=Paper=
{{Paper
|id=Vol-1388/PATH2015-paper2
|storemode=property
|title=An mHealth Intervention Strategy for Physical Activity Coaching in Cancer Survivors
|pdfUrl=https://ceur-ws.org/Vol-1388/PATH2015-paper2.pdf
|volume=Vol-1388
|dblpUrl=https://dblp.org/rec/conf/um/WolversV15
}}
==An mHealth Intervention Strategy for Physical Activity Coaching in Cancer Survivors==
https://ceur-ws.org/Vol-1388/PATH2015-paper2.pdf
An mHealth Intervention Strategy for Physical Activity
Coaching in Cancer Survivors
M.D.J. Wolvers and M.M.R. Vollenbroek-Hutten
Roessingh Research and Development, Enschede, The Netherlands
m.wolvers@rrd.nl
University of Twente, Enschede, The Netherlands
Abstract. Many cancer survivors experience severe fatigue long after they have
finished curative treatment. The aim of this study was to develop an intervention
strategy that aims to decrease cancer-related fatigue by integrating a physical ac-
tivity coaching system in primary care physiotherapy. This development started
from the current state of the art. Therefore, firstly, an overview is given about
physical activity goals for cancer-related fatigue, relevant cognitive behavioral
change factors in this context, and recommendations for using mobile Health ap-
plications. Subsequently, interviews with five experienced health professionals
were held to define recommendations for the first draft intervention strategy. Via
an iterative process with two physiotherapists and a patient, the final intervention
strategy was developed. The final result is a 9-week intervention strategy that
could benefit a large variety of patients with chronic cancer-related fatigue, that
has the potential to be integrated successfully in current primary health care, and
is currently evaluated in a large randomized controlled trial.
Keywords: physical activity ∙ activity monitoring ∙ cancer-related fatigue ∙
mHealth ∙ behavior change
1 Introduction
1.1 Chronic Cancer-Related Fatigue
Fatigue is a frequent and debilitating residual symptom of cancer and its treatment. It
is estimated that more than 20% of cancer survivors report severe fatigue one year after
treatment [1]. Survival rates and life expectancies of cancer patients are rising, and can-
cer is increasingly often considered a chronic disease. The 10-year prevalence of cancer
patients in the Netherlands is expected to grow by 40% between 2011 and 2020 [2]. As
a result, the number of patients suffering from cancer-related fatigue will increase rap-
idly.
Currently, cognitive behavioral therapy, multidisciplinary rehabilitation programs,
exercise, and energy conservation interventions seem effective in reducing fatigue. The
Dutch Cancer Society recommends to partially shift such oncological aftercare to pri-
mary care, and to encourage patients’ self-management with respect to their health
problems. It is expected that this will make health care accessible to a larger group of
patients, and is more cost-effective. In order to achieve the necessary changes, new
treatment strategies for the primary care need to be developed.
1.2 Physical Activity Coaching
Physical activity is considered an important element in treatments of chronic cancer-
related fatigue. An upcoming trend to achieve changes in physical activity is the use of
Mobile Health (mHealth) applications [3], such as UbiFit Garden [4] and Fish`n`Steps
[5]. Such systems use information from accelerometers or pedometers to send text mes-
sages to subjects in order to encourage physical activity, based on personalized step
goals. Another example is the Activity Coach, which has been developed by Roessingh
Research and Development (RRD, Enschede, The Netherlands) [6]. Previous research
showed that subjects with chronic fatigue syndrome and chronic obstructive pulmonary
disease were able to increase their daily physical activity by using this system [7, 8].
Based on this, it is expected that patients with chronic cancer-related fatigue might ben-
efit from using this system as well.
However, despite the short term effectiveness of the use of such mHealth systems,
current research shows that adherence and longer term effects are often still limited.
One reason could be that mHealth systems are often deployed as a standalone tool: It is
hypothesized that the use of mHealth systems should be better integrated in the every-
day care practice [9]. A motivating role of the health professional in using mHealth
systems will enhance a patient to generate insight in the usefulness and rationale of its
use, which will promote compliance. Also, the mHealth system can be used in a much
more personalized way, and behavior change processes can be supported more effec-
tively. Conversely, by using mHealth technology, the professional can monitor and
stimulate behavioral change in a patient’s home environment. Therefore, the aim of this
work was to develop an mHealth intervention strategy for patients who suffer from
chronic cancer-related fatigue that utilizes the Activity Coach, integrated in primary
care physiotherapy.
