Skip to main content

Adherence to a lifestyle monitoring system in patients with heart disease: protocol for the care-on prospective observational trial



Lifestyle factors such as physical fitness, dietary habits, mental stress, and sleep quality, are strong predictors of the occurrence, clinical course, and overall treatment outcomes of common cardiovascular diseases. However, these lifestyle factors are rarely monitored, nor used in daily clinical practice and personalized cardiac care. Moreover, non-adherence to long-term self-reporting of these lifestyle factors is common. In the present study, we evaluate adherence to a continuous unobtrusive and patient-friendly lifestyle monitoring system using evidence-based assessment tools.


In a prospective observational trial (N = 100), the project investigates usability of and adherence to a monitoring system for multiple lifestyle factors relevant to cardiovascular disease, i.e., daily physical activity levels, dietary habits, mental stress, smoking, and sleep quality. Patients with coronary artery disease, valvular disease and arrhythmias undergoing an elective intervention are asked to participate. The monitoring system consists of a secured online platform with a custom-built conversational interface—a chatbot—and a wrist-worn wearable medical device. The wrist-worn device collects continuous objective data on physical activity and the chatbot is used to collect self-report data. Participants collect self-reported lifestyle data via the chatbot for a maximum of 4 days every other week; in the same week physiological data are collected for 7 days for 24 h. Data collection starts one week before the intervention and continues until 1-year after discharge. Via a dashboard, patients can observe their lifestyle measures and adherence to self-reporting, set and track personal goals, and share their lifestyle data with practitioners and relatives. The primary outcome of the trial is adherence to using the integrated platform for self-tracking data. The secondary outcomes include system usability, determinants of adherence and the relation between baseline lifestyle behaviour and long-term patient-relevant outcomes.


Systematic monitoring during daily life is essential to gain insights into patients’ lifestyle behaviour. In this context, adherence to monitoring systems is critical for cardiologists and other care providers to monitor recovery after a cardiac intervention and to detect clinical deterioration. With this project, we will evaluate patients’ adherence to lifestyle monitoring technology. This work contributes to the understanding of patient-centered data collection and interpretation, to enable personalized care after cardiac interventions in order to ultimately improve patient-relevant outcomes and reduce health care costs.

Trial registration

Netherlands Trial Registry (NTR) NL9861. Registered 6th of November 2021.


Optimization of lifestyle behavior and psychological wellbeing are considered pivotal in cardiac rehabilitation, as physical fitness [1], daily physical activity (PA) levels [2], dietary habits [2], mental stress [2], sleep quality [3, 4], and smoking habits [5] are strongly related to the occurrence, clinical course and overall treatment results of common cardiovascular diseases (CVD’s) such as coronary artery disease (CAD) and cardiac arrhythmias such as atrial fibrillation (AF) [6]. Moreover, quality of life is often not improved after major cardiac interventions [7,8,9], which may be due to adverse effects of persistent unhealthy lifestyle behaviour on the clinical course of coronary artery disease and atrial fibrillation [9]. However, despite its undisputed relevance, lifestyle behaviour is currently not monitored systematically and therefore not optimally used to the advantage of patients in daily practice [8, 10].

In order to successfully implement lifestyle monitoring in cardiac care pathways, optimizing adherence to self-tracking of lifestyle behaviour via monitoring technologies is essential. Without data provided by the patients, personalized and improved treatment decisions cannot be made. However, there is a gap in literature regarding the adherence to continuous lifestyle monitoring technologies for a longer period of time. Whereas previous research showed high levels of adherence associated with monitoring technology [11, 12], these studies focused on relatively short programs (mostly 1 to 12 weeks). Secondly, studies typically focus on monitoring of one rather than multiple lifestyle domains. Yet, the use of technology to monitor multiple lifestyle domains over a prolonged period of time may be particularly useful as an assistive tool to achieve a healthy lifestyle, and subsequently, better health outcomes in special populations [11]. Therefore, there is a clear need for further research in evaluation of the adherence and usability of this kind of digital health technology in CVD care and management [13].

The Care-On trial evaluates patients’ adherence to monitoring of lifestyle behaviours (i.e. daily physical activity levels, dietary habits, mental stress and sleep quality) with a newly designed system that integrates innovative methods for continuous unobtrusive and patient-friendly in patients with coronary artery disease, valvular disease and arrhythmias. Subsequent system usability, determinants of adherence and the relation between baseline lifestyle behaviour and long-term patient-relevant outcomes are investigated. We postulate that a system that aids patients in monitoring their lifestyle factors will enable better self-management and improve self-motivation [14], with subsequent positive effects on the lifestyle factors themselves and, eventually, on long-term clinical outcomes.


Study design

This study is designed as a monocenter prospective observational trial. A total of 100 patients scheduled for, or clinically admitted after a major cardiac intervention will be recruited at Maxima Medical Center in both Eindhoven and Veldhoven. All participants are requested to provide written informed consent before study entry. Demographic and other patient-relevant data are collected at baseline: one week before intervention, or as soon as possible after intervention if it is not possible to include the patient before intervention. Periodic data are collected at baseline, one week, three months, six months, nine months and twelve months after intervention. Continuous lifestyle data are collected 7 days per month for one year after the intervention. The protocol for this study was approved by the Institutional Review Board of Maxima Medical Centre Veldhoven in the Netherlands. The trial is registered at the Netherlands Trial Registry (NTR: NL9861). An overview of the study design is provided in Fig. 1.

