=Paper=
{{Paper
|id=Vol-2730/paper9
|storemode=property
|title=Nudging chronic disease management for empowering citizens: the CHANGE project
|pdfUrl=https://ceur-ws.org/Vol-2730/paper9.pdf
|volume=Vol-2730
|authors=Giada Pietrabissa,Vanessa Bertuzzi,Emanuele Maria Giusti,Roberto Cattivelli,Gianluca Castelnuovo
|dblpUrl=https://dblp.org/rec/conf/psychobit/PietrabissaBGCC20
}}
==Nudging chronic disease management for empowering citizens: the CHANGE project==
https://ceur-ws.org/Vol-2730/paper9.pdf
nudging CHronic disease mANaGemEnt for empowering
citizens: the CHANGE project
Giada Pietrabissa1,2[0000-0002-5911-5748], Vanessa Bertuzzi2[0000-0002-8541-5357], Emanuele Ma-
ria Giusti1,2[0000-0001-5767-8785], Roberto Cattivelli1,2[0000-0002-5995-5456], and Gianluca Castel-
nuovo1,2 [0000-0003-2633-9822]
1 Istituto Auxologico Italiano IRCCS, Psychology Research Laboratory, Milan, Italy
2 Department of Psychology, Catholic University of Milan, Italy
giada.pietrabissa@unicatt.it
Abstract
Objective: Chronic diseases (CDs) are major causes of deaths, disabilities and healthcare ex-
penditure worldwide. Interventions aimed to prevent or mitigate the impact of CDs need to be
added to the traditional healthcare methods. The main purpose of the CHANGE project is the
development and validation of a new Nudge theory-based Information and Communications
Technology (ICT) coach system for monitoring and empowering patients with CDs. Methods: A
randomized controlled clinical trial involving 200 patients with CDs will be implemented. Online
assessment of demographic, psychological, neuropsychological, and behavioral outcomes will be
carried out through the users’ device (smartwatches). A machine learning algorithm-based profile
will elaborate specific nudge-based notifications, and suggestions will be returned to par- ticipants
via the CHANGE App. Expected results: real-time monitoring and tutoring will pre-
vent/decelerate the worsening of clinical conditions and will improve the physical and psycho-
social health of patients with CDs. Moreover, the provision of tailored care actions will contribute
to a more sustainable healthcare system.
Keywords: Chronic Diseases; Nudge Theory; Big Data; Machine Learning; Clinical Psychol-
ogy
1. Background
1.1. The global burden of chronic diseases
Chronic diseases (CDs), including cardiovascular diseases (CVD), obesity, cancer, di-
abetes and respiratory diseases, are collectively responsible for almost 70% of all deaths
worldwide [1]. Characteristic of CDs is to be long lasting and generally slow in pro-
gression. They, therefore, require a long period of supervision, observation or treat-
ment, but are not curable [2]. As a result, CDs are widely associated with increased
burden of disability, related economic losses, and healthcare expenditure [3].
Research also consistently demonstrates the link between CDs and mental health [1].
Data from the World Health Surveys indicate that people with two or more CDs are
seven times more likely to display symptoms of depression than their counterpart [4].
While mental health problems usually occur in people with CDs, there is also strong
evidence that existing emotional impairments increase the risk for the onset of a range
1
Copyright © 2020 for this paper by its authors. Use permitted under Creative Commons License Attribution
4.0 International (CC BY 4.0)
2
of chronic conditions [5]. In fact, enduring stress has a direct impact on the cardiovas-
cular, nervous and immune systems, thus leading to increased vulnerability to a range
of diseases [6, 7]
Common causes and manifestations of CDs have been identified and studied, and these
conditions have shown to be highly preventable and modifiable [8]. However,
healthcare systems around the world are meant to deal with acute episodic care, and
interventions aimed to prevent/mitigate the effects of CDs are still scant [9].
