic analysis approach was used in the qualitative synthesis of users’ perceptions. Results: Of the 40 participants invited to participate in the feasibility study, 10 declined, yielding an acceptance rate of 75%. Of the recruited participants, 6 participants were diagnosed with alcohol dependence; 17, with opioid dependence; 2, with cannabis dependence; and 5, with stimulant dependence. In addition, of the final 30 participants, 11 (37%) failed to complete all the planned interventions and 22 (73%) completed the perspective questionnaires; of these 22 participants, 100% rated the app as extremely and very easy, 77% rated it as extremely or very interactive, 54% rated it as extremely or very motivating, and 33% reported a change in their confidence levels. Conclusions: Our results highlight the feasibility of recruiting participants to undertake attention bias modification interventions. Participants generally accept use of a mobile version of such an intervention. Nevertheless, our acceptability data indicate that there could be improvements in the existing app, and a participatory design approach might be helpful in its future conceptualization. International Registered Report Identifier (IRRID): RR2-10.2196/11822

  • Using the K-CESD-R mobile app. Source: Image created by the Authors; Copyright: The Authors; URL: https://mhealth.jmir.org/2019/9/14657; License: Creative Commons Attribution (CC-BY).

    Response Time as an Implicit Self-Schema Indicator for Depression Among Undergraduate Students: Preliminary Findings From a Mobile App–Based Depression...

    Abstract:

    Background: Response times to depressive symptom items in a mobile-based depression screening instrument has potential as an implicit self-schema indicator for depression but has yet to be determined; the instrument was designed to readily record depressive symptoms experienced on a daily basis. In this study, the well-validated Korean version of the Center for Epidemiologic Studies Depression Scale-Revised (K-CESD-R) was adopted. Objective: The purpose of this study was to investigate the relationship between depression severity (ie, explicit measure: total K-CESD-R Mobile scores) and the latent trait of interest in schematic self-referent processing of depressive symptom items (ie, implicit measure: response times to items in the K-CESD-R Mobile scale). The purpose was to investigate this relationship among undergraduate students who had never been diagnosed with, but were at risk for, major depressive disorder (MDD) or comorbid MDD with other neurological or psychiatric disorders. Methods: A total of 70 participants—36 males (51%) and 34 females (49%)—aged 19-29 years (mean 22.66, SD 2.11), were asked to complete both mobile and standard K-CESD-R assessments via their own mobile phones. The mobile K-CESD-R sessions (binary scale: yes or no) were administered on a daily basis for 2 weeks. The standard K-CESD-R assessment (5-point scale) was administered on the final day of the 2-week study period; the assessment was delivered via text message, including a link to the survey, directly to participants’ mobile phones. Results: A total of 5 participants were excluded from data analysis. The result of polynomial regression analysis showed that the relationship between total K-CESD-R Mobile scores and the reaction times to the depressive symptom items was better explained by a quadratic trend—F (2, 62)=21.16, P<.001, R2=.41—than by a linear trend—F (1, 63)=25.43, P<.001, R2=.29. It was further revealed that the K-CESD-R Mobile app had excellent internal consistency (Cronbach alpha=.94); at least moderate concurrent validity with other depression scales, such as the Korean version of the Quick Inventory for Depressive Symptomatology-Self Report (ρ=.38, P=.002) and the Patient Health Questionnaire-9 (ρ=.48, P<.001); a high adherence rate for all participants (65/70, 93%); and a high follow-up rate for 10 participants whose mobile or standard K-CESD-R score was 13 or greater (8/10, 80%). Conclusions: As hypothesized, based on a self-schema model for depression that represented both item and person characteristics, the inverted U-shaped relationship between the explicit and implicit self-schema measures for depression showed the potential of an organizational breakdown; this also showed the potential for a subsequent return to efficient processing of schema-consistent information along a continuum, ranging from nondepression through mild depression to severe depression. Further, it is expected that the updated K-CESD-R Mobile app can play an important role in encouraging people at risk for depression to seek professional follow-up for mental health care.

  • Smartphone app for medication management. Source: Image created by the Authors; Copyright: The Authors; URL: https://mhealth.jmir.org/2019/9/e14914; License: Creative Commons Attribution (CC-BY).