2 Background
The next paragraphs describe the starting points for the development of the intervention
strategy. First, the activity coaching system is described in more detail. Without trying
to give a complete systematic review, the three subsequent paragraphs describe the state
of the art considering physical activity, behavioral change principles, and experiences
in the context of cancer-related fatigue with the use of mHealth systems.
2.1 The Activity Coach
The Activity Coach consists of a smartphone and a 3d-accelerometer (ProMove3D, In-
ertia Technology B.V., Enschede), shown in Figure 1. The sensor is worn on the hip by
means of an elastic belt or clipped onto the waistband. Both devices communicate with
each other real time through Bluetooth. The accelerometer converts the accelerometer
data into IMA’s, Integral of the Modulus of the Accelerometer output, as described by
Boerema et al. [10], which can be used as a measure of physical activity and correlates
well with energy expenditure as measured with oxygen consumption for many activities
[11]. The smartphone displays a real time visual of the patient’s cumulative activity,
relative to a line of reference, and generates automated feedback messages about the
patient’s current activity level relative to that line of reference. The smartphone uses its
wireless internet connection to send the converted data to a database, so that the data
can be retrieved on a web portal. The level and shape of the reference line, the content
of the feedback messages, and functionalities on the web portal were subject to change
in the development of this intervention strategy.
Fig. 1. The Activity Coach. Left: Smartphone (HTC Corporation, Taiwan) showing the applica-
tion. Right: ProMove 3D accelerometer (Inertia Technology, The Netherlands).
2.2 Physical Activity
Many behavioral change interventions that target fatigue in cancer survivors use phys-
ical activity goals such as increasing physical activity and/or physical exercise [12–16].
Walking programs, aerobic training, and resistance training have shown to be benefi-
cial. For example meta-analyses by Brown et al. (2011) [15] suggest that intensity of
exercise is strongly related to the effect of the intervention on cancer-related fatigue.
Two other reviews on the effects of exercise interventions are more cautious in their
conclusions, but acknowledge positive effects of strength training on physical function-
ing [17, 18]. In addition, Jacobsen et al. [19] did not find significant effect sizes of
physical activity interventions on fatigue outcomes in their meta-analysis. Multiple ac-
tivity types and intensities were included. However, they did find that home interven-
tions more often had a positive effect when compared to supervised interventions.
Other examples of goals that target physical behavior to reduce fatigue in cancer
survivors could include balancing activity throughout the day, or energy conservation
[20, 21]. This would include the management of opportunistic activities, which are ac-
tivities that a patient incorporates in their daily life, such as cycling to work and taking
the stairs.
So, despite contradictory results from various meta-analyses, relevant goals for pa-
tients with chronic cancer-related fatigue could be adjusting their physical behavior by
increasing the amount of opportunistic activities and the volume of aerobic or strength
training. However, energy conservation seems to be a promising focus for this popula-
tion too.
2.3 Cognitive Behavioral Change Principles
Exercise interventions seem more effective in reducing fatigue in cancer survivors
when they are guided by behavioral change or adaptation theory [15]. One of the rele-
vant factors in this context is improving self-efficacy over physical activity [22, 23], as
it seems to be one of the most important mediators of exercise interventions on fatigue
in cancer survivors. This can be achieved by (1) setting realistic but challenging sub-
goals and giving the possibility to monitor progress easily, so make sure the patient
experiences ‘he can do it’, (2) social comparison: make sure the patient knows that
comparable patients before him have been able to make comparable adjustments of
behavior, (3) verbal persuasion per e-mail. Learning to formulate implementation in-
tentions could help patients to change their physical behavior [24] in order to attain the
goals that they have set. The use of text messages in mHealth interventions can help
remind people of their implementation intentions [25].
