Mental Health Engagement Network

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DOI: 10.18848/2381-9251/CGP/v11i02/56499
Cite this publication
Journal of
Technologies in Society
TECHANDSOC.COM
VOLUME X
________________________________________________________________________
Mental Health Engagement Network
An Analysis of Outcomes Following a
Mobile and Web-based Intervention
CHERYL FORCHUK, ABRAHAM RUDNICK, JEFFREY
REISS, JEFFREY HOCH, LORIE DONELLE,
DEBORAH CORRING, MIKE GODIN, WALTER OSAKA,
ROBBIE CAMPBELL, MIRIAM CAPRETZ, JEFFEREY REED,
AND MEAGHAN MCKILLOP
JOURNAL OF TECHONOLOGIES IN SOCIETY
www.techandsoc.com
First published in 2015 in Champaign, Illinois, USA
by Common Ground Publishing LLC
www.commongroundpublishing.com
ISSN: 2381-9251
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Journal of Technology in Society
Volume X, 2015, www.techandsoc.com, ISSN 2381-9251
© Common Ground, Cheryl Forchuk, Abraham Rudnick, Jeffrey Reiss, Jeffrey Hoch,
Lorie Donelle, Deborah Corring, Mike Godin, Walter Osaka, Robbie Campbell,
Miriam Capretz, Jefferey Reed, Meaghan McKillop, All Rights Reserved
Permissions: cg-support@commongroundpublishing.com
Mental Health Engagement Network: An Analysis
of Outcomes Following a Mobile and Web-based
Intervention
Cheryl Forchuk, Western University, Canada
Abraham Rudnick, University of British Columbia, Canada
Jeffrey Reiss, Western University, Canada
Jeffrey Hoch, University of Toronto, Canada,
Lorie Donelle, Western University, Canada
Deborah Corring, Western University, Canada
Mike Godin, Canadian Mental Health Association, Canada
Walter Osaka, CanVoice, Canada
Robbie Campbell, Western University, Canada
Miriam Capretz, Western University, Canada
Jefferey Reed, Western University, Canada
Meaghan Mckillop, Western University, Canada
Abstract: The Mental Health Engagement Network (MHEN) project introduces, delivers and evaluates the use of
consumer driven web and mobile based technologies in the delivery of mental health care services to individuals living in
the community (approximately 400 clients and 54 mental health care providers). Through the MHEN intervention,
participants receive one Lawson SMART record account (electronic personal health record and health management tools
including secure messaging, symptom tracking, prompts and reminders), one iPhone 4S and a 12-month voice and data
plan. Previous publications have reported usability and adoption. Here we report the effects of the MHEN intervention
on primary outcomes among client participants. Significant improvements were found in quality of life, use of key health
services (outpatient visits, psychiatric admissions), and judicial system encounters. It is suggested that this new service
delivery model has the potential to reduce the use of costly health services and improve quality of life in those with
mental illness.
Keywords: Mobile Technology, Web Based Technology, Mental Health Care, Personal Health Records, Quality of Life,
Service Utilization
Introduction
ental illness can be debilitating to individuals. It can also have far reaching effects on
the wellness of the population and the economic sustainability of the Canadian
healthcare system. Mental illness indirectly affects all Canadians, with at least 20% of
the population personally experiencing some degree of mental illness in their lifetime (Canadian
Mental Health Association 2013). It is alarming that despite the unprecedented academic and
media focus on mental health and illness in recent years, only one third of Canadians who
experience mental illness actually receive the services and treatment they need (Statistics Canada
2003).
There is an overwhelming body of evidence to suggest that quality of life, an individual’s
overall well-being and life satisfaction (Angermeyer and Killian 1997) is significantly impaired
in those with psychosis, mood disorders and other major mental illnesses (Barrera and Norton
2009; Chand, Mattoo and Sharan 2004; El-Badri and Mellsop 2007; Olatunji, Cisler and Tolin
2007; Pirkola et al. 2009). One study found that individuals with mental illness had 17.6
unhealthy days every month; greater than any other chronic illness and considerably larger than
M
JOURNAL OF TECHNOLOGIES IN SOCIETY
the average adult score of 7.9 unhealthy days per month (Cook and Harman 2008, 48).
Furthermore, studies suggest that those with a mental disorder have a considerably shorter life
expectancy than the average adult (Wahlbeck 2011, 454-455) and die an average of 8 years
younger than the rest of the population (Druss et al. 2011, 601). This evidence presents a
disparity in the health and well-being of persons with mental illness and demonstrates the need
for health related and societal improvements.
