Barriers to and Facilitators of the Implementation of Digital Mental Health Interventions as Perceived by Primary Care Decision Makers: Content Analysis of Structured Open-Ended Survey Data

Abstract

Background:
Digital mental health represents a way to increase access to evidence-based psychological support. However, the implementation of digital mental health in routine health care practice is limited, with few studies focusing on implementation. Accordingly, there is a need to better understand the barriers to and facilitators of implementing digital mental health. Existing studies have mainly focused on the viewpoints of patients and health professionals. Currently, there are few studies about barriers and facilitators from the perspective of primary care decision makers, that is, the persons responsible for deciding whether a given digital mental health intervention should be implemented in a primary care organization.

Objective:
The objectives were to identify and describe barriers to and facilitators of the implementation of digital mental health as perceived by primary care decision makers, evaluate the relative importance of different barriers and facilitators, and compare barriers and facilitators reported by primary care decision makers who have versus have not implemented digital mental health interventions.

Methods:
A web-based self-report survey was conducted with primary care decision makers responsible for the implementation of digital mental health in primary care organizations in Sweden. Answers to 2 open-ended questions about barriers and facilitators were analyzed through summative and deductive content analysis.

Results:
The survey was completed by 284 primary care decision makers-59 (20.8%) decision makers representing implementers (ie, organizations that offered digital mental health interventions) and 225 (79.2%) respondents representing nonimplementers (ie, organizations that did not offer digital mental health interventions). Overall, 90% (53/59) of the implementers and 98.7% (222/225) of the nonimplementers identified barriers, and 97% (57/59) of the implementers and 93.3% (210/225) of the nonimplementers identified facilitators. Altogether, 29 barriers and 20 facilitators of implementation were identified related to guidelines; patients; health professionals; incentives and resources; capacity for organizational change; and social, political, and legal factors. The most prevalent barriers were related to incentives and resources, whereas the most prevalent facilitators were related to the capacity for organizational change.

Conclusions:
A number of barriers and facilitators were identified that could influence the implementation of digital mental health from the perspective of primary care decision makers. Implementers and nonimplementers identified many common barriers and facilitators, but they differ in terms of certain barriers and facilitators. Common and differing barriers and facilitators identified by implementers and nonimplementers may be important to address when planning for the implementation of digital mental health interventions. For instance, financial incentives and disincentives (eg, increased costs) are the most frequently mentioned barrier and facilitator, respectively, by nonimplementers, but not by implementers. One way to facilitate implementation could be to provide more information to nonimplementers about the actual costs related to the implementation of digital mental health.

Full text

Original Paper
Barriers to and Facilitators of the Implementation of Digital Mental
Health Interventions as Perceived by Primary Care Decision
Makers:Content Analysis of Structured Open-Ended Survey Data
Anders Brantnell1,2, PhD; Serdar Temiz3, PhD; Enrico Baraldi3, PhD; Joanne Woodford2, PhD; Louise von Essen2,
PhD
1Division of Industrial Engineering and Management, Department of Civil and Industrial Engineering, Uppsala University, Uppsala, Sweden
2Healthcare Sciences and e-Health, Department of Women’s and Children’s Health, Uppsala University, Uppsala, Sweden
3Division of Industrial Engineering and Management, Department of Civil and Industrial Engineering, Uppsala University, Uppsala, Sweden
Corresponding Author:
Anders Brantnell, PhD
Division of Industrial Engineering and Management
Department of Civil and Industrial Engineering
Uppsala University
Box 169
Uppsala, 751 04
Sweden
Phone: 46 729999825
Email: anders.brantnell@angstrom.uu.se
Abstract
Background: Digital mental health represents a way to increase access to evidence-based psychological support. However, the
implementation of digital mental health in routine health care practice is limited, with few studies focusing on implementation.
Accordingly, there is a need to better understand the barriers to and facilitators of implementing digital mental health. Existing
studies have mainly focused on the viewpoints of patients and health professionals. Currently, there are few studies about barriers
and facilitators from the perspective of primary care decision makers, that is, the persons responsible for deciding whether a given
digital mental health intervention should be implemented in a primary care organization.
Objective: The objectives were to identify and describe barriers to and facilitators of the implementation of digital mental health
as perceived by primary care decision makers, evaluate the relative importance of different barriers and facilitators, and compare
barriers and facilitators reported by primary care decision makers who have versus have not implemented digital mental health
interventions.
Methods: A web-based self-report survey was conducted with primary care decision makers responsible for the implementation
of digital mental health in primary care organizations in Sweden. Answers to 2 open-ended questions about barriers and facilitators
were analyzed through summative and deductive content analysis.
Results: The survey was completed by 284 primary care decision makers—59 (20.8%) decision makers representing implementers
(ie, organizations that offered digital mental health interventions) and 225 (79.2%) respondents representing nonimplementers
(ie, organizations that did not offer digital mental health interventions). Overall, 90% (53/59) of the implementers and 98.7%
(222/225) of the nonimplementers identified barriers, and 97% (57/59) of the implementers and 93.3% (210/225) of the
nonimplementers identified facilitators. Altogether, 29 barriers and 20 facilitators of implementation were identified related to
guidelines; patients; health professionals; incentives and resources; capacity for organizational change; and social, political, and
legal factors. The most prevalent barriers were related to incentives and resources, whereas the most prevalent facilitators were
related to the capacity for organizational change.
