ArticleJournal of medical Internet research2014
Daily collection of self-reporting sleep disturbance data via a smartphone app in breast cancer patients receiving chemotherapy: a feasibility study.
Article in Journal of medical Internet research, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT04172350 (Developing and Examining Preliminary Effects of An Innovative Care-improvement Smartphone-based Perioperative Solution for Women Undergoing Breast Cancer Surgery), which is not on this map. Cited by 72 papers, 9 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Developing and Examining Preliminary Effects of An Innovative Care-improvement Smartphone-based Perioperative Solution for Women Undergoing Breast Cancer Surgery (iCareBreast): A Pilot Randomized Controlled Trial
Who cites it
72 citing papers in PubMed, 9 syntheses or guidelines pooled it.
- Ecological Momentary Interventions in the Management of Eating Disorders and Obesity: A Systematic Review.The International journal of eating disorders · 2026Pooled it
- Patient preferences for breast cancer screening: a systematic review update to inform recommendations by the Canadian Task Force on Preventive Health Care.Systematic reviews · 2024Pooled it
- A Systematic Literature Review of Health Utility Values in Breast Cancer.Medical decision making : an international journal of the Society for Medical Decision Making · 2022Pooled it
- Factors Influencing Adherence to mHealth Apps for Prevention or Management of Noncommunicable Diseases: Systematic Review.Journal of medical Internet research · 2022Pooled it
- Acceptance and Use of Home-Based Electronic Symptom Self-Reporting Systems in Patients With Cancer: Systematic Review.Journal of medical Internet research · 2021Pooled it
- Research-Tested Mobile Apps for Breast Cancer Care: Systematic Review.JMIR mHealth and uHealth · 2019Pooled it
- Electronic Systems for Patients to Report and Manage Side Effects of Cancer Treatment: Systematic Review.Journal of medical Internet research · 2019Pooled it
- Mobilizing Breast Cancer Prevention Research Through Smartphone Apps: A Systematic Review of the Literature.Frontiers in public health · 2019Pooled it
- Mobile Applications for Type 2 Diabetes Risk Estimation: a Systematic Review.Journal of medical systems · 2015Pooled it
- Completion and Compliance Rates for an Intensive mHealth Study Design to Promote Self-Awareness and Self-Care Among Care Partners of Individuals With Traumatic Brain Injury: Secondary Analysis of a Randomized Controlled Trial.JMIR mHealth and uHealth · 2025Trial
- Effectiveness of an Innovative Mobile-Based Perioperative Care Program for Women Undergoing Breast Cancer Surgery (iCareBreast): Randomized Controlled Trial.Journal of medical Internet research · 2025Trial
- Access to Results of Patient Reported Outcome Surveys Does Not Improve Survey Response Rates.The Journal of surgical research · 2023Trial
- Smartphone-Delivered Progressive Muscle Relaxation for the Treatment of Migraine in Primary Care: A Randomized Controlled Trial.Headache · 2020Trial
- A mobile health app for real-time symptom monitoring in patients with cancer during COVID-19: feasibility, acceptability, and utility.BMC cancer · 2026Article
- Evaluating the effect of using a mobile app on the management of chemotherapy side effects and quality of life in breast cancer patients: Protocol for a randomized controlled trial.Journal of education and health promotion · 2026Article
- Symptom Management Preference and Persona Development for Mobile Health Design Targeting Chinese Older Adult Patients With Breast Cancer: Descriptive Qualitative Study.JMIR human factors · 2025Article
- Age-Specific Differences in Association Between Personality and Changes in Outing Behaviors During the COVID-19 Pandemic in Japan: Cross-Sectional Web-Based Questionnaire Survey.Online journal of public health informatics · 2025Article
- Article
- Article
- Intensive Longitudinal Methods Among Adults With Breast or Lung Cancer: Scoping Review.Journal of medical Internet research · 2024Article
12 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
14 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundImprovements in mobile telecommunication technologies have enabled clinicians to collect patient-reported outcome (PRO) data more frequently, but there is as yet limited evidence regarding the frequency with which PRO data can be collected via smartphone applications (apps) in breast cancer patients receiving chemotherapy.
objectiveThe primary objective of this study was to determine the feasibility of an app for sleep disturbance-related data collection from breast cancer patients receiving chemotherapy. A secondary objective was to identify the variables associated with better compliance in order to identify the optimal subgroups to include in future studies of smartphone-based interventions.
methodsBetween March 2013 and July 2013, patients who planned to receive neoadjuvant chemotherapy for breast cancer at Asan Medical Center who had access to a smartphone app were enrolled just before the start of their chemotherapy and asked to self-report their sleep patterns, anxiety severity, and mood status via a smartphone app on a daily basis during the 90-day study period. Push notifications were sent to participants daily at 9 am and 7 pm. Data regarding the patients' demographics, interval from enrollment to first self-report, baseline Beck's Depression Inventory (BDI) score, and health-related quality of life score (as assessed using the EuroQol Five Dimensional [EQ5D-3L] questionnaire) were collected to ascertain the factors associated with compliance with the self-reporting process.
resultsA total of 30 participants (mean age 45 years, SD 6; range 35-65 years) were analyzed in this study. In total, 2700 daily push notifications were sent to these 30 participants over the 90-day study period via their smartphones, resulting in the collection of 1215 self-reporting sleep-disturbance data items (overall compliance rate=45.0%, 1215/2700). The median value of individual patient-level reporting rates was 41.1% (range 6.7-95.6%). The longitudinal day-level compliance curve fell to 50.0% at day 34 and reached a nadir of 13.3% at day 90. The cumulative longitudinal compliance curve exhibited a steady decrease by about 50% at day 70 and continued to fall to 45% on day 90. Women without any form of employment exhibited the higher compliance rate. There was no association between any of the other patient characteristics (ie, demographics, and BDI and EQ5D-3L scores) and compliance. The mean individual patient-level reporting rate was higher for the subgroup with a 1-day lag time, defined as starting to self-report on the day immediately after enrollment, than for those with a lag of 2 or more days (51.6%, SD 24.0 and 29.6%, SD 25.3, respectively; P=.03).
conclusionsThe 90-day longitudinal collection of daily self-reporting sleep-disturbance data via a smartphone app was found to be feasible. Further research should focus on how to sustain compliance with this self-reporting for a longer time and select subpopulations with higher rates of compliance for mobile health care.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.