STAR-C (Sustainable Behaviour Change for Health Supported by Person-Tailored, Adaptive, Risk-aware Digital Coaching in a Social Context) Digital Coaching Intervention
Phase: NA
Conditions: Healthy Lifestyle
Sponsor: Umeå University

 
 
Title: 
STAR-C (Sustainable behaviour change for health supported by 
person-Tailored, Adaptive, Risk-aware digital Coaching in a 
social context) Digital Coaching Intervention 
 
Short Title 
STAR-C Digital Coaching Intervention 
 
Protocol Date: 
May 13, 2022 
 
Amendment Date 1: 
May 13, 2022 
 
Amendment Date 2: 
September 30, 2022 
 
Amendment Date 3: 
October 31, 2022 
 
Amendment Date 4: 
November 17, 2022 
 
Amendment Date 5: 
January 5, 2023 
Amendment Date 6: 
January 20, 2023 
Amendment Date 7: 
March 21, 2023 
Amendment Date 8: 
June 5, 2025 
 
Study Principal Investigator 
Patrik Wennberg, Umeå University, patrik.wennberg@umu.se 
 
Co-PIs 
Kristina Lindvall, Umeå University, kristina.lindvall@umu.se 
Nawi Ng, Umeå University, nawi.ng@umu.se 
 
Forskningshuvudman 
Umeå University 

2 
 
 
2 
TABLE OF CONTENTS 
Table of Contents 
2 
Abbreviations and Definitions of Terms 
4 
Abstract 
1 
1. 
BACKGROUND INFORMATION AND RATIONALE 
2 
1.1. 
Introduction 
2 
1.2. 
Name and Description of Intervention 
2 
1.3. 
Relevant Literature and Data 
2 
2. 
STUDY OBJECTIVES 
4 
2.1. 
Overall objective 
4 
2.2. 
Specific objectives 
4 
3. 
DESIGN, IMPLEMENTATION, AND EVALUATION OF THE STAR-C INTERVENTION 
5 
3.1. 
Intervention Plan 
5 
3.1.1. 
Study Design 
5 
3.1.1.1. 
Screening Phase 
5 
3.1.1.2. 
Baseline Phase 
5 
3.1.1.3. 
Follow-up Phase 
6 
3.1.1.4. 
Cost-effectiveness assessment 
6 
3.1.2. 
Allocation to Treatment Groups and Blinding 
7 
3.1.3. 
Study Duration, Enrolment and Number of Sites 
7 
3.1.3.1. 
Duration of Study Participation 
7 
3.1.3.2. 
Sample size and recruitment of participants 
8 
3.1.4. 
Study Population 
8 
3.1.4.1. 
Inclusion Criteria 
8 
3.1.4.2. 
Exclusion Criteria 
8 
3.2. 
Intervention Tools 
9 
3.2.1. 
Development of the STAR-C person-tailored coaching application 
9 
3.2.2. 
The functionality of the STAR-C digital application 
9 
3.2.2.1. 
General functionalities 
9 
3.2.2.2. 
STAR-C application with person-tailored coaching for the intervention group 
9 
3.2.2.3. 
STAR-C application without person-tailored coaching for the control group 
10 
3.2.3. 
Data collection using the STAR-C application 
10 
3.3. 
Study Evaluations and Measurements 
11 
3.3.1. 
Primary Endpoint 
11 
3.3.2. 
Secondary Endpoints 
11 
3.4. 
Data analysis 
11 

3 
 
 
3 
3.4.1. 
Baseline Data 
11 
3.4.2. 
Sub-study 1: Effectiveness Analysis 
12 
3.4.3. 
Sub-study 2: Cost-Effectiveness Analysis 
12 
4. 
IMPLEMENTATION RESEARCH ON THE STAR-C INTERVENTION 
13 
4.1. 
Sub-study 3: The lived experience of using the STAR-C application 
13 
4.2. 
Sub-study 4: Role of digital tool on behavioural change 
14 
4.3. 
Sub-study 5: Healthcare professionals’ experiences of using digital tool 
15 
4.4. 
Sub-study 6: The role and effectiveness of behavioural change techniques 
16 
5. 
STUDY ADMINISTRATION 
16 
5.1. 
Data Collection and Management 
16 
5.2. 
Confidentiality 
16 
5.3. 
Ethical considerations 
16 
5.3.1. 
Risk Assessment 
16 
5.3.2. 
Potential Benefits of Trial Participation 
17 
5.3.3. 
Risk-Benefit Assessment 
17 
6. 
Appendix 
18 
7. 
References 
19 

4 
 
 
4 
 
 
ABBREVIATIONS AND DEFINITIONS OF TERMS 
 
CVD 
Cardiovascular Diseases 
FGD 
Focus Group Discussion 
STAR-C Sustainable behaviour changes for health supported by person-Tailored, 
Adaptive, Risk-aware digital Coaching in a social context 
VIP 
Västerbotten Intervention Programme 

1 
 
 
ABSTRACT 
 Context: The growth of digital and health information technologies provides an 
opportunity to tailor and personalise health promotion messages to promote behavioural 
change for the primary prevention of chronic diseases. STAR-C digital coaching 
intervention aims to increase the efficiency and outreach of the Västerbotten 
Intervention Programme (VIP) in Northern Sweden, one of the few long-term 
cardiovascular diseases (CVD) prevention programmes globally integrated into routine 
primary healthcare. 
 Objectives: The overall objective is to assess the effectiveness of the STAR-C digital 
coaching intervention for health-related lifestyle behavioural change. More specifically, 
the study aims to: (1) analyse the effectiveness of the digital tool in enhancing readiness 
for behavioural change (primary outcome) and adoption and maintenance of healthier 
behaviours, self-rated health and well-being, and use of the digital tool (secondary 
outcomes); (2) understand the barriers and facilitators for the adoption and maintenance 
of utilising the STAR-C digital coaching intervention among health professionals and the 
adult population. 
 Study Design: This intervention is designed as a two-arm individual pragmatic 
one-sided crossover randomised controlled trial, with participants receiving immediate 
access to the digital coaching intervention (Arm 1) and no digital coaching intervention 
(Arm 2, delayed access three months). 
 Setting/Participants: The intervention is integrated into the Västerbotten Intervention 
Programme (VIP) in Västerbotten Region. In total, we will recruit 1000 participants from 
all VIP-participating primary healthcare centres in Västerbotten, proportional to the size 
of the VIP participants in each centre. All participants will receive regular VIP 
intervention with the VIP nurse before recruitment into the STAR-C intervention study. 
The intervention is planned to run for 6 months, with in-person recruitment at the 
baseline and follow-up at 1, 3, and 6 months. We exclude individuals who are 
bedridden, terminally ill or have severe communication problems and those who receive 
behavioural change treatment at the Beteendemedicin clinic. 
 Study Interventions and Measures: The primary outcome of the intervention is a 
change in the readiness for behaviour change. The secondary outcomes include: (i) 
actual changes in the behaviours, including use of tobacco cessation clinics, higher 
smoking and snus cessation rate, reduction in alcohol consumption, adoption of healthy 
food habits, increased level of physical activity and reduction of sedentary behaviours, 
self-rated health and well-being, comparing baseline and follow-up data; (ii) patterns 
and usage of the digital tool during the intervention period (for the intervention group). 
These outcomes will be measured quantitatively during the three follow-up periods 
using questionnaires. In addition, interviews and group interviews will be conducted to 
explore the barriers and facilitators for the adoption and maintenance of the STAR-C 
digital coaching intervention for VIP nurses and the adult population. 