Conclusions: A number of barriers and facilitators were identified that could influence the implementation of digital mental
health from the perspective of primary care decision makers. Implementers and nonimplementers identified many common barriers
and facilitators, but they differ in terms of certain barriers and facilitators. Common and differing barriers and facilitators identified
by implementers and nonimplementers may be important to address when planning for the implementation of digital mental
health interventions. For instance, financial incentives and disincentives (eg, increased costs) are the most frequently mentioned
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barrier and facilitator, respectively, by nonimplementers, but not by implementers. One way to facilitate implementation could
be to provide more information to nonimplementers about the actual costs related to the implementation of digital mental health.
(JMIR Hum Factors 2023;10:e44688) doi: 10.2196/44688
KEYWORDS
digital mental health; implementation; barriers; facilitators; internet-based cognitive behavioral therapy; survey; decision makers
Introduction
Background
Common mental health problems, such as depression and
anxiety, represent substantial global health challenges [1].
Depression is estimated to be the third-leading cause of disability
globally [2], and approximately 29% of all people will be
affected by an anxiety disorder during their lifetime [3].
Cognitive behavioral therapy (CBT) delivered face to face is a
common and effective treatment for depression and anxiety [4].
However, face-to-face treatments require large organizational
resources and visits to health care providers’ offices. Digital
mental health represents a way to improve access to care [5]
and decrease care costs [6]. Digital mental health can be defined
as mental health services and interventions delivered through
the internet, telephone, or connected technologies [7].
Internet-administered CBT (ICBT) is a form of digital mental
health and has been shown to be as effective as face-to-face
CBT for the treatment of depression and anxiety [4]. However,
although there is a growing body of research showing the
efficacy [8] and cost-effectiveness [9] of ICBT for common
mental disorders such as depression and anxiety [10-12], studies
of the implementation of ICBT in routine health care practice
are limited [13].
To enable implementation and increase access to digital mental
health, there is a need to understand aspects that may influence
the implementation of digital mental health interventions, that
is, barriers to and facilitators of implementation. Studies of the
implementation of digital mental health are relatively scarce
[13], and only a few reviews have identified barriers to and
facilitators of implementation [14-18]. By focusing only on the
views of patients and health professionals, existing studies have
identified barriers such as negative attitudes toward digital
mental health [14-16], the lack of suitability of digital mental
health for various mental health problems [14-16], low computer
literacy [15-17], the lack of training for health professionals
[17], and existing infrastructure [17]. Some identified facilitators
include training for health professionals [14,16], mild symptoms
[14,16], and ease of use [14]. A recent theoretical overview of
digital mental health interventions [18] identified barriers such
as privacy and security concerns, usability issues from patients’
point of view, patients’ knowledge and skills, and clinicians’
skills and capabilities. One qualitative study exploring mental
health professionals’ perspectives about digital mental health
implementation identified barriers, such as negative attitudes
of clinicians, existing infrastructure, and “one solution does not
fit all,” and facilitators, such as the packaging solutions [19].
Furthermore, continued implementation is also a challenge,
with a recent review identifying 131 empirical studies of the
rapid deployment of digital mental health interventions as a
response to the COVID-19 pandemic, with several barriers
identified regarding long-term sustainability [20].
Given the few studies focusing on the implementation of digital
mental health, it is reasonable to look broadly into digital health
implementation. Existing studies in the area have identified
several factors that could hinder or facilitate the implementation
of interventions. For example, a review studied the factors
influencing the adoption of digital applications by health care
professionals and identified 101 studies exploring barriers to
and facilitators of implementation [21]. Some of the most
frequent facilitators of implementation were the usefulness of
the innovation and compatibility, whereas some of the most
frequent barriers were related to the lack of knowledge among
health care professionals and the lack of compatibility [21].
A review of studies (n=16) of the implementation of digital
technologies to support patients with amyotrophic lateral
sclerosis identified several facilitators of implementation, such
as positive attitudes of health care professionals and the training
of health care professionals, and barriers, such as negative
attitudes of health care professionals and feasibility [22].
Another review of the barriers to the use of digital health by
older adults identified 57 studies detailing barriers, with the
most frequent barriers being the lack of interest and cost of use
[23]. A review focusing on digital health for self-management
of hypertension included studies (n=14) that identified barriers
to and facilitators of implementation [24]. Some of the most
frequent facilitators were access to technology, patient
knowledge, and ease of use. In contrast, some of the most
frequent barriers were the lack of evidence and added workload
[24].
However, none of the digital mental health or digital health
reviews identify barriers and facilitators experienced by health
care decision makers, that is, the professionals who take the
decision to implement or disregard new solutions. Although not
included in reviews, there are some qualitative studies that have
explored the barriers and facilitators experienced by health care
decision makers. A recent qualitative study in Sweden explored
policy makers’ views (ie, those who formulate rules and
regulations regarding digital health at the regional level, such
as politicians) about barriers to and facilitators of the
implementation of digital health [25]. Some identified barriers
included uncertainty about the impact of digital health on health
professionals and the lack of resources for digital health,
whereas facilitators included citizens’preferences and a strong
societal push for digital health [25]. Another qualitative study
focusing on barriers to the implementation of digital mental
health in the United Kingdom, as experienced by health decision
makers (health commissioners), identified barriers such as the
lack of decision-maker knowledge about the technology, digital
literacy among users and decision makers, high risk of investing
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in digital mental health, funding issues, and digital interventions
not being suitable for all patients [26]. In addition, our previous
findings from a web-based cross-sectional quantitative survey
about barriers to and facilitators of the implementation of ICBT
experienced by primary care decision makers identified a
number of barriers to and facilitators of implementation.
