Moving on! Can a Tailored Treatment in a Primary Care Setting Reduce Symptoms, Healthcare Consumption, Sick Leave and Prevent Pain and Hypertension in Individuals With Anxiety Disorders?
Phase: NA
Conditions: Anxiety
Sponsor: Vastra Gotaland Region

 
 
 
 
 
 
STUDY TITLE: 
Moving on! Can a tailored treatment in a primary care setting reduce 
symptoms, healthcare consumption, sick leave and prevent pain and 
hypertension in individuals with anxiety disorders? 
 
 
 
 
 
 
 
 
 
REVIEWED AND APPROVED BY ETHICAL BOARD: 
Dnr 2024-02725-01 (date 2024-06-27) 
 
 
 
DATE OF DOCUMENT: 
2024-11-25 
 
 
 
 
 
 
 
 
 
 
 
 
STUDY PROTOCOL WITH SAP 

PROJECT PLAN 
 
Moving on! Can a tailored treatment in a primary care setting reduce symptoms, 
healthcare consumption, sick leave and prevent pain and hypertension in 
individuals with anxiety disorders? 
 
Acronyms: CAU, care as usual. CBT, cognitive-behavioural therapy. CVD, cardiovascular diseases. GP, 
general practitioners. OR, odds ratio. PCCs, primary healthcare centers. PHRCs, primary healthcare 
rehabilitation centers. PHYSBI, Physical Fitness and Brain - an Interventional study. RMR, regional medical 
guidelines. VGR, region Västra Götaland. 
Purpose and aims 
 
Every third patient in the waiting room at a Swedish primary healthcare center is seeking help for 
common mental disorders including anxiety (1). Anxiety disorders substantially reduce quality of 
life and daily functioning and are, among mental disorders, the second leading cause of years lived 
with disability and the sixth-leading cause of disability globally (Global Burden of Disease Study, 
Lancet 2019). Anxiety disorders are also associated with elevated risks of pain, cardiovascular 
disease (CVD) and premature mortality (2), emphasizing the importance of finding effective 
treatment strategies that can be addressed in primary care.  
 
Specific research questions 
1. Can a tailored treatment and secondary prevention in a primary care setting reduce 
symptoms of anxiety, healthcare consumption, sick leave, pain and prevent hypertension 
in individuals with anxiety disorders? 
2. Are there long-term effects at 1 year of follow-up regarding the outcome measures? 
3. Is the treatment/secondary preventive strategy associated with health economic benefits? 
 
State-of-the art 
 
Anxiety disorders 
Anxiety disorders are the most common mental disorders; one out of three may be affected at 
some point in life (3). Symptoms of anxiety range from feelings of unpleasantness and worry to a 
state of anxious arousal with bodily symptoms frequently found in Panic Disorder (PD) including 
flushed skin, shortness of breath, tremors, sweating, increased pulse and blood pressure and pupil 
dilation. Individuals with Generalized Anxiety Disorders (GAD) experience more constant worry 
and anxiety in several aspects of their daily lives. Other symptoms are muscle tension, pain and 
sleep disturbances that often can be misinterpreted as physical disorders. Women suffer from 
anxiety disorders more often than men (33% and 22% respectively over a lifetime) and the course 
is often chronic-recurrent (4). It has been estimated that 70% of individuals in Sweden who seek 
health care for symptoms of anxiety and depression initially contact primary care. Moreover, it is 
estimated that the Covid-19 pandemic brought on an additional 76.2 million cases of anxiety 
disorders globally, an increase of 26% (5). The standard treatments for anxiety disorders include 
cognitive-behavioural 
therapy 
(CBT) 
and 
pharmacological 
treatment 
with 
selective 
serotonin/norepinephrine reuptake inhibitors (6). They are associated with treatment barriers and 
disadvantages. For example, nearly one third of patients do not respond to pharmacological 
treatment, which may in part be due to adverse side effects and noncompliance (6). Further, 
anxiety disorders substantially reduce quality of life, cognitive and daily functioning and comorbidity 
of psychiatric and somatic nature is common (7-9). Given the burden of comorbidity and reduced 
capacity to work in persons with anxiety, the lack of research is surprising and alarming. For 
comparison, a review of modifiable risk factors for depression yielded more than five times the 
number of publications compared to anxiety disorders (10, 11). 
 