2 
 
 
1. BACKGROUND INFORMATION AND RATIONALE 
1.1. Introduction 
As one of the very few long-term cardiovascular diseases (CVD) prevention programmes 
globally that is integrated into routine primary healthcare, the Västerbotten Intervention 
Programme (VIP) has been shown as a cost-effective intervention which combines low-risk 
population and high-risk individual approaches to reduce overall and CVD-specific 
premature mortality significantly (Blomstedt et al., 2015). However, little is known about how 
digital tools could be used to amplify the effect of VIP. The growth of digital and health 
information technologies provides an opportunity to tailor and personalise health promotion 
in the VIP while simultaneously widening its outreach to the population. STAR-C programme 
is designed to develop and evaluate a technical platform for personalised digital coaching to 
support behavioural changes to improve health (Ng et al., 2021). 
1.2. Name and Description of Intervention 
STAR-C Digital Coaching Intervention 
1.3. Relevant Literature and Data 
To understand individuals' propensity to engage in behaviour change, our work will be 
guided by the Health Belief Model and the Transtheoretical Model of Change. We use 
the Diffusion of Innovations theory to explore how digital technology is being utilised in 
promoting behaviour change at the individual level. 
The research field of health behaviour and behaviour change offers models and frameworks 
to explain and predict the mechanisms determining health behaviour (Davis et al., 2015). 
The Health Belief Model focuses on individuals’ health attitudes and beliefs that predict 
health behaviours (Edberg, 2015). The model comprises six elements: (a) self-efficacy - an 
individual’s belief in her own ability to take action; (b) a person’s perceived susceptibility to a 
given health problem; (c) the perceived severity of the health problem; (d) the perceived 
benefits of engaging in efforts to avoid this problem; (e) perceived barriers to engaging in 
those efforts; and (f) cues to action, or external incidents motivating action. The 
Transtheoretical Model of Change is a biopsychosocial, integrative model aimed at 
conceptualising intentional behaviour change processes (Prochaska & Velicer, 1997). The 
model recognises change as a process that unfolds over time, involving progress through a 
series of five (temporal) stages reflecting the process of individual change: (a) 
pre-contemplation; (b) contemplation; (c) preparation; (d) action; and (e) maintenance. 
Certain principles and change processes work best at each stage to reduce resistance, 
facilitate progress, and prevent relapse. Those principles include decisional balance, 
self-efficacy, and processes of change. Guidance based on the Transtheoretical Model 
results in increased participation in the change process because it appeals to the whole 
population rather than the minority ready to take action (Prochaska &Velicer, 1997). 
The Diffusion of Innovations Theory offers a systems approach to behavioural change 
and concerns the conditions and processes by which social system members adopt an 
innovation (Rogers, 2003). Innovation is more likely to be adopted if: (a) people perceive it 
as bringing relative advantages to their previous or current practice; (b) it is compatible with 
their existing values, norms and practices; (c) the results can be observed, and (d) it is 
perceived as being understandable and simple to use. The theory outlines five categories of 
‘adopters’ of an innovation: innovators, early adopters, early majority, late majority, and 

3 
 
 
laggards. It recognises that while some people can adopt what they consider an interesting 
innovation, others who would like to do so face internal, external or innovation-related 
barriers and therefore are unable to do so. 
The field of Behaviour Change Systems (BCS) and persuasive technology explores 
behaviour change techniques (BCT) (e.g., goal setting, feedback, personalisation) and 
their links to mechanisms of action (MoA) (e.g., motivation, readiness to change, 
self-efficacy, barriers) (Oinas-Kukkonen & Harjumaa, 2009; Connell et al., 2018). However, 
only a few studies base their system design and evaluation of behaviour change systems on 
theories of behaviour change (Taj et al., 2019). Further research aimed at exploring links 
between BCTs and MoAs is needed. The design of the STAR-C digital intervention is based 
on theories on behaviour change and BCS research (Lindgren et al., 2020; Lindgren & 
Weck, 2022). A theory-based conceptual model of the behaviour change process was 
developed (Lindgren & Weck, 2021) to tailor the intervention and study links between BCTs 
and MoAs and their effects over time. Further, ethical considerations relating to persuasive 
technology and artificial intelligence are considered in the design process, such as 
mechanisms for transparency and control (Lindgren et al., draft). 
To address the gaps of knowledge identified in the field of behaviour change systems, the 
intervention will address and contribute to the following: 
- 
STAR-C will test a digital coaching application that embeds a set of complementary 
behaviour change techniques to tailor the intervention to the individual and explore their 
effects over time on mechanisms of action, in particular, the level of readiness to change 
behaviour. 
- 
STAR-C will explore how participants choose to use the BCTs over time regarding 
changes in the level of readiness to change behaviour and concerning different domains 
of lifestyle changes (physical activity, recovery activity, nutrition, alcohol intake, tobacco 
use). 
- 
STAR-C will evaluate lived experiences of using the behaviour change application, the 
role of the application in influencing health beliefs, readiness for change and adoption of 
behaviour change, and healthcare professionals’ experience of the application as a tool 
for a behaviour change intervention. 
Two systematic reviews conducted with the STAR-C research programme assess the 
cost-effectiveness of digital health interventions for behavioural change (Kway, 2022) and 
medication adherence (Maria, 2022). Kway showed that all studies (n=20) included in the 
systematic review on the cost-effectiveness of digital health intervention for behavioural 
change originated from high-income countries. Most studies used telephone or text 
messaging, mobile health applications and online websites for the intervention. Of nine 
studies where full-scale economic evaluations were conducted, eight studies were identified 
as cost-effective. Six other studies indicated the potential cost-saving from such intervention 
(Kway, 2022). Similarly, Maria reviewed nineteen studies focusing on the cost-effectiveness 
of digital health interventions for medication adherence, and only one study was conducted 
in a low-income setting. The review indicates large heterogeneity in the design and 
outcomes of studies included in the systematic review. Most studies only conducted a partial 
economic assessment through cost-comparison analysis. Hence the results should be 
interpreted carefully (Maria, 2022). In the current STAR-C intervention, we will focus on 
using digital tool intervention to promote behaviour change. 

