Strengthening primary health care in Europe with digital solutions

This article provides an in-depth analysis of digital transformation in European primary healthcare (PHC). It assesses the impact of digital technology on healthcare delivery and management, highlighting variations in digital maturity across Europe. It emphasizes the significance of digital tools, especially during the COVID-19 pandemic, in enhancing accessibility and efficiency in healthcare. It discusses the integration of telehealth, remote monitoring, and e-health solutions, showcasing their role in patient empowerment and proactive care. Examples are included from various countries, such as Greece's ePrescription system, Lithuania’s adoption of remote consultations, Spain’s use of risk stratification solutions, and theNetherlands’ advanced use of telemonitoring solutions, to illustrate the diverse implementation of digital solutions in PHC. The article offers insights into the challenges and opportunities of embedding digital technologies into a multidisciplinary healthcare framework, pointing towards future directions for PHC inEurope

Turning the Crisis Into an Opportunity: Digital Health Strategies Deployed During the COVID-19 Outbreak

Coronavirus SARS-CoV-2; COVID-19; 2019-nCoV; Digital health; eHealth; Telemedicine; Public healthCoronavirus SARS-CoV-2; COVID-19; 2019-nCoV; Salud digital; eSalud; Telemedicina; Salud públicaCoronavirus SARS-CoV-2; COVID-19; 2019-nCoV; Salut digital; eSalut; Telemedicina; Salut públicaDigital health technologies offer significant opportunities to reshape current health care systems. From the adoption of electronic medical records to mobile health apps and other disruptive technologies, digital health solutions have promised a better quality of care at a more sustainable cost. However, the widescale adoption of these solutions is lagging behind. The most adverse scenarios often provide an opportunity to develop and test the capacity of digital health technologies to increase the efficiency of health care systems. Catalonia (Northeast Spain) is one of the most advanced regions in terms of digital health adoption across Europe. The region has a long tradition of health information exchange in the public health care sector and is currently implementin an ambitious digital health strategy. In this viewpoint, we discuss the crucial role digital health solutions play during the coronavirus disease (COVID-19) pandemic to support public health policies. We also report on the strategies currently deployed at scale during the outbreak in Cataloni

Enhancing lifestyle change in cardiac patients through the do change system ( Do cardiac health: Advanced new generation ecosystem ): Randomized controlled trial protocol

\u3cp\u3eBACKGROUND: Promoting a healthy lifestyle (eg, physical activity, healthy diet) is crucial for the primary and secondary prevention of cardiac disease in order to decrease disease burden and mortality.\u3c/p\u3e\u3cp\u3eOBJECTIVE: The current trial aims to evaluate the effectiveness of the Do Cardiac Health: Advanced New Generation Ecosystem (Do CHANGE) service, which is developed to assist cardiac patients in adopting a healthy lifestyle and improving their quality of life.\u3c/p\u3e\u3cp\u3eMETHODS: Cardiac patients (ie, people who have been diagnosed with heart failure, coronary artery disease, and/or hypertension) will be recruited at three pilot sites (Badalona Serveis Assistencials, Badalona, Spain [N=75]; Buddhist Tzu Chi Dalin General Hospital, Dalin, Taiwan [N=100] and Elisabeth-TweeSteden Hospital, Tilburg, The Netherlands [N=75]). Patients will be assisted by the Do Something Different (DSD) program to change their unhealthy habits and/or lifestyle. DSD has been developed to increase behavioral flexibility and subsequently adopt new (healthier) habits. In addition, patients' progress will be monitored with a number of (newly developed) devices (eg, Fitbit, Beddit, COOKiT, FLUiT), which will be integrated in one application.\u3c/p\u3e\u3cp\u3eRESULTS: The Do CHANGE trial will provide us with new insights regarding the effectiveness of the proposed intervention in different cultural settings. In addition, it will give insight into what works for whom and why.\u3c/p\u3e\u3cp\u3eCONCLUSIONS: The Do CHANGE service integrates new technologies into a behavior change intervention in order to change the unhealthy lifestyles of cardiac patients. The program is expected to facilitate long-term, sustainable behavioral change.\u3c/p\u3e\u3cp\u3eTRIAL REGISTRATION: Clinicaltrials.gov NCT03178305; https://clinicaltrials.gov/ct2/show/NCT03178305 (Archived by WebCite at http://www.webcitation.org/6wfWHvuyU).\u3c/p\u3