    A Smartphone App to Improve Medication Adherence in Patients With Type 2 Diabetes in Asia: Feasibility Randomized Controlled Trial

    Abstract:

    Background: The efficacy of smartphone apps for improving medication adherence in type 2 diabetes is not well studied in Asian populations. Methods: We block randomized 51 nonadherent and digitally literate patients with type 2 diabetes between the ages of 21 and 75 years into two treatment arms (control: usual care; intervention: usual care+Medisafe app) and followed them up for 12 weeks. Recruitment occurred at a public tertiary diabetes specialist outpatient center in Singapore. The intervention group received email reminders to complete online surveys monthly, while the control group only received an email reminder(s) at the end of the study. Barriers to medication adherence and self-appraisal of diabetes were assessed using the Adherence Starts with Knowledge-12 (ASK-12) and Appraisal of Diabetes Scale (ADS) questionnaires at baseline and poststudy in both groups. Perception toward medication adherence and app usage, attitude, and satisfaction were assessed in the intervention group during and after the follow-up period. Sociodemographic data were collected at baseline. Clinical data (ie, hemoglobin A1c, body mass index, low-density lipoprotein, high-density lipoprotein, and total cholesterol levels) were extracted from patients’ electronic medical records. Results: A total of 51 (intervention group: 25 [49%]; control group: 26 [51%]) participants were randomized, of which 41 (intervention group: 22 [88.0%]; control group: 19 [73.1%]) completed the poststudy survey. The baseline-adjusted poststudy ASK-12 score was significantly lower in the intervention group than in the control group (mean difference: 4.7, P=.01). No changes were observed in the clinical outcomes. The average 12-week medication adherence rate of participants tracked by the app was between 38.3% and 100% in the intervention group. The majority (>80%) of the participants agreed that the app was easy to use and made them more adherent to their medication. Conclusions: Our feasibility study showed that among medication-nonadherent patients with type 2 diabetes, a smartphone app intervention was acceptable, improved awareness of medication adherence, and reduced self-reported barriers to medication adherence, but did not improve clinical outcomes in a developed Asian setting.

  • Source: Image created by the Authors; Copyright: Yang-Yang Wang; URL: http://mhealth.jmir.org/2019/9/e11229/; License: Licensed by JMIR.

    Home Videos as a Cost-Effective Tool for the Diagnosis of Paroxysmal Events in Infants: Prospective Study

    Abstract:

    Background: The diagnosis of paroxysmal events in infants is often challenging. Reasons include the child’s inability to express discomfort and the inability to record video electroencephalography at home. The prevalence of mobile phones, which can record videos, may be beneficial to these patients. In China, this advantage may be even more significant given the vast population and the uneven distribution of medical resources. Objective: The aim of this study is to investigate the value of mobile phone videos in increasing the diagnostic accuracy and cost savings of paroxysmal events in infants. Methods: Clinical data, including descriptions and home videos of episodes, from 12 patients with paroxysmal events were collected. The investigation was conducted in six centers during pediatric academic conferences. All 452 practitioners present were asked to make their diagnoses by just the descriptions of the events, and then remake their diagnoses after watching the corresponding home videos of the episodes. The doctor’s information, including educational background, profession, working years, and working hospital level, was also recorded. The cost savings from accurate diagnoses were measured on the basis of using online consultation, which can also be done easily by mobile phone. All data were recorded in the form of questionnaires designed for this study. Results: We collected 452 questionnaires, 301 of which met the criteria (66.6%) and were analyzed. The mean correct diagnoses with and without videos was 8.4 (SD 1.7) of 12 and 7.5 (SD 1.7) of 12, respectively. For epileptic seizures, mobile phone videos increased the mean accurate diagnoses by 3.9%; for nonepileptic events, it was 11.5% and both were statistically different (P=.006 for epileptic events; P<.001 for nonepileptic events). Pediatric neurologists with longer working years had higher diagnostic accuracy; whereas, their working hospital level and educational background made no difference. For patients with paroxysmal events, at least US $673.90 per capita and US $128 million nationwide could be saved annually, which is 12.02% of the total cost for correct diagnosis. Conclusions: Home videos made on mobile phones are a cost-effective tool for the diagnosis of paroxysmal events in infants. They can facilitate the diagnosis of paroxysmal events in infants and thereby save costs. The best choice for infants with paroxysmal events on their initial visit is to record their events first and then show the video to a neurologist with longer working years through online consultation.

