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VOLUME 9 ISSUE 1

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VOLUME 7 ISSUE 2

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RECENT NEWS

VOLUME 9 ISSUE 1

Posted on Jul 28, 2023 in Featured | 0 Comments

The Editorial Board at the Journal of Mobile Technology in Medicine is proud to
present Volume 9, Issue 1. Mobile technology in Medicine is a rapidly developing
area, and we hope to continue accelerating research in the field. We look
forward to your submissions for Issue 2.

Journal of Mobile Technology Volume 9, Issue 1.PDF

ORIGINAL ARTICLES

001 “Hell, Yeah!” A Qualitative Study of Inpatient Attitudes towards Healthcare
Professionals’ Use of Mobile Devices
Lori Giles-Smith BA (Hons), Andrea Spencer RN

011 Mind the gap – a study on mHealth based treatment process optimization in
addiction medicine
Ulf Gerhardt, Thomas Gerlitzki, Ruediger Breitschwerdt, Oliver Thomas

029 A Pilot Study of Using a Personalised Video Message Delivered by Text
Message to Increase Maternal Influenza Vaccine Uptake
Khai Lin Kong, Sushena Krishnaswamy, Ryan Begley, Paul Paddle, Michelle L. Giles

035 Comparison of Mid-Sternum and Center of Mass Accelerometry to Force Plate
Measures for the Assessment of Standing Balance
Ryan Z. Amick, Nils A. Hakansson, David M. Jorgensen, Jeremy A. Patterson,
Michael J. Jorgensen

043 Teleultrasound in Remote and Austere Environments
Reuben J. Chen

048 Pilot Study: Real-Time Monitoring and Medication Reminders in Glaucoma
Patients
Alice H. Li, Yang Shou, Zhongqiu Li, Ann C. Fisher, Jeffrey L. Goldberg, Yang
Sun, Wen-Shin Lee, Robert T. Chang

In keeping with our open-access principles, all articles are published both as
full text and as PDF files for download. For your convenience, attached to this
post is a PDF file containing the complete Volume 9, Issue 1, which can be
easily downloaded and saved for viewing offline.

Journal of Mobile Technology Volume 9, Issue 1.PDF

We look forward to hearing from readers in the comments section, and encourage
authors to submit research to be considered for publication in this
peer-reviewed medical journal.
Yours Sincerely,
Editorial Board
Journal of Mobile Technology in Medicine

DESIGNING A WIC APP TO IMPROVE HEALTH BEHAVIORS: A LATENT CLASS ANALYSIS

Posted on Dec 4, 2018 in Original Article | 0 Comments

Designing a WIC App to Improve Health Behaviors: A Latent Class Analysis

Click Here to Download PDF of Article

Sylvia H. Crixell PhD, RD1, Brittany Reese Markides MS, RD2, Lesli
Biediger-Friedman PhD, MPH, RD3, Amanda Reat MS, RD2, Nicholas Bishop PhD4

1Nutrition and Foods Professor, School of Family and Consumer Sciences, Texas
State University, San Marcos, Texas; 2Nutrition and Foods Lecturer, School of
Family and Consumer Sciences, Texas State University, San Marcos, Texas;
3Nutrition and Foods Assistant Professor, School of Family and Consumer
Sciences, Texas State University, San Marcos, Texas; 4Family and Child
Development Assistant Professor, School of Family and Consumer Sciences, Texas
State University, San Marcos, Texas

Corresponding Author: scrixell@txstate.edu

Journal MTM 7:2:7–16, 2018

doi:10.7309/jmtm.7.2.2

--------------------------------------------------------------------------------

Background: Smartphone apps have potential to effectively deliver health
education and improve health behaviors among at-risk populations. To be
successful, apps should include user input during stages of development.
Previously, a prototype app designed for participants in the Texas Special
Supplemental Nutrition Program for Women, Infants, and Children (WIC) was
developed based on input from focus groups.

Aims: This research aimed to continue app design by soliciting user input via a
survey from a state-wide sample.

Methods: Texas WIC clients were asked about physical activity, healthy eating,
and breastfeeding behaviors, stage of change regarding health behaviors, current
use of health-related apps, and perceptions of app prototype features. Latent
class analysis (n=942) was used to identify mutually exclusive groups based on
the strength of participants’ agreement that prototype features would help them
exercise more or consume more fruits and vegetables. Logistic regression
examined health-related characteristics and sociodemographic differences between
classes.

Results: Response to app prototype features was positive. A 2-class model best
described latent classes. Class members that strongly agreed that prototype
features would help them improve health behaviors were younger (< 35 years), not
pregnant, already using health-related apps, and in the contemplation,
preparation, or action stages of change regarding physical activity.

Conclusion: Refinement of the Texas WIC app should incorporate input from
individuals who are pregnant, older than 35 years, or in pre-contemplation
regarding physical activity. The iterative process of user-centered design
applied in this research may serve as a useful framework for development of
other public health apps.

Keywords: health promotion, technology, vegetables, smartphone, exercise

--------------------------------------------------------------------------------

INTRODUCTION

In the United States, poverty affects women and children disproportionately, as
they make up approximately 70% of the low-income population.1 Poverty is
associated with deleterious health behaviors, such as consuming a low-quality
diet and being physically inactive, particularly among vulnerable populations
such as women and children.2 These behaviors contribute to serious health
concerns, including poor birth outcomes, obesity, heart disease, type 2
diabetes, and certain cancers.2,3 Limited access to evidence-based information
related to health, physical activity, nutrition, and infant care is a likely
contributor to poor health behaviors and outcomes among low-income individuals
and may be an important barrier that contributes to ongoing health disparities.2

Launched in 1972, the Special Supplemental Nutrition Program for Women, Infants,
and Children (WIC) is a federal grant program that serves low-income pregnant,
postpartum, and breastfeeding women, infants, and children up to age 5 who are
at nutritional risk, with the goal of improving health behaviors and outcomes
during critical periods of development.4 Annually, 8 million women, infants, and
children are enrolled in WIC, with approximately 886 thousand participating in
the state of Texas.5 WIC provides a number of resources, including vouchers for
healthful foods to support pregnancy, lactation, and growth, and referrals to
health care services.6 While they are enrolled in the program, WIC clients are
expected to regularly participate in education that focuses on promoting healthy
behaviors such as breastfeeding, exercise, and healthy eating (e.g. eating
fruits and vegetables, cooking meals at home, eating meals as a family).6
Historically, WIC clinics have worked to impart evidence-based information
related to health behaviors through education offered at clinics via
face-to-face education. This education modality presents barriers to an already
taxed population, which may lack reliable transportation to clinics and
childcare during education sessions.7 In an attempt to mitigate these barriers,
many WIC state agencies now offer online client education.8 However, reliable
access to a computer with internet connectivity is not ubiquitous among
Americans, and low-income smartphone owners are more likely to rely on their
smartphones as a primary way of connecting to the internet.9 Indeed, Texas WIC
clients have expressed a desire to receive education and services delivered via
phone.7,10 Thus, smartphone apps may offer a viable alternative interface for
providing innovative, accessible, and customizable health education to the WIC
population. Research has supported the use of smartphones as a behavioral
modification tool, with a number of applications developed to improve diet and
physical activity.11

Smartphone apps have unique characteristics that may make them particularly
ideal for delivering health education and supporting health behavior change. For
example, smartphone apps can be used to access clients in real time, offer
continual assessment of identified treatment goals, and deliver meaningful
information and support to reinforce behavior change.11 Despite the promise of
smartphone apps, individuals tend to discontinue app use after three months of
downloading.12 Therefore, app developers should consider, a priori, the
expressed needs of intended users. Indeed, all approaches to developing
technological tools to improve health outcomes should engage people first.13 One
approach to developing apps that prioritizes individuals is user-centered design
(UCD), an evidence-based, iterative process prioritizing user input and
engagement in designing products and services.14 Because UCD has been previously
used to develop appealing smartphone apps that target health behaviors, such as
physical activity,15 this process shows promise for developing an effective app
for WIC clients. In 2014, based on strong interest among Texas WIC clients for
delivery of nutrition education and services via their smartphones,16 the Texas
Department of State Health Services WIC program commissioned us to develop a
smartphone app prototype. To do this, we began the UCD process by conducting
focus groups with a diverse sample of female WIC participants in south central
Texas to explore current smartphone app use and preferences.17 Based on this
initial user-input and tenets of the Social Cognitive Theory,18 we developed an
app prototype, designed to provide customizable, interactive, and user-centered
health education to the Texas WIC population.17 The prototype included features
to support physical activity (i.e., activity calendar, activity tracker,
exercise videos, resource library), healthy eating (i.e., meal calendar, healthy
eating tracker, cooking videos, resource library, shopping list, fruit and
vegetable game, farmer’s market locator), and breastfeeding (i.e., breastfeeding
timer, growth chart, live assistant, resource library).17

The aim of this research was to continue the UCD process of developing an app
for Texas WIC clients by disseminating a statewide survey seeking input
regarding the app prototype features. Analysis of clients’ perceptions of
prototype features designed to support physical activity and healthy eating are
included in this report. Our approach was to use latent class analysis to
identify subgroups of respondents based on the extent to which they agreed that
the features would help them increase physical activity and intake of fruits and
vegetables, and logistic regression to examine how membership in the latent
classes were associated with sociodemographic and health-related
characteristics. Recommendations for continued user-centered design of the WIC
app were informed by characteristics of respondents in latent classes.

METHODS

SAMPLE

The survey was posted on the Texas WIC website from September 9, 2014 through
November 6, 2014. Clients visiting the website were greeted with a pop-up window
presenting an offer to take the survey in English or Spanish. Additionally,
clinics in central Texas who had access to client email addresses sent
invitations to clients to take the survey. Of the 606 emails sent, 88 addresses
were invalid, 102 began taking the survey, and 63 finished. Overall, 1,019 WIC
clients completed the survey. Participants who were younger than 18 (n=50), male
(n=14), and had an implausible reported height (shorter than 4 feet or taller
than 7 feet, n=27)19 were removed from the analytic sample, leaving a total
sample of 942 respondents. The Institutional Review Boards of Texas State
University and the Texas Department of State Health Services approved this
study.

SURVEY

The survey, developed in English in collaboration with Texas WIC staff, included
approximately 130 questions, depending on responses to logic-driven branches. To
develop a version of the client survey in Spanish, the English survey was
translated to Spanish, back-translated, and discrepancies were reconciled. The
survey was implemented using Qualtrics software (2014, Provo, UT). The welcome
page briefly described the survey, provided assurances of privacy, and described
incentives for survey completion, which included credit for taking a WIC
nutrition class and receiving a t-shirt. After giving informed consent,
participants were asked if they owned a smartphone. Those who responded with
‘no’ were routed to a thank you page and the survey was discontinued.

The survey was divided into 3 major sections corresponding to health behaviors
addressed by the WIC app prototype, including physical activity, healthy eating,
and breastfeeding, followed by a set of demographics questions. Each health
behavior section asked about current health practices, stage of change,
facilitators and barriers to performing the health behavior, belief that app
features would help to improve health behaviors, and current use of apps
regarding that health behavior. Facilitators and barriers to health behaviors
were drawn from focus groups held during the initial phase of the UCD of this
prototype app.17 The current study is an analysis of participant response to the
physical activity and healthy eating features and does not include
breastfeeding.

Current practices regarding physical activity were measured using the Godin
leisure-time exercise questionnaire, which creates a physical activity score
based on questions about intensity and duration of exercise.20 This score
classifies participant activity as insufficiently active, moderately active, or
active. For analysis, categories were collapsed into a binary variable (0 =
insufficient activity, 1 = moderately active or active). Stage of change for
physical activity behaviors was assessed using a 4-question system adapted from
Wolf et al. (1 = pre-contemplation, 2 = contemplation, 3 = preparation, 4 =
action).21 Survey respondents were asked to indicate on a 5-item Likert scale to
what extent they agreed that specific barriers and facilitators to physical
activity applied to them personally (1 = strongly disagree, 2 = disagree, 3 =
neither agree nor disagree, 4 = agree, 5 = strongly agree); these data were not
included in this analysis. Finally, after each of the four app prototype
features addressing exercise was displayed (an activity calendar, activity
tracker, exercise videos, and resource library), clients were asked whether they
agreed that the feature would help them exercise more often (1 = strongly
disagree, 2 = disagree, 3 = neither agree nor disagree, 4 = agree, 5 = strongly
agree).