Fig. 1
figure 1

Study design of the Care-On prospective observational trial

Study population

Patients scheduled for or recently having undergone coronary artery bypass surgery (CABG), a fractional flow reserve test (FFR) and/or a percutaneous coronary intervention (PCI), an electrophysiology study (EP) and/or radiofrequency catheter ablation (RFCA), a transcatheter aortic valve implantation (TAVI), and/or valve surgery will be considered for participation. We will include a total of 100 patients. Patients included before their intervention will be monitored one week extra (the week prior to the intervention). A complete list of inclusion and exclusion criteria is provided in Table 1.

Table 1 Inclusion and exclusion criteria for Care-On

After signing informed consent patients will receive instructions to use the lifestyle monitoring platform as part of the intake procedure with the researcher and nurse specialist. During this consultation patients will be instructed to use the platform on their personal computer and personal mobile phone. Patients will also receive a health watch, the Philips Health Band (PHB) (Health Band, Koninklijke Philips N.V. (KPNV), Amsterdam, The Netherlands).

Design of the lifestyle monitoring system

We aim to develop a platform that can adequately monitor lifestyle behaviors of cardiac patients in an integrated and holistic manner. For each lifestyle domain (physical activity, sleep, nutrition and stress) we consulted domain experts to determine which method to use, what data to collect and in what frequency we would be able to gain sufficient insight in the patient's lifestyle behavior. Secondly, a systematic review is being conducted [15] on validated self-assessment tools for cardiovascular risk behavior. Based on these sources, we chose data collection methods and set a data collection scheme. We created a Minimum Viable Product (MVP) of the lifestyle monitoring system and tested the platform with seven researchers, two dieticians with the aim to go through several quick design cycles (one week uses). We asked one patient to test the system for 15 weeks (from October 2021 to February 2022) and to provide feedback over time. These combined insights were used to mature the lifestyle monitoring system to a study ready state.

The lifestyle monitoring plan

The design process resulted in a combination of evidence-based monitoring methods and frequencies to monitor lifestyle efficiently, accurately, and with focus on the users’ needs.

Continuous lifestyle monitoring

Physical activity, daily nutrition intake, stress levels and sleep quality will be monitored continuously via the Care-On lifestyle monitoring system both objectively and subjective. The methods are displayed in Table 2.

Table 2 Continuous lifestyle monitoring plan

The methods were combined in one scheme with a focus on minimizing patient burden and creating regularity. Therefore, the basis of the scheme consists of seven measurement days (based on the minimum habitual sleep measurement frequency, and this includes all days of the week). These seven measurement days are divided over two weeks (for 3 or 4 days per week (2 or 3 weekdays and 1 weekend day)) per four weeks to minimize the activity of the chatbot per week and include measurement free weeks (pause weeks). The first week of reporting contains four reporting days: Monday, Wednesday, Friday and Sunday. The second week of reporting contains three reporting days: Tuesday, Thursday and Saturday. The reporting weeks will take place every other week. Thus, in one month seven days (one complete week of reporting) of measurements is collected. Patients will be asked to wear the health watch in the weeks the chatbot is active to simplify the scheme. The chatbot and health watch scheme can be found in Table 3.

Table 3 Continuous lifestyle monitoring scheme: “C” = day the chatbot is active. “Wear health watch” = patient is instructed to wear the health watch during the day (night is optional). In the row below the 'Week number' the days of the week are displayed from Monday (M) to Sunday (S)

A description of the monitoring method and frequency of each lifestyle domain is described in the following sections.

Daily physical activity level

Daily physical activity will be measured using a health watch (the Philips Health Band (PHB)) [22]. The health watch measures: activity counts, heartrate, respiration rate, total energy expenditure, active energy expenditure, steps, activity type, amount of time the watch has been worn, resting heartrate, recovery heartrate, cardio fitness index and the amount of time a person was asleep or awake.

Patients are asked to wear the health watch for 2 weeks per month (Table 3) for at least 12 h a day, excluding periods for charging the device and periods where the device cannot be worn (such as during washing, showering and swimming).

Daily dietary habits and drinking habit

Eating habits and drinking habits are measured via the chatbot, for seven days per month (Table 3). Participants will be asked to complete a 24 h food diary. The chatbot will aid in filling in the food dairy by prompting the patients after breakfast, after lunch, after dinner and the following morning to fill in their nutrition intake. Patients are prompted to report on the following morning as well to capture whether the patient has eaten something after dinner and before going to bed.

Daily nutrition intake is based on the Dutch Dietary Guideline of 2015 [23] and the Eetscore Food Frequency Questionnaire (FFQ) [24], and assessed through the following categories: vegetables; fruit; legumes; nuts and peanuts; dairy, cheese and cream; bread, cereal products and potatoes; meat, meat substitutes and egg; fish and shellfish; butter, fat and oil; drinks; salt; soup; sugar and confectionery; cake and pastry, savoury snacks and fast food.