Also, the patient’s stage of change should be acknowledged throughout the interven-
tion in order to decide on (when to change the) the focus of the intervention: i.e. in-
forming and raising awareness, motivating or maintenance [26]. The Activity Coach
could be used to give insight in the patient’s progression in order to increase the per-
ceived behavioral control.
Servaes et al. [27] reported on other cognitive elements that are associated with can-
cer-related fatigue: Patients with low sense of control over fatigue symptoms (and high
anxiety and high impairments in role functioning) are more likely to suffer from per-
sistent fatigue after cancer treatment. Therefore, targeting such cognitions could in-
crease the effect of interventions for fatigue. The involvement of a health professional
in the intervention could provide in this need, and make sure the patient is guided and
coached in a personalized manner.
2.4 mHealth Recommendations
A patient’s compliance can make or break a behavioral therapy, whether or not mHealth
technology is utilized. However, the use of mHealth brings new challenges considering
this topic, of which some are closely related to the previously mentioned cognitive as-
pects. According to Fogg [28], persuasive technologies should keep in mind three fac-
tors in order to be successful in their aim: motivation, ability, trigger. His framework
gives useful support for utilizing the Activity Coach. Consolvo et al. [29] formulated
recommendations more specifically for activity coaching applications successfully: 1)
give users proper credit for activities, 2) provide personal awareness of activity level,
3) support social influence, and 4) consider the practical constraints of users’ lifestyles.
Moreover, varying and personalizing feedback messages could make it more interesting
to use the system and therefore learn from it [8, 30]. It also possibly extends the pa-
tient’s use of, and compliance with, this system. Also, activity goals, when using a ref-
erence line in an mHealth application, should be based on a the individual patient’s
baseline activity pattern rather than on for example a “healthy” norm value of physical
activity [7].
Finally, “increased interaction with a counselor, more frequent intended usage, more
frequent updates and more extensive employment of dialogue support significantly pre-
dicted better adherence” [31].
3 Methods
Taking into consideration the existing system and background knowledge, the devel-
opment of the intervention strategy started. In order to do so, the guidelines published
by Huis in ‘t Veld et al. were used [32]. These guidelines suggest, as we did, to start
from current state of the art and evidence based medicine, and work in close co-opera-
tion with the intended users: both professionals and patients. In order to do so, first,
semi-structured interviews were held with five health professionals in the field and with
one patient. The interviews allowed plenty space for discussing new ideas and followed
the personal interests and concerns of the specific interviewee. The activity coaching
system was presented and discussed in these sessions in order to get first ideas about
how this system could be utilized successfully in their current practice. Ideas and rec-
ommendations were pooled and summarized. Then, a first version of the intervention
strategy was drafted.
Secondly, an iterative process of discussions and testing with two other physiother-
apists was performed. This was completed with a test session with a patient, after which
the intervention strategy was finalized.
4 Results
4.1 Step 1: Insights from Health Professionals
One psychotherapist, three physiotherapists, and an occupational therapist of the mul-
tidisciplinary cancer-rehabilitation team of Rehabilitation Centre Roessingh (Enschede,
The Netherlands) were approached for interviews, and all agreed to cooperate. The
health professionals were all very experienced with treating patients that suffer from
either chronic fatigue syndrome or chronic cancer-related fatigue, and two of them also
had prior experience with using a previous version of the activity coaching system.
These semi-structured interviews focused on three aspects: “How would you use the
activity coaching system in an intervention for chronic cancer-related fatigue”, “Given
the fact that such an intervention takes place at home solely, would e-mail be an appro-
priate means of communication?”, and “What would enable the system to be incorpo-
rated successfully in current primary health care?” The following issues arose:
1. E-mail was generally considered an efficient and effective medium to communicate
between patient and health professional.
2. In the Netherlands, complementary health insurance packages for physiotherapy of-
ten cover up to nine consults, this should be taken into account.
3. Two therapists would recommend at least one face-to-face contact.
4. One therapist was concerned about whether patients would like to be monitored all
over again, and questioned if patients would appreciate to wear the system.
5. There should be weeks planned in which the patient does not have to wear the sys-
tem. In that way, the patient will have to translate what he has learned to daily living
and compliance to the system in the other weeks might increase.