Intake of fruits and vegetables was used as an indicator of healthy eating
practices and was measured using a brief screener employed by Wolf et al. (fruit
and vegetable servings consumed on the previous day, excluding servings of white
potatoes, were summed for the final count and included in analysis as a
continuous variable).21 Participants were also asked how many family meals they
had each week, which was included as a continuous variable. Assessments of stage
of change and whether the seven prototype features (a meal calendar, healthy
eating tracker, cooking videos, a resource library, shopping list, fruit and
vegetable game, and a farmer’s market locator) that addressed healthy eating
would help them eat more fruits and vegetables were conducted in the same manner
as described for physical activity.

The survey included questions about demographic characteristics. Household size
was measured as a continuous variable. Age of participants (0 = 35 years or
older, 1 = younger than 35 years), education (0 = high school or less, 1 =
post-secondary education), race/ethnicity (White = reference, Black, Hispanic,
Other), language used to complete the survey (0 = English, 1 = Spanish),
employment status (0 = unemployed, 1 = employed), location of residence (0 =
urban, 1 = rural), and body mass index (BMI; < 18.5 = underweight, 18.5 – 24.9 =
normal weight, 25 – 29.9 = overweight, > 30 = obese) were coded as categorical
variables. Due to few participants having a BMI identifying them as underweight,
participants identified as underweight and normal weight were combined and used
as the BMI reference group. BMI was not calculated for women who were pregnant
(1 = pregnant, 0 = not pregnant). Food security was assessed with the U.S.
Household Food Security Survey Module: Six-Item Short Form.22 Food security
status was coded as a dichotomous variable (0 = very low or low food security, 1
= marginal or high food security). Current use of physical activity or healthy
eating apps were coded as dichotomous variables (0 = never or almost never use,
1 = sometimes or daily use).

STATISTICAL ANALYSES

Latent class analysis, a form of mixture modeling allowing for the
classification of unobserved heterogeneity in responses to multiple variables,
was used to identify homogenous, mutually exclusive groups of WIC clients based
on the extent to which they agreed that prototype features would help them
exercise more often or eat more fruits and vegetables.23 To identify the number
of classes that best represented the underlying response groups, a series of
model fit tests were conducted starting with a single-class model. Model fit
indices, including Akaike Information Criterion (AIC), Bayesian Information
Criterion (BIC), and sample-size adjusted AIC (SSA-AIC), were used to determine
whether the inclusion of each additional class provided improved model fit.
Model entropy, representing the accuracy of assigning individuals to classes,
was also considered. Finally, the Vuong-Lo-Mendell-Ruben (VLMR) likelihood ratio
provided a statistical test of whether the estimated model significantly
improved model fit compared to a model with one less class. Once the optimal
number of latent classes was determined, logistic regression was used to examine
differences between classes based on sociodemographic and health-related
characteristics. The latent class analysis and logistic regression was conducted
with Mplus 7.324 using maximum likelihood estimation with robust standard
errors, providing treatment of missing data with maximum likelihood and
estimation of standard errors robust to non-normality. All other analyses were
conducted using IBM SPSS Statistics for Windows, version 24 (IBM Corp., Armonk,
N.Y., USA).

RESULTS

Table 1 presents model fit indices used to identify the optimal number of latent
classes based on WIC participants’ responses to the survey questions “this app
feature would help me exercise more/eat more fruits and vegetables.” Compared to
the 1-class model, the 2-class model had improved AIC, BIC, and SSA-BIC model
fit indices; the rate of model fit improvement decreased with the 3-class model.
The VLMR test indicated that the 2-class model was a significant improvement on
the 1-class model (p < .001), but the 3-class model was not a significantly
better fit than the 2-class model (p = 0.8). Thus, based on model fit tests and
the necessity of parsimony, the 2-class model was identified as the best
description of latent classes.

Table 1: Goodness of fit indices for determining number of latent classes among
Texas WIC survey respondents.

Class 1 (strongly agree; 32.9% of the sample) was identified as the group that
strongly agreed that app features would help them improve targeted health
behaviors; those in class 2 (neutral, agree; 64.8% of the sample) agreed or were
neutral regarding whether the app features would help improve health behaviors.
Figure 1 shows the distribution of responses to questions asking if using the
app features would help respondents improve targeted health behaviors.

Figure 1: WIC clients’ agreement that app features would help improve targeted
health behaviors. a) Depicts Class 1 (strongly agree) and Class 2 (neutral,
agree) feedback regarding physical activity features. b) Depicts Class 1
(strongly agree) and Class 2 (neutral, agree) feedback regarding healthy eating
features.

Table 2 includes descriptive statistics for the complete analytic sample as well
as by latent class. On average, respondents in the complete sample had
approximately 5 household members and consumed 7.4 family meals per week,
including 3.5 servings of fruits and vegetables per day. Approximately three out
of four respondents were younger than 35 years of age at the time of the survey,
which categorizes them as millenials.25 Slightly more than half of participants
were Hispanic and the vast majority took the survey in English. Approximately
45% of respondents were employed and the majority were urban-dwellers.
Fifty-eight percent were overweight or obese. Sixteen percent of the sample was
pregnant. Approximately a third of the sample had completed post-secondary
education and a third had marginal or high food security. Two-thirds engaged in
at least 150 minutes of moderate or intense physical activity each week. Almost
two-thirds of participants used apps for exercise and three-quarters used
healthy eating apps. The vast majority recognized the benefits of being
physically active and eating fruits and vegetables. Likewise, the majority were
in contemplation, preparation, or active stages of change regarding being
physically active and eating fruits and vegetables.

Table 2: Description of the overall sample of Texas WIC clients and the 2 latent
classes.

The results of the multinomial logistic regression are shown in Table 3; class 2
(neutral, agree) was used as the reference group. Age, pregnancy, current app
use, and stage of change regarding exercise were significant predictors of class
membership. Specifically, respondents were more likely to be in class 1
(strongly agree) if they were younger than 35 years old (OR = 1.40; CI = 1.05,
1.86), were in contemplation, preparation, or active stages of change regarding
exercise (OR = 2.28, CI = 1.26, 4.11), or were currently using apps for exercise
(OR = 1.33; CI = 1.02, 1.78) or healthy eating (OR = 1.72, CI = 1.22, 2.42).
Respondents were significantly less likely to be in class 1 (strongly agree) if
they were pregnant (OR = 0.55, CI = 0.37, 0.82).

Table 3: Predictors of Texas WIC clients’ perceptions of WIC app prototype
features.

DISCUSSION

This paper describes an intermediate stage of UCD of an app designed for Texas
WIC participants. By investigating variation in responses to the app prototype
features, our aim was to identify characteristics of survey respondents
associated with the strength of their agreement that the physical activity and
healthy eating features would help them improve targeted health behaviors.
Importantly, survey respondents’ reactions to the app prototype features were
positive. Participants who strongly agreed that the app features would help
support behavior changes were more likely to be younger than 35 years of age, in
the contemplation, preparation, or action stages of change for the targeted
health behaviors, and currently using apps to foster these health behaviors.

The influence of age on class membership is not unexpected. Indeed, millennials,
or those born after 1980, are the age group most likely to be continually
engaged with smartphones26 and use them for a variety of activities such as
searching for jobs and accessing health information.27 One potential avenue for
increasing the acceptability of this app among older WIC participants would be
to link to or otherwise leverage platforms that have cross-generational
appeal.28 In the context of the transtheoretical model, it is also not
surprising that participants in contemplation, preparation, or action stages of
change were more likely to view the proposed app features as supportive, as they
were already interested in improving the targeted health behaviors.29 Similarly,
individuals currently using apps are likely more receptive to prototype app
features, in general. A surprising finding was that pregnancy was a significant
predictor of class membership, with pregnant women being half as likely to be in
class 1 (strongly agree). Previous research has reported that, in general,
pregnant women are interested in health-related apps with features that are
provide information specific to pregnancy, such as pregnancy-related risk
factors, gestational weight gain, diet and lifestyle, postpartum depression,
social support, and early infant feeding.30–32 In light of this, one explanation
for the relatively tepid response of pregnant women in this study could be that
the features were not specific to dietary and exercise recommendations for
pregnancy. Additionally, mobile health interventions targeting pregnant women
often suffer from low enrollment and high attrition, suggesting that, in
general, pregnancy may be a challenging time to address health behavior
change.33 However, firm conclusions about the allure of mobile apps to address
health behaviors during pregnancy cannot be drawn due to a dearth of relevant
studies.33 Given that health behaviors during pregnancy can have a profound
impact on maternal and child health, it is important for an app designed for WIC
clients to specifically address the needs of pregnant women to support a
healthful pregnancy.

A strength of this study was the use of a large sample of Texas WIC clients who
have experience with technology. Sample demographics were somewhat comparable to
Texas WIC, with 56% of the sample being Hispanic, compared to 68% in Texas
WIC,34 and 58% being overweight or obese, compared to 52% in Texas WIC.35
Limitations include electronic recruitment of individuals who owned a
smartphone, resulting in a sample biased towards technology use.

CONCLUSION

Given the enthusiastic response to the Texas WIC app prototype, it would be
tempting to finalize app development by simply incorporating the features
described in this study. However, while many factors may impact the ultimate
success of public health apps, perhaps a central piece revolves around the needs
and preferences of the intended user, framed within the context of his or her
specific life’s challenges.13 A user-centered approach to developing
technology-based tools, such as the UCD process, is a critical step in ensuring
that public health interventions reach their target audiences and elicit desired
health outcomes.14 Given the health status of vulnerable populations, such as
low income women and children participating in WIC,2 developing and implementing
efficacious, evidence-based, population-specific tools and technologies that
meet the needs of participants is of paramount importance, and may provide a
catalyst to improve health equity by removing barriers to accessing information.
In the case of the development of a Texas WIC app, engaging clients who are
older, in a pre-contemplation stage of change, or pregnant should occur next, so
that their specific needs and preferences can be incorporated into the final
version. The iterative process of UCD used in this research may serve as a
useful framework for development of public health apps.

ACKNOWLEDGEMENTS

This research was funded by a grant from the Texas Department of State Health
Services (contract number 2014-045584). WIC staff provided input on prototype
feature design,17 reviewed survey content, and facilitated survey dissemination
via their website.

DISCLOSURES

The Institutional Review Boards of Texas State University (2013E3835) and the
Texas Department of State Health Services (14-014) approved this study.

DECLARATION OF COMPETING INTERESTS

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: all authors had financial support from the Texas Department
of State Health Services 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.

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17. Biediger-Friedman L, Crixell SH, Silva M, et al. User-centered design of a
Texas WIC app: A focus group investigation. Am J Health Behav.
2016;40(4):461-71.

18. Bandura A. Self-efficacy: Toward a unifying theory of behavioral change. Adv
Behav Res Ther. 1978;1(4):139-161.

19. Koebnick C, Smith N, Huang K, et al. The prevalence of obesity and
obesity-related health conditions in a large multiethnic cohort of young adults
in California. Ann Epidemiol. 2012;22(9):609-616.

20. Godin G, Shephard R. Godin leisure-time exercise questionnaire. Med Sci
Sport Exerc. 1997;29(6):S36-S38.

21. Wolf RL, Lepore SJ, Vandergrift JL, et al. Knowledge, barriers, and stage of
change as correlates of fruit and vegetable consumption among urban and mostly
immigrant black men. J Am Diet Assoc. 2008;108(8):1315-1322.

22. United States Department of Agriculture Economic Research Service. Household
Food Security Survey Module: Six-Item Short Form. Available from:
https://www.ers.usda.gov/media/8282/short2012.pdf. Accessed May 2014.

23. Hagenaars J, McCutcheon A, eds. Applied Latent Class Analysis. Cambridge,
UK: Cambridge University Press; 2002.

24. Muthén L, Muthén B. Mplus User’s Guide. Vol 7th ed. Los Angeles, CA: Muthén
& Muthén; 2015.

25. Experian. Millennials and Technology: The Natural Order of Things. Available
from:
http://www.experian.com/blogs/marketing-forward/2014/07/07/millennials-and-technology-the-natural-order-of-things.
Accessed July 6, 2017.

