Abstract
Background
To promote physical activity in post-treatment cancer survivors, a mobile application WExercise was developed using the Multi-Process Action Control Framework. It contains 10 weekly online lesson to facilitate reflective, regulatory, and reflexive processes to help participants to form and sustain physical activity behavior.
Objectives
To test the usability and acceptability of WExercise in post-treatment cancer survivors.
Methods
This study involved four phases: (1) preparing application content, (2) expert panel review (comprising oncology healthcare workers, exercise specialists, and behavior change researchers), (3) developing the app, and (4) usability test. The usability test was conducted cross-sectionally using direct observation of application navigation tasks, a quantitative survey, and qualitative interviews among 10 post-treatment cancer survivors.
Results
In Phase 2, the expert panel rated the application highly on relevance, accuracy, comprehensiveness, meaningfulness, and easiness to understand (average score = 3.83 out of 4). The application was developed accordingly. In Phase 4, the System Usability Score was 75 %, greater than the cut-off point. Participants gave the items assessing acceptance of the application positive ratings (e.g., satisfaction = 4.30 out of 5). Based on the performance and feedback, the application was modified, including adjusting the font size and improving the visualization of buttons.
Conclusion
Overall, experts and potential users considered the application relevant, usable, and acceptable. It has the full potential for further testing in a larger trial for its effectiveness in promoting physical activity in cancer survivors.
Keywords: Usability, cancer survivor, Mobile application, mHealth, Multi-process action control framework, Physical activity, Behavior change
Highlights
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WExercise is a new application to promote physical activity in cancer survivors.
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WExercise has been tested rigorously and showed its usability and acceptance to users.
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WExercise is ready for further effectiveness testing in a fully-powered trial.
1. Introduction
Despite the well-established benefits of physical activity (PA) (Takemura et al., 2020; Nakano et al., 2018; Gerritsen and Vincent, 2016; Morishita et al., 2020), more than half of cancer survivors report no weekly moderate or vigorous PA (Mowls et al., 2016). Cancer survivors engage in significantly less PA than individuals with no previous cancer diagnosis (Mowls et al., 2016). Initiating and maintaining regular PA among cancer survivors is a major challenge, especially because cancer survivors may encounter side effects of treatment and have less motivation for PA than others (Elshahat et al., 2021).
Randomized controlled trials (RCTs) aimed at increasing PA among cancer survivors ranged from light approaches such as solely providing written information to more intensive designs including supervised exercise sessions (Grimmett et al., 2019; Sheeran et al., 2019). Compared to supervised sessions, light approaches employing theory-based behavioral change strategies (BCTs) using print, telephone, and technology may be translated into practice more easily (Goode et al., 2015). Particularly, technology-based approaches to promoting PA have various benefits over print and telephones: large reach at relatively low cost, ability to provide 24-h access to intervention materials, and the potential to personalize interventions for participants without delays.
Meta-analyses/systematic reviews of the effects of technology-based interventions to promote PA in cancer survivors have demonstrated a small significant improvement over usual care (Haberlin et al., 2018; Roberts et al., 2017; Khoo et al., 2021). Only a few RCTs have explicitly used theory to develop interventions, and they used intention-based theories that emphasize intention formation with less focus on how to translate intention into behavior and maintain behavior change (Rhodes and Yao, 2015). Of note, individuals do not always improve PA behavior despite their intentions. For example, one meta-analysis reported that 46 % of intenders failed to perform the PA behavior (Rhodes and de Bruijn, 2013).
The Multi-Process Action Control (M-PAC) framework builds on traditional social cognitive theories and focuses not only on the determinants of intention formation, but also on post-intentional constructs that bridge the intention-to-behavior gap (the failure to translate intentions into action) (Rhodes, 2017). It suggests that initiating reflective processes (e.g., instrumental attitude, perceived capability) influence intention formation, while translation of intention into PA relies on strong ongoing reflective processes (e.g., perceived opportunity, affective attitude) and regulation processes (e.g., action/coping planning, self-monitoring, emotion regulation, social support). Importantly, continuance of PA requires the development of reflexive processes of action control via habit and identity formation. While the overall evidence for the M-PAC framework is accumulating across populations for PA promotion including cancer populations (Liu et al., 2022a; Trinh et al., 2021; Perdew et al., 2021; Vallerand et al., 2018), to our knowledge, no application (app) intervention has been developed based on this framework.
Before implementing a technology innovation, it is important to conduct usability and acceptability testing (Mummah et al., 2016). The aims of the study are (1) to develop a 10-week technology-based PA promotion intervention (WExercise) using an app among post-treatment cancer survivors based on the M-PAC framework; and (2) to test the usability of WExercise among cancer survivors. The findings are useful for informing the design of the intervention for full trial evaluation in the future.
2. Methods
2.1. Study design
This study had four phases: (1) preparing app content, (2) expert panel review, (3) developing the app, and (4) usability test. The usability test was conducted cross-sectionally, using mixed methods, including direct observation of app navigation tasks, a quantitative survey, and qualitative interviews, to identify areas for improvement.