Daily mental stress levels

Daily self-reported mental stress levels are measured via the chatbot using Ecological Momentary Assessment (EMA) with questions derived on the Positive and Negative Affect Schedule (PANAS) [25]. The chatbot will ask participants to indicate their feelings 3 times per day (‘How did you feel over the past 10 min including now?’): at wake up, after lunch and after dinner (together with the nutrition notification). Participants are asked to what extent (on a 5-point Likert-scale: 0 = ‘very slightly or not at all’, 1 = ‘a little’, 2 = ‘moderately’, 3 = ‘quite a bit’ and 4 = ‘extremely’) they experience the following states: stressed, happy, irritated, anxious, active, whether they have control over what they do, feeling physically well (and if not, what is bothering them), a personalized stress symptom (the patient will be asked to provide this personalized stress symptom during first time use of the chatbot).

Daily sleep habits

Daily self-reported sleep quality and quantity is measured via the chatbot based on the Consensus Sleep Diary [19]. 5 items will be asked via the chatbot in the morning when the participant wakes up, with the following outcomes: usual bedtime (time), usual getting up time (time), hours of sleep per night (hours), perceived quality of sleep (Likert-scale), perceived feeling of restfulness (Likert-scale). Secondly, the PHB will objectively measure sleep quantity and quality.

Periodic lifestyle assessment

Patients will be asked to fill in a set of questionnaires (quarterly assessment) at baseline (t0) (one week before intervention), one week after discharge (t1) and at 3 months (t2), 6 month (t3), 9 months (t4) and 12 months after discharge (t5). The results of  the questionnaires related to the patients lifestyle (as described in Table 4) are visualized with a score in the lifestyle monitoring system.

Table 4 Periodic lifestyle assessment overview

The lifestyle monitoring system for patients

Patients will receive an account for the Care-On lifestyle monitoring system for administering daily health data. The patients will be asked to install the application of the platform (MijnFlowCoach [29]) on their mobile phone or tablet and get additional instructions to access the platform on their desktop.

The Care-On platform for lifestyle monitoring contains five main features:

  • Health watch (PHB) integration: Upload and review daily physical activity data collected via an integrated wrist-worn wearable device.

  • Chatbot module: The chatbot module will prompt the patients to self-report on their lifestyle behaviour regarding mental stress, nutrition intake and sleep habits.

    • Measuring daily dietary habits and drinking habit

    • Measuring daily mental stress

    • Measuring daily sleeping habits

  • A personal dashboard: A personal dashboard will give feedback on the patients’ adherence to self-report their lifestyle behaviour and provide an overview of the self-reported lifestyle data. Secondly, every quarter patients will be asked to fill in more extensive questionnaires on lifestyle parameters as an addition to the daily chatbot and smartwatch integration measurements via the personal dashboard.

  • A goal tracking module: The patient has the option to set and monitor monthly goals with the goals tracking module.

An impression is shown in Fig. 2.

Fig. 2
figure 2

Care-On lifestyle monitoring system with integrated health watch and lifestyle chatbot

Health watch

The PHB will be integrated in the platform and data visualizations of the data collected with the watch will be visualized on a personal dashboard. The patient will not be treated or coached based on the parameters measured by the devices as caretakers do not have access to these data. The patient will wear the device during a period of one week before intervention (if possible) and one year after intervention. After this period the patient will hand in the devices.

Patients are instructed to wear the PHB at least 12 h per day. The PHB will be paired with the mobile phone of the patient via the ‘Philips Gezondheid band’ application [30] which will be installed on the patient's phone at first visit. The patient is instructed to open the application to synchronize the data of the watch with the Philips-application once every two days. The application will provide the patients with data visualizations of the data collected by the PHB. The patients may wear the PHB continuously (also in weeks of no chatbot measurements) and during the night by choice.


A conversational agent (chatbot), called ‘Caro’, is integrated in the Care-On platform. Caro is represented by a female avatar and is programmed to ask the patients questions to monitor the lifestyle parameters sleep, stress, and nutrition intake.

During a measurement day Caro starts a chat session five times per measurement day: in the morning (sleep and stress), after breakfast (nutrition), after lunch (stress and nutrition) and after dinner (stress and nutrition) and the morning after (nutrition). During onboarding Caro will ask patients to fill in their daily schedule to personalize the chat timers.

If a patient is not able to answer a question directly, Caro will ask again after 30 min. The patient has until the next chat session to answer the questions. If a patient has answered all questions of the chat session the measurement is completed and counted for compliance.

The chatbot is available via the platform on desktop and via the ‘MijnFlowCoach’-application [29] which is installed on the patient's phone during onboarding. An impression is shown in Fig. 3.

Fig. 3
figure 3

Example of chatbot (Caro) questions regarding sleep quantity and quality

Personal dashboard

The data acquired via the PHB, the chatbot and the periodic lifestyle assessments are visualized and accessible for the patient in a dashboard in the lifestyle monitoring system. The data visualized on the dashboard is focused on the adherence to delivering self-reported data to the system and the content of their delivered data. An example is shown in Fig. 4.