Anxiety disorders, pain, and cardiovascular disease 

Anxiety disorders are associated with increased prevalence of concurrent severe medical 
conditions, including cardiovascular disease, chronic pain, functional gastrointestinal diseases, 
cancer and asthma (2, 12) as well as premature mortality (13). People diagnosed with anxiety 
disorders were reported to more likely have medical conditions including hypertension (OR 1.7), 
arthritis (OR 1.7), back/neck problems (OR 2.0), heart disease (OR 2.0), headache (OR 2.3), and 
multiple pains (OR 2.3), compared to a reference group in a large international study (14). The 
comorbidity of anxiety with cardiovascular disease and chronic pain could possibly be explained 
by common underlying biological processes. Allostatic load, inflammation, and hypothalamic–
pituitary–adrenal reactivity may be central (15). Also, lifestyle factors such as sedentary behavior 
and higher prevalence of alcohol/substance use may help to explain the increased risk of medical 
conditions in individuals with anxiety disorders (16, 17). 
 
Health promotion as a treatment and a secondary preventive measure 
As mentioned above, individuals with anxiety disorders very often also have other medical 
conditions, further affecting their general health and wellbeing in a negative way. Moreover, 
modifiable risk factors for anxiety have been reported including cigarette smoking, alcohol use, 
stress, low physical activity, low social support and avoidance behaviour (10). If lifestyle-
improvements can be obtained by health promotion interventions in this patient group, it may not 
only increase general health status but might also alleviate symptoms of anxiety.  
 
Physical activity as a treatment and a secondary preventive measure 
Similar to prevention and rehabilitation of cardiovascular disease and chronic pain (18), physical 
activity constitutes an important component in the prevention and treatment of anxiety (19). 
Physical activity can improve symptoms of anxiety (20) and recent findings indicate that anxiety 
could be an independent risk factor for cardiovascular disease (21), which further strengthens the 
use of exercise as treatment for patients with anxiety disorders. Hence, it is essential to develop 
interventions that may target not only mental but also physical health problems in people with 
anxiety disorders. The specific underlying mechanisms that may explain how exercise can reduce 
symptoms of anxiety are not fully explored. Theories include enhanced functional capacity, 
increased autonomy and improved self-esteem, and release of neurotrophic factors associated 
with neurogenesis, angiogenesis and neuroplasticity (22). For example, exercise per se may 
stimulate production of insulin-like growth factor 1 (IGF-1), which is associated with neuroplasticity 
and reduced anxiety-like behavior in mice (23).  
 
Preliminary and previous results 
 
The modified eHealth Lift in a primary health care context 
We have previously evaluated, in a primary healthcare setting in Western Sweden, the 
implementation of a low-budget lifestyle improvement method (Hälsolyftet [The Health Lift]). We 
found that increased awareness resulted in readiness for own positive lifestyle changes in persons 
motivated for change (n=3691), with longstanding beneficial results on several risk factors (24). 
The method included person-centered counseling through own participant reflection and tailored 
support at the local primary care centre (PCC), offered to all persons visiting the PCC (n= 8 PCCs) 
for a wide range of health problems (24). Among health problems, anxiety disorders may well be 
included. The patient received a material consisting of separate questionnaires dealing with dietary 
habits, smoking, alcohol habits, physical activity, stress, living conditions, waist-hip-ratio and well-
being expressed as a life ladder, present and future. Results were then converted into a health 
profile and a counsellor indicated what could be offered individually or in groups depending on the 
participant’s wishes. For example, a smoking cessation group, motivational interviewing for risky 
alcohol use, stress management classes, FaR® (physical activity on prescription), and weight 
reduction groups. This person-centered method was based on strengthening the individual’s own 
chosen health promoting activities and support for participation in municipal facilities. The program 
was shown to reach socioeconomically vulnerable and advantaged groups to the same extent and 
at least similar benefits (25). Through cooperation between the primary health care regional 