2.2. Procedures and measurement
2.2.1. Phase 1 preparing app content
The WExercise app is a mobile platform accessible across a variety of devices including tablets and smartphones. During app development, previous M-PAC-based RCTs for other populations were referenced (Vallerand et al., 2018; Liu et al., 2019). Besides, major exercise guidelines for cancer survivors were referenced, for example, the recommendations from the American College of Sports Medicine Roundtable (Campbell et al., 2019) and the World Health Organizations (Bull et al., 2020). The app integrated the three-layered processes (reflective, regulatory, reflexive) proposed by the M-PAC, guiding an individual from the formation of an intention to change, adoption of physical activity, to successful physical activity maintenance. Also, the research team applied principles of BCTs for supporting the processes based on the M-PAC framework (Appendix 1). For example, BCT 5.1 Information about health consequences was used to support reflective processes (instrumental attitude), BCT 11.2 Reduce negative emotions was used to support regulation processes (emotional regulation) (Michie et al., 2011). The content creation and adaptations were prepared and critically appraised by two researchers experienced in cancer care (DSTC and TWHK) made to ensure the app content was appropriate for cancer survivors, culturally sensitive to Chinese, and transformable to a technology-based format. Participants were encouraged to attain the recommended PA levels for post-treatment cancer survivors, i.e., perform at least 150 min of moderate aerobic exercise or 75 min of vigorous exercise and two resistance exercise sessions per week (Campbell et al., 2019).
The WExercise app comprised 10 weekly lessons structured as follows: (1) 1st-3rd lessons: initiating and ongoing reflective processes (instrumental attitude, perceived capability, affective attitude, perceived opportunity); (2) 4th–8th lessons: behavioral regulation through action and coping planning, self-monitoring, self-regulating alternative activities, and building a supportive social and physical environment; and (3) 9th–10th lessons: forming habits and identity (reflexive processes) for sustaining action control (Appendix 1). Each lesson took around 20–30 min depending on personalized progress.
2.2.2. Phase 2 expert panel review
The app content was reviewed by a local expert panel comprising a frontline clinical oncologist, a nurse specialist, an experienced oncology nurse researcher, a PA behavior change researcher, and an exercise physiologist experienced in providing supervised exercise training for cancer survivors. The panel gave ratings for each week's content on six domains, including (1) relevance to the overall study aim, (2) relevance to the lesson aim, (3) accuracy, (4) comprehensiveness, (5) meaningfulness, and (6) easiness to understand. For each domain, the rating was given on a 4-point Likert-type scale ranging from 1 (irrelevant/inaccurate/incomprehensive/not meaningful/difficult to understand) to 4 (relevant/accurate/comprehensive/meaningful/easy to understand). Changes were made according to expert panel's response and reviewed by the panel again to confirm no further change was needed.
2.2.3. Phase 3 app development
The app was developed based on the content approved by the expert panel using the Pathverse, a no-code app builder for research (Liu et al., 2022b). Each weekly lesson contains a combination of text, educational videos, infographics, and interactive activities. Various features were incorporated to engage participants and to strengthen the BCTs, for example, (1) integrating data from cellphones (e.g., step count) for self-monitoring, (2) creating an online diary for participants to log their PA sessions to reflect their progress, and (3) creating an online support community to offer social support (Appendix 1). The screenshots of the app are shown in Appendix 2.
2.2.4. Phase 4 usability test
The overall design of the usability test aimed to test the three measurable attributes of usability of the app: effectiveness (whether users can achieve specified goals), efficiency (the extent to which resources are exhausted by users in achieving the goals), and satisfaction (the comfort level and acceptability of use) (Brooke, 1996).
Participants were recruited from the outpatient clinic of the Department of Oncology in a public hospital in Hong Kong. All participants provided written informed consent. Inclusion criteria of participants include: (1) ≥18 years of age, (2) able to read Chinese and communicate in Cantonese or Putonghua, (3) completion of primary treatment (surgery/chemo−/radiation therapy) of curative intent for at least 12 months, (4) access to a smartphone, (5) no metastasis or recurrence at the time of recruitment, (6) not meeting the recommended PA guideline (<150 min of moderate intensity aerobic exercise and < 75 min of vigorous aerobic exercise per week), and (7) screened by a nurse and having no contraindications for engaging in unsupervised exercise using a risk-screening tool (Brown et al., 2015). Those with psychiatric disorders, significant cognitive impairment, or a history of more than one cancer were excluded.