However, the quantitative survey focused on comparing barriers
and facilitators between implementers and nonimplementers
but did not capture frequency and thus decision makers’
preferences [27].
Objectives
The objectives of this study were (1) to identify and describe
barriers to and facilitators of the implementation of digital
mental health as perceived by primary care decision makers;
(2) to evaluate the relative importance of the barriers and
facilitators; and (3) to compare the barriers and facilitators
between primary care decision makers who have implemented
versus have not implemented digital mental health.
Methods
Study Design
A web-based self-report survey was conducted between
February 2016 and May 2016 with decision makers responsible
for the implementation of digital mental health in primary care
organizations in Sweden. The survey focused on the
implementation of ICBT for depression and anxiety disorders.
Answers to the structured open-ended questions in the survey
are reported in this paper. Results from the rest of the survey
have been reported elsewhere [27].
Setting
Sweden was one of the first countries to conduct research on
ICBT for depression and anxiety [28]. Swedish national clinical
guidelines recommend that CBT and ICBT be provided to adults
with mild and moderate levels of depression and anxiety [29].
However, the implementation of ICBT is still in its infancy [30].
Sweden is divided into 21 geographically spread regions that
are responsible for health care provision. Each region has several
private and public primary care organizations that are publicly
funded and thus operate under the same conditions, for instance,
in terms of financial resources and adherence to national
guidelines. Primary care is the first point of care for patients
with mental health problems. The size of the primary care
organizations varies in terms of listed patients ranging from
3000 to 30,000. In addition to publicly funded primary care
organizations, there are private companies specialized in digital
mental health.
Primary care organizations are able to access ICBT through
three means: (1) contracting a private company to deliver digital
mental health, including support; (2) procuring ICBT program
licenses from companies and providing support by themselves;
or (3) connecting to the Platform for Support and Care run by
the Swedish Association of Local Authorities. Through the
Platform for Support and Care, primary care organizations can
access ICBT programs developed by private companies or other
organizations. There is a cost for connecting to the Platform for
Support and Care and for purchasing the treatment programs
with or without therapist support. There is also a cost to patients.
In the Stockholm Region (one of the 21 regions in Sweden), a
web-based meeting with therapist support costs approximately
€25 (US $26.7), and costs >€130 (US $138.8) for a patient
during the same year will be covered by the public insurance.
Furthermore, for patients, it is possible to access ICBT through
a private company; for instance, a company charges €75 (US
$80.1) for the first meeting and, subsequently, €75 (US $80.1)
per week.
As a response to the COVID-19 pandemic, there has been a
rapid increase of digital health solutions globally [20]. Available
data from 10 regions (including many of the large regions in
Sweden) show that 1781 treatments for digital mental health
started in 2019 and 4573 started in 2022, when the pandemic
had passed, and indicate a modest increase in the provision of
digital mental health treatments since the COVID-19 pandemic
(for details, refer to Multimedia Appendix 1 [30-32]). Available
data do not cover all regions, and according to estimations,
17,800 digital mental health treatments started in 2021 [31].
However, when compared with the number of registered cases
of major depression (670,980/5,397,675, 12.43%) and anxiety
disorders (536,279/5,397,675, 9.94%) in the first Primary Care
Registry in Sweden [33] (common mental health conditions
that may be treated with ICBT), the number of digital mental
health treatments started still appears to be very low.
Recruitment and Study Procedures
Study participants were directors of Swedish primary care
organizations. A list of 1156 primary care organizations was
compiled, and an invitation was sent to all decision makers.
Invitations were initially sent through regular mail and were
followed up via telephone and emails. Invitations included a
letter explaining the survey and information needed to
participate, link to the survey, participation number, and
password. Participants who completed the survey provided
informed consent through the survey platform (SurveyMonkey
[Momentive Global Inc]). No incentives were offered for survey
completion. Participants who did not complete the survey within
2 weeks received up to 2 telephone reminders and 1 email.
Details about the study procedures are reported elsewhere [27].
The Survey
Answers to the 2 open-ended questions in the survey are
reported in this study. The following questions were posed:
1. According to your understanding, what are the most
important factors that hinder the introduction of ICBT
programs? Please indicate a maximum of 5 factors.
2. According to your understanding, what are the most
important factors that facilitate introduction of ICBT
programs? Please indicate a maximum of 5 factors.
Questions were posed at the end of the survey and were preceded
by 37 Likert-scale questions about barriers and facilitators (for
details about the survey, refer to the paper by Brantnell et al
[27]).
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Data Analysis
Data analysis follows summative content analysis, which is a
suitable approach to analyze large amounts of open-ended
survey data [34]. As there is an abundance of studies of barriers
to and facilitators of the implementation of health care
interventions, deductive content analysis complemented the
summative content analysis [35] guided by the comprehensive
integrated checklist of determinants of practice (the Tailored
Implementation in Chronic Diseases [TICD] checklist) [36].
