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HEALTH CAREAPPS-WI
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SOLUTI
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ENABLI
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ENVI
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SUCCESSFUL
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Beyond theHype:Mobi
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Technologi
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Opportuni
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AddressHeal
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Di
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GOOGLE GLASS I
NDI
RECT OPHTHALMOSCOPY
Journal of
Mobile
Technology in
Medicine
editor-in-chief
Dr Chandrashan Perera
Dr Rahul Chakrabarti
section editors
Dr Jaime Lee
Dr Rafsan Halim
senior editor
multimedia editor
Dr Steven Steffensen
Dr Orrin Franko
senior editor
lead app editor
Dr Carlos Cabalag
A/Prof Vishal Jhanji
manuscript editor
ophthalmology & visual
science editor
peer review panel
Prof Aditya Ghose
Dr Saugato Mukerji
Dr Thomas Hardy
Dr George Kong
Dr Paul Paddle
Dr Ryan De Freitas
Dr Judith Proudfoot
Dr Mahendra Perera
Dr Eduardo Mayorga
Dr Jagadheesan Karuppiah
Dr Sud Agarwal
Dr Juston Sherwin
Dr Diab Mohamad
Dr Sanjiva Wijesinha
Dr Akbar Ashrafi
Dr Tissa Wijeratne
Dr Stanley Rajapakse
Dr Gayan Padmasekara
Dr Richard Brady
Dr Nitesh Nerlekar
Dr Simon Hew
Dr Andrew Bastawrous
Dr Vaidy Swaminathan
Dr Paula Ferrara
Mr Edward Bunker
Dr Jayantha Perera
Dr Patrick Mahar
Material published in the Journal of Mobile Technology in Medicine is published
under a Creative Commons 3.0 Attribution-NonCommercial-NoDerivs (CC-BY-NC-ND)
Unported license.
Electronic ISSN: 1839-7808
Journal of
Mobile
Technology in
Medicine
www.journalmtm.com
Vol: 4 Issue: 1
Editorial
001
JMTM Editorial Volume 4 (2015) Issue 1
R. Chakrabarti
Original Articles
002
Accuracy of Estimates of Step Frequency From a
Wearable Gait Monitor
M. Punt, H. Wittink, F. van der Bent, Jh. van Dieën
008
Health care apps- will they be a facelift for today’s
medical/dental practice?
D. Jasti, KVNR. Pratap, M. Padma.T, V.S. Kalyan, M.P. Sandhya,
ASK. Bhargava
015
Google Glass Indirect Ophthalmoscopy
A. Wang, A. Christoff, D.L. Guyton, M.X. Repka, M. Rezaei,
A.O. Eghrari
020
Development of an iPad version of the Kessler 10+ for
use in youth mental health outreach services
G. Furber, A.E. Crago, T.D. Sheppard, C. Skene
Perspective Piece
025
‘‘mHealth is an Innovative Approach to Address
Health Literacy and Improve Patient-physician
Communication – An HIV Testing Exemplar’’
D. Kumar, M. Arya
Original Article
031
Contextual Barriers to Mobile Health Technology in
African Countries: A Perspective Piece
Y. O’ Connor, J. O’ Donoghue
Perspective Piece
035
Taking mHealth Solutions to Scale: Enabling
Environments and Successful Implementation
J. Franz-Vasdeki, B.A. Pratt, M. Newsome, S. Germann
Letter To The Editor
039
Beyond the Hype: Mobile Technologies and
Opportunities to Address Health Disparities
Y. Hswen, K. Viswanath
EDITORIAL
JMTM EDITORIAL VOLUME 4 (2015) ISSUE 1
Rahul Chakrabarti1,2
1
Chief Editor, Journal of Mobil Technology in Medicine; 2Ophthalmology Registrar, Royal Victorian Eye and Ear Hospital,
Melbourne, Australia
Journal MTM 4:1:1, 2015
doi:10.7309/jmtm.4.1.1
It is with great pleasure that we present in this first
issue of 2015 a compendium of publications outlining the global impact of mHealth applications,
particularly in developing countries.
The perspective piece by Germann and Franz
articulates the challenges that need to be met in
order for mHealth to be utilised and upscaled in low
resource resource settings. Whilst low-resource
countries clearly have great potential to benefit
from expansion of mHealth applications, the
authors outline key barriers for mHealth expansion
in developing regions. They highlight the imperative
for strong governance. Critical elements identified
include appropriate planning, consideration of
feasibility, strengthening private and government
partnerships in mHealth projects, and ensuring
there is a dynamic auditing process that can record
the effectiveness and identify areas for improvement
during a project. The authors cite the example of
‘mTrac’, an e-Health initiative overseen by the
Ugandan Ministry of Health which is a central
auditing and data collection tool to monitor community based projects within their country. The tool
is intended to assist in collection, verification of
quality and reliability, and ultimately in analysis of
data collected across mHealth projects. Whilst the
tool is being disseminated in preliminary phases
across the country, it demonstrates a change in
paradigm particularly from within low-resource
countries to improve quality and rigour of data
collection and analysis.
Whilst there is an escalating demand for mHealth,
the emphasis must now shift to quality of evidence
rather than quantity. This is particularly important
#JOURNAL OF MOBILE TECHNOLOGY IN MEDICINE
given it is these low-resource areas where there is a
clear demand for data regarding effectiveness of
mHealth interventions yet a definite paucity of high
quality evidence.1 The publication of Germann anf
Franz echo similar themes highlighted previously in
our Journal by Bullen regarding the importance of
planning, consideration of local factors affecting
feasibility, and the tantamount importance of
governance in the success of translating mHealth
initiatives to real-world functional projects.2 Similarly, an independent report conducted by Price
Waterhouse Coopers on the emergence of mHealth
outlined that the key principles for upscaling
mHealth included finding applications that bring
concrete value to stakeholders, the imperative to
engage multiple stakeholders at a national level, and
to focus on solutions rather than technology.3 Thus,
in order to realise the full potential of mHealth
there must be a weight of high quality evidence in
order to engage the attention of government and
non-government investors, and most importantly,
patients and communities.
References
1. Free C, Phillips G, Watson L, et al. The effectiveness
of mobile-health technologies to improve health care
service delivery processes: a systematic review and
meta-analysis. PLoS medicine. 2013;10(1):e1001363.
2. Bullen P. Operational challenges in the Cambodian
mHealth revolution. Journal of Mobile Technology in
Medicine. 2013;2(2):203.
3. Emerging mHealth: Paths for growth. Price Waterhouse
Coopers;2012.
VOL. 4 | ISSUE 1 | JANUARY 2015 1
ORIGINAL ARTICLE
ACCURACY OF ESTIMATES OF STEP FREQUENCY FROM
A WEARABLE GAIT MONITOR
M Punt, MSc1, H Wittink, PhD1, F van der Bent, Ing1, Jh van Diee¨n, PhD2,3
1
Research group Lifestyle and Health, Utrecht University of Applied Sciences, Utrecht, The Netherlands; 2Move Research Institute
Amsterdam, Faculty of Human Movement Sciences, VU University Amsterdam, Amsterdam, the Netherlands; 3King Abdulaziz
University, Jeddah, Saudi Arabia
Corresponding Author: [email protected]
Background: Assessment of gait activity by accelerometry requires data analysis. Currently several
methods are used to estimate step frequency. At present the relation between step frequency
estimation, gait speed and minimal required time window length remains unknown.
Aims: The purpose of the study was to assess the accuracy of estimates of step frequency (SF) from
trunk acceleration data analyzed with commonly used algorithms and time window lengths, at a
wide range of gait speeds.
Method: Twenty healthy young subjects performed an incremental treadmill protocol from 1 km/h
up to 6 km/h, with steps of 1 km/h. Each speed condition was maintained for two minutes. A waist
worn accelerometer recorded trunk accelerations, while video analysis provided the correct number
of steps taken during each gait speed condition. Accuracy of two commonly used signal analysis
methods (autocorrelation, fast Fourier transformation) was examined with time windows of two,
four and eight seconds.
Results: Our main finding was that accuracy of SF estimates with fast Fourier transformation and
autocorrelation improved with increasing time window size, only at the lower gait speeds. Accuracy
of SF estimation was lower at low gait speeds independent of the algorithm and time window used.
Conclusion: We recommend a minimum TW length of 4 seconds when using AC and PSD algorithms
and when using the PSD algorithm to use spectral averaging, as this leads to better results at short
TW and low gait speeds.
Journal MTM 4:1:27, 2015
doi:10.7309/jmtm.4.1.2
Introduction
Quantitative assessment of gait patterns usually
involves laboratory methods, such as force plates
and optical motion analysis systems1. The ecological validity of such laboratory-based assessment can be questioned, i.e. does the movement
performed in the gait lab represent the normal
#JOURNAL OF MOBILE TECHNOLOGY IN MEDICINE
www.journalmtm.com
movement of a subject during daily life?2. Tri-axial
accelerometers fixed on the human body allow
quantitative analysis of gait patterns and offer the
advantage that they are not limited to a laboratory
setting3. Accelerometry-based assessment has become more and more accessible, with examples of
new applications being: real-time gait parameter
VOL. 4 | ISSUE 1 | JANUARY 2015 2
ORIGINAL ARTICLE
recognition4, continuous activity monitoring5 and
telerehabilitation6.
A basic parameter to be estimated in gait analysis is
the number of steps taken per unit of time, i.e. the
step frequency (SF). Algorithms based on Autocorrelation (AC)7,8 and Power Spectral Density
(PSD)9 have been used to this end. These methods
require analysis of data collected over a time
window (TW) of a given size. The choice of TW
size for data analysis depends on many factors.
Accuracy of the estimation increases with TW size.
In addition, smaller TWs reduce the frequency
resolution and increase the chance of ‘spectral
leakage’. However, step frequency (SF) within one
person may vary over time and the length of the TW
limits resolution in determining such variations. In
addition, a longer TW produces a time delay due to
the longer data collection and calculation times.
This time delay might be unacceptable in real-time
and continuous monitoring applications, especially
because compact wearable systems are restricted in
computational capacity10. Finally, gait speed is
often reduced in specific populations, such as
patients post stroke11 and with Parkinson’s disease12. Consequently, SF tends to be lower than in
normal populations11. Low SF may require a longer
TW as the events occur less frequently.
To the best of our knowledge, it is at present
unknown which TW size provides the most accurate
SF determination across the range of gait speeds
that humans produce. The main objective of this
study was to determine the effect of TW size on
accuracy of SF determination over a range of gait
speeds, for two most common used estimation
methods (based on AC and PSD). For the PSD
based estimates, we compared estimates of SF as the
frequency at the peak PSD to a weighted average of
the frequency at peak PSD and the nearest neighboring frequencies. We speculated that the latter
would improve the accuracy of SF estimation,
particularly for small time windows as spectral
averaging might overcome effects of spectral leakage. In addition, it has been assumed that combining acceleration signals from different directions
might improve the accuracy of gait parameter
detection10,4, at the cost of requiring more computational capacity. Therefore our second aim was to
determine whether or not including acceleration
signals from different directions improves the accuracy of measuring SF.
#JOURNAL OF MOBILE TECHNOLOGY IN MEDICINE
Method and data analysis
2.1 Participants
Twenty subjects (7 males, 13 females; age 28,6 9
11,2yr; height 172,6 9 8 cm; weight 69,4 9 9,7 kg;
BMI 23,2 9 2,5; mean 9SD) voluntarily participated in the study. This study was approved by the
local Ethics Committee and written informed consent was obtained from each participant. Treatment
of the participants was according to the Helsinki
declaration13.
2.2 Protocol
All subjects performed a twelve-minutes walk on a
calibrated treadmill (En Mill treadmill, Enraf Nonius, the Netherlands). The first speed was set at one
km/h and speed was increased by one km/h after
every two minutes. Walking speed ranged from one
km/h up to six km/h. The treadmill speed was
manually adjusted by a research assistant. Only the
last 88 seconds of each two-minutes walking bout
were analyzed in order to avoid acceleration effects.
The actual number of steps taken was derived from
video observation.
2.3 Materials
One tri-axial, piezo-capacitive accelerometer was
worn around the waist (70X80X25mm, 150 grams,
range 9 2.5g, output is in mV, a change of 1mV
corresponded to a change of 0.08 m/s2 (resolution)).
Acceleration signals were digitally stored on a
memory card with a sampling rate of 25 samples/s.
A camera was placed behind the treadmill (Panasonic type HC-V70, 50 samples/s).
2.4 Data analysis
Signal processing was performed using MATLAB
(Matlab 7.10.0, The MathWorks, USA). Based on
sensor alignment, acceleration signals were identified as anterior-posterior (AP), medio-lateral (ML)
and vertical (VT). A low-pass second-order Butterworth filter with a cut-off frequency of 10 Hz was
used. We compared two data analysis methods:
AC and PSD (with and without spectral averaging
with TW of two, four and eight seconds. Furthermore, accuracy of SF estimates derived from the
AP acceleration signal as well as from combined
AP and VT signals with of AC and PSD was
determined.
VOL. 4 | ISSUE 1 | JANUARY 2015 3
ORIGINAL ARTICLE
2.5 Autocorrelation (AC)
We used the unbiased AC sequence of the acceleration signals8 where in x(i) (1,2..N) represents the
time serie used, N represents the amount of samples
used in the equation and m represents the timelagged phase shift in samples from the same time
serie.
adðmÞ ¼
1
N jmj
X Njmj
i¼1
xðiÞxði þ mÞ
(1)
We used an algorithm for automatic detection of the
first dominant period (ad1) and second dominant
period (ad2)14. ad1 was subtracted from ad2,
resulting in the number of samples between two
contralateral steps (delta). Delta was divided by
sample frequency and subsequently multiplied by
the non-overlapping TW length. This procedure was
implemented for all time windows in the 88 seconds
walking bouts and results were summed, to estimate
the total number of steps in the trial.
2.6 Power Spectral Density (PSD)
Fast Fourier Transformation (FFT) was used to
estimate the PSD of the acceleration signals. A
custom-made algorithm searched for the peak in the
PSD. We assumed that the highest peak in the
power spectrum was the SF. For a more detailed
description of the calculations, See9.
2.7 Spectral averaging
As an extension of the above method, we calculated
a weighted average over the frequency at the highest
power density and its nearest neighboring frequen-
cies. Herein is Power(i) the amount of presents at a
certain frequency (Df) in the time serie.