Ten eligible cancer survivors were recruited to review the content of 2 weekly online lessons and to navigate the app in a laboratory setting using a standard phone provided by researchers. The think-aloud approach was adopted, in which participants were encouraged to say aloud their thoughts, actions, expectations, and observations when performing usability tasks (Van Someren et al., 1994). Each participant was assigned 6 specific usability tasks to test whether the app is easy to navigate. The tasks were designed and considered as representative by the research team based on real-life scenarios to ensure that all the main functionalities of the interfaces were used and tested. Also, the level of difficulty of the tasks should be appropriate so that the participants would not be able to solve the problem automatically (Van Someren et al., 1994). There were seven tasks: (1) browsing content, (2) filling in short quizzes and reviewing answers, (3) checking images, (4) playing videos, (5) filling in questionnaires and reviewing total scores, (6) browsing website links, and (7) looking for content based on keywords. Each task was assigned to at least 4 participants. The more common the task has to be performed when using the app in real-life, the more participants are assigned to perform the corresponding task. One trained RA took written notes of any observations or concerns raised, while another RA videotaped the task execution process. In addition, objective data were recorded by RAs watching the recorded videos, including the task completion level (from 0 [cannot complete] to 3 [completed successfully]), task success rate (proportion of participants successfully completing the task without assistance) and task time (time taken to complete the task successfully).
Upon completion of tasks, participants were invited to complete a self-reported evaluation questionnaire on usability and acceptability. First, the System Usability Scale (Michie et al., 2011), a widely used tool to assess usability for a variety of technologies (Brooke, 1996) was administered. It consists of 10 statements on a 5-point Likert-type scale ranging from 1 (strongly disagree) to 5 (strongly agree). The sum of the scores was multiplied by 2.5 to obtain the final standardized value (ranging 0 to 100), with higher scores indicating better usability. Scores above 68 indicated above-average usability (Brooke, 1996). Second, the User Acceptance Questionnaire, designed by the research team and used in a previous study (Cheung et al., 2018) was used to measure participants' acceptance of the app. The domains included attitude (4 items), perceived ease of use (four items), perceived usefulness (4 items), intention to use (2 items), and satisfaction (1 item) (Davis, 1989) (Appendix 3). Responses were rated on a 7-point Likert-type scale ranging from 1 (strongly disagree) to 7 (strongly agree), with the exception of satisfaction, which was rated from 1 (very unsatisfied) to 5 (very satisfied). Third, 10 specific questions relating to general feedback were asked, covering appropriateness of font size (1 item), difficulty of the app content (1 item), length of the content (1 item), appropriateness of images and videos (4 items), and appropriateness for cancer survivors (3 items) (Appendix 4). Finally, a demographic questionnaire was administered to elicit information on gender, age, education attainment, cancer diagnosis, treatment completed, and time since completion of treatment.
After participants completed the questionnaires, they undertook a qualitative interview with a researcher (TWHK) to explore their perspectives on the app using a semi-structured interview guide (Appendix 5). Modifications of the app were made at the end of this phase based on the findings.
2.3. Data analysis
Quantitative data were analyzed using SPSS 20. Descriptive analysis was performed to present the expert review rating (Phase 2), task completion level, task success rate, task time, SUS scores, user acceptance, and general feedback (Phase 4). The videos obtained in Phase 4 were reviewed by the researchers to elicit supplementary information on room for improvement of the app. For the qualitative interviews with participants in Phase 4, two researchers (DSTC, TWHK) confirmed the data was saturated, so data analysis proceeded. The interviews were analyzed using the six phases of thematic analysis (Braun and Clarke, 2006). First, two researchers read the transcripts repeatedly to get familiar with the data. Subsequently, the transcriptions were coded independently. Meaningful words and patterns were identified to search candidate themes, which were then reviewed by revisiting the interview data and coded extract to check if the themes fitted well with the coded data. The developed themes underwent a refining process and were defined and named clearly. Any disagreement was discussed, and revisions were made as appropriate. Last, the findings were presented as descriptive summaries with direct quotes from the raw data.
3. Results
3.1. Phase 2: expert panel review & phase 3: app development
In terms of domain scores (Appendix 6), the scores by domain ranged from 3.67 (SD 0.20) (Comprehensiveness) to 3.96 (SD 0.08) (Relevance to the lesson aim) out of 4, showing that experts' ratings on the domains were positive. Scores by week ranged from 3.60 (SD 0.18) to 3.93 (SD 0.10) out of 4. Open comments were also given by the panel. A major concern was that some of the content may be “too theoretical” and “very academic,” and that jargon, e.g., “self-efficacy” and “exercise identity,” needed elaboration to facilitate the understanding of participants. Suggestions were also given to rearrange the order of some content for a more logical flow (Table 1). Modifications were made to the app content accordingly, and the app was developed as planned.
Table 1.
Modifications made after Phase 2 (Expert review).
| Categories | Issues | Modifications |
|---|---|---|
| Content | Too theoretical and academic | Included more examples in daily life to better elaborate the concepts (e.g., self-efficacy, exercise identity) |
| Difficulty in associating the lessons with daily life | Provided concrete suggestions/guidelines on how to practice the concept (e.g., exercise progression principle) | |
| Gave examples of readily available resources in the community (e.g., links to non-governmental organizations) | ||
| Included more relevant short quizzes/questionnaires to provoke reflection | ||
| Jargon | Rephrased jargon into simpler terms and provided daily life examples | |
| Structure | Inappropriate sequence of introduction for some concepts | Introduced self-efficacy before exercise progression principles |
| Insufficient connections of concepts between lessons | Added some connective sentences in the beginning and end of the lesson to connect the previous and subsequent lesson |
3.2. Phase 4: usability test
3.2.1. Participant characteristics
After the development of the app, 10 patients were recruited for the usability test. Their demographic characteristics are summarized in Table 2. The mean age of the participants was 68.2 (SD 8.03). Most had received university education or above and self-reported as excellent in using smartphones. Breast cancer was the most common cancer type in the sample.