Fig. 4
figure 4

Personal dashboard: nutrition overview including visualized lifestyle goals

The periodic lifestyle assessments are also available via the dashboard. After having completed the periodic lifestyle assessment basic advice on the results is shown in the dashboard.

Goal tracking module

The patient has the option to set and monitor monthly goals with the goal tracking module. This module is optional. Default goals for dietary intake and physical activity will be provided based on the Dutch Dietary Guideline of 2015 and Dutch Physical Activity Guideline of 2017 [31]. The default goals for sleep and stress are based on the advice given by the Dutch General Practitioners Associations (in Dutch ‘Nederlands Huisartsen Genootschap (NHG)’) on the ‘Thuisarts’-website [32, 33]. The patient has the option to alter the default goals to fit their personal situation.

Quarterly consults by research nurse and final evaluation

Every quarter the research nurse will contact the patient via phone consultation to ask whether the patient has problems using the system. Secondly, the research nurse will ask for brief feedback on the use of the system. Thirdly, the research nurse will remind the patient to fill in the quarterly assessment when the patient has not completed it yet. At last, the research nurse will ask the patient to report medical events.

Within two weeks after the trial period has ended the SUS will be administered again and a semi-structured interview will be held with the participant to discuss their experience with the lifestyle monitoring system. During the semi-structure interview the following topics will be discussed: their quality of life after intervention, their experience with cardiac rehabilitation and/or cardiac care after intervention, their experience with the Care-On study in general, and their perceived usability of the different functionalities of the lifestyle monitoring system.

Outcome measures

Primary endpoint

The primary outcome is adherence providing self-tracking data, which is calculated as the total number of participants that completed the study at one year follow-up divided by the number who started using the system at baseline. Secondly, the general adherence rate and individual adherence rates of the chatbot, the quarterly assessments and wearing the health watch are compared. Adherence classifications and a dropout threshold were set up to adequately categorize patient’s adherence (see next section ‘Adherence classification and dropout threshold’). Other measures of adherence will also be explored (i.e., adherence rates per quarter, the success rate (percentage of the tasks that the patients complete correctly)).

Adherence classification and dropout threshold

Classifications for low, moderate and high adherence are set up to monitor patient’s compliance with providing lifestyle data. The health watch adherence rate, chatbot adherence rate and quarterly assessment completion rates make one-third of the general adherence rate:


The chatbot compliance rate is based on the minimum number of days of data collection necessary to gain insight in habitual intake of commonly consumed food. The minimum number of days of data to be collected to gain insight in habitual intake of commonly consumed food is 2–3 days (of food records, real-time monitoring) [17]. Thus, if a patient fills in at least 50% of the measurements, the minimum number of days is certainly met.

The health watch compliance rate is based on the minimum number of days necessary to gain insight in habitual physical activity and sedentary behavior. Previous studies [16] indicate that 3–5 days of monitoring is necessary to assess habitual PA and for sedentary behavior 5-days of monitoring will provide a reliable estimate. Patients were instructed to wear the PHB for at least 12 h a day on average during measurement weeks. Thus, if a patient wears the watch at least 50% of the prescribed time, the minimum number of days certainly is met.

The quarterly lifestyle assessment completion rate is based on the amount of completed quarterly assessments.

The adherence to providing self-tracking data is evaluated by the researchers every four weeks per patient. The total adherence score is a combined score of the chatbot adherence, activity tracker wear adherence and quarterly assessment adherence. The levels are as follows:

  • Low adherence classification: The patient's adherence is below 50% measured over a 4-week interval.

  • Moderate adherence classification: The patient's adherence is between 50 and 75% measured over a 4-week interval.

  • High adherence: The patient’s adherence is above 75% measured over a 4-week interval.

Patients who have a general adherence rate below 50% will be contacted and stimulated to resume using the system by the research nurse once; if patients did not resume after 1 month they are classified as non-adherent.

Secondary endpoints

The secondary endpoints are:

  • Usability (measured using the SUS, the feedback given by the patients during the phone consults with the research nurse, the quarterly questionnaires and the evaluation interview) and the success rate (percentage of tasks that the patients complete correctly)).

  • Predictors of adherence to the lifestyle monitoring system (demographic and disease characteristics, quality of life, self-efficacy, depressive symptoms and anxiety, motivation, stage of change, fatigue, physical fitness, levels of metal stress, use of a goal tracking functionality, perception of system usability and prior experience with technology. Standardized questionnaires will be used for self-report measures and objective ambulatory measures from the lifestyle monitoring system for objective lifestyle measures).

  • The association between lifestyle behaviour at baseline with clinical outcomes will be examined by evaluating patient clinical records (re-hospitalizations related to cardiac disease) and standardized self-report data (quality of life).

The data will be analyzed using multiple regression analysis. An overview of the questionnaires to be complete by the patients per timepoint for the secondary endpoints is shown in Table 5.

Table 5 Overview of the assessments of the secondary endpoints of the Care-On study

Sample size calculation and statistical analysis

The sample size is based on the primary objective adherence. Anticipating a dropout rate of 50% at 1 year follow up, 100 patients are needed to estimate this adherence rate with a precision of ± 10% (95% CI = [0.398, 0.601]).