1.2. Self-regulation and disease management
According to the World Health Organization (WHO), self-management is a key ele-
ment of CDs management [10]. It involves the person with CDs “engaging in activities
that protect and promote health, monitoring and managing the symptoms and signs of
illness, managing the impact of illness on functioning, emotions and interpersonal re-
lationships and adhering to treatment regimens” [11]. Lorig and Holman [12] as-
sumed that self-management is also about “enabling participants to make informed
choices, to adapt new perspectives and generic skills that can be applied to new prob-
lems as they arise, to practice new health behaviors, and to maintain or regain emo-
tional stability. In chronic medical conditions, this process involves management tasks
– management, role management, and emotional management as well as essential man-
agement skills - problem solving, decision making, resource utilization, the formation
of a patient–provider partnership, action planning, and self-tailoring” [12].
If this systematic learning and change process is successful, individuals (re-)gain their
ability to manage their lives without external professional support services, with con-
sequent increase of their self-efficacy and quality of life [13-15].
1.3. Collaborative - individualized stepped care approach in response to chronic
diseases
Systematic reviews have found that multidisciplinary - collaborative care programs can
improve the quality of care and improve patients’ outcomes [16]. Collaborative care is
consistent with the dimensions of the chronic care model (CCM) [17], which postulates
that four elements are desirable for an effective management of chronic conditions,
including patient’s self-management and decision support (clinical practice guidelines,
clinicians education), delivery system redesign (planned visits, case management, pri-
mary care team), and clinical information systems (registries, reminders, clinicians
feedback) [18]. Patient’s self-management support is aimed to empower individuals to
control their own health and their ability to access healthcare. Instead, decision support,
delivery system redesign and clinical information systems strategies are meant to re-
form care and care teams to better meet patients’ needs [19]. Research on the imple-
mentation of CCM in clinical practice demonstrated its capability to improve health
outcomes [20-22].
3
However, healthcare actions are still often inadequately organized and delivered, and a
fundamental problem in organizing the care of patients with CDs remains the provision
of the right level of support [23].
In this regards, the use of an individualized stepped care approach, defined by Donovan
and Marlatt [24] as “the least costly, least intensive, and least restrictive treatment
judged sufficient to meet the person's needs and goals should be attempted initially
before more costly and restrictive treatments are attempted” [24], has been supported
for many CDs, including hypertension [25], obesity and diabetes [26], pain [27-29], and
depression [30]. Stepped care is based on three assumptions: 1) different people require
different levels of care; 2) finding the right level of care depends on monitoring out-
comes; and 3) moving from lower to higher levels of care based on patient outcomes
often increases effectiveness and lowers costs [31]. The level or intensity of care re-
quired by each patient is selected according to evidence-based guidelines in line with
patient goals, treatment preferences, and clinical status [32].
The last decade has also seen growing popularity and uptake of self-monitoring tech-
nology including wearable devices, Apps and other communication platforms [33], and
the use of machine learning are now likely to revolutionize the delivery of chronic care
[34-36]. These technologies may sustain active self-management in patients with CDs
by facilitating sharing of treatment plans and online health communities, while lever-
aging the time and resources of healthcare professionals [37-39].
1.4. Hyper Nudges and Big Data
The nudge approach has emerged from the behavioral sciences to challenge potential
pitfalls of traditional regulation in public health strategies - such as costly procedures
or ineffective campaigning – so to address modifiable individual-level factors related
to the rise of CDs [40]. Thaler and Sunstein [41] defined a nudge as “a function of
(condition I) any attempt at influencing people’s judgment, choice or behavior in a
predictable way (condition a) that is made possible because of cognitive boundaries,
biases, routines, and habits in individual and social decision-making posing barriers
for people to perform rationally in their own self-declared interests, and which (con-
dition b) works by making use of those boundaries, biases, routines, and habits as
integral parts of such attempts” (20, p. 37). In other words, a nudge is “any aspect of
choice architecture that alters people’s behavior in a predictable way (a) forbidding
or adding any rationally relevant choice options; or (b) changing incentives, whether
regarded in terms of time, trouble, social sanctions, economics, etc.” [41].
