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

Fear of COVID-19 can have adverse mental health consequences in vulnerable patients with pre-existing conditions. We aimed to validate The COVID-19 Fears Questionnaire for Chronic Medical Conditions in Lithuania and to evaluate fears of COVID-19 in patients with pre-existing conditions. Between October and December, 2020, 371 respondents with pre-existing conditions completed an online survey including the 10-item COVID-19 Fears Questionnaire for Chronic Medical Conditions (COVID-19 fears), Patient Health Questionnaire-8 (PHQ-8; depressive symptoms), Generalised Anxiety Disorders-7 (GAD-7; anxiety symptoms) and Impact of Events Scale Revised (IES-R; posttraumatic stress symptoms). The majority of participants were women (84%) and from 41 to 60 years old (46%). The most common pre-existing conditions were respiratory diseases (33%) and diabetes (22%). Mean total score on the COVID-19 Fears Questionnaire was 24.89 ± 8.79. Exploratory factor analysis yielded one-factor solution with the Cronbach's coefficient alpha of 0.913. The highest rated COVID-19 fears were infection of close people, inability to receive treatment for COVID-19 and pre-existing condition and fear of severe complications because of the pre-existing condition. There was moderate to strong correlation of the 10-item COVID-19 Fears Questionnaire for Chronic Medical Conditions score with scores on the PHQ-8 (r = 0.371), GAD-7 (r = 0.358) and IES-R (r = 0.553; all p < 0.001). Test-retest reliability was adequate. The 10-item COVID-19 Fears Questionnaire for Chronic Medical Conditions has good psychometric properties in patients with pre-existing conditions and greater fear is associated with worse mental health. Consideration of COVID-19 fears is important for optimised care of patients with pre-existing during the pandemic.

Management of glioblastoma: a perspective from Lithuania

This article examines social workers’ experiences, perceptions, and understanding of the role of digital advocacy in the rehabilitation and reintegration process of incarcerated people. The current study, located in Lithuania, is based on three focus group discussions with 15 social workers working at local prisons and NGOs. The research reveals that people in prison experience limited access to digital technologies, including the internet, mobile phones, and even TV, due to strict institutional regulations. Social workers reflect on the needs of people in prison for digital literacy, self‐efficacy, and advocacy to counteract stigma and to promote a more nuanced understanding of incarceration. Public prejudices and media stigmatisation reinforce social exclusion, while proactive digital advocacy could contribute to the humanization process and community acceptance of individuals with justice system involvement. Based on the social workers’ voices, this article addresses digital advocacy and digital literacy as a shifting paradigm in prison‐based social work, which requires the balancing of digital provisions and risk management on the individual, professional, and institutional levels.

Complications are frequent in cancer patients and contribute to adverse outcomes and higher healthcare costs, underscoring the need for earlier identification and prediction. This study evaluated the feasibility of using passively generated smartphone sensor data to explore early-warning signals of complications and symptom worsening during cancer treatment. A total of 108 patients were continuously monitored using accelerometer, GPS, and screen on/off data collected through the LAIMA application, while symptoms of depression, fatigue, and nausea were assessed every two weeks and complications were confirmed during clinic visits or emergency presentations. Smartphone data streams were aggregated into variables describing activity and sociability patterns. Machine learning models, including Decision Tree, Extreme Gradient Boosting, K-Nearest Neighbors, and Support Vector Machine, were used for complication prediction, and time-series models such as Autoregressive Integrated Moving Average, Holt–Winters, TBATS, Long Short-Term Memory neural network, and General Regression Neural Network were applied to identify early behavioral changes preceding symptom reports. In this exploratory analysis, the ensemble model demonstrated high sensitivity (89%) for identifying complication events. Smartphone-derived behavioral indicators enabled earlier detection of depression, fatigue, and vomiting by about nine days in a subset of patients. These findings demonstrate the feasibility of passive smartphone sensor data as exploratory early-warning signals, warranting validation in larger cohorts.