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Published in final edited form as: ANS Adv Nurs Sci. 2021 Jul-Sep;44(3):268–278. doi: 10.1097/ANS.0000000000000359

Symptom Monitoring in Children with Life-Threatening Illness: A Feasibility Study using mHealth

Jacqueline Vaughn 1, Nirmish Shah 2, Sharron L Docherty 3, Qing Yang 4, Ryan J Shaw 5
PMCID: PMC8368073  NIHMSID: NIHMS1660024  PMID: 33624987

Children and adolescents with life-threatening illness (C-LTI) such as cancer or those undergoing blood and marrow transplantation (BMT), experience considerable symptom distress from both their disease and its treatment.1-3 Annually, more than 15,000 children in the US are diagnosed with cancer 4 and more than 1,500 undergo a BMT.5 Evidence shows their symptoms are poorly recognized, under-reported, and thus inadequately treated,1,6 leading to a cycle of ongoing and escalating symptom distress, ultimately placing them at risk for overall poor outcomes.7,8 In addition, C-LTI with significant symptom distress are less likely to adhere to treatment regimens9 and are more likely to experience treatment delays.10

Most research to better understand symptom dynamics (occurrences, clusters, trajectories) in C-LTI has been cross-sectional, giving a snapshot view, and often relies on patient recall at the time of data collection. Thus, knowledge gaps exist about ongoing symptom complexity, patterning, and how and why these symptoms change over time.3,11 Longitudinal research has the potential to give deeper insight into the ongoing, dynamic, and changing nature of the symptom experience. Yet limited longitudinal research has been conducted for this age group.3,11 Longitudinal research can advance understanding of symptom dynamics and ultimately lead to interventions that specifically target symptom distress.3

Mobile health (mHealth) technology may be one solution to improve longitudinal research for symptom understanding in C-LTI. Evidence shows mHealth technology is increasingly used to manage chronic illness.12 The use of mHealth technology may improve symptom assessment for C-LTI by capturing patient-generated health data in a minimally burdensome way.13,14 Wearables and sensors that collect physiologic data are increasing in accuracy, reliability, and predictive capabilities.13,15,16 Additional mHealth technologies such as smartphone applications (apps) show promise in helping persons with chronic illness manage their health and symptoms. For C-LTI, the ability to report symptoms in an app enables the ability to collect longitudinal data from patients in their daily environment and gives a “voice” to their symptom experience. Importantly, there is increasing evidence that children and adolescents prefer using technological approaches, such as mHealth, for health management.17 Given the prevalence of mHealth technologies and the strong developmental fit for children and adolescents, these devices and the health data they collect are likely to enhance our understanding of symptom dynamics18,19 and notably inform symptom management interventions for C-LTI. Yet limited research has been conducted to explore the use of mHealth in C-LTI.

Purpose:

The purpose of this study was to explore the use of mHealth technologies for C-LTI to advance understanding of symptom dynamics. Our aims to achieve this were: 1) to explore the feasibility of using mHealth devices to obtain patient generated symptom data in C-LTI; and 2) through interviews, explore C-LTI perceptions of facilitators and barriers to symptom management using mobile health devices.

Theoretical Framework

The Theory of Unpleasant Symptoms provided the framework for this study. The theory broadly guides symptom science research by identifying symptoms, examining the influencing factors, illuminating determinants, and guiding intervention development.20 We explored the theory’s key components of the symptom experience (timing, intensity, duration, distress, and quality) using mHealth technologies to better understand the symptom dynamics C-LTI experience.

Methods

Study Design:

A longitudinal mixed methods study design was used to assess the feasibility of using mHealth technologies in C-LTI. The medical center Institutional Review Board approved the study protocol (Pro00068979).

Procedure:

We loaned participants a wearable device and a smartphone with an installed study application or ‘app’. We asked participants to wear the wearable as much as possible each day and to record their symptoms in the study app daily. We previously conducted a pilot study to test our study approach, design, and procedures.21 Based on our findings, we made modifications to the study app to encourage increased symptom reporting and chose the Apple Watch as the study wearable. We also expanded our sample to include more diverse populations of C-LTI.

Participants:

After obtaining written permission from the parent and assent from the child, we enrolled a convenience sample of pediatric patients (N = 20) aged 8-17, undergoing cancer treatment or blood and marrow transplantation at a major medical facility in the southeastern United States. Participants diagnosed with cancer were hospitalized for myelosuppressive chemotherapy and/or radiation. These participants were recruited irrespective of cancer diagnosis and not during their first round of treatment. We avoided enrolling participants during their first round of treatment since it is a particularly vulnerable time-period, and they would likely not be able to engage in the study. Participants requiring blood and marrow transplantation were hospitalized for the acute phase of treatment. As this study was exploratory in nature, we planned that participants could remain in the study for up to 120 days with the aim of determining a reasonable benchmark for future research. We chose the diagnostic groups since they both share an aggressive treatment regimen and have a possibility of intense symptom distress.