P jþ1
i¼j1 ðPowerðiÞ ðiDf ÞÞ
estimated frequency ¼
P jþ1
i¼j1 PowerðiÞ
(2)
2.8 Combining AP and VT signals
Similar calculations for AC and PSD were made;
however, input was the vertical (VT) acceleration
signal. AC and PSD results from AP and VT signals
were averaged. This resulted in AC and PSD derived
estimates from both AP and VT signals. Further
analyses were similar to the description above.
2.9 Statistics
The difference in estimated number of steps from
acceleration data and number of steps counted from
video observation was expressed as mean absolute
percentage error. A smaller mean absolute percentage error reflects a higher accuracy. Normality of
the data was confirmed by the Kolmogorov-Smirnov test. We used a repeated measure ANOVA, to
test for effects of gait speed, TW and algorithm type
and their interactions. Tukey HSD tests were used
for post-hoc analyses. To compare the different
algorithms in relation with TW and gait speed we
used a three-way factorial ANOVA repeated measures to test for any interaction effects between: gait
speed * TW * and type of algorithm used.
Results
3.1 Power Spectral Density and Autocorrelation
Figure 1 illustrates the accuracy of the autocorrelation (AC) and Power spectral density (PSD) based
Figure 1: Autocorrelation and Power Spectral Density results for estimating step frequency at a wide range of gait speeds
(1 6km/h), at different TW lengths * 2 second window length, h 4 second window and o an 8 second window.
Data is presented as the absolute percentage differences against camera observation. Please notice the difference in
vertical scale between left and right panel.
#JOURNAL OF MOBILE TECHNOLOGY IN MEDICINE
VOL. 4 | ISSUE 1 | JANUARY 2015 4
ORIGINAL ARTICLE
estimates of SF, at all TW lengths and gait speeds. A
three-way ANOVA repeated measures for type of
algorithm (PSD and AC), TW length and gait speed
demonstrated a main effect of algorithm (F 69.11, P B0.001), a main effect of TW length
(F 97.11, P B0.001) a main effect on gait speed
(F 8.26 P B0.001) and a significant interaction
effect between these three factors (F 4.33, P B
0.001). Post-hoc analyses demonstrated a higher
accuracy for PSD at 2 seconds TW at all gait speed
conditions compared to AC at a 2 seconds TW.
Comparing AC with PSD at the 4-seconds TW
revealed a higher accuracy for AC for the first two
gait speed conditions. No differences were found at
the 8-seconds TW.
3.2 Autocorrelation
Evaluation of the AC algorithm revealed a significant effect of TW size (F 106.3, P B0.0001), gait
speed (F 6.02, P B0.0001) and an interaction
effect of TW and gait speed (F 3.52, P B0.001).
Post-hoc analyses revealed that SF estimates were
less accurate for 2-seconds TW compared to 4- and
8-seconds TW for all gait speeds. Accuracy for 4seconds TW was higher compared to 8-seconds TW
at slow and fast speeds (1, 2 and 5, 6 km/h). Posthoc analyses within the TW size conditions revealed
a significant increase in accuracy with increasing
gait speed for TW of 2 and 8 seconds.
3.3 Power Spectral Density (PSD)
PSD based estimates were compared between 2-,
4- and 8-seconds TW and all gait speed conditions.
A significant effect of TW (F 16.3, P B0.0001)
was found indicating higher accuracy with longer
time windows. Furthermore a significant effect of
gait speed (F 6.8, P B0.0001) indicating that
accuracy improved with increasing speed, moreover
no significant interaction effect between TW and
gait speed (F 1.7, P 0.07).
3.4 Spectral averaging
Figure 2 illustrates the effect of spectral averaging
on the PSD based estimates. Significant main effects
were found for gait speed (F 8.58, PB 0.001) for
TW length (F 28.79, P B0.001) and type of
algorithm used (PSD and PSD with spectral averaging) (F 18.21, P B0.001). A significant interaction effect was found between gait speed, TW
length and algorithm (F 2.15, P 0.022). Posthoc analyses demonstrated that spectral averaging
improved accuracy for 2-seconds TW across all gait
speed conditions in comparison to PSD. In addition, spectral averaging for a TW of 4 seconds
significantly improved accuracy in comparison to
PSD at a TW length of 4 seconds at gait speeds of 1
and 2 km/h. No differences were found between
PSD and spectral averaging at 8-seconds TW.
However, accuracy improved for both methods at
gait speeds of 3 km/h and faster.
3.5 Combining AP and VT accelerations
Figures 3 and 4 illustrates the effect on accuracy of
combining the AP and VT accelerations for the AC
algorithm and PSD algorithms. Addition of the VT
signals did yield similar and in some cases less
accurate results for both algorithms.
Discussion
Motivated by the possibility of developing wearable
gait systems for real-time gait parameter recognition, real-time gait parameter feedback and gait
activity monitoring, our first aim was to examine
the accuracy of SF estimates over a wide range of
gait speeds for AC and PSD algorithms at different
time window (TW) lengths. We found more accurate
SF estimates when both gait speed and TW were
Figure 2: Results for the spectral averaging in comparison with PSD algorithm at different time windows and at a wide
range of gait speeds. h spectral averaging and * represents PSD. A 2 seconds TW, B 4 seconds TW and C 8
seconds TW. Data is presented as the mean absolute percentage error against camera observation.
#JOURNAL OF MOBILE TECHNOLOGY IN MEDICINE
VOL. 4 | ISSUE 1 | JANUARY 2015 5
ORIGINAL ARTICLE
Figure 3: Results for the superimposed autocorrelation in comparison to autocorrelation at different time windows and at a
wide range of gait speeds. Superimposed autocorrelation h and regular autocorrelation *. A 2 seconds TW, B 4
seconds TW and C 8 seconds TW. Data is presented as the mean absolute percentage error against camera observation.
Figure 4: Results for the superimposed PSD in comparison to PSD at different time windows and at different gait speeds.
Superimposed PSD h and regular PSD *. Respectively A 2 seconds TW, B 4 seconds TW and C 8 seconds
TW. Data is presented as the mean absolute percentage error against camera observation.
higher. Furthermore, we found an interaction effect
between TW length and gait speed for AC. These
results support the idea to enlarge the TW length
when the population of interest consists of slow
walkers. Differences between the AC and PSD
based estimates were small and not consistent
across TW and speeds. Spectral averaging did
improve accuracy for short TW lengths i.e. 2 or 4
seconds and at slow gait speeds. Combining estimates from AP accelerations with estimates form
VT accelerations did not yield more accurate SF
estimates. Finally, independent of the algorithm and
TW used, SF estimation accuracy is always less at
slow gait speeds compared to higher gait speeds.
To the best of our knowledge, only a limited number
of studies have presented guidance on minimum
numbers of meters or steps for valid and reliable
gait parameter recognition. For example, MoeNilssen et al.8 used five strides, while Auvinet
et al.15 recommend 40 m. However, these studies
did not explore effects of TW length and changes in
gait speed systematically. Yang et al.4 recently
pointed out that the use of time windows may be
necessary in view of variations in gait speed over
time. Therefore future research in the field of
activity monitoring should focus on the determination of the optimization of window length with
#JOURNAL OF MOBILE TECHNOLOGY IN MEDICINE
respect to accuracy, reliability and sensitivity to
variation in gait speed.
Study limitations
Over ground walking is different compared to
treadmill walking16; in treadmill walking gait variability is reduced compared to over ground walking17. However, dominant frequencies measured
around the waist in over ground walking reflect
SF as well3. Therefore, we expect no major differences in effects of the methodological choices
investigated when this experimental protocol had
been carried out in an over ground condition. Most
advanced wearable gait systems are and will be
developed for abnormal or pathological gaits such
as in stroke and Parkinson’s disease5,18. Our conclusions and recommendations can be useful in
developing algorithms for pathological gait, but
have to be interpreted with caution as our study
used young, healthy subjects only.
4.1 Conclusions
We examined the accuracy of estimating step
frequency over a wide range of gait speeds derived
from accelerometer signals in relation to signal
analysis. When developing specific algorithms for
the detection of step frequency, the optimal TW
VOL. 4 | ISSUE 1 | JANUARY 2015 6
ORIGINAL ARTICLE
length depends on gait speed for both AC and PSD
based estimates. We recommend a minimum TW
length of 4 seconds when using AC and PSD
algorithms and when using the PSD algorithm to
use spectral averaging, as this leads to better results
at short TW and low gait speeds. Combining AP
with VT acceleration data did not improve estimates
of step frequency.
Conflict of interest statement
The authors state that there was no conflict of
interests with any financial or personal relationships
or organizations that could influence the research
results.
Acknowledgements
We would like to thank our research assistants
Femmy Troost and Desiree Pultrum for their
contribution in data collection and data analysis.
We also would like to thank our financial funder
SIA RAAK, 2010-2-024 INT.
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11. Goldie PA, Matyas TA, Evans OM. Deficit and
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12. Egerton T, Williams DR, Iansek R. Comparison of
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disease and healthy older adults. BMC Neurol
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3. Moe-Nilssen R. A new method for evaluating motor
control in gait under real-life environmental conditions.
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14. Tura A, Rocchi L, Raggi M, Cutti AG, Chiari L.
Recommended number of strides for automatic
assessment of gait symmetry and regularity in
above-knee amputees by means of accelerometry
and autocorrelation analysis. J Neuroeng Rehabil
2012;9:11.
4. Yang C-C, Hsu Y-L, Shih K-S, Lu J-M. Real-time
gait cycle parameter recognition using a wearable
accelerometry system. Sensors 2011;11:731426.
15. Auvinet B, Chaleil D, Barrey E. Accelerometric gait
analysis for use in hospital outpatients. Rev Rhum
Engl Ed 1999;66:38997.
5. Wagenaar RC, Sapir I, Zhang Y, Markovic S, Vaina
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17. Dingwell JB, Cusumano JP, Cavanagh PR, Sternad
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VOL. 4 | ISSUE 1 | JANUARY 2015 7
ORIGINAL ARTICLE
HEALTH CARE APPS- WILL THEY BE A FACELIFT
FOR TODAY’S MEDICAL/DENTAL PRACTICE?
Deepika Jasti1, KVNR Pratap, MDS2, Madhavi Padma.T, MDS3, V. Siva Kalyan, MDS4,
M. Pavana Sandhya, MDS5, ASK. Bhargava, MDS6
1
Final year Post graduate student, Department Of Public Health Dentistry, Mamata Dental College, Khammam-507002, Andhra
Pradesh, India; 2Professor and Head, Department Of Public Health Dentistry, Mamata Dental College, Khammam-507002, Andhra
Pradesh, India; 3Professor, Department Of Public Health Dentistry, Mamata Dental College, Khammam-507002, Andhra Pradesh,
India; 4Reader, Department Of Public Health Dentistry, Mamata Dental College, Khammam- 507002, Andhra Pradesh, India; 5Senior
Lecturer, Department Of Public Health Dentistry, St. Joseph Dental College, Eluru-534003, Andhra Pradesh, India; 6Senior Lecturer,
Department Of Public Health Dentistry, Mamata Dental College, Khammam-507002, Andhra Pradesh, India
Corresponding Author: [email protected]
Background: With the recent advent of smart phones, usage of medical apps is on rise. Smart phones
are powerful devices that combine the conventional functions of a mobile phone with advanced
computing capabilities enabling users to access software applications commonly termed as ‘‘apps’’.
Health care applications (apps) that are downloadable on to smart phones are increasingly
becoming popular among clinicians.
Aim: The aim of the present study was to assess the usage of health care apps among Medical and
Dental doctors.
Methodology: A descriptive cross sectional questionnaire based study was conducted on medical and
dental doctors of Mamata hospitals, Khammam, Andhra Pradesh. A pretested, self administered
questionnaire was used and it consists of questions regarding demographic data followed by usage of
health care apps. Descriptive statistics were computed to demonstrate the frequency of responses and
the comparisons were made using chi-square test. A p-value less than or equal to 0.05 was considered
to be significant.
Results: A total of eighty doctors (48 Medical and 32 dental) completed the questionnaire. More
males (n 63) than females (n 17) participated in the study. Participants had a mean age of 32.5
years. It was found that 68% of dental doctors and 70.45% of medical doctors are using health care
apps on their smart phone. Most of the participants (58.8% of dental and 77.4% medical doctors)
use the health care apps for knowledge purposes, while no dental doctors used the apps for diagnosis
or treatment purposes. The majority of the dental doctors (41.17%) are using these apps for patient
education purpose when compared to the medical doctors (3.22%).
Conclusion: There is a high usage rate of health care apps among both medical and dental doctors,
with medical doctors using the apps for informational purposes, whereas dental doctors used the
apps for patient education.
Journal MTM 4:1:814, 2015
doi:10.7309/jmtm.4.1.3
#JOURNAL OF MOBILE TECHNOLOGY IN MEDICINE
www.journalmtm.com
VOL. 4 | ISSUE 1 | JANUARY 2015 8
ORIGINAL ARTICLE
INTRODUCTION
Smart phones have become ubiquitous among
general public. Advanced mobile communications
and portable computations are now combined in a
handheld device called as ‘‘smart phone’’. These
phones are capable of running third party software
‘‘applications’’ commonly termed as ‘‘apps’’1,2
Of late there is an exponential growth of Smartphone users in India. A survey conducted by
Eriksson reported that smart phone penetration
will grow from 10% in 2013 to 45% by 2020 i.e. from
90 million subscribers to 520 million subscribers3.
It is no surprise, therefore that such devices have
become a part of health care system. Health care
applications that are downloadable on to smart
phones are increasingly becoming popular among
clinicians. Smart phone technology is changing the
way that the healthcare is being practiced, with
professionals becoming likely to access regularly
updated, more convenient, web based literature
than refer to hard copies of text books or journals.
Around 500 million smart phone users worldwide
will be using some kind of health care app by 20154.
There are many health care apps both medical57
and dental8 that focused on patient education,
demonstration, provision of library which include
drug information, drug dosage and effect. Although
the number of health related apps has skyrocketed,
it is unknown how many of these are evidence based
or developed by reliable health organizations. Other
concerns include confidentiality of patient information and usage of apps in front of patients9. Thus a
few studies1012 have evaluated the efficacy of some
apps and revealed wide adaptation of these apps by
health care professionals during recent years.