26. Nielsen. Millennials are Top Smartphone Users. Available from:
http://www.nielsen.com/us/en/insights/news/2016/millennials-are-top-smartphone-users.html.
Accessed July 2017.

27. Pew Research Center. U.S. Smartphone Use in 2015. Available from:
http://www.pewinternet.org/2015/04/01/us-smartphone-use-in-2015/. Accessed June
2015.

28. Pew Research Center. Social Media Update 2016. Available from:
http://www.pewinternet.org/2016/11/11/social-media-update-2016/. Accessed May
2018.

29. Prochaska JO, Velicer WF. The transtheoretical model of health behavior
change. Am J Heal Promot. 1997;12(1).

30. Osma J, Barrera AZ, Ramphos E. Are pregnant and postpartum women interested
in health-related apps? Implications for the prevention of perinatal depression.
Cyberpsychology, Behav Soc Netw. 2016;19(6):412-415.

31. Krishnamurti T, Davis A, Wong-Parodi G, et al. Development and testing of
the MyHealthyPregnancy App: A behavioral decision research-based tool for
assessing and communicating pregnancy risk. JMIR Mhealth Uhealth. 2017;5(4):e42.

32. Waring ME, Moore Simas TA, Xiao RS, et al. Pregnant women’s interest in a
website or mobile application for healthy gestational weight gain. Sex Reprod
Healthc. 2014;5(4):182-184.

33. O’Brien CM, Cramp C, Dodd JM. Delivery of dietary and lifestyle
interventions in pregnancy: Is it time to promote the use of electronic and
mobile health technologies? Semin Reprod Med. 2016;34(2):e22-e27.

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Racial-Ethnic Group Enrollment Data 2012. Available from:
https://www.fns.usda.gov/wic/wic-racial-ethnic-group-enrollment-data-2012.
Accessed July 2017.

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Characteristics 2012. Alexandria, VA: United States Department of Agriculture,
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Accessed June 2017.

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EVALUATION OF FREE ANDROID HEALTHCARE APPS LISTED IN APPSANITARIE.IT DATABASE:
TECHNICAL ANALYSIS, SURVEY RESULTS AND SUGGESTIONS FOR DEVELOPERS

Posted on Dec 4, 2018 in Original Article | 0 Comments

Evaluation of Free Android Healthcare Apps Listed in appsanitarie.it Database:
Technical Analysis, Survey Results and Suggestions for Developers

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Dr. Lorenzo Di Matteo1, Dr. Carmela Pierri2, M.D. Sergio Pillon3, Eng. Giampiero
Gasperini4, Eng. Paolo Preite1, Dr. Edoardo Limone4, Dr. Silvia Rongoni1

1Department of Training, Formit Foundation; 2Board of Directors, UNINT
University/ Department of Training, Formit Foundation; 3Department of
Cardiovascular Telemedicine, Azienda Ospedaliera San Camillo-Forlanini;
4Department of Strategy and Technologies, Formit Foundation

Corresponding Author: l.dimatteo@formit.org

Journal MTM 7:2:17–26, 2018

doi:10.7309/jmtm.7.2.3

--------------------------------------------------------------------------------

Background: Health apps catalogued in dedicated databases are not scarce but
still little is known about the situation concerning their technical aspects
such as the general level of privacy and security.

Aims: This study aims to analyze android free health apps in a specific
database.

Methods: A systematic technical analysis on a population of 275 android free app
among the ones listed in the appsanitarie.it database (“Banca Dati delle app
sanitarie”). Analysis has been carried out following a defined protocol with a
survey as operative support tool to examine aspects such as the app rating in
the store.

Results: The analysis concerned 275 health apps. Cardiology (38 apps) resulted
to be the most populous medical branch. The overall app ratings average is 4,10.
18,54% of the apps required personal data at first launch. 84,36% of the apps
allowed only manual data entry. Data sharing has been detected in 133 cases.
9,45% of the apps provides a backup option. 13% of the apps declare to be
compliant to some kind of privacy regulation. Among this 13% of apps only 19%
showed relevance to the EU privacy regulation. The 61,1% of the apps presented
no reference for scientific background of the contents.

Conclusions: Manual data entry when redundant should be avoided by developers in
favour of automatic calculation of derived parameters. Moreover a limited number
of the analyzed apps adopt data protection mechanisms and declare privacy
compliance. Security and Privacy are generally poor. Survey results suggest
there is large room for improvement in app design.

Keywords: Telemedicine, eHealth, mHealth, Data Security, Baseline Survey

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INTRODUCTION

Apps on mobile devices such as smartphone offer a lot of perspectives of use in
health and medical fields. App economy as the whole range of economic activity
related to mobile applications evolve rapidly as the smartphone market. Other
studies report that only the first 10 top mobile health apps generate up to 4
million free and 300.000 paid downloads per day1.

On the other side Healthcare researches find that vast majority of professionals
is conscious of an interoperability lack for a better use of patient generated
data2. Other researches show that more than half of the interviewed patients
assert to have used a digital device including mobile apps to manage their
health and almost two thirds think it would be helpful for their healthcare
providers to have access to their patient generated data as part of their
medical history3.

Studies showed that for patients with chronic diseases it is a comfortable
solution sharing data with healthcare providers via online patient portal,
mobile apps or message texts4. This could lead to some sort of benefits for both
patients and healthcare providers but also expose to some risks, especially the
first ones5,6. Unclear disclosures about data processing terms could lead to
privacy risks for the user and insufficient security could bring to data
breaches or loss risks, considering also that a smartphone loss could bring to a
leakage7. Security or data protection could be not sufficient if the user is not
fully capable to prevent the loss of data from the device or mechanisms as
encryption or passwords are not available8.

On the other hand sharing patients health data with messaging and multimedia
mobile applications as communication channels it’s handy for a professional but
non completely compliant with health data protection standards a healthcare
trust certainly adopt9. On the patient side new findings concluded that while
less than half of the analyzed apps are useful to the targeted user, some apps
seemed to sacrifice quality and safety to add more functionalities10.

The purpose of this study is to make a technical analysis of free android apps
listed in a dedicated “healthcare apps” database, “Banca Dati delle app
sanitarie” (at http://www.appsanitarie.it/banca-dati-app-sanitarie). The
database has been developed as part of a Formit Foundation project financed by a
grant of the General Directorate of Medical Devices and Pharmaceutical Service
of the Italian Ministry of Health in 2015-2016. Launched in 2015 the database
was created to list results of the apps census operated by the Observatory of
the health apps established by Formit Foundation.

Apps in the database has been selected through a specific definition,
“healthcare apps”, and selection workflow (see methods section for a full
description). Database apps, both Android and iOS, have been selected through
specific criteria in the stores (summoned in a workflow), and could be used in
an healthcare context by patients and physicians. The database apps considered
are 659 “healthcare apps”, divided in medical branches and 2% of them present a
CE mark as medical device. The database has been chosen as starting point for
the selection of listed apps because of a clear definition and a selection
workflow.

The study which results will be here presented has not been conducted looking in
the inner working mechanisms of the apps but with a highly technical analysis of
the functionalities available to users. Analysis has been carried out facing
four different groups of app characteristic: the app general details; the
features as requested data, data entry, data access, connect-ability, online and
sharing feature; password and backup security mechanisms; privacy terms and
scientific references. Regulatory framework considered in matter of privacy is
the General Data Protection Regulation, GDPR (Regulation EU 2016/679)11 due to
its validity all over national member states legislations, and the Privacy Code
of Conduct on mHealth apps for what concerns guidelines to enhance privacy in
this field12.

METHODS

ETHICAL STATEMENT

This research project has been conducted with full compliance of research ethics
norms. Research involved usage of mobile devices and apps. Survey development
and data gathering involved part of the research team while survey fulfillment
another one. Results analysis has been carried out by the whole team.

APP SELECTION

Apps has been selected among the list of free Android ones (Google play
downloadable) in every medical branch composing the database (Banca Dati App
sanitarie, BDA http://www.appsanitarie.it/banca-dati-app-sanitarie). It was also
decided to exclude from the analysis apps requiring registration with medical
credentials to dedicated platform or specific devices to work. Apps listed in
the database has been chosen before this study following a specific definition
and a workflow. In this sense not all the health apps could be listed in the
database.

Apps defined as healthcare apps (App sanitarie) in the database are:

• CE marked Medical device apps (in order to achieve CE mark for their products
in Europe, medical device manufacturers must comply with the appropriate medical
device directive set forth by the EU Commission);

• Apps not developed with medical purpose by the producer but responding to one
of this characteristics:

– receive data from medical devices;

– elaboration and transformation of healthcare and patient-related data;

– interaction with a non medical device that visualize, memorize, analyze and
transmit data;

– receive health data by user with manual entry that are not only diet and
fitness oriented.

According to the app definition this workflow was used:

Figure 1: App Selection Criteria

The apps selected from the database to be analyzed satisfy the following
operative criteria:

1. – Available for Android (downloadable from Google play);

2. – Free;

3. – With no mandatory registration to platform requiring medical credentials;

4. – Usable independently from connection with external devices.

TECHNICAL ANALYSIS

Apps have been under a phase of technical analysis for 2 months, from March to
April 2017.

The scope of the technical analysis is to examine some of the operating
mechanisms of the selected apps. This has been done following a Technical
Analysis Scheme characterized by different technical macro-area to identify
diverse functional aspects and a metrical-statistical question-answer structure
to ensure results measurability and repeatability.

To reach a technical analysis of the software, a survey has been designed and
fulfilled. The analysis has been conceived to focus on the following elements:

* Information useful to identify the app;
* Operating characteristics of the app;
* Security related to password, back-up and data encryption;
* Presence of privacy and condition terms.

The technical analysis has been composed by the survey development,
comprehensive of design and deployment, and a consequent phase of app analysis,
then data gathering and results analysis.

SURVEY DEVELOPMENT

To analyze selected apps a survey has been designed with different sections
related to different type of data to collect about the four analytics aspects
and organized following an answer-question structure. In this sense the sections
which composed the survey are:

* App general characteristics, as name, version, developer name, rating on the
store;
* App features, as requested personal data, modality of data entry, possibility
to delete/change data, connect-ability, online platform registration, sharing
on social media;
* App security, as password registration, password recovery, password security
level, back-up possibility, backup destination, backup encryption;
* App privacy and reliability, as declaration of compliance to some privacy
regulation, European privacy regulation compliance, scientific source or
bibliography.

The online survey has been realized with the open source application Lime
Survey.

APP ANALYSIS

Technical-functional analysis has been performed accessing the survey through
authentication via username and password. Mobile devices with Android operative
system have been used with the newest version of operative system available at
the time. App search has been performed on the Google Play store. Download has
followed if for free and if available in the country where the study took place
(Italy). Once installation terminated, mandatory healthcare professional-only
platform registration and necessary external device connection has been checked.
If negative, the app has been analyzed through the survey fulfillment.

DATA GATHERING AND RESULTS ANALYSIS

Through Lime survey data gathering has furnished the overall amount of data from
the technical analysis. Results analysis instead has been realized on a compiled
single dataset, using descriptive statistics to summarize and underline aspects
of data collection. Collected data analysis has been accomplished through the
data stored in a database managed directly by the application Lime survey, this
allowed to export information in formats suitable for statistical work purposes.

RESULTS

APP GENERAL CHARACTERISTICS

The analysis concerned 275 apps on the total amount of 659 in the database at
the time the study took place, due to the existence of operative criteria
described in the methods section. Most populous medical branch resulted
cardiology (38 apps), oncology (22) and health & well-being (21), as shown in
Figure 2.

Figure 2: Number of apps per medical branch

App rating in the store is expressed on a Likert scale from 1 to 5 by the user
and shows the average of the overall amount of rating for an app on an
incremental scale. Rating average of an app has been rounded down due to
simplify data collection management. The average of the overall app rating
averages resulted 4,10, where the lowest app rating average is 1 and the higher
is 5. In the most populous medical branches, average of the app rating averages
in cardiology is 3,66, while in oncology is 4,33 and in health & well-being is
4,60.