The negative impacts of mental illness are not limited to the domain of health and quality of
life. A number of estimates suggest that mental illness has a considerable impact on the
economic stability and sustainability of the healthcare system. The Public Health Agency of
Canada reported that neuropsychiatric disorders had the highest expenditure for direct health
services and the third-highest expenditure for indirect costs to the economy related to illness,
disability or premature death (Public Health Agency of Canada 2009). Recent estimates of the
economic burden of mental illness in Canada were found to be approximately $50 billion
annually (Lim et al. 2008; Smetanin et al. 2011). These estimates are modest, and do not include
costs incurred by the justice system, social services, and by families and informal caregivers
(Mental Health Commission of Canada 2012). It is evident that an overhaul to the current
provision of care is needed to ensure a sustainable and efficient healthcare system for those
experiencing mental illness.
Innovations in Care
The current system of care remains disjointed and has been unable to effectively and efficiently
support those with mental illness. However, the advent of innovative health technologies presents
an opportunity to transform the current approach to delivering mental health care. A growing
body of evidence demonstrates the efficacy of consumer-driven technologies in augmenting the
care of physical illness (Carlisle et al. 2012; Gubrium 2009; Logan 2013; Mattila et al. 2010), but
literature surrounding the use of technology in mental health care is limited to technology
usability, acceptability and client readiness (Proudfoot 2004; Stewart et al. 2010; Van der Krieke
et al. 2014). A deeper understanding of how consumer-driven technology affects the health,
quality of life, and service use in persons with mental illness would be instrumental in policy
development and the planning of mental health services.
Proposed Solution
The Mental Health Engagement Network (MHEN) is a client-centered intervention, funded by
Canada Health Infoway, which is based on the principles of health promotion and early
intervention through the use of mobile technology and a Personal Health Record (PHR). This
intervention was designed by an interdisciplinary group of healthcare providers, researchers,
health information technology experts and mental health clients. Its purpose is to augment the
delivery of community-based mental health care by electronically connecting clients to their
health care professional and providing them with tools to improve health management. A
detailed overview of this innovative solution and its adoption can be explored in detail in the
initial publications (Forchuk et al. 2013; Forchuk et al. 2014). It is expected that this new service
delivery model has the potential to improve quality of life and enhance health status in those with
mental illness, while reducing the use of costly health services (Forchuk et al. 2013; Forchuk et
al. 2014).
A previous publication reported that client and mental health care professionals have
adopted the MHEN intervention into their care (Forchuk et al. 2014). Here we report on the
effectiveness of the MHEN intervention in terms of quality of life, health status and service
utilization.
FORCHUK ET AL.: MENTAL HEALTH ENGAGEMENT NETWORK
Research Questions
1. Did health outcomes change following implementation of the MHEN intervention?
2. Did quality of life change following implementation of the MHEN intervention?
3. Did the use of key health services (outpatient visits, hospitalizations, and Emergency
Room visits) change following implementation of the MHEN intervention?
4. Did the number of judicial system encounters change following implementation of the
MHEN intervention?
Methods
Study Design
The MHEN project is a mixed method, delayed implementation design. Individuals from the
caseloads of participating mental health care professionals were randomized into either Group 1
(early intervention) or Group 2 (delayed intervention). Group 1 received the MHEN intervention
first, while participants in Group 2 initially acted as a control group and received the MHEN
intervention 6 months later (Forchuk et al. 2013; Forchuk et al. 2014). A comparison of Group
demographics at baseline was conducted and no significant differences were found (See Table 1).
Sample
The sample was comprised of 400 clients recruited from the caseloads of 54 mental health care
professionals within 4 community mental health agencies in London and the surrounding area:
London Health Sciences Center, St. Joseph’s Health Care (London and St. Thomas), the
Canadian Mental Health Association, and WOTCH Community Mental Health Care Services
(Forchuk et al. 2013, Forchuk et al. 2014). See initial publications for detailed sampling strategies
(Forchuk et al. 2013, Forchuk et al. 2014). Table 1 presents baseline descriptive statistics for the