According to survey by Epocrates, more than 40%
of medical students indicated that they turn to
smart phone medical apps as their first choice of
reference13. Robinson et al conducted a study on
medical students and found that 84% students
believed that smart devices were a useful addition
to their education14. Research by Vigmen and
Williamson indicate that the use of smart phone
leads to improve patient care and diagnosis, and
choice of therapy15.
Most of these international studies were conducted
on usage of health care apps among medical
students. To the best of our knowledge, little is
known in regard to the usage of health care apps
#JOURNAL OF MOBILE TECHNOLOGY IN MEDICINE
among Medical and Dental professionals. Adding
to these, India stood one among the top countries in
smart phone subscription16, but still there are no
studies conducted in Indian context till date on
usage of smart phone among medical and dental
professionals. Thus a study was planned to assess
the usage of health care apps among Medical and
Dental doctors of a tertiary care dental college and
hospital, Khammam, A.P, India.
Aim of the study
To assess the usage of health care apps among
Medical and Dental doctors of Mamata hospitals,
Khammam, A.P. We hypothesized there is similar
frequency, and usage pattern of use of health care
apps amongst medical and dental doctors.
Objectives
1. To assess the frequency of usage of health
care apps among Medical and Dental doctors
2. To compare the usage of health care apps
among Medical and Dental doctors
Methodology
A descriptive cross sectional questionnaire based
study was conducted. Ethical clearance was obtained from the Institutional Research Ethics Committee, Mamata dental college, Khammam, India.
Study participants include medical and dental
doctors from all the specialties including post
graduates of Mamata hospitals. Informed consent
was taken from all the participants before the start
of the study.
All the Medical and Dental doctors who owned
smart phone were included in the study. Those
doctors who were not willing to participate and
were busy or out of station during the period of the
survey were excluded.
Study instrument
A pre tested self administered 9-item questionnaire
was used. Questionnaire consists of two parts. First
part consists of three questions which collected data
on the demographic details like age, gender and
profession followed by second part of six questions
related to awareness, usage and usefulness of health
care apps, how often the apps were used etc.
VOL. 4 | ISSUE 1 | JANUARY 2015 9
ORIGINAL ARTICLE
The content of the questionnaire was derived from
the previous literature and was modified according
to Indian nativity. Pretest was done to ensure the
content validity of a questionnaire with the help of a
panel of experts of size six. Certain questions which
were found to be irrelevant were deleted and those
questions which were found to be incomprehensive
were modified. Reliability of the questionnaire was
checked with the help of a test- retest method using
kappa statistic and it was found that 90% agreement
for responses. Test-retest was done with a time
interval of 2 weeks. Pilot study was conducted on a
sample of 40 subjects who were not included in the
main study and the results were analyzed to ensure
that the aim and objectives of the study were
obtained.
Study procedure
The duration of survey was one month conducted
in October on all the medical and dental doctors of
Mamata hospitals, Khammam. Participants were
explained about the purpose of the study before the
questionnaire was distributed to them. The questionnaire was designed to take maximum of five
minutes to complete and these questionnaires were
collected back after providing the required time to
fill the form. The doctors who were not available at
the time of the study were noted and tried to meet
again for two times. Those who are not available
even at the third visit were not included in the study.
As this was an exploratory study, a convenience
based sampling method was adopted for determining sample size.
DENTAL
MEDICAL
TOTAL
MALE
FEMALE
TOTAL
24
39
63
8
9
17
32
48
80
Table 1: Demographic Details
Health care apps awareness and usage
Among all the smart phone holders, 86% (78%
dental and 91.6% medical) doctors are aware of the
health care apps and 69.56% (68% of dental doctors
70.45% of medical doctors) are using health care
apps on their smart phone.
Of the 30.43% doctors who are not using health care
apps on their smart phone, 7.69% of medical and no
dental doctors said that they don’t know how to
obtain the apps on their smart phone, 30.7%
medical and 12.5% dental doctors felt that they
don’t need the health care apps and 61.5% medical
and 87.5% dental doctors felt that they prefer the
computer instead of smart phone.
Majority of the dental doctors (58.8%) and only a
few medical doctors (22.58%) felt that the health
care apps are very useful to them in their clinical
practice. But majority of the medical doctors
(64.50%) and only a few dental doctors (41.17%)
felt that the health care apps are moderately useful
for their clinical practice. This difference is found
to be statistically significant with p B 0.05 (Figure 1).
Data analysis
The data collected was analyzed using statistical
package for social sciences (SPSS version 18). Chi
square test is used to test the significance. P-value
less than 0.05 was considered as significant.
RESULTS
Demographics
A total of eighty health care professionals completed the questionnaire. More males (n 63)
than females (n 17) participated in the study.
Participants had a mean age of 32.5 years. Out of
eighty health care professionals, 48 are Medical
professionals and 32 are dental professionals.
(Table 1)
#JOURNAL OF MOBILE TECHNOLOGY IN MEDICINE
Figure 1: Usefulness of Health Care Apps Among Medical
and Dental Doctors
Note: The above graph depicts usefulness of health
care apps among medical and dental doctors. The
chi-square value of this data is 7.3278, degree of freedom
is 2 and p-value is 0.025 which is found to be
statistically significant.
VOL. 4 | ISSUE 1 | JANUARY 2015 10
ORIGINAL ARTICLE
professionals were aware of the medical apps prior
to the study18. Various resources aid to promote
the awareness regarding health care apps among
doctors like fellow medical students, app stores,
clinicians etc.
Figure 2: Purpose of Healthcare Apps Usage Among
Medical and Dental Doctors
Note: The above graph depicts the purpose of usage of
health care apps among medical and dental doctors. The
chi-square value for the above data is 13.3394, degree of
freedom is 3 and the p-value is 0.0039 which is
statistically significant.
Most of the participants (58.8% of dental and
77.4% medical doctors) use the health care apps
for knowledge purpose, while no dental doctors use
the apps for diagnosis or treatment purpose. High
proportion of the dental doctors (41.17%) are using
these apps for patient education purpose when
compared to the medical doctors (3.22%). This
difference is found to be statistically significant with
p B 0.05 (Figure 2)
Participants most commonly used medical apps
when ever required (33%) followed by few times
per month (31%), few times per week (23%) and
once in a day (13%).
DISCUSSION
Smart phones have rapidly become part of everyday
life. The worldwide number of mobile device users
who have downloaded mobile health care applications nearly doubled from 127 million in 2011 to
247 million in 201217. The widespread adoption and
use of mobile technologies is opening new and
innovative ways to improve health and health care
delivery. They are playing a key role in transforming
the efficiency, delivery and access to the health care
system.
The present study findings reveal that 78% of
dental and 91.6% of medical doctors were aware of
health care apps. These results were in accordance
with the previous study, where 83% of health care
#JOURNAL OF MOBILE TECHNOLOGY IN MEDICINE
Also many medical universities have embraced this
mobile application technology as part of training or
for the usage of their students and staff19,20. Present
study findings showed that 68% of dental and
70.45% of medical doctors are using health care
apps on their smart phone. These results are in
accordance with the previous studies21,22. This
suggests that it is a global trend and practice for
health care professionals to own a smart device and
use medical apps to support their study and clinical
sessions.
Choy koh et al found in their study22, that majority
of their study participants have positive perceptions
on health care apps usage believing that, these apps
were essential tools for their studies, allowing for
faster and reliable access to clinical guidelines,
knowledge and skill as well as helping in decision
making. Several studies performed in UK and
Australia among medical faculty staff and students
showed similar positive attitude towards health care
apps usage18 23,24.
A Study found that smart mobiles improved
physician’s response time, accuracy, data management and record keeping practices25. Other potential benefits of mobile technology include tools to
overcome language barriers and increase patient
attendance rates by providing virtual reminders17.
Majority of the medical and dental doctors, who are
not using health care apps on smart phone, felt that
they prefer computer instead of smart phone apps
for accessing medical information. This might be
due to dislike towards relatively small mobile phone
screens. Participants of previous studies felt that,
medical apps cannot replace the use of traditional
textbooks. They felt that, unlike textbooks and
journals, there is no official peer review process
for apps and thus doctors may be skeptical of the
content26. In a study conducted by Laucher et al,
82% of their participants expressed concerns about
confidentiality and correctness of the information16.
In present study, 58.8% of dental doctors and
22.58% of medical doctors felt that the health care
apps as very useful while 64.5% of medical doctors
VOL. 4 | ISSUE 1 | JANUARY 2015 11
ORIGINAL ARTICLE
and 41.17% of dental doctors felt that the apps are
moderately useful for their clinical practice. Emergencies due to long waiting in the dental clinics can
be reduced due to appropriate use of patient
appointment apps. Also dental doctors are using
the health care apps more than medical doctors for
patient education purpose, finding these apps very
useful. We recognize that an arbitrary likert scale
was used in the questionnaire, and as such it is
difficult to further define the ‘‘usefulness’’ to each
practitioner
More medical doctors are using the health care apps
for diagnosis purpose when compared to dental
doctors. This might be due to availability of more
number of multiple medical applications than
dental applications21.
Most of the medical and dental doctors use the
health care apps for knowledge purpose. This is
because apps allow easy access to information
within seconds, which would take longer in searching a text book. Smart phones are handy, very
convenient and portable to access information while
on public transport or on clinical sessions compared
to relatively bulky textbooks22.
Conclusion
Both medical and dental doctors are using health
care apps at an equal level. Majority of the dentists
felt health care apps as very useful while majority of
the medical doctors felt these apps as moderately
useful. Dentists are using health care apps for
knowledge and patient education purpose while
majority of the medical doctors are using apps
mainly for knowledge purpose.
Limitations
The results from this study cannot be generalized,
as the sample size of the present study was small
and confined to one particular area. Multi centric
studies of this kind with large sample size are
needed for the results to be generalized.
Future prospects
Further studies can be done to evaluate the usage of
health care apps among patients. Also frequency
and type of medical and dental apps can be
compared on a same smart phone platform or in
different platforms.
#JOURNAL OF MOBILE TECHNOLOGY IN MEDICINE
References
1. Md Mosa A, Yoo I, Sheets L. A Systematic Review
of Healthcare Applications for Smartphones. BMC
Medical Informatics and Decision Making 2012;
12(67):131.
2. Telecom: Enabling growth and serving the masses.
Accessed at: http://www.deloitte.com/assets/Dcom-India/Local%20Assets/Documents/Thoughtware/ 2014/
Telecom_Enabling_growth_and_serving_the_masses.
pdf. Last accessed on 09.05.2014.
3. Tech Desk. Smartphone penetration to reach 45% in
India by 2020: Ericsson. May 9, 2014. Accessed at:
http://indianexpress.com/article/technology/technology-others/ericsson-identifies-key-elements-of-mobilebroadband-growth-in-india. Last accessed on may27th.
4. U.S. Food and Drug Administration. Mobile medical
applications. Accessed at: http://www.fda.gov/MedicalDevices/ProductsandMedicalProcedures/ConnectedHealth/MobileMedicalApplications/ucm255978.htm.
Last accessed on 09.05.2014.
5. Havelka S. Mobile resources for nursing students
and nursing faculty. Journal of Electronic Resources
in Medical Libraries. 2011;8:1949.
6. Dasari KB, White SM, Pateman J. Survey of iPhone
usage among anaesthetists in England. Anaesthesia.
2011;66:62031.
7. Franko OI, Tirrell TF. (2011). Smartphone app
use among medical providers in ACGME training
programs. Journal of Medical Systems [Online].
Available: http://www.springerlink.com/content/p6t
82ph541835u75.
8. Top 15 Mobile Applications for Dental & Oral
Health. http://www.medscape.com/features/slideshow/
dentalapps. last accessed on 10.05.2014.
9. Public Health Smartphone Apps: Disadvantages.
http://www.medscape.com/viewarticle/776278_3. Last
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10. Josephson CB, Salman R. Smartphones: Can an
iPhone App help stroke physicians? The Lancet.
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11. Low D, Clark N, Soar J, Padkin A, Stoneham A,
Perkins GD, Nolan J. A randomised control trial to
determine if use of the iResus application on a smart
phone improves the performance of an advanced life
support provider in a simulated medical emergency.
Anaesthesia. 2011;66:25562.
12. Zanner R, Wilhelm D, Feussner H, Schneider G.
Evaluation of M-AID, a first aid application for
mobile phones. Resuscitation. 2007;74:48794.
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13. Epocrates invests in future physicians. http://www.
epocrates.com/who/media/news/press-releases/epocrates-invests-future-physicians. Last accessed on
10.05.2014.
21. Rung A, Warnke F, Matteos N. Investigating the use
of Smart phones for learning purposes by Australian
Dental Students. JMIR mHealth. 2014;2(2):18.
14. Robinson T, Cronin T, Ibrahim H, et al. Smartphone
use and acceptability among clinical medical students: a questionnaire based study. J Med Syst.
2013;37:9936.
22. Koh KC, Wan JK, Selvanathan S, Vivekananda C,
Lee G, Tau Ng C. Medical Students’ Perceptions
Regarding The Impact Of Mobile Medical Applications On Their Clinical Practice. Journal Mob
Technol Med. 2014;3(1):4653.
15. Safdari R, Jebraeily MD, Rahimi B, Doulani A.
Smartphone medical applications use in the clinical
training of medical students of UMSU and its
influencing factors. European Journal of Experimental
Biology, 2014;4(1):6337.
23. Payne K, Wharrad H, Watts K. Smartphone and
medical related App use among medical students
and junior doctors in the United Kingdom (UK): a
regional survey. BMC Medical Informatics and
Decision Making 2012;12(121):211.
16. Subscriber base- Indian Brand Equity Foundation.
August 2013. Accessed at: http://www.ibef.org/
download/telecommunication-august-2013.pdf. Last
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24. Aungst T. Survey results show how medical student
use of medical apps differs from resident physicians.
iMedicalapps April 25, 2013. http://www.imedicalapps.com/2013/04/survey-medical-student-medicalappresident-physician/ (last accessed on 10 may 2014).
17. Advancements in mobile health technology. Health
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18. Koehler N, Vujovic O, McMenamin C. Healthcare
professionals’ use of mobile phones and the internet
in clinical practice. Journal Mob Technol Med. 2013;
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19. Stanford School of Medicine. http://med.stanford.