  • Source: Foter; Copyright: Phillips Communications; URL: https://foter.com/fff/photo/15028021079/68c6d814c0/; License: Creative Commons Attribution + Noncommercial + NoDerivatives (CC-BY-NC-ND).

    Wearable Health Technology and Electronic Health Record Integration: Scoping Review and Future Directions

    Abstract:

    Background: Due to the adoption of electronic health records (EHRs) and legislation on meaningful use in recent decades, health systems are increasingly interdependent on EHR capabilities, offerings, and innovations to better capture patient data. A novel capability offered by health systems encompasses the integration between EHRs and wearable health technology. Although wearables have the potential to transform patient care, issues such as concerns with patient privacy, system interoperability, and patient data overload pose a challenge to the adoption of wearables by providers. Methods: We used a scoping process to survey existing efforts through Epic’s Web-based hub and discussion forum, UserWeb, and on the general Web, PubMed, and Google Scholar. We contacted Epic, because of their position as the largest commercial EHR system, for information on published client work in the integration of patient-collected data. Results from our searches had to meet criteria such as publication date and matching relevant search terms. Results: Numerous health institutions have started to integrate device data into patient portals. We identified the following 10 start-up organizations that have developed, or are in the process of developing, technology to enhance wearable health technology and enable EHR integration for health systems: Overlap, Royal Philips, Vivify Health, Validic, Doximity Dialer, Xealth, Redox, Conversa, Human API, and Glooko. We reported sample start-up partnerships with a total of 16 health systems in addressing challenges of the meaningful use of device data and streamlining provider workflows. We also found 4 insurance companies that encourage the growth and uptake of wearables through health tracking and incentive programs: Oscar Health, United Healthcare, Humana, and John Hancock. Conclusions: The future design and development of digital technology in this space will rely on continued analysis of best practices, pain points, and potential solutions to mitigate existing challenges. Although this study does not provide a full comprehensive catalog of all wearable health technology initiatives, it is representative of trends and implications for the integration of patient data into the EHR. Our work serves as an initial foundation to provide resources on implementation and workflows around wearable health technology for organizations across the health care industry.

  • Source: Image created by the Authors; Copyright: The Authors; URL: http://mhealth.jmir.org/2019/8/e13608/; License: Creative Commons Attribution (CC-BY).

    Mobile Apps for Medication Management: Review and Analysis

    Abstract:

  • Source: Flickr; Copyright: V.T. Polywoda; URL: https://www.flickr.com/photos/vtpoly/17994243416/in/album-72157652613135665/; License: Creative Commons Attribution + Noncommercial + NoDerivatives (CC-BY-NC-ND).

    Using the Unified Theory of Acceptance and Use of Technology (UTAUT) to Investigate the Intention to Use Physical Activity Apps: Cross-Sectional Survey

    Abstract:

  • Source: freepik; Copyright: katemangostar; URL: https://www.freepik.com/free-photo/close-up-content-lady-sweater-reading-online-article_3798992.htm; License: Licensed by JMIR.

    Mobile Health Divide Between Clinicians and Patients in Cancer Care: Results From a Cross-Sectional International Survey

    Abstract:

    Background: Mobile technologies are increasingly being used to manage chronic diseases, including cancer, with the promise of improving the efficiency and effectiveness of care. Among the myriad of mobile technologies in health care, we have seen an explosion of mobile apps. The rapid increase in digital health apps is not paralleled by a similar trend in usage statistics by clinicians and patients. Little is known about how much and in what ways mobile health (mHealth) apps are used by clinicians and patients for cancer care, what variables affect their use of mHealth, and what patients’ and clinicians’ expectations of mHealth apps are. Objective: This study aimed to describe the patient and clinician population that uses mHealth in cancer care and to provide recommendations to app developers and regulators to generally increase the use and efficacy of mHealth apps. Methods: Through a cross-sectional Web-based survey, we explored the current utilization rates of mHealth in cancer care and factors that explain the differences in utilization by patients and clinicians across the United States and 5 different countries in Europe. In addition, we conducted an international workshop with more than 100 stakeholders and a roundtable with key representatives of international organizations of clinicians and patients to solicit feedback on the survey results and develop insights into mHealth app development practices. Results: A total of 1033 patients and 1116 clinicians participated in the survey. The proportion of cancer patients using mHealth (294/1033, 28.46%) was far lower than that of clinicians (859/1116, 76.97%). Accounting for age and salary level, the marginal probabilities of use at means are still significantly different between the 2 groups and were 69.8% for clinicians and 38.7% for patients using the propensity score–based regression adjustment with weighting technique. Moreover, our analysis identified a gap between basic and advanced users, with a prevalent use for activities related to the automation of processes and the interaction with other individuals and a limited adoption for side-effect management and compliance monitoring in both groups. Conclusions: mHealth apps can provide access to clinical and economic data that are low cost, easy to access, and personalized. The benefits can go as far as increasing patients’ chances of overall survival. However, despite its potential, evidence on the actual use of mobile technologies in cancer care is not promising. If the promise of mHealth is to be fulfilled, clinician and patient usage rates will need to converge. Ideally, cancer apps should be designed in ways that strengthen the patient-physician relationship, ease physicians’ workload, be tested for validity and effectiveness, and fit the criteria for reimbursement.