The TICD checklist [36] divides barriers and facilitators into
each of the seven domains: (1) guidelines; (2) health
professionals; (3) patients; (4) professional interaction; (5)
incentives and resources; (6) capacity for organizational change;
and (7) social, political, and legal factors. The checklist is based
on a rigorous review of existing studies of barriers to and
facilitators of implementation [36] and provides a good basis
for identifying barriers to and facilitators of implementation.
The survey that was reported by Brantnell et al [27] adjusted
the TICD checklist according to the Swedish conditions and
the Likert-scale approach of the survey questions and thus
originated from 5 domains. With open-ended structured data,
there was no need to adjust the original TICD checklist because
the domains, barriers, and facilitators that would be irrelevant
would not be included in the analysis. The analysis was
conducted in 6 steps.
First, following a summative content analysis approach [34],
data were divided into four small blocks administered through
separate Microsoft Excel files: (1) barriers mentioned by
implementers (ie, decision makers of organizations that had
implemented ICBT); (2) barriers mentioned by nonimplementers
(ie, decision makers of organizations that had not implemented
ICBT); (3) facilitators mentioned by implementers; and (4)
facilitators mentioned by nonimplementers. Second, the
Leximancer software was used to identify the most frequent
words in each of the 4 data blocks. Subsequently, Excel files
were searched for each of the frequent words. To identify
possible synonyms for each word, a web-based database,
Synonymer.se [37] was used, and the words were added in the
search. When applicable, some area-specific synonyms that
were not identified by Synonymer.se [37] were added. For
example, synonyms to “staff” were “therapist,” “speech
therapist,” “psychologist,” “the one treating patients”
(behandlare in Swedish), and “medical doctor.”
Third, all hits in the Excel files were marked, and frequent words
and phrases were copy-pasted into a Microsoft Word file. The
frequency of the copy-pasted words and phrases was recorded.
At this stage, no interpretation of data was conducted, but similar
words and phrases were combined into large units. Fourth, the
words and phrases were translated into English to try to maintain
the Swedish phrasing while also capturing the essence of the
words and phrases. Fifth, following deductive thematic analysis
[35], two researchers independently placed the words and
phrases into the TICD checklist under suitable domains (eg,
guidelines) and barriers and facilitators (eg, the accessibility of
the intervention). Sometimes, the respondents did not provide
the direction of the barrier and facilitator. An example of this
could be “health professional interest.” In such cases, we
modified the word or phrase to include the direction such as
“health professionals not interested” (if provided as an answer
regarding barriers) or “health professionals interested” (if
provided as an answer regarding facilitators). There are some
overlaps in the TICD checklist, and thus, the 2 researchers
placed some words and phrases under >1 barrier and facilitator.
If words and phrases did not fit with existing barriers and
facilitators, new barriers and facilitators were created and
integrated into the checklist. To increase the credibility of the
deductive analysis, coding was conducted by 2 independent
coders, which is a recommended procedure when using an
existing checklist or framework [38].
An internal workshop was conducted to compare and discuss
the outcomes from the deductive analysis. All disagreements
were solved through discussion during the workshop. A decision
was made to place all words and phrases that lacked a subject
(ie, the actor experiencing the barrier or facilitator) such as
“leadership” under leadership barriers and facilitators because
it was the decision makers who answered the survey. In many
cases, respondents provided the subject such as patients or health
professionals, and thus, when the subject was missing, a
reasonable conclusion was that the words and phrases referred
to leadership. While placing words and phrases into the TICD
checklist, their frequencies were recorded. All words and phrases
mentioned by at least 2 participants were included. Throughout
the process, all the authors were involved in discussing and
following up on the analysis to increase rigor and trustworthiness
[39]. Finally, following the deductive content analysis, the
frequencies of each barrier and facilitator were summarized
using descriptive statistics, and a comparison between
implementers and nonimplementers was conducted. The number
of barriers and facilitators was counted for implementers and
nonimplementers by adding all words and phrases relating to
specific barriers and facilitators.
Ethics Approval
The study was performed in accordance with the Swedish ethical
law and the Declaration of Helsinki. The ethical review board
of Sweden, Uppsala, approved the study (application number
2015/461).
Results
Respondents
A total of 1156 survey invitations were sent, of which 1130
(97.8%) were shown to be eligible. Noneligible answers that
were excluded were duplicate answers (13/26, 50%), bankruptcy
or closed down (10/26, 38%), and not a primary care
organization (3/26, 12%). A total of 284 decision makers
answered the 2 open-ended survey questions.
Characteristics of the Decision Makers
Most decision makers (277/284, 97.5%) were health care center
directors or chief executive officers. The 3 most frequent
professions of decision makers were nurse (154/284, 54.2%),
general practitioner (59/284, 20.8%), and physiotherapist
(20/284, 7%). Among the respondent organizations, 20.8%
(59/284) provided ICBT and were thus implementers. Overall,
63% (179/284) of the decision makers represented public
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organizations and the remaining represented private
organizations. Both types of organizations were publicly funded.