Table 2.
Demographic characteristics of the participants in Phase 4 (Usability test) (n = 10).
| Variable | Number (%) or Mean (SD) |
|---|---|
| Gender | |
| Male | 6 (60) |
| Female | 4 (40) |
| Age | 68.2 (8.03) |
| Education level | |
| Primary education or lower | 0 (0) |
| Junior secondary education | 1 (10) |
| Senior secondary education | 1 (10) |
| University education or above | 8 (80) |
| Cancer type | |
| Head and neck | 1 (10) |
| Nasopharyngeal | 1 (10) |
| Breast | 3 (30) |
| Prostate | 1 (10) |
| Colon | 1 (10) |
| Othera | 3 (30) |
| Years since treatment completion | |
| > 5 years | 6 (60) |
| 3–5 years | 3 (30) |
| 1–2 years | 1 (10) |
| Smartphone usage competencies | |
| Weak | 0 (0) |
| Average | 3 (30) |
| Above average | 4 (40) |
| Excellent | 3 (30) |
SD = standard deviation.
Other cancer types include cholangiocarcinoma, mucosal melanoma, and mucoepidermoid carcinoma.
3.2.2. Navigation tasks performance
The range of mean task time was especially wide for “Checking images” (time range = 18–346 s) and “Browsing content” (time range = 24–311 s) (Table 3). “Playing videos” was the task with the highest task success rate (8/10, 80 %). It was also the task with the highest mean completion level (mean = 2.80, SD 0.42). The task with the lowest task success rate was “Checking images” (2/8, 25 %) and the second lowest was “Filling in short quizzes and reviewing answers” (3/6, 50 %). Consistently, these 2 tasks had the lowest mean completion level of 1.38 (SD 1.30) and 2.00 (SD 1.26) respectively, meaning that participants generally required considerable support to complete these tasks.
Table 3.
Performance of navigation tasks in Phase 4 (Usability test).
| Task category | Mean completion levela (mean (SD)) |
Task success rateb (%) |
Task time (mean (SD), s) (min-max) |
Difficulties encountered by observations |
|---|---|---|---|---|
| Browsing content | 2.42 (0.79) | 58 | 74.25 (76.53) (24–311) |
Some participants might take longer to read jargon; Some participants might not realize that the pages could be scrolled down. |
| Filling in short quizzes and reviewing answers | 2.00 (1.26) | 50 | 110.50 (62.40) (36–200) |
Some participants were unsure how to submit the answers because of confusing buttons. The fonts for the questions were too small for most participants. |
| Checking images | 1.38(1.30) | 25 | 91.50 (107.78) (18–346) |
Some of the contents of the pictures were too small for some participants. |
| Playing videos | 2.80 (0.42) | 80 | 36.00 (77.92) (3–255) |
Some participants had difficulty in pausing the video. |
| Filling in questionnaires and reviewing total scores | 2.25 (1.39) | 75 | 41.75 (55.95) (10–176) |
Similar issue to Task 2: some participants had issues submitting the questionnaires due to confusing buttons. |
| Browsing website links | 2.50 (0.84) | 67 | 61.50 (75.15) (5–209) |
Some participants were not aware that the button could link to an external link. |
| Looking for content based on keywords | 2.30 (1.06) | 60 | 59.10 (76.53) (5–230) |
Some participants needed hints to revisit the correct module. |
SD = standard deviation; s = second.
Range = 0 (cannot complete) to 3 (completed successfully).
Refers to the proportion of participants successfully completing the task without assistance (i.e., scored 3 out of 3 in the navigation tasks).
From observations of recorded videos and RA notes, participants took longer to read some of the jargon (e.g., “cancer-related fatigue,” “morbidities”). Common reasons for failing at “Filling in short quizzes and reviewing answers” were that participants found font size small and the buttons for submitting and reviewing the answers confusing. Also, participants found the images too small and difficult to read, causing long task times and high failure rates for the task “Checking images.”
3.2.3. Self-reported measures
For the SUS, the overall mean score was 75 % (SD 13.01), which is higher than the acceptable cut off of 68 % (Table 4). Regarding acceptance, participants' ratings were generally positive for all domains, including attitude, perceived usefulness, perceived ease of use, intention to use, and satisfaction. The feedback regarding the general interface and content were also generally good, while the appropriateness of font size yielded the lowest rating (mean = 1.58, SD 0.52), consistent with the navigation task results.
Table 4.