Our sample size was based on the aims to examine the feasibility of using the mHealth devices and to receive diverse qualitative feedback from participants. Our sample was consistent with other studies piloting mobile devices for persons with diseases including cancer,17 diabetes,22 and those undergoing blood and marrow transplantion,23 all which found the size adequate to evaluate study feasibility.

Data Collection:

Quantitative data collection:

Demographic data were collected at baseline, while mHealth data were collected daily as patients used the devices. Feasibility was assessed as mobile device acceptability and engagement.

Mobile device data collection:

1) An Apple Watch Series 1 collected daily physiologic data (heart rate, (HR), step count) continuously when a patient was wearing the device. Additionally, a sleep app was installed on the Apple Watch for participants interested in monitoring their sleep (n=15), which monitored hours of sleep and deep sleep. Patients were asked to wear the Apple Watch except when bathing or charging.

2) A smartphone (Apple iPhone 6 or 6S) with the study app installed was used to record daily symptom data. The investigative team self-developed the study app known as Technology Recordings for better Understanding Pediatric Blood and Marrow Transplant (TRU-PBMT) app (Figure 1a).24 This same app was re-designed for use with the pediatric oncology patients and named the Technology Recordings for better Understanding Pediatric Oncology (TRU-Onc) app.25 The apps were similar except for the health provider specified daily care goals (bathing, mouth care), number of laps walked, and the addition of symptom emojis to the text list in the TRU-Onc app (Figure 1b). Both apps had a key feature, the “Symptom Tracker” tab that served as a symptom diary. Participants were asked to record symptoms such as pain, fatigue, and nausea each day in the app. Participants rated their symptom intensity using a numeric rating scale (0 = no symptom; 10 = worst intensity of symptom). They recorded the timing of symptoms and any pharmacologic or non-pharmacologic interventions used to treat the symptom(s). The study apps had child friendly language and graphics and an audible daily prompt set for 6:00 pm to remind participants to record their data. The study apps were developed in Apple HealthKit with patient, parent, and healthcare provider input. Recording on any tab in the study app was used as a measure for study app engagement.

Figure 1.

Figure 1

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a. Study Symptom App showing the text list of symptoms and the Intensity Rating Scale. b. The symptom emoji added to the study app.

Qualitative Data Collection:

A study team member trained in qualitative methods conducted individual, face to face, semi-structured interviews with participants at study discharge. Fourteen study participants agreed to be interviewed. The interviews consisted of open-ended questions designed to explore the participant’s experiences with the mHealth devices and perspectives on feasibility and acceptability of the devices.

Data Analysis

We used a mixed methods approach to integrate quantitative and qualitative data to examine the feasibility of using the mHealth devices. After the initial quantitative data was obtained, the results were used to plan the follow up qualitative data collection.26 The quantitative data helped inform additional areas to probe to better understand feasibility for using the mHealth devices. We assessed feasibility as acceptability of the devices, engagement with the devices, and trends of engagement with the devices throughout the study.

Quantitative Data Analysis:

We analyzed the quantitative data using SAS 9.4 (Cary, NC) statistical software, and used graphics to describe frequencies, percentages, means, and measures of dispersion. We summarized participant characteristics (age, gender, ethnicity, and treatment) using measures of central tendency and dispersion.

Acceptability:

Acceptability was measured as study enrollment rate and attrition rate for participants who withdrew from the study within the first week. We also assessed the number of weeks that participants participated in the study to inform future study endpoints.

Engagement and engagement trends:

  1. Apple Watch engagement: Engagement was defined as number of days the participant wore the watch over the total number of days in the study and represented as a percentage. In addition, these same data were aggregated on a weekly level to explore the engagement trend during the study period. These data were plotted as a proportion using an empirical summary plot. Frequencies, means, medians, interquartile, ranges, and standard errors were assessed for Apple Watch data (HR) and provided a quantitative description of the Apple Watch engagement. We confirmed use of the Apple Watch each day by the heart rate being recorded.

