mHealth Technologies for Palliative Care Patients at the Interface of In-Patient to Outpatient Care: Protocol of Feasibility Study Aiming to Early Predict Deterioration of Patient’s Health Status

Introduction

Severely ill patients and their caregivers face many challenges. Suffering from a life-threatening disease not only confronts people with deeply existential fears that demand complex coping strategies. It also means to be subdued to numerous diagnostic and therapeutic interventions, which sometimes themselves include relevant risks of side effects and complications. In the last months of their lives many palliative care patients are afflicted with different significant symptoms, such as pain, nausea, dyspnea, fatigue, and sleeplessness, just to name the most frequently occurring complaints [1-3].

One of the particular critical phases in patients’ trajectories is discharge from hospital care. After structured and multiprofessional care within the hospital environment, maybe even at a specialized Palliative Care Unit, the quality of care at home depends on many local factors that are difficult or impossible to be changed: patients` environment, including housing, social, and family situation, as well as availability and training of local professional health services. Due to different initiatives on the federal and cantonal level, the availability of ambulant specialized palliative care services in Switzerland is on the rise. Still, most patients discharged from hospital are lost to follow-ups by a palliative care specialist. As a consequence, many patients present for unplanned readmissions at hospitals and emergency departments [4]. Depending on different settings, between 17% and 50% of these (re)admissions and emergency visits (EV) of cancer patients are deemed avoidable [5-7]. Men, lung cancer patients, patients with low continuity of care, and those not attending (ambulant) palliative care have been shown to be particularly vulnerable [8,9]. Main causes for EV in cancer patients are uncontrolled symptoms, predominantly pain, nausea, vomiting, constipation, and dyspnea followed by complications associated with cancer treatment [10-12]. Currently, disease trajectories leading to EV are not well understood and this is not only true for cancer patients, but also applying to patients with life-limiting chronic diseases (eg, heart failure); continuity of care seems to also be one of the key factors in both disease spectra [13,14]. Thus, better coordination and communication between services, establishing transitional palliative care (based in hospital, collaborating with primary care physicians) are repeatedly named as potentially effective tools to diminish unplanned readmissions and EVs [2,4,9,15].

Due to a dramatic development of mHealth the use of apps and texting for patients’ support, but also wireless tracking of physical activity using mobile phones and wearable sensors has been expeditiously adopted in western societies in the last few years. In 2015, 63% of the US adult population have owned a mobile phone device and even in the age group >65-years old, mobile phones were owned by 27% [16]. A study conducted in patients from a department of Internal Medicine in Los Angeles in 2014 to 2015 showed that 91% of those patients owned a mobile phone, with 76% of these reporting having Internet capability. Mobile apps were used by 75% of patients and 32% of these even used health apps [17]. The use of mobile phones and mobile sensor devices in medical research is also growing fast: almost US $1 billion in grant money was invested in mHealth research in 2013 resulting in approximately 1000 web-of-science publications in 2013 [18]. Reasons for this tremendous hope on mHealth are their possibilities to obtain various objective patient-related parameters in a noninvasive, passive, continuous, and real-time fashion.

Meanwhile mHealth has been explored in many diseases and patient groups, for example, in cardiovascular diseases [19], mental disorders [20,21], obese people [22], hemodialysis patients [23], chronic pain [24], children with cerebral palsy [25], pulmonary rehabilitation [26], Parkinson’s disease [27], and stressed persons [28]. Also, the collection of patient subjective reported outcome using mHealth is already established in oncology and yet has been proven feasible in palliative care patients of different age groups [29-33]. In a study with 162 cancer patients in different disease stages (early to advanced) plus 20 healthy controls, Ferrioli et al. [34] could demonstrate a strong correlation between daily physical activity monitored by an accelerometer sensor and disease stage, functional status, fatigue, and quality of life (QoL) in cancer patients [34]. Patients were equipped with a mobile sensor that was attached to the anterior thigh using adhesive dressings (thus, being much more uncomfortable than current bracelet devices); nevertheless, 98% of patients judged the sensor as acceptable and wore it 7 days as proposed. Arguments based on the assumption the population of elderly people could not adapt to the technical progress have been successfully refuted by several studies [35-37]. Consequently, the international scientific palliative care community promotes the use of mHealth for quite a while, but up to now there has not been much experience gained and published [38].

