Guide Development for eHealth Interventions Targeting People With a Low Socioeconomic Position: Participatory Design Approach.

BACKGROUND: People with a low socioeconomic position (SEP) are less likely to benefit from eHealth interventions, exacerbating social health inequalities. Professionals developing eHealth interventions for this group face numerous challenges. A comprehensive guide to support these professionals in their work could mitigate these inequalities. OBJECTIVE: We aimed to develop a web-based guide to support professionals in the development, adaptation, evaluation, and implementation of eHealth interventions for people with a low SEP. METHODS: This study consisted of 2 phases. The first phase involved a secondary analysis of 2 previous qualitative and quantitative studies. In this phase, we synthesized insights from the previous studies to develop the guide's content and information structure. In the second phase, we used a participatory design process. This process included iterative development and evaluation of the guide's design with 11 professionals who had experience with both eHealth and the target group. We used test versions (prototypes) and think-aloud testing combined with semistructured interviews and a questionnaire to identify design requirements and develop and adapt the guide accordingly. RESULTS: The secondary analysis resulted in a framework of recommendations for developing the guide, which was categorized under 5 themes: development, reach, adherence, evaluation, and implementation. The participatory design process resulted in 16 requirements on system, content, and service aspects for the design of the guide. For the system category, the guide was required to have an open navigation strategy leading to more specific information and short pages with visual elements. Content requirements included providing comprehensible information, scientific evidence, a user perspective, information on practical applications, and a personal and informal tone of voice. Service requirements involved improving suitability for different professionals, ensuring long-term viability, and a focus on implementation. Based on these requirements, we developed the final version of "the inclusive eHealth guide." CONCLUSIONS: The inclusive eHealth guide provides a practical, user-centric tool for professionals aiming to develop, adapt, evaluate, and implement eHealth interventions for people with a low SEP, with the aim of reducing health disparities in this population. Future research should investigate its suitability for different end-user goals, its external validity, its applicability in specific contexts, and its real-world impact on social health inequality.

Cost-effectiveness of Cardiac Telerehabilitation With Relapse Prevention for the Treatment of Patients With Coronary Artery Disease in the Netherlands.

Importance: Cardiac telerehabilitation (CTR) has been found to be a safe and beneficial alternative to traditional center-based cardiac rehabilitation (CR) and might be associated with higher participation rates by reducing barriers to CR use. However, implementation of CTR interventions remains low, which may be owing to a lack of cost-effectiveness analyses of data from large-scale randomized clinical trials. Objective: To assess the cost-effectiveness of CTR with relapse prevention compared with center-based CR among patients with coronary artery disease. Design, Setting, and Participants: This economic evaluation performed a cost-utility analysis of data from the SmartCare-CAD (Effects of Cardiac Telerehabilitation in Patients With Coronary Artery Disease Using a Personalized Patient-Centred ICT Platform) randomized clinical trial. The cost-effectiveness and utility of 3 months of cardiac telerehabilitation followed by 9 months of relapse prevention were compared with the cost-effectiveness of traditional center-based cardiac rehabilitation. The analysis included 300 patients with stable coronary artery disease who received care at a CR center serving 2 general hospitals in the Netherlands between May 23, 2016, and July 26, 2018. All patients were entering phase 2 of outpatient CR and were followed up for 1 year (until August 14, 2019). Data were analyzed from September 21, 2020, to September 24, 2021. Intervention: After baseline measurements were obtained, participants were randomly assigned on a 1:1 ratio to receive CTR (intervention group) or center-based CR (control group) using computerized block randomization. After 6 supervised center-based training sessions, patients in the intervention group continued training at home using a heart rate monitor and accelerometer. Patients uploaded heart rate and physical activity data and discussed their progress during a weekly video consultation with their physical therapist. After 3 months, weekly coaching was concluded, and on-demand coaching was initiated for relapse prevention; patients were instructed to continue using their wearable sensors and were contacted in cases of nonadherence to the intervention or reduced exercise or physical activity volumes. Main Outcomes and Measures: Quality-adjusted life-years were assessed using the EuroQol 5-Dimension 5-Level survey (EQ-5D-5L) and the EuroQol Visual Analogue Scale (EQ-VAS), and cardiac-associated health care costs and non-health care costs were measured by health care consumption, productivity, and informal care questionnaires (the Medical Consumption Questionnaire, the Productivity Cost Questionnaire, and the Valuation of Informal Care Questionnaire) designed by the Institute for Medical Technology Assessment. Costs were converted to 2020 price levels (in euros) using the Dutch consumer price index (to convert to US dollars, euro values were multiplied by 1.142, which was the mean exchange rate in 2020). Results: Among 300 patients (266 men [88.7%]), the mean (SD) age was 60.7 (9.5) years. The quality of life among patients receiving CTR vs center-based CR was comparable during the study according to the results of both utility measures (mean difference on EQ-5D-5L: -0.004; P = .82; mean difference on EQ-VAS: -0.001; P = .92). Intervention costs were significantly higher for CTR (mean [SE], €224 [€4] [$256 ($4)]) compared with center-based CR (mean [SE], €156 [€5] [$178 ($6)]; P < .001); however, no difference in overall cardiac health care costs was observed between CTR (mean [SE], €4787 [€503] [$5467 ($574)] and center-based CR (mean [SE], €5507 [€659] [$6289 ($753)]; P = .36). From a societal perspective, CTR was associated with lower costs compared with center-based CR (mean [SE], €20 495 [€ 2751] [$23 405 ($3142)] vs €24 381 [€3613] [$27 843 ($4126)], respectively), although this difference was not statistically significant (-€3887 [-$4439]; P = .34). Conclusions and Relevance: In this economic evaluation, a CTR intervention with relapse prevention was likely to be cost-effective compared with center-based CR, suggesting that CTR maybe used as an alternative intervention for the treatment of patients with coronary artery disease. These results add to the evidence base in favor of CTR and may increase the implementation of CTR interventions in clinical practice.