  2. Study App engagement: Engagement was defined as number of days the participant recorded in the study app over the total number of days in the study and represented as a percentage. Study app engagement was also calculated on a weekly basis to explore the trend over time. These same data were plotted as a proportion using an empirical summary plot. Frequencies, means, medians, interquartile, ranges, and standard errors were assessed for the study app data. Any daily data recorded in the app was used as a measure of app engagement. For example, a participant might record symptoms in the symptom tracker one day, then record stool output in the Bristol stool record on another day, and thus received credit for recording on 2 days.

Qualitative Data Analysis:

Semi-structured interview data were obtained to gain in-depth understanding of participants’ experiences with and perceptions of the mobile devices. Interview data were analyzed using conventional content analysis27 and NVIVO 12 software (QSR International 2018) supported the analysis. Interview data were audio-recorded for 11 of the participants. Three participants agreed to be interviewed but declined being audio-recorded, thus we used field notes to obtain their perceptions. All interviews were transcribed, reviewed in their entirety, and systematically coded by the research team, initially by the first author then reviewed and discussed with study team members. Symptom and mHealth literature guided the development of the semi-structured interview guide. Emergent codes were generated based on participants’ responses. Credibility and rigor were ensured by consistent study team meetings to review data collection and analysis, creation of a codebook, and maintenance of an audit trail which documented codes, themes, and analysis progression.28 Codes were refined as the analysis progressed then grouped into categories. This process led to the establishment of the broader themes which described relationships among categories that illuminated key elements of feasibility.

Results

Quantitative Results:

Table 1 presents the demographic and clinical characteristics of the sample.

Table 1.

Sample Characteristics

N=20 n % M (SD) Range
Gender
  Male 9 45%
  Female 11 55%
Race
  Black 7 35%
  Hispanic 3 15%
  White 10 50%
Age 13.25 (3.04) (8-17 years)
  8-12 years 9 45%
  13-17 years 11 55%
Treatment Group
  Pediatric Oncology 12 60%
  Pediatric BMT 8 40%
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SD=Standard Deviation

Acceptability:

A total of 20 patients were approached and all 20 enrolled in the study (enrollment rate 100%). Of the 20 patients who enrolled in the study, 15 (75%) remained in the study for at least one week. Three participants withdrew from the study within 4 days and two more withdrew within seven days (attrition rate 25%). All five participants who withdrew from the study described feeling too ill to continue participation. The median number of weeks in the study was 3.9 weeks (27 days), IQR 44 (9, 53), range 4 - 107 days for all 20 participants.

Engagement:

Overall, participants wore the Apple Watch 56% of their days in the study and engaged with the study app 63% of their days in the study. Figure 2 shows the proportion of each device’s weekly engagement throughout the study.

Figure 2.

Figure 2.

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Empirical Summary Plot of Apple Watch and Study App Engagement

Qualitative Results:

Participants discussed their perspectives on the use of mobile devices for symptom monitoring. Three major themes emerged from the analysis of the interview data related to the mobile devices: feasibility, engagement, and satisfaction.

Theme 1. Feasibility of using the Mobile Technologies:

We defined feasibility as the participant’s ease or difficulty using or managing both the Apple Watch and the study app. When asked to describe how easy or difficult the Apple Watch was to use, most participants (93%) responded it was “do-able” to wear the watch and they rarely had any technical difficulties. For example, when asked if they experienced difficulty learning to use or operate the Apple Watch one child reported, “It’s very do-able, um it’s not something you need to think about…It’s not like a continuously conscious effort, you just put it on, and it does the stuff it needs to do.”

We also asked for participants’ perceptions on the feasibility of using the study app to record their symptoms as recording in the study app required more effort than simply wearing a device. The participants reported it was easy to learn how to use the app, and they had no difficulty navigating and recording their symptoms in the study app. When asked about any difficulty related to the study app one child described, “Not really, I had to get used to it but that’s pretty much it, but now I’m used to it.”

The most common feasibility challenge expressed by the participants was the limited battery life of the Apple Watch (18-20 hours) resulting in difficulty keeping the device charged. One child commented on the limited battery life, “I feel the only thing is like charging it…it doesn’t stay charged long.”

Feasibility challenges were not limited to the devices themselves. Participants described how they would forget to wear the Apple Watch or record their symptoms in the app. One participant noted, “At first it was do-able but towards the end I kind of forgot to charge it…I forgot to record,” Another reported they would forget to put the Apple Watch on after charging or showering, “I forgot to put it back on after it was charged…I’d just forget or not think about it.”

Some participants described how their symptom distress interfered with the feasibility of using the devices. One expressed “Just cause you know I don’t feel so good, my tummy hurts so like I just didn’t want to,” while another described how they could not wear the Apple Watch due to a skin rash, “Yeah, my skin was burning up, I didn’t want anything touching me.”