  • Heart rate variability biofeedback. Source: Image created by the RTI Authors; Copyright: The RTI Authors; URL: http://mhealth.jmir.org/2019/7/e12590/; License: Licensed by the authors.

    Biofeedback-Assisted Resilience Training for Traumatic and Operational Stress: Preliminary Analysis of a Self-Delivered Digital Health Methodology

    Abstract:

    Background: Psychological resilience is critical to minimize the health effects of traumatic events. Trauma may induce a chronic state of hyperarousal, resulting in problems such as anxiety, insomnia, or posttraumatic stress disorder. Mind-body practices, such as relaxation breathing and mindfulness meditation, help to reduce arousal and may reduce the likelihood of such psychological distress. To better understand resilience-building practices, we are conducting the Biofeedback-Assisted Resilience Training (BART) study to evaluate whether the practice of slow, paced breathing with or without heart rate variability biofeedback can be effectively learned via a smartphone app to enhance psychological resilience. Objective: Our objective was to conduct a limited, interim review of user interactions and study data on use of the BART resilience training app and demonstrate analyses of real-time sensor-streaming data. Methods: We developed the BART app to provide paced breathing resilience training, with or without heart rate variability biofeedback, via a self-managed 6-week protocol. The app receives streaming data from a Bluetooth-linked heart rate sensor and displays heart rate variability biofeedback to indicate movement between calmer and stressful states. To evaluate the app, a population of military personnel, veterans, and civilian first responders used the app for 6 weeks of resilience training. We analyzed app usage and heart rate variability measures during rest, cognitive stress, and paced breathing. Currently released for the BART research study, the BART app is being used to collect self-reported survey and heart rate sensor data for comparative evaluation of paced breathing relaxation training with and without heart rate variability biofeedback. Results: To date, we have analyzed the results of 328 participants who began using the BART app for 6 weeks of stress relaxation training via a self-managed protocol. Of these, 207 (63.1%) followed the app-directed procedures and completed the training regimen. Our review of adherence to protocol and app-calculated heart rate variability measures indicated that the BART app acquired high-quality data for evaluating self-managed stress relaxation training programs. Conclusions: The BART app acquired high-quality data for studying changes in psychophysiological stress according to mind-body activity states, including conditions of rest, cognitive stress, and slow, paced breathing.

  • Source: freepik; Copyright: katemangostar; URL: https://www.freepik.com/search?dates=any&format=search&page=2&people=include&query=shoulder+workout&selection=1&sort=popular; License: Licensed by JMIR.

    Reliability of a Smartphone Compared With an Inertial Sensor to Measure Shoulder Mobility: Cross-Sectional Study

    Abstract:

    Background: The shoulder is one of the joints with the greatest mobility within the human body and its evaluation is complex. An assessment can be conducted using questionnaires or functional tests, and goniometry can complement the information obtained in this assessment. However, there are now validated devices that can provide more information on the realization of movement, such as inertial sensors. The cost of these devices is usually high and they are not available to all clinicians, but there are also inertial sensors that are implemented in mobile phones which are cheaper and widely available. Results from the inertial sensors integrated into mobile devices can have the same reliability as those from dedicated sensors. Objective: This study aimed to validate the use of the Nexus 4 smartphone as a measuring tool for the mobility of the humerus during shoulder movement compared with a dedicated InertiaCube3 (Intersense) sensor. Methods: A total of 43 subjects, 27 affected by shoulder pathologies and 16 asymptomatic, participated in the study. Shoulder flexion, abduction, and scaption were measured using an InertiaCube3 and a Nexus 4 smartphone, which were attached to the participants to record the results simultaneously. The interclass correlation coefficient (ICC) was calculated based on the 3 movements performed. Results: The smartphone reliably recorded the velocity values and simultaneously recorded them alongside the inertial sensor. The ICCs of the 3 gestures and for each of the axes of movement were analyzed with a 95% CI. In the abduction movement, the devices demonstrated excellent interclass reliability for the abduction humeral movement axis (Cronbach alpha=.98). The axis of abduction of the humeral showed excellent reliability for the movements of flexion (Cronbach alpha=.93) and scaption (Cronbach alpha=.98). Conclusions: Compared with the InertiaCube3, the Nexus 4 smartphone is a reliable and valid tool for recording the velocity produced in the shoulder.

Citing this Article

Right click to copy or hit: ctrl+c (cmd+c on mac)

Latest Submissions Open for Peer-Review:

View All Open Peer Review Articles
  • Development and Evaluation of Culturally Tailored Mobile Phone-Based Application to Promote Breast Cancer Preventive Behaviors among the Iranian Women: A randomized controlled trial

    Date Submitted: Sep 8, 2019

    Open Peer Review Period: Sep 8, 2019 - Nov 3, 2019

    Background: Despite the first rank of breast cancer incidence diagnosed in Iranian women, they have one of the lowest rates of breast cancer preventive behaviors. So that these behaviors have not been...

    Background: Despite the first rank of breast cancer incidence diagnosed in Iranian women, they have one of the lowest rates of breast cancer preventive behaviors. So that these behaviors have not been applied as routine care for healthy Iranian women. To improve preventive behaviors, a reliable and practical approach is needed. This study aimed to develop and test the culturally tailored mobile Phone-based application to promote breast cancer preventive behaviors called ASSISTS among Iranian women. Objective: This study explores the impact of ASSISTS mobile application on changes to study Iranian women’ preventive behaviors toward breast cancer and recommends suggestions for how the intervention can be enhanced for varied dissemination and performance in the Iranian community. Methods: Applying a randomized controlled trial design, 140 Iranian women aged 40 years and above were recruited and randomly appointed to either the intervention group (n=70) to receive culturally and personally tailored multilevel messages by a smartphone application accompanied by the routine health care facilities and control group (n=70) to receive just a routine health care. Outcome measures included attitude, stimulus, self-efficacy, information seeking, stress management, self-care, and social support, readiness for performing breast cancer preventive behaviors. The feasibility and acceptability of the ASSISTS mobile application intervention were also assessed. Results: Significant intervention effects were observed for objectively measured knowledge and self-reported variables of ASSISTS scale (all P<.05). The intervention group indicated significantly more change on scores of knowledge of preventive behaviors procedures and self-care (P=.01). The ASSISTS app group indicated significantly more readiness for breast exams use after the intervention compared with the booklet group. Furthermore, the intervention group evaluated consent with the intervention (P=.001) and increase of knowledge on breast cancer preventive behaviors (P=.001) more significant than the booklet group. About 68.2% ASSISTS app participants assessed this app as self-explanatory (44/70), fun (64/70, 66.5%), and interesting (63/70, 88.8%). Conclusions: An ASSISTS smartphone app-based intervention was a practical, useful, and suitable intervention means to improve breast cancer preventive behaviors in Iranian women. A flexible, directly tailored method that depends on current technical progress can gain underserved and hard-to-employee populations that bear unequal cancer loads.

  • Evaluating the feasibility of frequent cognitive assessment using the Mezurio smartphone app: Observational and Interview Study in PREVENT dementia cohort members.

    Date Submitted: Sep 5, 2019

    Open Peer Review Period: Sep 5, 2019 - Oct 31, 2019

    Background: Smartphones may significantly contribute to the detection of early cognitive decline at scale by enabling remote, frequent, sensitive, economic assessment. Several prior studies have susta...