Therefore, the nudge theory proposes positive reinforcement and indirect suggestions
as ways to influence the behavior and decision making of groups or individuals with-
out further restricting freedom of choice or imposing mandatory obligations to people
[42]. This framework has demonstrated to be an effective and viable public tool in
encouraging healthier eating choices in adults [43]. By reducing the set of choices,
therefore decreasing the cognitive effort associated with processing information,
Nudges may challenge the needs of people of remembering or doing things, beside
promoting healthier behaviors and an active lifestyle.
The big data concept refers to vast quantities of data created by the adoption of the
Internet and digitization of all sorts of information, including health records. Therefore,
within the machine learning spectrum, big data-driven nudges allow health data man-
agement to be prescriptive, rather than only descriptive [44].
4
2. Main objectives of the CHANGE project
The main objective of the present work is to describe the protocol of the CHANGE
project. It is aimed to provide an innovative, scalable, secure and intelligent system by
1) designing a virtual coach architecture and platform to manage and record selected
parameters through the employment of the big data-machine learning approach, and 2)
by using a smartwatch-based technology in the frame of the nudge theory to enhance self-
monitoring of patients with CDs by providing them with tailored feedbacks and
strategies. This will enhance the cognitive, biomedical, psychological, social, and
behavioral well-being of patients with CDs via self-management.
The clinical efficacy and effectiveness of the new approach will be evaluated in a ran-
domized control trial (RCT) comparing the CHANGE platform (experimental condi-
tion) with standard care (control condition). The costs and resources of the CHANGE
approach will be also assessed.
3. Methods
In order to provide evidence for the effectiveness of the CHANGE project, a multi-
center RCT involving 200 patients with CDs (100 using the CHANGE virtual coach
approach and 100 with standard approach-treatment as usual) will be implemented ac-
cording to the latest CONSORT statements (Consolidated Standards of Reporting Tri-
als - www.consort-statement.org). The CHANGE protocol will be evaluated by the new
model for assessment of telemedicine (MAST- Methodology to assess telemedi- cine
applications - https://ec.europa.eu/digital-single-market/en/news/methotelemed-
framework-methodology-assess-effectiveness-telemedicine-applications-europe).
3.1. Participants
Participants will be recruited and screened for admission into the study from the Istituto
Auxologico Italiano IRCCS1 on a 3-month basis and monitored for the following 12
months. Patients will be included into the study if: 1) aged between 65 and 85 years; 2)
diagnosed with a CDs, 3) have basic knowledge of informatics; 4) will provide written
and informed consent to participate. Exclusion criteria, instead, will be the presence of
severe 1) psychiatric disturbance according to the Diagnostic and Statistical Manual for
Mental Disorder 5th edition (DSM-5); 2) cognitive impairment affecting the partici-
pants’ self-management opportunities and the reliability of self-report data; or 3) med-
ical conditions that prevent from independent daily activities and need continuous sur-
veillance.
3.2. Randomization procedure
1 The Istituto Auxologico Italiano IRCCS is one of the main Italian research sites, with four main
hospitals and many clinical units located in northern Italy.
5
All participants will be randomly assigned to the intervention or control group. The
randomization scheme will be generated by using the Web site Randomization.com:
http://www.randomization.com. Randomization will take place after the baseline meas-
urement.
3.3. Measures
The Structured Clinical Interview for DSM-5 Disorders (SCID) [45] will be used to
screen for the presence of psychiatric disorders. Moreover, a neuropsychological as-
sessment comprising the Mini-Mental State Examination (MMSE) questionnaire [46]
and the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS)
[47, 48] will be administered by two independent psychologists at the beginning of the
rehabilitation program to screen patients for inclusion of into the study.
Moreover, the following selected parameters will be collected data at baseline (paper
and pencil) and at different follow-up points (through the users’ device).
Demographic information: age, gender, education, civil status, and socio-economic sta-
tus (SES).
Medical outcomes: body mass index (BMI), blood pressure, and presence of comorbid-
ities (Charlson comorbidity index - CCI [49]).