Attitudes Toward Health, Healthcare, and eHealth of People With a Low Socioeconomic Status: A Community-Based Participatory Approach.

Low socioeconomic status (SES) is associated with a higher prevalence of unhealthy lifestyles compared to a high SES. Health interventions that promote a healthy lifestyle, like eHealth solutions, face limited adoption in low SES groups. To improve the adoption of eHealth interventions, their alignment with the target group's attitudes is crucial. This study investigated the attitudes of people with a low SES toward health, healthcare, and eHealth. We adopted a mixed-method community-based participatory research approach with 23 members of a community center in a low SES neighborhood in the city of Rotterdam, the Netherlands. We conducted a first set of interviews and analyzed these using a grounded theory approach resulting in a group of themes. These basic themes' representative value was validated and refined by an online questionnaire involving a different sample of 43 participants from multiple community centers in the same neighborhood. We executed three focus groups to validate and contextualize the results. We identified two general attitudes based on nine profiles toward health, healthcare, and eHealth. The first general attitude, optimistically engaged, embodied approximately half our sample and involved light-heartedness toward health, loyalty toward healthcare, and eagerness to adopt eHealth. The second general attitude, doubtfully disadvantaged, represented roughly a quarter of our sample and was related to feeling encumbered toward health, feeling disadvantaged within healthcare, and hesitance toward eHealth adoption. The resulting attitudes strengthen the knowledge of the motivation and behavior of people with low SES regarding their health. Our results indicate that negative health attitudes are not as evident as often claimed. Nevertheless, intervention developers should still be mindful of differentiating life situations, motivations, healthcare needs, and eHealth expectations. Based on our findings, we recommend eHealth should fit into the person's daily life, ensure personal communication, be perceived usable and useful, adapt its communication to literacy level and life situation, allow for meaningful self-monitoring and embody self-efficacy enhancing strategies.

Proof of concept of a 45-second cardiorespiratory fitness self-test for coronary artery disease patients based on accelerometry.

Cardiorespiratory fitness (CRF) provides important diagnostic and prognostic information. It is measured directly via laboratory maximal testing or indirectly via submaximal protocols making use of predictor parameters such as submaximal [Formula: see text], heart rate, workload, and perceived exertion. We have established an innovative methodology, which can provide CRF prediction based only on body motion during a periodic movement. Thirty healthy subjects (40% females, 31.3 ± 7.8 yrs, 25.1 ± 3.2 BMI) and eighteen male coronary artery disease (CAD) (56.6 ± 7.4 yrs, 28.7 ± 4.0 BMI) patients performed a [Formula: see text] test on a cycle ergometer as well as a 45 second squatting protocol at a fixed tempo (80 bpm). A tri-axial accelerometer was used to monitor movements during the squat exercise test. Three regression models were developed to predict CRF based on subject characteristics and a new accelerometer-derived feature describing motion decay. For each model, the Pearson correlation coefficient and the root mean squared error percentage were calculated using the leave-one-subject-out cross-validation method (rcv, RMSEcv). The model built with all healthy individuals' data showed an rcv = 0.68 and an RMSEcv = 16.7%. The CRF prediction improved when only healthy individuals with normal to lower fitness (CRF<40 ml/min/kg) were included, showing an rcv = 0.91 and RMSEcv = 8.7%. Finally, our accelerometry-based CRF prediction CAD patients, the majority of whom taking ß-blockers, still showed high accuracy (rcv = 0.91; RMSEcv = 9.6%). In conclusion, motion decay and subject characteristics could be used to predict CRF in healthy people as well as in CAD patients taking ß-blockers, accurately. This method could represent a valid alternative for patients taking ß-blockers, but needs to be further validated in a larger population.