organization in region Västra Götaland (VGR) and the University of Gothenburg’s Dept. of Primary 
Health Care, an academic course for primary care nurses and health educators was launched. 
The Health Lift method has been implemented in ordinary care at some PCCs in the VGR since 
2015. Recently, researchers at Karolinska Institute developed a web-based version of the Health 
Lift (26). The eHealth Lift, here referred to as modified eHealth Lift, which adds counselling and 
supporting, will be applied in the current project, including support from a care manager 
(vårdsamordnare) according to regional medical guidelines (RMR).  
 
Exercise in the treatment of anxiety - The PHYSBI Intervention Study 
We carried out a blinded, randomized, controlled clinical exercise intervention (RCT) within the 
primary care context (for study protocol, see (27)). There were key gaps in the literature regarding 
how the effects of physical exercise affect not only symptoms of anxiety but also other mental and 
physical health variables in patients with anxiety disorders including cognitive function, 
cardiovascular fitness, and work ability/sick leave. These were all issues that we addressed in the 
PHYSBI Study (Clinical Trial NCT03247270). Around 280 patients with anxiety disorders, aged 
18-65 years, from six PCCs in two Swedish regions (VGR and Halland) took part in a 12-week 
exercise intervention of two different intensity levels (27). At study start, severity of anxiety among 
these patients was associated with impaired executive function related to working memory, 
independently of comorbid major depression (8). Both low and moderate/high intensity exercise 
reduced levels of anxiety and depression from baseline to post-treatment compared to the control 
group who received advice on physical activity according to public health recommendations (Fig 
1) (16). Including both exercise intensities as a continuous parameter showed a significant intensity 
trend for anxiety symptoms. In addition, quality of life and workability (Wall et al., manuscript) and 
visuospatial ability (Nyberg et al., manuscript) was improved. We have recently decoded the 1-
year follow-up data where the anxiety scores for the exercise intervention remained at the low level 
attained already at the 12-week follow-up, while anxiety scores reduced in the control group (Fig. 
2). Between the 12-week follow-up and the 1-year follow-up, quality of life increased significantly 
in the intervention group (p=0.002) but not in the control group (p=0.356) (Fig. 3). At the 1-year 
follow-up, the intervention group still reported higher quality of life and work ability compared to the 
control group (p=0.006) (Wall et al., manuscript). 
 
Taken together these findings strengthen the view that supervised, individualized physical exercise 
represents an effective treatment and secondary preventive long-lasting strategy for anxiety 
disorders and should be made more frequently available for persons with anxiety issues in primary 
care. 
 

 
Fig. 1. 12-week follow-up data – symptoms of anxiety and depression. 
Between-group treatment effects on self-rated anxiety symptoms (BAI scores) and depression symptoms 
(MADRS-S scores) (a). Effect sizes were accessed using analysis of covariance (ANCOVA) in a general 
linear model. For comparison the standardized mean differences, as expressed by Cohen’s d are reported. 
a, Adjusted for sex and age b, Adjusted for sex, age and baseline psychoactive medication, major 
depression, BAI-score, cardiovascular and respiratory disorders, smoking and physical exercise at baseline. 
Mean BAI (b) and MADRS-S (c) scores at baseline and at post-intervention by exercise intervention group. 
Error bars show standard error of the estimated means. Severity levels for anxiety (b) minimal/mild (score 
0–15), moderately (score 16–25) and severely (score 26–63) and for depression (c) no/minimal (score 0–
12), mild (score 13–19) and moderate (score 20–34) are indicated in shades of gray.  
 
