Health apps are supposed to support fighting sedentary lifestyles and, consequently, a variety of chronic diseases. For promoting physical activity in a sustained manner, these apps and corresponding research draw upon a variety of behavior change techniques and visualizations. To provide a structured overview of recent approaches and identify research gaps, we conducted a systematic literature review of empirical research works on app-based approaches for promoting everyday physical activity. In the 42 relevant studies identified, we thoroughly analyzed the applied behavior change techniques and in-app visualization types. We found a recent emphasis on feedback and monitoring as well as goal setting techniques, while the application of others such as informing about health consequences or shaping the user's knowledge are applied only in rare cases. The range of visualization types is limited. Traditional charts and gamified illustrations turned out to be predominant. However, empirical research on alternative approaches such as innovative chart visualizations is scarce.

Compared to parents of healthy children, parents of severely or chronically ill children have significantly worse physical and mental health and a lower quality of life, e.g. because of lack of sleep. The proposed solution aims at assisting caregivers by means of a remote monitoring service run by professional nursing staff which should allow parents to get a good night’s sleep. A smart algorithm has been developed to detect if a particular parameter (heart rate, respiration rate or oxygen saturation) has exceeded a pre-defined threshold and thus may imply an emergency. Parents are only alerted after a professional nurse in the monitoring centre has cross-checked vital parameter trends and carried out an audio-visual inspection. The quality and accuracy of the system has been validated through iterative testing including a test performed in a children’s hospital to ensure that the monitoring system is not inferior to a hospital set-up.

Health care delivery is undergoing a rapid change from traditional processes toward the use of digital health interventions and personalized medicine. This movement has been accelerated by the COVID-19 crisis as a response to the need to guarantee access to health care services while reducing the risk of contagion. Digital health scale-up is now also vital to achieve population-wide impact: it will only accomplish sustainable effects if and when deployed into regular health care delivery services. The question of how sustainable digital health scale-up can be successfully achieved has, however, not yet been sufficiently resolved. This paper identifies and discusses enablers and barriers for scaling up digital health innovations. The results discussed in this paper were gathered by scientists and representatives of public bodies as well as patient organizations at an international workshop on scaling up digital health innovations. Results are explored in the context of prior research and implications for future work in achieving large-scale implementations that will benefit the population as a whole.

Der digitale Wandel führt zu mehr Flexibilisierung, welche sich auch auf das betriebliche Gesundheitsmanagement (BGM) auswirkt. Die Arbeitswelt 4.0 birgt Chancen wie mehr Selbstbestimmung und bessere Vereinbarkeit von Beruf und Familie, aber auch Risiken wie psychische Überlastung und interessierte Selbstgefährdung, besonders in Organisationen mit indirekter Steuerung. Digitale Lösungen können eine wichtige Rolle bei der Stressbewältigung spielen und klassische BGM-Maßnahmen ergänzen.

Healthcare delivery is undergoing a rapid change from traditional processes towards the use of digital health interventions and personalized medicine. Hospitals and health care providers are introducing hospital information systems, electronic health records

The paper describes the design rationales and algorithms underlying SmartCoping, a mobile solution for recognizing stress based on the continuous monitoring of heart rate variability (HRV). A central contribution is the calibration of HRV values to each user of the SmartCoping app. Calibration is crucial since HRV varies greatly among people and must be interpreted individually. Calibration is implemented in two variants, one which requires user feedbacks on perceived stress levels during an initial learning phase and one where no feedback is needed, and a user’s maximal and minimal HRV values are determined during an initial usage phase. The variant with user feedbacks results in stress warnings which better reflect a user’s stress perception. The app can be used to trigger stress warnings, identify recurrent stress situations from the history data, and give relaxation support via a biofeedback component based on breathing exercises. SmartCoping helps people improve their self-perception and develop appropriate stress avoidance and coping strategies. The system has been evaluated in a field test with healthy volunteers.

This paper introduces an alternative approach to conventional pedometer apps which measure the wide-spread goal of 10,000 steps a day. Instead we focus on the intensity of physical activity, which is in line with recent recommendations of renowned health institutions such as the WHO. These promote a minimum of moderate to vigorous physically active time per week to achieve the desired health benefits. The paper discusses how the guidelines have been implemented. It also outlines how we help maintain user motivation over time (e.g. by integrating and personalising "nudges") and how we intend to solve the challenges posed by different fitness levels and personal lifestyles.

