A pilot intervention of using a mobile health app (ONC Roadmap) to enhance health-related quality of life in family caregivers of pediatric patients with cancer
Original Article

A pilot intervention of using a mobile health app (ONC Roadmap) to enhance health-related quality of life in family caregivers of pediatric patients with cancer

Sarah B. Koblick1^, Miao Yu1^, Matthew DeMoss1^, Qiaoxue Liu1, Charles N. Nessle1^, Michelle Rozwadowski1^, Jonathan P. Troost2^, Jennifer A. Miner3^, Afton Hassett4^, Noelle E. Carlozzi3,5^, Debra L. Barton6^, Muneesh Tewari7,8,9,10^, David A. Hanauer11^, Sung Won Choi1,8^

1Department of Pediatrics, University of Michigan, Ann Arbor, MI, USA; 2Michigan Institute for Clinical and Health Research, University of Michigan, Ann Arbor, MI, USA; 3Department of Physical Medicine and Rehabilitation, University of Michigan, Ann Arbor, MI, USA; 4Department of Anesthesiology, University of Michigan, Ann Arbor, MI, USA; 5Department of Surgery, University of Michigan, Ann Arbor, MI, USA; 6School of Nursing, University of Michigan, Ann Arbor, MI, USA; 7Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA; 8Rogel Comprehensive Cancer Center, University of Michigan, Ann Arbor, MI, USA; 9Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA; 10Center for Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA; 11Department of Learning Health Sciences, University of Michigan, Ann Arbor, MI, USA

Contributions: (I) Conception and design: SW Choi; (II) Administrative support: SW Choi; (III) Provision of study materials or patients: SB Koblick, SW Choi; (IV) Collection and assembly of data: SB Koblick, M Yu, M DeMoss, Q Liu, M Rozwadowski; (V) Data analysis and interpretation: SB Koblick, M Yu, M DeMoss, Q Liu, JP Troost, JA Miner, A Hassett, NE Carlozzi, DL Barton, M Tewari, DA Hanauer; (VI) Manuscript writing: All authors; (VII) Final approval of manuscript: All authors.

^ORCID: Sarah Koblick 0000-0003-1936-7526; Miao Yu, 0000-0002-8245-0552; Matthew DeMoss, 0000-0003-1556-7563; Charles Nessle, 0000-0001-7119-1658; Michelle Rozwadowski, 0000-0001-7864-383X; Jonathan P. Troost, 0000-0002-1183-8330; Jennifer A. Miner, 0000-0003-2722-1121; Afton Hassett, 0000-0003-2982-484X; Noelle E Carlozzi, 0000-0003-0439-9429; Debra L Barton, 0000-0001-6624-3476; Muneesh Tewari, 0000-0002-7781-3152; David A Hanauer, 0000-0001-6931-3791; Sung Won Choi, 0000-0002-6321-3834.

Correspondence to: Sung Won Choi, MD, MS. Edith Briskin and Shirley K. Schlafer Foundation Research Professor, Professor of Pediatrics, University of Michigan, Michigan Medicine, Blood and Marrow Transplantation Program, 1500 East Hospital Drive, MPB D4118 SPC 5718, Ann Arbor, MI 48109, USA. Email: sungchoi@med.umich.edu.

Background: The Roadmap mobile health (mHealth) app was developed to provide health-related quality of life (HRQOL) support for family caregivers of patients with cancer.

Methods: Eligibility included: family caregivers (age ≥18 years) who self-reported as the primary caregiver of their pediatric patient with cancer; patients (age ≥5 years) who were receiving cancer care at the University of Michigan. Feasibility was calculated as the percentage of caregivers who logged into ONC Roadmap and engaged with it at least twice weekly for at least 50% of the 120-day study duration. Feasibility and acceptability was also assessed through a Feasibility and Acceptability questionnaire and the Mobile App Rating Scale to specifically assess app-quality. Exploratory analyses were also conducted to assess HRQOL self- or parent proxy assessments and physiological data capture.

Results: Between September 2020–September 2021, 100 participants (or 50 caregiver-patient dyads) consented and enrolled in the ONC Roadmap study for 120-days. Feasibility of the study was met, wherein the majority of caregivers (N=32; 65%) logged into ONC Roadmap and engaged with it at least twice weekly for at least 50% of the study duration (defined a priori in the Protocol). The Feasibility and Acceptability questionnaire responses indicated that the study was feasible and acceptable with the majority (>50%) reporting Agree or Strongly Agree with positive Net Favorability [(Agree + Strongly Agree) – (Disagree + Totally Disagree)] in each of the domains (e.g., Fitbit use, ONC Roadmap use, completing longitudinal assessments, engaging in similar future study, study expectations). Improvements were seen across the majority of the mental HRQOL domains across all groups; even though underpowered, there were significant improvements in caregiver-specific aspects of HRQOL and anxiety and in depression and fatigue for children (ages 8–17 years), and a trend toward improvement in depression for children ages 8–17 years and in fatigue for adult patients.

Conclusions: This study supports that mHealth technology may be a promising platform to provide HRQOL support for caregivers of pediatric patients with cancer. Importantly, the findings suggest that the study protocol was feasible, and participants were favorable to participate in future studies of this intervention alongside routine cancer care delivery.