Descriptive statistics will be used to report demographics and baseline characteristics. The primary outcome (i.e., ‘adherence’ based on continued use status at 1-year follow-up) will be analysed as a dichotomous outcome measure. Predictors of adherence status and which factors predict re-hospitalizations related to cardiac disease (i.e., the secondary aims) will be investigated using multiple logistic regression analyses. Predictors continuous outcome measures (e.g., QoL) will be examined using linear regression models. Analysis will be carried out in the statistical software package SPSS (version 24, SPSS Inc.).

Trial status

The Institutional Review Board of the hospital has approved the study protocol and its amendments prior to the start of the study. The inclusion of the patients started in November 2021 and is expected to be completed in June 2023. The expected study end date is one year (June 2024) later as the last included patient has finished their study year.


The Care-On trial is one of the first trials to evaluate long term adherence to monitoring multiple lifestyle behaviour domains with a monitoring system after major cardiac events. We postulate that a system that aids patients in monitoring their lifestyle will enable better self-management and improve self-motivation [14], with subsequent positive effects on the lifestyle factors themselves and, eventually long-term health outcomes. Adherence to monitoring devices that continuously provide self-tracking data is key and therefore the primary outcome of this study. Furthermore, we investigate the determinants of adherence to and usability of the system to further improve and personalize the lifestyle monitoring system to be easily integrated in the cardiac rehabilitation (CR) programs and to, consecutively, enhance adherence and eventually (lifestyle) behavior change.

For cardiac patients, CR programs are crucial for secondary prevention of cardiovascular events and optimization of risk factors and lifestyle behavior. Despite the undisputed advantages of participation in CR, too few patients participate in CR programs and adherence to maintaining a healthy lifestyle after a cardiac event or intervention is poor [44]. Secondly, standard CR and tele-rehabilitation programs aim to accomplish and maintain healthy lifestyle behaviour, however we often see a relapse in lifestyle behaviour during and after CR programs [45, 46]. The shortcomings of current CR programs may at least be partly due to insufficient long-term guidance by medical professionals, insufficient focus on sustainable behavioral change and self-management, and the fact that current group-based CR programs often do not meet patients’ individual needs and competences [47]. The latter may contribute to suboptimal adherence to prescriptions of lifestyle behavior (e.g. daily physical activity, dietary advice) and advices on self-monitoring, both in conventional CR programs and cardiac tele-rehabilitation programs. There is a clear need for long term strategies for sustainable behaviour change to achieve long term results.

Increased adherence to self-monitoring of lifestyle behavior can improve behavioral change by incorporating self-monitoring into patients’ daily routines, thus stimulating the development of self-management skills [48,49,50], even beyond phase II CR programs. Also, these data enable health care professionals to provide more suitable and sustainable advices and guidance. We envision that these insights will contribute to identify profiles that will help in optimizing personalized decision making. Also, insights in patients lifestyle data will add in achieving more long-lasting improvement in lifestyle as personalized regimes can be applied (e.g., pre and/or post-intervention rehabilitation, lifestyle management). Furthermore, continuous monitoring of lifestyle behaviour may enable early detection and treatment of clinical deterioration which will have a positive impact on the disease course (e.g., prevention of heart failure).

Research on the adherence to continuous lifestyle monitoring technologies for a longer period of time is scarce. In fact, previous research showing high levels of adherence associated with monitoring technology focused on relatively short programs (1 week to 6 months). Furthermore, these studies typically focused on monitoring only one or two lifestyle domains rather than all lifestyle domains that are part of secondary prevention cardiovascular diseases. The Care-On lifestyle system plan has been designed with a user-centered design approach to design a system suitable for long term use and monitoring of multiple lifestyle domains. Monitoring methods where chosen based on two important factors to optimize adherence: high usability in combination with low patient burden. The result was a lifestyle monitoring system with integrated chatbot and a wearable device, with a monitoring plan that is tailored to the patients’ daily routines.


The Care-On study investigates a newly designed lifestyle monitoring system for cardiac patients that enables long-term continuous monitoring of multiple lifestyle domains. It will provide insight in the adherence and usability of continuous lifestyle monitoring and it will give insight into the association between lifestyle behavior and clinical outcomes and patient-relevant outcomes. These insights can enable the design of personalized cardiac interventions, which may lead to enhanced patient participation and improved long-term adherence in cardiac rehabilitation, resulting in improved patient-relevant outcomes and reduce healthcare costs.

Availability of data and materials

Not applicable.