6. Personalized and well-justified goals are easier to attain than acting upon a standard,
“healthy” reference line, so a therapist should be able to adjust that line. In that way,
the end goal can be divided into sub-goals and adjusted throughout the intervention
in order to support the patient in a flexible manner.
7. Large inter-individual differences exist in baseline activity patterns and personal
goals should be set, which requires tailoring of the automated feedback.
4.2 Draft of the Intervention Strategy after Step 1
Based on the background knowledge and the results of the interviews, a first draft of
the intervention strategy was developed with as main characteristics that it includes a
theoretical framework, weekly instructions, e-mail examples, and guidelines for the in-
corporation and use of the activity coaching system.
The Activity Coach. Adjustments to the technology were made to the web portal and
the software on the smartphone that generates the feedback messages.
Web Portal. The therapist enters the web portal at the home page, which shows a “traf-
fic light”-visual of each patient’s compliance to wearing the accelerometer of the cur-
rent week. More detailed information on each patient is shown in three tabs:
1. “Patient”: a summary of demographics and contact details of the patient;
2. “Activity monitor”: tab on which different graphs of the patient’s activity are
shown in line charts that show either the cumulative (Figure 2) or raw IMA data
from each day, or in a bar plot that represent the three day-parts or separate days.
3. “Measurement settings”: tab in which the Activity Coach can be set up for pa-
tients: level and shape of the reference line and the content of the feedback mes-
sages on the smartphone.
Fig. 2. Screenshot of the activity viewer on the therapist portal, showing the reference line (green)
and the actual cumulative activity (blue). Grey segments represent missing data, which are inter-
or extrapolations of the reference line.
Feedback Scenarios. In order to create flexibility for the therapist, and acknowledging
the great inter-individual differences between patients, three different feedback scenar-
ios were created. They differ from each other in terms of content of the feedback mes-
sages. The first scenario is for persons who are prone to being not physically active
enough (activate). The second scenario is meant for patients who are used to push their
boundaries, and could use encouragement of taking rest above a certain point (temper).
The third scenario (balance) is the most neutral scenario, and can be used for patients
who require to balance their activities throughout the day, and especially to conserve
energy in the morning. Figure 3 shows a visual of the classification of the three scenar-
ios. The messages differ on three scales. Firstly, the goal of the feedback message can
be to reward or acknowledge the physical behavior (green), or to stimulate the patient
to change the physical behavior (yellow, orange, red). These messages differ in rigor-
ousness of the feedback or the proposed behavior (for example “a nice stroll” (yellow)
versus “a brisk walk” (red)), as can be seen in the intensity of the colors in Figure 3.
Boundaries for all three scenarios are set at a deviation of respectively +/-10 and +/-
20% from the reference line. Secondly, the messages can either be suggestive or im-
posing, for example “Is there any chance that you can plan a brisk walk this afternoon?”
or “Is your current activity in line with your intentions?” versus “Time for a brisk walk”.
Fig. 3. Visual representation of the feedback scenarios. Left: activate, middle: temper, right: bal-
ance. The black line in the middle of the green strip represents the reference line.
Process Guidelines. The intervention strategy starts as the patient completes an intake
questionnaire about demographics, medical condition, and fatigue complaints. Ques-
tionnaires can be administered online, and the hardware can be sent by direct mail eas-
ily. The patient wears the system for a week to create a baseline activity measurement.
In this week, the smartphone does not display any feedback about the patient’s activity.
However, the therapist should keep in mind that the simple act of wearing the device
might influence the results of this measurement.
After the baseline week, the therapist logs into the web portal to see the results of
the baseline measurement, and to change the settings of the Activity Coach. The thera-
pist selects a reference line that is equal to, or is based on the patient’s average daily
activity during the baseline week. In that way, the patient can get used to using the
Activity Coach. Subsequently, the therapist approaches the patient through e-mail,
gives an introduction about himself and the intervention, and gives a rough planning
for the upcoming 9 weeks. The patient is asked to introduce himself too and to use the
system for a minimum of three days to get used to the feedback scenario.
For the patient, the first feedback period now starts. Each hour, a feedback message
is selected and pops up at the smartphone. The patient can retrieve the message the
entire hour, until another message is generated.