Results of self-reported questionnaires in Phase 4 (Usability test) (n = 10).
| Variable | Mean (SD) |
|---|---|
| System usability score (0−100) | 75 (13.02) |
| User acceptance score | |
| Attitude (1–7) | 5.78 (0.82) |
| Perceived usefulness (1–7) | 5.62 (0.86) |
| Perceived ease of use (1–7) | 6.00 (0.58) |
| Intention (1–7) | 5.67 (1.15) |
| Satisfaction (1–5) | 4.30 (0.48) |
| Questionnaire on general interface and content | |
| Appropriateness of font size (1–3) | 1.58 (0.52) |
| Difficulty of the application content (1–3) | 2.00 (0) |
| Length of the content (1–3) | 2.17 (0.39) |
| Appropriateness of images and videos (1–3) | 1.69 (0.13) |
| Appropriateness of content for cancer survivors (1–4) | 3.31 (0.09) |
SD = standard deviation.
3.2.4. Qualitative feedback
Two major themes emerged: positive feedback and issues identified. The representative quotes are shown in Appendix 7.
Theme 1: Positive feedback
Positive feedback related to three subthemes: (1) interface, (2) content, and (3) app features. Regarding the interface, comments were mainly “easy to use” and “smooth.” The content of the app was “informative,” “systematic,” “concise,” and “inspiring.” For the app features, participants commented positively particularly on the videos because they helped to consolidate the knowledge of the week. The online forum was also commended because it bonded a group of people sharing similar experiences, and it was interactive and motivating.
Participant 5: “The app is easy to use.”
Participant 9: “The interface is very smooth and responsive.”
Participant 3: “The information is very useful for cancer patients and focused.… I don't need to spend time searching for information.”
“Videos can help me [to] recap the information, easier to understand the concept. It offers an alternative to read[ing] all over again.”
Participant 7: “Having a community can give great motivation because everyone shares similar experiences.”
Theme 2: Issues
Issues identified related to two subthemes: (1) interface and (2) content. The interface had a small font size for text and in pictures, as well as some light-colored buttons and scrollbars, making them easily missed. For the content, one participant doubted the relevance of the app to older adults and another comment was that patients' role was passive in the app.
Participant 6: “Some words are too small for me … especially those in the pictures. The rest are fine.”
Participant 4: “I understand what the emoji means, but how do I pick my answer?”
Participant 9: “The app may be suitable for younger patients.… [The] elderly [are] unlikely to do high-intensity exercise, e.g., running, swimming. They mostly just walk and stretch.”
Participant 7: “I feel a bit like students listening to teacher's seminars and then work on the assignment.… My role is a bit passive. It would be better if it [was] more engaging.”
3.2.5. Modifications
After the usability test, the findings were discussed among the research team. Modifications were made to improve the interface, simplify the content, enhance the relevance to older adults, and reduce the sense of passiveness. Details are shown in Appendix 8.
4. Discussion
To design a feasible, effective, and scalable technology-based behavior change intervention, the development phase is crucial (Mummah et al., 2016). This study describes and reports the results of this iterative process. First, the experts rated the app content highly as comprehensive and relevant to the study aim, and they provided suggestions for improvement. Accordingly, the app was developed. A sample of cancer survivors then completed a usability test consisting of objective navigation tasks, self-reported measures on usability and acceptance, and qualitative interviews. Participants overall found the app informative and easy to use, though there were rooms for improvement. Finally, the app was updated based on feedback. Collectively, the iterative process and results conveyed confidence to the research team that the final app used in the subsequent full trial was usable and acceptable to users.
There is an increasing trend of using apps to promote PA in individuals with chronic illnesses (Silva, 2023; Aslam et al., 2020; Pradal-Cano et al., 2020). Although there were a few RCTs tested the effects of apps for promoting PA in cancer survivors (Mayer et al., 2018; Uhm et al., 2017; Ormel et al., 2018), none conducted usability tests before conducting the RCT. Usability tests represent an important component in a user-centered app-development process (Zapata et al., 2015). Testing for usability minimizes errors in real-life, reduces the need for user training and support, and enhances user acceptance of technology-based interventions. In general, people tend to use an app if they perceive it as useful, easy to use, and enjoyable; this will probably lead to better compliance with the intervention. This study employed 4 phases, which combined expert-based and user-based testing, resulting in rich data to identify usability issues from different perspectives. Such a user-centered iterative design process is in line with the few usability tests of mobile apps designed to promote PA in cancer populations (Henshall and Davey, 2020; Monteiro-Guerra et al., 2020; Welch et al., 2022).