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25. ‘‘The Impact of Mobile Handheld Technology on
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Johanna Westbrook, Journal of the American Medical Informatics Association, Volume 16, No. 6,
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26. Koehler N, Yao K Dr , Vujovic O Dr, McMenamin.
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20. Top five medical apps at Harvard Medical School.
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10 may 2014).
#JOURNAL OF MOBILE TECHNOLOGY IN MEDICINE
VOL. 4 | ISSUE 1 | JANUARY 2015 13
ORIGINAL ARTICLE
ANNEXURE- QUESTIONNAIRE
HEALTH
CARE
APPS-
WILL
THEY
BE
A
FACELIFT
FOR
TODAY’S
MEDICAL/DENTAL PRACTICE?
QUESTIONNAIRE:
PRINCIPAL INVESTIGATOR: Dr. Deepika Jasti
DEMOGRAPHIC DETAILS
1. Age :
2. Gender:
3. Profession :
AWARENESS & USAGE OF HEALTH CARE APPS
4. Are you aware of health care apps?
a. Yes
b. no
5. If yes, do you use health care apps on your smart phone?
a. Yes
b. no
6. If no, what is the reason?
a. Don’t know how to obtain them?
b. Health care apps are too expensive
c. No need to use health care apps
d. I prefer to use computer
7. Are these applications useful for your clinical practice?
a. Very useful b. Moderately useful c. Not useful
8. For what purpose, do you use the health care apps?
a. For knowledge purpose
b. For diagnosis purpose
c. For treatment purpose
d. For patient education purpose
9. How often do you use these health care apps?
a. Few times per month
b. Few times per week
c. Once in a day
d. whenever required
#JOURNAL OF MOBILE TECHNOLOGY IN MEDICINE
VOL. 4 | ISSUE 1 | JANUARY 2015 14
ORIGINAL ARTICLE
GOOGLE GLASS INDIRECT OPHTHALMOSCOPY
Aaron Wang, MD, PhD1, Alex Christoff, CO, COT1, David L. Guyton, MD1, Michael X. Repka, MD1,
Mahsa Rezaei, MS1, Allen O. Eghrari, MD1
1
Department of Ophthalmology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
Corresponding Author: [email protected]
Background: Google Glass is a wearable, head-mounted computer with display, photographic and
videographic imaging capability, and connectivity to other devices through Wi-Fi and Bluetooth
signaling.
Aims: To describe for the first time the use of Google Glass for use in indirect ophthalmoscopy and
modification techniques to assist with its use.
Methods: A lightweight, portable light source was installed above the Glass aperture, a small tissue
paper used to diffuse the light, and the arm of the headset was taped to the examiner’s glasses in
order to bring the display into the right eye’s central visual field.
Results: Using a slightly modified Glass headset, the examiner documented the central and
peripheral retina in a young male with ease.
Conclusion: We demonstrate for the first time that Glass, with minor modifications, can be used as a
simple and effective method to perform and record a fundus examination.
Journal MTM 4:1:1519, 2015
doi:10.7309/jmtm.4.1.4
Introduction
In April 2013, Google released a beta version of the
Google Glass for developers for $1500, termed the
Explorer version. Glass is a wearable headset
weighing 50 grams with a prismatic heads-up color
display in the superior visual field of the right eye. It
includes a built-in 5 megapixel camera with 1280 x
720 pixel HD video at 30 frames per second
ambient light sensor, Wi-fi and Bluetooth connectivity, a capacitive touchpad on the temple frame,
16GB of flash memory, and is powered by a 2.1
Watt-hour lithium polymer battery. Among the
earliest adopters of this wearable technology were
physicians, who quickly integrated this tool into
medical and surgical practice.1
#JOURNAL OF MOBILE TECHNOLOGY IN MEDICINE
www.journalmtm.com
In health care, the application of the Glass has
largely centered on the ability to access patientspecific medical records in a convenient manner.
Reports of its use to display radiographic images by
the bedside or intraoperatively,2 allergy information
in an emergency medicine setting,3 or medical
records by facial recognition,4 demonstrate the
unique utility of this commercially available headmounted tool to present physicians with needed
information quickly and effectively. Patient privacy
has been addressed by customization of the Glass to
shut off social media sharing.5
The use of imaging through Glass for health
delivery has been particularly relevant for surgery.
VOL. 4 | ISSUE 1 | JANUARY 2015 15
ORIGINAL ARTICLE
Advantages include its head mounted, hands-free
use6 and the ability to allow other physicians and
observers to view surgical techniques from the
visual perspective of the surgeon.7,8
To our understanding, however, the use of Glass for
imaging of fine details or microscopy in vivo has not
been explored. This may be due in part to the lack
of manual controls in image acquisition and the
inability to zoom, as well as the superiorly displaced
display over the right eye visual field, which may
cause symptoms of strain9 for the user and prevent
comfortable real-time imaging. In contrast to most
current-generation smartphones, the Glass also
does not have an illuminating light source, and
therefore no flash photography.
Here, we describe for the first time the use of the
Glass to acquire images of the retina. Through
simple, affordable modifications of the commercially available headset, ‘‘Glass indirect ophthalmoscopy’’ can assist with disease management and
education.
camera aperture, affects the character of the glare
and reflections for the condensing lens and cornea.
Affixing the light source above the aperture preserves the familiar movements of the user’s head
and tilting of the condensing lens to optimize
fundus viewing and minimize glare and reflections.
The light itself needs to be bright enough to view
the fundus, but not too bright that it overwhelms
the autoexposure capability of the Glass. It should
be battery operated to prevent the need for additional power or obtrusive wiring.
After utilizing several portable LED lights of
various shapes and sizes, we identified a small
keychain LED light from http://meritline.com
(SKU 600-773-001) to be particularly useful, as its
switch allows the light to be continually powered.
To create a soft diffuse light, we attached a small
piece of tissue paper in front of the LED. The light
was then affixed to the Glass with Velcro. This
allows the position of the light to be easily adjusted
to ensure that the direction of the light is the same
as that of the aperture. This apparatus is demonstrated in Figure 1.
Methods
Minimal modification of Glass is necessary for use
in indirect ophthalmoscopy, as the headset already
includes an onboard camera and display. We affixed
an external light source, small enough to be
attached securely to the device, on top of the
aperture, as the standard indirect binocular
ophthalmoscopes with which ophthalmologists are
accustomed include a light source above the viewing
optics. The orientation of the light with respect to
the viewing optics, or in this case, the Glass’s
The exam of the fundus is performed by viewing the
onboard display only, while the Glass is viewing the
aerial image of the condensing lens. This technique
ensures that what is seen is what is captured. In its
current form, the user starts video capture, taps the
Glass arm to extend the length of the video, and
taps to stop the recording after the exam. Although
the default assembly of the Glass requires the
examiner to direct gaze superotemporally, for Glass
Indirect ophthalmoscopy the user should position
Figure 1: Google Glass with LED lighting apparatus fixed directly superior to aperture. During use, tissue paper is placed in
front of the light for diffusion.
#JOURNAL OF MOBILE TECHNOLOGY IN MEDICINE
VOL. 4 | ISSUE 1 | JANUARY 2015 16
ORIGINAL ARTICLE
To perform the exam, the light is toggled on and the
video capture is started. The exam is then performed in the same manner as with the traditional
binocular indirect ophthalmoscope (BIO) by holding the condensing lens in one hand. Since the
camera application for the Glass currently is wide
angle and zooming options are not currently
present, the fundus will appear small in the display.
To address this issue, the examiner should position
his or her head about 25cm (rather than arm’s
length) from the patient’s eye so that the fundus can
fill the field of view of the Glass camera. This
represents the only change in examination technique that differs from use of the traditional BIO, and
is demonstrated in Figure 3.
Figure 2: The Google Glass unit is attached to the user’s
own glasses with tape around the arm, allowing the
display to be positioned directly in the center of the
user’s field of view.
the display centrally, to be in primary gaze for
extended comfortable viewing with the Glass for the
duration of the exam. This is accomplished by
unscrewing the Google Glass frame from the main
unit and taping it to the frame of the user’s own
glasses (see Figure 2), or a blank pair.
After discussion of risks and benefits, a 31-year-old
male volunteer provided informed consent to proceed with Glass indirect ophthalmoscopy and
recording, as part of a study of head-mounted
digital camera indirect ophthalmoscopy for which
Institutional Research Board approval was obtained. The participant’s right eye was dilated with
2.5% phenylephrine and 1% tropicamide.
All authors have completed the Unified Competing
Interest form at www.icmje.org/coi_disclosure.pdf
and declare: no support from any organisation for
the submitted work; no financial relationships with
any organisations that might have an interest in the
submitted work in the previous 3 years; no other
relationships or activities that could appear to have
influenced the submitted work.
Figure 3: The examiner uses Glass on a subject for fundoscopy at a relatively close distance, positioned between the typical
near (direct ophthalmoscopy) and far (indirect ophthalmoscopy) locations encountered with standard techniques.
#JOURNAL OF MOBILE TECHNOLOGY IN MEDICINE
VOL. 4 | ISSUE 1 | JANUARY 2015 17
ORIGINAL ARTICLE
Figure 4: Still photographs from Video 1 reveal an image of the disc, macula, choroidal vasculature and vitreoretinal
interface centrally (left). Glass may also be oriented to acquire images of the retinal periphery; details of a single peripheral
retinal vessel are appreciated inferiorly (right). We expect resolution of Glass imaging to improve in future iterations.
Results
With the appropriate external light fixated in
the manner described above, and the shorter
examination distance, the user can obtain fundus
examination and video recording using the Google
Glass. The learning time to use such an apparatus
is minimal. Video 1 demonstrates the ease of
examination, and simplicity of marching around
the periphery of the fundus. Still photographs
from the center and periphery are demonstrated in
Figure 4.
The quality of viewing is based on the resolution of
the display, and the quality of the video capture is
based on the camera; both of which will only
improve with time. Currently, the resolution of the
display and camera are sufficient to obtain a good
fundus exam. Auto-exposure and autofocusing was
rapid enough to not hinder the speed of the exam.
Discussion
The use of mobile technology in daily ophthalmic
practice is widespread, and the vast majority of
Video 1: Video of Glass reveals ease of fundoscopy.
Details of the optic nerve, macula, vessels, and peripheral
pigmentary changes are all recognized through this view.
#JOURNAL OF MOBILE TECHNOLOGY IN MEDICINE
ophthalmologists have integrated the use of smartphones into their professional responsibilities.9
To date, portable digital fundoscopy has been
largely smartphone-based. This includes the iExaminer (WelchAllyn, Skaneateles Falls, NY, USA)
which pairs with a panoptic ophthalmoscope, and
PEEK (Peek Vision). In contrast to these options,
Glass indirect ophthalmoscopy allows the practitioner to maneuver the photographic unit with the
head, utilizing the familiar technique of indirect
ophthalmoscopy, and providing a view of the
peripheral retina.
Although the resolution of cameras associated with
popular commercially available mobile devices is
often less than slit-lamp mounted digital SLR
cameras, image quality is adequate for documentation of gross and, with proper magnification, fine
features of the retina. A direct head-to-head diagnostic comparison of images from digital SLR vs.
smartphone photography in a dermatological setting demonstrated that a difference in resolution,
although noticeable, does not necessarily translate
to clinical utility.11 For instance, the RetCam
(Clarity Medical Systems, Pleasanton, California,
USA), widely used for retinal imaging under
anesthesia, produces 640 x 480 images; our institution has found it useful clinically despite the fact
that alternative fundus imaging systems Panoramic
200 (Optos, Fife, Scotland) and Optooret-1000
(Medibell Medical VIsion Technologies, Haifa,
Israel) have higher resolutions (2000 x 2000 and
1024 x 1024, respectively). In comparison, the Glass
demonstrates relatively high resolution, increased
portability, and a price two orders of magnitude less.
This balance of portability and digital video or
photographic documentation may open new avenues
VOL. 4 | ISSUE 1 | JANUARY 2015 18
ORIGINAL ARTICLE
for clinical care and education. A physician, for
instance, may find it helpful to document examination findings seen through Glass for integration into
electronic medical records. Similar to operating
rooms in which medical students may view surgery
on a screen through the perspective of the operating
microscope, students learning the details of indirect
ophthalmoscopy may find it beneficial to view the
retina from the perspective of the examiner. In rural
settings or in developing countries, a portable battery
could be used to charge the Glass through its USB
connection and the LED-powered light source is
independent in its battery life from the Glass itself. An
additional advantage in such settings is the ability to
record audio narration while examining patients, and
the possibility of geotagging photographs based on
GPS/spatial and temporal coordinates during rural
camps. Further development of the Glass device, its
operating system, storage, and imaging capabilities
will facilitate its use in a variety of settings.
The indirect ophthalmoscopic exam method is
preserved except for the examination distance.
Although a telephoto lens in front of the aperture
may preserve the examination distance, adapting to
a closer viewing distance is not impractical, as it
requires the head to be positioned between what
would be generally utilized for direct and indirect
ophthalmoscopy. Also, as applications on the Glass
develop, it is likely that a zoom feature will be
incorporated into the video application to allow for
traditional arm’s length working distance, enabling
the Glass to be used through smaller pupils.
Applications could be developed for the Glass to
enhance video ophthalmoscopy and its use in
medicine.
Conclusion
In summary, we describe the use of Google’s Glass
device for indirect ophthalmoscopy and documentation of the optic nerve, macula and peripheral
retina. Its portability, affordability relative to other
retinal imaging techniques, and ease of use allows it
to be applied in a variety of clinical and research
settings.
Acknowledgements
The authors wish to thank Jessica Chang and Sonya
Thomas for photographic support.
#JOURNAL OF MOBILE TECHNOLOGY IN MEDICINE
References
1. Eng, J. British doctor livestreams cancer surgery
using google glass. NBC News May 23 2014.
http://www.nbcnews.com/science/science-news/britishdoctor-livestreams-cancer-surgery-using-google-glassn113596 (Accessed on June 15, 2014).
2. Kim, L. Google glasss delivers new insight during
surgery. UCSF October 13 2013. http://www.ucsf.
edu/news/2013/10/109526/surgeon-improves-safetyefficiency-operating-room-google-glass (Accessed on
June 15, 2014).