Barriers to and Facilitators of Implementation of ICBT
Overview
Altogether, 59 implementers responded, of which 57 (97%)
listed the facilitators of implementation and 53 (90%) listed the
barriers to implementation. In contrast, 225 nonimplementers
responded, of which 210 (93.3%) listed the facilitators of
implementation, whereas 222 (98.7%) listed the barriers to
implementation.
In total, 29 barriers to and 20 facilitators of the implementation
of ICBT were identified (Tables 1 and 2), and these were
grouped within 6 domains based on the TICD checklist
(guidelines; health professionals; patients; incentives and
resources; capacity for organization change; and social, political,
and legal factors). No barriers and facilitators were mentioned
regarding the seventh domain in the TICD checklist, namely,
professional interaction. For detailed outcomes of the summative
and deductive content analysis, refer to Multimedia Appendix
2. All the barriers and facilitators are presented in Tables 1 and
2. The most frequently mentioned barriers were related to
incentives and resources (ie, availability of necessary resources;
14/53, 26%) and capacity for organizational change (ie, capable
leadership; 14/53, 26%; Table 1), whereas the most frequently
mentioned facilitators were related to capacity for organizational
change (ie, assistance for organizational change; 57/210, 27.1%;
Table 2).
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Table 1. Barriers mentioned by implementers and nonimplementers, distributed according to the Tailored Implementation in Chronic Diseases checklist.
Nonimplementers (n=222), n (%)Implementers (n=53), n (%)Domains and barriers
Guidelines
—a
8 (15.1)Compatibility
23 (10.4)—Feasibility
20 (9)—Strength of the recommendation
4 (1.8)2 (3.8)Accessibility of the intervention
2 (0.9)2 (3.8)Quality of evidence supporting the recommendation
4 (1.8)—Effort
2 (0.9)—Clarity
2 (0.9)—Cultural appropriateness
2 (0.9)—Trialability
Health professionals
23 (10.4)10 (18.9)Intention and motivation
2 (0.9)6 (11.3)Nature of the behavior
14 (6.3)—Attitudes
2 (0.9)2 (3.8)Skills needed to adhere
5 (2.2)—Awareness and familiarity with the recommendation
Patients
15 (6.8)4 (7.5)Patient motivation and interest
5 (2.2)3 (5.7)Patient behavior
11 (4.9)2 (3.8)Patient preferences
4 (1.8)—Patient beliefs and knowledge
Incentive and resources
29 (13.1)14 (26.4)Availability of necessary resources
33 (14.9)5 (9.4)Financial incentives and disincentives
15 (6.8)3 (5.7)Information system
11 (4.9)3 (5.7)Availability of supporting infrastructure
Capacity for organizational change
53 (23.9)14 (26.4)Capable leadership
25 (11.3)6 (11.3)Organizational readiness
11 (4.9)5 (9.4)Assistance for organizational change
—2 (3.8)Mandate, authority, and accountability
2 (0.9)—Regulations, rules, and policies
Social, political, and legal factors
18 (8.1)—Health care system
3 (1.4)—Contracts
aNot available.
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Table 2. Facilitators mentioned by implementers and nonimplementers, distributed according to the Tailored Implementation in Chronic Diseases
checklist.
Nonimplementers (n=210), n (%)Implementers (n=57), n (%)Domains and facilitators
Guidelines
36 (17.1)8 (14)Accessibility of the intervention
14 (6.6)5 (8.8)Feasibility
2 (1)4 (7)Strength of the recommendation
—a
3 (5.3)Observability
—3 (5.3)Clarity
3 (1.4)—Compatibility
Health professionals
24 (11.4)11 (19.3)Intention and motivation
Patients
5 (2.4)4 (7)Patient motivation and interest
6 (2.9)2 (3.5)Patient beliefs and knowledge
6 (2.9)—Patient behavior
Incentives and resources
37 (17.6)2 (3.5)Financial incentives and disincentives
9 (4.3)5 (8.8)Availability of necessary resources
12 (5.7)3 (5.3)Information system (people, platform, and technology combined)
2 (1)2 (3.5)Availability of supporting infrastructure
2 (1)2 (3.5)Nonfinancial incentives and disincentives
Capacity for organizational change
57 (27.1)11 (19.3)Assistance for organizational change
44 (21)5 (8.8)Capable leadership
7 (3.3)2 (3.5)Relative strength of supporters and opponents
2 (1)—Mandate, authority, and accountability
Social, political, and legal factors
2 (1)—Health care system
aNot available.
Guidelines
Overall, 9 barriers and 6 facilitators were identified regarding
the guidelines for ICBT interventions. Implementers most often
mentioned the lack of compatibility with existing technology
(8/53, 15%) as a barrier. In contrast, nonimplementers most
often mentioned the feasibility of the intervention (ie, the extent
to which the intervention is practical; 23/222, 10.4%). Of the 9
barriers identified, 2 (the accessibility of the intervention and
quality of evidence supporting the recommendation) were
mentioned by both implementers and nonimplementers. Both
implementers (8/57, 14%) and nonimplementers (36/210, 17.1%)
most often mentioned the accessibility of the intervention as a
facilitator. Of the 6 facilitators identified, 3 (the accessibility
of the intervention, feasibility, and strength of recommendation)
were mentioned by both implementers and nonimplementers.