As Figure 3 shows average app rating in the store is high almost for every
medical branch in line with the data of an average of the app rating averages of
4,10 on the Likert scale. In fact most of the analyzed apps resulted to be
placed in the high ranks of the rating scale. Excluding 5 apps with no rating on
the store, 105 on 270 apps, the 38,89% of the overall rated apps resulted having
a rating average of 4 while 91 apps, the 33,70% is ranked with an average of 4,5
and 34 apps, the 12,60% showed a rating average of 5. However rating in the
store could be subjected to distortive mechanisms such as comments directly or
indirectly linked to the developers or an exiguous amount of them.

Figure 3: Average app rating per medical branch

Table 1: Rating average per number of apps

APP FEATURES

Generally health-ish apps need data input to perform one or more of their
features. In this sense 18,54% of the analyzed apps showed to require personal
data at their first launch in order to create a user profile. An app can request
to the user one or more of the data listed in Table 2. The most frequently
required data resulted to be gender for the 14,91% of the apps (41), followed by
age the 11,62% (32) and weight the 8,62% (24).

Table 2: Personal Data required at first launch per number of apps

Table 3: Modality of data entry per number of apps

Modality of data entry followed the part of the survey section concerning
personal data request. Data entry could happen through a possible
synchronization with an external device in order to acquire data automatically,
or at the contrary only manually or both. The great majority of the apps allowed
only manual data entry, exactly 84,36% (232) of the apps. Only automatic and
both data entry modality are allowed by the 8% and the 7,64% of the apps.

Similarly results about possibility to change and delete entered data showed
that it was possible manual change for the 84% of the apps and manual deletion
for the 72,72%. It has been noticed that it was not possible change manual
entered data only for 18 apps and no possibility to delete for 47. Only 5,45% of
the analyzed apps resulted to allow the modification of automatic entered data
and 6,18% the deletion. In this sense results of N/A change and delete of
automatic entered data and the possibility to change and delete manual entered
data or vice versa almost match.

Table 4: Communication protocols per number of apps

Table 5: Communication protocols per number of apps

In line with the previous survey results, regarding communication protocols to
exchange data with other systems or external medical devices, the most used
resulted Wi-Fi for the 5,45% of the apps, followed by Bluetooth for the 5,1%.
USB and a dedicated interface resulted to be used as communication protocols
only by two of the analyzed apps.

In relation to communication and data exchange with online data storage services
it was analyzed diffusion of mandatory registration to online platform in order
to completely use the app. Generally apps requiring registration to an online
platform permit backup of the data composing the user profile through a
dedicated feature. In this sense it turned out to be only a 3,63% of the apps to
require a mandatory registration to an online platform.

A considerable number of analyzed apps presented the feature “share” on
different communication channels and social media. An app can allow more than
one data sharing possibility. On the overall 133 times data sharing has been
detected, the most frequent data sharing feature resulted to be e-mail (45
apps), almost doubling the second one that is SMS (23). Sharing on social media
resulted to be possible only with 17 apps on Facebook and 14 on Twitter. Other
channels not considered initially in the survey but of which it has been taken
note in dedicated blank spaces, were hangouts resulting 12 times as social media
and 10 times google drive as other sharing channel.

Table 6: Mandatory registration to online platforms per number of apps

APP SECURITY

Results regarding app security and data protection showed that only few of the
analyzed apps provides password registration. The 5,1% of the apps shown to
provide the creation of a password at the app start and only the 1.1% a secured
password. On the overall amount of apps only 1,81% provides the password
recovery generally known as “Forget Password?” button sending the new password
to a previously saved email address.

For what concerns data storage option it resulted to be possible both locally,
on the smartphone memory, that remotely with online storage services. Globally
9,45% of the overall analyzed apps provides a backup option. An online backup
has been possible for the 5,1% of the apps, while a local memory back-up for the
4,36%. Regarding a clear-to-the-user encryption of the backup, 5,81% of the
overall apps, more or less the all apps with backup option, showed no
possibility to have clear information about encryption. Naturally it has been
not feasible to check for encryption for the vast majority of the apps (93,81%)
having no back-up.

Figure 4: Data sharing channels per number of apps

Table 7: App security and data protection

APP PRIVACY AND RELIABILITY

App analysis concerning privacy showed that only 13% of the all apps declare to
be compliant to any kind of privacy regulation for what concerns personal and
health data about the user. The 87% of the apps showed no declaration of
compliance to any kind of privacy regulation with any kind of message to the
user, nor at launch nor in the menu.

Among this 13% of apps showing a declaration of privacy regulation compliance
only 19% showed some sort of relevance to the EU privacy regulation. This is due
to the fact that national regulation of Member States has been considered in
relation to a wider European privacy regulation. In fact before General Data
Protection Regulation, Directive 95/46/CE has been adopted by data protection
and privacy national acts. The 81% of the remaining apps showed instead an
international declaration related to an End-User License Agreement (EULA) model
or some other type of generic declaration. For what concerns reliability it has
been considered the presence of references quoted in the app regarding
scientific sources. Generally scientific references and quotes has been found in
the info or in the bibliography section of the app menu. The 61,1% of the
analyzed apps presented no reference or quote regarding the scientific
background of the contents, while 38,18% presented a bibliography or quoted
studies in a dedicated part of the menu and 1,81% resulted to be not applicable
to this check.

Figure 5: Percentage of Apps Showing Declaration of Privacy Regulation
Compliance

Figure 6: Percentage of apps showing EU privacy regulation Compliance among apps
declaring privacy regulation compliance

Table 8: Scientific references in the app

DISCUSSION

It is expected that Bring-Your-Own-Device connectivity will be preferred by
select patient groups and will be used for the remote monitoring of 22.9 million
patients in 202113. In this sense it is no surprise the number of health apps in
the stores like Google Play, although the number of apps wears thin using a
database based on a specific definition of mHealth app with a clearly defined
selection workflow. Another boundary for the analysis has been represented by
the possibility of free download and analyze functionalities without
restrictions of use by necessary external device to operate or mandatory
registration to platform requiring medical credentials.

On the other hand the strict database selection criteria and subsequently the
limits of analysis operative criteria have brought to a homogeneity of apps
population and uniformity of characteristics to analyze. Concerning the
definition of app on which the database is based certainly the app analysis
selection has been made among a population of apps that excludes low quality
apps from the analysis spectrum. In this sense it has to be explained the small
numbers of analyzed apps for some specialized medical branches and limits of
findings for apps in those branches.

Likert scale is an important score to observe regarding adoption of an app by
users. Although this data could be subjected to some distortions and elaboration
of new assessing tools doesn’t miss14–18. Usability is generally measured with
the perceived ease and enjoyment experiencing in using the app19, but the user
could rate poorly an application with solid data security mechanisms but no
catchy layout and the opposite with a more attractive but less secure app.
Therefore rating of the apps is certainly important albeit partial.

The request of personal data opens up other considerations. Processing personal
data would suggest the adoption of a database encryption mechanism, but once
“compiled” in the form of “application package” can no longer be opened and
verified without violating the copyright of the developer. It is also true that
both Apple and Google have enabled encryption of the application database in the
default mode.

It should also be noted that requiring to the user both age and date of birth
impacts on data quality management. Most of the apps allow only manual data
entry. It is an important factor due to its possible repercussion on data
quality. Essentially more it is reduced input error, more data quality will be
achieved. However data entry could be manual due to a developer lack or to a
functional condition to respect in order to let the app run.

Regarding automatic data entry some applications use communication protocols
with other systems or external devices as Bluetooth, wi-fi or USB. Regarding
data exchange, Android allows the developer to easily implement communication to
social networks or instant messaging systems with the possibility of data
sharing with other users. Mail choice to share data is no surprise, the
versatility of the medium is certainly more suitable to send ordinary messages
to a doctor. Similarly SMSs are used by apps to share data since many
applications elaborate a set of numeric values that can be easily included into
a text message.

In matter of data protection the use of a secure password with a minimum of
eight letters and alpha-numeric symbols seems to be rare. In this sense a
personal identification number of five digits can be easily forced by a brute
force attack trying all the possible combinations. It’s worth to mention also
that a strong password is a condition that could be set up by design.

The apps including a backup function resulted to be limited too, but when the
app not requires “persistent” data to trace, backup is useless. More than half
of the analyzed apps has backup on online services (a cost to the app provider
that local memory backup isn’t). The user could be not adequately aware of the
technical and legal mechanisms that regulate the cloud computing service20,
local backup instead allows complete data management. No sufficient information
about data protection controls has been noticed. This is because the focus in
the app description on the store generally seems on advertising, while
neglecting privacy and security reliability.

The great majority of the apps showed no declaration of compliance to any kind
of privacy regulation. Calculator apps or similar apps resets at every exit
deleting all the entered data with no real data processing. Anyway it doesn’t
really explain the absence of scientific references and quotes in almost 6 on 10
apps. In fact only a few apps presented clear scientific references. For some
apps, especially scale calculator a professional may not need scientific
references to identify or use a well-known tool in his or her medical branch,
but for a user with no particular knowledge in medical science the lack of
information could lead to misreading the outcome and to false negative
self-diagnosis.

CONCLUSIONS

Considering that the analysis has been carried out on a limited number of apps,
data-quality oriented approach should be used anyway by developers in order to
realize a correct balance between manual data entry and automatic calculation.
Manual data-entry should be reduced and the automation should be increased.
Moreover format-control parameters or different controls (as sliders, or
date-picker) should be used to reduce data-entry mistakes. Replacement of the
classic text-field produces an increasing of speed during the filling process
and reduces typing errors. In this sense could be useful to look for a better
understanding of the perceived and desired usability by the user, rising
research attention on this side. On the other hand it is necessary to examine
feasibility of mHealth in the healthcare context, as effectiveness of mobile
phone applications in healthcare services. In this sense a pathway could be an
observational studies by experimenters with patient or physicians adopting
mHealth solutions.

ACKNOWLEDGEMENTS

We would like to thank the General Directorate for Medical Devices and the
Pharmaceutical Service of the Ministry of Health (Italy), especially Dir.
Marcella Marletta, Eng. Pietro Calamea and Dr. Paola D’Alessandro for their
support.

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USE OF PERSONAL DEVICES IN HEALTHCARE: GUIDELINES FROM A ROUNDTABLE DISCUSSION

Posted on Dec 4, 2018 in Original Article | 1 Comment

Use of Personal Devices in Healthcare: Guidelines From A Roundtable Discussion

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Laura Vearrier, MD1, Kyle Rosenberger, M.Ed2, Valerie Weber, DMD, MA3

1Assistant clinical professor, Department of Emergency Medicine, Drexel
University College of Medicine, Philadelphia, PA; 2Instructional Designer, Ohio
University Heritage College of Osteopathic Medicine and Ohio University’s Office
of Instructional Innovation, Athens, OH; 3Assistant clinical professor,
Department of General Dentistry and Oral Medicine, University of Louisville
School of Dentistry, Louisville, KY.

Note: The corresponding author is not a recipient of a research scholarship.

Journal MTM 7:2:27–34, 2018

doi:10.7309/jmtm.7.2.4

--------------------------------------------------------------------------------

Background: In recent years, smartphone use in professional settings has been
increasing, particularly with physicians. There are benefits and drawbacks that
result from this increase. Despite this, there is relatively limited
peer-reviewed medical literature on the subject. Thus, suitable guidelines for
smartphone use in the health care setting is needed.

Aims: This article present guidelines for professional conduct related to the
use of personal devices, such as smartphones, in the healthcare setting.

Methods: These guidelines were developed through an interdisciplinary roundtable
discussion at the 2016 Academy for Professionalism in Health Care Conference in
Philadelphia, PA.

Results: As a result of the roundtable discussions, several guidelines were
developed. First, healthcare providers should be trained on the danger of
distractions caused by personal devices and how to minimize them in a clinical
setting. Second, the use of smartphones for personal use should be limited to
specified use areas; however, if they are present during a patient encounter,
they should be set to a mode that eliminates or minimizes interruptions. Third,
providers should seek permission from patients prior to integrating smartphones
into the provider-patient relationship. Finally, smartphone photography, while
being a potential tool to improve patient care, should be used with caution
concerning patient autonomy and privacy.

Conclusion: The guidelines serve as a foundation from which professionalism with
regard to personal device use can be further developed.