400 MHEN participants (Forchuk et al. 2013, Forchuk et al. 2014).
Table 1: Description of Client Participants (n=394)
Characteristic
Intervention Group
n=192
Control Group
n=202
Total Sample
n=394
Age (years) [Mean (SD)]
38.2 (14.62)
37.1 (12.9)
37.6 (13.76)
Sex
Male
125 (65.1%)
114 (56.4%)
239 (60.7%)
Female
67 (34.9%)
88 (43.6%)
155 (39.3%)
Marital Status
Single, never married
140 (72.9%)
136 (67.3%)
276 (70.1%)
Married/Common-law
18 (9.4%)
15 (7.4%)
33 (8.4%)
Separated/Divorced
32 (16.7%)
50 (24.8%)
82 (20.8%)
Widowed
2 (1.0%)
1 (0.5%)
3 (0.8%)
Highest Level of Education
Grade school
52 (27.1%)
66 (32.8%)
118 (30.0%)
High school
93 (48.4%)
84 (41.8%)
177 (45.0%)
Community college/University
46 (24.0%)
51 (25.4%)
97 (24.7%)
Currently Employed
47 (24.5%)
50 (24.8%)
97 (24.6%)
Psychiatric Diagnoses
Psychotic disorder
111 (57.8%)
123 (60.9%)
234 (59.4%)
Mood disorder
119 (62.0%)
107 (53.0%)
226 (57.4%)
Anxiety disorder
60 (31.2%)
64 (31.7%)
124 (31.5%)
Substance related disorder
30 (15.6%)
20 (9.9%)
50 (12.7%)
Personality disorder
12 (6.2%)
12 (5.9%)
24 (6.1%)
Disorder of childhood/adolescence
9 (4.7%)
13 (6.4%)
22 (5.6%)
Other/organic/unknown
8 (4.0%)
11 (5.5%)
19 (4.8%)
JOURNAL OF TECHNOLOGIES IN SOCIETY
Data Collection
Each client participant completed four interviews throughout the study (baseline, 6, 12 and 18
months). Eight questionnaires were administered during these interviews. The questionnaires
included: a demographic form, the Quality of Life Brief Version (QoL-BV), Health, Social, and
Justice Service Use form, Medical Outcomes Study 36-item Short-Form Health Survey (SF-36),
the European Quality of Life -5 Dimensions (EQ-5D), the Community Integration Questionnaire,
the Adult Consumer Empowerment Scale, and the Perception of Smart Technology Form.
Data Analysis
Target enrollment was 200 participants per group. The outcomes of interest included quality of
life (general life satisfaction, number between 1 (terrible) and 7 (delighted)), overall health score
(number between 0 and 100), number of arrests in the previous 6 months, number of outpatient
visits in the previous 6 months, number of visits to the Emergency Department for psychiatric
reasons, and presence of a psychiatric hospitalization in the previous 6 months.
Changes in outcomes over time were assessed using statistical methods for repeated
measuresANOVAs for continuous outcomes, and Generalized Estimating Equations for binary
outcomes. Main effects were modeled first, with subsequent models including a time*group
interaction to examine any potential differences between the two treatment groups. Only
individuals with complete data across all four time points were included in each model. All
analyses were conducted using SPSS 22.0.
Results
Did Health Outcomes Change Following Implementation of the MHEN Intervention?
Table 2 outlines the results of the repeated measures analysis on health outcomes. The three p-
values reflect the significance of trends over time(pTime), between groups (pGroup), and any impact
the group may have had on the trends over time, (i.e. group*time interaction) (pGroup*Time). The
mean overall health score, as measured by the EQ-5D, did not significantly change following
implementation of the MHEN intervention (70.0 at 0 months vs. 70.1 at 18 months, p=0.987).
Additionally, there were no differences between the two treatment groups (p=0.993), nor did the
treatment group impact the trajectory of the trends in overall health status over time (p=0.980).
Table 2: Overall Health Score by Treatment Group Following Implementation of the MHEN
Intervention
Treatment
Group
6 Months
Mean
(SD)
12 Months
Mean
(SD)
18 Months
Mean
(SD)
Results of Repeated
Measures Models
(p-values)
Early
70.0 (19.5)
69.8 (18.2)
70.0 (18.9)
pTime=0.987
pGroup=0.993
pGroup*Time=0.980
Delayed
69.4 (22.2)
70.1 (18.5)
70.2 (19.8)
Total
69.7 (20.8)
69.9 (18.3)
70.1 (19.3)
Did Quality of Life Change Following Implementation of the MHEN Intervention?
As displayed in Table 3, there was a small but significant improvement in the quality of life over
time (4.7 at 0 months vs. 5.0 at 18 months, p<0.001). This trend did not differ between the
groups (p=0.414) nor were there differences in quality of life overall between the groups (0.364).