Health Professionals
Overall, 5 barriers and 1 facilitator were identified related to
health professionals. Implementers (10/53, 19%) and
nonimplementers (23/222, 10.4%) most often mentioned the
lack of intention and motivation as barriers. Of the 5 barriers
identified, 3 (intention and motivation, the nature of the
behavior, and skills needed to adhere) were mentioned by both
implementers and nonimplementers. Both implementers and
nonimplementers mentioned only 1 facilitator—intention and
motivation (11/57, 19% for implementers and 24/210, 11.4%
for nonimplementers).
Patients
Overall, 4 barriers and 3 facilitators were identified related to
patients. Implementers (4/53, 8%) and nonimplementers (15/222,
6.8%) most often mentioned the barrier, lack of patient
motivation and interest. Of the 4 barriers identified, 3 (patient
motivation and interest, patient behavior, and patient
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preferences) were mentioned by both implementers and
nonimplementers. The most frequently mentioned facilitator
by implementers was patient motivation and interest (4/57, 7%).
In contrast, the most frequently mentioned facilitator by
nonimplementers were patient beliefs and knowledge (6/210,
2.9%) and patient behavior (6/210, 2.9%). Of the 3 facilitators
identified, 2 (patient motivation and interest and patient beliefs
and knowledge) were mentioned by both implementers and
nonimplementers.
Incentives and Resources
Overall, 4 barriers and 5 facilitators were identified regarding
incentives and resources. Implementers most often mentioned
the barrier, the availability of necessary resources (14/53, 26%).
In contrast, nonimplementers most often mentioned the barriers,
financial incentives and disincentives (33/222, 14.9%) and the
information system (33/222, 14.9%). All the 4 barriers identified
were mentioned by both implementers and nonimplementers.
Implementers most often mentioned the facilitator, the
availability of necessary resources (5/57, 9%), whereas
nonimplementers most often mentioned the facilitator, financial
incentives and disincentives (37/210, 17.6%). All the 5
facilitators identified were mentioned by both implementers
and nonimplementers.
Capacity for Organizational Change
Overall, 6 barriers and 5 facilitators were identified regarding
capacity for organizational change. Both implementers and
nonimplementers most often mentioned the barrier, capable
leadership (ie, leadership interest and knowledge; 14/53, 26%
for implementers and 53/222, 23.9% for nonimplementers). Of
the 5 barriers identified, 3 (capable leadership, organizational
readiness, and assistance for organizational change) were
mentioned by both implementers and nonimplementers.
Organizational readiness was not part of the TICD checklist but
originated from the summative content analysis and was added
to the checklist. Implementers (11/57, 19%) and
nonimplementers (57/210, 27.1%) most often mentioned the
facilitator, assistance for organizational change. Of the 4
facilitators identified, 3 (assistance for organizational change,
capable leadership, and relative strength of supporters and
opponents) were mentioned by both implementers and
nonimplementers.
Social, Political, and Legal Factors
Overall, 2 barriers and 1 facilitator were mentioned by
nonimplementers related to social, political, and legal factors.
The barrier that was most often mentioned was the health care
system (18/210, 8.6%). The health care system was not part of
the TICD checklist but was added based on the summative
content analysis. The only facilitator was the health care system
(2/210, 0.9%).
Discussion
Principal Findings
A total of 284 decision makers participated in the survey and
provided answers to 2 open-ended questions. The majority of
respondents (277/284, 97.5%) were health care center directors
or chief executive officers. The 3 most common professions
among the decision makers were nurses (154/284, 54.2%),
general practitioners (59/284, 20.8%), and physiotherapists
(20/284, 7%). Out of all the organizations represented, 20.8%
(59/284) offered ICBT and were labeled as implementers.
Among the implementers, 90% (53/59) identified barriers to
implementation, while 97% (57/59) listed facilitators of
implementation. On the other hand, among the nonimplementers,
98.7% (222/225) listed barriers to implementation and 93.3%
(210/225) listed facilitators of implementation. In total, 29
barriers to and 20 facilitators of implementing ICBT were
identified.
Findings identified barriers to and facilitators of the
implementation of digital mental health related to 6 domains in
the TICD checklist: guidelines; health professionals; patients;
incentives and resources; capacity for organizational change;
and social, political, and legal factors. First, we conducted
summative content analysis based on the responses. During this
phase, we were able to capture barriers and facilitators, as
expressed by the respondents. Second, we connected the
responses with the TICD checklist [36]. No barriers or
facilitators were identified related to the TICD checklist domain,
professional interaction. In addition, we identified 3 new barriers
and facilitators that were added to the TICD checklist: the
availability of supporting infrastructure (domain: incentives and
resources), organizational readiness (domain: capacity for
organizational change), and the health care system (domain:
social, political, and legal factors).
Findings show that the most frequently mentioned barriers
related to availability of necessary resources (14/53, 26%) and
capable leadership (14/53, 26%), whereas the most frequently
mentioned facilitators related to assistance for organizational
change (57/210, 27.1%). Existing studies of the implementation
of digital mental health [26] and digital health [25] interventions
focusing on decision makers imply that the availability of
necessary resources is an important barrier to implementation.
This barrier (availability of necessary resources) is further
supported by existing studies of policy makers’use of evidence
[39] and barriers to implementation related to third-sector actors
providing health care [40]. Our findings align with a review
focusing on health professionals’views that identified important
barriers and facilitators related to organizations, systems, and
health professionals including assistance for organizational
change [16]. However, our findings also suggest that assistance
for organizational change also relates to decision makers.