4 
 
 
To address the knowledge gaps identified in the systematic reviews, the current intervention 
will address and contribute to the following: 
- 
STAR-C will test an integrated approach to behavioural change using a digital tool in a 
real-life primary healthcare setting, which is unique from other interventions identified in 
the systematic reviews. 
- 
The cost-effectiveness analysis in STAR-C will focus on the differences in costs and 
outcomes comparing the intervention groups (i.e. compare against the ongoing VIP 
intervention as received by the control group). 
- 
STAR-C will quantify the outcomes regarding changes in the readiness to change 
behaviour. A unique and novel contribution is methods to translate these outcomes into 
cost evaluation in the economic evaluation. 
 
2. STUDY OBJECTIVES 
2.1. Overall objective 
To evaluate the effectiveness of the STAR-C digital coaching intervention for health-related 
lifestyle behavioural change. 
 
 
2.2. Specific objectives 
The specific objectives are to 
● Investigate to what extent STAR-C digital coaching intervention leads to change in the 
readiness for behavioural change (sub-study 1). 
● Evaluate the effect of the digital intervention on behavioural change, including a visit to 
tobacco cessation clinics, higher smoking and snus cessation rate, reduction in alcohol 
consumption, adoption of healthy food habits, increased level of physical activity and 
reduction of sedentary behaviours (sub-study 1). 
● Evaluate the effect of the digital intervention on self-rated health and well-being 
(sub-study 1). 
● Evaluate the cost-effectiveness of STAR-C digital coaching intervention compared to the 
existing VIP intervention in promoting behavioural change among the adult population 
(sub-study 2). 
● Evaluate barriers and facilitators for adopting and maintaining the utilisation of STAR-C 
digital coaching intervention among the adult population (sub-study 3) and health 
professionals (sub-study 5). 
● Evaluate barriers and facilitators for adopting and maintaining health-related lifestyle 
behavioural change among the adult population (sub-study 4). 
● Analyse the different patterns of digital tool usage that could explain the effectiveness of 
the different behaviour change techniques embedded in the intervention and their 
relation to the participants and their characteristics (readiness for change, motivation, 
barriers, etc.), observed across age groups, gender, education level and geographical 
area of residence (sub-study 6). 

5 
 
 
 

6 
 
 
3. DESIGN, IMPLEMENTATION, AND EVALUATION OF THE STAR-C 
INTERVENTION 
3.1. Intervention Plan 
3.1.1. Study Design 
This intervention is designed as a two-arm individual pragmatic one-sided crossover 
randomised controlled trial conducted within the ongoing Västerbotten Intervention 
Programme (VIP) in Västerbotten Region in Sweden. VIP is integrated into primary care 
and invites inhabitants in Region Västerbotten to a health survey, including a 
comprehensive questionnaire and a systematic screening for cardiovascular risk factors, 
followed by a single individual health counselling with a trained nurse every ten years, at 
ages 40, 50, and 60 years. During the counselling, a “star profile” is used to illustrate the 
interrelationships between risk markers and behaviours and options for health promotion 
(Norberg et al., 2010). Recently, a digital platform has been developed to support 
counselling, including a digital form, a decision support system, and a star profile. This 
digital platform is now being implemented and offered at all 38 healthcare centres, 
allowing the participant to provide the basis for a health consultation via the digital health 
form on 1177.se. The compilation of form responses and health examination results in 
the support system reduces the healthcare staff's time spent on oral questions, 
compilation, calculation, documentation, remittance, and follow-up. Instead, the VHU 
nurse can focus on the dialogue with the participant and contribute with various methods 
that support changing unhealthy lifestyles, focusing on assessing the examination and 
counselling results. 
In the STAR-C intervention study, all participants will receive the regular VIP intervention 
(health survey and individual health counselling) before recruitment into the STAR-C 
intervention study. See Appendix 1 for the study flowchart and steps in the intervention. 
In intervention arm 1 (intervention arm), the participant will receive immediate access 
to digital coaching with personalisation. 
In intervention arm 2 (control arm), the participant will receive no digital coaching 
immediately but delayed access at three months. 
 
 
3.1.1.1. Screening Phase 
We will identify the intervention participants among those participating in the VIP. The 
VIP nurse will conduct the screening among VIP participants. If a participant fulfils the 
eligibility criteria, the participant will be invited to participate in the STAR-C digital 
coaching intervention. As recruitment occurs after the VIP participation, all participants 
will receive the standard VIP health dialogue with the nurse. See Section 3.1.4.1 for the 
inclusion criteria. 
3.1.1.2. Baseline Phase 
Upon receiving the participant's verbal consent, the VIP staff will help them download 
and install the STAR-C application on their mobile phones (by scanning a QR code or a 
link to a web page for instructions on how to download the software) or give instructions 
on how to install the application on a later occasion. All participants will receive the 