FORCHUK ET AL.: MENTAL HEALTH ENGAGEMENT NETWORK
Table 3: General Life Satisfaction by Treatment Group Following Implementation of the
MHEN Intervention
Treatment
Group
6 Months
Mean
(SD)
12 Months
Mean
(SD)
18 Months
Mean
(SD)
Results of Repeated
Measures Models
(p-values)
Early
4.9 (1.2)
5.0 (1.3)
5.0 (1.3)
pTime<0.001
pGroup=0.364
pGroup*Time=0.414
Delayed
4.8 (1.4)
4.9 (1.4)
5.0 (1.3)
Total
4.8 (1.3)
4.9 (1.3)
5.0 (1.3)
Did the Use of Key Health Services (Outpatient Visits, Hospitalizations, Emergency Room
Visits) Change Following Implementation of the MHEN Intervention?
Table 4 displays the results of the repeated measures analyses on the use of key health services
(outpatient visits, psychiatric admissions, ER visits for psychiatric reasons). There was a
significant decrease in the average number of outpatient visits over time (8.6 at 0 months vs. 3.7
at 18 months, p=0.005). This trend did differ between the two treatment groups and the
difference over time appeared to be larger in the early treatment group (difference of 5.5 vs. 2.1,
p=0.005).
There was also a significant decrease in the proportion of individuals with a psychiatric
admission over the course of the study (p<0.001). This trend did not differ between the groups
(p=0.412) and there appeared to be no difference in overall proportions of individuals with a
psychiatric admission between the groups (p=0.618).
There appeared to be a slight downward trend in the proportion of individuals with an ER
visit for psychiatric reasons over time, however this was not significant (9.9% vs. 5.3%,
p=0.055). No differences between the groups were found (p=0.222), and the treatment group did
not impact trends over time (p=0.881).
Table 4: Use of Key Health Services (Outpatient Visits, Psychiatric Admissions, ER Visits for
Psychiatric Reasons) by Treatment Group Following Implementation of the MHEN Intervention
Treatment
Group
6 Months
Mean
(SD) / %
12 Months
Mean
(SD) / %
18 Months
Mean
(SD) / %
Results of Repeated
Measures Models
(p-values)
Number of Outpatient Visits in the Previous 6 Months
Early
6.1 (12.1)
5.7 (9.4)
3.7 (7.8)
pTime=0.005
pGroup=0.659
pGroup*Time=0.005
Delayed
7.6 (15.3)
5.9 (13.0)
5.8 (14.5)
Total
6.9 (13.8)
5.8 (11.3)
4.8 (11.6)
Presence of a Recent Psychiatric Admission
Early
9.2%
5.7%
4.0%
pTime<0.001
pGroup=0.618
pGroup*Time=0.412
Delayed
13.8%
7.5%
6.3%
Total
11.4%
6.6%
5.1%
Presence of a Recent Visit to the ER for a Psychiatric Reason
Early
4.4%
6.6%
3.6%
pTime=0.055
pGroup=0.222
pGroup*Time=0.881
Delayed
6.3%
7.9%
7.1%
Total
5.3%
7.2%
5.3%
JOURNAL OF TECHNOLOGIES IN SOCIETY
Did the Number of Individuals with a Judicial System Encounter Change Following
Implementation of the MHEN Intervention?
As shown in Table 5, the proportion of individuals with a judicial system encounter significantly
decreased over the course of the study (6.8% vs. 1.1%, p=0.008). There were no differences
between the groups overall (p=0.832), nor did the group impact this downward trend (p=0.419).
Table 5: Proportion of Individuals with a Recent Judicial System Encounter by Treatment Group
Following Implementation of the MHEN Intervention
Treatment
Group
6 Months
12 Months
18 Months
Results of Repeated
Measures Models
(p-values)
Early
4.3%
4.3%
1.4%
pTime=0.008
pGroup=0.832
pGroup*Time=0.419
Delayed
6.3%
3.1%
0.8%
Total
5.3%
3.8%
1.1%
Discussion
The purpose of the MHEN project was to develop, design and evaluate the effectiveness of a
client-centered community based mental health care service delivery model. The MHEN
intervention utilizes mobile phone and web based technologies to support individuals living in
the community with mental illness. The efficacy of consumer-driven interventions for physical
health and chronic conditions such as diabetes, asthma and HIV is well established in the existing
literature (Anhoj and Moldrup 2004; Ferrer-Roca et al. 2004; Logan 2013; Mattila et al. 2010),
however, there has been less evaluation of its efficacy in individuals with mental illness.
