1. Introduction
stress reduction
CARDIOVASCULAR DISEASES
Detrimental effects of chronic stress on healthcare professionals have been well-established, but the implementation and evaluation of effective interventions aimed at improving distress coping remains inadequate. Individualized mHealth interventions incorporating sensor feedback have been proposed as a promising approach. This study aimed to investigate the impact of individualized, sensor-based mHealth interventions focusing on stress and physical activity on distress coping in healthcare professionals. The study utilized a multi-arm, parallel group randomized controlled trial design, comparing five intervention groups (three variations of web-based training and two variations of an app training) that represented varying levels of individualization to a control group. Both self-reported questionnaire data (collected using Limesurvey) as well as electrocardiography and accelerometry-based sensory data (collected using Mesana Sensor) were assessed at baseline and post-intervention (after eight weeks). Of the 995 eligible participants, 170 (26%) completed the post-intervention measurement (Group 1: N = 21; Group 2: N = 23; Group 3: N = 7; Group 4: N = 34; Group 5: N = 16; Control Group: N = 69). MANOVA results indicated small to moderate time-by-group interaction effects for physical activity-related outcomes, including moderate to vigorous physical activity (F(1,5) = 5.8, Occupational psychosocial stress can increase the risk of developing psychological, musculoskeletal, or cardiovascular diseases [In general, psychosocial stressful stimuli activate neuronal, neuroendocrine, and endocrine pathways [While health complaints are frequently observed, there are personal and organizational resources which can improve resilience toward occupational stress [Within the health care sector, efficacious stress reduction programs include yoga and qigong [Despite the plethora of studies confirming the efficacy of stress reduction interventions, the evidence for health personnel is insufficient. Study rigor issues, for instance low total intervention time, small sample sizes, and high dropout rates, undermine intervention quality [Digital health promotion programs can come in different modalities: web-based trainings (WBT) are presented on a secured online platform and assessed through an internet browser either on a smartphone or on a computer/laptop [Potential opportunities for individualization are (1) the adaptation of intervention content to individual needs for behavior change, (2) individual coaching based on intervention results, (3) direct biofeedback via app and sensor interfaces, (4) visualization of health data, e.g., in the form of health dashboards or health reports, or (5) the adaptation of content based on psychological characteristics, such as personality traits. Needs assessment, health reports, and coaching have already been discussed above. The most commonly used devices in biofeedback differed depending on the outcome of the evaluation. For physical activity parameters ActiGraph accelerometer (Actigraph, LLC, Pensacola, FL, USA, SenseWear wristbands (BodyMedia, Inc., Pittsburgh, PA, USA), Actical (Mini Mitter Co., Inc., Bend, OR, USA), or Active style Pro (Omron Healthcare Co., Ltd., Kyoto, Japan) are the most common [Advantages of accelerometer sensors include: (1) High sensitivity: Accelerometer sensors are highly sensitive and can accurately measure even small movements or changes in position; (2) Compact size: Accelerometer sensors are small and lightweight, making them easy to incorporate into a variety of devices and systems; (3) Low power consumption: Accelerometer sensors have low power requirements and can operate for long periods of time without needing to be recharged; (4) Versatility: Accelerometer sensors can be used in a wide range of applications, including motion sensing, activity tracking, and gesture recognition. Potential disadvantages of accelerometer sensors include: (1) Limited accuracy: While accelerometer sensors are highly sensitive, they may not be as accurate as other types of sensors, such as gyroscopes, in certain applications; (2) Vulnerability to noise: Accelerometer sensors may be prone to interference or “noise” from external sources, which can affect their accuracy and performance; (3) Limited range: Accelerometer sensors may have a limited range of movement or acceleration that they can measure, depending on the specific device or application. Nevertheless, accelerometer sensors are useful and versatile devices that have a wide range of applications. However, it is important to consider the potential limitations and challenges of using accelerometer sensors in order to ensure the best possible performance and accuracy.For stress-related parameters, photoplethysmography (PPG)-based wearable devices such as earlobe sensors, blood pressure monitors, finger bracelets and wristwatches or electrocardiogram (ECG)-based devices such as chest belts or patches are commonly used, with the latter exhibiting higher sensitivity and specificity values [Beside sensory based biofeedback, another approach to design individualized digital solutions could be the integration of personality traits. A smartphone app that focuses on stress reduction may firstly focus on personality characteristics, as studies showed that personality characteristics are associated with specific coping behavior [Therefore, the present study aims to compare both web-based vs. app-based and individualized vs. non-individualized stress management interventions in terms of their effectiveness. The main research question is whether eight weeks of differentially individualized sensor-based mHealth interventions (1 = WBT, 2 = WBT + Need, 3 = WBT + Need + Coaching; 4 = APP + Biofeedback, 5 = App + Biofeedback + Healthreport) focusing on stress management and physical activity can impact HRV-related stress parameters (SDNN, RMSSD, LFHF, and Baevsky Index) and accelerometry related physical activity parameters (Steps, MVPA, Inactivity, and Inactivity disruption) and therefore improve distress coping in health professions. We hypothesize that individualized interventions will have small to moderate positive effects for physical activity and stress-related outcomes in relation to distress coping in health professions, whereas non-individualized interventions will not show significant effects. To the best of our knowledge, this study complements the described existing body of research and is the first to:Scientifically validate a sensor-based mHealth intervention for distress coping in the healthcare setting.Compare the efficacy of different individualization levels of mHealth interventions by a multi-arm study design.Combine components of physical activity and relaxation techniques in an mHealth application using a multimodal intervention approach to improve distress coping among healthcare professionals.Measure multiple clinically relevant stress and physical activity-related outcomes during the intervention and adapt the content of the intervention based on these measures.Enable unrestricted implementation within the daily work routine of healthcare professionals by means of a mobile and low-threshold intervention.