The overall aim of our feasibility study is to advance transitional care and continuity of care of seriously ill patients by application of mHealth technologies. We assume that by monitoring patients` activity using mHealth technology, we will be able to predict a decline of the patients` health status in time: this could then trigger intensified care by palliative care professionals. The future goal of our research is a clinical interventional trial, which aims at reducing unplanned hospital readmissions and EV of ambulant palliative care patients by the use of mHealth. The feasibility study presented in this paper will evaluate and optimize patients’ acceptance of mHealth technologies in the palliative care setting of patients discharged from in-patient care. We aim to analyze the correlation between patient’s behavioral patterns and changes in crucial symptoms as well as elaborate algorithms for trend detection.

Methods

Aims of the Study

The first aim of this study is to evaluate palliative care patients’ acceptance with regard to the supply with a commercially available wireless physical activity tracking bracelet and a mobile phone in order to monitor objective health and behavioral data and to capture subjective symptom assessment. The second aim is the evaluation of correlations between the patient-specific activity patterns (physical, social activity, and vital signs) and the subjective patients’ ratings of pain, distress, and QoL in order to early detect changes in the patient-specific pattern of physical and social behavior, vital sign patterns, and the named symptom ratings.

Characteristics of Participants

Patients will be consecutively recruited at the 2 wards of the clinic of radiation-oncology of the University Hospital Zurich (USZ). One ward is part of the Competence Center Palliative Care USZ and offers specialized palliative care for patients with life-threatening diseases from all clinics of the university hospital. Most of these patients do suffer from cancer, a minority from incurable heart failure, severe pulmonary affections, or neurologic diseases. Inclusion criteria are: patients with established diagnosis of metastatic cancer or other severe illness with limited life-expectancy (physicians guess <12 months, >8 weeks), Karnofsky Index ≥50%, scale of performance status developed by the Eastern Cooperative Oncology Group (ECOG) ≤2, aged >18 years. Eligible patients with interest of participation will have to pass a practical minitest concerning the handling of the devices (ie, simple operations on a mobile phone and handling of activity bracelet). Exclusion criteria are relevant cognitive impairment and insufficient knowledge of German language. Similar to related work [20,39] a number of 30 participants is intended to ensure relevant findings by capturing a representative cohort regarding sex, age, health status (Karnofsky Index/ECOG), life-limiting diseases, and different grades of pre-existing experience in the use of mobile phones or other electronic devices. Patients interested in the study will receive a leaflet with sound information on the aim of the study, a rough sketch of study procedure, and requirements with regard to participants (eg, wearing a tracking bracelet). Attached at this information leaflet is the informed consent. Moreover, we hand out a second leaflet with detailed information on the handling of the tracking bracelet, the app, and characteristics of data gathered by device and app.

Study Design

For this feasibility study, we chose an explorative descriptive design observing palliative care patients discharged from hospital and equipped with physical activity tracker and mobile phone technology. The underlying assumption is that wireless tracking of physical and social activity as well as of vital sign data in the palliative care setting is accepted by patients, is feasible, and its pattern analysis allows the generation of objective information on the current health status in the vulnerable phase of leaving inpatient care. For specific study question, see Textbox 1.

