Development of an eHealth System to Capture and Analyze Patient Sensor and Self-Report Data: Mixed-Methods Assessment of Potential Applications to Improve Cancer Care Delivery

The Utilization of e-Health in Lymphedema Care: A Narrative Review

Background: Electronic health (e-Health), refers to technologies that can be utilized to enhance patient care as well as collect and share health information. e-Health comprises several umbrella terms, including telehealth, mobile health, e-Health, wearables, and artificial intelligence. The types of e-Health technologies being utilized in lymphedema (LE) care are unknown. Method: In this narrative review, a search of published research on the utilization of e-Health technologies in LE-related care was conducted. Results: Five different types of e-Health modalities were found (robotics, artificial intelligence, electronic medical records, smart wearable devices, and instructive online information) spanning 14 use cases and 4 phases of care (preventative, diagnostic, assessment, and treatment phases). Broad e-Health utilization examples were found including robotic-assisted surgery to reduce the likelihood of LE after lymphadenectomy, machine learning to predict patients at risk of filarial-related LE, and a novel wearable device prototype designed to provide lymphatic drainage. Conclusions: e-Health has reported merit in the prevention, diagnoses, assessment, and treatment of LE with utilization demonstrating cutting edge applicability of e-Health for achieving optimal patient care and outcomes. As technology continues to advance, additional research into the utilization of e-Health in LE care is warranted.

Physician perspectives on chronic pain management: barriers and the use of eHealth in the COVID-19 era

BACKGROUND

Chronic pain is a highly prevalent and disabling condition which is often undertreated and poorly managed in the community. The emergence of COVID-19 has further complicated pain care, with an increased prevalence of chronic pain and mental health comorbidities, and burnout among physicians. While the pandemic has led to a dramatic increase in virtual health care visits, the uptake of a broader range of eHealth technologies remains unclear. The present study sought to better understand physicians' current needs and barriers in providing effective pain care within the context of COVID-19, as well as gauge current use, interest, and ongoing barriers to eHealth implementation.

METHODS

A total of 100 practicing physicians in British Columbia, Canada, completed a brief online survey.

RESULTS

The sample was comprised of physicians practicing in rural and urban areas (rural = 48%, urban = 42%; both = 10%), with the majority (72%) working in family practice. The most prominent perceived barriers to providing chronic pain care were a lack of interdisciplinary treatment and allied health care for patients, challenges related to opioid prescribing and management, and a lack of time to manage the complexities of chronic pain. Moreover, despite expressing considerable interest in eHealth for chronic pain management (82%), low adoption rates were observed for several technologies. Specifically, only a small percentage of the sample reported using eHealth for the collection of intake data (21%), patient-reported outcomes (14%), and remote patient monitoring (26%). The most common perceived barriers to implementation were cost, complexity, and unfamiliarity with available options.

CONCLUSIONS

Findings provide insight into physicians' ongoing needs and barriers in providing effective pain management during the COVID-19 pandemic. Despite the potential for eHealth technologies to help address barriers in pain care, and strong interest from physicians, enhanced useability, education and training, and funding are likely required to achieve successful implementation of a broader range of eHealth technologies in the future.

Using Continuous Passive Assessment Technology to Describe Health and Behavior Patterns Preceding and Following a Cancer Diagnosis in Older Adults: Proof-of-Concept Case Series Study

BACKGROUND

Describing changes in health and behavior that precede and follow a sentinel health event, such as a cancer diagnosis, is challenging because of the lack of longitudinal, objective measurements that are collected frequently enough to capture varying trajectories of change leading up to and following the event. A continuous passive assessment system that continuously monitors older adults' physical activity, weight, medication-taking behavior, pain, health events, and mood could enable the identification of more specific health and behavior patterns leading up to a cancer diagnosis and whether and how patterns change thereafter.

OBJECTIVE

In this study, we conducted a proof-of-concept retrospective analysis, in which we identified new cancer diagnoses in older adults and compared trajectories of change in health and behaviors before and after cancer diagnosis.

