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Posted on Jul 28, 2016 in Original Article | 0 comments

An integrated mHealth model for type 2 diabetes patients using mobile tablet devices

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Sora Park, PhD1, Sally Burford, PhD1, Leif Hanlen, PhD2, Paresh Dawda, MBBS/DRCOG3, Paul Dugdale, PhD/FAFPHM4, Christopher Nolan, MBBS/PhD5, John Burns, Adjunct Professor6

1News & Media Research Centre, University of Canberra, ACT, Australia; 2Data61, University of Canberra, Australian National University, ACT, Australia; 3Ochre Health Medical Centre, ACT, Australia; 4College of Medicine, Biology & Environment, Australian National University, ACT, Australia; 5College of Medicine, Biology & Environment, Australian National University, Canberra Hospital, ACT, Australia; 6University of Canberra, ACT, Australia

Corresponding Author: sora.park@canberra.edu.au

Journal MTM 5:2:24–32, 2016

doi: 10.7309/jmtm.5.2.4

Background: Ease of use, proximity to the user and various health maintenance applications enable mobile tablet devices to improve patient self-management. With mobile phones becoming prevalent, various mobile health (mHealth) programs have been devised, to improve patient care and strengthen healthcare systems.

Aims: This study explored how mHealth programs can be developed for type 2 diabetes patients through a co-design participatory workshop between practitioners and researchers. The aim was to design a mHealth pilot program from the input.

Methods: A co-design workshop was conducted with 15 participants, including general practitioners, specialists, nurses and a multidisciplinary research team. Participants generated 31 statements in response to a trigger question and engaged in a structured discussion. Thematic cluster analysis was conducted on the statements and discussions.

Results: Through the analysis, patients’ self-management and health system integration emerged as the main topics. Further analysis revealed that there were two distinct areas of patient self-management; ‘compelled’ and ‘empowered’.

Conclusion: With the results, a loose-knit mHealth pilot program was developed wherein patients with various levels of conditions and digital skills could be incorporated. In order to encourage sustainable changes, practitioners proposed that mobile devices must be situated in the patients’ everyday settings and that digital training should be provided.

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Posted on Jul 28, 2016 in Original Article | 0 comments

Scope of Mobile Phones in Mental Health Care in Low Resource Settings

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M Sood, Additional Prof.1, RK Chadda, Prof.1, K Sinha Deb, Assistant Prof.1, R Bhad, Senior Resident1, A Mahapatra, Senior Resident1, R Verma, Assistant Prof.1, AK Mishra, Assistant Prof.1

1Department of Psychiatry, All India Institute of Medical Sciences, Ansari Nagar, New Delhi 110029, India

Corresponding Author: soodmamta@gmail.com

Journal MTM 5:2:33–37, 2016

doi: 10.7309/jmtm.5.2.5

Introduction: Mobile apps are used as an aid in the mental health services in many high income countries. The present study was conducted to assess frequency of mobile phone use amongst patients with mental illness.

Methods: Patients attending psychiatric outpatient department of a public funded tertiary care hospital in India were assessed for use of mobile phone and its possible utility in mental health service delivery using a semi structured questionnaire.

Results: The study had 350 subjects, out of whom 307 (87.7%) reported using mobile phone on a regular basis. Mobile phone was used for phone calls, sending and receiving short text messages (SMS) recreation, and accessing social networking sites. Most of the users agreed that the mobile phone could be used as an aid in mental health service delivery, and expressed willingness to receive educational messages.

Conclusion: Patients with mental illness attending psychiatric outpatient services in India use mobile phones and are willing to use as a treatment aid.

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Posted on Jul 28, 2016 in Perspective Pieces | 0 comments

The Future of Automated Mobile Eye Diagnosis

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Cassie A. Ludwig, BS1, Mia X. Shan, BS, BAH1, Nam Phuong H. Nguyen1, Daniel Y. Choi, MD1, Victoria Ku, BS1, Carson K. Lam, MD1

1Byers Eye Institute, Stanford University School of Medicine 2405 Watson Drive, Palo Alto, CA, USA 94305

Corresponding Author: carsonl@stanford.edu

Journal MTM 5:2:44–50, 2016

doi: 10.7309/jmtm.5.2.7

The current model of ophthalmic care requires the ophthalmologist’s involvement in data collection, diagnosis, treatment planning, and treatment execution. We hypothesize that ophthalmic data collection and diagnosis will be automated through mobile devices while the education, treatment planning, and fine dexterity tasks will continue to be performed at clinic visits and in the operating room by humans. Comprehensive automated mobile eye diagnosis includes the following steps: mobile diagnostic tests, image collection, image recognition and interpretation, integrative diagnostics, and user-friendly, mobile platforms. Completely automated mobile eye diagnosis will require improvements in each of these components, particularly image recognition and interpretation and integrative diagnostics. Once polished and integrated into greater medical practice, automated mobile eye diagnosis has the potential to increase access to ophthalmic care with reduced costs, increased efficiency, and increased accuracy of diagnosis.