The influence of training characteristics on the effect of exercise training in patients with coronary artery disease: Systematic review and meta-regression analysis.

BACKGROUND: Although exercise-based cardiac rehabilitation improves exercise capacity of coronary artery disease patients, it is unclear which training characteristic determines this improvement. Total energy expenditure and its constituent training characteristics (training intensity, session frequency, session duration and programme length) vary considerably among clinical trials, making it hard to compare studies directly. Therefore, we performed a systematic review and meta-regression analysis to assess the effect of total energy expenditure and its constituent training characteristics on exercise capacity. METHODS: We identified randomised controlled trials comparing continuous aerobic exercise training with usual care for patients with coronary artery disease. Studies were included when training intensity, session frequency, session duration and programme length was described, and exercise capacity was reported in peakVO2. Energy expenditure was calculated from the four training characteristics. The effect of training characteristics on exercise capacity was determined using mixed effects linear regression analyses. The analyses were performed with and without total energy expenditure as covariate. RESULTS: Twenty studies were included in the analyses. The mean difference in peakVO2 between the intervention group and control group was 3.97ml·min-1·kg-1 (p<0.01, 95% CI 2.86 to 5.07). Total energy expenditure was significantly related to improvement of exercise capacity (effect size 0.91ml·min-1·kg-1 per 100J·kg, p<0.01, 95% CI 0.77 to 1.06), no effect was found for its constituent training characteristics after adjustment for total energy expenditure. CONCLUSIONS: We conclude that the design of an exercise programme should primarily be aimed at optimising total energy expenditure rather than on one specific training characteristic.

Clinical and cost-effectiveness of home-based cardiac rehabilitation compared to conventional, centre-based cardiac rehabilitation: Results of the FIT@Home study.

Aim Although cardiac rehabilitation improves physical fitness after a cardiac event, many eligible patients do not participate in cardiac rehabilitation and the beneficial effects of cardiac rehabilitation are often not maintained over time. Home-based training with telemonitoring guidance could improve participation rates and enhance long-term effectiveness. Methods and results We randomised 90 low-to-moderate cardiac risk patients entering cardiac rehabilitation to three months of either home-based training with telemonitoring guidance or centre-based training. Although training adherence was similar between groups, satisfaction was higher in the home-based group ( p = 0.02). Physical fitness improved at discharge ( p < 0.01) and at one-year follow-up ( p < 0.01) in both groups, without differences between groups (home-based p = 0.31 and centre-based p = 0.87). Physical activity levels did not change during the one-year study period (centre-based p = 0.38, home-based p = 0.80). Healthcare costs were statistically non-significantly lower in the home-based group (€437 per patient, 95% confidence interval -562 to 1436, p = 0.39). From a societal perspective, a statistically non-significant difference of €3160 per patient in favour of the home-based group was found (95% confidence interval -460 to 6780, p = 0.09) and the probability that it was more cost-effective varied between 97% and 75% (willingness-to-pay of €0 and €100,000 per quality-adjusted life-years, respectively). Conclusion We found no differences between home-based training with telemonitoring guidance and centre-based training on physical fitness, physical activity level or health-related quality of life. However, home-based training was associated with a higher patient satisfaction and appears to be more cost-effective than centre-based training. We conclude that home-based training with telemonitoring guidance can be used as an alternative to centre-based training for low-to-moderate cardiac risk patients entering cardiac rehabilitation.

Effects of cardiac telerehabilitation in patients with coronary artery disease using a personalised patient-centred web application: protocol for the SmartCare-CAD randomised controlled trial.