In the second week, by phone contact, the patient and the therapist set personal goals
for the upcoming eight weeks, and define and plan tasks to accomplish these goals.
Goals and sub-goals can vary from “doing groceries independently by bike in week 9”
to “Being able to take effective rest moments during the week”. Accordingly, the ther-
apist translates sub-goals into a set of reference activity patterns that will be adjusted
throughout the nine weeks of intervention. When desired, also the feedback scenario
can be adjusted by the therapist.
The intervention strategy suggests to change the reference activity pattern of the Ac-
tivity Coach in at least three steps throughout the 9-week intervention. This likely stim-
ulates the use of feasible goals and consequently increases the self-efficacy of the pa-
tient. The therapist supports and coaches the patient with weekly e-mails during nine
weeks. The intervention strategy suggests that in week 7, the patient is asked to not
wear the system, and the patient is stimulated to translate his experiences and future
goals in terms that relate to day-to-day activities and planning. Exercises that could be
used in this week include keeping a fatigue or energy diary. The intervention is con-
cluded by evaluating the progress of the patient, the benefits and difficult parts of the
intervention, and setting goals for the future.
4.3 Step 2: Feedback from the iterative test phase
The first draft of the intervention strategy was presented, explained, and discussed ex-
tensively with two physiotherapists (PMI Rembrandt, Veenendaal, The Netherlands),
after which it was tested and evaluated with these therapists and a patient.
The most important results from the therapists are that it is difficult to formulate
goals and tasks for the intervention, and to explain the use of the system by e-mail.
Also, it was recommended that the patient should get access to an online environment
in which he can look up his past physical behavior in order to monitor and evaluate his
own progress. Finally, it was suggested that a normative reference line could support
the therapist to value a patient’s activity level.
The patient’s feedback was that the system is bulky and can be bothering to wear
during exercise. Also, it is sometimes short of power for an entire day. Furthermore,
more information about the reasoning behind the suggested activities in the automated
feedback messages would be considered useful. The informative feedback messages
were preferred over the direct messages. Finally, the lacking recognition of activities,
and underestimations of certain physical activities was sometimes frustrating for them.
4.4 Adjustments to the draft intervention strategy after Step 2
The Activity Coach. Power-saving software adjustments were made to ensure that the
battery of both devices will last an entire day. However, no adjustments to address the
bulkiness of the system were made, because the choice for hardware was among the
starting points for this study. Also, the system was not adapted to recognize activities.
It is expected that this issue will be only a minor limitation in the current intervention,
because individual goals are based on patients’ own baseline activity patterns, which
likely incorporate a constant underestimation throughout the intervention.
Web Portal. In order to support the decision making of the therapist, a normative refer-
ence line was incorporated in the portal. It represents the average daily activity pattern
of twenty patients who suffered from severe chronic cancer-related fatigue, and wore
the activity coaching system for one week consecutively. This reference line is shown
when the therapist reviews the baseline activity of the patient.
Patients were also enabled to have access to a web portal. For patients, it consists of
an ‘activity viewer’ that is similar to the one that is shown in the therapist portal, but
without plots of the raw data.
Feedback Scenarios. The content of the messages was not further adjusted as a reaction
to the patient’s feedback. We hypothesize that such preferences are likely dependent on
for example the stage of change of the patient, learning style, and personality. Adjusting
the system to tailor the set of feedback messages for each individual was not technically
feasible for this project. A mixed approach was therefore maintained.
Process Guidelines. A phone-call was implemented in the protocol during the second
week in order to set goals. Also, the intervention strategy now suggests introducing the
patient to the portal from the fifth week on. It is expected that from that moment on,
patients are used to wearing and using the Activity Coach, and can interpret the line
charts properly. The use of this portal creates an evaluation moment, and goals can be
adapted accordingly if necessary. Also, example exercises were added to the interven-
tion strategy that review earlier physical behavior and achievements during the inter-
vention, thereby using the patient portal.
As the Activity Coach is known to underestimate the intensity of certain activities,
caution should be taken when interpreting absolute IMA counts, and (any change of)
type of activity should be kept in mind when doing so. The intervention strategy there-
fore now includes thorough recommendations for the therapist on informing patients
explicitly about the possibilities, strengths and weaknesses of the system.