7 
 
 
application, with different functionalities according to their allocation to the intervention 
arms. Upon registration and login to the application, the participants will obtain detailed 
information about the intervention, and they have to provide their digital consent to 
participate. See Appendix 2 for the Informed Consent form. Finally, the nurse will make 
notes in the log book about the recruitment of the participants (simply by sticking the 
barcode label to the intervention register). 
Upon consent, the participants will need to complete a baseline assessment through a 
digital questionnaire embedded in the application (See Appendix 3 for information 
collected in the baseline). In the baseline questionnaire, we will also ask if the VIP 
participants must come back for another visit at three months (usually for those with high 
blood pressure, blood glucose, or blood cholesterol). The participant can complete the 
assessment at their convenient time after the VIP visit. A few reminders will be sent, 
after which a research assistant will conduct the baseline assessment through a 
telephone interview. Relevant information collected in the VIP questionnaire will be 
linked to the baseline data later. 
After completing the baseline assessment, the application will automatically randomise 
the participants into one of the intervention arms. We will use block randomisation to 
ensure a balanced number of participants recruited into the different arms in each 
intervention site. Participants in Arm 1 will have direct access to digital coaching 
applications. See Section 3.2 for the description of the digital coaching application and 
its functionality. Participants in Arm 2 will receive delayed access to the coaching 
functionalities of the application after three months. The application will show participants 
in the control group a page with information about the study and a list of follow-up 
checkpoints, which will be accessible when they reach each checkpoint, and a 
countdown that shows the number of days until they receive access to the full 
application. 
3.1.1.3. Follow-up Phase 
The follow-up phase will continue for six months, with follow-ups at one, three, and six 
months. The application will send notifications two weeks before the follow-up time (a 
window period of plus and minus two weeks) for the participants to fill in an online 
follow-up questionnaire as in the baseline. Three reminders will be sent to the 
participants. 
In addition to the quantitative data, we will also conduct qualitative studies to understand 
the usage of digital coaching apps and the extent to which the use of the digital tool has 
influenced behaviour change among the study participants. The consent form will 
contain a question where the participant can indicate their interest in an individual or 
group interview. Those who indicate interest will be contacted and invited to participate in 
the qualitative study. See interview guides in Appendix 4. 
3.1.1.4. Cost-effectiveness assessment 
As the measure of the effectiveness of the intervention, we will use the primary and 
secondary outcomes described below. Information on the costs of application 
development and implementation of the intervention will be collected throughout the 
programme. We will also use the EQ 5D 5L tool to follow the potential differences 
between the groups. See Appendix 3. 

8 
 
 
3.1.2. Allocation to Treatment Groups and Blinding 
Random allocation to treatment arms: The randomisation of study participants will be 
conducted by the digital system. As we are interested in studying differences in the 
treatment outcomes and digital literacy between population groups, the randomisation 
list will be stratified by gender (male/female) and age group (40, 50 and over). We will 
conduct block randomisation for the study site to ensure an approximately similar 
number of study participants in each treatment arm. An example of a stratified block 
randomisation list for a site that will recruit about 48 participants is as follows. In each 
block, six participants will be recruited and randomly allocated to one of the two research 
arms. 
 
Male, 40 year 
Male, 50 and 60 years 
Female, 40 year 
Female, 50 and 60 years 
Block Sequence Allocation Block Sequence Allocation Block Sequence Allocation Block Sequence Allocation 
1 
1 
Arm B 
3 
1 
Arm B 
5 
1 
Arm B 
7 
1 
Arm A 
1 
2 
Arm A 
3 
2 
Arm A 
5 
2 
Arm A 
7 
2 
Arm B 
1 
3 
Arm B 
3 
3 
Arm A 
5 
3 
Arm B 
7 
3 
Arm A 
1 
4 
Arm A 
3 
4 
Arm A 
5 
4 
Arm A 
7 
4 
Arm B 
1 
5 
Arm B 
3 
5 
Arm B 
5 
5 
Arm B 
7 
5 
Arm A 
1 
6 
Arm A 
3 
6 
Arm B 
5 
6 
Arm A 
7 
6 
Arm B 
2 
1 
Arm A 
4 
1 
Arm B 
6 
1 
Arm A 
8 
1 
Arm A 
2 
2 
Arm B 
4 
2 
Arm A 
6 
2 
Arm B 
8 
2 
Arm A 
2 
3 
Arm B 
4 
3 
Arm A 
6 
3 
Arm A 
8 
3 
Arm B 
2 
4 
Arm A 
4 
4 
Arm B 
6 
4 
Arm A 
8 
4 
Arm B 
2 
5 
Arm B 
4 
5 
Arm A 
6 
5 
Arm B 
8 
5 
Arm B 
2 
6 
Arm A 
4 
6 
Arm B 
6 
6 
Arm B 
8 
6 
Arm A 
 
 
Blinding: Upon their consent to participate in the study, baseline measurement will be 
conducted for each participant before their random allocation to different treatment arms. 
The research group will be blinded to the treatment assignment. The treatment 
assignment key will be revealed only after the intervention has been concluded and the 
analysis has been conducted. 
3.1.3. Study Duration, Enrolment and Number of Sites 
3.1.3.1. Duration of Study Participation 
Duration of study: The participants are planned to be recruited from September 2025 – 
February 2026. For each participant, the intervention duration will be up to three months, 
with in-person recruitment at the baseline and follow-up at 1, 3, and 6 months on the 
application. 
3.1.3.2. Sample size and recruitment of participants 
Sample size: We estimate the sample size for randomised controlled trials following 
Wittes (Hayes and Bennett 1999). We assume a control prevalence of populations with 

9 
 
 
readiness for behaviour change of 25% to yield a sample size of 328 participants in each 
arm that give 80% power at 95% confidence to detect a minimum of 40% increase (from 
25% to 35%) in the prevalence of people with readiness for behaviour change in the 
intervention arm 1. After adjusting for a 50% oversample to allow for non-response, 
refusal or loss to follow-up, we obtained the minimum sample size of 492 participants per 
arm, yielding a total of approximately 1000 participants being recruited into the trial. 
Recruitment of participants: We will recruit participants from all primary healthcare 
centres in Västerbotten which run the Västerbotten Intervention Programme. We expect 
the primary healthcare centre to represent centres in urban and rural areas in 
Västerbotten. 
3.1.4. Study Population 
3.1.4.1. Inclusion Criteria 
- 
VIP participants who have received the VIP intervention 
- 
Has a smartphone  
 
3.1.4.2. Exclusion Criteria 
- 
Individuals who are bedridden, terminally ill, have severe vision/hearing problems, or 
with other hindrances to fulfilling the study protocol. 
- 
Individuals who will be referred to the Behavioural medicine clinic for behavioural 
change treatment. 