BACKGROUND:Most people wish to die at home, but most people in Switzerland die in hospitals or nursing homes. Family caregivers often offer support so patients with palliative care needs can stay at home for as long as possible. However, crises and unplanned hospital admissions often occur in this setting because of family caregiver strain and symptom severity in patients. The so-called smart devices such as wearables or smartphones offer the opportunity to continuously monitor certain parameters and recording symptom deteriorations. By providing professionals with this information in a timely manner, crises in the home could be avoided.OBJECTIVE:The aim of this interdisciplinary study is to explore the symptom burden of people with palliative care needs who are cared for at home and to understand the development of crises in the home care setting. On the basis of the findings from this study, we will develop an early warning system to stabilize the home care situation and to prevent critical events from happening, thereby reducing avoidable hospitalizations.METHODS:A mixed method study is being conducted consisting of 4 main consecutive phases: (1) developing the monitoring system; (2) pretesting the system and adapting it to user needs; (3) conducting the study in the palliative home care setting with approximately 40 patients; and (4) distinguishing symptom patterns from the collected data specific to crisis emergence, followed by the development of an early warning system to prevent such crises. In study phase 3, each patient will receive an upper arm sensor and a symptom diary to assess symptom burden related to patients and family caregivers. A within-case analysis will be conducted for each patient's situation followed by a cross-case comparison to identify certain symptom patterns that may predict symptom deterioration (study phase 4).RESULTS:The collaboration with the local mobile palliative care team for participant recruitment and data collection has been established. Recruitment is forthcoming.CONCLUSIONS:We expect the findings of this study to provide holistic insight into symptom burden and the well-being of patients with palliative care needs and of their family caregivers. This information will be used to develop an early warning system to avoid the occurrence of potential crises, thereby improving palliative care provision at home.INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID):PRR1-10.2196/13933.

The paper presents results from the SmartSleep project which aims at developing a smartphone app that gives users individual advice on how to change their behaviour to improve their sleep. The advice is generated by identifying correlations between behaviour during the day and sleep architecture. To this end, the project addressed two sub-tasks: detecting a user’s daytime behaviour and recognising sleep stages in an everyday setting. In the case of daytime activity detection the best results were achieved using an accelerometer at the wrist and another one at the ankle (87%). A subsequent smoothing step increased the accuracy to over 90%. For recognising sleep architecture we experimented with various consumer wearables that we used in addition to the usual PSG sensors in a sleep lab. Several sleep stage classifiers were learned from the resulting sensor data streams segmented into labelled sleep stages of 30 s each. Apart from handcrafted features we experimented with unsupervised feature learning based on the deep learning paradigm. Our best results for correctly classified sleep stages are between 86 and 90% for Wake, REM, N2 and N3, while the best recognition rate for N1 is 37%. Finally, we discuss a preliminary design of the algorithm for determining correlations between daytime behaviour and sleep architecture.

The paper describes a mobile solution for the early recognition and management of stress based on continuous monitoring of heart rate variability (HRV) and contextual data (activity, location, etc.). A central contribution is the automatic calibration of measured HRV values to perceived stress levels during an initial learning phase where the user provides feedback when prompted by the system. This is crucial as HRV varies greatly among people. A data mining component identifies recurrent stress situations so that people can develop appropriate stress avoidance and coping strategies. A biofeedback component based on breathing exercises helps users relax. The solution is being tested by healthy volunteers before conducting a clinical study with patients after alcohol detoxification.

The paper analyzes current weaknesses of behavioral change support systems such as the lack of adequately taking into account the heterogeneity of target users. Based on this analysis the paper presents an application framework that comprises various components to accommodate user preferences and to adapt system interventions to individual users: a goal hierarchy which users can tailor to their needs, dividing nudges into different types that correspond to speech acts, rules for context-specific triggering of nudges. User adaptation is realized with approaches from user modeling and collaborative filtering. The result is a self-learning application that changes in line with a user's progress, which is expected to enhance user acceptance and increase and sustain people's motivation for behavioral change. The application framework will be evaluated by comparing a mobile health app using the framework with a simplified version of the app that does not support user tailoring and adaptation.

People increasingly use the Internet for obtaining information regarding diseases, diagnoses and available treatments. Currently, many online health portals already provide non-personalized health information in the form of articles. However, it can be challenging to find information relevant to one's condition, interpret this in context, and understand the medical terms and relationships. Recommender Systems (RS) already help these systems perform precise information filtering. In this short paper, we look one step ahead and show the progress made towards RS helping users find personalized, complex medical interventions or support them with preventive healthcare measures. We identify key challenges that need to be addressed for RS to offer the kind of decision support needed in high-risk domains like healthcare.

The paper describes the development of a mobile solution based on smartphones and sensors for the early recognition of stress. The solution is based on real-time capture and analysis of vital data such as heart rate variability as well as activity and contextual data such as location and time of day. Individual recognition patterns for stress are derived from combining vital and contextual data by using subjective stress assessments via mood maps as additional input during an initial learning phase. The reliability of stress alerts and therapeutic impact will be tested in a clinic specialised on the treatment of alcoholics since stress tends to cause craving and therefore trigger relapses.