Keywords: Professionalism, Smartphone, Physicians, Photography, Delivery of
Health Care, Clinical Practice, Telemedicine

--------------------------------------------------------------------------------

INTRODUCTION

In the last few years, the use of personal devices such as smartphones has been
rapidly increasing and smartphone ownership is highest among young adults of
higher income and education level.1–2 This trend is being mirrored in the
healthcare setting. Nearly all physicians and nurses own smartphones.3–4
Physicians’ usage of smartphones for professional purposes has been steadily
increasing from 68% in 2012 to 84% in 2015.5 The Boston Consulting Group and
Telenor Group remarked that the “smartphone is the most popular technology among
doctors since the stethoscope”.6 A survey study of nurses reported that more
than half of nurses have used their smartphone instead of asking a colleague for
information.4

There are benefits and drawbacks of providers utilizing their smartphones in the
healthcare setting for personal and professional purposes. With computing power
and Internet connectivity, personal devices give providers access to textbooks,
journal articles, practice guidelines, clinical calculators, and medical
applications. Smartphones are improving the efficiency and accuracy of
communication. Physicians and nurses are using short messaging services (SMS) to
communicate patient information and smartphones have been reported to increase
the connectedness of medical trainees’ with their supervisors.7–9 Smartphones
are also improving communication between providers and patients. The use of
videos on personal devices has been reported to be an efficient and effective
way to educate patients on their disease that resulted in increased medication
compliance and physicians are using smartphones to monitor patients
remotely.10,11 Drawbacks of such constant connectivity include a risk for
distraction from patient care. Providers may be interrupted for less acute
clinical issues in addition to personal calls, texts, emails, social media, and
applications. Personal devices also create a physical barrier between the user
and the rest of the world. This barrier translates into cognitive and
psychological barriers, and patients are often unaware of the clinical benefits
of smartphones.12,13

Despite the ubiquity of smartphones in healthcare, there is limited
peer-reviewed medical literature on issues with respect to the professionalism
of smartphone use in the healthcare setting. An Ovid Medline keyword search of
“professionalism” and the intersection of any of the following: “smartphone”,
“smart phone”, “cell phone”, “mobile phone”, “tablet” or “personal device”,
yielded only seven results (search performed May, 2016). There is a need for
guidance and education regarding professional conduct and personal device use in
the healthcare setting. In a survey study of medical students, the majority
reported insufficient education from either their medical school curriculum or
their senior residents or attendings on appropriate/inappropriate use of mobile
devices to communicate patient information and how to conduct themselves
professionally with mobile technology.14

METHODS

A roundtable workshop exploring issues of professionalism and smartphone use in
the healthcare setting was held at The Academy for Professionalism in Health
Care 2016 conference in Philadelphia, PA. Participants included physicians,
nurses, medical students, dentists and academic researchers. Participant
occupational settings included medical education, academic clinical practice,
and private practice. Four case-based scenarios (Figure 1) were discussed in
small focus groups and then presented to the entire workshop for further
analysis. The results of these discussions were compiled into the following
guidelines.

Figure 1: Case-based scenarios and questions

RESULTS

DISTRACTION AND SMARTPHONE USE

Smartphone use may result in provider distraction or so-called “distracted
doctoring” and increase the risk for patient care errors. Smartphone use in the
healthcare setting has the potential to result in distraction in a manner
similar to driving. Use of smartphones may involve cognitive, visual, and/or
manual tasks that divert the provider’s attention from their patient care
responsibilities. As such, frequently repeated activities or procedures may
increase the risk that providers will engage in distracting secondary tasks,
such as smartphone use. “Bring Your Own Device” (BYOD) policies that require
providers to utilize their personal devices for professional purposes may
increase the risk of distraction due to non-professional phone calls, text
messages, and app notifications. Further research is needed in the area of
distracted doctoring and smartphone use.

Healthcare providers commonly hold the misperception that utilizing smartphones
for multi-tasking in the healthcare setting improves efficiency and patient
care. Professional and personal smartphone utilization in the healthcare setting
increases the number of interruptions and the amount of information received and
processed by providers while engaging in patient care. While multi-tasking is a
necessary skill, it should be minimized when possible. Attentional shifts while
multi-tasking interrupt the cognitive processing of information, situational
awareness, and may increase the likelihood of patient care errors. Interruptions
may be minimized by use of “silent”, “airplane”, and “do not disturb” modes,
particularly during important patient care activities. The amount of information
received by providers may be controlled through specialized ring or text message
tones and disabling of app notifications.

Healthcare providers should be educated on the dangers of “distracted
doctoring”. Individuals may overestimate their ability to engage in smartphone
use without significant distraction. Education on distraction, generally, and
specifically smartphone-associated distraction should be implemented at the
undergraduate and graduate medical education levels and in
patient-safety-related continuing education. Simulation exercises allow
participants to experience the detrimental effects of distraction and develop
skills for dynamic prioritization of incoming information.

Appropriate smartphone use may decrease distractions and should be encouraged.
Smartphones may be utilized to reduce distractions from handheld pagers and
overhead paging systems. Use of calendar functions, alarms, and notes apps may
be used to reduce the cognitive load of “to-do lists”. Alarms for medication
administration or other time-sensitive tasks may improve timeliness of
administration. Smartphone information resources and clinical calculators reduce
the need to interrupt current tasks to find a computer. Policies to utilize
smartphones to reduce distractions should be considered at the institutional
level.

SMARTPHONE PHOTOGRAPHY IN THE HEALTHCARE SETTING

Smartphone photography is an advantageous learning and communication tool;
however, respect for the patient and the patient’s privacy must be paramount.
Smartphone photography has the potential to capture disease conditions and
procedures that may otherwise be difficult or impossible to record and which may
be used in educational materials or the peer-reviewed literature and therefore
improve patient care on a global level. Smartphone photography may be used to
transfer information about patients (e.g. lesion, electrocardiogram) to other
providers, improving the clinical decision-making process and therefore improve
patient care on the individual level. Pitfalls of photography in the healthcare
setting include capture of patients while they are vulnerable or when they are
unable to fully consent. Patients may perceive an element of coercion when asked
to be photographed even if no direct coercive statement is made. The individual
right to privacy, respect, and autonomy are paramount. Respect for privacy and
autonomy as it pertains to smartphone photography should be taught at the
undergraduate, graduate and continuing education levels. When institutional
photography equipment is available, that equipment should be used in lieu of a
smartphone.

Consent should be obtained at the time of image capture for the photograph, the
intended use and any transmission. Consent should not be obtained at time of
admission or triage for later photography. Consent for photography at that time
contains an element of implied coercion and is too abstract to be considered
informed consent. Informed consent should include the elements of the body part
to be photographed, the intended use, and any transmission of the photograph.
When possible, written consent should be obtained. If written consent is not
possible, verbal consent should be witnessed and documented.

Photographs should be obtained in such a way as to minimize or eliminate the
amount of protected health information that is captured. Photographs of the face
are typically not necessary. Patient identifiers such as name, date of birth,
and medical record numbers should not be included in photographs. Tattoos,
piercings, skin conditions, and other unique identifiers compromise patient
confidentiality and should be included only with explicit consent and when
capture of those elements is required.

Smartphone photography in the healthcare setting for personal or entertainment
purposes is inappropriate and should be avoided. Such photography contains too
much potential for abuse to be acceptable. Unintended consequences include the
inadvertent capture of protected health information or other patient
identifiers. The content of seemingly innocuous photographs in the healthcare
setting have the potential to distress patients, family members and others.

Healthcare providers have a duty to intervene in situations involving
inappropriate smartphone photography. When possible, inappropriate photography
should be prevented. If such photography has already occurred, appropriate
interventions may include education, deletion of the photograph, or report at
the institutional or law enforcement level depending on the scenario. Healthcare
institutions should have protocols in place for reporting inappropriate
smartphone photography with a well-defined chain of command and protections
against retribution including the ability to anonymously report. Patients may
similarly take inappropriate smartphone photographs in the healthcare setting
and providers should intervene in those situations as well.

SMARTPHONE ETIQUETTE AND PERCEPTIONS

Use of smartphones for personal calls, texting and social media apps is to be
avoided in patient care areas. Personal use of smartphones in patient care areas
may convey an informality or lack or professionalism to patients, their
families, and other staff. Even when providers are not actively engaged in
patient care activities, personal smartphone use should be avoided. Patients may
perceive that aspects of their care are being adversely affected by personal
smartphone use such as wait time, face time with providers, or attention to
their complaints. Personal smartphone use may be permitted in staff lounge
areas, provided that it does not adversely affect patient care.

Smartphones should be set to “silent”, “airplane”, or “do not disturb” modes
during patient encounters. Vibrate modes are frequently audible and should not
be utilized. App notifications should be turned off using one of the
above-mentioned modes. Smartphone interruptions during sensitive discussions may
be particularly distressing to patients. It is encouraged to remind colleagues
to put their smartphone into one of the above-mentioned modes prior to such a
discussion. Most healthcare providers do not need to be immediately available to
colleagues. In the event that a healthcare provider must be immediately
available during a patient encounter, the possibility of interruption should be
communicated to the patient at the outset. The use of a “do not disturb” mode
that permits calls from only pre-identified emergency contacts may reduce the
risk of interruption and is recommended.

Patient permission should be obtained for professional smartphone utilization
during patient care activities. As discussed above, smartphones are a resource
for healthcare professionals, allowing increased communication with other
providers, interface with EMR, clinical calculators, and immediate access to
information resources (e.g. pharmacopeia). The inherent portability of
smartphones over other electronic devices makes them particularly useful during
patient care activities. However, explicit patient permission should be obtained
prior to their utilization during patient care activities. Permission introduces
the device into the patient-provider relationship and serves the dual purpose of
informing the patient that the device is being used to facilitate their care and
to confirm that use of the device will not be unduly distressing to the patient.
Patients should be encouraged to ask their physicians what they are using their
devices for to facilitate communication regarding this practice.

Transparency during professional smartphone use minimizes negative patient
perceptions associated with provider smartphone utilization and is encouraged.
Patients should be informed of the specific tasks being performed by the
provider that are facilitating their care. When smartphones are being used to
access EMR, clinical calculators, and information resources, sharing the
smartphone screen with the patient provides transparency and empowers the
patient to participate in their care. Visual information shared by smartphone
device augments the verbal exchange of information between patient and provider
and facilitates shared decision making.

INSTITUTIONAL POLICIES ON SMARTPHONE USE

Institutions should have policies delineating appropriate and inappropriate
smartphone use and notify employees of these policies. Considerations in the
development of such policies must include patient care versus non-patient care
areas, the rights of employees, patient safety and privacy, and professional
versus personal use. Disciplinary actions may range from verbal warnings with
documentation to dismissal depending on the offense. Administration should
develop smartphone photography policies and outline the consequences for misuse.
Institutional policies should also address smartphone use by patients and their
families.

When personal smartphones are utilized for patient care activities,
institutional policy should address device encryption and password protection.
Institutions have a duty to patients and providers to reasonably protect their
privacy against breaches. Difficulties associated with institutional oversight
of personal devices may require that efforts are directed at provider education
and institution-provider contracts on device parameters and use. Mandatory
reporting of lost devices and remote wiping capabilities are appropriate
policies. Multi-factor authentication should be considered for access to
protected health information. Long-term storage of patient-related data or
photographs on a personal device is inappropriate. While not all breaches in
confidentiality are preventable, reasonable institutional oversight minimizes
the risk and ramifications of such a breach.

When smartphone photography is used for patient care, the images should be
integrated into the medical record. Institutions should have a mechanism by
which smartphone photographs may be readily incorporated into the paper or
electronic medical record and provide education to providers in this regard.
Requiring integration into the medical record at the time that patient care is
delivered minimizes the length of time that the image is retained on a personal
device and risk that the provider fails to upload the photograph to the medical
record.

Policies delineating the appropriateness of smartphones for telemedicine are
encouraged. Such policies should be in accordance with state and federal laws.
Video and conferencing apps enable remote care of patients and interdisciplinary
cooperation, which enhance patient care when utilized appropriately.
Telemedicine is not a replacement for bedside patient care when such care is
reasonably available.