10 
 
 
3.2. Intervention Tools 
3.2.1. Development of the STAR-C person-tailored coaching application 
The STAR-C coaching application is developed in a participatory design process 
involving experts from the domains of nursing, psychology, medicine, public health, 
nutrition, social work, ethnology, epidemiology, health economics, health informatics, 
human-computer interaction, and artificial intelligence, employed at Umeå University 
and/or Region Västerbotten. Further, tentative users and participants in VIP have been 
participating in the design process. 
The theoretical foundation of the design includes behaviour change theories, activity 
theory, and argumentation theory. The basis for system design includes participatory 
design to include diverse views on purposes, values, and functionalities; value-based 
design taking ethical aspects into the design process of systems embedding artificial 
intelligence (AI) techniques; and behaviour change systems/persuasive technology 
design. 
A combination of AI techniques is being developed, where the data collected through the 
version used in the study will be used to further refine and develop the models for 
personalisation during the study and future versions of the system. 
3.2.2. The functionality of the STAR-C digital application 
3.2.2.1. General functionalities 
To access the application, a user account is needed to authenticate a user and to store 
data relating to the use of the application. 
The system architecture consists of a mobile application communicating with a server at 
ATEA 
Besides specific functionalities described in the next sections, both versions of the 
mobile application also contain functionality that allows the participant to communicate 
with the project team to troubleshoot problems in using the application and provide 
suggestions for improvement, or in case the participant wants to withdraw from the study 
and to have their data erased. 
3.2.2.2. STAR-C application with person-tailored coaching for the intervention group 
The intervention version contains information about the study and a baseline 
assessment to create an initial user model, which can be used for tailoring the 
application's behaviour. The assessment is repeated at months 1, 3, and 6.During daily 
use, the user model is updated with information about changed preferences 

11 
 
 
regarding activities, motives for changing behaviour, perceived barriers, and 
accomplishments related to short- and long-time goals. 
The STAR-C intervention embeds a set of behaviour change techniques (BCT) that, 
according to earlier studies, have some links to mechanisms of action (MoA) such as 
motivation, readiness to change, beliefs about capability, and attitudes towards the 
behaviour in focus. 
● Goal setting (long- and short-term) for each domain of behaviour the participant 
wants to address; 
● Self-monitoring of behaviour: 
○ List of chosen activities relating to short and long-term goals for logging 
accomplishments; 
○ Visualisation of progress relating to particular selected behaviour(s) and the 
selected activities; 
○ Visualisation of overall status (STAR profile); 
● Evidence/knowledge-based information and person-tailored advice, including 
information about health consequences; 
● Digital coach character with person-tailored behaviours to challenge and to support 
by providing reminders, feedback, and suggestions of goals, partly through 
text-based dialogues with the user that are also being stored for later analysis.  
How these BCTs are linked to MoAs is an open research question, which will be 
explored by allowing participants to use the behaviour change techniques that they 
prefer, which may also change over time 
 
1. How ATEA’s Language Model Has Been Trained 
The language model used in the STAR-C intervention has been developed using 
IBM Watsonx AI on a secure, on-premises system called the Fusion Box. This 
model has been refined using publicly available data and prompt engineering 
techniques, without exposing any sensitive user information. The training involved 
fine-tuning of a pre-trained model to understand and respond to user prompts in 
Swedish and other supported languages, using established IBM AI services and 
tools hosted locally within ATEA's infrastructure. 
 
2. Limiting the Dialogue to Activities and Goal-Setting (Not Health Advice) 
To ensure ethical and safe interaction, the digital coach in STAR-C is programmed 
to focus strictly on behavioral support, such as reminders, activity tracking, and 
motivational nudges. The language model does not give health or medical advice. 
This is enforced by restricting the dialogue scope to predefined templates and 
prompts that support general well-being and goal-setting. All conversations are 
guided and aligned with the behavioral change methods outlined in the study 
protocol. 
 
3. Data Privacy and Security (Not Cloud-Based) 
All data processed by the language model remains within ATEA's secure 
environment. The Fusion Box is a dedicated local server based in Umeå, equipped 
with enterprise-grade security. No participant data is sent to external cloud services. 
The system uses Red Hat OpenShift and IBM Watsonx AI services installed directly 
on the Fusion Box, ensuring full control and local data residency. Access is 
restricted via role-based permissions, and all model interactions are logged and 
protected. The system also utilizes an internal service, also hosted on the ATEA 

12 
 
 
data center in Umeå, to handle identification management. Some personal 
information required for authentication is sent and stored. External services are used 
to facilitate remote notifications, no personal identifiable information is sent to these 
services. 
 
 
. 
3.2.2.3. STAR-C application without person-tailored coaching for the control group 
The application for the control group contains information about the study and the 
participant’s participation throughout the study, including the baseline questionnaire, 
which is followed up with questionnaires including a sub-set of questions at 1, 3, and 6.. 
The control group’s version also embeds a countdown to when they can access the full 
version when completing the questionnaires after three months. 
3.2.3. Data collection using the STAR-C application 
Data collected is anonymous and transmitted securely through https, using encryption 
and stored in temporarily in servers at ATEA for before transfer to ITS (IT-stöd och 
systemutveckling) at Umeå University national and local regulations for research at the 
University. The following data will be collected in the intervention study. 
● Information necessary for setting up a user account and for the linkage to the VIP 
database: person number, email address, telephone number, age and gender. 
● Participant’s responses to baseline and follow-up assessments. 
● Participant’s defined goals, related activities and their characteristics, and logs of 
their activities relating to the goals. 
● Information about when and how the participant responds to suggestions and 
reminders. 
● Aggregated information about physical activity, psychological well-being, alcohol 
consumption, tobacco use, and nutrition habits. This information is numbers used to 
create a star profile and measure short- and long-term accomplishments. 
● Information about the user’s clicks on menus and buttons and time spent using the 
application. 

13 
 
 
● Information about the number of steps measured by sensors in the mobile phone. 
● Participant’s preferences regarding how the coaching functionalities should appear. 
● Participant’s suggestions about improvements of the application. 
 