Type D personality and global positioning system tracked social behavior in patients with cardiovascular disease

Objective: Social behavior (e.g., loneliness, isolation) has been indicated as an important risk factor for cardiovascular disease. Recent studies show that Type D personality might be an important predictor of social behavior. Hence, the current exploratory study aims to examine, using ecological assessment, whether Type D personality is associated with a lower likelihood to engage in social encounters in patients with cardiovascular disease. Method: Cardiac patients who participated in the Do CHANGE (Phase 2) trial were included in current analysis. As part of the Do CHANGE intervention, real-life data were collected in the intervention group using the MOVES app, which was installed on patients' mobile phones. For a period of 6 months, Global Positioning System (GPS) data from the participating patients were collected. From the GPS data, 3 target variables were computed: (a) general activity level, (b) social variety, and (c) social opportunity. Results: A total of 70 patients were included in the analysis. Patients with a Type D personality had lower scores on the "social opportunity" variable compared to non-Type D patients (F = 6.72; p = .01). Type D personality was associated with lower social participation after adjusting for depression and anxiety. No association between Type D personality and general activity or behavioral variety was observed. Conclusions: This is the first study to use an ecological measure to assess social behavior of cardiac patients with a Type D personality. Results show that Type D personality might be associated with lower social engagement, which could, in turn, partly explain its association with adverse health outcomes

Type D personality and global positioning system tracked social behavior in patients with cardiovascular disease

Objective:  Social behavior (e.g., loneliness, isolation) has been indicated as an important risk factor for cardiovascular disease. Recent studies show that Type D personality might be an important predictor of social behavior. Hence, the current exploratory study aims to examine, using ecological assessment, whether Type D personality is associated with a lower likelihood to engage in social encounters in patients with cardiovascular disease.  Method:  Cardiac patients who participated in the Do CHANGE (Phase 2) trial were included in current analysis. As part of the Do CHANGE intervention, real-life data were collected in the intervention group using the MOVES app, which was installed on patients' mobile phones. For a period of 6 months, Global Positioning System (GPS) data from the participating patients were collected. From the GPS data, 3 target variables were computed: (a) general activity level, (b) social variety, and (c) social opportunity.  Results:  A total of 70 patients were included in the analysis. Patients with a Type D personality had lower scores on the "social opportunity" variable compared to non-Type D patients (F = 6.72; p = .01). Type D personality was associated with lower social participation after adjusting for depression and anxiety. No association between Type D personality and general activity or behavioral variety was observed.  Conclusions:  This is the first study to use an ecological measure to assess social behavior of cardiac patients with a Type D personality. Results show that Type D personality might be associated with lower social engagement, which could, in turn, partly explain its association with adverse health outcomes