3. Borchers, C. Google glass embraced at Beth Israel
Deaconess. Boston Globe April 4 2014. http://www.
bostonglobe.com/business/2014/04/08/beth-israel-usegoogle-glass-throughout-emergency-room/WhIXcV
zkpn7MOCAhKuRJZL/story.html (Accessed on June
15, 2014)
4. MedRef : https://medrefglass.appspot.com (Accessed
on June 15, 2014).
5. Pai, A. At least four startups are now focused on
Google Glass apps for doctors. Mobihealthnews April
17 20014. http://mobihealthnews.com/32170/at-leastfour-startups-are-now-focused-on-google-glass-appsfor-doctors/ (Accessed June 15, 2014).
6. Muensterer OJ, Lacher M, Zoeller C, Bronstein M,
Kubler J. Google glass in pediatric surgery: an
exploratory study. Int J Surg. 2014; 12(4): 2819.
7. O’Conner, A. Google glass enters the operating room.
The New York Times June 1 2014. http://mobile.
nytimes.com/blogs/well/2014/06/01/google-glass-entersthe-operating-room (Accessed on June 15, 2014).
8. Lutz B, Kwan N. Chicago Surgeon to use google
glass in operating room. NBC Chicago Dec 26 2013.
http://www.nbcchicago.com/news/tech/google-glasssurgery-237305531.html (Accessed on June 15, 2014).
9. Smith, J. Google’s eye doctor admits glass can cause
pain. Betabeat May 19 2014 http://betabeat.com/2014/
05/googles-eye-doctor-admits-glass-can-cause-pain/
(Accessed on June 15, 2014).
10. Davis EA, Hovanesian JA, Katz JA, Kraff MC,
Trattler WB. Professional Life and the Smartphone.
Cataract Refract Surg Today. 2010 Sep 21-2.
11. Asaid R, Boyce G, Padmasekara G. Use of smartphone for monitoring dermatological lesions compared to clinical phothography. Journal of Mobile
Technology in Medicine. 2012; 1(1) 0168.
VOL. 4 | ISSUE 1 | JANUARY 2015 19
ORIGINAL ARTICLE
DEVELOPMENT OF AN IPAD VERSION OF THE
KESSLER 10 FOR USE IN YOUTH MENTAL
HEALTH OUTREACH SERVICES
Gareth Furber, PhD1, Ann E Crago2, Tom D Sheppard3, Clive Skene4
1
(Clinical Psychology) Health Economics and Social Policy Group, School of Population Health, South Australian Health
and Medical Research Institute (SAHMRI), North Terrace, Adelaide, SA, 5000; 2Bachelor of Nursing Youthlink,
Women’s and Children’s Health Network, SA Health, GP Plus Health Care Centre Marion, 10 Milham Street,
Oaklands Park, Adelaide, SA, 5046; 3Registered Nurse (Mental Health) Youthlink, Women’s and Children’s Health
Network, SA Health, GP Plus Health Care Centre Marion, 10 Milham Street, Oaklands Park, Adelaide, SA, 5046; 4Master of
Psychology CAMHS Executive, Level 1, 55 King William Road, North Adelaide, SA, 5006
Corresponding Author: E [email protected]
In this case report we describe the development and early trialling of an iPad application replicating
the Kessler 10 (K10), a widely used brief measure of psychological distress. The application was
the result of a collaboration between a youth mental health outreach service (Youthlink), a private
application developer (Enabled) and local health service IT support. Therapists reported greater
engagement with the iPad version of the K10 compared to the pen/paper version and described
how the application assisted them to collaboratively reflect with consumers on treatment progress.
Journal MTM 4:1:2024, 2015
doi:10.7309/jmtm.4.1.5
Case study
Youthlink is an Australian Child and Adolescent
Mental Health early intervention service (CAMHS)
for 16 to 19-year olds who are experiencing mental
health difficulties or having trouble making the
transition between CAMHS and adult mental health
services. Youthlink services approximately 150 young
people a year through the work of 6-7 staff (nursing,
psychiatry, social work). The clinical presentation of
these young people is complex. They present with
Axis 1 disorders such as depression, psychosis and
anxiety, have diagnostic comorbidity, and report
histories of abuse, trauma, family or peer group
problems, school/work issues, harmful substance use
and homelessness.
Youthlink consumers can be difficult to engage
through traditional channels (e.g. clinic based consultations, home phones, and reminder letters).
Thus much of the work consists of connecting
with these consumers in flexible ways, on the run,
#JOURNAL OF MOBILE TECHNOLOGY IN MEDICINE
www.journalmtm.com
via home visits, last minute appointments, and
mobile phone calls. This flexibility ensures that
these clients are regularly monitored as they exhibit
a number of high-risk behaviours (e.g. impulsivity,
self-harm and substance use) as well as high risk
exposure (e.g. homelessness, legal issues, financial
problems).
One of the instruments that Youthlink use to
monitor the outcomes of consumers is the K10.
The original K101 is a 10-item consumer-reported
questionnaire intended to yield a global measure of
distress based on questions about anxiety and
depressive symptoms that a person has experienced
in the most recent 4 week period. The ‘‘’’ refers to
a version with 4 additional questions relating to
degree of work impairment and health service
utilisation. The K10 was developed by Kessler and
colleagues for use in the US National Health
Interview Survey as a screening scale for nonspecific distress. Validation studies have supported
VOL. 4 | ISSUE 1 | JANUARY 2015 20
ORIGINAL ARTICLE
its appropriateness as a brief measure of psychological distress in Australian samples2,3.
Until recently, Youthlink administered the K10 as
a pen/paper instrument. In outreach work this
process had a number of limitations. The instrument
was unengaging to consumers and therapists had to
remember to carry paper copies and conduct hand
scoring. More importantly, therapists had to remember to carry a consumer’s previous K10s if
wanting to discuss progress over time. When the
first version of the iPadTM was released in 2010, the
team proposed to develop a tablet-based version of
the K10 to address these limitations. A tablet
version of the K10 would be engaging, readily
available, automatically scored, and much better
suited to collecting multiple K10 over the course
of treatment. This case report outlines Youthlink’s
development of the K10 iPad application and the
early experiences of the therapists in using the
application in clinical practice.
Development of the application
Development and trialling was conducted in 5
stages. Stage 1 (Consultation) involved working
with local Information Technology personnel to
develop a functional specification document outlining the expected screens, functionality, security
features and basic design of the application. Stage 2
(Approvals) involved confirmations from local and
state clinical reference groups that the project
adequately protected consumer privacy, and from
local and state health IT that the application met
guidelines for use within existing health IT infrastructure. Stage 3 (Tendering) involved tendering
the project out to private developers and receiving
approvals to release funds for iPads TM and software development. Stage 4 (Development) was led
by a local application developer called Enabled
(www.enabled.com.au). Enabled used the functional
specification document from Stage 1, which was
further supplemented through direct consultation
with Youthlink and contributions of their own
ideas. Enabled used an agile development process
whereby iterative design and app decisions were
showcased regularly to the team before making
adjustments/changes and moving to the next step of
development. Early versions of the application were
distributed to team members to trial in the field, to
identify useability issues and software bugs. Finally
in Stage 5 (Field trial), all six Youthlink therapists
received iPads and a working version of the
application to use in practice. The field trial went
#JOURNAL OF MOBILE TECHNOLOGY IN MEDICINE
for 6-months, 3-months to allow familiarisation
with the application, and 3-months using as standard practice. Therapists’ experiences with the
application were gathered during a focus group,
6 months after the cessation of the field trial. In
addition, K10 collection rates during the second
3-month period of the field trial were compared
against the same period in the previous year (when
iPads were not being used).
The application
Screenshots from the application are presented in
Figure 1. The application is password protected (A).
After sign-in, the therapist searches for the relevant
client and can either review previous K10 or
launch a new one. Upon launching a new K10,
the tablet is handed to the client. Each item of the
K10 is replicated on the device, with one question
per page and ability to move backwards and
forward through the questionnaire. As per the
standard K10, clients are asked to answer in
relation to the last 4 weeks. The 10 Likert scale
distress items (rated ‘‘none of the time’’ through to
‘‘all of the time’’) are represented with large blue
button choices (B). Items relating to days unable to
work/restricted in working and visits to a doctor/
health professional were visualised using a grid of
28 dots (4 lines of 7), reflecting the last 4 weeks (C).
Each dot selected represented a day. This visual
metaphor provided a neater way for clients to think
back over the past 4 weeks and report their work
capacity and medical appointments. This visual
metaphor reflected one of a number of suggestions
the team at Enabled made in the development of the
application.
Upon completion, the K10 is automatically
scored and represented as a single score in a
progress chart (D). If multiple K10 have been
completed by the client, all are displayed along a
timeline allowing for a visual representation of a
consumer’s distress scores over time. Any individual
measurement instance can be ‘‘exploded’’ to examine the client’s scores across the 14 items (E). The
10 distress items are organised into four categories
(negative affect, nervous, agitation fatigue) based
on a factor analysis by Brooks4. The use of these
categories helps consumers understand the different
symptom clusters that make up their distress. In
addition to the current score, this exploded chart
shows how that item has changed since the last
completion of the K10. This allows a more
fine-grained analysis of which areas are showing
VOL. 4 | ISSUE 1 | JANUARY 2015 21
ORIGINAL ARTICLE
Figure 1: Screenshots from the iPad application
improvements or worsening. Finally for data entry
purposes, the app can generate a historical table of a
client’s answers over time (F), and data for all
clients on the iPad can be exported through iTunes
to a csv file for further analysis. The app does not
require a network connection to function.
Therapists’ positive experiences of the
application
Therapists reported that the application was well
received by consumers who found it easy and quick
to use, well-designed and liked the visual representation of their therapy progress. Therapists found
themselves using the application collaboratively,
with consumers and therapists reviewing the
results of assessments together and incorporating
the results into the treatment. Therapists noted a
number of benefits of this process. It increased
consumers’ emotional self-awareness and vocabulary, giving them words for their different symptoms. It provided a more concrete basis for
treatment decisions, identifying which symptoms
were more troublesome. It provided useful evidence for consumers who felt they were not
making progress but whose symptoms were improving. It acted as a clearer prompt for therapists
#JOURNAL OF MOBILE TECHNOLOGY IN MEDICINE
to act on declining scores, inquire about specific
symptoms or investigate inconsistencies in clients’
report (e.g. showing high distress in person but low
distress on K10). Finally it provided a mechanism for consumers who were uncomfortable talking about their feelings to express how they were
doing in a less confronting way.
Therapists reported being much more engaged with
the application over the pen/paper measure which
reflected in their use of the application. Therapists
were more likely to use the K10 at intake to get a
baseline level of functioning. For example, 57%
of admissions to the service during the 3-month
audit when using the iPad version had a K10,
compared to 27% of admissions in the previous
6-months. They were also more likely to use the
application at review points during therapy where
they would discuss with consumers their progress
over time. The convenience of having the application on the iPad (which they carried with them like a
diary) was a primary driver of these benefits. In
addition, other features of the application such as
its design (consumers ‘‘thought it was cool’’), and
use of progress charts to show change in K10
scores over time, made the application highly suited
VOL. 4 | ISSUE 1 | JANUARY 2015 22
ORIGINAL ARTICLE
to repeated measurement and considerably more
attractive to use than the pen/paper version.
Therapists’ negative experiences of the
application
The application had a number of functional limitations reflecting the budget of the project and the
organisational challenges of embedding new IT
technologies within existing systems. These included
not integrating with therapists’ calendars and the
client record system, features identified by therapists as crucial for the long-term adoption of
tablet-based outcome assessments. Additionally,
for security purposes the application required
secure login, de-identified codes for individual
clients, and a search-based method for accessing
client information from the device. These organisational requirements made some aspects of using the
application, most notably, finding and sorting
through clients, somewhat cumbersome.
A significant frustration with the application was
due to provisioning issues. Provisioning refers to the
means by which therapists download the application to their device. As the application was not
intended to be released in the Apple store, provisioning of the application was initially handled by
Enabled and then by the IT department of the local
health service. Having not been involved with
provisioning prior to this project, the IT department
initially struggled with providing a reliable means of
accessing the application, which meant all therapists
experienced the application ceasing to work during
the 6-month trial leading to lost data. At the time of
writing, these provisioning issues had been solved.
Finally, using the application required therapists
to adopt the iPad as part of their daily workflow
(e.g. email, calendaring). Three of the six therapists
found this easy and embedded the iPad and
application in their standard practice and continued
to use it post-trial period. However three found it
was a poor fit to their workflow and used the iPad
(and hence application) only intermittently after the
field trial.
Discussion
The Youthlink team set out to develop an iPad
version of the K10 to overcome the barriers of
pen/paper measurement in youth outreach work.
The development process engaged clinicians to
rethink their use of a standard routine outcome
measure (K10) and imagine ways of making
#JOURNAL OF MOBILE TECHNOLOGY IN MEDICINE
assessment a more collaborative process. The resulting application was engaging, clinically useful,
convenient, and a valid replacement for the pen/
paper K10. Feedback from therapists was that the
use of the application changed their behaviour
regarding use of the K10 thus creating a more
collaborative measurement process which had tangible benefits for clients in terms of mental health
literacy, communication of distress, therapy planning and monitoring. In this respect, our experiences are illustrative of the purported benefits
of routine outcome assessment5 and consistent
with positive reports of tablet-based assessment in
other fields6,7.
Two aspects of translating the K10 to the iPad
stood out. The first was the ability to harness the
touchscreen interface to increase engagement. Features such as single question per page, larger text,
clearer response options, dynamic colour contrasts,
assistive visual cues for difficult questions, and
responsive touch made completing the K10 on
the iPad faster, more intuitive and more enjoyable.
The second aspect was the application’s ability to
automatically generate and display feedback charts
that provided therapists and consumers with information that could be used to structure their
treatment. This process was facilitated by the wide
viewing angles on the iPad screen which allowed
therapist and consumer to review charts simultaneously. A number of authors have identified feedback as a crucial ingredient in improving compliance
in outcome measurement810.