DISCUSSION

Professional and non-professional uses of smartphones can create distractions
that can be detrimental to patient care. Healthcare providers should be educated
on the dangers of distractions and be trained on how to minimize distractions
related to personal devices. The use of smartphone functions, such as alarms,
notes, and direct inter-provider communications, to decrease distraction is
encouraged. Further research is needed in the area of distracted doctoring to
determine the scope of this problem and the most effective intervention
strategies.

Smartphone photography has the potential to improve patient care on the global
and individual level but is associated with many pitfalls due to the ease and
ubiquity of smartphone photography in general. Patient autonomy and privacy are
paramount. Respect for the patient always trumps any potential benefit, global
or individual, afforded by a photograph. Consent should be obtained just prior
to obtaining a photograph and should be witnessed and documented. Photographs or
videos for entertainment or personal uses is not appropriate. Providers have a
duty to peer-regulate and intervene in the case of inappropriate smartphone use.
Healthcare institutions should have protocols for reporting and addressing
inappropriate use of smartphone photography.

The use of smartphones for personal applications should be limited to designated
break or lounge areas. Smartphones should be set to modes that eliminate or
minimize interruptions during patient encounters. Providers should seek
permission from patients prior to integrating smartphones into the
provider-patient relationship to improve communication and education. Providers
should strive for transparency with regard to their professional use of
smartphones explain and show to patients the clinical applications of
smartphones.

These guidelines do not address legal aspects of restricting mobile phone use in
clinical settings; however, they serve as a basis for the conversation to begin.
While the Medicines and Healthcare Products Regulatory Agency (MHRA) does not
support a blanket ban on the use of mobile phones in hospitals, some health
systems are taking it upon themselves to implement regulations.15 For example,
the Jewish General Hospital in Montreal, Quebec, has instituted a policy that
addresses “the use of cell phones in the hospital for phone calls and for data
usage (including text messages, browsing the internet or other). This policy
applies to all cell phone users in the hospital, including staff, members of the
CPDP, consultants, volunteers, visitors, and patients.”16 This policy looks to
mitigate the use of mobile devices to respect the patients’ rights, with a focus
on safety and confidentiality. Another hospital, Union Hospital in Eklton,
Maryland, has issued a policy that includes advising employees that the “use of
cell/camera phone during work, for other than hospital business should be
avoided. Personal calls should be limited to break and meal breaks. All
employees are required to silence their cell/camera phones while they are
working.”17 Similarly, Greenville Hospital System in Greenville, South Carolina
does not look to outright band mobile phone use, but advises that,

“During work time, employees are expected to exercise the same discretion with
the use of personal communication devices as is expected with the use of any
business phone. Personal phone calls (including text messaging) during the work
day, regardless of the phone or device used, are not appropriate can interfere
with productivity and be distracting to others.”18

While these are only a few examples of hospitals and health systems that have
instituted policies around mobile devices, they do demonstrate a growing
movement towards regulating such devices. This movement, while presumably
controversial, is a natural occurrence as the prevalence of mobile technology
increases. As such, hospitals and health systems will need to take a position on
how strict their regulations will be, with the goal of improving patient care
and safety.

CONCLUSION

As personal devices are becoming a technology that is integral to patient care,
healthcare institutions should develop written policies regarding smartphone
use. Policies should address appropriate use, consequences of misuse, mechanisms
to protect patient information, integration of communication and images into
medical records, and guidelines for telemedicine.

These guidelines establish a foundation for the professional use of smartphones
in the healthcare setting. The very nature of smartphones, as personal devices,
mandates that the use of smartphones be largely regulated on the individual and
peer level. This self-regulation demands a strong internal locus of
professionalism that must be developed, practiced, and assessed among all
healthcare professionals.

ACKNOWLEDGEMENTS

The authors wish to acknowledge Nicholas DeVito, MPH; Lucy Hammond, PhD;
Marguerite Heyns; Mark Kuczewski, PhD; Dennis Novack, MD; Steven Rosenzweig, MD;
and Glen Solomon, MD for their significant contributions to the roundtable
discussions.

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Click Here to Download PDF of Article

DEVELOPING A USER-CENTERED MOBILE APPLICATION FOR STROKE CAREGIVERS: A PILOT
NATIONAL SURVEY

Posted on Dec 4, 2018 in Original Article | 0 Comments

Developing a User-centered Mobile Application for Stroke Caregivers: A Pilot
National Survey

Click Here to Download PDF of Article

Jonathan Singera,*, Sarah Weingastb, Nadege Gillesb, Mohammad Fayseld, Dimitre
G. Stefanovc, Shirley Girouarde, Alyssa Conigliarof,g, Marilyn Fraser Whiteh,i,
Amy Jensenj, Dee Burtonk, Steven R. Levineb,l,m

aDepartment of Psychology, University of Nevada, Reno, NV, USA; bDepartment of
Neurology, SUNY Downstate Medical Center, Brooklyn, NY, USA; cAcademic
Computing, SUNY Downstate Medical Center, Brooklyn, NY, USA; dMedical
Informatics Program, SUNY Downstate Medical Center, Brooklyn, NY, USA; eCollege
of Nursing, SUNY Downstate Medical Center, Brooklyn, NY, USA; fDepartment of
Emergency Medicine, Kings County Hospital Center, Brooklyn, NY, USA; gDepartment
of Emergency Medicine, SUNY Downstate Medical Center, Brooklyn, NY, USA; hArthur
Ashe Institute for Urban Health, Brooklyn, NY, USA; iBrooklyn Health Disparities
Center, Brooklyn, NY, USA; jNational Stroke Association, Centennial, CO, USA;
kCenter for Health, Media and Policy, Hunter College, NY, USA; lStroke Center,
SUNY Downstate Medical Center, Brooklyn, NY USA; mDepartment of Neurology, Kings
County Hospital Center, Brooklyn, NY, USA and The Mobile Applications for Stroke
(MAPPS) Investigative Team

Corresponding Author: jonathan.singer@nevada.unr.edu

Journal MTM 7:2:35–45, 2018

doi:10.7309/jmtm.7.2.5

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Abstract: Inadequate support, along with the stroke patient’s level of
disability, can have a negative impact on informal caregivers’ quality of life
and well-being. Yet, there is a lack of research and interventions focused on
improving the health and well-being of informal caregivers. To determine the
most salient potential resources and features for stroke patient caregivers
regarding the use of mobile apps to improve caregiver’s health. A nationwide
survey of caregivers was mailed to stroke survivors through the National Stroke
Association, which included questions on demographics, cell phone/smartphone
ownership, and caregiver’s opinion about mobile app resources– specifically 1)
scheduling multiple tasks, 2) finding resource information, 3) finding local
resources, 4) tracking fitness and diet, and 5) communication with the stroke
survivor. 396 stroke caregivers [(299 (76%) female, 96 (24%) African-American,
42 (11%) Hispanic/Latino, and 210 (53%) Caucasian], aged 20-99 years (mean 58.2
± 11.30), returned surveys; 96% owned a cell phone and 60% owned a smartphone.
Most caregivers reported aspects of the app to be useful, including,
doctor/rehab appointments [80% (95% CI 76-84%)], links to reliable medical
information [84% (95% CI 80-87%)], local stroke support groups [81% (95% CI
77-85%)], exercises [76% (95% CI 71-80%)], and touch screen with useful phrases
[76% (95% CI 71-80%)]. Latino (88%-74%) and African-American (84%-77%)
caregivers reported the highest rate of usefulness. Implementation of a mobile
app unique to stroke caregivers with multiple resources is desired by this
diverse, national sample of informal caregivers. Such a mobile app holds
potential to reduce the disparities gap for resource use.

Keywords: Stroke, Caregiver, Rehabilitation, Stroke mHealth, Mobile health

--------------------------------------------------------------------------------

INTRODUCTION

Stroke is one of the leading causes of long-term disability in the United States
and is a substantial burden on informal caregivers1. These individuals play a
vital role in the health and well-being of stroke survivors. Usually, informal
caregivers are family members who have little to no training on how to care for
a stroke patient. This training deficit creates additional physical,
psychological, and economic strain on both the caregiver and patient1–4.

Inadequate support, along with the stroke patient’s level of disability, can
have a major negative impact on informal caregivers’ quality of life (QoL) and
well-being5. Yet, there is a lack of research focused on the health and
well-being of informal caregivers1,2,5,6. New approaches have been reported as
possible avenues to improve caregiver QoL including patient and
caregiver-centered models5 and mobile health (mHealth) technology7. In 2015,
smartphone ownership rates in America were reported as 64%; while 90% owned any
type of cell phone, 32% owned an e-reader and 42% owned a tablet computer8. Also
in 2015, 64% of Latinos and 68% of African Americans owned a cell phone8.
Numerous studies have shown that Latinos and African Americans have disparate
access to healthcare9,10. These populations have some of the highest rates of
obesity, diabetes, and hypertension – all major risk factors for stroke10. New
mobile-based technologies may narrow the gap in access to care and health
related resources for minority populations11–13. To our knowledge, there is no
medical app specifically dedicated to stroke patient caregivers to help improve
the caregiver’s quality of life.

Many resources can be implemented in a mobile app for stroke patient caregivers,
which could help both the caregiver and the stroke survivor (e.g. managing task
scheduling, finding resources, exercise routines, fitness and diet/nutrition
tracking, etc.). In order to design and develop a useful mobile app for stroke
patient caregivers, identification of appropriate resources and features for
inclusion is critical. A caregiver-centered survey regarding the use of mobile
apps was designed to determine the most salient potential resources and features
for stroke patient caregivers taking race/ethnicity, age, and gender into
account.

MATERIALS AND METHODS

Design: A nationwide, observational survey. Participants: Using a national
database of stroke survivors maintained by the National Stroke Association
(NSA), stroke survivors were mailed a cover letter and a 17-question survey
(Appendix B). The 17 questions were designed by the stroke experts on this
manuscript from SUNY Downstate as well National Stroke Association. Stroke
survivors were asked to give the survey to their informal caregiver, if
applicable. The NSA database does not have a record of how many stroke survivors
have caregivers and to our knowledge there are no studies that have determined a
ratio of caregivers to stroke survivors. Therefore, it is impossible to conclude
response rate. Caregivers were not provided compensation for participating in
the study.

Measures: The study was approved by the SUNY Downstate Institutional Review
Board. After consent was signed, the survey was administered. The Survey
included demographic questions, as well as questions regarding the caregiver’s
opinion on what features in a mobile app (if any) would be most useful to
include, specifically: 1) scheduling multiple tasks, 2) finding information and
resources about stroke care on the Internet, 3) local stroke-related resources,
4) tracking fitness and diet, and 5) communication tools for stroke survivors.
The caregivers were also surveyed on their ownership of a cell phone and/or
smartphone on which the app could be used. Data Analysis: Multiple logistic
regressions were performed to analyze the data. In the five logistic regression
models, the independent variables were age (computed into tertiles 20-53, 54-62,
63-91), race (African American, White, Hispanic/Latino, and responses of Asian,
Native American and mixed were grouped as Other), and gender. The dependent
variable was overall caregiver rating of the potential usefulness of the app,
with regard to the five possible resources, dichotomized as useful or not
useful. Responses were coded as “not useful” if they reported, “this app would
not be useful” and coded as “useful” if they reported one of the aspects of the
mobile app would be useful to help with the given task. Statistical significance
was set at .05 and SAS 9.4 (SAS Institute, Inc Cary, NC) was used for
statistical analysis.

RESULTS

A sample of 396 informal stroke patient caregivers (299 females) ranging from 20
to 91 years of age participated in this study. The mean age of the caregivers
was 58.2 (SD = 11.30) years. Of the 396 caregivers, 210 (53%) were
White/Caucasian, 96 (24%) were African-American, 42 (11%) were Hispanic/Latino,
and the remainder (12%) self-identified as Asian, Native American, Mixed and
Other. Ninety percent of the caregivers reported owning a cell phone, and 60% of
all caregivers reported owning a smartphone.

After examining the five questions to see which resources caregivers want
implemented to improve both their and their stroke survivors’ lives, high rates
of usefulness were found between all races and genders (Tables 1–5). As age
increased, caregivers’ reports of each resource’s potential utility in a mobile
app decreased ( p < .05). However, even in the oldest group (63+), the majority
of caregivers reported that each of the five types of resources could be useful
in a mobile app, ranging from the lowest at 67% to the highest at 79%. “Doctor
or rehab appointments,” “Links to trustworthy medical information,” “stroke
support groups in my or my stroke survivors area,” “app with exercises for
stroke survivors,” and “a touch screen with useful phrases” were reported as
potentially the most useful features in a mobile app regardless of race, age, or
gender for the five questions.