5 Discussion
This paper has described the development of an mHealth intervention strategy that tar-
gets chronic cancer-related fatigue. Feedback was obtained by involving potential end-
users with various backgrounds in all phases of the development process. Such devel-
opment was intended to result in a highly accepted intervention, contrasting technol-
ogy-driven approaches that often do not come beyond the pilot stage [32].
The added value of this work is mostly the explicit involvement of a health profes-
sional for deploying the mHealth technology. Although this seems to be an obvious
improvement, to our best knowledge, other examples of such use of activity coaching
systems have not been published so far [33, 34]. By involving a health professional,
more subtle and tailored physical behavior goals can be attained, such as creating
awareness and improving energy conservation. Being able to set flexible goals is a huge
advantage for the targeted population because of the population’s heterogeneous char-
acter.
Another important feature of this intervention is that it is directed at opportunistic
physical activities and at low-to-moderate intensity exercise, rather than high-intensity
exercise. This serves two goals: to accommodate the diverse nature of the population,
and to establish safety of the patient; physical tests cannot be performed because no
face-to-face sessions were incorporated. However, we are confident that increasing the
volume of opportunistic activities and actively managing their daily activities will have
beneficial health outcomes for many patients. This could be strengthened by improving
cognitions about physical behavior: Some argue that perceived amount of physical ac-
tivity or the self-efficacy over physical activity is even more important than the amount
of the physical activity itself [35]. Future research that focusses on the role of physical
activity in interventions for fatigue should therefore also focus on cognitions and on
other dimensions of physical behavior than just the objective daily amount.
Although the current employment of the Activity Coach was realized by extensive
collaboration with experts and based on a broad spectrum of literature, many of the
features have not been optimized so far. Firstly, the bulky hardware can be an important
bias for the effectiveness of this intervention strategy. Also, personalizing the feedback
messages to the subject’s stage of change or learning style, and the way that the bound-
aries are set within the feedback scenarios have not been subject of this work, but could
be an interesting topic for subsequent studies. Currently, the system is being adjusted
to generate tailored motivational feedback messages considering for example timing
and content [36]. Also, the visual representation of the activity measurement on both
the smartphone and the web portal should be improved and personalized. The current
visualization is rather simplistic, however, ideally they should explicitly support the
goals they serve: visualize the longitudinal change or highlight improvement of the pa-
tient in order to strengthen self-efficacy and sense of control. Relevant examples for
comparable goals yet exist [37]. Finally, the current experiments are limited due to the
small number of patients that were involved, and the limited structure of the interviews.
Conclusion. This paper is a first step in order to develop an mHealth intervention to
support patients who suffer from chronic cancer-related fatigue. The intervention strat-
egy succeeds in meeting many of the recommendations that were extracted from rele-
vant literature or formulated by health professionals in the field. However, the actual
usefulness, acceptability, and effectiveness of the final intervention strategy have not
been established yet. A randomized controlled trial (The Netherlands Trial Register,
number NTR3483) is conducted currently to study the effectiveness, working mecha-
nisms, and effect predictors of the intervention within the target group.
Acknowledgements. This work is part of the “Fitter na kanker” project, which is
funded by the “Alpe d’HuZes/KWF-fonds”, administered by the Dutch Cancer Society.
The authors declare that in relation to this study, they have no conflicts of interest.
6 References
1. Goedendorp, M.M., Gielissen, M.F.M., Verhagen, C.A.H.H.V.M., Bleijenberg, G.:
Development of fatigue in cancer survivors: a prospective follow-up study from
diagnosis into the year after treatment. J. Pain Symptom Manage. 45, 213–22 (2013).
2. Meulepas, J.M., Kiemeney, L.A.L.M., Benraadt, J.: Kanker in Nederland tot 2020:
trends en prognoses. (2011).
3. Fanning, J., Mullen, S.P., McAuley, E.: Increasing physical activity with mobile
devices: a meta-analysis. J. Med. Internet Res. 14, e161 (2012).