Adoption of new technologies into mental health
services is aided by case studies of services that have
experimented with implementing new practices. For
example in previous work we explored the use of
Short Messages Service to foster better communication with consumers11. This work was promoted
nationally through the professionals sections of the
Reachout website by inclusion as a case study for
training for mental health professionals12. We hope
that this brief case study similarly inspires mental
health services to think about how mobile technologies can be used in clinical practice. Future work
should explore whether introduction of such applications reliably increases therapists’ use of routine
outcome measurement.
Acknowledgements
The authors would like to acknowledge the Mental
Health Unit within the Department of Health in
South Australia for the financial support of this
VOL. 4 | ISSUE 1 | JANUARY 2015 23
ORIGINAL ARTICLE
project. We’d also like to thank the team at Enabled
for their professionalism in building the application
and Greg Chambers and Jon Holloway for local IT
support and helping get the project off the ground.
All those who contributed significantly to this paper
are reflected in the authorship list.
References
1. Kessler RC, Barker PR, Colpe LJ, Epstein JF,
Gfroerer JC, Hiripi E, et al. Screening for serious
mental illness in the general population. Arch Gen
Psychiatry 2003;60(2):1849.
2. Furukawa TA, Kessler RC, Slade T, Andrews G. The
performance of the K6 and K10 screening scales for
psychological distress in the Australian National
Survey of Mental Health and Well-Being. Psychol
Med 2003;33(2):35762.
3. Andrews G, Slade T. Interpreting scores on the
Kessler Psychological Distress Scale (K10). Aust N
Z J Public Health 2001;25(6):4947.
4. Brooks RT, Beard J, Steel Z. Factor structure and
interpretation of the K10. Psychol Assess 2006;18(1):
6270.
5. Boswell JF, Kraus DR, Miller SD, Lambert MJ.
Implementing routine outcome monitoring in clinical practice: Benefits, challenges, and solutions.
Psychother Res 2013;(February 2014):3741.
6. Stukenborg GJ, Blackhall L, Harrison J, Barclay JS,
Dillon P, Davis MA, et al. Cancer patient-reported
outcomes assessment using wireless touch screen
#JOURNAL OF MOBILE TECHNOLOGY IN MEDICINE
tablet computers. Qual Life Res 2013; Epub ahead
of print.
7. Zargaran E, Schuurman N, Nicol AJ, Matzopoulos
R, Cinnamon J, Taulu T, et al. The electronic trauma
health record: design and usability of a novel tabletbased tool for trauma care and injury surveillance
in low resource settings. J Am Coll Surg 2014;218(1):
4150.
8. Batty MJ, Moldavsky M, Foroushani PS, Pass S,
Marriott M, Sayal K, et al. Implementing routine
outcome measures in child and adolescent mental
health services: from present to future practice. Child
Adolesc Ment Health 2013;18(2):827.
9. Bickman L. A measurement feedback system (MFS)
is necessary to improve mental health outcomes.
J Am Acad Child Adolesc Psychiatry 2008;47(10):
11149.
10. Miller SD, Duncan BL, Sorrell R, Brown GS. The
partners for change outcome management system.
J Clin Psychol 2005;61(2):199208.
11. Furber G V., Crago AE, Meehan K, Sheppard TD,
Hooper K, Abbot DT, et al. How Adolescents Use
SMS (Short Message Service) to Micro-Coordinate
Contact With Youth Mental Health Outreach
Services. J Adolesc Heal 2010;48(1):1135.
12. Using technology in practice: Case studies. Reachout
Pro website. http://au.professionals.reachout.com//
media/PDF/Professionals/Support%20workers/Part
%203%20-%20Case%20Studies.ashx. Accessed January 24, 2014.
VOL. 4 | ISSUE 1 | JANUARY 2015 24
PERSPECTIVE PIECE
‘‘MHEALTH IS AN INNOVATIVE APPROACH TO
ADDRESS HEALTH LITERACY AND IMPROVE PATIENTPHYSICIAN COMMUNICATION AN HIV TESTING
EXEMPLAR’’
Disha Kumar1,2, Monisha Arya, M.D., M.P.H3,4
1
School of Social Sciences, Rice University, 6100 Main St., Houston, Texas 77005, U.S.A; 2Wiess School of Natural Sciences, Rice
University, 6100 Main St., Houston, Texas 77005, U.S.A; 3Department of Medicine, Section of Infectious Diseases and Section of
Health Services Research, Baylor College of Medicine, One Baylor Plaza, Houston, Texas 77030, U.S.A; 4Center for Innovations in
Quality, Effectiveness and Safety, Michael E. DeBakey VA Medical Center 2002 Holcombe Blvd (Mailstop 152), Houston, Texas
77030, U.S.A
Corresponding Author: [email protected]
Low health literacy is a barrier for many patients in the U.S. Patients with low health literacy have
poor communication with their physicians, and thus face worse health outcomes. Several
government agencies have highlighted strategies for improving and overcoming low health literacy.
Mobile phone technology could be leveraged to implement these strategies to improve communication between patients and their physicians. Text messaging, in particular, is a simple and
interactive platform that may be ideal for patients with low health literacy. We provide an exemplar
for improving patient-physician communication and increasing HIV testing through a text message
intervention.
Journal MTM 4:1:2530, 2015
doi:10.7309/jmtm.4.1.6
Low health literacy leads to poor patientphysician communication and worse health
outcomes
Health literacy is ‘‘the degree to which individuals
have the capacity to obtain, communicate, process,
and understand basic health information and services needed to make appropriate health decisions.’’1
According to the 2003 National Assessment of
Adult Literacy, over 36% and 14% of U.S. adults
have below intermediate and below basic health
literacy, respectively.2 Racial and ethnic minorities
are most impacted by low health literacy, with 41%
of Hispanics and 24% of African-Americans having
below basic health literacy.2 Patients with low health
literacy make less use of preventive healthcare
services3,4 and suffer worse health outcomes.5,6
#JOURNAL OF MOBILE TECHNOLOGY IN MEDICINE
www.journalmtm.com
Moreover, as noted by the American Medical
Association, patients with poor health literacy
have poor communication with their physicians,
leading to poor health outcomes.7 Interventions
aimed at improving patient-physician communication positively correlate with improved health.8
Mobile health could improve patient-physician
communication
As noted by former U.S. Department of Health and
Human Services Secretary, Kathleen Sebelius, in her
keynote address at the annual Mobile Health
(mHealth) Summit, mobile technologies are ‘‘opening up new lines of communication between patients
and their physicians’’ and are an innovative strategy
to engage traditionally hard-to-reach populations
VOL. 4 | ISSUE 1 | JANUARY 2015 25
PERSPECTIVE PIECE
such as racial and ethnic minority communities.9
mHealth could be an innovative way to overcome
health literacy barriers because of its reach: mobile
phone ownership is ubiquitous across race, ethnicity, education, and income levels.10,11 The Institute
of Medicine Roundtable on Health Literacy’s Collaborative on New Technologies highlighted how
the ubiquity of mobile phones is closing the digital
divide faced by many low health literacy patients.12
mHealth offers potential to engage patients with
low health literacy by conveniently delivering relevant health information that could improve patientphysician communication.
Text messaging is the most common activity performed on a mobile phone, with 81% of mobile
phone owners sending and receiving text messages.11 Thus, text messaging is an ideal platform
for delivering health interventions to patients.
Studies have found that text messages have been
successful at promoting patient-physician communication,13 smoking cessation,14,15 weight loss,16,17
and immunization coverage.18 This may be because
text messages have several salient health promotion
features, especially beneficial for low health literacy
patients. mHealth text messages can be: 1) written
in simple text, 2) personalized based on the patient’s
health literacy level, and 3) interactive to facilitate
communication between patient and physician.
Based on these many aspects, text message interventions hold great potential to engage patients
and improve communication between patients and
physicians.
Adopting text messages to empower patients
with low health literacy
Government agencies have highlighted the importance of designing health interventions that are
appropriate for patients with low health literacy.
The U.S. Department of Health and Human
Services’ Quick Guide to Health Literacy recommends using health literacy strategies, such as
improving the usability of and access to health
information.19 Additionally, the Agency for Healthcare Research and Quality’s Health Literacy Universal Precautions Toolkit recommends using
patient feedback to evaluate the usability of the
health information presented.20 Finally, the U.S.
Department of Health and Human Services’ National Action Plan to Improve Health Literacy
emphasizes targeting and tailoring communication
in health interventions and the use of UserCentered Design (UCD).21 UCD employs strategies
#JOURNAL OF MOBILE TECHNOLOGY IN MEDICINE
Figure 1: The User-Centered Design Process23 (Adapted
from McCurdie et. al)
for end-users to influence iterative prototypes of a
product (Figure 1).22,23 Although UCD strategies
are generally employed in products more complex
than text messaging, UCD offers valuable insight to
ensure text messages result in increased patientphysician communication, and positive and sustained health engagement.
Challenges to note in developing a text message
intervention
Certain challenges may exist in text message interventions; however, these challenges can be addressed if they are identified early in campaign
development. First, although text messaging transcends race, ethnicity, education, and income levels,11
text messaging is not pervasive among the elderly.
Compared to over 94% of adults 18-49 years who
own a cell phone use text messaging, only 35% of
adults over 65 years who own a cell phone use text
messaging.11 However, older adults are increasingly
using text messaging. In 2013, 35% of adults 65
years or older used text messaging, compared to
only 11% of adults in this age group in 2009.24
Based on current trends, mHealth interventions
targeting older adults may be better suited for these
end-users as their familiarity with text messaging
increases. Second, the privacy and security of
patient information sent over text message should
comply with local and national regulations (e.g. the
U.S. Health Insurance Portability and Accountability Act of 1996). Additional research is needed to
identify risks associated with text messaging;25
updated security measures and regulations may
need to be implemented.26 Third, it is important
to note that text messages are limited to 160
characters. While text messages can effectively reach
target audiences, the campaign message must be
VOL. 4 | ISSUE 1 | JANUARY 2015 26
PERSPECTIVE PIECE
succinct enough to convey the intended information. Similarly, costs may be incurred by the enduser in receiving text messages. However, one in
three U.S. adults have unlimited text plans, limiting
the patients who will have to bear a cost burden.27
Finally, mHealth interventions may not always
improve health to the full expectations of the
campaign designers. For instance, Sweet Talk, a
text message system that supported adolescents
with diabetes, did not improve glycemic control.28
However, the system improved additional goals
of the campaign: diabetes self-efficacy and selfmanagement.28 Despite these limitations, strategic
text message campaigns have been successfully
implemented internationally and could be used as
a model.1318,29,30 Text message campaign designers
should be flexible and aware of the abilities and
preferences of the target audiences.
Exemplar: Text messaging could engage patients
and increase HIV testing
The HIV epidemic continues in the U.S. as approximately 50,000 persons contract HIV each year.31
HIV disproportionately affects racial and ethnic
minorities. Despite national recommendations for
routine HIV testing,32,33 several reports highlight
that physicians are not recommending HIV testing
to their patients even those at highest risk for
HIV.3438 Interestingly, patients want and expect
HIV testing to be done and want their physician to
test them.39 Conversely, physicians want their
patients to ask them for the HIV test.40
Health literacy impacts HIV health disparities.41
Low health literacy and poor patient-physician
communication are associated with poorer HIV
knowledge.42 Low health literacy may be a contributing factor to low HIV testing rates, particularly among the racial and ethnic minority
communities hardest hit by the HIV epidemic.
Because studies have found that text messages can
promote patient-physician communication,13 HIV
informational text messages could motivate patients
to ask their physicians about the HIV test. This
intervention could thereby increase patient-physician communication and HIV testing. A study of
predominately African-American patients found
that 77% of them felt they could be convinced by
a text message to get HIV tested.43 Unlike static
HIV testing campaigns, such as those on billboards,
HIV text message interventions could be sent near
the time of patients’ appointments with their physicians. Targeted text messages could revolutionize
#JOURNAL OF MOBILE TECHNOLOGY IN MEDICINE
preventive health practices, such as HIV testing, by
facilitating communication between low health
literacy patients and their physicians.
Conclusion
As highlighted in the Institute of Medicine report
Health Literacy: A Prescription to End Confusion,
the health system has significant opportunity and
responsibility to improve health literacy.44 The
health system should capitalise on the potential of
mHealth to engage people in their health and
overcome some of the barriers faced by patients of
low health literacy. Despite the proven positive
effects that mHealth campaigns have had on health
behaviors,13,45,46 mHealth as a patient-empowerment tool remains in its infancy. More research is
needed on the ability of mHealth to improve health
for the hardest-to-reach populations, such as those
with low health literacy. Successful mHealth strategies should incorporate health literacy strategies1921
and user-centered design.21,23 Prompting patients
with a simple health message before their physician
appointment could motivate patients to talk to their
physician about a pertinent health issue, thus overcoming low health literacy barriers. Utilizing
mHealth specifically for HIV testing, as in the
exemplar provided, could achieve several Healthy
People 2020 objectives: improve patient-physician
communication, improve HIV testing, and increase
use of mHealth.47
Acknowledgements
This work was supported by the Rice University
Janus Award (Disha Kumar), an undergraduate
research scholarship, and by a National Institutes
of Health/National Institute of Mental Health K23
grant (MH094235-01A1, PI: Arya). This work was
supported in part by the Center for Innovations in
Quality, Effectiveness and Safety (#CIN 13-413),
Michael E. Debakey VA Medical Center, Houston,
TX. The views expressed in this article are those
of the authors and do not necessarily represent
the views of the National Institutes of Health, the
Department of Veterans Affairs, or Rice University.
We have no conflict of interests to disclose.
All authors have completed the Unified Competing
Interest form at www.icmje.org/coi_disclosure.pdf
(available on request from the corresponding
author) and declare: D. Kumar reports stipend
from Rice University, M. Arya reports grant from
the National Institutes of Health/National Institute
of Mental Health; no financial relationships with
VOL. 4 | ISSUE 1 | JANUARY 2015 27
PERSPECTIVE PIECE
any organisations that might have an interest in the
submitted work in the previous 3 years; no other
relationships or activities that could appear to have
influenced the submitted work.
This paper or papers similar to it have not been
published previously by any of the authors.
The authors wish to thank Ms. Sajani Patel and Ms.
Anna Huang for their thoughtful comments and
editorial assistance on the manuscript.
