Purpose: Integrating patient-reported outcomes (PROs) into clinical practice is an increasingly promising strategy for improving patients’ symptoms, communication, and clinical outcomes. The objective of this study was to assess the feasibility, acceptability, and perceived effectiveness of a mobile health intervention designed to collect PROs and activity data as a measure of health status. Patients and Methods: Pilot intervention with 10 patients with gynecologic cancers receiving palliative chemotherapy. The HOPE (Helping Our Patients Excel) study used wearable accelerometers to assess physical activity and the Beiwe research platform to collect PROs, risk stratify patient responses, provide tailored symptom management, and notify patients and clinicians of high-risk symptoms. Feasibility and acceptability were assessed through enrollment and adherence rates; perceived effectiveness was evaluated by patients and oncologists at study completion. Results: The approach-to-consent rate was 100% and participants were 90% and 70% adherent, respectively, to the wearable accelerometers and smartphone surveys. Participants’ mean daily step count was 4,040 (SD=2,270) and increased from week 1 (mean=3,520, SD=1578) to week 3 (mean=4,136, SD=1,578). Active monitoring of participants’ heart rates, daily steps, and PROs throughout the study identified anomalies in participants’ behavior patterns that suggested poor health for 20% (2 patients). Patients and clinicians indicated that the intervention improved physical activity, communication, and symptom management. Conclusion and Relevance: A mobile health intervention that collects PROs and activity data as a measure of health status is feasible, acceptable, and was perceived to be effective in improving symptom management patients with advanced gynecologic cancers. A larger, multisite randomized clinical trial to assess the efficacy of the HOPE intervention on patients’ symptoms, health-related quality of life, clinical outcomes, and health care utilization is warranted.
Objective The amyotrophic lateral sclerosis (ALS) trial outcome measures are clinic based. Active and passive smartphone data can provide important longitudinal information about ALS progression outside the clinic. Methods We used Beiwe, a research platform for smartphone‐based digital phenotyping, to collect active (self‐report ALSFRS‐R surveys and speech recordings) and passive (phone sensors and logs) data from patients with ALS for approximately 24 weeks. In clinics, at baseline and every 3 months, we collected vital capacity, ALSFRS‐R, and ALS‐CBS at enrollment, week 12, and week 24. We also collected ALSFRS‐R by telephone at week 6. Results Baseline in‐clinic ALSFRS‐R and smartphone self‐report correlation was 0.93 ( P < 0.001). ALSFRS‐R slopes were equivalent and within‐subject standard deviation was smaller for smartphone‐based self‐report (0.26 vs. 0.56). Use of Beiwe afforded weekly collection of speech samples amenable to a variety of analyses, and we found mean pause time to increase by 0.02 sec per month across the sample. Interpretation Smartphone‐based digital phenotyping in people with ALS is feasible and informative. Self‐administered smartphone ALSFRS‐R scores correlate highly with clinic‐based ALSFRS‐R scores, have low variability, and could be used in clinical trials. More research is required to fully analyze speech recordings and passive data, and to identify optimal digital markers for use in future ALS clinical trials.
Sleep abnormalities are considered an important feature of schizophrenia, yet convenient and reliable sleep monitoring remains a challenge. Smartphones offer a novel solution to capture both self-reported and objective measures of sleep in schizophrenia. In this three-month observational study, 17 subjects with a diagnosis of schizophrenia currently in treatment downloaded Beiwe, a platform for digital phenotyping, on their personal Apple or Android smartphones. Subjects were given tri-weekly ecological momentary assessments (EMAs) on their own smartphones, and passive data including accelerometer, GPS, screen use, and anonymized call and text message logs was continuously collected. We compare the in-clinic assessment of sleep quality, assessed with the Pittsburgh Sleep Questionnaire Inventory (PSQI), to EMAs, as well as sleep estimates based on passively collected accelerometer data. EMAs and passive data classified 85% (11/13) of subjects as exhibiting high or low sleep quality compared to the in-clinic assessments among subjects who completed at least one in-person PSQI. Phone-based accelerometer data used to infer sleep duration was moderately correlated with subject self-assessment of sleep duration (r = 0.69, 95% CI 0.23–0.90). Active and passive phone data predicts concurrent PSQI scores for all subjects with mean average error of 0.75 and future PSQI scores with a mean average error of 1.9, with scores ranging from 0–14. These results suggest sleep monitoring via personal smartphones is feasible for subjects with schizophrenia in a scalable and affordable manner.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.