Digital Phenotyping for Monitoring and Disease Trajectory Prediction of Patients With Cancer: Protocol for a Prospective Observational Cohort Study.

BACKGROUND: Timely recognition of cancer progression and treatment complications is important for treatment guidance. Digital phenotyping is a promising method for precise and remote monitoring of patients in their natural environments by using passively generated data from sensors of personal wearable devices. Further studies are needed to better understand the potential clinical benefits of digital phenotyping approaches to optimize care of patients with cancer. OBJECTIVE: We aim to evaluate whether passively generated data from smartphone sensors are feasible for remote monitoring of patients with cancer to predict their disease trajectories and patient-centered health outcomes. METHODS: We will recruit 200 patients undergoing treatment for cancer. Patients will be followed up for 6 months. Passively generated data by sensors of personal smartphone devices (eg, accelerometer, gyroscope, GPS) will be continuously collected using the developed LAIMA smartphone app during follow-up. We will evaluate (1) mobility data by using an accelerometer (mean time of active period, mean time of exertional physical activity, distance covered per day, duration of inactive period), GPS (places of interest visited daily, hospital visits), and gyroscope sensors and (2) sociability indices (frequency of duration of phone calls, frequency and length of text messages, and internet browsing time). Every 2 weeks, patients will be asked to complete questionnaires pertaining to quality of life (European Organization for Research and Treatment of Cancer Core Quality of Life Questionnaire [EORTC QLQ-C30]), depression symptoms (Patient Health Questionnaire-9 [PHQ-9]), and anxiety symptoms (General Anxiety Disorder-7 [GAD-7]) that will be deployed via the LAIMA app. Clinic visits will take place at 1-3 months and 3-6 months of the study. Patients will be evaluated for disease progression, cancer and treatment complications, and functional status (Eastern Cooperative Oncology Group) by the study oncologist and will complete the questionnaire for evaluating quality of life (EORTC QLQ-C30), depression symptoms (PHQ-9), and anxiety symptoms (GAD-7). We will examine the associations among digital, clinical, and patient-reported health outcomes to develop prediction models with clinically meaningful outcomes. RESULTS: As of July 2023, we have reached the planned recruitment target, and patients are undergoing follow-up. Data collection is expected to be completed by September 2023. The final results should be available within 6 months after study completion. CONCLUSIONS: This study will provide in-depth insight into temporally and spatially precise trajectories of patients with cancer that will provide a novel digital health approach and will inform the design of future interventional clinical trials in oncology. Our findings will allow a better understanding of the potential clinical value of passively generated smartphone sensor data (digital phenotyping) for continuous and real-time monitoring of patients with cancer for treatment side effects, cancer complications, functional status, and patient-reported outcomes as well as prediction of disease progression or trajectories. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/49096.

Smartphone sensors for evaluating COVID-19 fear in patients with cancer: a prospective study.

Objective: This study aimed to analyze the association between the behavior of cancer patients, measured using passively and continuously generated data streams from smartphone sensors (as in digital phenotyping), and perceived fear of COVID-19 and COVID-19 vaccination status. Methods: A total of 202 patients with different cancer types and undergoing various treatments completed the COVID-19 Fears Questionnaire for Chronic Medical Conditions, and their vaccination status was evaluated. Patients' behaviors were monitored using a smartphone application that passively and continuously captures high-resolution data from personal smartphone sensors. In all, 107 patients were monitored for at least 2 weeks. The study was conducted between August 2022 and August 2023. Distributions of clinical and demographical parameters between fully vaccinated, partially vaccinated, and unvaccinated patients were compared using the Chi-squared test. The fear of COVID-19 among the groups was compared using the Mann-Whitney and the Kruskal-Wallis criteria. Trajectories of passively generated data were compared as a function of fear of COVID-19 and COVID-19 vaccination status using local polynomial regression. Results: In total, 202 patients were included in the study. Most patients were fully (71%) or partially (13%) vaccinated and 16% of the patients were unvaccinated for COVID-19. Fully vaccinated or unvaccinated patients reported greater fear of COVID-19 than partially vaccinated patients. Fear of COVID-19 was higher in patients being treated with biological therapy. Patients who reported a higher fear of COVID-19 spent more time at home, visited places at shorter distances from home, and visited fewer places of interest (POI). Fully or partially vaccinated patients visited more POI than unvaccinated patients. Local polynomial regression using passively generated smartphone sensor data showed that, although at the beginning of the study, all patients had a similar number of POI, after 1 week, partially vaccinated patients had an increased number of POI, which later remained, on average, around four POI per day. Meanwhile, fully vaccinated or unvaccinated patients had a similar trend of POI and it did not exceed three visits per day during the entire treatment period. Conclusion: The COVID-19 pandemic continues to have an impact on the behavior of cancer patients even after the termination of the global pandemic. A higher perceived fear of COVID-19 was associated with less movement, more time spent at home, less time spent outside of home, and a lower number of visited places. Unvaccinated patients visited fewer places and were moving less overall during a 14-week follow-up as compared to vaccinated patients.

The impact of pre-existing conditions and perceived health status on mental health during the COVID-19 pandemic.

BACKGROUND: Patients with pre-existing conditions and poor health status are vulnerable for adverse health sequalae during the COVID-19 pandemic. We investigated the association of pre-existing medical conditions and self-perceived health status with the risk of mental health complications during the COVID-19 pandemic. METHODS: In October-December, 2020, 1036 respondents completed online survey that included assessment of pre-existing conditions, self-perceived health status, depressive (Patient Health Questionnaire-8 score ≥ 10), anxiety (Generalized Anxiety Disorders-7 score ≥ 10) and post-traumatic stress (Impact of Events Scale Revised) symptoms, alcohol use (AUDIT), and COVID-19 fear (COVID-19 Fears Questionnaires for Chronic Medical Conditions). RESULTS: Study participants were predominantly women (83%), younger than 61 years of age (94%). Thirty-six percent of respondents had a pre-existing condition and 5% considered their health status as bad or very bad. Pre-existing conditions and poor perceived health status were associated with increased risk for moderate to severe depressive and anxiety symptoms, fear of COVID-19 and post-traumatic stress symptoms, independently from respondents' age, gender, living area, smoking status, exercise, alcohol consumption and diet. CONCLUSIONS: Pre-existing medical conditions and poor perceived health status are associated with increased risk of poor mental health status during the COVID-19 pandemic.

Can mHealth interventions improve quality of life of cancer patients? A systematic review and meta-analysis.

mHealth can be used to deliver interventions to optimize Health-related quality of life (HRQoL) of cancer patients. In this systematic-review and meta-analysis, we explored the possible impact of health interventions delivered via mHealth tools on HRQoL of cancer patients. The systematic literature search was performed on July 20, 2019, to identify studies that evaluated the impact of mHealth intervention on HRQoL of cancer patients. We identified 25 studies (17 randomized controlled trials and 8 pre-post design studies; 957 patients) that evaluated mHealth interventions. The most commonly studied mHealth interventions included physical activity/ fitness interventions (9 studies), cognitive behavioral therapy (6 studies), mindfulness/ stress management (3 studies). In the majority of studies, mHealth interventions were associated with an improved HRQoL of cancer patients. The meta-analysis of the identified studies supported the positive effect of mHealth interventions for HRQoL of cancer patients. mHealth interventions are promising for improving HRQoL of cancer patients.