3.3. Study Evaluations and Measurements 
3.3.1. Primary Endpoint 
The primary endpoint will be the change in readiness for behavioural change between 
baseline and follow-ups at 1, 3, and 6 months. The proportion of participants in different 
stages of behavioural change of (a) pre-contemplation; (b) contemplation; (c) 
preparation; (d) action; and (e) maintenance for different behaviours will be evaluated at 
different time points. 
3.3.2. Secondary Endpoints 
The secondary endpoints include the change in behaviours or adaptation of healthier 
behaviours in the form of: 
- 
use of tobacco cessation clinics or smoking, snus and other tobacco products 
cessation 
- 
reduction in alcohol consumption 
- 
the adoption of healthy food habits (using the four index questions) 
- 
increase level of physical activity, and reduction of sedentary behaviours. 
 
We will also assess the following additional secondary outcomes: 
- 
the use and pattern of use of the digital tools 
- 
health-related quality of life using the EQ5D instrument 
- 
WHO-5 well-being index 
(https://ogg.osu.edu/media/documents/MB%20Stream/who5.pdf). 
 
We will identify all costs related to the intervention or non-intervention, value the costs 
and, in the end, measure the relevant costs. It will be important to distinguish between 
costs for the project and costs relevant to implementing ordinary VIP intervention. 
Identifying the effect in a health economic evaluation is as important since the costs must 
be related to an effect to assess the cost-effectiveness. More details about the 
cost-effectiveness measurement will be described in Section 3.4.3. 
 
3.4. Data analysis 
3.4.1. Baseline Data 
We describe participants’ characteristics at baseline and follow-up. Baseline and 
demographic characteristics will be summarized by standard descriptive summaries 
(e.g., means and standard deviations for continuous variables such as age and 
percentages for categorical variables such as gender). 
Stages of change will be compared for each behaviour at different time points and across 
population groups. We will conduct latent class analysis to group the participants 

14 
 
 
into several classes of readiness for behavioural change for different combinations of 
behaviours (using the baseline data). 
3.4.2. Sub-study 1: Effectiveness Analysis 
We will use an intention-to-treat approach in the analysis of the RCT data. We assess 
differences in the groups using ANOVA and Chi-square tests for continuous and 
categorical variables, respectively. We analyse the longitudinal data using generalised 
estimating equations (GEE) and mixed models to assess the effect of time and the 
intervention on the outcomes. 
The primary effectiveness endpoint will be the change in readiness for behavioural 
change between baseline and follow-up visits at 1, 3, and 6, months. Using the follow-
up data, we will conduct latent transition analysis to map different trajectories of 
readiness for behavioural change, comparing participants in different intervention arms. 
Similar methods will be adopted for the secondary endpoints, change in health 
behaviours and well-being. 
3.4.3. Sub-study 2: Cost-Effectiveness Analysis 
Identification of costs is the first step in health economic evaluation. Costs for the 
development of the digital solution (the app), the running costs for app maintenance, and 
costs for data storage in the server are regarded as fixed investment costs. The variable 
costs include costs for reimbursing the nurse’s time spent in the initial training to 
understand the intervention and get familiar with the apps' functionalities and time spent 
in implementing the intervention to the participants. When using a societal perspective in 
the analysis, the time used by the participants to participate in the intervention should 
also be accounted for as a cost. In the next step, the different costs are valued. When 
considering technically-related costs, it is important to distinguish between costs related 
to the project and costs related to the introduction of the regular VIP intervention. In the 
valuation of various costs identified, we will include costs from public material (average 
salaries x used time), and the participants’ time will be quantified with the opportunity 
cost approach. Based on published materials, we will also value future savings related to 
reduced morbidity. 
The cost of developing the app will be measured by multiplying the estimated time by the 
current salaries. One hypothesis, however, is that the cost will be so low per user that 
this cost will be excluded from the analysis. Other continuous salary costs, time costs, 
and continuous update costs, however, become important to follow. 
A cost-effectiveness analysis must also have a clear effect to measure. Alternative 
calculations will be carried out. A first analysis relates the cost to the number of users. At 
the second step, we can also relate the cost to users with positive readiness to change 
their behaviours. As the expected effect is multidimensional, it is difficult to do a 
complete cost-effect analysis without simplifying the reality. We will therefore carry out a 
number of scenario-based cost effect analyses, where we will ultimately use published 
risk equations to draw conclusions. Another way to simplify the analysis is to use 
indicators to measure health-related quality of life. We collected EQ5D 5L data and can 
calculate the number of gained QALY due to the intervention. 
A result of scenario-based cost effect analysis based on our data could look like this, for 
example, the cost of the app is x kronor. If y number of people were to stop smoking, the 
intervention cost could be justified by this measure of effectiveness. 

15 
 
 
All these mean that we will have different CEAs to strive for; cost/user; cost/user with 
positive change; cost/smoke quitter, or cost per person with improved physical 
behaviour. The different analyses will together form the conclusion. 
 
 
4. IMPLEMENTATION RESEARCH ON THE STAR-C INTERVENTION 
We integrate the implementation of the intervention into the ongoing VIP in place; hence, 
this RCT is pragmatic and more scalable in the Swedish primary healthcare settings. We will 
use implementation research design throughout the STAR-C intervention to describe and 
understand the enabling factors and barriers to the implementation. We use the data to 
develop a theory about how the interventions impact health outcomes by assessing 
feasibility, acceptability, scalability, and replicability. 
The implementation research consists of four sub-studies with different aims and is 
conducted among different participant groups as described below. 
4.1. Sub-study 3: The lived experience of using the STAR-C application 
Aim: to explore the lived experience of using the STAR-C application among the adult 
population, including barriers and facilitators for use. 
 
Sample and selection: A sample of 8-10 STAR-C participants will be selected if they: 
a) agreed to participate in the evaluation of the STAR-C application and 
b) have downloaded the application with full functionalities. 
 
The sample represents a variation in age, gender, geographical location, and experience 
using digital tools. 
 
Methods: The sub-study is based on ethnographic research methods. Personal photo/text 
journals (Photovoice) and in-depth interviews will be used to explore the lived experience of 
using the STAR-C application, including barriers and facilitators for use. Study participants 
will be asked to document their use of the application visually and textually in a journal on 
three to five occasions (one week for each occasion), for three months. Visual and written 
journals allow individual participants to highlight matters they perceive to be important in 
long-term and day-to-day use of the application. Visual and textual journals will be 
complemented with in-depth interviews with study participants. In-depth interviews will allow 
participants to elaborate on matters identified and described in their visual and textual 
documentation, as well as for researchers to explore more specific matters related to 
participants’ day-to-day use of the application, including barriers and facilitators for use. The 
study starts with an individual introduction where participants are informed about the 
purpose of the photo/text journal concerning their use of the STAR-C application. In-depth 
interviews will be conducted after three months of use of the application. Overall matters 
that will be explored are: 
● 
How do participants experience using the STAR-C application, and what cultural, social 
and technological contexts are activated in their descriptions of their experiences? 
● 
What are the potential barriers and facilitators for continuous use of the STAR-C 
application? 
● 
What could be suggested for future interventions implementing similar digital tools? 