Keywords: Mobile health (mHealth); wearable sensors; pediatric oncology

Received: 15 July 2022; Accepted: 25 December 2022; Published online: 28 January 2023.

doi: 10.21037/mhealth-22-24

Highlight box

Key findings

• ONC Roadmap was shown to be feasible to use by caregivers of pediatric patients with cancer. This mHealth intervention may provide health-related quality of life (HRQOL) support in this population.

What is known and what is new?

• Cancer care delivery has focused primarily on involving the patient, and to an even lesser extent involving caregivers (in isolation). However, cancer experiences are shared by both members, which invariably influences each member of the dyad individually (through independent effects) as well as bidirectionally (through interdependent effects).

What is the implication and what should change now?

• Despite clear advantages of mHealth technology with regards to convenience and reach, designing dyadic interventions has been limited. Thus, in cancer care settings where family support is critical, including a dyadic mHealth approach has the potential to enhance HRQOL for both members.


Over the past decade, our interdisciplinary team developed a positive psychology-based mHealth app (Roadmap) (1). We created this app, because when a child is diagnosed with cancer, the entire family is impacted (2,3). Invariably, cancer alters the health-related quality of life (HRQOL) of family members and their care recipients (patients) (4). Family caregivers face a myriad of challenges navigating the demands of paid jobs with unpaid caregiving tasks (5). Unsurprisingly, caregivers who assume significant caregiving responsibilities at home face higher physical and emotional stressors, impeding their ability to provide care of loved ones, make decisions, and manage self-care (6-9). These chronic stressors can lead to adverse psychological and physiological effects on their daily lives that can adversely impact the patient (10,11).

The Roadmap mHealth app was developed to provide HRQOL support for family caregivers of patients with cancer (12,13). Applying Carbonneau’s conceptual framework on the positive aspects of caregiving (14), iterative cycles of user-centered design were utilized (15). This framework included three central positive aspects of caregiving: (I) quality of caregiver and patient daily relationship; ii) caregiver feeling of accomplishment; and (III) meaning of the caregiver role in daily life. These domains were considered interdependent and working together to reinforce positive outcomes (e.g., caregiver well-being) and protect caregiver HRQOL. Caregiver self-efficacy and caregiver enrichment events in daily life influenced the enhancement of positive aspects of caregiving (14).

Guided by this framework (14), the Roadmap mHealth app was studied in a pilot intervention to support the HRQOL of family caregivers of pediatric patients with cancer. Herein, this Roadmap app was leveraged to: (I) assess the feasibility and acceptability of the Roadmap mHealth app (henceforth, ONC Roadmap, abbreviated for “oncology”) in caregivers of pediatric patients with cancer; (II) characterize self-reported outcomes of physical, mental, and social HRQOL domains; and (III) evaluate the wearable sensor data outputs in both caregivers and patients. This work is important because it may inform future mHealth design and intervention considerations for families of children with cancer. We present the following article in accordance with the Transparent Reporting of Evaluations with Nonrandomized Designs (TREND) reporting checklist (16) (available at https://mhealth.amegroups.com/article/view/10.21037/mhealth-22-24/rc).


Ethical statement

The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013). The study was approved by the Institutional Review Board of the University of Michigan Medical School (IRBMED HUM#01176584) and was registered on ClinicalTrials.gov (NCT04480541). IRBMED-approved informed consent/assent was taken from all the study participants.

Study site

The study was conducted at the University of Michigan, Ann Arbor, MI (U-M). All study activities were conducted remotely with no in-person contact, and all study materials were mailed to participants’ residences. The design and development of ONC Roadmap have been previously published, including graphical images of the app (https//:www.roadmap.study) (1).

Recruitment and enrollment

Inclusion Criteria: Eligibility for study participation of caregivers included: age ≥18 years and self-reported as the primary caregiver of their pediatric patient with cancer. Patients were required to be at least age ≥5 years and receiving cancer care at the data coordinating site. The study team has other IRB-approved studies in this patient population (age ≥5 years) where technology and wearable sensors are being examined. Patients in this age group have been participants in similar studies (17). While the intervention specifically targeted the caregiver, both members of the dyad (i.e., caregiver and patient) had to agree to participate. Additional eligibility requirements included both members having access to necessary resources for participating in an mHealth technology-based intervention (i.e., smartphone/tablet and internet access), and being willing to use personal equipment/internet for the study. All participants provided informed consents/assents within the ONC Roadmap app. Of note, children (age 10–14 years) signed the IRB-approved Assent Form document and adolescents (age 15–17 years) signed the IRB-approved Consent Form document; children (age 5–9 years) did not sign any Assent Form documents.

Recruitment occurred between September 2020–September 2021. IRBMED-approved paper flyers and postings were distributed throughout the outpatient Pediatric Hematology/Oncology (PHO) waiting rooms, clinic rooms, and infusion center. Interested participants who contacted the study team by phone or email received additional study information (e.g., overview of study procedures). All recruitment and participant onboarding were conducted remotely due to the COVID-19 pandemic. The target sample was 50 dyads (see Figure 1: TREND (16) Diagram of Participant Flow).

Figure 1 Transparent Reporting of Evaluations with Nonrandomized Designs (TREND) diagram of participant flow.

The intervention: study procedures

The study procedures of the intervention are outlined in Figure S1.