METHODS

Participants were 10 older adults (mean age 71.8, SD 4.9 years; 3/10, 30% female) with various self-reported cancer types from a larger prospective cohort study of older adults. A technology-agnostic assessment platform using multiple devices provided continuous data on daily physical activity via wearable sensors (actigraphy); weight via a Wi-Fi-enabled digital scale; daily medication-taking behavior using electronic Bluetooth-enabled pillboxes; and weekly pain, health events, and mood with online, self-report surveys.

RESULTS

Longitudinal linear mixed-effects models revealed significant differences in the pre- and postcancer trajectories of step counts (P<.001), step count variability (P=.004), weight (P<.001), pain severity (P<.001), hospitalization or emergency room visits (P=.03), days away from home overnight (P=.01), and the number of pillbox door openings (P<.001). Over the year preceding a cancer diagnosis, there were gradual reductions in step counts and weight and gradual increases in pain severity, step count variability, hospitalization or emergency room visits, and days away from home overnight compared with 1 year after the cancer diagnosis. Across the year after the cancer diagnosis, there was a gradual increase in the number of pillbox door openings compared with 1 year before the cancer diagnosis. There was no significant trajectory change from the pre- to post-cancer diagnosis period in terms of low mood (P=.60) and loneliness (P=.22).

CONCLUSIONS

A home-based, technology-agnostic, and multidomain assessment platform could provide a unique approach to monitoring different types of behavior and health markers in parallel before and after a life-changing health event. Continuous passive monitoring that is ecologically valid, less prone to bias, and limits participant burden could greatly enhance research that aims to improve early detection efforts, clinical care, and outcomes for people with cancer.

Exploring the Level of Post Traumatic Growth in Kidney Transplant Recipients via Network Analysis

Although kidney transplant can lead to psychiatric disorders, psychosocial syndromes and demoralization, a positive post-traumatic growth (PTG) can occur in kidney transplant recipients (KTRs). However, the PTG-Inventory (PTGI), a reliable tool to measure PTG is scarcely used to explore the effect of this stressful event in KTRs. Thus, the purpose of our study was to assess the level of PTG and its correlation with demoralization, physical and emotional symptoms or problems via network analysis in KTRs. Additionally, we aimed at exploring the association of PTG with psychiatric diagnoses, Diagnostic Criteria for Psychosomatic Research (DCPR) conditions, and medical variables. A total of 134 KTRs were tested using MINI International Neuropsychiatric Interview 6.0 (MINI 6.0), DCPR interview, PTGI, Edmonton Symptom Assessment System (ESAS), Canadian Problem Checklist (CPC) and Demoralization scale (DS-IT). PTGI was used to investigate the positive psychological experience of patients after KT. It consists of 21 items divided in five factors. Routine biochemistry, immunosuppressive agents, socio-demographic and clinical data were collected. A symptom network analysis was conducted among PTGI, ESAS and DS-IT. Mean score of PTGI total of sample was 52.81 ± 19.81 with higher scores in women (58.53 ± 21.57) than in men (50.04 ± 18.39) (p < 0.05). PTGI-Relating to Others (16.50 ± 7.99) sub-score was markedly higher than other PTGI factor sub-scores. KTRs with DCPR-alexithymia or International Classification of Diseases, tenth revision (ICD-10) anxiety disorders diagnosis had lower PTGI total score and higher PTGI-Personal Strength sub-score, respectively (p < 0.05). The network analysis identified two communities: PTGI and ESAS with DS-IT. DS-IT Disheartenment, DS-IT Hopelessness and PTGI Relating to Others were the most central items in the network. After 1000 bootstrap procedures, the Exploratory graph analysis revealed the presence of a median of two communities in the network in 97.5% of the bootstrap iterations. A more extensive use of PTGI should be encouraged to identify and enhance the positive psychological changes after KT.