16 
 
 
Questions to interview guide: Given the study's methodological approach, including 
photo/text journals and in-depth interviews, questions discussed during interviews will be 
generated by both study participants and researchers. During in-depth interviews, 
participants will be asked to elaborate on the research material generated through their 
personal photo/text journals. The photo/text journals will also work as a base from which 
researchers identify important matters and ask questions for participants to elaborate further 
on during interviews. Given the semi-structured character of the interviews, researchers will 
ask questions that are centred around four general themes, aligning with the overall aim of 
the sub-study: 
● 
In what ways are you using the application in your day-to-day life? 
● 
When are you using the application in your day-to-day life? 
● 
How is it like to use the application in your day-to-day life? 
● 
What do you find to be barriers and facilitators for using the application? 
 
Analysis: The research material will be analysed using an ethnographic explorative 
approach based on Discourse theory and Science and Technology Studies (STS). This 
methodological stance helps to explore how the cultural, social, and technological contexts 
in which participants are situated can influence experiences and uses of the STAR-C 
application, including barriers and facilitators for use. 
 
4.2. Sub-study 4: Role of digital tool on behavioural change 
Aim: To understand the role of a digital tool in influencing health beliefs, readiness for 
change, and adoption of behavioural change. 
 
Sample: A sample will be selected based on participants that have: 
a) responded to be in different stages of willingness for change at baseline 
b) represent a variation in geographical residence, age, gender, and experience of using 
digital tools 
 
The participants will be invited at 3 months after being included in the intervention. Baseline 
data will be used as the foundation for the interview, where the participants will be asked to 
reflect upon the answers given at baseline. About 10-15 participants will be included in the 
study. 
 
Methods: In-depth interviews will be used for the following study and if willing, interviewed 
at two occasions. Overall questions that will be explored are: 
● 
How, what, and for whom can a digital tool complement the role of social networks and 
social environment in influencing health beliefs, readiness for change and adoption of 
behavioural change? 
● 
What would be suggestions for future interventions using similar tools to further facilitate 
the adoption of and maintenance of health-related lifestyle behavioural change? 
 
Questions to interview guide: Below are examples of the questions that will be included in 
the interview guide. Further questions are given in the Appendix. As the study is based on 
an emergent design, additional questions can be added during the study. 
● 
Do you recall your reasoning when responding to questions on readiness for change? 
● 
How would you rate yourself today about readiness for change? 
● 
How do you experience the use of the app in influencing your readiness for change and 
any potential change between baseline and today? 

17 
 
 
● 
Have you made any changes since baseline, if yes please describe 
● 
What do you see as the contributing factors for these changes? 
● 
How do you utilize the app vs. your social networks for health-related behaviour change? 
 
Analysis: The research material will be analysed using an inductive and deductive thematic 
analysis. In the deductive approach, the health belief model and the transtheoretical model 
of change will be used as frames of analysis. 
 
4.3. Sub-study 5: Healthcare professionals’ experiences of using digital tool 
Aim: To explore healthcare professionals’ experiences of using the STAR-C application as 
part of the VIP interventions, including perceptions of barriers and facilitators of using digital 
applications to help VIP participants to change and sustain health-related behaviours. 
Sample and selection: Approximately 15 healthcare professionals (nurses, assistant 
nurses, etc.) working with VIP will be included in the sub-study. Participants will be selected 
based on having a variation in years of experience in working with the VIP, including health 
dialogues, and also experience in using digital tools for facilitating behavioural change. They 
will also represent a geographical variation in the location of healthcare centres. 
Methods: The sub-study is based on group interviews with 15 participants, with 2-6 
participants in each group interview. At least one group interview per the three broad 
geographical areas of the VIP (Lycksele, Umeå, Skellefteå) will be conducted. The group 
interviews aim to identify pivotal matters connected to the overall aim of the sub-study. The 
sub-study will be conducted at least six months into the intervention so that staff have 
experience introducing and using the STAR-C application as part of the VIP. Overall matters 
that will be discussed are: 
● 
How do participants experience using the STAR-C application within the context of the 
VIP intervention? 
● 
What are perceived to be potential barriers and facilitators for continuous use of the 
STAR-C application within the VIP intervention? 
● 
What organisational, professional and technological contexts are activated in their 
descriptions of their experiences using the STAR-C application within the VIP 
intervention? 
● 
What could be suggestions for future initiatives that want to implement similar digital 
tools in public health interventions? 
 
Questions to use: Given the explorative approach of the sub-study, group interviews will be 
centred around four general themes, aligning with the overall aim of the sub-study: 
● 
How do participants experience using the STAR-C application within the context of the 
VIP intervention? 
● 
What are perceived to be potential barriers and facilitators for continuous use of the 
STAR-C application within the VIP intervention? 
● 
What organisational, professional and technological contexts are activated in their 
descriptions of their experiences using the STAR-C application within the VIP 
intervention? 
● 
What could be suggestions for future initiatives that want to implement similar digital 
tools in public health interventions? 

18 
 
 
Analysis: The research material will be analysed using an approach based on Qualitative 
content analysis. This methodological stance helps to identify potential barriers and 
facilitators and explore how organisational, professional and technological contexts can 
influence staff’s experiences and uses of the STAR-C application within the context of the 
VIP intervention. 

19 
 
 
4.4. Sub-study 6: The role and effectiveness of behavioural change techniques 
Aims: To understand the role of the different behaviour change techniques embedded in the 
digital tool in influencing health beliefs, readiness for change and adoption of behavioural 
change, and other mechanisms of action, and how different participants utilise these for 
promoting healthy behaviour. Furthermore, the design and implementation of the behaviour 
change techniques will be further evaluated and refined, partly based on the participants’ 
suggestions provided through the application. 
 