The need to promote PA as part of oncology care is widely recognized due to its multifaceted and well-documented benefits (Coletta et al., 2022). To develop interventions that can enhance PA in cancer survivors, theory-based research should be conducted. A review rated a total of 64 mobile apps for promoting PA in the general public based on the taxonomy of BCTs, and found the average number of BCTs used was 5 (range 2–8) (Middelweerd et al., 2014), whereas our app applied a total of 16 BCTs. Regarding app functionalities, the most common ones of the past apps include self-monitoring, feedback provision, and goal-setting (Pradal-Cano et al., 2020; Middelweerd et al., 2014; Coughlin et al., 2016), which are also included in our app. Of note, all reviews found that majority of apps did not report whether or not their apps are driven by behavior change theories (Pradal-Cano et al., 2020; Middelweerd et al., 2014; Coughlin et al., 2016). It is known that behavior change theory is an important determinant of successful behavior change interventions (Rhodes, 2017; Hagger and Weed, 2019). Relatively, our app is based on a comprehensive behavior change framework (i.e., M-PAC), under which users are guided from exercise intention formation to translation into action, and ultimately maintenance of exercise behavior. This is the first study employing the M-PAC framework for PA promotion using an app-based intervention delivery mode. Compared to traditional behavior change theories, the M-PAC involves more diverse components relating to reflective, regulatory, and reflexive processes to promote and sustain behavior change (Rhodes, 2021). To maximize the cost-effectiveness, accessibility, and scalability of PA promotion interventions, eHealth technologies offer an unprecedented opportunity. The overall satisfaction with the system usability and acceptance suggested that the M-PAC framework is feasible for a smartphone app. However, whether the app developed based on this framework is effective for promoting behavior change is yet to be established. A full scale RCT is needed to test the effectiveness of the intervention in the future. Also, investigations on which behavioral change theories and BCTs best promote positive PA behavior change may guide future PA-related app development.
4.1. Limitations
First, the participants were recruited by convenience sampling from an oncology department in Hong Kong, and most participants had university education or above. The results may not be generalizable to potential users with other demographic characteristics. Second, there was a voluntary bias in the sampling as the participants in this study might have intended to become physically active anyway. Third, our sample was small. Nevertheless, some have suggested that over two-thirds of usability issues can be identified with 4 or 5 participants (Virzi, 1992), and our results based on mixed methods also showed data saturation. Fourth, the expert panel did not involve real patients, resulting in limited patient engagement in the initial stage. Fifth, the usability test was conducted on a standard phone rather than patients' own phone. This might have confounded the usability findings. Finally, the usability test was conducted in a controlled setting over a short period of time. The experience of using the app in a real setting over a longer period of time may differ.
4.2. Implications
Our study informs researchers and healthcare services of a user-centered iterative approach to design and test an app prior to deploying it in a full-scale intervention. The advantage is that it reflects the real-world difficulties that might arise, thus allowing for integration of feedback and revisions to enhance the chance of success. This helped to ensure that the app design was relevant and usable by the intended population. It may pave the way for a larger scale RCT, which will test the effectiveness of the app in improving PA levels, with the long-term aim of rolling it out into mainstream clinical practice.
4.3. Conclusions
This study demonstrates that cancer survivors found the app usable and acceptable. The findings may lead to a larger RCT to confirm the effectiveness of the app to promote PA in cancer survivors.
Summary table
| What was already known on the topic |
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| What this study added to our knowledge |
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Funding
Funding support from the Research Fellowship Scheme of Health and Medical Research Fund, Food and Health Bureau, Hong Kong (Ref: 06200067) to Dr. Denise Shuk Ting Cheung.
CRediT authorship contribution statement
All authors have contributed to the study design and rationale, interpretation of data and approval of the submitting version of the manuscript.
Declaration of competing interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Acknowledgements
We would like to thank Mr. Henry La and Ms. Amanda Willms for their assistance in developing the WExercise app, Mr. Stephen Ip and Ms. Chi Wah Lau for serving as expert reviewers during the development phase, and the participants for taking part in the usability test.
Footnotes
Supplementary data to this article can be found online at https://doi.org/10.1016/j.invent.2024.100730.
Contributor Information
Denise Shuk Ting Cheung, Email: denisest@hku.hk.
Tiffany Wan Han Kwok, Email: tiffkwok@hku.hk.
Sam Liu, Email: samliu@uvic.ca.
Ryan E. Rhodes, Email: rhodes@uvic.ca.
Chi-Leung Chiang, Email: chiangcl@hku.hk.
Chia-Chin Lin, Email: cclin@hku.hk.