BACKGROUND: Cardiac rehabilitation has beneficial effects on morbidity and mortality in patients with coronary artery disease, but is vastly underutilised and short-term improvements are often not sustained. Telerehabilitation has the potential to overcome these barriers, but its superiority has not been convincingly demonstrated yet. This may be due to insufficient focus on behavioural change and development of patients' self-management skills. Moreover, potentially beneficial communication methods, such as internet and video consultation, are rarely used. We hypothesise that, when compared to centre-based cardiac rehabilitation, cardiac telerehabilitation using evidence-based behavioural change strategies, modern communication methods and on-demand coaching will result in improved self-management skills and sustainable behavioural change, which translates to higher physical activity levels in a cost-effective way. METHODS: This randomised controlled trial compares cardiac telerehabilitation with centre-based cardiac rehabilitation in patients with coronary artery disease. We randomise 300 patients entering cardiac rehabilitation to centre-based cardiac rehabilitation (control group) or cardiac telerehabilitation (intervention group). The core component of the intervention is a patient-centred web application, which enables patients to adjust rehabilitation goals, inspect training and physical activity data, share data with other caregivers and to use video consultation. After six supervised training sessions, the intervention group continues exercise training at home, wearing an accelerometer and heart rate monitor. In addition, physical activity levels are assessed by the accelerometer for four days per week. Patients upload training and physical activity data weekly and receive feedback through video consultation once a week. After completion of the rehabilitation programme, on-demand coaching is performed when training adherence or physical activity levels decline with 50% or more. The primary outcome measure is physical activity level, assessed at baseline, three months and twelve months, and is calculated from accelerometer and heart rate data. Secondary outcome measures include physical fitness, quality of life, anxiety and depression, patient empowerment, patient satisfaction and cost-effectiveness. DISCUSSION: This study is one of the first studies evaluating effects and costs of a cardiac telerehabilitation intervention comprising a combination of modern technology and evidence-based behavioural change strategies including relapse prevention. We hypothesise that this intervention has superior effects on exercise behaviour without exceeding the costs of a traditional centre-based intervention. TRIAL REGISTRATION: Netherlands Trial Register NTR5156 . Registered 22 April 2015.

Energy expenditure estimation in beta-blocker-medicated cardiac patients by combining heart rate and body movement data.

BACKGROUND: Accurate assessment of energy expenditure provides an opportunity to monitor physical activity during cardiac rehabilitation. However, the available assessment methods, based on the combination of heart rate (HR) and body movement data, are not applicable for patients using beta-blocker medication. Therefore, we developed an energy expenditure prediction model for beta-blocker-medicated cardiac rehabilitation patients. METHODS: Sixteen male cardiac rehabilitation patients (age: 55.8 ± 7.3 years, weight: 93.1 ± 11.8 kg) underwent a physical activity protocol with 11 low- to moderate-intensity common daily life activities. Energy expenditure was assessed using a portable indirect calorimeter. HR and body movement data were recorded during the protocol using unobtrusive wearable devices. In addition, patients underwent a symptom-limited exercise test and resting metabolic rate assessment. Energy expenditure estimation models were developed using multivariate regression analyses based on HR and body movement data and/or patient characteristics. In addition, a HR-flex model was developed. RESULTS: The model combining HR and body movement data and patient characteristics showed the highest correlation and lowest error (r2 = 0.84, root mean squared error = 0.834 kcal/minute) with total energy expenditure. The method based on individual calibration data (HR-flex) showed lower accuracy (i2 = 0.83, root mean squared error = 0.992 kcal/minute). CONCLUSIONS: Our results show that combining HR and body movement data improves the accuracy of energy expenditure prediction models in cardiac patients, similar to methods that have been developed for healthy subjects. The proposed methodology does not require individual calibration and is based on the data that are available in clinical practice.

Effects of home-based training with telemonitoring guidance in low to moderate risk patients entering cardiac rehabilitation: short-term results of the FIT@Home study.

BACKGROUND: Home-based exercise training in cardiac rehabilitation (CR) has the potential to improve CR uptake, decrease costs and increase self-management skills. The FIT@Home study evaluates home-based CR with telemonitoring guidance using coaching interventions including strategies for behavioural changes with the aim to maintain adherence to a healthy lifestyle and to improve long-term effects. In this interim analysis we provide short-term results on exercise capacity, quality of life and training adherence of the first 50 patients included in the FIT@Home study. DESIGN: The study design was a randomised controlled trial. METHODS: Low to moderate risk CR patients were randomised to a 12-week home-based training (HT) programme or a 12-week centre-based training (CT) programme. In both groups, training was performed at 70-85% of maximal heart rate (HRmax) for 45-60 min, 2-3 times per week. The HT group received three supervised training sessions, before commencing training with a heart rate monitor in their home environment. These patients received individual coaching by telephone weekly, based on training data uploaded on the Internet. The CT programme was performed under the direct supervision of a physical therapist. Exercise capacity and health-related quality of life were assessed at baseline and at 12 weeks. RESULTS: CT (n = 25) and HT (n = 25) both showed a significant improvement in peak oxygen uptake (peak VO2) (10% and 14% respectively) and quality of life after 12 weeks of training, without significant between-group differences. The average training intensity of the HT group was 73.3 ± 3.5% of HRmax. Training adherence was similar between groups. CONCLUSION: This analysis shows that HT with telemonitoring guidance has similar short-term effects on exercise capacity and quality of life as CT in CR patients.