Patients leaving inpatient care of the Clinic of Radiation-Oncology will be equipped with a mobile phone and tracking bracelets. We will use the Samsung Galaxy S5 mobile phone and it will capture body motion data (by acceleration signals), location (anonymized global positioning system [GPS] data), encrypted speech, and phone call statistics. Furthermore, in this study, a mobile phone questionnaire app will be installed, a visual pain scale, and the Distress Thermometer. Patients will be requested to answer these simple to handle questionnaire app twice a day. The tracking bracelet used in this feasibility study is provided by Biovotion company, it is worn at the upper arm, easy to put on, and it will capture heart rate, heart rate variability, blood oxygen, blood perfusion, skin temperature, stress (by galvanic skin response), and body motion data (by acceleration data). Figure 1 gives a detailed overview on study procedures.

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Planned Data Collection and Analysis

With regard to analysis, the study is composed of 3 work packages: A, B, and C. Work packages A and B enclose interims analysis; that way preliminary evaluation results can be implemented during patient recruitment phase aiming continuously to improve feasibility and data quality. In Textboxes 2-4, we describe the different work packages.

Work Package B – Technological Feasibility of Behavior Tracking and Monitoring of Health Status

Work package B evaluates whether physical activity parameters, such as step count, sleep duration, social activity patterns like making calls, going out to meet people, and vital signs (eg, heart rate) can be technically measured over sufficiently long periods and whether this data correlate with changes in the health status of patients. Health status will be assessed in terms of the following parameters in Textbox 3.

Work Package C - Data Analysis, Correlation, and Trend Detection

The recorded data of the bracelet and mobile phone are uploaded to a secured webserver of the Swiss Federal Institute of Technology once per day to allow remote access to the data (only for the conductors of the study) in order to ensure accurate recording and for evaluation purposes. We chose the Biovotion bracelet, because it gives us the possibility to avoid data storage in a cloud. Additionally, it is an officially recognized medical device. Privacy sensitive data as real location (GPS) is anonymized, speech is encrypted, names and phone numbers of contacts are not recorded. All patient sensitive data is encoded and accessed only by people determined by the principal investigator. The local Ethics Committee (Kantonale Ethikkommission Zürich) has already approved the study and agreed to detailed specifications on data security we gave to them. Textbox 4 gives an overview on planned data collection and analysis.

Results

Enrollment began in February 2017. Data collection will be completed in January 2018, first results will be reported in the middle of the year.

Discussion

Continuous symptom assessment and critical review of symptoms by patient and doctor have been defined as a mainstay of effective ambulant palliative care [39]. Yet, in many European countries primary care practices are rarified [43], reimbursement of home visits are insufficient [44], and despite different movements to embed palliative care in the public health care system ambulatory palliative care institutions are still lacking. Our study aims to detect early health status changes with the help of mHealth technology in order to improve health care for ambulatory palliative care patients and avoid unplanned or emergency hospital readmissions. As to our knowledge with regard to this special patient population, no data on correlation of subjective symptom burden and objective activity parameters do exist until now, this project will deliver highly novel data. Not only on aspects of feasibility and patients` acceptance, but particularly on the proof (or the rejection) of the presumed existence of a correlation between subjective and objective data. Due to the fact, we collect a broad range of activity data reaching form blood oxygen, perfusion, skin temperature and heart beat to call statistics, and voice modulation, we will be able to identify parameters that correlate best with the patient’s subjective symptom burden. This is important for the future development of effective mHealth systems supporting remote symptom management by wearables combined with proactive care in a patient group that is in constant danger of a rapid and/or unexpected deterioration in health status. With the help of advanced data analysis algorithm we will be able not only to detect subtle changes in health status sometimes initially unnoticed by the patient and his/her family, but relevant precursor of possible symptom exacerbations. This approach will furthermore allow for even predicting these sometimes very finely graduated changes in time; thus, enabling professionals with data access to initiate effective intervention in time. We are aware of the fact that equipment of palliative patients with tracking devices requires sensitivity and the goal of palliative care remains to provide the best QoL possible, not only with respect to medical, but also to psychosocial and spiritual needs. This project therefore aims to supplement and assist existing palliative care structures and resources to build-up effective home care programs, which provide comprehensive and coordinated care closely adapted to the patient individual needs.