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ORIGINAL ARTICLE
CONTEXTUAL BARRIERS TO MOBILE HEALTH
TECHNOLOGY IN AFRICAN COUNTRIES:
A PERSPECTIVE PIECE
Yvonne O’ Connor, PhD1, John O’ Donoghue, PhD2
1
Health Information Systems Research Centre, University College Cork, Cork, Ireland; 2Global eHealth Unit, Imperial College
London, UK
Corresponding Author: [email protected]
Journal MTM 4:1:3134, 2015
doi:10.7309/jmtm.4.1.7
On a global scale, healthcare practitioners are now
beginning to move from traditional desktop-based
computer technologies towards mobile computing
environments1. Consequently, such environments
have received immense attention from both academia and industry, in order to explore these promising opportunities, apparent limitations, and
implications for both theory and practice2. The
application of mobile IT within a medical context,
referred to as mobile health or mHealth, has
revolutionised the delivery of healthcare services
as mobile technologies offer the potential of retrieving, modifying and entering patient-related data/
information at the point-of-care. As a component of
the larger health informatics domain mHealth may
be referred as all portable computing devices (e.g.
mobile phones, mobile clinical assistants and medical sensors) used in a healthcare context to support
the delivery of healthcare services.
The usefulness of implementing IT in healthcare is
reflected in current eHealth initiatives in resourcepoor settings (e.g. Baobab Health in Malawi, MPedigree in Ghana, Nigeria and Kenya; Cell-Life in
South Africa). In recent years attempts have being
made to digitise WHO/UNICEF paper-based clinical guidelines when delivering paediatric healthcare
services, namely: Integrated Management of Childhood Illness (IMCI) and Community Case Management (CCM). Both IMCI and CCM are stepwise
and structured approaches, employed by Community Health Workers (CHW), towards reducing
death, illness and disability while promoting improved growth and development among children
#JOURNAL OF MOBILE TECHNOLOGY IN MEDICINE
under five years of age3,4. Digitising the IMCI and
CCM guidelines offer profound opportunities to
CHW (also referred to as Health Surveillance
Assistants in Malawi, Africa) in terms of improving
adherence to clinical guidelines, offering instant
access to patient data independent of location and
time and facilitating drug ordering via Short
Message Service (SMS)5.
However, introducing mobile technology in a medical context within resource-poor communities is
not without its challenges6. One obstacle faced by
mHealth users is lack of user acceptance of the
technology. Common factors which influence the
decision making process of accepting mobile technology in medicine may include perceived usefulness, perceived ease-of-use of the technological
tool7, performance expectancy, effort expectancy,
social influence, facilitating conditions8. Arguably,
the most imperative barrier faced by mHealth users
in Africa is that of a contextual nature. The
underlying premise behind this argument is that
many mHealth solutions for use in developing
countries are often developed in western societies.
Such solutions have been criticised for failing to
recognise the unique contextual factors associated
with developing regions9. Contextual factors reflect
external or driving elements that comprise the
environment or conditions for decision making
tasks10 and as a result, such factors can vary across
populations and industries. Cultural, economic,
political and cognitive dimensions are contextual
factors which could influence how end users interact
VOL. 4 | ISSUE 1 | JANUARY 2015 31
ORIGINAL ARTICLE
Figure 1: Contextual factors which should be incorporated into mHealth solutions
with mobile technology in medicine (referenced 14,
Figure 1).
Cultural factors (1, Figure 1) denote a set of beliefs
and norms that are both consciously and subconsciously held by any individual in the given
society11. In the context of this paper, this refers
to the principles/customs held by CHW in rural
regions of Africa. Culture diversity between developing and developed countries can be observed
based on ‘‘Individualism versus Collectivism’’,
‘‘Power distance’’, and ‘‘Masculinity versus Femininity’’12. That is, developed countries such as
Europe and U.S.A. are driven by individualist
approaches whereas developing countries are concerned with collectivist strategies. Power distance
reflects the way society distributes, shares, and
enforces the power among its members13. Power
holders in high power distance cultures such as
Africa are much more comfortable with a larger
status differential than low power distance cultures.
Additionally, research in African countries shows
preferential treatment towards males over females.
It is worth noting, however, that cultural values
cannot be easily adjusted to conform to any changes
introduced by mHealth. This conformity, therefore,
may have an impact on individual users’ intentions
to adopt mHealth technologies in Africa. The
authors suggest that ethnographic studies should
be performed to capture local cultural dimensions
similar to the work of Kitson (2011)14. In her work
Kitson identified a number of cultural factors
impacting the implementation of the Care2x hospital information system in Tanzania.
Economic factors (2, Figure 1) refer to the direct and
indirect financial opportunities attributable to
CHW in rural areas of developing regions. Without
the necessary economic support for sufficient tools
#JOURNAL OF MOBILE TECHNOLOGY IN MEDICINE
and resources, technology transfer from developed
regions to Africa becomes very complicated, given
the existing technological infrastructures at many
African locations15. To help ensure that mHealth
solutions are a viable option for African countries a
cost analysis should be performed as advocated by
Schweitzer and Synowiec (2012)16. Increased mobile
coverage in rural areas, including faster network
connectivity, is essential to realising the potential
and scope of mHealth in developing countries.
However, western societies should develop solutions
that operate on commonly used mobile devices in
developing regions. Many mHealth initiatives in
Africa utilise the SMS functionality of mobile
communication systems as a core connectivity
method. The underlying rationale for using this
low-cost functionality is that high-performance
devices are not required to transmit data. For
example, the effects of mobile phone SMS on
antiretroviral treatment adherence in Kenya was
examined17,18. These studies provide empirical evidence that mobile health initiatives can improve
HIV treatment outcomes.
Political factors (3, Figure 1) refer to the governmental agenda of central administrations within
developing regions. The planning and budgeting
process in resource-poor areas are often constrained
by expenditures in previous years. As a result,
developing regions often face difficulty to mobilise
funds for full-scale mHealth implementation as
there may be no reliable or guaranteed governmental financial support for sustaining mHealth initiatives. If mobile technologies are to be successfully
introduced across healthcare within developing
regions, it is an essential that government agencies
provide the necessary support, such as local Ministries of Health and local software industries to
manage and maintain the software artefact.
EHealth Nigeria is an example whereby an organisation collaborates closely with appropriate political
powers to ensure the sustainability of Health
Management Information Systems.
Cognitive factors (4, Figure 1) refer to users’
personal self-beliefs and opinions ability to interact
with mobile technologies in a medical domain. That
is, the degree to which a CHW perceives his or her
ability to use mHealth technologies in the accomplishment of a task19. Cognitive dimensions do play
an integral role in the use of mHealth technologies
in developing countries as it is reported that such
regions face education limitations (computer illiteracy) and a lack of English language skills. Research
VOL. 4 | ISSUE 1 | JANUARY 2015 32
ORIGINAL ARTICLE
conducted in the health domain of Mozambique
revealed that a limited amount of participants were
computer literate, with only a minority of health
workers at health facilities having the cognitive
ability to interpret health data20. MHealth initiatives promoted by developed countries are often
developed using the English language. This can
hinder the use of mobile technology in medicine due
to the lack of language translation abilities implemented within the software solution. It is therefore
imperative that developers facilitate multi-language
support to enhance the usability of mHealth
technologies. Furthermore, training workshops
should be provided to end users of mHealth
solutions to enhance proficiency with the technology21. The importance of providing training workshops is reflected in the work performed by Baobab
health in Malawi. They offer initial and refresher
training courses to end users of their eHealth
systems arguing that training is essential.
Conclusion
The status quo of the healthcare sector in Africa
is plagued with uncertainty surrounding lack of
resources (financial, technical and human), inadequate training to support health care providers, lack
of technical infrastructure, limited participation in
the development of medical/clinical standards, and
lack of understanding of standards at national
level)9. As a result, extant research on IT in the
less-developed world has been severely limited. To
add to this complexity IT solutions designed in
developed countries have often failed to transfer
effectively to African regions. To ensure that
mHealth is a viable option for the health services
sector in African countries many eHealth initiatives
are attempting to address contextual factors as part
of their development. This perspective piece argues
that it is imperative for developers to encompass
local cultural, economic, political and cognitive
factors to ensure intentions, use and diffusion of
mHealth initiatives.
Acknowledgements
‘‘The Supporting LIFE project (305292) is funded
by the Seventh Framework Programme for
Research and Technological Development of the
European Commission www.supportinglife.eu’’
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PERSPECTIVE PIECE
TAKING MHEALTH SOLUTIONS TO SCALE: ENABLING
ENVIRONMENTS AND SUCCESSFUL IMPLEMENTATION
Jennifer Franz-Vasdeki, PhD1, Beth Anne Pratt, PhD2, Martha Newsome, MPH3, Stefan Germann, PhD4
1
Independent Consultant; 2Global Health Insights, LLC; 3World Vision International; 4World Vision International
Corresponding Author: [email protected]
Journal MTM 4:1:3538, 2015
doi:10.7309/jmtm.4.1.8
Introduction
The increasing availability and capacity of mobile
devices is transforming accessibility and coverage in
the health field. Globally, there are nearly 6 billion
mobile cellular subscriptions with penetration
reaching 80% in the developing world.1 Particularly
in low and middle-income countries, the use of
mobile telecommunication and multimedia technologies, known as mobile health or mHealth, can
improve the quality of care and enhance efficiency
of service delivery within healthcare systems. In
particular, mHealth innovations offer tremendous
opportunities to improve access to health-related
information in hard to reach areas.2 Such opportunities include increased operational efficiencies, low
cost delivery, as well as enhanced diagnosis, treatment and tracking of diseases.3 Like many resources
and devices in the larger field of health informatics,
mHealth solutions can also improve consumer
access to and control over information they receive
about health and can help to advance knowledge
and skills while reducing complexity.4 mHealth
tools can provide improved access to healthcare
while creating cost efficiency and increasing capacity and quality of healthcare.5
Frontline health workers can benefit significantly
from mHealth technologies, particularly in maternal
and newborn health (MNH) as they can increase
autonomy and improve motivation by facilitating
and streamlining workloads and automating tedious
duties.
Despite its demonstrated potential, mHealth tools
and applications often struggle in practice. The
mHealth landscape is comprised by a large number
of pilot projects that are successful in one location,
but do not make it to scale.5 This has lead
to widespread scepticism of pilot projects and
#JOURNAL OF MOBILE TECHNOLOGY IN MEDICINE
small-scale mHealth interventions in many parts of
the world, particularly in low-income countries. In
fact, the term ‘pilotitis’ has been coined in response
to frequently expressed dissatisfaction from donors
and governments about isolated mHealth interventions that are successful in one context, but not
‘rolled out’ due to a variety of technical, practical,
economic and often institutional and political
barriers.68
Initially, there was a distinct need for small-scale
projects to gain a deeper understanding of technologies and applications. Likewise, not all mHealth
projects are appropriate for scale; some serve a
specific function or geographical area and are
designed for the short-term. To integrate and build
cross-sector partnerships around mHealth solutions, however, there is a growing need to coordinate
activities and build an evidence base that allows for
learning, communication, and understanding across
sectors and contexts.
There is growing evidence to support the emergence
of a new era in the mHealth evolutionary process
with more and more successful mHealth intitiatives
making it to scale and being rolled into national
health schemes. Research around mHealth initiatives and their impact is evolving rapidly and the
tracking of progress and success is increasing.9 The
mHealth Alliance’s mHealth and MNCH: State of
the Evidence report has found the increase remarkable and has called for even greater investment of
resources in studying the effect mHealth interventions have on health outcomes, and emphasises the
need to view gaps in the mHealth evidence as
opportunities for future research.9
In particular, to make it out of the pilot phase, these
initiatives require a number of key elements from
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PERSPECTIVE PIECE
the very beginning to ensure the possibility of scale.
The mHealth Alliance has identified five key
components that have increased the likelihood of
a pilot project being mainstreamed into health
systems, including: improved evidence, technology
integration and interoperability, sustainable financing for mHealth, global and national policies that
support the use of mHealth, and a health community that can design and deploy mobile technologies
for health.9
Lemaire (2011) cites a number of complementary
criteria required for overcoming ‘pilotitis’ and
successfully scaling up of mHealth initiatives at
national level. These criteria include:
1. Building sustainability plans into mHealth
initiatives from the point of planning;
2. Ensuring that technological and logistical
solutions to problems are locally feasible and
appropriate;
3. Securing buy-in from, and creating strategic
partnerships with, key stakeholders, including national Ministries of Health, private
sector mobile technology partners, technical
agencies, local non-governmental organisations, and potential sources of financing
whether private sector or donor-based;
4. Aligning mHealth initiatives with local and
national health priorities, and integrating
initiatives into existing national- and subnational health systems, structures, and policies;
5. Putting in place data and interoperability
standards so information fluidly feeds back
into and informs national and sub-national
health management information systems;
6. Ensuring monitoring and evaluation is built
into implementation plans, and provided
with a sufficient budget.
The following case study offers a look at some of
these basic ingredients and approaches that can help
to achieve both scale and sustainability in the
African context. We focus in particular on how
closing persistent gaps increases the likelihood of
mainstreaming mHealth initiatives into health systems.
Health Management Information Systems:
mTrac
In 2012, following reports of an uncoordinated,
‘chaotic mushrooming’ of mHealth projects across
#JOURNAL OF MOBILE TECHNOLOGY IN MEDICINE
the country, the Government of Uganda placed a
moratorium on all mobile technology pilots until
such time a coordinated set of technical standards
and government strategies could be put in place.10
On the surface, the government’s declaration of a
moratorium might suggest public sector mistrust in
mHealth innovations. However, there have been a
number of notable mHealth success stories in
Uganda. One such success has been the government’s adoption and scale up of the mTrac platform
for health information management. The Ugandan
MoH was recognized by the African Development
Bank for mTrac and it was rated one of the top ten
eHealth projects of 2013.11
mTrac is a government initiative that originated as a
pilot project within a Millennium Villages Project
and Foundation for Innovative New Diagnostics
(FIND). It was then handed over to the Government of Uganda for launch and scale up in
December 2011. The Ministry of Health (MoH)
fully owns and operates mTrac and it began to roll it
out in four phases, each covering approximately
twenty-eight districts.
mTrac is meant to be used as both an auditing and
data collection tool. In particular, mTrac focuses on
the collection, verification, accountability and analysis of data generated at community and health
facility levels. With financial support primarily from
the UK Department for International Development
(DFiD), this is done in three key ways.12,13 Firstly,
via SMS, in order to transmit weekly surveillance
reports (i.e. information on disease outbreaks and
stocks of anti-malarials) from health facilities to
the MoH and District Health Offices (DHOs).