Appendix A. Supplementary data
Supplementary material
References
- Aslam A.S., et al. A systematic review on the use of mHealth to increase physical activity in older people. Clinical EHealth. 2020;3:31–39. [Google Scholar]
- Braun V., Clarke V. Using thematic analysis in psychology. Qual. Res. Psychol. 2006;3(2):77–101. [Google Scholar]
- Brooke J. CRC Press; 1996. SUS: A 'Quick and Dirty' Usability Scale; pp. 207–212. [Google Scholar]
- Brown J.C.M.A., Ko E.M.M.D.M., Schmitz K.H.P.M.P.H. Development of a risk-screening tool for Cancer survivors to participate in unsupervised moderate- to vigorous-intensity exercise: results from a survey study. PM & R. 2015;7(2):113–122. doi: 10.1016/j.pmrj.2014.09.003. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Bull F.C., et al. World Health Organization 2020 guidelines on physical activity and sedentary behaviour. Br. J. Sports Med. 2020;54(24):1451–1462. doi: 10.1136/bjsports-2020-102955. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Campbell K.L., et al. Exercise guidelines for Cancer survivors : consensus statement from international multidisciplinary roundtable. Med. Sci. Sports Exerc. 2019;51(11):2375–2390. doi: 10.1249/MSS.0000000000002116. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Cheung D.S.T., et al. Usability testing of a smartphone application for delivering qigong training. J. Med. Syst. 2018;42(10):191–198. doi: 10.1007/s10916-018-1048-9. [DOI] [PubMed] [Google Scholar]
- Coletta A.M., Basen-Engquist K.M., Schmitz K.H. Exercise across the cancer care continuum: why it matters, how to implement it, and motivating patients to move. Am. Soc. Clin. Oncol. Educ. Book. 2022;42:932–938. doi: 10.1200/EDBK_349635. [DOI] [PubMed] [Google Scholar]
- Coughlin S.S., et al. A review of smartphone applications for promoting physical activity. Jacobs journal of community medicine. 2016;2(1) [PMC free article] [PubMed] [Google Scholar]
- Davis F.D. Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q. 1989:319–340. [Google Scholar]
- Elshahat S., Treanor C., Donnelly M. Factors influencing physical activity participation among people living with or beyond cancer: a systematic scoping review. Int. J. Behav. Nutr. Phys. Act. 2021;18:1–20. doi: 10.1186/s12966-021-01116-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Gerritsen J.K.W., Vincent A.J.P.E. Exercise improves quality of life in patients with cancer: a systematic review and meta-analysis of randomised controlled trials. Br. J. Sports Med. 2016;50(13):796–803. doi: 10.1136/bjsports-2015-094787. [DOI] [PubMed] [Google Scholar]
- Goode A.D., et al. Telephone, print, and web-based interventions for physical activity, diet, and weight control among cancer survivors: a systematic review. J. Cancer Surviv. 2015;9(4):660–682. doi: 10.1007/s11764-015-0442-2. [DOI] [PubMed] [Google Scholar]
- Grimmett C., et al. Systematic review and meta-analysis of maintenance of physical activity behaviour change in cancer survivors. The international journal of behavioral nutrition and physical activity. 2019;16(1):37. doi: 10.1186/s12966-019-0787-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Haberlin C., et al. The use of eHealth to promote physical activity in cancer survivors: a systematic review. Support. Care Cancer. 2018;26(10):3323–3336. doi: 10.1007/s00520-018-4305-z. [DOI] [PubMed] [Google Scholar]
- Hagger M.S., Weed M. DEBATE: do interventions based on behavioral theory work in the real world? Int. J. Behav. Nutr. Phys. Act. 2019;16(1):1–10. doi: 10.1186/s12966-019-0795-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Henshall C., Davey Z. Development of an app for lung cancer survivors (iEXHALE) to increase exercise activity and improve symptoms of fatigue, breathlessness and depression. Psycho-oncology. 2020;29(1):139–147. doi: 10.1002/pon.5252. [DOI] [PubMed] [Google Scholar]
- Khoo S., et al. mHealth interventions to address physical activity and sedentary behavior in Cancer survivors: a systematic review. Int. J. Environ. Res. Public Health. 2021;18(11):5798. doi: 10.3390/ijerph18115798. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Liu S., et al. Development of a self-guided web-based intervention to promote physical activity using the multi-process action control framework. Internet interventions : the application of information technology in mental and behavioural health. 2019;15:35–42. doi: 10.1016/j.invent.2018.11.003. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Liu S., et al. Dose-response relationship of a blended in-person and online family-based childhood obesity management program: secondary analysis of a behavior intervention. JMIR Pediatr Parent. 2022;5(3) doi: 10.2196/36770. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Liu S., et al. A “no-code” app design platform for Mobile Health Research: development and usability study. JMIR Formative Research. 2022;6(8) doi: 10.2196/38737. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Mayer D.K., et al. SurvivorCHESS to increase physical activity in colon cancer survivors: can we get them moving? J. Cancer Surviv. 2018;12(1):82–94. doi: 10.1007/s11764-017-0647-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Michie S., et al. A refined taxonomy of behaviour change techniques to help people change their physical activity and healthy eating behaviours: the CALO-RE taxonomy. Psychol. Health. 2011;26(11):1479–1498. doi: 10.1080/08870446.2010.540664. [DOI] [PubMed] [Google Scholar]
- Middelweerd A., et al. Apps to promote physical activity among adults: a review and content analysis. Int. J. Behav. Nutr. Phys. Act. 2014;11(1):1–9. doi: 10.1186/s12966-014-0097-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Monteiro-Guerra F., et al. A personalized physical activity coaching app for breast cancer survivors: design process and early prototype testing. JMIR Mhealth Uhealth. 2020;8(7) doi: 10.2196/17552. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Morishita S., et al. Effect of exercise on mortality and recurrence in patients with Cancer: a systematic review and Meta-analysis. Integr. Cancer Ther. 2020;19 doi: 10.1177/1534735420917462. p. 153473542091746–1534735420917462. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Mowls D.S., et al. Lifestyle behaviors among US cancer survivors. J. Cancer Surviv. 2016;10(4):692–698. doi: 10.1007/s11764-016-0515-x. [DOI] [PubMed] [Google Scholar]
- Mummah S.A., et al. IDEAS (integrate, design, assess, and share): a framework and toolkit of strategies for the development of more effective digital interventions to change health behavior. J. Med. Internet Res. 2016;18(12) doi: 10.2196/jmir.5927. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Nakano J., et al. Effects of aerobic and resistance exercises on physical symptoms in Cancer patients: a Meta-analysis. Integr. Cancer Ther. 2018;17(4):1048–1058. doi: 10.1177/1534735418807555. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Ormel H.L., et al. Self-monitoring physical activity with a smartphone application in cancer patients: a randomized feasibility study (SMART-trial) Support. Care Cancer. 2018;26(11):3915–3923. doi: 10.1007/s00520-018-4263-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Perdew M., Liu S., Rhodes R., Ball G.D.C., Mâsse L.C., Hartrick T., Strange K., Naylor P.J. The effectiveness of a blended in-person and online family-based childhood obesity management program. Child. Obes. 2021;17(1):58–67. doi: 10.1089/chi.2020.0236. [DOI] [PubMed] [Google Scholar]
- Pradal-Cano L., et al. Using mobile applications to increase physical activity: a systematic review. Int. J. Environ. Res. Public Health. 2020;17(21):8238. doi: 10.3390/ijerph17218238. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Rhodes, R.E., Chapter Five - The Evolving Understanding of Physical Activity Behavior: A Multi-Process Action Control Approacha, in Advances in Motivation Science, A.J. Elliot, Editor. 2017, Elsevier. p. 171–205.