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Posted on Jul 28, 2016 in Letter to the Editor | 0 comments

A Survey of Japanese Young Adults’ Postures When Using Smartphones before Sleeping

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Michitaka Yoshimura, MA1, Momoko Kitazawa, MA1, Taishiro Kishimoto, MD, PhD2,3*, Masaru Mimura, MD, PhD2, Kazuo Tsubota, MD, PhD1

1Department of Ophthalmology, Keio University School of Medicine, Tokyo, Japan; 2Department of Neuropsychiatry, Keio University School of Medicine, Tokyo, Japan; 3Hofstra Northwell School of Medicine, Hempstead, New York, USA

*Taishiro Kishimoto is not a recipient of a research scholarship.

Corresponding Author: t-kishimoto@keio.jp

Journal MTM 5:2:51–53, 2016

doi:10.7309/jmtm.5.2.8

Although mobile technologies, devices and software have enriched our lives in many ways, including medical applications, the potential negative effects are often overlooked. A growing amount of evidence suggests that there are potential negative impacts of smartphones on biophysiological processes, especially on sleep.1–6 Studies have shown that blue lights, especially the short-wavelength light (380 ~ 495 nm) emitted from smartphone monitors, disrupts circadian rhythm by retarding nocturnal melatonin production.7

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Posted on Jul 28, 2016 in Editorial | 0 comments

The era of automated systems to facilitate health care

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Rahul Chakrabarti, Dr1

1Chief Editor, Journal of Mobile Technology in Medicine

Journal MTM 5:2:1, 2016

doi:10.7309/jmtm.5.2.1

Dear Readers,

7th July, 2016

It is with great pleasure that we present the second issue of the Journal of Mobile Technology in Medicine for 2016 with excellent examples of translational mHealth research. One of the great challenges confronting global health care is accessibility and affordability to diagnostic technologies and timely referral to specialist services.

In this issue, Ludwig et al provide a brief overview of the existing technologies available to aid automated diagnostic and referral in the field of the ophthalmology. The authors provide a summary of a potential pathway for automated ophthalmic care through the use of mobile diagnostic devices that can facilitate image collection. The first step in the clinical algorithm is safe and accurate image capturing technologies. The authors highlight examples of mobile diagnostic adapters developed by the Peek Vision group (UK), D-eye system (Italy), and iExaminer (Welch Allyn) which convert the modern smartphone into an anterior and posterior segment image capturing device. These images can then be collated, filtered for quality, and interpreted by automated software and results can, in theory, be graded in real-time to provide risk stratification and triaging of patients.

Whilst the concept of automated diagnostics in ophthalmic care is not new, the challenge over the last 20 years has been to develop algorithms that meet sensitivity and specificity criteria to be safe for day to day real world clinical practice.1 Ludwig et al succinctly illustrate examples whereby the two common modes of automated image analysis, neural networks and deep learning are now meeting the level of reliability and reproducibility for safe clinical practice. Importantly, Ludwig et al highlight examples of the utility of automated grading technologies developed for two of the most common, yet insidious causes of global vision loss, glaucoma and diabetic retinopathy.

The evolution of automated diagnostic technologies now truly positions health care in the 21st century to reach and provide care to a greater population breadth than ever before. The benefits of such technologies will always be balanced by the caveats of the necessity for clinical correlation by a specialist or appropriately trained medical professional, the costs of equipment, and the need for further evidence in larger population based studies. This is particularly poignant for automated software based learning. Nevertheless, there is a clear value in ability of these technologies to facilitate early diagnosis, triaging and timely referral of patients in rural and remote and low-resourced settings, where the greatest burden of global morbidity exists.

Reference

1. Gardner GG, Keating D, Williamson TH, Elliott AT. Automatic detection of diabetic retinopathy using an artificial neural network: a screening tool. The British journal of ophthalmology. Nov 1996;80(11): 940–944.

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Posted on Jul 28, 2016 in Perspective Pieces | 0 comments

Pretesting mHealth: Implications for Campaigns among Underserved Patients

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Disha Kumar, BS, BA1,2, Monisha Arya, MD, MPH3,4

1Rice University, 6100 Main Street, Houston, Texas 77005, USA; 2School of Medicine, Baylor College of Medicine, One Baylor Plaza, Houston, Texas 77030, USA; 3Department of Medicine, Section of Infectious Diseases and Section of Health Services Research, Baylor College of Medicine, One Baylor Plaza, Houston, Texas 77030, USA; 4Center for Innovations in Quality, Effectiveness and Safety, Michael E. DeBakey VA Medical Center 2002 Holcombe Blvd (Mailstop 152), Houston, Texas 77030, USA

Corresponding Author: disha.kumar@bcm.edu

Journal MTM 5:2:38–43, 2016

doi: 10.7309/jmtm.5.2.6

Background: For health campaigns, pretesting the channel of message delivery and process evaluation is important to eventual campaign effectiveness. We conducted a pilot study to pretest text messaging as a mHealth channel for traditionally underserved patients.

Aims: The primary objectives of the research were to assess 1) successful recruitment of these patients for a text message study and 2) whether recruited patients would engage in a process evaluation after receiving the text message.

Methods: Recruited patients were sent a text message and then called a few hours later to assess whether they had received, read, and remembered the sent text message.

Results: We approached twenty patients, of whom fifteen consented to participate. Of these consented participants, ten (67%) engaged in the process evaluation and eight (53%) were confirmed as receiving, reading, and remembering the text message.

Conclusion: We found that traditionally underserved and under-researched patients can be recruited to participate in a text message study, and that recruited patients would engage in a process evaluation after receiving the text message.

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