High rates of potential usefulness were found for the minority caregivers in our
sample for these five resources; consequently, each question was investigated
further to examine racial differences for each of the five questions
individually. Table 1 outlines gender, age, and racial differences for the
caregivers’ opinions on mobile app features to schedule multiple tasks.
Implementation of an app to help caregivers with scheduling multiple tasks for
the stroke survivor was found to be useful to 80% (95% CI 76-84%) of the
population, with no significant differences found between races when controlling
for age and gender ( p = .073). Caregivers stated that “stroke rehab exercises”
and “links to trustworthy medical information” are the most desired features for
an app used to find resources about stroke. Table 2 outlines data (App most
useful to find information and resources about stroke) based on race, gender,
and age, and they were similar between all groups. An app for stroke resources
was reported as useful to 84% (95% CI 80-87%) of the caregivers, and no racial
differences were found when controlling for age and gender ( p = .89; Appendix
A).

Table 1: App most useful when scheduling multiple tasks for stroke survivor

Table 2: App most useful to find information and resources about stroke

Eighty one percent (95% CI 77-85%) of caregivers believed a mobile app would be
useful for finding local resources, and no racial differences were found when
controlling for age and gender (p = .49; Appendix A). More specifically,
caregivers indicated that access to “stroke support groups in my area” and
“stroke physicians and rehab specialists in my area” would be most beneficial
(Table 3).

Table 3: App most useful to find local resources

Caregivers reported that having a mobile app with “exercises for stroke
survivors” and that “tracks stroke survivor’s diet” would be the most beneficial
features in an app to track stroke survivors’ fitness and diet. However, 21% of
the caregivers reported that having an app to track the survivors’ fitness and
diet would not be useful (Table 4). Caregivers reported that this resource was
useful at the lowest percentage of any of the five resources. Racial differences
for caregivers’ reporting of potential usefulness of an app to track the stroke
survivors’ fitness and diet, after controlling for age and gender, was not
significant (p = .19; Appendix A).

Table 4: App most useful to track fitness and diet

The fifth question investigating the most useful features for an app focused on
communication with the stroke survivor. Caregivers reported that having a touch
screen with useful phrases such as “I am hungry” would be the most useful.
However, 21% of the caregivers reported that an app to communicate with the
stroke survivor would not be useful (Table 5). Racial and gender differences
were not found when investigating caregivers’ opinions on usefulness of an app
to help the caregiver communicate with stroke survivors (p =.67; Appendix A).

Table 5: App most useful for Stroke Survivor to communicate with you

DISCUSSION

To our knowledge, this study is the first to formally investigate a national
sampling of informal stroke caregivers’ opinions on potential usefulness of
features to be made available in a mobile stroke app. Despite the millions of
informal caregivers who suffer economic, physical and emotional burdens14, there
are limited resources that are both useful and cost effective. Forty percent of
caregivers reported having an app for “doctor or rehab appointments” as
potentially the most useful, which is consistent with stroke patients often
having multiple health care providers, including neurologists, cardiologists,
primary care physicians, physical therapists and/or speech therapists. Tracking
numerous appointments and ensuring that these are kept can become challenging
and increase stress, especially when coordinating with existing commitments.
Therefore, implementing a mobile app that assists caregivers in organizing the
stroke survivor’s schedule could alleviate some of the caretaker’s burden.

Caregivers’ QoL and well-being have been found to be associated with the
disability level of the stroke survivors5, and providing a tool to help manage
the severity of survivor’s disability may help improve the caregiver’s QoL and
well-being. Moreover, by improving caregivers QoL, it may in turn improve the
survivor’s QoL and well-being. Caregivers reported that an app which would
provide “links to trustworthy medical information,” “exercises for stroke
survivors,” “stroke support groups in my or survivor’s area,” and “stroke
physicians and rehab specialists in my or survivor’s area,” would be the most
useful. Creating an app that would provide caregivers with this information
might not only improve stroke survivors’ QoL and well-being, but potentially
improve the caregiver’s health by allowing the caregiver opportunities to
exercise with the patient or bring the stroke survivor to support groups. In
turn, these activities could reduce caregivers’ feelings of alienation and
stress5. Also, many of these apps hold the potential to decrease economic burden
if a caregiver is given resources that will improve recovery for the stroke
patient, thereby reducing the number of practitioners or appointments required
for future care. Evaluating resources for stroke patients and their caregivers
is important following a stroke, and especially for minority groups who may have
limited or no access to healthcare9,10.

One of the objectives of this study was to examine racial/ethnic differences in
caregivers’ assessment of the potential utility of a mobile app, and to identify
specific features caregivers would want in a mobile app. Despite not finding any
significant differences among races/ethnicities for all five questions regarding
desirable features, possibly due to an underpowered sample, Hispanic/Latino and
African American respondents reported the highest rates of potential utility for
all five-resource types. Minority populations also have highest rates of poverty
and are often underserved15, so reducing the economic burden on these caregivers
is critical. Therefore, implementation of a mobile app with the features desired
by the minority informal caregivers in this sample may narrow the gap by
providing resources that will improve health outcomes for minority populations.

Mobile technology was not available until later in the lives of older adults. As
expected, the five resources of the mobile app were reported as potentially less
useful for the older cohort, as compared to a younger sample16. However, in our
oldest group of 63+ years of age, more than half of caregivers reported all five
resources could be useful in a mobile app. The argument against mHealth
technology for caregivers15,17 has been that older generations will not have
access to, or benefit from, these resources in a mobile app. However, high rates
of cellphone/smartphone ownership among older participants (63+; cell phone
ownership= 86%, smartphone ownership= 43%) were reported, and this older cohort
reported that the resources could be potentially useful. Despite arguments
stating that older adults wouldn’t benefit from mHealth15, our data suggest that
mHealth has the potential to be an innovative, universal resource accessible to
all populations.

There were several limitations in this study. First, the study was survey-based
from a national database and the respondents may not necessarily be
representative of the general stroke caregiver population. Also, the response
rate is impossible to identify due to the NSA not having records of how many
stroke survivors had caregivers. Although the app resources and features
selected for the survey were developed with input from two focus groups of
stroke survivors18, the questionnaire did not allow for open-ended responses.
Therefore, it could be argued that caregivers in the sample may desire
alternative features built into a mobile app, which were not captured by the
closed-ended questionnaire. Another potential weakness is that we do not know
the symptoms of the caregivers’ stroke survivors’ strokes or their concurrent
illnesses. These items could have impacted the responses of the respondents
(e.g. survivors with/without aphasia may or may not need communication tools;
survivors with diabetes or hypertension may need more diet tools than those
without).

CONCLUSION

Despite these limitations, at least 80% of caregivers said they want these
features in a mobile app, which provides a foundation for future app/studies to
build upon. Future directions for research that may benefit the field and
improve both stroke patient and caregivers health and well-being are, developing
a mobile app for caregivers, and completing beta testing to make sure it meets
the needs of the caregivers, is user friendly, and can be disseminated to all
races, ethnicities and age groups.

ACKNOWLEDGEMENT

Funding: This study was supported by The Patient-Centered Outcomes Research
Institute (PCORI) pilot award 1IP2PI000781.

CONFLICT OF INTEREST

There is no conflict of interest in this research.

REFERENCES

1. Bakas T, Austin JK, Habermann B, et al. Telephone Assessment and
Skill-Building Kit for Stroke Caregivers: A Randomized Controlled Clinical
Trial. Stroke. 2015; 46: 3478–3487.

2. Han B, Haley WE. Family caregiving for patients with stroke review and
analysis. Stroke. 1999; 30: 1478–1485.

3. Gaugler JE, Kane RL, Kane RA, et al. Caregiver and institutionalization of
cognitively impaired older people: Utilizing dynamic predictors of change. The
gerontologist. 2003; 43: 219–229.

4. Joo H, Dunet DO, Fang J, et al. Cost of informal caregiving associated with
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5. Kerr SM, Smith LN. Stroke: an exploration of the experience of informal
caregiving. Clinical rehabilitation. 2001; 15: 428–436.

6. Bergstrom AL, von Koch L, Anderson M, et al. Participation in everyday life
and life satisfaction in person with stroke and their caregivers 3-6 months
after onset. Journal of rehabilitation medicine. 2015; 8: 508–515.

7. Fens M, van Heugten CM, Beusmans G, et al. Effect of a stroke-specific
follow-up care model on the quality of life of stroke patients and caregivers: A
controlled trial. Journal of rehabilitation medicine. 2014; 9: 7–15.

8. Anderson M. Technology device ownership: 2015.
http://www.pewinternet.org/2015/10/29/technology-device-ownership-2015/. 2015.
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9. Cho MJ, Sim JL, Hwang SY. Development of smartphone educational application
for patients with coronary artery disease. Healthcare informatics research.
2014; 20: 117–124.

10. George S, Duran N, Norris K. A systematic review of barriers and
facilitators to minority research participation among African Americans,
Latinos, Asian Americans and Pacific Islanders. American journal of public
health. 2014; 104: 16–31.

11. Hayes SL, Riley P, Radley DC, et al. Closing the gap: Past performance of
health insurance in reducing racial and ethnic disparities in access to care
could be an indication of future results. The commonwealth fund. 2015; 5: 1–11.

12. Bender MS, Choi J, Arai S, et al. Digital technology ownership, usage and
factors predicting downloading health apps among Caucasian, Filipino, Korean,
and Latino Americans: the digital link to health survey. JMIR mHealth and
uHealth. 2014; 4: 43.

13. Pierce LL, Steiner VL, Khuder SA, et al. The effect of a web-based stroke
intervention on carers’ well-being and survivors’ use of healthcare services.
Disability and rehabilitation. 2009; 31: 1676–1684.

14. Hickenbottom SL, Fendrick AM, Kutcher JS, et al. A national study of the
quantity and cost of informal caregiving for the elderly with stroke. Neurology.
2002; 58: 1754–1759.

15. Greene SK, Levin-Rector A, Hadler JL, et at. Disparities in reportable
communicable disease incidence by census tract-level poverty. Journal of public
health. 2015; 105: e27-e34.

16. Krebs P, Duncan DT. Health app use among United States mobile owners: A
national survey. JMIR mHealth and uHealth. 2015; 4: e101.

17. Gurman TA, Rubin SE, Roess AA. Effectiveness of mHealth behavior change
communication interventions in developing countries: a systematic review of the
literature. Journal of health communication. 2012; 17: 82–104.

18. Nadege G, Zelonis S, Beving L, et al. Stroke Survivors & Caregivers
Preferences for Mobile APPs: A nationwide population-based survey. Neurology.
2015; 84(Supplement): S5.003.

APPENDIX A

Table 1a: App most useful when scheduling multiple tasks for stroke survivor
(Dichotomized)

Table 2a: App most useful to find information and resources about stroke
(Dichotomized)

Table 3a: App most useful to find local resources (Dichotomized)

Table 4a: App most useful to track fitness and diet (Dichotomized)

Table 5a: App most useful for Stroke Survivor to communicate with you
(Dichotomized)

APPENDIX B

TEN QUESTIONS MAILED TO STROKE CAREGIVERS AND ANALYZED IN THIS STUDY

1. 1. Which of these mobile apps would be the MOST useful to you when
scheduling your stroke survivor’s multiple tasks? (Please check only one)

1. a. Doctor or rehab appointments

2. b. A medication reminder

3. c. Coordinating meals with medication schedule

4. d. Scheduling social activities

5. e. Blood pressure tracking

6. f. This type of app would not be useful

2. 2. Which of these mobile apps would be the MOST useful to help you find
how-to information about stroke care on the Internet? (Please check only
one)

1. a. A medication resource guide

2. b. Suggested stroke rehab exercises

3. c. Links to trustworthy medical information

4. d. This type of app would not be useful

3. 3. Which of these mobile apps would be the MOST useful to help you find
LOCAL stroke-related resources? (Please check only one)

1. a. Stroke support groups in my or my stroke survivor’s area

2. b. Stroke physicians and rehab specialists in my or my stroke survivor’s
area

3. c. Reviews of stroke physicians and rehab specialists in my or my stroke
survivor’s area

4. d. Locations of certified stroke centers in my or my stroke survivor’s
area

5. e. This type of app would not be useful

4. 4. Which of these mobile apps would be the MOST useful to you to track your
stroke survivor’s fitness and diet? (Please check only one)

1. a. An app that tracks distance when I or my stroke survivor exercise

2. b. An app that tracks how much time I or my stroke survivor exercise

3. c. An app with exercises for stroke survivors

4. d. An app that offers healthy meal suggestions for stroke survivors

5. e. An app that tracks my stroke survivor’s diet, including calories,
sodium, fat, etc.