 
 
 
Fig. 2. 1-year follow-up data – symptoms 
of anxiety. 
Mean (95% CI) scores of symptoms of 
anxiety (BAI) in the intervention group 
(n=75) and the control group (n=36) at 
baseline, after the 12-weeks intervention 
and at the 1-year follow up. Both within-
group and between-group analyses were 
performed, and corresponding p-values are 
reported. 
 

 
Fig. 3. 1-year follow-up data – quality of 
life. 
Mean (95% CI) scores of quality of life 
(EQ5D 1-100 scale) in the intervention 
group (n=75) and the control group (n=36) 
at baseline, after the 12-weeks intervention 
and at the 1-year follow-up. Both within-
group 
and 
between 
group 
analyses 
(Wilcoxon signed-rank test and Mann-
Whitney 
U 
test, 
respectively) 
were 
performed and corresponding p-values are 
reported in the figure. 
 
 
 
Significance and scientific novelty 
 
Despite the high burden of anxiety, there are major knowledge gaps when it comes to secondary 
preventive measures. The intervention described in this application aim to reduce anxiety, improve 
the quality of life, work ability and health in primary health care patients with anxiety disorders. 
Little research has focused on lifestyle improvements or exercise specifically in the treatment of 
anxiety. In Sweden there are guidelines from The National Board of Health and welfare for 
treatment of depression with exercise, but guidelines for treatment of anxiety disorders are 
currently lacking. Also results from this study may extend the current understanding and practice 
in the field of treatment and secondary prevention of anxiety disorders within primary care.  
 
There have been many different types of individual studies relating to improving physical activity, 
but the question remains which type of intervention is most effective? How should it be 
implemented in practice and from a system perspective? This project aims to combine, for the first 
time, evidence-based interventions to tailor a lifestyle intervention with different steps focusing on 
patient control and participation since the individual chooses the intervention arm. We will combine 
and evaluate two evidence-based lifestyle interventions: the modified eHealth Lift/Care manager 
according to RMR, and PHYSBI, a promising new strategy with an intensive training program, a 
new approach for treating anxiety disorders. In PHYSBI we have already examined different dose 
regimens for anxiety in the primary care context regarding secondary preventive outcomes. 
Therefore, the most effective high dose regimen will be used. 
If a simple intervention program in primary care can alleviate anxiety, improve quality of life, 
cognitive performance, and work ability, prevent pain and hypertension, this may have a great 
positive impact both at the individual level and from a societal perspective. In a longer-term 
perspective, improved treatment of anxiety might have the potential to prevent future 
marginalization and premature death among individuals with anxiety disorders. Should the health 
economic analysis show that the intervention is associated with reduced medical and sick-leave 
costs, these resources could be used elsewhere in an economically strained health care system. 
 
We hope to increase the availability of person-centered, evidence-based and cost-effective 
measures to promote both physical and mental health in patients with anxiety disorders. Moreover, 
the interventions are designed to easily be implemented in regular primary care activities. Hence, 
the results from this study will contribute with knowledge, competence development and valuable 
practical guidelines for the primary care regarding treatment and secondary prevention for patients 
with anxiety disorders.  
Project description (Fig. 4) 
 
Design of study: A parallel cluster RCT design with three assessment points (baseline, post-
intervention and 1 year of follow-up). The protocol was prepared in accordance with the SPIRIT 
2013 statement (28) and will be registered at ClinicalTrial.gov. 