Secondly, mTrac operates as an anonymous hotline
providing a service delivery complaints toll-free
number through which community members can
report health service-related issues, including operating hours of health centres and stock outs of
essential drugs in hospitals. Thirdly, through a
mechanism known as ‘u-report’- where 235,000
registered stakeholders representing every community in Uganda collect regular feedback on developmental issues and engage elected representatives
to discuss these issues.
mTrac is also available at the Village Health Team
(VHT) level, feeding into the system information
collected by community health workers. Data
collected at this level includes information on
malaria, severe malnutrition, and referrals to health
facility, as well as on ACT and Amoxycillin stock.
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PERSPECTIVE PIECE
mTrac was designed from the start to work within
and through both the MoH’s existing software and
paper systems of collection at the community and
facility-level. By prioritising interoperability from
the beginning, mTrac has aligned and integrated
fully with the MoH and required limited additional
investment in IT infrastructure or project implementation.
The MoH receives technical support from UNICEF
and WHO, as well as financing from DfID, but
mTrac is formally governed via a government-led
Steering Committee chaired by the National Medical Stores, as well as via a dedicated eHealth
Technical Working Group (TWG).13
The impact of mTrac is not yet known as it is still in
its early stages. There is some evidence of low
reporting rates by end users, and also weak health
system responsiveness (e.g. supply chain inefficiencies mean that reports of drug stock outs cannot be
acted upon).14 Such problems serve to highlight the
fact that mHealth initiatives are not magic bullets,
and their success is largely dependent on the
strength of the health system into which they are
introduced. Nevertheless, mTrac serves as a model
for mHealth scale up as it focused, from the
beginning, on a) designing interoperable systems
that can be implemented to scale; b) aligning these
systems into existing national structures, policies,
and institutions; c) coordinating multiple public and
private sector stakeholders, and leveraging their
strengths; and d) focusing on minimising additional
investment by government to ensure sustainability.
Conclusions
Achieving comprehensive health delivery systems
supported by mHealth tools requires meaningful,
productive communication across numerous sectors
and stakeholders. Such stakeholders may include
public health and healthcare delivery personnel,
information technology and communication specialists, economists and finance professionals as well as
evaluation and monitoring experts. Leadership is
also needed from ministries, acting in concert, to
provide the necessary guidance for innovators,
companies, and organisations in order to develop
meaningful mHealth tools targeted at national
priorities. Patients or end users should also be
included. There is a distinct need for engaging peers
in the technology sector, thus requiring discussion
to be extended to include stakeholders from across
the full healthcare spectrum and along the full
continuum of care.
#JOURNAL OF MOBILE TECHNOLOGY IN MEDICINE
Bringing mHealth solutions and interventions to
scale requires cross-sector partnership brokering
expertise, increased awareness, coordinated and
directed financing, national and global policies to
establish interoperability standards and data protection, and investment in the workforce to support
long-term applications and initiatives.3 It also
requires numerous and multi-sector stakeholders
to be involved in national discussions to set
strategies and operational plans to move forward
in a unified fashion. The way forward needs to draw
upon governments’ formal health sector development strategies and formal commerce, communications and related national strategies.
There remains limited evidence on the improved
health outcomes generated from these projects.
Better evidence would ensure improved coordination of activities to allow for learning, communication and thus understanding across sectors. For
example, a project that demonstrates an increased
quality of care or improved capacity of health care
workers to manage patient load will provide the
necessary evidence to funding organisations and
national health systems that these projects are not
only possible, but effective in changing delivery
methods and improving efficiency, as well as generating tangible health benefits for the often most
difficult to reach populations and doing so in a
sustainable and affordable manner.
The mHealth field is currently dominated by people
from the technology sector rather than health
delivery practitioners. As mHealth is still in its
nascent state, limited cross sector expertise is to be
expected but has also been a contributor to
‘pilotitis’. Technologists can dazzle healthcare providers in the field or even policy makers with a new
screen on a smartphone or a clever way to eliminate
paperwork using SMS, however, this leads to a
solution-based approach that does not consider
long-term adaptability and sustainability implications. Once the technologist leaves, the pilot often
falls apart because those in the field cannot support
the application, or the technology/solution does not
take into account the fact that the ‘solution’ needed
to interoperate with some other equally important
systems or processes.
As demonstrated with mTrac, there is a growing
consensus among governments, funding bodies,
and international organisations for the need of
greater efforts in cross-sector partnerships to bring
various stakeholders together. The Commission on
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PERSPECTIVE PIECE
Information and Accountability for Women’s and
Children’s Health has called for 74 countries to have
integrated the use of Information and Communication Technologies (ICT) in their national health
information systems and infrastructure by 2015.15
The implementation plan of the Commission’s
recommendation on use of ICT has three elements:
develop a national mHealth plan, identify scalable
projects, and build a knowledgebase of lessons
learned and best practices.
Bringing mHealth solutions and interventions to
scale requires numerous and multi-sector stakeholders to be involved in national discussions to set
strategies and operational plans to move forward in
a unified fashion. The way forward needs to draw
upon government‘s formal health sector development
strategies and formal commerce, communications
and related national strategies.
References
1. International Technical Union. The World in 2013:
ICT Facts and Figures. (2013).
2. Chang, L. W. et al. Impact of a mHealth intervention
for peer health workers on AIDS care in rural
Uganda: a mixed methods evaluation of a clusterrandomized trial. AIDS and behavior 15, 177684,
doi:10.1007/s10461-011-9995-x (2011).
technology in public health; Public Health Institute,
Oakland, CA, 2010).
6. Kuipers, P. et al. Collaborative review of pilot
projects to inform policy: A methodological remedy
for pilotitis? Australia and New Zealand health policy
5, 17, doi:10.1186/1743-8462-5-17 (2008).
7. van Velthoven, M. H., Brusamento, S., Majeed, A. &
Car, J. Scope and effectiveness of mobile phone
messaging for HIV/AIDS care: a systematic review.
Psychology, health & medicine 18, 182202,
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8. Free, C. et al. The effectiveness of mobile-health
technology-based health behaviour change or disease
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on Hold, Bhttp://opinionator.blogs.nytimes.com/
2013/03/13/the-benefits-of-mobile-health-on-hold/?_
r0 (2013).
11. Ugandan Ministry of Health. Health ministry initiative wins Africa e-health award. (http://www.mtrac.
ug/mtrac-news, 2013).
12. DfID. Increasing Access to Antimalarial drugs in
Uganda: Second Annual Review. (DfID, iati.dfid.gov.
uk/iati_documents/4295929.doc, 2013).
3. Mechael, P. N., Batavia, h., Kaonga, N., Searle, S.,
Kwan, A., Goldberger, A., Fu, L., Ossman, J,.
Barriers and gaps affecting mHealth in low and middle
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13. Government of Uganda. Report on the mTrac
National Launch. (Ministry of Health, Kampala,
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4. Ratzan, S. C. Connecting the MDGs and NCDs with
digital health. Journal of health communication 16,
6815, doi:10.1080/10810730.2011.600623 (2011).
14. Martyris, D. mHealth in Uganda. (mHealth Working Group, http://www.mhealthworkinggroup.org/
resources/mhealth-uganda, 2013).
5. mHealth Alliance. Leveraging mobile technologies
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landscape & opportunities for advancement in lowresource settings. (The center for innovation &
15. WHO. Keeping promises, measuring results: Commission on information and accountability for women’s
and children’s health. (World Health Organization,
Geneva, 2011).
#JOURNAL OF MOBILE TECHNOLOGY IN MEDICINE
VOL. 4 | ISSUE 1 | JANUARY 2015 38
LETTER TO THE EDITOR
BEYOND THE HYPE: MOBILE TECHNOLOGIES AND
OPPORTUNITIES TO ADDRESS HEALTH DISPARITIES
Yulin Hswen, MPH1,2, Kasisomayajula Viswanath, PhD1,3
1
Department of Social and Behavioral Sciences, Harvard School of Public Health, Boston, MA, USA; 2Center on Media and
Child Health, Boston Children’s Hospital, Boston, MA, USA; 3Health Communication Core, Dana-Farber/Harvard Cancer Center,
Boston, MA, USA
Corresponding Author: [email protected]
doi:10.7309/jmtm.4.1.9
In recent years, countless news stories, blog posts,
and academic commentaries have highlighted the
growing excitement surrounding the potential of
mobile health (mHealth) technologies. Whether it is
for treatment, diagnosis, illness monitoring or
promoting healthy lifestyle behaviors, mHealth
refers to the use of mobile and wireless devices
such as smartphones or tablet computers for health
or medical purposes, and as regularly illustrated in
the Journal of Mobile Technology in Medicine, these
emerging technologies offer innovative approaches
to addressing complex medical and population
health concerns.1,2 For example, leaders in medicine, government, and industry have championed
mHealth as a strategy for treating acute and chronic
illnesses, more efficiently conducting clinical and
population-based health research, and addressing
healthcare workforce shortages.35 In a recent
commentary published in the Journal of the American Medical Association, Steinhubl and colleagues
discussed the potential for emerging mobile technologies to transform health care.6 While we
entirely agree that mHealth holds tremendous
potential, we caution readers that these benefits
may be differentially experienced across diverse
groups, and may exacerbate as opposed to close
health disparities.
The Internet revolution is a case in point. It was
championed as a means to overcome socioeconomic, demographic and geographic barriers, yet
considerable evidence shows a digital divide and
fewer opportunities for disadvantaged individuals.
It is possible that mHealth may be different given the
rapid worldwide penetration of mobile telecommunication technologies; however, despite the decreasing
costs of owning the actual devices, continuing access
to data services through subscription represents a
considerable expense for low-income individuals7 and
#JOURNAL OF MOBILE TECHNOLOGY IN MEDICINE
limits access and use of these services.8 This creates
challenges for many people to maintain a continuous and reliable wireless connection to the Internet, which would severely limit their ability to
benefit from mHealth applications requiring continual illness monitoring, real-time data collection,
or remote syncing to the virtual cloud. A recent
survey conducted in the United States highlighted
that expense was the single greatest barrier to
owning a mobile device among a predominantly
African American sample of low-income individuals with serious mental health concerns.9
Access, however, does not guarantee benefit from
mHealth technologies. Difficult or unfamiliar userinterface may deter people from lower socioeconomic status to make effective use of mobile
technologies for their health. Significant gaps in
trust of health information from Internet sources
has also been observed across low-income and
ethnic groups.7 It is likely that such digital inequalities and lack of trust of health information may
significantly limit the potential for mHealth to
enable minority and low-income individuals to
benefit through self-diagnosing acute symptoms,
or tracking and managing chronic health conditions. This is of particular concern given the
disproportionately elevated chronic disease burden
impacting these individuals.10
Our aim is not to question the promise of mHealth,
but rather to emphasize that just as stated by
Steinbuhl and colleagues in their concluding remarks, ‘‘much remains to be done’’.6 Just as clearly
defined government regulations3, internationally
recognized research guidelines11, and robust clinical
trial evidence6 are critically necessary for advancing
this nascent field, consideration of how mHealth
technologies can be adapted and strategically
VOL. 4 | ISSUE 1 | JANUARY 2015 39
LETTER TO THE EDITOR
delivered to address the needs of the most vulnerable low-income patients is of equal value. The role
of mHealth technologies for addressing health
disparities has received less attention12, though
important opportunities exist. For instance, trends
of increasing mobile phone penetration among lowincome groups, evidence that at-risk minorities are
more likely to search for health related information
on their phones or on the Internet than mainstream
populations, and the capacity to engage at-risk
patients through greater personalization, facilitating
social connections, or community outreach further
support the promise of using these emerging technologies for reaching marginalized individuals.13,14
It is imperative that efforts to address health
disparities through the elimination of health communication inequalities, targeted dissemination of
culturally appropriate health information to at-risk
minority groups10, or incentives programs aimed at
addressing gaps in affordability and access to
mobile health technologies7, must not be overshadowed by the hype or excitement of only the
newest hi-tech devices. We sit at an exciting time
where patients, researchers, clinicians, entrepreneurs
and policy makers can shape how emerging mobile
technologies will transform health care; let’s not
squander this opportunity.
Disclosures
None for any author.
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3. Cortez NG, Cohen IG, Kesselheim AS. FDA regulation of mobile health technologies. New England
Journal of Medicine. 2014;371:3729.
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tsunami*older adults and mental health care. New
England Journal of Medicine. 2013;368:4936.
6. Steinhubl SR, Muse ED, Topol EJ. Can mobile
health technologies transform health care? Journal of
the American Medical Association. 2013;310:23956.
7. Viswanath K, Nagler RH, Bigman-Galimore CA,
McCauley MP, Jung M, Ramanadhan S. The
communications revolution and health inequalities
in the 21st century: implications for cancer control.
Cancer Epidemiology Biomarkers & Prevention. 2012;
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8. Zickuhr K, Smith A. Digital differences. Pew Research
Center’s Internet & American Life Project. 2012:141.
9. Ben-Zeev D, Davis KE, Kaiser S, Krzsos I, Drake
RE. Mobile technologies among people with serious
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Administration and Policy in Mental Health and
Mental Health Services Research. 2013;40:3403.
10. Gibbons MC, Fleisher L, Slamon RE, Bass S,
Kandadai V, Beck JR. Exploring the potential of
Web 2.0 to address health disparities. Journal of
Health Communication. 2011;16:7789.
11. Tomlinson M, Rotheram-Borus MJ, Swartz L, Tsai
AC. Scaling up mHealth: where is the evidence?
PLoS Medicine. 2013;10:e1001382.
12. Horn IB, Mendoza FS. Reframing the disparities
agenda: a time to rethink, a time to focus. Academic
Pediatrics. 2013;14:1156.
13. Martin T. Assessing mHealth: opportunities and
barriers to patient engagement. Journal of Health
Care for the Poor and Underserved. 2012;23:93541.
14. Naslund JA, Grande SW, Aschbrenner KA, Elwyn
G. Naturally occurring peer support through social
media: the experiences of individuals with severe
mental illness using YouTube. PLoS One.
2014;9:e110171.
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