Daily collection of self-reporting sleep disturbance data via a smartphone app in breast cancer patients receiving chemotherapy: a feasibility study

Yul Ha Min, Jong Won Lee, Yong-Wook Shin, Min-Woo Jo, Guiyun Sohn, Jae-Ho Lee, Guna Lee, Kyung Hae Jung, Joohon Sung, Beom Seok Ko, Jong-Han Yu, Hee Jeong Kim, Byung Ho Son, Sei Hyun Ahn, Yul Ha Min, Jong Won Lee, Yong-Wook Shin, Min-Woo Jo, Guiyun Sohn, Jae-Ho Lee, Guna Lee, Kyung Hae Jung, Joohon Sung, Beom Seok Ko, Jong-Han Yu, Hee Jeong Kim, Byung Ho Son, Sei Hyun Ahn

Abstract

Background: Improvements 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.

Objective: The 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.

Methods: Between 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.

Results: A 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).

Conclusions: The 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.

Keywords: breast cancer; compliance; mobile applications; self report.

Conflict of interest statement

Conflicts of Interest: None declared.

Figures

Figure 1
Figure 1
Study design and participant flow.
Figure 2
Figure 2
Screenshot of the app for self-reporting of sleep disturbance data.
Figure 3
Figure 3
Distribution of individual patient-level reporting rates.
Figure 4
Figure 4
Changes in compliance over time.
Figure 5
Figure 5
Intervals from enrollment to first self-reporting. X and O indicate day of enrollment and day of start of self-reporting, respectively.
Figure 6
Figure 6
Comparison of longitudinal compliance rates according to different time units.
Figure 7
Figure 7
Push notifications and distribution of self-reporting time.

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