 
Setting: Primary care centers in VGR, Sweden 
 
Participants: Patients aged 18-65 diagnosed with anxiety disorders attending 20 PCCs within the 
VGR. Participants will be recruited by personnel at the PCCs (doctors, psychologists, nurses), 
through information material at the PCCs (posters, flyers and handouts) and by using the primary 
care system Medrave to identify and contact suitable patients. Patients diagnosed, by a GP, with 
anxiety disorders, including panic syndrome (F41.0), generalized anxiety (F41.1), mixed anxiety- 
and depression (F41.2 and F41.3), as well as anxiety NOS (F41.9) are included in the study. 
Patients with and without ongoing treatment with psychoactive medication (antidepressants or 
anxiolytics) are included. Exclusion criteria are pregnancy, physical difficulties in performing a 
physical exercise program, pathological electrocardiogram, low BMI (under 17.5), ongoing 
alcohol/substance abuse, ongoing exhaustion disorder or psychotic disorder, newly discovered 
(within 6 months) atrial fibrillation, high suicide risk as assessed by the GP as well as limited 
knowledge in the Swedish language. Participants will sign a written informed consent and informed 
about the possibility to withdraw from the study at any time without disadvantages (an interpreter 
will be provided if needed). 
 
Randomization: Intervention or Care as usual/control (CAU). Cluster-randomization at PCC level 
to PCCs connected to primary healthcare rehabilitation centers (PHCRs). It will be performed by a 
independent statistician at Akademistatistik/Gothenburg University using a computer-based method. 
Randomization will occur accounting for socioeconomy (high or low) and size (over or under 5000 listed 
patients) of the PCC. The PCCs will be divided into four blocks: high socioeconomy/small size, high 
socioeconomy/large size, low socioeconomy/small size and low socioeconomy/large size. Within each 
block, the intervention as well as CAU/control will be randomized to either two or three PCCs, resulting 
in 10 PCCs with intervention and 10 PCCs with CAU/control.  
 
The intervention: The 10 PCCs randomized to intervention will offer all patients with a registered 
anxiety diagnosis in the PCCs electronic patient register as well as patients newly diagnosed with 
any of the above listed disorders to participate in an intervention program. The patient chooses the 
intervention arm, and the focus is on person-centredness, control, empowerment and participation.  
The two intervention-options are: 
 
1. Modified eHealth Lift, a digital development of the Health Lift, an evidence based lifestyle-
intervention for health promotion and prevention in primary health care. This intervention 
will be executed together with care manager contact according to RMR. 
 
2. The moderate-high intensity arm of PHYSBI, an individualized 12-week training program 
for anxiety disorders including 3 occasions/week. The moderate intensity arm of PHYSBI, 
which also will contain elements of high intensity, corresponds to 3.0-8.9 METs, Borg RPE 
12-17 and 60-94% of maximal heart rate. The intervention includes cardiorespiratory and 
resistance training in a group-session (26). The study physiotherapists design 
individualized exercise programs during a single one-to-one session with the patients.  
 
CAU (PCCs randomized to non-intervention): At Swedish PCCs, care as usual (CAU) for 
patients with anxiety disorders can consist of visits to various healthcare professionals (such as 
physicians, nurses, psychologists, therapists, physiotherapists). The treatment should follow the 
regional guidelines, which are based on the national evidence-based clinical guidelines for anxiety 
disorders (National guidelines for care of depression and anxiety disorders, The Swedish National 
Board of Health and Welfare, Stockholm 2020). These guidelines include psychopedagogic 
support, psychotherapy (cognitive behavioural therapy (CBT) face-to-face or internet-mediated), 
psychopharmaceutical treatment and/or sick listing (preferably partial). Patients diagnosed with 
anxiety disorders at PCCs randomized to CAU will be informed that their PCC is engaged in an 
ongoing comparative study. All individuals will be asked if they would like to participate in data 
collection and must give informed consent. 

 
Outcome measures: Individual data on symptoms of anxiety, depression, general health, pain, 
sleep, fatigue, work ability, physical activity and perceived quality of life will be obtained through 
established self-assessment scales and collected for each intervention arm. Patients will self-
report smoking, concurrent illnesses, pain, usage of prescribed drugs and physical activity using a 
questionnaire designed by the research team. For participants in the modified eHealth lift, the 
health profile at baseline and after one year will be compared. We will also obtain data from the 
healthcare database VEGA (Database for health-care consumption in VGR), the national Patient 
register, the regional prescribed drug register Digitalis, the national Prescribed drug register and 
the MiDAS/DOA database at Försäkringskassan (includes data on sick leave >14 days).  
 