Self-reported assessments

All self-reported HRQOL data were collected using ONC Roadmap, which utilized Qualtrics (Qualtrics, Provo, UT), an online research tool that enables researchers to create study-specific websites for administering study surveys and storing participant data. Participants were prompted by app alert to complete assessments at baseline (pre-study period (T0) and days 30 and 120 (post-baseline assessments at T1 and T2) using ONC Roadmap. Caregiver socio-demographic data (e.g., age, gender, race, ethnicity, education, occupation), household finances, caregiving experiences, and use of mobile devices/technology were obtained at T0, based on our National Caregiver Health Survey (18-20). A list of the HRQOL PROMIS® measures assessed at T0, T1, and T2 by self-report or parent proxy are provided in Table S1 (21,22). The reference population for PROMIS® measures is the U.S. general population (23), whereas the reference population of the affiliate PROMIS® measures (the Neuro-QoL TBI-CareQoL measures) is other caregivers (24). PROMIS® Measure-Specific Scoring Guides available online through the PROMIS® Assessment Center were used to score the measures and calculate T-scores (25). A higher PROMIS® T-score represents more of the concept being measured. For example, an individual with T-score of 60 for the Global Mental or Physical Health scale is one standard deviation better (i.e., healthier) than the U.S. general population. While patients (age ≥8 years) and family caregivers (age ≥18 years) completed self-assessments, parent proxy assessments were also completed by family caregivers for patients (age 5–17 years only).

Roadmap and Fitbit® Apps

Participants (caregivers and patients) were instructed to download ONC Roadmap and Fitbit® apps on their smartphone or other mobile device (both free of charge and publicly available via Apple and Google app stores). As previously described (1), caregivers received the full-version of ONC Roadmap, which included positive activities, chat forums, resources, and graphs (mood, sleep, and steps). Graphs only were visible to patients (i.e., they did not have access to positive activities, chat forums, or resources).

Wearable sensor

Fitbits® were mailed to the participants’ homes. They were instructed to wear it continuously, except while charging, to measure their physical activity, heart rate, and sleep during the 120-day monitoring period.

Feasibility and acceptability

Feasibility of the study, defined a priori in the Protocol, was calculated as the percentage of caregivers who logged into ONC Roadmap and engaged with it at least twice weekly for at least 50% of the 120-day study duration. These data were based on data use logs (i.e., timestamps) of: (I) positive activity completed; (II) chat/reply to chat posted or viewed in the forum, (III) and mood score reported (1).

Caregivers completed a Feasibility and Acceptability questionnaire, which was informed by existing measures of feasibility and acceptability (26,27), and the Mobile App Rating Scale (MARS) (28) at the end of study (i.e., day 120) to specifically assess app-quality. Our a priori hypothesis was that more than 50% of respondents would Agree or Strongly Agree with the feasibility and acceptability of ONC Roadmap. The MARS is a simple, objective, and reliable tool for assessing the quality of mHealth apps and has demonstrated internal consistency (alpha =0.90) and interrater reliability intraclass correlation coefficient (ICC =0.79) (28). The MARS was scored by calculating the mean scores of engagement, functionality, aesthetics, and information quality objective subscales, and an overall mean app quality total score. Each MARS item used a 5-point scale (1-Inadequate, 2-Poor, 3-Acceptable, 4-Good, 5-Excellent). Higher total and subscale scores indicate better app-quality.

Statistical analyses

For the descriptive statistics, continuous measures were described using means/medians (M) and standard deviation (SD)/interquartile range (IQR), while categorical measures were summarized using frequencies and proportions. These data were analyzed using R (version 4.1.1).

Fitbit® automatically generated accelerometer-based summary data (per proprietary algorithms) based on “activity counts” collected over the course of the day. We assessed participant compliance in wearing the Fitbit® by identifying when heart rate data were present through the Roadmap platform using the Fitbit application programming interface (API) (29). As previously reported, we measured daily wear time using heart rate data with a minutes-level resolution. Compliance was expressed both in hours (0–24 h) and in percentages (i.e., by dividing the hours spent wearing the device by 24 h) (30,31). Using this assessment of compliance, we calculated the average daily step count for participants who wore the Fitbit® more than 6 h between 8 AM and 8 PM. We chose a cut-off of 6 h because the distribution of average daily step count did not change significantly for higher cut-offs. No compliance cut-off was applied for the calculation of asleep hours because the daily average changed by only about 0.05 h between a cut-off of 0 h and a cut-off of 11 h between 8 PM and 8 AM.

Although this pilot study was not powered to examine efficacy, exploratory analyses were conducted to assess for changes in HRQOL scores across time (i.e., T0, T1, T2). Baseline vs. day 30 and baseline vs. day 120 HRQOL mean T-scores with SD were compared using two-tailed T tests with probability level of 0.05.

Next, we used a longitudinal regression model with random effects to determine whether caregiver PROMIS® global health outcome changed over time. The model included age, gender, self-report of any mental health condition, and caregiving hours/week.

To examine the relationship between caregiver and patient, we treated data from care partners as a paired or dyadic longitudinal series where the pairing was modeled at each timepoint in the series. Because longitudinal, dyadic data present a special case of nested data whereby interdependence exists at two hierarchies in the data, we employed the Actor-Partner Interdependence Model (APIM) (32-35). An important feature of the APIM analyses was to create within- and between-member versions of the outcome variable (e.g., PROMIS® global mental health), separately for caregivers and patients. The APIM also included age, gender, baseline self-report of any mental health condition, and caregiving hours/week. We specifically examined whether baseline (T0) caregiver and patient HRQOL (anxiety, depression) domains influenced caregiver and patient global (mental) health at 120-day (T2).