Cardiac rehabilitation


Physical activity


Cardiovascular disease


Coronary artery disease


Atrial fibrillation


Coronary artery bypass grafting


Radiofrequency catheter ablation


Electrophysiology study


Transcatheter aortic valve implantation


Fractional flow reserve test


Percutaneous coronary intervention


Netherlands trial registry


Philips Health Band


Koninklijke Philips N.V


Minimum viable product


Food frequency questionnaire


Positive and Negative Affect Schedule


Ecological momentary assessment


Perceived Stress Scale


Pittsburgh Sleep Quality Index


IPhone Operating System


Nederlands Huisartsen Genootschap


System Usability Scale


Quality of Life


Short Form Health Survey


General Self-Efficacy Scale


Hospital Anxiety and Depression Scale


Fatigue Assessment Scale


Mobile Device Proficiency Questionnaire


General Adherence Scale


Confidence interval


Statistical package for the social sciences


Consumer satisfaction score


Single ease question


  1. Sallis JF, Patterson TL, Buono MJ, Nader PR. Relation of cardiovascular fitness and physical activity to cardiovascular disease risk factors in children and adults. Am J Epidemiol. 1988;127(5):933–41.

    Article  CAS  PubMed  Google Scholar 

  2. Sabzmakan L, Morowatisharifabad MA, Mohammadi E, Mazloomy-Mahmoodabad SS, Rabiei K, Naseri MH, et al. Behavioral determinants of cardiovascular diseases risk factors: A qualitative directed content analysis. ARYA Atheroscler. 2014;10(2):71–81.

    PubMed  PubMed Central  Google Scholar 

  3. Jackson CL, Redline S, Emmons KM. Sleep as a potential fundamental contributor to disparities in cardiovascular health. Annu Rev Public Health. 2015;36:417–40.

    Article  PubMed  PubMed Central  Google Scholar 

  4. Shahrbabaki SS, Linz D, Hartmann S, Redline S, Baumert M. Sleep arousal burden is associated with long-term all-cause and cardiovascular mortality in 8001 community-dwelling older men and women. Eur Heart J. 2021;42(21):2088–99.

    Article  PubMed  PubMed Central  Google Scholar 

  5. How Tobacco Smoke Causes Disease: The Biology and Behavioral Basis for Smoking-Attributable Disease: A Report of the Surgeon General. Atlanta (GA): Centers for Disease Control and Prevention (US); 2010.

  6. Ambrosetti M, Abreu A, Corrà U, Davos CH, Hansen D, Frederix I, et al. Secondary prevention through comprehensive cardiovascular rehabilitation: From knowledge to implementation. 2020 update. A position paper from the Secondary Prevention and Rehabilitation Section of the European Association of Preventive Cardiology. Eur J Prev Cardiol. 2020;2047487320913379.

  7. Risom SS, Zwisler AD, Thygesen LC, Svendsen JH, Berg SK. High Readmission Rates and Mental Distress 1 yr After Ablation for Atrial Fibrillation or Atrial Flutter: A NATIONWIDE SURVEY. J Cardiopulm Rehabil Prev. 2019;39(1):33–8.

    Article  PubMed  Google Scholar 

  8. Maisano F, Viganò G, Calabrese C, Taramasso M, Denti P, Blasio A, et al. Quality of life of elderly patients following valve surgery for chronic organic mitral regurgitation. Eur J Cardio-Thorac Surg Off J Eur Assoc Cardio-Thorac Surg. 2009;36(2):261–6 (Discussion 266).

    Article  Google Scholar 

  9. Kotseva K, Wood D, De Bacquer D, De Backer G, Rydén L, Jennings C, et al. EUROASPIRE IV: A European society of cardiology survey on the lifestyle, risk factor and therapeutic management of coronary patients from 24 European countries. Eur J Prev Cardiol. 2016;23(6):636–48.

    Article  PubMed  Google Scholar 

  10. Charitakis E, Barmano N, Walfridsson U, Walfridsson H. Factors predicting arrhythmia-related symptoms and health-related quality of life in patients referred for radiofrequency ablation of atrial fibrillation: an observational study (the SMURF Study). JACC Clin Electrophysiol. 2017;3(5):494–502.

    Article  PubMed  Google Scholar 

  11. Albergoni A, Hettinga FJ, La Torre A, Bonato M, Sartor F. The role of technology in adherence to physical activity programs in patients with chronic diseases experiencing fatigue: a systematic review. Sports Med - Open. 2019;5(1):41.

    Article  PubMed  PubMed Central  Google Scholar 

  12. Piette JD, List J, Rana GK, Townsend W, Striplin D, Heisler M. Mobile health devices as tools for worldwide cardiovascular risk reduction and disease management. Circulation. 2015;132(21):2012–27.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Santo K, Redfern J. Digital health innovations to improve cardiovascular disease care. Curr Atheroscler Rep. 2020;22(12):71.

    Article  PubMed  PubMed Central  Google Scholar 

  14. Riegel B, Moser DK, Buck HG, Dickson VV, Dunbar SB, Lee CS, et al. Self-care for the prevention and management of cardiovascular disease and stroke. J Am Heart Assoc. 2017;6(9):e006997.

    Article  PubMed  PubMed Central  Google Scholar 

  15. Rutger Brouwers, Ilse Rongen, Jos Kraal, Hareld Kemps, Danny van de Sande, Tom Vromen. Validity and acceptance of self-assessment tools for cardiovascular risk behaviour: a systematic review [Internet]. [cited 2022 Oct 9]. Available from:

  16. Hart TL, Swartz AM, Cashin SE, Strath SJ. How many days of monitoring predict physical activity and sedentary behaviour in older adults? Int J Behav Nutr Phys Act. 2011;8(1):62.