The outcome measures for patients aged 18-65 diagnosed with anxiety disorders recorded after 3 
months and 1 year are as follows: 
 
Ø Main/primary outcome is symptoms of anxiety, reported using established psychiatric self-
assessment scales (BAI, GAD7, HAD and MADRS-S; question 2) 
Ø Symptoms of depression, pain, sleep, alcohol habits, fatigue, general health, work ability, 
physical activity and perceived quality of life: established self-assessment scales (EQ-5D, 
PSQ, ISI, AUDIT, MFS, MADRS-S, IPAQ and C2WI) and questionnaire data 
Ø BMI and blood pressure (measured at PCC) 
Ø Lifestyle questions (modified version of eHealth Lift) 
Ø Health profile (modified version of eHealth Lift) 
Ø Number of health care contacts with anxiety disorders: VEGA data including primary health 
care and hospital outpatient and inpatient care and the National Patient register, and 
questionnaire data. 
Ø Psychopharmaceuticals and analgesics prescribed: Digitalis data and the Prescribed drug 
register and questionnaire data. 
Ø Sick leave: MiDAS/DOA data and questionnaire data. 
Ø Mental and Physical health: VEGA data and data from the National Patient register of ICD-10 
diagnoses including common mental disorders (depression and different anxiety syndromes), 
pain and hypertension and questionnaire data.  
 
 
 
Timeline and Workplan 
During early 2024 a Clinical Trial registration will be submitted. We already have established 
contacts with gyms, the PHCRs and the Research and Development primary healthcare centres. 
During 2024 the collaborations with the primary healthcare organization, the PHCRs and health 
centres will be further established. A steering committee will be formed and the RCT procedures 
tested. During autumn 2024 the PCCs will be invited to participate. At the intervention PCCs, the 
recruitment of patients will be facilitated through a research nurse. At every PCC, a unique care 
manager manager which has been implemented at all PCCs in VGR since 2016 (29) handles the 
modified eHealth Lift, and the physiotherapists implement PHYSBI. The PHCRs will perform the 
PHYSBI arm and the involved PCCs will implement the modified eHealth Lift arm continuously 
during the intervention year of 2025. Study start date is anticipated to be in Jan 2025 and primary 
completion date in Dec 2026 (1 year of follow-up). Outcome data will be collected throughout the 
period 2025-2026. In 2026-2027 data will be processed, analyzed and scientific papers produced.  
 
We cannot see any crucial risks that would hinder the execution of the proposed project. One 
difficulty may be the recruitment of participants, as always with RCTs especially in primary health 
care. However, we will have a unique care manager and a research nurse to facilitate patient 
recruitment. In our previous PHYSBI study, the adherence rate was high (70%) at the 12-week 
follow-up.  

 
Fig. 4. Study design. 
 
 
Statistics analysis plan (SAP): The majority of outcome variables are scale variables that may 
be treated as continuous variables and therefore primarily modelled using linear regression. These 
are scales for symptoms of anxiety, depression, pain, sleep, general health, perceived quality of 
life, sick leave days, number of health care contacts and number and types of 
psychopharmaceuticals prescribed. Data will be presented using descriptive statistics, including 
means and standard deviations for continuous variables, and frequencies and percentages for 
categorical variables Group differences by intervention status (modified eHealth Lift, PHYSBI, or 
CAU) will be assessed by Kruskal-Wallis test and chi-square tests, respectively. The average 
change in continuous outcomes will be analyzed using linear regression, with intervention as 
categorical predictor and baseline levels as covariates. 
 