The existing literature on consumer-driven mobile phone and web based technology
interventions for individuals with mental illness has largely focused on the development of
interventions and considered primary outcomes of user satisfaction, preferences, acceptance, and
compliance (Van der Krieke et al. 2013; Depp et al. 2010). These “proof of concept” studies
suggest that mobile phone and web based interventions may be an effective, efficient and safe
way to support individuals with mental illness in their recovery.
The MHEN project has contributed to the existing literature by evaluating the effectiveness
and usability of a consumer driven mobile phone and web based technology intervention among
individuals with mental illness. Specifically, the MHEN project has examined the effect of
consumer driven mobile phone and web based technologies on an individual’s quality of life,
health status and service utilization (psychiatric Emergency Department visits, outpatient visits to
a day hospital, arrests and psychiatric hospitalizations).
Between baseline and 6 months, both the intervention and control groups had a reduction in
the proportion of arrests, the mean number of outpatient visits to a day hospital, psychiatric
hospitalizations, and psychiatric emergency department visits. Although statistically significant
results are only reported for a reduction in outpatient visits between the intervention and control
groups at Time 2, these results may have clinical relevance (recognizing that reduction in
outpatient visits may imply but does not necessarily demonstrate better clinical outcome). For
example, the intervention group experienced a 48.6% reduction in the number of individuals with
a psychiatric hospitalization between baseline and 6 months. This reduction would be clinically
relevant to the individual and to the broader health care system as there are significant costs
associated with psychiatric hospitalizations.
The intervention did not have a statistically significant effect on health status or general life
satisfaction. It may be that 6 months is too short of a period to find such differences, or that the
FORCHUK ET AL.: MENTAL HEALTH ENGAGEMENT NETWORK
sample size was too small to demonstrate such differences. It is recommended that future studies
retain the control/intervention comparisons for a longer period of time and with larger samples.
The results of this project indicate that the MHEN intervention may have the potential to
change mental health care service delivery. As discussed in the previous publications (Forchuk et
al. 2014), participating clients and mental health care professionals have incorporated the MHEN
intervention into their care, demonstrating its adoption and usability. Here we report on the
effectiveness of the MHEN intervention on quality of life, health status and service utilization
between baseline and 6 months. Continued research is required to understand the long term
effects of mobile phone and web-based technology interventions for individuals with mental
illness.
Conclusion
Consumer-driven technology has the potential to be used in the provision of mental health care.
This initial analysis of the MHEN intervention has demonstrated that technology-enabled models
of care have the potential to reduce costly health and social service use, for instance, by
decreasing outpatient visits, hospitalizations and arrests. No significant changes to health status
and quality of life were reported. Additional research is needed, including further examination of
longer term outcomes from this project.
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JOURNAL OF TECHNOLOGIES IN SOCIETY
ABOUT THE AUTHORS
Dr. Cheryl Forchuk: Associate Director, Nursing Research, Western University, and Assistant
Director, Lawson Health Research Institute, London, Ontario, Canada
Dr. Abraham Rudnick: Associate Professor, Department of Psychiatry, University of British
Columbia, Victoria, British Colombia, Canada
Dr. Jeffrey Reiss: Professor and Chair, Division of General Adult Psychiatry, Western
University, London, Ontario, Canada
Dr. Jeffrey Hoch: Scientist, Keenan Research Center, Li Ka Shing Knowledge Institute of St.
Michael’s Hospital, University of Toronto, Ontario, Canada,
Dr. Lorie Donelle: Associate Professor, Faculty of Health Sciences, Western University,
London, Ontario, Canada
Dr. Deborah Corring: Adjunct Professor, Department of Psychiatry and School of Occupation
Therapy, Western University, London, Ontario, Canada
Mike Godin: Team Leader, Housing Advocacy Program, Canadian Mental Health Association,
London Middlesex Branch, London, Ontario, Canada
Walter Osaka: Peer Support Specialist, CanVoice, London, Ontario, Canada
Dr. Robbie Campbell: Associate Professor and Research Scientist, Western University and
Lawson Health Research Institute, London, Ontario, Canada
Dr. Miriam Capretz: Associate Professor, Department of Electrical and Computer Engineering,
Western University, London, Ontario, Canada
Jefferey Reed: Student, Western University, London, Ontario, Canada
Meaghan McKillop: Student, Western University, London, Ontario, Canada
Journal of Technologies in Society
is one of the
four thematically focused journals that comprise the
Technology Collection and support the Technology,
Knowledge, and Society knowledge community—its
journals, book series, and online community.
The journal focuses on the roles of technologies in
community formation, maintenance, and change. It
examines communities of practice and knowledge-
creating communities; technologies for participatory
citizenship; issues of identity, disability and access;
and technical and social systems of sustainability.