Socio-demographic characteristics

One hundred participants consented/assented and enrolled in this study (Figure 1) with 50 family caregivers and 50 pediatric patients with cancer. There was low study attrition (<5%; Table 1). Nearly half of the caregivers were unable to work (N=21; 42%) due to caregiving responsibilities or unemployed (N=2; 4%). Twenty-nine caregivers (58%) reported annual family income ≤$99,999 and three (6%) <$10,000 for a mean number of 4.5 persons in the household (range, 2–8). Only one caregiver did not own a smartphone and opted to use a mobile tablet for study participation.

Table 1

Participant demographics

Characteristic Family caregiver, N=50 Care recipient (Patient), N=50
Age in years, mean [range] 41.2 [18–56] 11.9 [5–20]
Gender (Female), n [%] 42 [84] 24 [48]
Ethnicity (Non-Hispanic), n [%] 48 [96] 48 [96]
Race (White), n [%] 44 [88] 42 [84]
Marital status (Married), n [%] 37 [74]
Education (Some college or more), n [%] 35 [70]
Disease characteristics, n [%]
   Leukemias/Lymphomas* 21 [42]
   Solid tumors 29 [58]
Adults (≥18 years) in household, mean (standard deviation) 2.34 (0.82)
Children in household (<18 years), mean (standard deviation) 2.12 (1.30)
Employment status, n [%]
   Full-time or self-employed 22 [44]
   Part-time 3 [6]
   Retired 2 [4]
   Unemployed 2 [4]
   Unable to work 21 [42]
Annual household income§, n [%]
   <$10,000 3 [6]
   $10,000–$14,999 1 [2]
   $15,000–$24,999 2 [4]
   $25,000–$34,999 4 8]
   $35,000–$49,999 6 [12]
   $50,000–$74,999 8 [16]
   $75,000–$99,999 5 [10]
   $100,000–$200,000 13 [26]
   >$200,000 2 [4]
   Prefer not to answer 6 [12]
Overall health (1–7, very poor–excellent), n [%]
   1 0 [0]
   2 1 [2]
   3 7 [14]
   4 16 [32]
   5 18 [36]
   6 3 [6]
   7 5 [10]
Most Common health conditions (more than one response allowed), n [%]
   Anxiety 17
   Seasonal allergies 16
   Depression 15
   High blood pressure 12
Weekly caregiving hours, n [%]
   <5 9 [18]
   5–9 11 [22]
   10–19 6 [12]
   20–29 2 [4]
   30–39 5 [10]
   >40 17 [34]
Patient proximity (same household) 47 [94]
Providing additional medical care toψ
   Child(ren) 24
   Spouse 11
   Parent 4
   Sibling 3
   No one else 18
Technology owned/used by caregivers
   Primary type of cell phone, n [%]
    Apple iPhone 29 [58]
    Android phone 20 [40]
    Do not own cell phone 1 [2]
   Type of tablet device (more than one response allowed), n
    Apple iPad 21
    Android tablet 15
    Microsoft Windows tablet 2
    Kindle 1
    Do not own tablet device 13
   Type of Fitness or Smart Watch (more than one response allowed), n
    Apple 9
    Fitbit 13
    Garmin 2
    Other 2
    Do not own fitness/smart watch 25
   Number of apps downloaded on mobile device (cell or tablet), n [%]
    ≤5 2 [4]
    6–10 4 [8]
    11–20 18 [36]
    21–50 18 [36]
    >50 8 [16]
   Number of apps used at least once a day on mobile device (cell or tablet), n [%]
    ≤5 19 [38]
    6–10 26 [52]
    11–20 5 [10]
   Health or wellness-related apps used on mobile device (more than one response allowed, n
    Fitness 8
    Counting steps 15
    Nutrition (e.g., tracking calories, recording foods) 12
    Meditation or stress management 5
    Sleep 11
    None 18

*, B-cell ALL: N=10; T-cell ALL N=5; Hodgkin lymphoma (HL) N=4; non-Hodgkin lymphoma N=2. , Osseous sarcoma N=9; soft tissue sarcoma N=8; neuroblastoma N=5; brain tumor N=5; wilms tumor N=1; ovarian tumor N=1. §, When we transformed baseline family income into a percentage of FPL for the year the survey was completed (2021) and stratified into two levels (≤200%, >200%), between 32%–50% of the study population met the criteria of ≤200% FPL (depending on their salary range). This stratification of ≤200% FPL is consistent with published definitions of low-income families and identifies those eligible for government support. Of note, year-specific FPLs are based on the Department of Health and Human Services Poverty Guidelines, which is calculated as baseline family income divided by the year-specific poverty guideline for household size and multiplied by 100 to achieve the percentage of FPL. ψ, more than one category can be selected (i.e., can surpass 100%). ALL, acute lymphoblastic leukemia; FPL, federal poverty level.