    Article  PubMed  PubMed Central  Google Scholar 

  17. Brouwer-Brolsma EM, Lucassen D, de Rijk MG, Slotegraaf A, Perenboom C, Borgonjen K, et al. Dietary Intake Assessment: From Traditional Paper-Pencil Questionnaires to Technology-Based Tools. In: Athanasiadis IN, Frysinger SP, Schimak G, Knibbe WJ, editors. Environmental Software Systems Data Science in Action. Cham: Springer International Publishing; 2020. p. 7–23. (IFIP Advances in Information and Communication Technology).

  18. de Vries LP, Baselmans BML, Bartels M. Smartphone-based ecological momentary assessment of well-being: a systematic review and recommendations for future studies. J Happiness Stud. 2021;22(5):2361–408.

    Article  PubMed  Google Scholar 

  19. Carney CE, Buysse DJ, Ancoli-Israel S, Edinger JD, Krystal AD, Lichstein KL, et al. The consensus sleep diary: standardizing prospective sleep self-monitoring. Sleep. 2012;35(2):287–302.

    Article  PubMed  PubMed Central  Google Scholar 

  20. Borba D, Reis R, Lima P, Facundo L, Narciso F, Silva A, et al. How many days are needed for a reliable assessment by the sleep diary? Sleep Sci Sao Paulo Braz. 2020;1(13):49–53.

    Google Scholar 

  21. Lau T, Ong JL, Ng B, Chan L, Koek D, Tan C, et al. Minimum number of nights for reliable estimation of habitual sleep using a consumer sleep tracker. SLEEP Adv. 2022;31:3.

    Google Scholar 

  22. Hendrikx J, Ruijs LS, Cox LGE, Lemmens PMC, Schuijers EGP, Goris AHC. Clinical evaluation of the measurement performance of the philips health watch: a within-person comparative study. JMIR Mhealth Uhealth. 2017;5(2): e10.

    Article  PubMed  PubMed Central  Google Scholar 

  23. Looman M, Feskens EJ, de Rijk M, Meijboom S, Biesbroek S, Temme EH, et al. Development and evaluation of the dutch healthy diet index 2015. Public Health Nutr. 2017;20(13):2289–99.

    Article  PubMed  Google Scholar 

  24. van Lee L, Feskens EJM, Meijboom S, Hooft van Huysduynen EJC, van’t Veer P, de Vries JHM, et al. Evaluation of a screener to assess diet quality in the Netherlands. Br J Nutr. 2016;115(3):517–26.

    Article  PubMed  Google Scholar 

  25. Tran V. Positive Affect Negative Affect Scale (PANAS). In: Gellman MD, Turner JR, editors. Encyclopedia of Behavioral Medicine [Internet]. New York: Springer; 2013. p. 1508–9. [cited 2023 Apr 6]. Available from:

  26. Meijer R, van Hooff M, Papen-Botterhuis NE, Molenaar CJL, Regis M, Timmers T, et al. Estimating VO2peak in 18-91 year-old adults: Development and Validation of the FitMáx©- Questionnaire. medRxiv [Internet]. 2021; Available from:

  27. Cohen S, Kamarck T, Mermelstein R. A global measure of perceived stress. J Health Soc Behav. 1983;24(4):385–96.

    Article  CAS  PubMed  Google Scholar 

  28. Buysse DJ, Reynolds CF 3rd, Monk TH, Berman SR, Kupfer DJ. The pittsburgh sleep quality index: a new instrument for psychiatric practice and research. Psychiatry Res. 1989;28(2):193–213.

    Article  CAS  PubMed  Google Scholar 

  29. MijnFlowCoach - Apps op Google Play [Internet]. [cited 2022 Oct 9]. Available from:

  30. Philips Gezondheid band - Apps op Google Play [Internet]. [cited 2022 Oct 9]. Available from:

  31. Weggemans RM, Backx FJG, Borghouts L, Chinapaw M, Hopman MTE, Koster A, et al. The 2017 dutch physical activity guidelines. Int J Behav Nutr Phys Act. 2018;15(1):58.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Slaapproblemen | [Internet]. [cited 2021 Jun 9]. Available from:

  33. Stress | [Internet]. [cited 2021 Jun 9]. Available from:

  34. Failde I, Medina P, Ramirez C, Arana R. Construct and criterion validity of the SF-12 health questionnaire in patients with acute myocardial infarction and unstable angina. J Eval Clin Pract. 2010;16(3):569–73.

    PubMed  Google Scholar 

  35. Schwarzer R, Jerusalem M. The general self-efficacy scale (GSE). Anxiety Stress Coping. 2010;1(12):329–45.

    Google Scholar 

  36. Spinhoven P, Ormel J, Sloekers PP, Kempen GI, Speckens AE, Van Hemert AM. A validation study of the hospital anxiety and depression scale (hads) in different groups of dutch subjects. Psychol Med. 1997;27(2):363–70.