Psychological variables: the individuals’ perceived health-related quality of life
(HRQoL) - measured by the SF-36 health survey [50] and psychological status (Symp-
tom Checklist-90-R [51]), the Beck anxiety inventory (BAI) and the Beck depression
Inventory (BDI) [52], and the Paykel Scale of Stressful life events [53]. The users’
general perceived self-efficacy (GSE) [54] will be also registered, together with their
sleep quality (Pittsburgh sleep Quality index - PSQI [55]). Patients’ engagement and
motivation to change will be also evaluated via the Patients Health Engagement (PHE)
scale [56] and the University of Rhode Island Change and Assessment Scale (IT-
URICA) [57], respectively. The Multidimensional Scale of Perceived Social Support
(MSPSS) will be further administered [58]; and the users’ satisfaction in managing
technological devices and platform assessed using the Telemedicine Satisfaction Ques-
tionnaire (TSQ) [59].
Behavioral outcomes: the patients’ self-reported lifestyle (Self-Report Habit Index -
SRHI [60]), adherence to healthy diet (Mediterranean Diet Scale - MDS), and physical
activity level (Recent Physical Activity Questionnaire – RPAQ [61]). Moreover, ac-
cording to the International Classification of Functioning, Disability and Health (ICF)
[62], the individuals basic instrumental activities of daily living will be assessed using
the WHO Disability Assessment Schedule 2.0 (WHO-DAS 2.0) [63].
A “health score” will be elaborated by integrating target behaviors.
Cost-effectiveness of the CHANGE project will be evaluated considering its direct and
indirect cost savings.
Procedure
The users will install the CHANGE App in their smartwatch and will access to the
CHANGE on-line service.
6
Devices (smartwatch or smartphone) will use two different networks: a local wireless
network (Bluetooth LE) to dialogue with local sensors and measuring instruments, and
a 3G/4G network to send and receive data from the remote back-end server.
Different signals will be collected from the user's device and delivered by the CHANGE
App to the remote backend server, whose software will 1) analyze the data in order to
elaborate a machine learning algorithm able to identify a given pattern, 2) alert the cli-
nicians in case of problems. The system will then transmit a real-time "nudge action"
driven by the CHANGE App to the user (Figure 1). In other words, to help patients with
CDs to improve their health and social outcomes, the personalized virtual coach will
elaborate a customized profile (built on the machine learning theory-based algorithm)
providing the users with specific nudge-based advices. The CHANGE App will be self-
adaptive to the patient information technology (IT) skills and level of engagement.
The security of the communications will be guaranteed by the protocols used (Secure
Pairing and Connections for BTLE and HTTPS for app-to-backend communications),
while data privacy will be ensured by encrypted communications channels and the use
of encrypted/obfuscated databases.
Figure 1. CHANGE Project IT architecture - Retrieved from Pietrabissa G. et al., 2019 [40]
3.4. Sample size and Statistical analysis
According to a-priori power analysis (G*Power 3.1.3 was used for calculation), 200
participants are required to detect a small interaction effect of treatment × time on out-
comes with a 90% statistical power. Two-tailed t-tests for continuous variables and
Fisher's exact tests for binary variables will be used for descriptive purposes. Treatment
effects (treatment × time interaction effects) as well as moderating and mediating ef-
fects will be tested with the General Linear Model. Repeated-measure factorial AN-
COVAs will be used for each outcome. Critical alpha for the treatment effects will be
set to 0.01 in order to limit the inflation of the type I error rate due to multiple tests,
while no adjustment will be applied to alpha=0.05 for the statistical testing of the other
effects. Further analysis will be performed using advanced informatics approaches to
detect the most useful sorting algorithms to understand and address chronic conditions.
4. Expected results and conclusion
Continuous data integration through predictive analytic methods will improve
knowledge of CDs and its determinants. Moreover, real-time monitoring and tutoring
(experimental condition) are expected to prevent or decelerate the worsening of clinical
conditions of patients with CDs – compared to the control condition. Decreased
hospitalizations, and improved high-quality care will follow. Increased pa- tient-
7
provider interactions and direct involvement of patients with CDs in decision- making
processes and virtual communities will also enhance individuals’ engagement,
motivation to change and perceived self-efficacy. In addition, high-quality and
personalized care will contribute to a more sustainable healthcare system.
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