Digital health for optimal supportive care in oncology: benefits, limits, and future perspectives

BACKGROUND

Digital health provides solutions that capture patient-reported outcomes (PROs) and allows symptom monitoring and patient management. Digital therapeutics is the provision to patients of evidence-based therapeutic interventions through software applications aimed at prevention, monitoring, management, and treatment of symptoms and diseases or for treatment optimization. The digital health solutions collecting PROs address many unmet needs, including access to care and reassurance, increase in adherence and treatment efficacy, and decrease in hospitalizations. With current developments in oncology including increased availability of oral drugs and reduced availability of healthcare professionals, these solutions offer an innovative approach to optimize healthcare resource utilization.

DESIGN

This scoping review clarifies the role and impact of the digital health solutions in oncology supportive care, with a view of the current segmentation according to their technical features (connection to sensors, PRO collection, remote monitoring, self-management in real time…), and identifies evidence from clinical studies published about their benefits and limitations and drivers and barriers to adoption. A qualitative summary is presented.

RESULTS

Sixty-six studies were identified and included in the qualitative synthesis. Studies supported the use of 38 digital health solutions collecting ePROs and allowing remote monitoring, with benefits to patients regarding symptom reporting and management, reduction in symptom distress, decrease in unplanned hospitalizations and related costs and improved quality of life and survival. Among those 38 solutions 21 provided patient self-management with impactful symptom support, improvement of QoL, usefulness and reassurance. Principal challenges are in developing and implementing digital solutions to suit most patients, while ensuring patient compliance and adaptability for use in different healthcare systems and living environments.

CONCLUSIONS

There is growing evidence that digital health collecting ePROs provide benefits to patients related to clinical and health economic endpoints. These digital solutions can be integrated into routine supportive care in oncology practice to provide improved patient-centered care.

The Use of Demoralization Scale in Italian Kidney Transplant Recipients

Demoralization is a commonly observed syndrome in medically ill patients. The risk of demoralization may increase in patients after a kidney transplant (KTRs) because of the stressful nature of renal transplantation, psychosocial challenges, and adjustment needs. No study is available on demoralization amongst KTRs. The purpose of our study was to evaluate the validity of the Italian version of the Demoralization Scale (DS-IT) and the prevalence of demoralization in KTRs. Also, we aimed at exploring the association of the DS-IT with International Classification of Diseases (ICD) psychiatric diagnoses, post-traumatic growth (PTG), psychological and physical symptoms, and daily-life problems. A total of 134 KTRs were administered the MINI International Neuropsychiatric Interview 6.0. and the Diagnostic Criteria for Psychosomatic Research-Demoralization (DCPR/D) Interview. The DS-IT, the Edmonton Symptom Assessment System (ESAS), the Canadian Problem Checklist (CPC), were used to measure demoralization, physical and psychological symptoms, and daily-life problems; also, positive psychological experience of kidney transplantation was assessed with the PTG Inventory. Routine biochemistry and sociodemographic data were collected. Exploratory factor analysis demonstrated a four-dimensional factor structure of the DS-IT, explaining 55% of the variance (loss of meaning and purpose, disheartenment, dysphoria, and sense of failure). DS-IT Cronbach alpha coefficients indicated good or acceptable level of internal consistency. The area under the Receiving Operating Characteristics (ROC) curve for DS-IT (against the DCPR/D interview as a gold standard) was 0.92. The DS-IT optimal cut-off points were ≥20 (sensitivity 0.87, specificity 0.82). By examining the level of demoralization, 14.2%, 46.3%, 24.6%, and 14.6% of our sample were classified as having no, low, moderate, and high demoralization, respectively, with differences according to the ICD psychiatric diagnoses (p < 0.001). DS-IT Total and subscales scores were positively correlated with scores of ESAS symptoms and CPC score. A correlation between DS-IT loss of meaning and purpose subscale and PTGI appreciation of life subscale (p < 0.05) was found. This study shows, for the first time, a satisfactory level of reliability of the DS-IT and a high prevalence of severe demoralization in KTRs.