Theme 2. Engagement with the mHealth devices:

We defined engagement with the Apple Watch and study app as “how and when” participants interacted with the devices. Participants expressed their views on how and when they engaged with the devices and addressed why engagement changed over time. For example, one of the younger children explained, “It’s harder because I’m always feeling kind of sleepy and nauseous, and so I don’t want to get up and grab the phone and start recording my symptoms.” When asked why they stopped recording symptoms one adolescent stated, “Once I got my chemo and got sick it was harder. When I was so nauseous and throwing up, I didn’t care at all about the watch or the phone, I just wanted to sleep.”

Theme 3. Satisfaction with study devices:

An additional theme that emerged from the interview analysis was satisfaction with the mHealth technologies, where participants expressed their pleasure or displeasure. The Apple Watch had features that participants enjoyed and encouraged them to use the device. One child described, “I like how the Mickey Mouse laughs! Yeah, it’s really fun… it’s really cool how I can see how I slept.”

Most of the participants (71%) expressed satisfaction with the study app and those who used the app with the emojis noted liking the symptom emojis. They stated the emojis made recording symptoms and mood more fun. One said, “the emojis, yeah, it was easy, and I liked them.” Other study app features the participants expressed satisfaction with were the colorful radio button prompts reminding them to have “fun and do something relaxing” twice each day, “I like the little bubbles that say ‘have you done anything fun today?’ it’s kind of fun.” When asked overall to describe their satisfaction or dissatisfaction with the study app one child said, “It was fun! I got like something to do every day.”

Study participants who used the sleep app function to monitor and track their sleep expressed that they liked the feature, “I liked the sleeping thing, at first I thought I wasn’t going to do it, then I saw the thing recording my sleep and I thought that was kind of interesting. [I liked seeing] just how much I sleep, how much I don’t sleep.”

Discussion

The mobile health phenomenon aided by the prolific adoption and use of wearables and smartphones creates new opportunities to assess and monitor patients’ symptoms. Determining the feasibility of C-LTI use of mHealth devices to capture symptom data is a critical first step to advance understanding of symptom dynamics. Guided by the Theory of Unpleasant Symptoms, we obtained the key symptom characteristics of timing, intensity, and duration using the Apple Watch and symptom study app.

A mixed methods approach gave us an in-depth way to explore the feasibility issues related to the use of mHealth technologies. The quantitative results, when integrated with the interview findings allowed us to further understand the many complexities related to feasibility. These include the challenges of using mHealth technologies over time, the reasons that led to decreased engagement, and factors that led to study attrition.

One example of deeper understanding through integration of the quantitative and qualitative findings was illustrated with the device engagement findings. Our quantitative findings showed participants engaged with the Apple Watch 56% of their study days. Through interviews we found it was easy to use and minimally burdensome, however, interview findings also revealed participants would forget to put it back on after showering. This detailed information helped us better interpret this finding and will inform future research as we look to incorporate wearable devices that remain easy to use but minimize disruption to data collection such as devices that are waterproof and have longer battery life.

The same approach using mixed methods integration helped us better understand the 63% study app engagement finding. The interview data enriched the quantitative findings and provided an explanation of why participants did or did not record their symptoms (participants’ level of symptom distress, forgetfulness). Using the mixed methods approach contributed insight into our study findings that neither a qualitative or quantitative method alone could provide and helped us cultivate ideas for future research.29

Acceptability:

The participant enrollment rate (100%) and attrition rate (25%) indicate that C-LTI found the study acceptable. The interview findings gave context and helped us understand the reasons for attrition for those who withdrew within the first week by noting their decision was primarily influenced by their level of symptom distress rather than finding the study unacceptable.

Engagement:

Our findings indicate it was feasible for C-LTI to record and track relevant symptom-related data using two mHealth devices. Despite undergoing intense treatment for their disease, participants participated in the study a median of 27 days ranging from 4-107 days. Participants wore the Apple Watch an average 56% of the study days and recorded in the study app 63% of the study days. Plans to improve engagement with the devices and encourage adherence to symptom reporting are underway for the next phase of the study. Based on participant feedback we plan to incorporate engaging features such as symptom data visualizations, educational modules, mindfulness sessions, breathing exercises, and the ability to win badges and trophies.