    Background: Smartphones may significantly contribute to the detection of early cognitive decline at scale by enabling remote, frequent, sensitive, economic assessment. Several prior studies have sustained engagement with participants remotely over a period of a week; extending this to a period of a month would clearly give greater opportunity for measurement. However, as such study durations are increased, so too is the need to understand how participant burden and scientific value might be optimally balanced. Objective: We explore the ‘little but often’ approach to assessment employed by the Mezurio app, interacting with participants every day for over a month. We aim to understand whether this extended remote study duration is feasible, and which factors might promote sustained participant engagement over such study durations. Methods: Thirty-five adults (aged 40-59 years) with no diagnosis of cognitive impairment were prompted to interact with the Mezurio smartphone app platform for up to 36 days, completing short, daily episodic memory tasks in addition to optional executive function and language tests. A subset (n=20) completed semi-structured interviews focused on their experience using the app. Results: Average compliance with the schedule of learning for subsequent memory test was 80%, with 88% of participants still actively engaged by the final task. Thematic analysis of participants’ experiences highlighted schedule flexibility, a clear user-interface, and performance feedback as important considerations for engagement with remote digital assessment. Conclusions: Despite the extended study duration, participants demonstrated high compliance with the tasks scheduled and were extremely positive about their experiences. Long durations of remote digital interaction are therefore definitely feasible, but only when careful attention is paid to the design of the users’ experience.

  • Wear-IT: Low-burden Mobile Monitoring and Intervention through Real-time Analysis

    Date Submitted: Aug 30, 2019

    Open Peer Review Period: Aug 30, 2019 - Oct 25, 2019

    Background: Mobile health methods often rely on active input from participants, for example in the form of self-report questionnaires delivered via web or smartphone, to measure health and behavioral...

    Background: Mobile health methods often rely on active input from participants, for example in the form of self-report questionnaires delivered via web or smartphone, to measure health and behavioral indicators and deliver interventions in everyday life settings. For short-term studies or interventions, these techniques are deployed intensively, causing nontrivial participant burden. For cases of where the goal is long-term maintenance, limited infrastructure exists to balance information needs with participant constraints. Yet the increasing precision of passive sensors such as wearable physiology monitors, smartphone-based location history, and Internet-of-Things (IoT) devices, in combination with statistical feature selection and adaptive interventions have begun to make such things possible. Objective: We introduce Wear-IT, a smartphone app and cloud framework intended to begin to fill this gap by allowing researchers to leverage commodity electronics and real-time decision-making to optimize the amount of useful data collected while minimizing participant burden. Methods: By using a combination of active and passive sensing technology, real-time analysis tools, responsive intervention and assessment, individualized visualization, modeling and feedback, and novel data collection methods, it is possible to actively balance participant burden and engagement with data quality within the resource limitations of the device. Results: Wear-IT provides a novel framework for both researchers seeking to study ways of optimizing burden and engagement for mHealth and uHealth research; it also can be deployed as a means of using these techniques to balance these quantities against data quality for adaptive interventions, especially for longer-term deployments. We provide use cases from ongoing deployments, and a brief example of visualization tools for an addiction recovery intervention. Conclusions: Engagement and participant burden are serious concerns for any mHealth or uHealth deployment, whether for research or active intervention, and balancing these quantities against data quality and intervention precision represents a nontrivial task. The use of individualized modeling to combine passive and active sensing, feedback, and responsiveness seems necessary to balance these concerns against the need for quality real-time data. Wear-IT takes the first step towards this goal by providing a general-purpose framework for mHealth and uHealth researchers seeking to study these concerns, and for practitioners and clinicians seeking to optimize these quantities in their interventions.

  • Social, Organizational, and Technological Factors Impacting Clinicians’ Adoption of Mobile Health Tools: A Systematic Literature Review

    Date Submitted: Aug 22, 2019

    Open Peer Review Period: Aug 20, 2019 - Oct 15, 2019

    Background: There is a growing body of evidence highlighting the potential of Mobile Health in reducing healthcare costs, enhancing access, and improving the quality of patient care. However, user acc...