Feasibility and acceptability

The majority of caregivers (N=32; 65%) logged into ONC Roadmap and engaged with it at least twice weekly for ~50% of the study duration. Eighty percent of caregivers (N=39) logged in at least once weekly. The four most common activities used were gratitude journal, pleasant activity scheduling, savoring, and engaging with beauty (Table 2). Not surprisingly, caregiver app use declined over time from 18-days during the first 30-days compared with 12-days during the last 30-days (Figure 2). Nonetheless, the Feasibility and Acceptability questionnaire responses indicated that the Fitbit®, ONC Roadmap app, and longitudinal self-reported assessments were feasible and acceptable with the majority reporting Agree or Strongly Agree with positive Net Favorability in all the categories (Table S2).

Table 2

Caregiver engagement with positive activities

Activity name Activity description Completed number of activities Unique number of caregivers
Gratitude journal Feeling grateful is a powerful way to ward off depression and inspire feelings of optimism. It is perhaps the easiest positive emotion to tap in to when things are difficult. For that reason, we encourage you to keep a gratitude diary. You can do that right here! This is how to go about it:
Step 1: Every day, note at least 2 things for which you are grateful. It can be anything – your friends and family, your pets, feeling the sunshine on your face, happy that a friend phoned, receiving a present, being able to take a walk, chocolate cupcakes … anything. Evenings, right before you go to sleep, usually works best.
Step 2: Make a commitment to yourself that you will note at least 2 things every day, but here is a twist - the things you list MUST be DIFFERENT. Try never to repeat anything.
Step 3: Smile as you write these things down. This will help you to feel even more grateful.
108 19
Pleasant activity scheduling Providing care for loved ones can be incredibly time consuming. You might have already noticed that you have stopped doing many of the fun things you used to do. Yet, these pleasant activities are incredibly important and can help you better cope with stress. By scheduling and taking part in pleasant activities, you may find that you feel happier and have more energy.
Step 1: Identify activities that you find to be pleasant. These activities do not have to be expensive or time consuming – they just need to be things you enjoy. Activities could include taking a walk in the park, listening to music, working on your hobby, seeing a movie with a friend or reading a great book.
Step 2: Set aside time in the next week to do at least two of these activities. Put them on your calendar like an appointment and treat them with the same importance as you would a doctor’s appointment.
Step 3: Log what you did for your pleasant activity. Have fun, it’s good for you!
94 17
Savoring Savoring involves recognizing special moments and taking efforts to make them last and be more memorable. You can savor food, experiences, moments with loved ones, anything that brings you pleasure.
Step 1: Consider a typical weekday. Review your morning routine, your daily activities, and your evening rituals, and consider how much time you spend noticing and enjoying the pleasures of the day, both small and large.
Step 2: Every day for the next week, be sure to savor at least two experiences (for example, your morning coffee, or the sun on your face as you walk to your car). Spend at least 2–3 minutes savoring each experience.
Step 3: Log these savoring experiences here so you can revisit them later.
70 13
Engaging with beauty Beauty in nature can inspire the emotion of ‘awe,’ beauty in art and skill can inspire admiration, and the witnessing of beauty in positive acts of human behavior can inspire more positive acts echoing like a ripple in a pond.
Step 1: Create a Beauty Log where you will add your observations about three different types of beauty: beauty in nature, beauty that is man-made (e.g., art, music, dance, architecture) or beautiful human behavior (e.g., kind acts, brave acts).
Step 2: Look for beauty as you go through the day. When you observe something that is beautiful, add it to your log in text or photo form.
55 14
Signature strengths Character strengths are connected with resilience and buffer people from vulnerabilities that can lead to depression and anxiety. Your unique set of character strengths make you, you. Using these strengths more regularly and in different ways can help you lead a more successful and rewarding life.
Step 1: Based on the Brief Strengths Test, note your top seven strengths.
Step 2: Every day for the next week, use one of these strengths in a way that you have not used it before.
Step 3: Each night, note how you used one of your strengths that day, including what strength you used, how you felt before, during, and after the activity, and whether you plan to repeat it in the future.
35 6
Positive piggy bank As human beings, we tend to focus on negative things, people and events. This focus on the negative can undermine our happiness. Keeping a Positive Piggy Bank can help us focus on all the good things in our world, too.
Step 1: When you observe something that makes you happy, take a moment as savor it. Think about what makes this so special to you.
Step 2: Make a note to capture this thing or moment with enough detail that you can immediately recall what happened later.
Step 3: Now, tap the coin and it will drop into your positive piggy bank.
Step 4: You can make as many of these happy memory “deposits” as you like. The best part is that when you need a little pick-me-up, you may “break” open your piggy bank and read all of these happy notes.
33 12
Random acts of kindness Although we do kind things daily, we often do not set out to intentionally do something nice for somebody else. Kindness is something always available for us to both give and receive.
Step 1: For this activity, one day this week, do five kind acts all in one day. Take a little time to plan what you are going to do. For the first four acts, do these for other people. These people can be complete strangers or friends and family members. These can be small acts of kindness such as holding a door open, sharing a genuine compliment or giving somebody a hug.
Step 2: You must also do one kind thing for you. People who take care of others tend to put them first and forget to be kind to themselves. It’s important to take care of yourself, too! Perhaps, you could take a long bubble bath, go for a walk in the park, enjoy a Popsicle or sleep an extra 20 minutes.
Step 3: Smile as you do these kind acts. You are putting good into the world!
15 8
Love letter Finding ways to express warmth, care, deep positive regard, and authentic appreciation to those we love is important to us (the giver) to express, and for the receiver to hear and experience.
Step 1: Think about the love you have for the person for whom you are providing care.
Step 2: Write a brief love letter to this person. In the letter, tell your loved one about your love for him or her, offering your thoughts, feelings and specific examples. Also, consider linking your love to something that happened today or recently.
Step 3: Share your letter with the person you care for.
12 9
Figure 2 ONC Roadmap App use over time in family caregivers. Each boxplot represents the daily compliance averaged chronologically for each 30-day of the 120-day study period (N=49). M1 vs. M2, P=0.002; M1 vs. M3, P<0.001; M1 vs. M4, P<0.001. M, month; IQR, interquartile range.