Effects and costs of home-based training with telemonitoring guidance in low to moderate risk patients entering cardiac rehabilitation: The FIT@Home study.

BACKGROUND: Physical training has beneficial effects on exercise capacity, quality of life and mortality in patients after a cardiac event or intervention and is therefore a core component of cardiac rehabilitation. However, cardiac rehabilitation uptake is low and effects tend to decrease after the initial rehabilitation period. Home-based training has the potential to increase cardiac rehabilitation uptake, and was shown to be safe and effective in improving short-term exercise capacity. Long-term effects on physical fitness and activity, however, are disappointing. Therefore, we propose a novel strategy using telemonitoring guidance based on objective training data acquired during exercise at home. In this way, we aim to improve self-management skills like self-efficacy and action planning for independent exercise and, consequently, improve long-term effectiveness with respect to physical fitness and physical activity. In addition, we aim to compare costs of this strategy with centre-based cardiac rehabilitation. METHODS/DESIGN: This randomized controlled trial compares a 12-week telemonitoring guided home-based training program with a regular, 12-week centre-based training program of equal duration and training intensity in low to moderate risk patients entering cardiac rehabilitation after an acute coronary syndrome or cardiac intervention. The home-based group receives three supervised training sessions before they commence training with a heart rate monitor in their home environment. Participants are instructed to train at 70-85% of their maximal heart rate for 45-60 minutes, twice a week. Patients receive individual coaching by telephone once a week, based on measured heart rate data that are shared through the internet. Primary endpoints are physical fitness and physical activity, assessed at baseline, after 12 weeks and after one year. Physical fitness is expressed as peak oxygen uptake, assessed by symptom limited exercise testing with gas exchange analysis; physical activity is expressed as physical activity energy expenditure, assessed by tri-axial accelerometry and heart rate measurements. Secondary endpoints are training adherence, quality of life, patient satisfaction and cost-effectiveness. DISCUSSION: This study will increase insight in long-term effectiveness and costs of home-based cardiac rehabilitation with telemonitoring guidance. This strategy is in line with the trend to shift non-complex healthcare services towards patients' home environments. TRIAL REGISTRATION: Dutch Trial Register: NTR3780. Clinicaltrials.gov register: NCT01732419.

Assessing gait adaptability in people with a unilateral amputation on an instrumented treadmill with a projected visual context.

BACKGROUND: Gait adaptability, including the ability to avoid obstacles and to take visually guided steps, is essential for safe movement through a cluttered world. This aspect of walking ability is important for regaining independent mobility but is difficult to assess in clinical practice. OBJECTIVE: The objective of this study was to investigate the validity of an instrumented treadmill with obstacles and stepping targets projected on the belt's surface for assessing prosthetic gait adaptability. DESIGN: This was an observational study. METHODS: A control group of people who were able bodied (n=12) and groups of people with transtibial (n=12) and transfemoral (n=12) amputations participated. Participants walked at a self-selected speed on an instrumented treadmill with projected visual obstacles and stepping targets. Gait adaptability was evaluated in terms of anticipatory and reactive obstacle avoidance performance (for obstacles presented 4 steps and 1 step ahead, respectively) and accuracy of stepping on regular and irregular patterns of stepping targets. In addition, several clinical tests were administered, including timed walking tests and reports of incidence of falls and fear of falling. RESULTS: Obstacle avoidance performance and stepping accuracy were significantly lower in the groups with amputations than in the control group. Anticipatory obstacle avoidance performance was moderately correlated with timed walking test scores. Reactive obstacle avoidance performance and stepping accuracy performance were not related to timed walking tests. Gait adaptability scores did not differ in groups stratified by incidence of falls or fear of falling. LIMITATIONS: Because gait adaptability was affected by walking speed, differences in self-selected walking speed may have diminished differences in gait adaptability between groups. CONCLUSIONS: Gait adaptability can be validly assessed by use of an instrumented treadmill with a projected visual context. When walking speed is taken into account, this assessment provides unique, quantitative information about walking ability in people with a lower-limb amputation.