In the case of count data we will test whether Poisson or negative binomial models are more 
appropriate than linear regression. The correlation of outcomes between patients from the same 
PCC will be modelled using a hierarchical linear model including PCC as random effect.  A more 
detailed analysis of the longitudinal change will be based on repeated-measure models that also 
account for the correlations within individuals. Interaction terms for PCC status and time will test 
whether time trends differ by intervention status. Binary endpoints such as sick leave will be 
analyzed using logistic regression with adjustment for correlations as described for the linear 
models. All regression models will be adjusted for age, sex, as well as for variables on PCC-level 
such as socioeconomy, number of patients listed per PCC (size), and proportion of patients with 
anxiety diagnoses. 
 
Power considerations: We have performed power calculations based on 20 health centres 
distributed 50:50 i.e. 10 controls and 10 with two interventions. However, as patients are allowed 
to choose between the two intervention types (modified eHealth Lift or PHYSBI) we will consider 
an unequal distribution of intervention types, e.g. 70:30 % for the modified eHealth Lift and 
PHYSBI. As the main outcome measures are continuous we consider Cohen’s d = mean 
difference/SD as outcome measure, assuming small to medium effect size, i.e. d = 0.2 – 0.5 [51]. 
Power analysis was performed using a STATA procedure for one-way ANOVA. For a change 
(baseline to 3 months) we can observe effects sizes with d ≥ 0.2 and a power of ≥0.9 (significance 
level = 0.05) with a total of 320 individuals, distributed as 160 patients without intervention, 112 
with the modified eHealth Lift, and 48 with PHYSBI. The same sample size will yield even larger 
statistical power if the group size for the modified eHealth Lift and PHYSBI were more alike.  
 
Larger sample size is needed for binary endpoints such as sick leave. Assuming that about 5% of 
the patients will experience these endpoints during follow-up a total number of 1050 – 2200 will be 
needed to observe intervention-control differences by 2-3%. All our main outcome measures are 
continuous and since the numbers of health centres are comprehensive we conclude that the 
proposed analyses have sufficient statistical power to discriminate between the intervention-
specific time trends. Based on our experience from the PHYSBI RCT we estimate that we can 
include 450 individuals with anxiety disorders undergoing the intervention year.  

 
Health-economic analyses 
Cost-effectiveness analyses will be performed separately, comparing the two intervention arms to 
no CAU, including both direct (healthcare and pharmaceutical utilization) and indirect (long-term 
sickness and absenteeism from work) costs. Cost-effectiveness analyses will be based on the 
primary data collected within the project and based on a combination of primary data and 
secondary data from other sources for lifetime time perspectives. The long-term analyses will be 
performed using a health-economic simulation model projecting the outcomes and cost-
effectiveness at the 1-year follow-up to a longer time period (5 years and lifelong). More 
specifically, the within-study observed evolvement of the outcomes will be used to statistically 
project these outcomes for a 5-year and a lifelong time perspective. The most frequently used cost-
effectiveness measures is the incremental cost-effectiveness ratio (ICER). This measure together 
with the net monetary benefit (NMB) will be used to characterize the cost-effectiveness of each 
intervention.  
Sensitivity analyses with respect to these measures will be performed using either bootstrap 
techniques (when primary data is analyzed) or Monte Carlo simulation. The cost-effectiveness of 
an intervention aiming at improving health by targeting and affecting health-related behaviors 
depends on a couple of factors. Both the effect of the intervention on the propensity of the 
participants to maintain the within-intervention level of the targeted behavior over time, and the 
health effects attained by the new, improved level of the targeted behavior are of importance. Thus, 
the information about physical activity levels collected at the 1-year follow-up is essential to the 
health economic analyses. All analyses will follow the well-established methodology for performing 
health economic evaluations (30, 31). The simulation model that the planned analyses require will 
be constructed within the project using an accessible platform (Excel and the Visual Basic for 
Application facility). All health-economic analyses will be performed in collaboration with Kristian 
Bolin, Professor of health economics. 
 
 
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