4. Consolvo, S., McDonald, D.W., Toscos, T., Chen, M.Y., Froehlich, J., Harrison, B.,
Klasnja, P., LaMarca, A., LeGrand, L., Libby, R., Smith, I., Landay, J.A.: Activity
sensing in the wild: a field trial of UbiFit Garden. CHI 2008 Proceedings. (2008).
5. Lin, J., Mamykina, L., Lindtner, S., Delajoux, G., Strub, H.: Fish’n'Steps: encouraging
physical activity with an interactive computer game. UbiComp 2006: Ubiquitous
Computing. pp. 261–278. Springer, Berlin (2006).
6. op den Akker, H., Tabak, M., Marin-Perianu, M., Huis in ’t Veld, M.H.A., Jones,
V.M., Hofs, D.H.W., Tönis, T.M., van Schooten, B.W., Vollenbroek-Hutten, M.M.R.,
Hermens, H.J.: Development and evaluation of a sensor-based system for remote
monitoring and treatment of chronic diseases - the continuous care & coaching
platform. Proceedings of the 6th International Symposium on eHealth Services and
Technologies, EHST 2012. pp. 19–27 (2012).
7. Evering, R.M.H., Vollenbroek-Hutten, M.: Ambulatory activity-based feedback for
improving daily physical activity patterns in patients with the chronic fatigue
syndrome. Assistive Technology Research Series. Volume 33: Assistive Technology:
From Research to Practice. pp. 727 – 732 (2013).
8. Tabak, M., op den Akker, H., Hermens, H.: Motivational cues as real-time feedback
for changing daily activity behavior of patients with COPD. Patient Educ. Couns. 94,
372–8 (2014).
9. Jansen-Kosterink, S., Huis in ’t Veld, R., Kranen, C., Hermens, H., Vollenbroek-
Hutten, M.M.R.: Why telemedicine does not find its way towards sustainable
implementation [in press]. Int. J. Telemed. Appl. (2014).
10. Boerema, S.T., van Velsen, L., Schaake, L., Tönis, T.M., Hermens, H.J.: Optimal
sensor placement for measuring physical activity with a 3D accelerometer. Sensors
(Basel). 14, 3188–206 (2014).
11. Bouten, C., Westerterp, K.R., Verduin, M., Janssen, J.: Assessment of energy
expenditure for physical activity using a triaxial accelerometer. Off. J. Am. Coll.
Sport. Med. 26, 1516–1523 (1994).
12. Schmitz, K.H., Holtzman, J., Courneya, K.S., Ma, L.C., Duval, S., Kane, R.:
Controlled physical activity trials in cancer survivors: a systematic review and meta-
analysis. Cancer Epidemiol Biomarkers Prev. 14, 1588–1595 (2005).
13. Prue, G., Rankin, J., Allen, J., Gracey, J., Cramp, F.: Cancer-related fatigue: a critical
appraisal. Eur. J. Cancer. 42, 846–63 (2006).
14. Kangas, M., Bovbjerg, D.H., Montgomery, G.H.: Cancer-related fatigue: a systematic
and meta-analytic review of non-pharmacological therapies for cancer patients.
Psychol. Bull. 134, 700–41 (2008).
15. Brown, J.C., Huedo-Medina, T.B., Pescatello, L.S., Pescatello, S.M., Ferrer, R.A.,
Johnson, B.T.: Efficacy of exercise interventions in modulating cancer-related fatigue
among adult cancer survivors: a meta-analysis. Cancer Epidemiol. Biomarkers Prev.
20, 123–33 (2011).
16. Cramp, F., Byron-Daniel, J.: Exercise for the management of cancer-related fatigue in
adults. Cochrane database Syst. Rev. 11, CD006145 (2012).
17. Galvão, D.A., Newton, R.U.: Review of exercise intervention studies in cancer
patients, (2005).
18. Stevinson, C., Lawlor, D.A., Fox, K.R.: Exercise interventions for cancer patients:
Systematic review of controlled trials, (2004).
19. Jacobsen, P.B., Donovan, K.A., Vadaparampil, S.T., Small, B.J.: Systematic review
and meta-analysis of psychological and activity-based interventions for cancer-related
fatigue. Heal. Psychol. 26, 660–7 (2007).