Journal of Technologies in Society
is a peer-
reviewed scholarly journal.
ISSN 2381-9251
  • Article
    Depression has been identified as the single largest contributor to poor health and functioning worldwide. Global estimates indicate that 4.4% of the world's population lives with depression, equating to about 322 million individuals. Research demonstrates that telehealth interventions (i.e. delivering therapy by phone or videoconferencing) have potential for improving mental health care among community‐based older adults. This review analyses scholarly literature on telehealth interventions among older adults with depressive symptoms. Following PRISMA guidelines, a systematic search of peer‐reviewed papers was conducted using the following key terms: telemedicine, telepsychogeriatrics, telepsychiatry, eHealth, mental health, depression, and geriatric. The review included nine articles examining telehealth for mental health care, published in English between 1946 and 26 September 2017. Telehealth for mental health care among older adults demonstrates a significant impact on health outcomes, including reduced emergency visits, hospital admissions, and depressive symptoms, as well as improved cognitive functioning. Positive or negative influences on the use of telehealth among older adults are identified. This review highlights keys aspects to consider in using telehealth interventions, including levels of education, cognitive function, and prior technology experience. The review highlights vital factors for designing interventions which aim to capitalize on the benefits of the use of telehealth for mental healthcare service delivery, especially in older adults with depressive symptoms.
  • Article
    The Youth-Mental Health Engagement Network study explored the use of mobile technology to support youth in managing their mental health. The Lawson SMART record (LSR) is a web-based application that allows individuals to create and manage an electronic personal health record (ePHR). Through this record, individuals can store their personal health information (e.g., list of medications, family history, immunization records, allergies, care provider contact information, care plans, and crisis plans) and use interactive tools to manage their health care (e.g., mood monitor, reminder prompts, alerts, and mental health assessments). The purpose of this study was to evaluate the usability and acceptability of using an ePHR in providing mental health care to youth with mental illnesses who were experiencing depressive symptoms, from the perspectives of both the patients and their care providers. This 6-month study employed an exploratory descriptive design. Mixed methods research methodology was used to determine the uptake of the LSR as part of their mental health treatment by youth experiencing depressive symptoms. Participants included 41 patients, aged 16 to 21, receiving outpatient services from one of 9 care providers employed within a hospital-based youth mental health care program. Focus groups were held with participating care providers and patients throughout the study to explore youth and care provider perceptions of using the LSR for mental health care management. In addition, both patients and care providers completed surveys to elaborate on their use of and comfort with the technology. This study found that the majority of patients used a phone (90.6%) and computer (70.7%) daily and felt extremely comfortable using cell phones (71.9%) and computers (58.5%). Of the 41 patients, 9 (22%) had their smartphone lost, stolen, broken, or given away. Qualitative analysis of focus group discussions with patients and care providers revealed 6 themes: 1) extent of LSR use was related to the severity of illness and treatment intensity; 2) symptom tracking was the most common use of the LSR; 3) patients reported increased self-awareness and autonomy with LSR use; 4) use of the LSR changed and had the potential to enhance the therapeutic relationship; 5) use of the LSR was a good fit with Cognitive Behavioural Therapy and Dialectical Behaviour Therapy treatment requirements; and 6) the LSR should be personalized and simplified. In a population of youth with mental illnesses who were experiencing depressive symptoms, the LSR was found to be useful and to fit well with standard treatment practices. However, youth would prefer a simpler tool. Future research should evaluate the clinical- and cost-effectiveness of using an ePHR in a larger sample of youth with mixed clinical presentations and over a longer period of time. This additional research would be valuable in understanding the usability and acceptability of smart technologies as an integral part of usual mental health care for youth. 2016
  • Article
    Full-text available
    This research study introduces, delivers, and evaluates the benefits of using web and mobile technology to provide consistent supportive health care to individuals living within London, Ontario and the surrounding area who have been diagnosed with a mental illness. This longitudinal, mixed method study consists of 400 (245 men and 155 women) individuals who have been diagnosed with either a mood or a psychotic disorder who are currently working with mental health care professionals (54 mental health care providers across 4 agencies). The participants will have access to the Lawson SMART record, a web-based application that provides individuals with a personal health record, and tools to help them manage their health. Participants will access the Lawson SMART record using an iPhone 4S. Based on preliminary findings, client participants are generally comfortable with the use of technology. Most indicated that they were either extremely comfortable (26.3%) or slightly comfortable (20.3%) with technology generally, while only a minority said that they were either slightly uncomfortable (4.0%) or extremely uncomfortable (5.8%). It is hypothesized that the use of smart technologies in the treatment of mood and psychotic disorders will improve quality of life while reducing health care costs through a decrease in hospitalizations and hospital room visits. The Mental Health Engagement Network: Connecting Clients with their Health Team project was presented at IARIA-Smart 2012 Conference by Dr. Cheryl Forchuk in May 2012 [1]. As a result of the presentation, this paper was developed. ©Copyright by authors, Published under agreement with IARIA.