The MARS was also utilized to provide a multidimensional measure of ONC Roadmap app quality indicators of engagement, functionality, aesthetics, and information quality (28). The ONC Roadmap app quality total mean score was 3.59 (SD =0.78) and overall star rating was 3.38 (SD =0.86). The sub-scales, functionality and aesthetic, had the highest reported mean subscale scores of 4.01 (SD =0.66) and 3.88 (SD =0.74), respectively (Table 3), followed by information and engagement [mean subscale scores of 3.76 (SD =0.75) and 2.99 (SD =0.87), respectively].

Table 3

Mobile App rating scale of ONC Roadmap

Subscale/item Mean SD Mean* SD*
Section A: Engagement 2.99 0.87
   1 Entertainment 2.81 1.00 2.49 1.24
   2 Interest 2.88 1.01 2.52 1.20
   3 Customization 2.97 1.20 2.27 1.15
   4 Interactivity 3.07 0.98 2.70 1.22
   5 Target group 3.52 1.02 3.41 0.93
Section B: Functionality 4.01 0.66
   6 Performance 4.10 0.66 4.00 0.93
   7 Ease of use 3.93 0.88 3.93 0.87
   8 Navigation 3.87 1.11 4.00 0.94
   9 Gestural design 4.18 0.67 4.10 0.79
Section C: Aesthetic 3.88 0.74
   10 Layout 4.03 0.72 3.91 0.87
   11 Graphics 3.87 0.81 3.41 0.92
   12 Visual appeal: How good does the app look? 3.74 0.89 3.14 0.91
Section D: Information 3.76 0.75
   13 Accuracy of app description 3.77 0.92 3.66 1.03
   14 Goals 3.48 1.05 3.43 1.10
   15 Quality of information 3.86 0.92 3.18 1.46
   16 Quantity of information 3.97 1.00 2.87 1.54
   17 Visual information 4.00 0.83 1.35 1.89
   18 Credibility 3.72 1.07 2.79 0.95
   19 Evidence base 3.50 0.95
Section E: Subjective quality 2.77 0.72
   20 Would you recommend this app? 3.50 1.14 2.31 1.17
   21 How many times do you think you would use this apps? 3.00 1.05 2.46 1.12
   22 Would you pay for this app? 1.27 0.69 1.31 0.60
   23 What is your overall star rating of the app? 3.38 0.86 2.69 1.06
Section F: App specific 3.70 1.02
   24 Awareness 3.77 1.01
   25 Knowledge 3.73 1.08
   26 Attitudes 3.60 1.07
   27 Intention to change 3.66 1.04
   28 Help Seeking 3.73 1.08
   29 Behaviour to change 3.82 1.06

*, The App Quality Total Mean Score for ONC Roadmap was 3.59 (SD =0.78); Stoyanov SR, Hides L, Kavanagh DJ, et al. Mobile app rating scale: a new tool for assessing the quality of health mobile apps. JMIR Mhealth Uhealth 2015;3:e27. SD, standard deviation.

Using ONC Roadmap to obtain HRQOL data

Completion rates for the HRQOL assessments by caregivers at T0, T1, and T2 were 100% (N=50/50), 88% (N=43/49), and 88% (N=38/49), respectively. Completion rates by patients (age 8 years and older) were also 100% (N=34/34) at T0 but were lower than caregivers at T1 and T2 [61% (N=20/33), and 45% (N=15/33), respectively]. The Parent Proxy (age 5–17 years) assessments were completed at similar rates to the caregiver self-assessments [100% (N=41/41), 85% (N=35/41), and 76% (N=31/41)], respectively.

In caregivers, the median wear time of sensors across the 120-day study period was: 17.8 h (of the 24 h day); 9.2 h during daytime (between 8 AM–8 PM), and 8.4 h during nighttime (between 8 PM–8 AM). In patients, the median wear time of sensors was: 6.3 h (of the 24 h day); 4.1 h during daytime (between 8 AM–8 PM), and 3.0 h during nighttime (between 8 PM–8 AM). Figure S2 shows the distribution of compliance (24 h) of Fitbit® wear across the study period. A decline in caregiver compliance was observed from a median of 19.3 h (first 30 days) to 15.8 h (last 30 days) of the study period (Figure S2A), while patient compliance declined from a median of 11.0 h (first 30 days) to 1.3 h (last 30 days, Figure S2B).

When we explored potential differences of steps, sleep, and self-reported mood, caregiver and patient mood were higher in the 5–11 years compared with 12–17 or 18+ years age-groups, P=0.003 or P=0.022 (caregiver) and P<0.001 or P<0.001 (patient), respectively (Table S3). Patient step count was also higher in those aged 5–11 compared with those aged 12–17 (P=0.024). There were no differences in sleep among caregiver and patient age-groups.

Exploratory analyses: Roadmap’s preliminary efficacy on HRQOL outcomes

The change in mean pre- (T0) and post- (T1 and T2) HRQOL scores for participants are shown in Table S4. In general, although most analyses failed to meet conventional levels of statistical significance, there were improvements in almost all the different mental HRQOL domains across all of the groups over time. Specifically, at 30-day (T1), caregivers had higher levels of global mental health and lower levels of caregiver-specific anxiety. At 120-day (T2), caregiver-specific anxiety and strain and general anxiety were lower compared with baseline. Interestingly, caregiver-specific anxiety at T0 was negatively correlated with app use at T1 (i.e., higher baseline anxiety scores were associated with less app use over the next 30 days; Table S5).

In patients (8–17 years), there were lower levels of depression at T1, without reaching statistical significance at T2; patients 18+ years reported better global mental health at T1, without reaching statistical significance at T2. However, in parent proxy reports (8–17 years), depression and fatigue were both rated lower at T2. No significant changes were observed by parent proxy in patients 5–7 years; however, the means were in the anticipated direction.

To adjust for participant age, gender, number of caregiving hours per week, and baseline self-report of any caregiver mental health condition, we then performed linear mixed models. While caregiver age >40 years and self-report of any mental health condition were negatively associated with global mental health of the caregiver, this outcome was improved at T1 compared with T0 (Table S6). Additionally, patient (8–17 years) depression was significantly lower over at T1 and T2 compared with T0. In our generalized linear APIM models, we found that “actor” (i.e., caregiver or patient) anxiety or depression at baseline influenced day 120 global mental health outcomes of the caregiver or patient, respectively. Interestingly, when we also assessed for “partner” effects (Table S7), caregiver depression at T0 was negatively associated with patient global mental health at T2.


In this study, family caregivers of pediatric patients with cancer met our a priori defined measure for Feasibility for the study duration (120-day). Most family caregivers agreed that the ONC Roadmap app, Fitbit®, and study design were feasible and acceptable. They also indicated they were likely to engage in a similar future study lasting up to 6-month.

Our study also incorporated the MARS, which provided a multidimensional measure of engagement, functionality, aesthetics, and information quality (28). ONC Roadmap caregivers reported the sub-scale mean scores as well as overall mean app quality total score at least consistent with or higher than 50 apps that were previously selected for rating by the MARS (28). Caregivers generally Agreed/Strongly Agreed with the perceived impact of ONC Roadmap on users’ knowledge and attitudes. Specific strengths of ONC Roadmap were functionality, aesthetic, and information quality. While overall a specific weakness was engagement, specific elements of entertainment and interest had mean scores higher than previously published studies on independent ratings of 50 mental health and well-being apps (20). This likely contributed to the decline in app use over the 120-day study period. Thus, we are considering strategies in future app refinements to enhance engagement by presenting content in interesting ways (e.g., alerts, messages, reminders, feedback) (36,37).

Nonetheless, caregivers were compliant with completing assessments, reporting mood scores, and wearing Fitbit®, consistent with what we observed in our recently completed college student study (30). However, we found patients to be more variable in completing the study-related procedures. With the near ubiquitous use of technology in children, adolescents, and young adults (38), this was somewhat surprising. It is possible that caregivers experienced the benefit of having access to positive activities, chat forums, mood/steps/sleep graphs, and caregiving resources, whereas for patients, their only access to ONC Roadmap was limited to graphs. Alternatively, patients may have established their technology-related support and did not leverage ONC Roadmap to the extent that caregivers did. Semi-structured interviews with our dyads are ongoing to better understand these factors.

Not surprisingly, over one-third of the caregivers reported significant burden (i.e., providing more than 40 hours of unpaid caregiving) and up to one-half were unable to work due to caregiving responsibilities or unemployment. We also found that between 32–50% of our caregivers reported household incomes as less than or equal to 200% federal poverty level ($55,500 for a family of 4 in 2022) (39). While these data were in line with reported rates of financial hardship in Michigan and U.S. families with children below 200% (~45%) (40), they highlight the potential implications of poverty, such as material hardship, when designing interventions to address the needs of families undergoing intensive medical management, such as cancer care. Indeed, psychosocial support that is low-burden and dyadic-focused are needed to integrate seamlessly with cancer care delivery (4,41).

Research across multiple disciplines is emerging on the importance of designing interventions that are truly dyadic in nature, integrating both caregiver and patient (42). Dyadic-level processes have been shown to influence the health and well-being of members within a dyad (43). For example, pediatric and adolescent and young adult patients’ reports of subjective illness severity may indicate their own as well as their caregiver’s mental health (44). Accordingly, while exploratory, herein we examined the influence of baseline HRQOL domains of the caregiver and patient on their mental health and found that baseline depression in caregiver influenced patient’s global health 120-day later. Thus, with growing emphasis on interpersonal, dyadic-level processes likely contributing to the health, illness, recovery, treatment, and/or overall well-being of both members of the dyad (41,43), future studies should integrate both members of the dyad. Indeed, mHealth technologies offer scalable and flexible solutions for delivering family-based or dyadic-level interventions (45,46).

While this study was not powered to assess the efficacy of the ONC Roadmap in HRQOL outcomes, exploratory analyses suggest preliminary efficacy on HRQOL outcomes. In general, we saw improvements in mental HRQOL over the 120-day study period for all groups, although this difference only met conventional levels of significance in a few instances, primarily for the caregiver group (where the intervention was intended for). Specifically, there were significant improvements in caregiver-specific strain, caregiver-specific anxiety, and general anxiety at day 120. Interestingly, higher baseline care-specific anxiety scores were associated with less app use; and higher baseline patient (8–17 years) depression scores were associated with less caregiver app use (data not shown). There was also evidence to suggest that patient fatigue improved with caregivers receiving the intervention. Accordingly, while the intervention’s primary target was caregivers, it was unexpected that patients experienced reduced depression. Indeed, the “partner effects” observed in our APIM analyses suggest a potential interaction between both members of the dyad. Interestingly, while caregiver global mental health improved at day 30, this was not sustained at day 120, and physical function and positive affect declined at day 120.

It is possible that caregivers experienced increased physical burden while caring for other family members at home during the pandemic. We also speculate that with the PROMIS® positive affect’s measure of “In the past 7 days: I felt cheerful,” it may better reflect pleasurable engagement (e.g., ecstatic happiness) (47), separate from general well-being, as assessed by the PROMIS® global mental health (21). Lyubomirsky and Layous’ positive activity model (48) suggests that the dosage and variety of positive activities coupled with motivation and effort of the individual (i.e., so called, person-activity fit) may influence the degree to which well-being is enhanced. Thus, this intersection between characteristics of the individual and the positive activities may be important considerations in how well those activities are able to enhance individual well-being. In the present research, we did not obtain information regarding psychological conditions/disorders or personality traits. These variables could have impacted the findings and will be considered in the design of our future studies. In our ongoing qualitative interviews, we are assessing these considerations in more detail.

Strengths of this study included the broad inclusion criteria of pediatric cancer diagnoses and phases of care, low-burden on participants, and a pre-registered study design plan. Our study also had limitations. Our findings are likely more generalizable to caregivers who were similar, mostly White, non-Hispanic, with at least some college education, and who own mobile devices and routinely use apps. In the present study, patient compliance waned over time. The patient was not emphasized as the primary member of the study. It is possible their involvement was no longer considered novel or important over time. In future studies, we hope to incorporate the dyad as its primary target rather than one member of the dyad alone (e.g., caregiver). Additionally, we recognize the inherent biases afforded by single-arm, single-center study designs. Nonetheless, we are encouraged with the high proportion of caregivers who reported the intervention to be feasible and acceptable with no adverse events reported. While caregivers were compliant with completing assessments, reporting mood scores, and wearing Fitbit®, patients were more variable in completing the study-related procedures.


Our findings suggest that mHealth technology can be used to support the HRQOL of caregivers and their patients. We are currently exploring mechanisms of ONC Roadmap on HRQOL outcomes. We are also examining strategies to enhance engagement, such as just-in-time adaptive interventions (36) or digital coaching (49). Considering the growing populations of survivors and aging caregivers (50), developing and rigorously testing mHealth platforms that provide cancer supportive care for both members of the dyad are needed.


Funding: This work was supported by an American Society of Hematology Bridge Grant and National Institute of Health/National Heart, Lung, and Blood Institute grant (No. 1R01HL146354) and the Edith S. Briskin and Shirley K Schlafer Foundation (SWC).


Reporting Checklist: The authors have completed the TREND reporting checklist. Available at https://mhealth.amegroups.com/article/view/10.21037/mhealth-22-24/rc

Data Sharing Statement: Available at https://mhealth.amegroups.com/article/view/10.21037/mhealth-22-24/dss

Peer Review File: Available at https://mhealth.amegroups.com/article/view/10.21037/mhealth-22-24/prf

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://mhealth.amegroups.com/article/view/10.21037/mhealth-22-24/coif). CNN is supported by an NIH/NCI T32 Training Grant (T32CA236621). JPT reports that the analytic work for this study was supported by the National Center for Advancing Translational Sciences (NCATS Grant Number: UL1TR002240) for the Michigan Institute for Clinical and Health Research. JAM is supported as a research staff member on HL146354 (Roadmap mHealth Study). AH, NEC, DLB, DAH report that being Co-I of National Institute of Health/National Heart, Lung, and Blood Institute grant (1R01HL146354). SWC reports that this work was supported by an American Society of Hematology Bridge Grant and National Institute of Health/National Heart, Lung, and Blood Institute grant (1R01HL146354) and the Edith S. Briskin and Shirley K Schlafer Foundation (SWC). SWC is supported by grants 1R01HL146354 and K24HL156896. The other authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013). The study was approved by the Institutional Review Board of the University of Michigan Medical School (IRBMED HUM# 01176584) and IRBMED-approved informed consent/assent was taken from all the study participants.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.


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doi: 10.21037/mhealth-22-24
Cite this article as: Koblick SB, Yu M, DeMoss M, Liu Q, Nessle CN, Rozwadowski M, Troost JP, Miner JA, Hassett A, Carlozzi NE, Barton DL, Tewari M, Hanauer DA, Choi SW. A pilot intervention of using a mobile health app (ONC Roadmap) to enhance health-related quality of life in family caregivers of pediatric patients with cancer. mHealth 2023;9:5.

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