20. Barsevick, A.M., Dudley, W., Beck, S., Sweeney, C., Whitmer, K., Nail, L.: A
randomized clinical trial of energy conservation for patients with cancer-related
fatigue. Cancer. 100, 1302–10 (2004).
21. Servaes, P., Werf, S. van der, Prins, J.: Fatigue in disease-free cancer patients
compared with fatigue in patients with chronic fatigue syndrome. Support. Care
Cancer. 9, 11–7 (2001).
22. Buffart, L., Ros, W., Chinapaw, M.: Mediators of physical exercise for improvement
in cancer survivors’ quality of life. Psychooncology. (2013).
23. Pinto, B.M., Ciccolo, J.T.: Physical activity motivation and cancer survivorship. In:
Courneya, K.S. and Friedenreich, C.M. (eds.) Recent results in cancer research. pp.
381–401. Springer, Berlin (2011).
24. Prestwich, A., Perugini, M., Hurling, R.: Can implementation intentions and text
messages promote brisk walking? A randomized trial. Health Psychol. 29, 40–9
(2010).
25. Murray, T.C., Rodgers, W.M., Fraser, S.N.: Examining implementation intentions in
an exercise intervention: the effects on adherence and self-efficacy in a naturalistic
setting. J. Appl. Soc. Psychol. 39, 2303–2320 (2009).
26. Prochaska, J.O., Velicer, W.F.: The transtheoretical model of health behavior change.
Am. J. Heal. Promot. (1997).
27. Servaes, P., Gielissen, M.F.M., Verhagen, S., Bleijenberg, G.: The course of severe
fatigue in disease-free breast cancer patients: a longitudinal study. Psychooncology.
16, 787–95 (2007).
28. Fogg, B.: A behavior model for persuasive design. Proceedings of the 4th International
Conference on Persuasive Technology. ACM Press, New York (2009).
29. Consolvo, S., Everitt, K., Smith, I., Landay, J. a.: Design requirements for
technologies that encourage physical activity. CHI 2006 Proceedings. Design for
Tangible Interactions. p. 457. ACM Press, New York (2006).
30. Kuijpers, W., Groen, W., Aaronson, N., Van Harten, W.: A systematic review of web-
based interventions for patient empowerment and physical activity in chronic diseases:
relevance for cancer survivors. J. Med. Internet Res. 15, e37 (2013).
31. Kelders, S.M., Kok, R.N., Ossebaard, H.C., Van Gemert-Pijnen, J.E.W.C.: Persuasive
system design does matter: A systematic review of adherence to web-based
interventions, (2012).
32. Huis in ’t Veld, R.M.H.A., Widya, I.A., Bults, R.G.A., Sandsjö, L., Hermens, H.J.,
Vollenbroek-Hutten, M.M.R.: A scenario guideline for designing new teletreatments: a
multidisciplinary approach. J. Telemed. Telecare. 16, 302–307 (2010).
33. Goode, A.D., Lawler, S.P., Brakenridge, C.L., Reeves, M.M., Eakin, E.G.: Telephone,
print, and Web-based interventions for physical activity, diet, and weight control
among cancer survivors: a systematic review. J. Cancer Surviv. (2015).
34. Blackman, K.C., Zoellner, J., Berrey, L.M., Alexander, R., Fanning, J., Hill, J.L.,
Estabrooks, P.A.: Assessing the internal and external validity of mobile health
physical activity promotion interventions: a systematic literature review using the RE-
AIM framework. J. Med. Internet Res. 15, e224 (2013).
35. Kummer, F., Catuogno, S., Perseus, J.M., Bloch, W., Baumann, F.T.: Relationship
between cancer-related fatigue and physical activity in inpatient cancer rehabilitation.
Anticancer Res. 33, 3415–22 (2013).
36. op den Akker, H., Cabrita, M., op den Akker, R., Jones, V.M., Hermens, H.J.: Tailored
Motivational Message Generation: A Model and Practical Framework for Real-Time
Physical Activity Coaching. J. Biomed. Informatics. 55, 104–115 (2014).
37. Havre, S., Hetzler, B., Nowell, L.: ThemeRiver: Visualizing Theme Changes over
Time. IEEE symposium on information visualization. pp. 115–123 (2000).