  • Article
    Background: Canadian mental health care reform calls for new service delivery models that capitalize on health promotion, support and early intervention as patients and services are transitioning from institutions to communities. The Mental Health Engagement Network (MHEN) intervention is a smart technology enabled service delivery model that electronically links individuals to their health care professionals, promoting information sharing between individuals and their health care professionals, and promoting access to mental health care services. This project, funded by Canada Health Infoway, began in September 2011 and will complete in March 2013. Methods: The MHEN project is a longitudinal, mixed qualitative and quantitative research study which has recruited 400 (245 men and 155 women) research participants diagnosed with a mood or a psychotic disorder who are currently working with community based mental health care professionals (54 mental health care professionals across 4 agencies in the London and surrounding area). Each participant has been randomly assigned into Group 1 (early intervention) or Group 2 (later intervention). Group 1 participants received an iPhone 4S, a TELUS health space™ account, and version 1.0 of the Lawson SMART record (a web-based application that provides individuals with a personal health record and tools to help them manage their health) in July, 2012. Participants in Group 2 initially acted as a control group, and received the version 2 intervention in March, 2013. Results: Participants felt the Lawson SMART record was quite (33.1%) or extremely (29.2%) helpful, and gave participants quite a bit more (26.8%) and an extreme amount more (21%) independence. Web analytics demonstrated that participants visited the Lawson SMART record mobile and desktop home page a total of 16, 928 times. Conclusion: This new service delivery model has the potential to provide quality care to those living in the community with mental illness, enhance health status and quality of life, and reduce the burden of mental illness on the healthcare system by decreasing more costly service uses.
  • Article
    Full-text available
    The objective of this study was to estimate the health and economic impact of major mental illnesses in Canada, beginning in 2011 and annually over the next three decades. For the purposes of this study the major mental illnesses included: mood disorders, anxiety disorders, schizophrenia, substance use disorders, ADHD, conduct disorders, ODD and cognitive impairment including dementia. Using RiskAnalytica’s Life at Risk simulation platform, measures of incidence, prevalence and mortality were simulated for the total Canadian population (ages 9 and over) to project the impact of major mental illnesses over a 30-year time horizon. The outcomes, assuming a steady-state prevalence were then linked to estimates of health service use and workplace productivity to forecast the economic impact of mental illness now and into the future.
  • Article
    Objective: The aim of this review was to investigate to what extent information technology may support self-management among service users with psychotic disorders. The investigation aimed to answer the following questions: What types of e-mental health self-management interventions have been developed and evaluated? What is the current evidence on clinical outcome and cost-effectiveness of the identified interventions? To what extent are e-mental health self-management interventions oriented toward the service user? Methods: A systematic review of references through July 2012 derived from MEDLINE, PsycINFO, AMED, CINAHL, and the Library, Information Science and Technology database was performed. Studies of e-mental health self-management interventions for persons with psychotic disorders were selected independently by three reviewers. Results: Twenty-eight studies met the inclusion criteria. E-mental health self-management interventions included psychoeducation, medication management, communication and shared decision making, management of daily functioning, lifestyle management, peer support, and real-time self-monitoring by daily measurements (experience sampling monitoring). Summary effect sizes were large for medication management (.92) and small for psychoeducation (.37) and communication and shared decision making (.21). For all other studies, individual effect sizes were calculated. The only economic analysis conducted reported more short-term costs for the e-mental health intervention. Conclusions: People with psychotic disorders were able and willing to use e-mental health services. Results suggest that e-mental health services are at least as effective as usual care or nontechnological approaches. Larger effects were found for medication management e-mental health services. No studies reported a negative effect. Results must be interpreted cautiously, because they are based on a small number of studies.