Education and support, which are important components of
organizational change (for details, refer to the codes in
Multimedia Appendix 2—under the column heading Words and
phrases mentioned by respondents, grouped as Assistance for
organizational change), have been identified as barriers in
existing studies focusing on implementation facilitators for
third-sector actors providing health care [40] and studies of
health policy makers’use of evidence [41].
Assistance for organizational change and capable leadership
are closely related, and thus, it is unsurprising that capable
leadership is one of the most frequently mentioned barriers to
implementation. That is, if managers are not trained and
educated, they will not be able to support implementation.
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Increasing knowledge requires an implementation strategy [42],
and in general, implementation strategies are reported to have
between 4% and 10% effect sizes in changing behavior [43].
Furthermore, the lack of knowledge could be dependent on other
factors such as attitudes and outcome expectations, and thus,
addressing these barriers is also needed [42]. Therefore, it is
not likely that COVID-19 and similar disruptions could wipe
out complex and sizable barriers to implementation, and thus,
a reasonable assumption is that many barriers are persistent and
require structured implementation efforts rather than sudden
external pressure. These assumptions are also supported in
existing studies of barriers to sustain digital mental health
interventions after the COVID-19 pandemic [20].
Comparing our findings with those of existing studies of
frequent barriers and facilitators, we underscore 3 important
findings. First, existing studies of digital mental health have
identified very few barriers and facilitators related to guidelines
or the therapy in itself, such as the strength of the
recommendation (ie, 1 solution does not fit all) [19,26].
However, the frequently mentioned guideline-related barriers
and facilitators in our findings, the lack of compatibility [21],
feasibility [22,44], and the accessibility of the intervention [24],
are well established in existing studies of digital health
implementation. Second, existing studies of digital mental health
[26] and digital health [25] with focus on decision makers do
not raise the importance of health professionals’ intention and
motivation as both barriers to and facilitators of implementation.
In our findings, this barrier and facilitator was the most
frequently mentioned related to health professionals, and it is
also identified in existing studies of digital mental health [19]
and digital health [22]. Third, patient’s motivation and interest
are identified as important barriers to and facilitators of
implementation in existing studies of digital health
implementation [23,24], but they are not very prevalent among
our findings regarding the implementation of digital mental
health from the perspective of decision makers.
Implementers and nonimplementers identified a number of
similar barriers and facilitators relating to 4 (health
professionals, patients, incentives and resources, and capacity
for organizational change) of the 6 domains. Most similarities
were identified in relation to incentives and resources. However,
there were differences in how frequently these barriers were
mentioned. Implementers report availability of necessary
resources as the most frequent barrier and facilitator, whereas
these are not the most frequently reported by nonimplementers.
These findings imply that the implementation of digital mental
interventions is not dependent on available resources, albeit
may be hindered by lack of them. This, in turn, could be
encouraging for nonimplementers that lack the necessary
resources to invest in digital mental health. One way to facilitate
implementation could be to communicate to nonimplementers,
especially persons with budgetary responsibilities, that
maintaining implementation requires additional resources.
Whether maintaining implementation requires extra resources
is an empirical question for further studies.
Financial incentives and disincentives are the barriers and
facilitators most frequently mentioned by nonimplementers but
not by implementers, which implies that nonimplementers
perceive structural hinders for implementation related to
financial aspects such as the reimbursement system and
increased costs. In contrast, implementers do not perceive the
financial incentives and disincentives as highly problematic,
which, in turn, could be motivating for nonimplementers that
assume these to be sizable barriers to implementation. One way
to facilitate implementation could be to provide more
information to nonimplementers regarding the actual costs
related to the implementation of digital mental health. Whether
the benefits of digital mental health interventions are related to
financial incentives or other aspects such as improved care
warrants further studies.
The most obvious difference between implementers and
nonimplementers was found in barriers and facilitators related
to guidelines. The 2 barriers most frequently mentioned by
nonimplementers related to guidelines are the feasibility and
strength of recommendation, whereas these are not mentioned
by implementers. Whether these 2 barriers are real barriers based
on experience or only based on assumptions is unclear; however,
overcoming these barriers, for instance, through education could
improve the possibilities for implementation, and thus, it could
be beneficial to educate nonimplementers regarding the
feasibility and strength of the recommendation. Implementers
most frequently mention compatibility as a barrier related to
guidelines, whereas nonimplementers do not mention this
barrier, which implies that implementers perceive that there is
not an optimal fit between the digital mental health intervention
and existing work practices. This type of barrier is difficult to
overcome because it is at the core of the intervention, that is,
starting to use the intervention requires work with computers
and thus requires a more complex implementation strategy
targeting possible barriers such as digital literacy, attitudes
toward digital mental health, and adaptation of existing work
routines to accommodate the provision of digital mental health.
Limitations
Our study has some limitations. First, we collected structured,
open-ended data using a survey, which is not an optimal way
to gain an in-depth understanding of the barriers and facilitators
because no follow-up questions can be posed. However, we
followed a well-structured and rigorous analysis process that
should be able to provide a good overview of barriers to and
facilitators of ICBT implementation in Sweden from the
perspective of decision makers. Second, we collected data from
1 country. Sweden has publicly funded health care, with good
access to care. There could be certain contextual differences
between different health care systems, but some barriers and
facilitators such as capable leadership could apply to several
contexts and digital mental health more generally. Whether
capable leadership and the other identified barriers and
facilitators also apply to other health care systems and
technologies is an empirical question for further studies.