Usefulness of a lifestyle intervention in patients with cardiovascular disease

\u3cp\u3eThe importance of modifying lifestyle factors in order to improve prognosis in cardiac patients is well-known. Current study aims to evaluate the effects of a lifestyle intervention on changes in lifestyle- and health data derived from wearable devices. Cardiac patients from Spain (n = 34) and The Netherlands (n = 36) were included in the current analysis. Data were collected for 210 days, using the Fitbit activity tracker, Beddit sleep tracker, Moves app (GPS tracker), and the Careportal home monitoring system. Locally Weighted Error Sum of Squares regression assessed trajectories of outcome variables. Linear Mixed Effects regression analysis was used to find relevant predictors of improvement deterioration of outcome measures. Analysis showed that Number of Steps and Activity Level significantly changed over time (F = 58.21, p < 0.001; F = 6.33, p = 0.01). No significant changes were observed on blood pressure, weight, and sleep efficiency. Secondary analysis revealed that being male was associated with higher activity levels (F = 12.53, p < 0.001) and higher number of steps (F = 8.44, p < 0.01). Secondary analysis revealed demographic (gender, nationality, marital status), clinical (co-morbidities, heart failure), and psychological (anxiety, depression) profiles that were associated with lifestyle measures. In conclusion results showed that physical activity increased over time and that certain subgroups of patients were more likely to have a better lifestyle behaviors based on their demographic, clinical, and psychological profile. This advocates a personalized approach in future studies in order to change lifestyle in cardiac patients.\u3c/p\u3

Personalized eHealth program for lifestyle change:results from the do cardiac health advanced new generated ecosystem (do CHANGE 2) randomized controlled trial

\u3cp\u3eOBJECTIVE: Unhealthy lifestyle factors have adverse outcomes in cardiac patients. However, only a minority of patients succeed to change unhealthy habits. Personalization of interventions may result in critical improvements. The current randomized controlled trial provides a proof of concept of the personalized Do CHANGE 2 intervention and evaluates effects on: 1) lifestyle, and 2) quality of life over time.\u3c/p\u3e\u3cp\u3eMETHODS: Cardiac patients (N=150; mean age=61.97±11.61 years; 28.7% women; heart failure N=33, coronary artery disease N=50, hypertension N=67) recruited from Spain and The Netherlands were randomized to either the 'Do CHANGE 2' or 'Care as Usual' group. The Do CHANGE 2 group received ambulatory health-behaviour assessment technologies for six months combined with a 3-month behavioural intervention program. Linear Mixed Models (LMM) analysis wERE used to evaluate the intervention effects and latent class analysis (LCA) was used for secondary subgroup analysis.\u3c/p\u3e\u3cp\u3eRESULTS: LMM analysis showed significant intervention effects for lifestyle behaviour (Finteraction(2,138.5)=5.97, p =.003), with improvement of lifestyle behaviour in the intervention group. For quality of life, no significant main effect (F(1,138.18)=.58, p=.447) or interaction effect (F(2,133.1)=0.41, p=.67) were found. Secondary LCA revealed different subgroups of patients per outcome measure. The intervention was experienced as useful and feasible.\u3c/p\u3e\u3cp\u3eCONCLUSION: The personalized eHealth intervention resulted in significant improvements in lifestyle. Cardiac patients and health care providers were also willing to engage in this personalized digital behavioural intervention program. Incorporating eHealth lifestyle programs as part of secondary prevention would be particularly useful when taking into account which patients are most likely to benefit.\u3c/p\u3e\u3cp\u3eTRIAL REGISTRATION: https://clinicaltrials.gov/ct2/show/NCT03178305.\u3c/p\u3

Personalized eHealth program for life-style change:Results from the "do Cardiac health advanced new generated ecosystem (Do CHANGE 2)" randomized controlled trial