Our study app engagement results concurred with other studies that evaluated the use of mHealth apps in adult women with breast cancer30 and e-diaries in adolescents with depression.31 However, our Apple Watch use results were slightly lower than those found in other feasibility studies using wearables in adults with chronic illness12 and adults undergoing blood and marrow transplant.32 These studies both used Fitbit wearables which have a battery life of approximately five days compared to the Apple Watch battery life of 18-20 hours. Moreover, the participants in our study were children and adolescents with acute illness as opposed to adults.

Engagement for both devices dropped sharply between weeks 3-5 (Figure 2). One possible explanation could be related to the health status of the participants during this time-period. For participants undergoing blood and marrow transplantation, this time reflects the period of engraftment, a time of increased symptom distress.33 For participants receiving oncology treatment, this time period may reflect the intense effects from the chemotherapy.2 We explored this further through interviews to better understand this finding. Participants expressed how severe symptoms such as pain and fatigue interfered with their ability to engage with the devices daily thus complementing this finding. Future research plans will target increasing parental involvement to optimize symptom data collection during times when symptom distress becomes a barrier.

Another possible explanation for the drop in engagement at this time could be related to device fatigue, a concept where users tire of using devices and lose interest. One study using multiple mobile devices for diabetes management showed the same trend and the authors proposed device fatigue as a possible cause.34 Through interviews, we found that participants did lose interest with the devices as time progressed. This information will inform future study designs and implementation to minimize controllable barriers such as device fatigue.

Implications for Nursing Practice and Research:

Our findings have several important clinical and research implications. mHealth technologies and the patient generated health data they obtain can be leveraged as powerful tools across the health and illness continuum. mHealth devices are familiar tools frequently and easily used by patients of all ages. Nurses play a critical role in symptom identification, assessment, and management thus, they should consider using mHealth data to improve understanding of patient’s day to day symptom experience. As mHealth technology evolves, nurses will use real-time patient generated health data to enhance symptom knowledge, guide clinical practice, and inform personalized symptom management interventions. Electronic health records are increasingly incorporating patient-generated health data from mHealth devices for patient self-management and clinical care and further research is warranted.35,36 Finally, nurses can leverage mHealth data to educate and empower patients to better understand their symptoms and be actively involved in their care.

Nevertheless, as with any novel area of inquiry, further rigorous research is needed to refine which mobile technologies and approaches best allow for comprehensive symptom data collection with the lowest participant burden. Research is warranted to examine ways we can use mHealth data to monitor, track, detect, classify, and predict symptoms to lessen symptom distress thus improving patient outcomes.

Limitations:

There were several limitations to our study. First, we provided the mHealth devices to participants, if participants did not previously own these devices there was potential to influence study engagement. Another limitation concerns the attrition of the five participants who withdrew from the study within the first week citing they felt too ill to continue. This may indicate that children with the most distressing symptoms were not represented in the study findings. Finally, although this study included a diverse sampling of children with significant symptom distress, it was conducted at a single institution with a design-appropriate sample size, which may limit generalizability of the findings.

Conclusion

Increasing evidence demonstrates mHealth technologies are impacting the management of wellness and illness for many populations. Our mixed method approach provides important insights into the feasibility of using these devices to monitor symptoms in C-LTI. Our findings suggest mHealth use is feasible and may provide insight into the symptoms of acutely ill children and adolescents. Symptom data from mobile technologies can be leveraged to help C-LTI, their parents, and providers monitor symptoms and develop symptom management strategies. These strategies, in turn, may improve outcomes and reduce the symptom distress in C-LTI.

Acknowledgments

Sources of Funding: This work was supported by the U.S. National Institutes of Health, National Institute of Nursing Research (NINR) Grant No. 1F31NR018100 and NINR T32NR007091 to the first author; The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Duke Pediatric Chairs Award 2016 and Duke Institute for Health Innovation Grant to the second author.

Footnotes

Conflicts of Interest: Dr. Shah reports GBT—consultant, research, speaker; Alexion— speaker; Novartis – consultant, research, speaker; Bluebird bio – consultant. The remaining authors have no financial relationships relevant to this article to disclose. The remaining authors have no competing interests to declare.

Contributor Information

Jacqueline Vaughn, University of North Carolina School of Nursing, Carrington Hall, S Columbia St, Chapel Hill, NC 27599.

Nirmish Shah, Department of Hematology, Duke University School of Medicine, 40 Duke Medicine Circle, Durham, NC 27705..

Sharron L. Docherty, Duke University School of Nursing, 307 Trent Dr. Durham, NC 27710..

Qing Yang, Duke University School of Nursing, 307 Trent Dr. Durham, NC 27710..

Ryan J. Shaw, Duke University School of Nursing; 307 Trent Dr. Durham, NC 27710..

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