6. f. This type of app would not be useful

5. 5. Which of these mobile apps would be the MOST useful to your stroke
survivor to communicate with you and/or others? (Please check one)

1. a. A touch screen with useful phrases (e.g., I want to go to the park; I
am hungry)

2. b. An app that translates your stroke survivor’s typed text into spoken
words

3. c. An app that translates a text message your stroke survivor receives
into spoken words

4. d. An app that my stroke survivor can show people to let them know they
have had a stroke

5. e. An app that uses GPS to show my stroke survivor’s location

6. f. This type of app would not be useful

6. 6. Please check if you are:

1. ___ female ___ male

7. 7. What is your age? _______

8. 8. Please check the ethnicity that best describes you:

1. ___ Black or African-American

2. ___ Afro-Caribbean

3. ___ White or Caucasian

4. ___ Hispanic or Latino

5. ___ Asian

6. ___ Native American

7. ___ Mixed

8. ___ Other

9. 9. Do you have a cell phone?

1. ___ Yes ___ No

10. 10. If Yes, is your cell phone a smartphone (i.e., you can use it to
connect to the Internet or web)?

1. ___ Yes ___ No

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EXPANDING THE USE OF TELEMEDICINE IN NEUROLOGY: A PILOT STUDY

Posted on Dec 4, 2018 in Original Article | 0 Comments

Expanding The Use of Telemedicine in Neurology: A Pilot Study

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Melissa M. Reider-Demer1, D.N.P, MN, CNP and Dawn Eliashiv1, M.D.

1Department of Neurology, University of California, Los Angeles

Corresponding Author: mrdemer@mednet.ucla.edu

Journal MTM 7:2:46–50, 2018

doi:10.7309/jmtm.7.2.6

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Background: Telemedicine enables providers to connect with patients and
consultants remotely in a cost-effective, convenient manner. Telemedicine in
various forms—from store-and-forward technology to real-time
videoconferencing—has demonstrated good outcomes in specialties including
neurosurgery, general neurology, stroke, and epilepsy.

Aim: To demonstrate the feasibility of telemedicine in an outpatient
university-based neurology clinic, and assess patient and provider satisfaction
with telemedicine.

Methods: Forty neurologically stable adult patients were recruited. We excluded
patients who were non-English speaking, had intellectual disability, lacked
caregiver availability, or had unstable neurological conditions. After each
telemedicine visit a patient standardized satisfaction survey was completed,
comprising 11 questions assessing patient willingness to participate, technical
issues, and satisfaction with the clinic experience and medical provider. A
provider satisfaction survey was obtained at the end of the study.

Results: Forty of fifty patients meeting criteria consented to a telemedicine
visit in lieu of a routine visit, and 15 of these responded to survey. All felt
confident when meeting with their provider met their needs. Ninety percent of
participants agreed that the technical process of joining the on-line session
was easy. All patients agreed that they were satisfied with their telemedicine
session and would choose telemedicine over face-to-face visits in the future.
Cost analysis demonstrated a UCLA healthcare system saving of $28.00 for each
telemedicine replacing an in-person clinic visit. Patients saved the cost of
auto fuel, missed work, and parking when using the telemedicine option.

Conclusions: Telemedicine in neurology patients is feasible and satisfies
patients and providers.

--------------------------------------------------------------------------------

INTRODUCTION

Traditional ways of providing health care are constantly being improved as
evidenced by the increasing use of telemedicine. This option enables providers
to connect with patients and consultants across challenging geographic distances
in a cost-effective, convenient manner (Reider-Demer, 2017). In neurology and
neurosurgery populations, telemedicine in various forms—from store-and-forward
technology to real-time videoconferencing—has demonstrated good outcomes.
(Reixeira-Poit, 2017). In an effort to maximize resource efficiency, continuity
of care and patient access, the Veterans Health Administration has tested
workflows using allied health professionals to conduct telemedicine follow-up
care in lieu of in person clinical visits to maximize response efficiency and
patient access (Dadlani, 2014).

Further studies have shown that the use of telemedicine in neurology is an
alternative to traditional health care (Hwa, 2013). In major urban centers,
increased patient demands can cause neurology practices to struggle, resulting
in patients having to travel within large catchment areas for follow-up care.
This is especially true for subspecialty care where a significant shortage of
neurologists limits the number of patients who can be seen (Reixeira-Poit,
2017). Many patients have to travel long distances, take days off work without
pay, and absorb the costs of motor fuel and parking fees. Many have disabilities
that hinder travel mobility, and many have restricted driving privileges, so
travel to appointments places a burden on them and their caregivers. While the
use of telemedicine may not be feasible for patients with complex neurological
conditions, it is a viable alternative for stable patients (Pulley, 2015).
Neurology visits are typically carried out face-to-face by trained allied health
professionals, and have shown good patient satisfaction and high-quality care.
We performed a prospective feasibility pilot study to determine if real-time
videoconferencing (telemedicine sessions) with allied health professionals can
successfully substitute for in-person clinic visits and still maintain
patient/provider satisfaction in neurology (Govindarajan, 2017).

METHODS

The study was prospectively approved by our local institutional review board,
and included random, stable neurology patients who accepted the use of
telemedicine for their routine follow-up appointments, or results review
sessions, among patients with ataxia, epilepsy, stroke, genetic disorders,
dementia, multiple sclerosis, Parkinson’s disease and neuro rehabilitation. This
project began in September 2017 and ended in December 2017. When established
neurology patients required a follow-up clinic visit and/or a results review
session, they were offered a telemedicine session as an option. Patients were
required to have a stable neurology disorder, be over the age of 18 years, and
be able to verbally consent to the study. Patients were offered this alternative
provided they had internet access via a smart phone, tablet, or laptop equipped
with a video camera, similar to that of the participating medical providers.
Patients lacking one of these devices could not participate. Exclusion criteria
were inability to speak the English language, inability to consent, or an
unstable neurological condition. If the patient selected a telemedicine session,
the clinic coordinators sent an invitation to the patient. To ensure that the
telemedicine consult was feasible, a technology support designee contacted the
patient prior to the visit and offered to test the internet connection and
assist to download the application program “ZOOM.” The telemedicine consult was
performed utilizing ZOOM, a secure web-based, HIPPA compliant software
application (https://twitter.com/zoom_us).

Verbal consent was obtained and documented within the clinic note at each clinic
encounter with patients willing to participate in the telemedicine study. If the
telemedicine visit resulted in an unexpected assessment, an in-person visit was
arranged. Otherwise at the end of the session the patient was offered a future
telemedicine follow up visit or a result summary session as appropriate.

After each telemedicine session, patients were queried about their satisfaction
with the telemedicine session via an automatically generated electronic patient
satisfaction survey. At the end of the study the provider satisfaction survey
was electronically sent to the participating providers. The patient survey
instrument is provided as Survey 1(Figure 1), and the provider survey as Survey
2 (Figure 2).

Figure 1: Survey One: Patient Satisfaction

Figure 2: Survey Two Provider Satisfaction

Outcome measures were used to evaluate patient/provider satisfaction and the
feasibility of using telemedicine.

RESULTS

Forty of 55 identified eligible patients accepted telemedicine sessions.
Twenty-five participants used tablet computers, ten used laptop computers, and
five used smartphones. All patient survey responders indicated that they wanted
to participate in the patient satisfaction survey. All participants either
agreed or strongly agreed that the telemedicine session was clearly explained to
them. Ninety percent of the participants underwent a follow-up appointment.
Ninety percent did not require a session prior to the actual clinic visit, and
ninety percent agreed or strongly agreed that the technical process of joining
the on-line session was easy. All participants either agreed or strongly agreed
that they felt confident when meeting with the provider, could clearly visually
see their provider on the device screen, and that the telemedicine session met
their expectations. All patients agreed or strongly agreed that they were
satisfied overall with their telemedicine session and would choose telemedicine
over face-to-face visits in the future.

Only one provider had previously used a form of telemedicine prior to this
study. Among the nine participating providers, six responded to the
questionnaire. All were satisfied that the telemedicine session met both their
provider expectations as well as their medical needs for examining the patient,
and were either satisfied or strongly satisfied with audio-visual quality. All
were very satisfied with the overall quality of the care provided and would
participate in telemedicine in the future.

DISCUSSION

This prospective feasibility pilot study supports the effective use of
videoconferencing by allied health professionals for established neurology
follow-up clinic visits that may include a review of previously obtained tests.
The clinical setting of this study supported using telemedicine as a tool in
evaluating patients with follow-up clinic visits in a multi subspecialty
outpatient neurology clinic with a varied diagnoses including epilepsy, genetic
disorders, stroke, ataxia, dementia, neurorehabilitation, and other general
neurology disorders.

Telemedicine technology can bridge care gaps and help meet the increasing
demands for neurologists. Twenty-one small studies within the United States were
published between 2010 and 2015 and review telemedicine intervention outcomes
for follow up care. These studies demonstrated showed a high level satisfaction
in a majority of patients and providers who used telemedicine, and noted
reductions in travel time and cost (Snyder, 2018). Telemedicine has proven to be
an increasingly viable supplement to traditional health care delivery, enabling
providers to connect with patients and consultants across distances in a cost
effective and convenient manner (Armstrong, 2014).

Telemedicine is an innovative tool for improving access to care reducing burden
in clinics, and providing cost savings to patients, their families and the
healthcare system. Telemedicine is convenient for patients who routinely use
cellular phones, tablets, or laptop computers. Patients are willing and ready to
utilize telemedicine as indicated by 40 out of 55 eligible patients opting to
participate in this study. The post visit survey showed favorable patient and
provider satisfaction. Telemedicine allowed patients to reduce driving time,
fuel usage, and parking costs. Although total costs were not assessed, some
specific cost savings were noted throughout the feasibility study. Typically,
patients and families that drove to and from the medical center often missed
work (Reider-Demer, 2017). Costs for fuel in the state of California averaged
$0.54/mile. Parking at our institute was $12.00 daily. As this was a pilot study
a health economics analysis including administrative, pre and post visit
functions were not assessed. Note, telemedicine visits did not incur clerical
cost for registering patients or medical assistant costs to obtain vital signs
which saved the department $28.00 per visit in our department. The reduction in
clinic load would allow to schedule more patients as well as decrease wait time
for new or established clinic visits, decompressing clinics.

The use of telemedicine, employing technological devices such as cellular
phones, tablets and laptops, can improve access to healthcare providers. It can
provide medical care providers an equivalent alternative to in-person clinic
visits, ultimately providing better service to patients and their families. As
hospitals acquire electronic health-record systems, wireless telephones, and
other technological devices, telemedicine will likely become an integral part of
the institutional infrastructure. The limitations of this study are the
relatively small number of patients, the lack of access for patient without
acceptable devices, lack of internet connection in some households, and language
barriers. As this as a pilot study the applicability of telemedicine to unstable
patients was not assessed.

Presently the use of telemedicine can only be used within the state in which a
practitioner is licensed to practice. Additionally, Medicare does not reimburse
for telemedicine. Both are limiting factors that could be surmounted in the
future.

CONCLUSION

Telemedicine is an increasingly viable, cost-effective, convenient alternative
to traditional neurological care delivery, enabling providers to connect with
patients and consultants across distances (Reider-Demer, 2017). This pilot study
supports telemedicine as an option for neurological patients with favorable
patient/provider satisfaction. Further studies should be performed in additional
patient populations at other institutions.

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clinic visits. Neurosurgical Focus. May 2018;44:5:18.

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