Third, our data were collected in 2016. However, these data are
still relevant for several reasons: neither the intervention nor
the context has changed substantially since 2016. Swedish
primary care organizations can still access ICBT in several
ways, and it is often the primary care director who can make
the decision regarding whether to offer ICBT. We acknowledge
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that during the past years, the technology has probably matured
and could have become less costly and more easily available.
However, it is unlikely that disruptions such as COVID-19 and
technology advancements have overcome the complex and
sizable barriers to implementation that were identified, and thus,
a reasonable assumption is that many barriers are persistent and
require structured implementation efforts. For instance, it is
unlikely that there has been substantial increase in available
resources for digital mental health, and although regions in
Sweden are investing in digital innovations, mental health has
not been their priority [45]. Similarly, leadership and public
health workforce capacity building requires structured and
complex implementation efforts [46].
Moreover, despite expectations of a massive increase of digital
mental health treatments after the COVID-19 pandemic, the
number of treatments in Sweden was still modest during the
COVID-19 pandemic (refer to the Methods section), which
makes the identified barriers and facilitators relevant. One reason
for the relatively low numbers of digital mental health treatments
provided during the COVID-19 pandemic could be the Swedish
government’s decision to not use lockdown measures, meaning
that health care was still provided face to face [47,48]. However,
we do not present any data about digital mental health provision
in other comparable countries, such as Denmark and Norway,
which have similar public health systems but adopted different
COVID-19 responses [48]. Thus, how and whether COVID-19
influenced digital mental health provision in these countries is
an empirical question for further studies.
Fourth, the 2 open-ended questions were posed at the end of
the survey, after asking 37 questions to be answered on a Likert
scale, which could risk priming the responses. However, we
deem this risk to be low because the abundance of Likert-scale
questions would rather provide information overload than clear
advice about possible barriers and facilitators for a respondent
who has not considered them beforehand.
Conclusions
Globally, the COVID-19 pandemic resulted in rapid deployment
of various digital health solutions such as telehealth and
videoconferencing to provide continued care despite distancing
requirements. However, given the complex nature of digital
mental health solution implementation, it is not probable that
implementation based on sudden external pressure will be
maintained. So far, few studies have examined the barriers to
and facilitators of the implementation of digital mental health,
and even fewer studies have examined the perspectives of
decision makers. In this study, we report about various barriers
and facilitators related to guidelines; health professionals;
patients; incentives and resources; capacity for organization
change; and social, political, and legal factors. Commonly
reported barriers, by both implementers and nonimplementers,
related to incentives and resources, whereas common facilitators
were related to capacity for organizational change, and most
differences were identified in relation to guidelines.
Understanding similarities and differences can provide advice
to future implementers of digital health regarding barriers and
facilitators to take into consideration and inform the
development of implementation strategies.
Acknowledgments
The authors thank all decision makers who participated in the study. The authors thank several colleagues for their input. This
study was supported by Uppsala University Psychosocial Care Programme (U-CARE), which is a strategic research environment
funded by the Swedish Research Council (dnr 2009–1093). The funder did not contribute to the design or conduct of the study
and did not participate in the analysis of the results.
Conflicts of Interest
None declared.
Multimedia Appendix 1
Started digital mental health treatments during 2018 and 2022. The file illustrates the number of started digital mental health
treatments during 2018 and 2022. Data in the file are based on these studies [30,31,33].
[DOCX File , 110 KB-Multimedia Appendix 1]
Multimedia Appendix 2
Outcomes of summative content analysis and deductive analysis based on the Tailored Implementation in Chronic Diseases
checklist. The file describes in detail the outcomes of the 2 coding procedures, listing the words and phrases, barriers, facilitators,
and domains.
[DOCX File , 28 KB-Multimedia Appendix 2]
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Abbreviations
CBT: cognitive behavioral therapy
ICBT: internet-administered cognitive behavioral therapy
TICD: Tailored Implementation in Chronic Diseases
Edited by A Kushniruk; submitted 29.11.22; peer-reviewed by J Long, A Bucher; comments to author 11.02.23; revised version received
05.04.23; accepted 14.05.23; published 26.06.23
Please cite as:
Brantnell A, Temiz S, Baraldi E, Woodford J, von Essen L
Barriers to and Facilitators of the Implementation of Digital Mental Health Interventions as Perceived by Primary Care Decision
Makers: Content Analysis of Structured Open-Ended Survey Data
JMIR Hum Factors 2023;10:e44688
URL: https://humanfactors.jmir.org/2023/1/e44688
doi: 10.2196/44688
PMID:
©Anders Brantnell, Serdar Temiz, Enrico Baraldi, Joanne Woodford, Louise von Essen. Originally published in JMIR Human
Factors (https://humanfactors.jmir.org), 26.06.2023. This is an open-access article distributed under the terms of the Creative
Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and
reproduction in any medium, provided the original work, first published in JMIR Human Factors, is properly cited. The complete
bibliographic information, a link to the original publication on https://humanfactors.jmir.org, as well as this copyright and license
information must be included.
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