Methods: Data collection is done using the STAR-C digital application. Data includes 
answers to questions, defined activities to meet goals, logging of activities, experienced 
effects of logged activities, sensor data counting steps, and logs of usage of different 
functionalities in the application. 
 
Analysis: We will employ several analytical approaches in this sub-study. 
● 
Quantitative analysis of use patterns and links between behaviour change techniques, 
mechanisms of action and domains of behaviour change. 
● 
Ontological analysis of how participants define and adjust baby-step goals in the 
different domains of behaviour change. 
● 
Qualitative analysis of open-ended questions embedded in the application using 
thematic analysis. 
● 
Case studies of usage and behaviour change patterns over time. 
 
5. STUDY ADMINISTRATION 
5.1. Data Collection and Management 
All the quantitative and qualitative data collected during the study will be stored and 
catalogued using standard methods. All Investigators and study site staff involved with this 
study will conform to the General Data Protection Regulation (GDPR) 2018 concerning the 
collection, storage, processing and disclosure of personal information and uphold the Act’s 
core principles. 
5.2. Confidentiality 
All participants will be informed that their information will be treated anonymously and will 
only be presented on an aggregate level. These data will be anonymised before sharing 
them with the collaborators. Only the research group will have access to the project data. 
5.3. Ethical considerations 
5.3.1. Risk Assessment 
We are aware of potential ethical issues regarding asking sensitive questions concerning 
individuals’ behaviours, perceptions and attitudes towards health, use of digital 
applications, and their encounters with healthcare. Interviews to explore the reasons for 
not changing behaviours might also be sensitive. However, none of our data collection 
methods poses any risk of harm to the participants, non-participants, or healthcare 
providers. Participants will be reassured that their withdrawal will not affect their future 
healthcare. 

20 
 
 
5.3.2. Potential Benefits of Trial Participation 
Participation in the study may give valuable support for a healthy lifestyle, improved 
health and well-being. 
5.3.3. Risk-Benefit Assessment 
The risk for participants is low and is outweighed by the scientific benefit of finding 
improved methods to enhance healthy lifestyles and improved health in the population. 
This project aims to create equal opportunity and to ensure equitable access to 
high-quality health promotion activities at both societal and individual levels. 

21 
 
 
6. APPENDIX 
 
 
Appendix 1. Study flowchart 
 
+

22 
 
 

23 
 
 
7. REFERENCES 
Blomstedt Y, Norberg M, Stenlund H, Nystrom L, Lonnberg G, Boman K, et al. Impact of a 
combined community and primary care prevention strategy on all-cause and cardiovascular 
mortality: a cohort analysis based on 1 million person-years of follow-up in Vasterbotten 
County, Sweden, during 1990-2006. BMJ Open. 2015;5(12):e009651. 
Center for Clinical Management Research (2014). Consolidated framework for implementation 
research. Ann Arbor, Center for Clinical Management Research. 
Connell, L.E.; Carey, R.N.; de Bruin, M.; Rothman, A.J.; Johnston, M.; Kelly, M.P.; Michie, S. 
Links Between Behavior Change Techniques and Mechanisms of Action: An Expert 
Consensus Study. Ann. Behav. Med. 2018, 53, 708–720. 
Davis, R., R. Campbell, Z. Hildon, L. Hobbs and S. Michie (2015). "Theories of behaviour and 
behaviour change across the social and behavioural sciences: a scoping review." Health 
Psychol Rev 9(3): 323-344. 
Edberg, M. (2015). Essentials of Health Behavior: Social and Behavioral Theory in Public 
Health., Burlington: Jones and Bartlett 
Hayes, R. J. and S. Bennett (1999). "Simple sample size calculation for cluster-randomized 
trials." Int J Epidemiol 28(2): 319-326. 
Kway, T. L. (2022). Cost-effectiveness of using digital tools for behaviour change interventions 
among people with chronic diseases. Master of Public Health, Umeå University. 
Lindgren H, Guerrero E, Jingar M, Lindvall K, Ng N, Richter Sundberg L, Santosa A, Weinehall 
L. The STAR-C Intelligent Coach: a Cross-Disciplinary Design Process of a Behaviour 
Change Intervention in Primary Care. Stud Health Technol Inform, 2020; 273: 203-208. 
Lindgren H, Lindvall K, Richter-Sundberg L. Responsible design of an AI System for Improving 
Health – An Ethics Perspective on a Participatory Design Process of the STAR-C Digital 
Coach for Behaviour Change. Draft. 
Lindgren H, Weck S. Conceptual Model for Behaviour Change Progress – Instrument in Design 
Processes for Behaviour Change Systems. Stud Health Technol Inform 2021 
Oct;285:277-280. 
Lindgren, L, Weck S. 2022. Contextualising Goal Setting for Behaviour Change – from Baby 
Steps to Value Directions. In 33rd European Conference on Cognitive Ergonomics 
(ECCE2022), October 4–7, 2022, Kaiserslautern,Germany. ACM, New York, NY, USA, 7 
pages. https: //doi.org/10.1145/3552327.3552342. 
Maria, T. M. (2022). Cost-effectiveness of digital coaching in improving medication adherence 
among individuals with chronic diseases. Master of Public Health, University of Gothenburg. 
Ng N, Eriksson M, Guerrero E, Gustafsson C, Kinsman J, Lindberg J, et al. Sustainable 
Behavior Change for Health Supported by Person-Tailored, Adaptive, Risk-Aware Digital 
Coaching in a Social Context: Study Protocol for the STAR-C Research Programme. 
Frontiers in public health. 2021;9:593453. 
Norberg M, Wall S, Boman K, Weinehall L. The Vasterbotten Intervention Programme: 
background, design and implications. Glob Health Action. 2010;3. 
Oinas-Kukkonen H, Harjumaa M. Persuasive Systems Design: Key Issues, Process Model, and 
System Features. Communications for the Association for Information Systems 2009;24,28: 
485-500. 
Prochaska, J. O. and W. F. Velicer (1997). "The transtheoretical model of health behaviour 
change." Am J Health Promot 12(1): 38-48. 
Rogers, E. (2003). Diffusion of Innovations. New York, Simon and Schuster. 
Taj, F.; A Klein, M.C.; van Halteren, A. Digital Health Behavior Change Technology: Bibliometric 
and Scoping Review of Two Decades of Research. JMIR mHealth uHealth 2019, 7, e13311. 
[CrossRef] [PubMed] 

24