  • Article
    Achieving and sustaining good blood pressure control continues to be a challenge for many reasons including nonadherence with prescribed treatment and lifestyle measures, shortage of primary care physicians especially in less populated areas, and variations in physicians' practice behaviour. Many strategies have been advocated to improve outcomes with the greatest success being achieved using nurse or pharmacist-led interventions in which they were given the authority to prescribe or alter antihypertensive treatment. However, this treatment approach, which historically involved 1-on-1 visits to a doctor's office or pharmacy, proved costly, was not scalable, and did not actively engage patients in treatment decision-making. Several electronic health interventions have been designed to overcome these limitations. Though more patient-centred and often effective, they required wired connections and a personal computer, and logging on for Internet access and navigating computer screens greatly reduced access for many older patients. Furthermore, it is unclear whether the benefits were related to better case management or technological advances. Mobile health (mHealth) technology circumvents the technical challenges of electronic health systems and provides a more flexible platform to enhance patient self-care. mHealth applications are particularly appropriate for interventions that depend on patients' sustained adherence to monitoring schedules and prescribed treatments. Studies from our group in hypertension and other chronic conditions have shown improved health outcomes using mHealth applications that have undergone rigourous usability testing. Nonetheless, the inability of most electronic medical record systems to receive and process information from mobile devices continues to be a major impediment in realizing the full potential of mHealth technology.
  • Article
    Full-text available
    Type 2 diabetes is a leading cause of death and morbidity and is a health priority in Australia. This randomised controlled trial will explore whether remote access to clinical care, supported by telehealth technologies over high speed broadband, leads to improved diabetes control in a way that benefits patients, carers and clinicians and improves the overall health system. People in the intervention arm of the trial will receive additional diabetes care from a care coordinator nurse via an in-home broadband communication device that can capture clinical measures, provide regular health assessments and videoconference with other health professionals when required. Patients in the control arm of the trial will receive usual care from their GP and participate in the clinical measurement and quality of life components of the evaluation. The trial evaluation will include biomedical, psychological, self-management and quality of life measures. Data on utilisation rates and satisfaction with the technology will be collected and cost -effectiveness analyses undertaken. The role of this technology in health care reform will be explored.
  • Article
    Full-text available
    People with mental disorders evince excess mortality due to natural and unnatural deaths. The relative life expectancy of people with mental disorders is a proxy measure of effectiveness of social policy and health service provision. To evaluate trends in health outcomes of people with serious mental disorders. We examined nationwide 5-year consecutive cohorts of people admitted to hospital for mental disorders in Denmark, Finland and Sweden in 1987-2006. In each country the risk population was identified from hospital discharge registers and mortality data were retrieved from cause-of-death registers. The main outcome measure was life expectancy at age 15 years. People admitted to hospital for a mental disorder had a two- to threefold higher mortality than the general population in all three countries studied. This gap in life expectancy was more pronounced for men than for women. The gap decreased between 1987 and 2006 in these countries, especially for women. The notable exception was Swedish men with mental disorders. In spite of the positive general trend, men with mental disorders still live 20 years less, and women 15 years less, than the general population. During the era of deinstitutionalisation the life expectancy gap for people with mental disorders has somewhat diminished in the three Nordic countries. Our results support further development of the Nordic welfare state model, i.e. tax-funded community-based public services and social protection. Health promotion actions, improved access to healthcare and prevention of suicides and violence are needed to further reduce the life expectancy gap.
  • Article
    Although growing concern has been expressed about premature medical mortality in persons with mental illness, limited data are available quantifying the extent and correlates of this problem using population-based, nationally representative samples. The study used data from the 1989 National Health Interview Survey mental health supplement, with mortality data through 2006 linked through the National Death Index (80,850 participants, 16,435 deaths). Multivariable models adjusting for demographic factors assessed the increased hazard of mortality adding socioeconomic status, healthcare variables, clinical factors first separately, and then together. Persons with mental disorders died an average of 8.2 years younger than the rest of the population (P < 0.001). Adjusting for demographic factors, presence of a mental illness was associated with a significant risk of excess mortality, (hazard ratio=2.06, 95% confidence interval=1.71-2.40), with 95.4% of deaths owing to medical rather than unnatural causes. Adding socioeconomic variables to the model, the hazard ratio was 1.77 (P < 0.001); adding health system factors, it was 1.80 (P < 0.001)); adding baseline clinical characteristics, the hazard ratio was 1.32 (P < 0.001). After adding all the 3 groups of variables simultaneously, the association was reduced by 82% from baseline and became statistically nonsignificant (hazard ratio=1.19, P=0.053). The results of the study underscore the complex causes and high burden of medical mortality among persons with mental disorders in the United States. Efforts to address this public health problem will need to address the socioeconomic, healthcare, and clinical risk factors that underlie it.