- Rhodes R.E. Multi-process action control in physical activity: a primer. Front. Psychol. 2021;12:797484. doi: 10.3389/fpsyg.2021.797484. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Rhodes R.E., de Bruijn G.-J. How big is the physical activity intention-behaviour gap? A meta-analysis using the action control framework. Br. J. Health Psychol. 2013;18(2):296–309. doi: 10.1111/bjhp.12032. [DOI] [PubMed] [Google Scholar]
- Rhodes R.E., Yao C.A. Models accounting for intention-behavior discordance in the physical activity domain: a user’s guide, content overview, and review of current evidence. The international journal of behavioral nutrition and physical activity. 2015;12(1):9. doi: 10.1186/s12966-015-0168-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Roberts A.L., et al. Digital health behaviour change interventions targeting physical activity and diet in cancer survivors: a systematic review and meta-analysis. J. Cancer Surviv. 2017;11(6):704–719. doi: 10.1007/s11764-017-0632-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Sheeran P., et al. Promoting physical activity among Cancer survivors: Meta-analysis and Meta-CART analysis of randomized controlled trials. Health Psychol. 2019;38(6):467–482. doi: 10.1037/hea0000712. [DOI] [PubMed] [Google Scholar]
- Takemura N, C.D.c.-f.a., Smith R, Deng W, Ho KY, Lin J, Lin CC., Effectiveness of aerobic exercise and mind-body exercise in cancer patients with poor sleep quality: a systematic review and meta-analysis of randomized controlled trials. Sleep Med. Rev. , 2020. 53:101334. [DOI] [PubMed]
- Silva J., et al. Technological features of smartphone apps for physical activity promotion in patients with COPD: a systematic review. Pulmonology. 2023 doi: 10.1016/j.pulmoe.2023.06.005. Advance online publication. S2531-0437(23)00124-1. [DOI] [PubMed] [Google Scholar]
- Trinh L., et al. A pilot feasibility randomized controlled trial adding behavioral counseling to supervised physical activity in prostate cancer survivors: behavior change in prostate cancer survivors trial (BOOST) J. Behav. Med. 2021;44(2):172–186. doi: 10.1007/s10865-020-00185-8. [DOI] [PubMed] [Google Scholar]
- Uhm K.E., et al. Effects of exercise intervention in breast cancer patients: is mobile health (mHealth) with pedometer more effective than conventional program using brochure? Breast Cancer Res. Treat. 2017;161(3):443–452. doi: 10.1007/s10549-016-4065-8. [DOI] [PubMed] [Google Scholar]
- Vallerand J.R., et al. Feasibility and preliminary efficacy of an exercise telephone counseling intervention for hematologic cancer survivors: a phase II randomized controlled trial. J. Cancer Surviv. 2018;12(3):357–370. doi: 10.1007/s11764-018-0675-y. [DOI] [PubMed] [Google Scholar]
- Van Someren M., Barnard Y.F., Sandberg J. vol. 11. AcademicPress; London: 1994. The Think Aloud Method: A Practical Approach to Modelling Cognitive; pp. 29–41. [Google Scholar]
- Virzi R.A. Refining the test phase of usability evaluation: how many subjects is enough? Hum. Factors. 1992;34(4):457–468. [Google Scholar]
- Welch W.A., et al. User-centered development of a smartphone application (Fit2Thrive) to promote physical activity in breast cancer survivors. Transl. Behav. Med. 2022;12(2):203–213. doi: 10.1093/tbm/ibab112. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Zapata B.C., et al. Empirical studies on usability of mHealth apps: a systematic literature review. J. Med. Syst. 2015;39:1–19. doi: 10.1007/s10916-014-0182-2. [DOI] [PubMed] [Google Scholar]
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