    Article  CAS  PubMed  Google Scholar 

  37. Gillespie ND, Lenz TL. Implementation of a Tool to Modify Behavior in a Chronic Disease Management Program. Kohl KS, editor. Adv Prev Med. 2011 Mar 8;2011:215842.

  38. Hendriks C, Drent M, Elfferich M, De Vries J. The fatigue assessment scale: quality and availability in sarcoidosis and other diseases. Curr Opin Pulm Med. 2018;24:1.

    Google Scholar 

  39. Peres S, Pham T, Phillips R. validation of the system usability scale (sus). Proc Hum Factors Ergon Soc Annu Meet. 2013;57:192–6.

    Article  Google Scholar 

  40. Roque NA, Boot WR. A new tool for assessing mobile device proficiency in older adults: the mobile device proficiency questionnaire. J Appl Gerontol Off J South Gerontol Soc. 2018;37(2):131–56.

    Article  Google Scholar 

  41. Hays RD, Sherbourne CD, Mazel R. User’s Manual for the Medical Outcomes Study (MOS) Core Measures of Health-Related Quality of Life [Internet]. RAND Corporation PP - Santa Monica, CA; 1995. Available from:

  42. What is CSAT and how do you measure it? // Qualtrics [Internet]. Qualtrics. [cited 2021 Dec 16]. Available from:

  43. Experience WL in RBU. Beyond the NPS: Measuring Perceived Usability with the SUS, NASA-TLX, and the Single Ease Question After Tasks and Usability Tests [Internet]. Nielsen Norman Group. [cited 2021 Dec 16]. Available from:

  44. Kotseva K, De Backer G, De Bacquer D, Rydén L, Hoes A, Grobbee D, et al. Lifestyle and impact on cardiovascular risk factor control in coronary patients across 27 countries: Results from the European Society of Cardiology ESC-EORP EUROASPIRE V registry. Eur J Prev Cardiol. 2019;26(8):824–35.

    Article  PubMed  Google Scholar 

  45. Smith KM, McKelvie RS, Thorpe KE, Arthur HM. Six-year follow-up of a randomised controlled trial examining hospital versus home-based exercise training after coronary artery bypass graft surgery. Heart Br Card Soc. 2011;97(14):1169–74.

    Article  Google Scholar 

  46. Brouwers RWM, Kemps HMC, Herkert C, Peek N, Kraal JJ. A 12-week cardiac telerehabilitation programme does not prevent relapse of physical activity levels: long-term results of the FIT@Home trial. Eur J Prev Cardiol. 2022;29(7):e255–7.

    Article  PubMed  Google Scholar 

  47. Brouwers RWM, Brini A, Kuijpers RWFH, Kraal JJ, Kemps HMC. Predictors of non-participation in a cardiac telerehabilitation programme: a prospective analysis. Eur Heart J Digit Health. 2022;3(1):81–9.

    Article  PubMed  Google Scholar 

  48. Thomas RJ, Beatty AL, Beckie TM, Brewer LC, Brown TM, Forman DE, et al. Home-based cardiac rehabilitation: a scientific statement from the american association of cardiovascular and pulmonary rehabilitation, the american heart association, and the american college of cardiology. J Am Coll Cardiol. 2019;74(1):133–53.

    Article  PubMed  PubMed Central  Google Scholar 

  49. Kenealy TW, Parsons MJG, Rouse APB, Doughty RN, Sheridan NF, Hindmarsh JKH, et al. Telecare for diabetes, CHF or COPD: effect on quality of life, hospital use and costs. A randomised controlled trial and qualitative evaluation. PloS One. 2015;10(3):0116188.

    Article  Google Scholar 

  50. Janssen V, De Gucht V, van Exel H, Maes S. A self-regulation lifestyle program for post-cardiac rehabilitation patients has long-term effects on exercise adherence. J Behav Med. 2014;37(2):308–21.

    Article  PubMed  Google Scholar 

Download references


Not applicable.


This study is part of the Care-On project, which is funded by the Dutch Heart Foundation, grant agreement number 2019B015.


Author information

Authors and Affiliations



WG participated in the design of the study, conducted the trial and drafted the manuscript. NT participated in the design of the study, helped to conduct the trial, HK, LY, YL, MB and WK participated in the design of the study and helped to draft the manuscript. RB helped to draft the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to W. F. Goevaerts.

Ethics declarations

Ethics approval and consent to participate

See the “Study design” subsection in the “Methods” section. The protocol for this study was approved by the Institutional Review Board of Máxima Medical Center Veldhoven in the Netherlands (reference number 1449). Written informed consent to participate will be obtained from all subjects before study entry. All methods are carried out in accordance with the Declaration of Helsinki.

Consent for publication

Consent for publication was obtained from Mrs. W.F. Goevaerts (Mayra) for publication of Fig. 4 in the manuscript.

Competing interests

The authors declare that they have no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit The Creative Commons Public Domain Dedication waiver ( applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Goevaerts, W.F., Tenbült - van Limpt, N.C.C.W., Kop, W.J. et al. Adherence to a lifestyle monitoring system in patients with heart disease: protocol for the care-on prospective observational trial. BMC Cardiovasc Disord 23, 196 (2023).

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: