Beating cancer‐related fatigue with the Untire mobile app: Results from a waiting‐list randomized controlled trial

Abstract Objective This waiting‐list randomized controlled trial examined the effectiveness of a self‐management mHealth app in improving fatigue and quality of life (QoL) in cancer patients and survivors. Methods Persons with cancer‐related fatigue (CRF) were recruited across four English speaking countries, via social media, and randomized into intervention (n = 519) and control (n = 280) groups. Whereas the intervention group received immediate access to the Untire app, the control group received access only after 12‐weeks. Primary outcomes fatigue severity and interference, and secondary outcome QoL were assessed at baseline, 4, 8, and 12‐weeks. We ran generalized linear mixed models for all outcomes to determine the effects of app access (yes/no), over 12‐weeks, following the intention‐to‐treat principle. Results Compared with the control group, the intervention group showed significantly larger improvements in fatigue severity (d = 0.40), fatigue interference (d = 0.35), and overall QoL on average (d = 0.32) (P's < .01), but not for overall QoL in the past week (P = .07). Sensitivity analyses indicated that participants with medium or high app use benefited most when compared with nonusers and control participants (P's ≤ .02). The intervention effect on fatigue interference was slightly stronger in younger participants (≤56 vs. >56). Effects did not depend on education and cancer status. Reliable change analyses indicated that significantly more people showed full recovery for fatigue in the intervention vs the control group (P's = .02). Conclusions The Untire app can be an effective mHealth solution for cancer patients and survivors with moderate to severe CRF.


| BACKGROUND
Cancer-related fatigue (CRF), commonly defined as a distressing, persistent, subjective sense of physical, emotional, and/or cognitive tiredness or exhaustion, is the number one side effect of cancer and its treatment, affecting cancer patients' and survivors' quality of life (QoL) profoundly. 1,2 In-person psychosocial treatments based on cognitive behavior therapy (CBT), mindfulness-based stress reduction (MBSR), psycho-education, and physical exercises have been found to reduce symptoms of fatigue effectively, but are limited in reach, as one therapist can only treat one patient or group at a time. 3 Employing eHealth interventions, or the more specific mHealth interventions (via the mobile phone, eg, apps), might help to overcome this limitation as many patients worldwide could be reached. Advantages of mHealth apps include immediate access to the app (and does not require traveling), the low-threshold to use it (and at a moment of patients' convenience), and the focus on selfmanagement (without contact with a therapist, which reduces costs). 4 Apps can be used anywhere, anytime, and for an undetermined period of time. For patients with CRF, web-based and therapist-guided interventions 3,5-10 have shown to reduce CRF effectively. However, no mHealth interventions have been available, 3 and empirical evidence to support the effectiveness of apps, in general, is limited. 11 2 | PURPOSE OF THE RESEARCH This study examines the Untire self-management app, which is based on clinical practice and the abovementioned evidence-based methods for managing CRF. 5 We study the app's effectiveness as a mHealth solution for cancer patients and survivors (ie, persons who once received the diagnosis of cancer, but stated not to have cancer at the moment) with moderate to severe levels of fatigue.
The primary aim of this RCT is:

| Study design and setting
The Untire app study is an international waiting-list randomized controlled trial (RCT) aimed to examine the effectiveness of the Untire app in improving levels of fatigue and QoL in patients and survivors of cancer in four countries (ie, Australia, Canada, the United Kingdom, and the United States). Details are provided in the protocol. 16 The trial has been registered in the Trial Registry on November 29, 2017 (https://www.trialregister.nl/trial/6642).

| Study population
The Untire app study included cancer patients and survivors of ≥18 years, who experienced fatigue at moderate/severe levels (ie, having an average composite score of ≥3 17 on items 1-3 of the FSI [12][13][14] and owned a smartphone, tablet, or iPad (ie, iOS/Android).
Exclusion criteria were a diagnosis of and receiving treatment for a severe mental disorder since these persons may need more intensive treatment than offered by the app. Also excluded were persons having a diagnosis of myalgic encephalomyelitis/chronic fatigue syndrome or fibromyalgia, since the app focusses on improving physical activity, which could be potentially harmful to these patients. 18  On the study landing page, interested persons were screened for eligibility. 16 Based on the e-mail address and/or name, we checked for multiple registration attempts at the eligibility screening and removed duplicate entries. Eligible persons received the information letter (online) and gave electronic informed consent. After completing a baseline questionnaire, participants were randomized into the intervention or control group. Intervention participants received a personal access code that could be used to activate the Untire app after downloading it from the app store, enabling free usage. All participants received e-mail invitations and reminders at 4, 8, 12, and 24-weeks to complete the questionnaires. This article focusses on the outcomes of the baseline (T0)-T12. Once control participants completed the 12-week questionnaire, they also received an access code that could be used to activate the app for free. All participants were allowed to seek or accept other treatments for their fatigue during the study.

| Randomization and blinding
Participants who completed the baseline were automatically randomized 2:1 by the online survey tool into the intervention and control groups. Since intervention participants received the incentive (app access) right away, we expected more dropout in the intervention group at 12-weeks. 19,20 The randomization ratio was chosen to balance the number of 12-week assessments between intervention and control, and enabling a sufficient number of assessments for in-depth analyses within the intervention group. Due to the character of the intervention, it was not possible to blind participants and researchers to the intervention or control allocation.

| Intervention
The Untire app intervention is based on evidence-based methods for patients with CRF in clinical practice and comprises four modules (ie, My themes, My exercises, Physical activity, and Tips), see Appendix for more information. It is hypothesized that the app addresses dysfunctional thoughts via CBT and psycho-education (My themes), reduces stress and improves sleep via MBSR (My exercises), helps to improve physical fitness through exercise instructions (Physical Activity), and empowers via positive psychology (Tips). 3,5,[21][22][23][24][25][26][27] In addition, through Quick scans, participants get weekly insight into their levels of fatigue, burden, happiness, satisfaction, and energy leaks over time (see Appendix). Participants in the intervention group were instructed to use the app at their own pace and could freely choose modules to work on. While daily app use was recommended, participants received instructions to use the app at least once a week. We provided automatic push notifications within the first 2 weeks of app use to support participants to activate the app. In case that participants did not use the app, reminders where send.
3.6 | Outcome assessment 3.6.1 | Primary outcome measure The primary outcome was the change in fatigue severity and interference from baseline to 12-weeks, assessed with the self-report questionnaire FSI. [12][13][14] Fatigue severity and interference were assessed by calculating the average of three severity (FSI-items 1-3) and seven interference items (FSI-items 5-11), respectively. The items were answered on an 11-point Likert-scale ranging from 0 (not at all fatigued/no interference) to 10 (as fatigued as I could be/extreme interference). A higher score indicates higher fatigue severity or interference.

| Secondary outcome measures
The secondary outcome was change in QoL from baseline to 12 weeks, with the 1-item QoL EORTC-QLQ-30 questionnaire (QoL during the past week 15 ) as well as a self-constructed item of QoL on average (How would you rate your overall QoL on average?), on a 7-point Likert-scale ranging from 0 (very poor) to 7 (excellent). A higher score indicates a better QoL.

| Moderating factors
Demographic data such as age, gender, country of residence, level of education, cancer type, and cancer status (cancer patient vs survivor) were collected as potential moderators.

| In-app log data
Once participants activated their personal access code and gave consent to the privacy terms in the Untire app, participants were registered, and their in-app activities were automatically stored anonymized (log data). Log data was used to determine the degree of app use, which was calculated as the number of days with ≥1 activity performed within the 12-week intervention period.

| Descriptive statistics
We will present baseline characteristics (ie, demographics and health characteristics) of participants who completed the baseline fatigue assessment. Alpha was set to .05.

| Fatigue
Fatigue severity and interference were analyzed using generalized linear mixed models (GLMM) with four repeated measures (baseline,

4, 8, and 12 weeks) and two groups (intervention and control).
Intention-to-treat (ITT) is used as the primary approach in all outcome analyses. Based on previous studies testing health-related apps targeting behavior change, [28][29][30][31][32][33] we estimated the percentage of participants that would lose interest in the app or study assessments, and therefore would be lost to follow-up at the 12-weeks measurement, at 60%. Outcomes of the model run in the ITT approach and the outcomes of a model run among T12-completers were carried out to demonstrate the robustness of the primary findings. A sensitivity analysis explored whether the degree of app use is related to the degree of change in fatigue severity or interference over time. The 33% most active participants were categorized as "high users," the middle 33% as "medium," the least active 33% as "low," and intervention participants who had not downloaded the app as "nonusers." Differences in outcomes between intervention participants who showed high, medium, or low app use, nonusers, and control participants were tested by repeating the GLMM analysis of the primary outcome, but now comparing these five groups.
F I G U R E 1 Participant flow (CONSORT diagram) in the Untire app study. The figure presents the FSI at T0 and at T12, the number of completed assessments, and the number of participants who underwent the intervention (i.e., activated the app) T A B L E 1 Baseline characteristics of study participants who completed at least the baseline fatigue assessment in the intervention or control group

| Quality of life
QoL of the past week and overall QoL on average were analyzed by repeating the same GLMM analysis of the primary outcome using the ITT approach.

| Moderators
To gain insight into whether the hypothesized intervention effect varies among different groups of participants, potential moderating factors of the intervention effect on fatigue were explored by testing a three-way interaction (time*condition*moderator) using GLMM. Variables with ≥30 participants per category were analyzed.

| Clinical significance
To determine validly whether the degree of change in fatigue was clinically meaningful, the reliable change index (RCI) was calculated. 34  [FSI severity composite score ≤ 3]). We tested for differences between the intervention and control group by carrying out chi-square (X 2 ) tests.

| Sample size
Sample size calculations 16   use, "high users" used the app ≥9 days, "medium users" used the app ≥3 days, "low users" used the app ≥1 day. The reduction in fatigue severity and interference was seen, especially in participants who showed high and medium app use, in comparison to nonusers and participants in the control group (P's < .02; see Figure 2, and Supporting Information 2). The levels of fatigue in low app users, nonusers, and control participants did not statistically differ after 12-weeks (P's > .05).
The clinical relevance analysis for fatigue interference showed differences in the number of people who recovered, improved, not-chan-

| Moderation of intervention effects
The variables age, education, and cancer status were taken as moderators into account. The significant intervention effect on fatigue inter-   reduced with a self-management app for cancer patients and survivors. While just a few web-based/eHealth interventions targeted at reducing fatigue in cancer patients and survivors exist, 5-10 the effects of mHealth delivery on fatigue remain unclear. 3 Our data showed that having 12-week access to the Untire app significantly improved levels of fatigue and QoL on average. These effects were found, especially F I G U R E 2 Change in levels of fatigue severity, A and in fatigue interference, B, over time and between conditions. Sensitivity analyses for fatigue severity, C and fatigue interference, D, based on four groups of app use (high, medium, low, nonusers' participants) and waiting-list control participants. Change in overall QoL during the past week, E, and change in overall QoL on average over time and between conditions, F. Error bars present the 95% confidence interval. QoL, quality of life among those participants who used the app to a medium or high degree, which was in practice related to ≥3 days of app use. In addition, significantly more participants in the intervention group showed full recovery from fatigue when compared with the control group. Although we only explored moderators (ie, age, level of education, and cancer status) and more research on potential moderating factors is needed, the present results suggest that the selfmanagement app could be beneficial for different types of individuals.

| Clinical implications
Our study demonstrated small-to-moderate treatment effects targeted at CRF. However, it is important to note that with selfmanagement (ie, not therapist guided) mHealth interventions, a higher number of patients and survivors can be reached and helped simultaneously at much lower costs. Although the high attrition rates in the intervention group reflect that a self-management mHealth intervention might not be suitable for everyone, we assume that an app due to its low-threshold supply could be especially helpful for patients who would otherwise not engage in any form of treatment. In line with this, we appeared to have attracted participants who did not receive any help previously.

| Study limitations
In addition to our study's international focus and large-scale set-up, we addressed the issue of widespread CRF by an innovative lowthreshold mHealth intervention. A specific advantage of the Untire app itself is its flexibility, as the patients are in the lead to select the content or exercises that match their interests and needs instead of following a predetermined week-by-week program. Current in-app engagement strategies include different modes of content delivery, persuasive elements, reminders, and social support. Increasing personalized feedback may further engage participants and foster retention.
Carrying out a waiting-list RCT helped us to demonstrate causality, and to control for potential spontaneous improvements during the study period. A drawback of this design is that it might artificially inflate intervention effect estimates since participants in the control group could have been influenced by design to literally "wait-tochange" and thus do not improve. 37 However, this does not seem to be the case in our study, since control participants also showed improvements. Another limitation is that, in order to limit patient burden, QoL was assessed with two questions instead of using a complete, validated QoL questionnaire.
A drawback of using an online recruitment procedure is that multiple registrations by one person are possible. We removed duplicate registrations based on email address and/or name. In the study sample, the percentage of persons who completed the registration more than once did not differ between conditions, assuming that those attempts were not motivated by the idea of receiving immediate app access while being initially allocated to the waitinglist-control group (ie, no evidence for a broken randomization). Another drawback of online recruitment is the lack of access to hospital records, meaning that we cannot validate medical information (eg, cancer type and status). However, participants had nothing to gain by providing invalid medical information. Another limitation is that remotely conducted mHealth studies regularly show high attrition rates concerning study assessments and intervention participation (in our case, app use). High dropout rates and decreasing app use may be common, 28,30,38 as the choice to quit is uncomplicated owing to the impersonal character of the online study and intervention. 39 We intended to counteract the law of attrition 39 by sending reminder e-mails supporting participants to complete study assessments. Due to the design and the methodology used, it was not possible to have personal contact with the participants, and increasing adherence by sending reminders was partially successful. Low retention rates prohibit any inferences about participants who dropped out and might introduce selection bias. It is important to realize that unequal dropout rates in intervention and control groups do not necessarily imply that effect estimates are biased. 40 As aforementioned, our data analysis did not find any substantial differences when comparing baseline characteristics (Table 1) of T12-completers with T12-dropouts, and the results were consistent between analytic strategies (ie, ITT and T12-completers). Furthermore, we acknowledge that blind analyses would have been the optimal way to eliminate the experimenter's bias. We aimed to reduce experimenter bias by publishing the methodology and the statistical analysis plan in the Trial Registry prior to analyzing our data. 16 We should be aware that the recruitment strategy may have influenced our study sample, and that our findings might be limited to middle-aged female breast cancer patients who are using Facebook.
However, our sample is likely to represent future users who might find the Untire app in a similar way, that is, through finding information on the internet or Facebook. This idea is supported by data from users who downloaded and used the Untire app after the study had been completed, which showed that most users were indeed middleaged and female.

CONFLICT OF INTEREST
The authors declare that they have no conflict of interest. The University Medical Center Groningen received funding from Tired of Cancer BV., the developer of the Untire app, to study its effectiveness independently. Independence is declared in a research agreement.

ETHICAL APPROVAL STATEMENT
The Untire app study has been approved by the Medical Ethical Committee of the University Medical Center Groningen (UMCG), the Netherlands. Hereafter, the study received either ethical approval or a waiver from authorized institutions in the four English-speaking countries targeted (ie, Australia, Canada, the United Kingdom, and the United States). The trial has been registered on November 29, 2019 on the Netherlands Trial Registry (NL6642).

DATA AVAILABILITY STATEMENT
The data that support the findings of this study are available from the corresponding author upon reasonable request.