    Background: There is a growing body of evidence highlighting the potential of Mobile Health in reducing healthcare costs, enhancing access, and improving the quality of patient care. However, user acceptance and adoption are key prerequisites to harness this potential, hence, a deeper understanding of the factors impacting this adoption is crucial for its success. Objective: The aim of this review is to systematically explore relevant published literature in order to synthesize the current understanding of the factors impacting clinicians’ adoption of mHealth tools, not only from a technology perspective but also from social and organizational perspectives. Methods: A structured search was carried out of Medline PubMed, the Cochrane Library, and SAGE database for studies published between January 2008 and July 2018 in the English language; yielding 4993 results, of which 171 met the inclusion criteria. Results: The technological factors impacting clinicians’ adoption of mobile health were categorized into 8 key themes: Usefulness, Ease of use, Design, Compatibility, Technical issues, content, Personalization and convenience. These were in turn divided into 14 sub-themes altogether. Social and organizational factors were much more prevalent and were categorized into 8 key themes: Workflow related, Patient related, Policy and regulations, Culture or attitude or social influence, Monetary factors, Evidence base, Awareness, and User engagement. These were in turn divided into 41 sub-themes, highlighting the importance of considering these factors when addressing potential barriers to mHealth adoption and how to overcome them. Conclusions: The study results can help inform mHealth providers and policy makers regarding the key factors impacting mHealth adoption, guiding them into making educated decisions to foster this adoption and harness the potential benefits. Clinical Trial: NA

  • Facilitating Management of Opioid Use Disorder: A Technology Landscape Analysis

    Date Submitted: Aug 2, 2019

    Open Peer Review Period: Aug 6, 2019 - Oct 1, 2019

    Background: Advances in technology engender investigation of technology solutions to Opioid Use Disorder (OUD). However, in comparison to chronic disease management, the application of mobile health (...

    Background: Advances in technology engender investigation of technology solutions to Opioid Use Disorder (OUD). However, in comparison to chronic disease management, the application of mobile health (mHealth) to OUD has been limited. Objective: The objectives of this paper are to (1) document the currently available opioid-related mHealth applications (apps) and (2) review past and existing technology solutions that address OUD. Methods: We used a two-phase parallel search approach: (1) app search to determine availability of opioid-related mHealth apps, and (2) focused review of literature to identify relevant technologies and mHealth apps used to address OUD. Results: The app search revealed a steady rise in app development, with the majority of apps being clinician-facing. A majority of the apps were designed to aid in opioid dose conversion. Despite the availability of these apps, the focused review found no study that investigated the efficacy of mHealth apps to address OUD. Conclusions: Our findings highlight a general gap in technological solutions of OUD management, and the potential for mHealth apps and wearable sensors to address OUD.

  • A theory-based content analysis on mhealth applications for obesity

    Date Submitted: Aug 1, 2019

    Open Peer Review Period: Aug 6, 2019 - Oct 1, 2019

    Background: With the availability of handy mobile devices and high-speed internet, much information in the field of health, wellness and fitness is now more accessible to the public. People of almost...

    Background: With the availability of handy mobile devices and high-speed internet, much information in the field of health, wellness and fitness is now more accessible to the public. People of almost all age groups use mhealth apps (Mobile health applications) to know about common diseases and their symptoms, medicine uses and side effects, diet plans and calculates BMI to keep them fit, etc. Obesity is considered as a growing threat to our society, especially for kids. Mobile apps related to obesity are available in large numbers. The potentials of such obesity-related mobile apps have to be investigated for better understanding of these apps, for using them in an effective way and for their influencing behavioural change on the users. There are prevalent studies on health & fitness apps in general but studies rarely focused on a particular health issue related apps. Objective: Thus the aim of the study is to explore the potentials of obesity-related apps. Methods: The content analysis method was adopted to analyze the contents of the top 35 obesity-related mhealth apps. A framework based on Precede-Proceed Model (PPM) was used to explore the chosen apps. The three factors of PPM model are a pre-disposing factor, enabling factor and reinforcing factor. Results: The analysis resulted that 26% of the apps satisfied all the variables of pre-disposing factor, only 3% of the apps satisfied all the variables of enabling factor and 6% of the apps satisfied all the variables of reinforcing factor. Conclusions: Entirely only 9% of the apps taken for the study satisfied the maximum variables of PPM to influence the health behavioural change of the app users. The researchers strongly recommend health professionals to involve in the development of obesity-related mhealth apps rather than some third-party developers. Lastly, a few suggestions regarding how users can adapt an obesity-related mhealth app were provided.

Advertisement