OBJECTIVE: Unhealthy lifestyle factors have adverse outcomes in cardiac patients. However, only a minority of patients succeed to change unhealthy habits. Personalization of interventions may result in critical improvements. The current randomized controlled trial provides a proof of concept of the personalized Do CHANGE 2 intervention and evaluates effects on: 1) lifestyle, and 2) quality of life over time. METHODS: Cardiac patients (N=150; mean age=61.97±11.61 years; 28.7% women; heart failure N=33, coronary artery disease N=50, hypertension N=67) recruited from Spain and The Netherlands were randomized to either the 'Do CHANGE 2' or 'Care as Usual' group. The Do CHANGE 2 group received ambulatory health-behaviour assessment technologies for six months combined with a 3-month behavioural intervention program. Linear Mixed Models (LMM) analysis wERE used to evaluate the intervention effects and latent class analysis (LCA) was used for secondary subgroup analysis. RESULTS: LMM analysis showed significant intervention effects for lifestyle behaviour (Finteraction(2,138.5)=5.97, p =.003), with improvement of lifestyle behaviour in the intervention group. For quality of life, no significant main effect (F(1,138.18)=.58, p=.447) or interaction effect (F(2,133.1)=0.41, p=.67) were found. Secondary LCA revealed different subgroups of patients per outcome measure. The intervention was experienced as useful and feasible. CONCLUSION: The personalized eHealth intervention resulted in significant improvements in lifestyle. Cardiac patients and health care providers were also willing to engage in this personalized digital behavioural intervention program. Incorporating eHealth lifestyle programs as part of secondary prevention would be particularly useful when taking into account which patients are most likely to benefit. TRIAL REGISTRATION: https://clinicaltrials.gov/ct2/show/NCT03178305

Turning the Crisis Into an Opportunity: Digital Health Strategies Deployed During the COVID-19 Outbreak

Coronavirus SARS-CoV-2; COVID-19; 2019-nCoV; Digital health; eHealth; Telemedicine; Public healthCoronavirus SARS-CoV-2; COVID-19; 2019-nCoV; Salud digital; eSalud; Telemedicina; Salud públicaCoronavirus SARS-CoV-2; COVID-19; 2019-nCoV; Salut digital; eSalut; Telemedicina; Salut públicaDigital health technologies offer significant opportunities to reshape current health care systems. From the adoption of electronic medical records to mobile health apps and other disruptive technologies, digital health solutions have promised a better quality of care at a more sustainable cost. However, the widescale adoption of these solutions is lagging behind. The most adverse scenarios often provide an opportunity to develop and test the capacity of digital health technologies to increase the efficiency of health care systems. Catalonia (Northeast Spain) is one of the most advanced regions in terms of digital health adoption across Europe. The region has a long tradition of health information exchange in the public health care sector and is currently implementin an ambitious digital health strategy. In this viewpoint, we discuss the crucial role digital health solutions play during the coronavirus disease (COVID-19) pandemic to support public health policies. We also report on the strategies currently deployed at scale during the outbreak in Cataloni

Dimensionality of the system usability scale among professionals using internet-based interventions for depression: a confirmatory factor analysis

Background The System Usability Scale (SUS) is used to measure usability of internet-based Cognitive Behavioural Therapy (iCBT). However, whether the SUS is a valid instrument to measure usability in this context is unclear. The aim of this study is to assess the factor structure of the SUS, measuring usability of iCBT for depression in a sample of professionals. In addition, the psychometric properties (reliability, convergent validity) of the SUS were tested. Methods A sample of 242 professionals using iCBT for depression from 6 European countries completed the SUS. Confirmatory Factor Analysis (CFA) was conducted to test whether a one-factor, two-factor, tone-model or bi-direct model would fit the data best. Reliability was assessed using complementary statistical indices (e.g. omega). To assess convergent validity, the SUS total score was correlated with an adapted Client Satisfaction Questionnaire (CSQ-3). Results CFA supported the one-factor, two-factor and tone-model, but the bi-factor model fitted the data best (Comparative Fit Index = 0.992, Tucker Lewis Index = 0.985, Root Mean Square Error of Approximation = 0.055, Standardized Root Mean Square Residual = 0.042 (respectively χ2diff (9) = 69.82, p < 0.001; χ2diff (8) = 33.04, p < 0.001). Reliability of the SUS was good (ω = 0.91). The total SUS score correlated moderately with the CSQ-3 (CSQ1 rs = .49, p < 0.001; CSQ2 rs = .46, p < 0.001; CSQ3 rs = .38, p < 0.001), indicating convergent validity. Conclusions Although the SUS seems to have a multidimensional structure, the best model showed that the total sumscore of the SUS appears to be a valid and interpretable measure to assess the usability of internet-based interventions when used by professionals in mental healthcare