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Does feedback from this device change unhealthy habits? Lessons from my PhD project

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Feedback from digital technology has often been used to support people in changing undesired, unhealthy habits. As yet, there has been little research into the efficacy of these designs. In my PhD project, I evaluated the acceptance, sustained use, and effect of four designs that provide feedback on undesired habitual behaviour through digital technology. Findings are that the disruptive effect of feedback on undesired habits has been proven, and there is some evidence that feedback may have a lasting effect on behavioural change. (Sustained) use of digital designs that provide feedback is moderated by motivation, age, goal-related aspects, and user experience. The necessity of high motivation to use a device poses challenges for the acceptance of and sustained engagement with designs for behaviour change that rely on feedback. Further challenges concern privacy and the quality of the evaluations of our designs.
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Does feedback from this device change unhealthy habits?
Lessons from my PhD project
HERMSEN Sandera*
a Utrecht Unive rsity of Applied Sciences, Utrec ht, Th e Netherlands
* Corresponding author e-mail:
doi: 10.21606/dma.2017.306
Feedback from digital technology has often been used to support people in changing
undesired, unhealthy habits. As yet, there has been little research into the efficacy of
these designs. In my PhD project, I evaluated the acceptance, sustained use, and
effect of four designs that provide feedback on undesired habitual behaviour
through digital technology. Findings are that the disruptive effect of feedback on
undesired habits has been proven, and there is some evidence that feedback may
have a lasting effect on behavioural change. (Sustained) use of digital designs that
provide feedback is moderated by motivation, age, goal-related aspects, and user
experience. The necessity of high motivation to use a device poses challenges for the
acceptance of and sustained engagement with designs for behaviour change that
rely on feedback. Further challenges concern privacy and the quality of the
evaluations of our designs.
feedback; digital devices; behaviour change; health behaviour
1 Introduction
Undesirable habits can be very hard to change. In recent years, we have seen a growing number of
digital designs that claim to provide a solution. Many of these designs (automatically) record our
behaviour and give us feedback on our performance. Evidence of the efficacy of designs that provide
feedback on behaviour is slowly accumulating but remains limited to academic outlets that are
historically less accessible to non-behavioural scientists, such as HCI researchers, designers and
design researchers (Hekler, Klasnja, Froehlich, & Buman, 2013). This paper aims to provide designers
and design researchers with an accessible overview of my PhD project, which contributes to
answering the question whether feedback through digital technology is effective to change habitual
behaviour. To do so, the paper provides a summary of a recent analysis of the current literature, and
an evaluation of four existing designs for behaviour change that provide feedback on undesired
In literature, habits are commonly defined as "behaviour (...) prompted automatically by situational
cues, as a result of learned cue-behaviour associations" (Wood & Neal, 2009, pp. 580; Gardner,
2014, p.1). They help us to come to terms with the enormous complexity of everyday life, by taking
away the burden of conscious deliberation from many uncritical decisions. Unfortunately, many of
our habits have adverse effects on our own health and that of the planet we live on. The rigid cue-
response-chain of a strong habit overrides contradictory behavioural intentions (Verplanken & Faes,
1999; Verplanken & Wood, 2006). This may lead to undesired results when habits have a satisfying
short-term effect but damaging health consequences in the long run, as with snacking, a lack of
physical activity, or alcohol abuse. Furthermore, since habits do not take into account current
context, changed circumstances may render habits unproductive for contemporary life, even though
the behaviour may have led to rewards in the past.
The major benefit of habitual behaviour is that it circumvents active consideration of the current
context, but this also makes it very hard to change habits using interventions aimed at controlled
processing, e.g. through persuasive messages related to the consequences of behaviour (Verplanken
& Wood, 2006) or changing behavioural intentions (Sheeran, 2002). A more successful way to
disrupt undesired habits is to bring habitual behaviour and its context to (conscious) awareness. Self-
monitoring, the procedure by which individuals record the occurrences of their own target
behaviours (Nelson & Hayes, 1981), enables perception of our own behaviour and adaption to the
current context. This leads to a decrease in unwanted behaviour (Quinn, Pascoe, Wood, & Neal,
2010). Unfortunately, self-monitoring is difficult for even the most motivated individual (Wilson,
2002). There is often a discrepancy between self-reported and actual performance in health
behaviours such as calorie intake and physical activity (Lichtman et al., 1992),
Accurate self-monitoring is greatly improved by personalized information from external sources (Kim
et al., 2013; Li, Dey, & Forlizzi, 2010). The advent of mobile and interactive media has given us an
unsurpassed opportunity to support people in self-monitoring, by providing them with tailored
feedback. Feedback has been defined as "actions taken by (an) external agent(s) to provide
information regarding some aspect(s) of one's task performance" (Kluger & Denisi, 1996), on their
behaviour. Digital technology can offer constant, real-time updates, powered by sensitive
measurement devices, often worn on the body. Besides data generation, digital technology can offer
habit-disrupting cues such as light and sound signals, buzzes, and push messages. Digital technology
is not only useful to present users with evaluations of past behaviour ("reflection-on-action");
because of the ubiquity of wearables and mobile devices, feedback from digital technology offers an
unprecedented opportunity for "reflection-in-action" (Schön, 1984): the analysis of behaviour as it
occurs. This could greatly increase people's efficacy in self-managing healthy behavioural change.
1.1 Solutionism or smart solutions?
The rapid rise of the technological possibilities has been matched by a similar rise in the number of
designs on the market that make use of these possibilities. Wearable activity trackers (cf. Kooiman et
al., 2015) give us feedback on whether we walk enough; sleep monitors monitor our sleep (e.g.
Ogihara & Eshita, 2016); smart devices track our eating habits (e.g. Zandian et al., 2009), an app can
warn us about situations in which we are likely to smoke a cigarette (e.g. Naughton et al., 2016) and
a growing number of devices tell us (and others) what emotions we experience in cases where we
are unable to do so ourselves (e.g. Van Dijk, 2017). This increased attention in health design practice
is closely followed by a growing body of literature in design research and human-computer
interaction research in the past decades (e.g. Darby, 2001; Fischer, 2008; Frohlich, Findlater, &
Landay, 2010; Ludden, 2013; Hänsel et al., 2015; Gouveia et al., 2016). By far the biggest part of this
literature researches the different channels, modalities, and other properties of feedback through
digital technology: how to optimally design the feedback technology.
Considering all this attention, it may come as a surprise that there has been relatively little research
into whether all this feedback on health behavioural is as effective as we implicitly presume. After
all, the rise in designs and research based on these designs may very well be a case of technocratic
solutionism (Morozow, 2013): we have sensors and actuators, especially in smartphones, and we
have wearables. Now that we've been provided with these hammers, we suddenly see nails
everywhere. But are these really nails?
1.2 When we build it, they will change?
In my PhD project, I investigated whether feedback through digital technology is an effective way to
support people in changing their undesired, unhealthy habitual behaviour. Theory supports this
hypothesis; with Control Theory (Carver & Scheier, 1985) delivering the best explanation: reflective
behaviour change resembles a thermostat. When looking to change their behaviour, people
compare their performance to a behavioural goal. When a discrepancy between goal and
performance is noted given enough motivation, opportunity, and the right abilities people will
attempt to reduce this discrepancy. This process depends on conscious scrutiny of behaviour and its
effects. Knowing that habitual behaviours are mostly automatic, and thereby outside of conscious
scrutiny, the strength of feedback lies in delivering exactly that cue that is needed to make
automatic behaviour available for conscious deliberation. Feedback may also increase motivation to
change the target behaviour (Northcraft, Schmidt, & Ashford, 2011): feedback places the target
behaviour higher on a hypothetical list of priorities. When given feedback on the number of steps we
take, we may prioritise walking over other modes of transportation or other physical activity choices,
because feedback diverts our attention towards this behaviour.
The question is, of course, whether practice follows theory. To find out, we
examined the available
evidence from literature, to evaluate whether current literature provides an answer to the following
Is feedback through digital technology an effective way to change habitual behaviour?
Is feedback through digital technology effective for each user in every context, or are there
intrapersonal (e.g. character traits, psychological states such as motivation) or interpersonal
(contextual or systemic) moderators? What feedback properties are most effective in
different circumstances?
To provide further answers to these questions, we then evaluated 4 existing designs for behavioural
change. Inclusion criteria for the designs were: a) the design addresses habitual behaviour, b) the
design uses feedback on behavioural performance as its (primary) behaviour change technique, c)
the design can be tested in real-life conditions (beyond the lab). To obtain valid results, we only
included participants who could reasonably be expected to be motivated to change their behaviour,
for instance because they chose to purchase or download the design of their own accord. The first
design we evaluate in this paper is a physical activity tracker which is currently available in the
market; the second design is a commercially available app that gives feedback on water drinking.
The third is an online solution (web-based platform and app), currently available from a public
institution, that gives feedback on the nutritional content of meals. The evaluations of these three
designs help answering questions about what determinants and design properties enable the design
to be effective for what audience. The fourth design is a 'smart' fork that registers eating rate and
gives feedback when you eat too fast. This evaluation contributes to answering whether feedback is
an effective way to durably change undesired behaviours.
2 Literature review: The efficacy of feedback technology for habit change
To evaluate current practices and the state of the art, we reviewed the available scientific evidence
for the effect of feedback through digital technologies on habitual behaviour. A combined search in
a range of scientific and design- and HCI- oriented databases, and auxiliary ancestry searches,
yielded a set of 69 original papers (with a total of 72 studies) that matched our inclusion criteria:
digital technology that delivers tailored feedback by an external agent to provide information
All research projects have been performed together with a range of partners from academia, hence the 'we'.
regarding task performance, aimed at automatic (habitual) behaviour, with an analysis of the
design's efficacy.
The included studies covered a range of dependent variables, varying from energy consumption to
motor skills and physical activity. We thematically classified target behaviours of the intervention,
feedback technology, feedback characteristics (content (feedback sign, comparison, and level of
tailoring), timing, modality, frequency, duration, data source), and the availability of visual examples
of the design and provided feedback. For each intervention, number of participants, independent
and variables, analysis method, results, and possible methodological concerns were assessed. A
complete overview of the search terms and analysis of the interventions is available in Hermsen,
Frost, Renes, & Kerkhof, 2016.
2.1 Feedback disrupts habitual behaviour
Our analysis showed strong evidence for the idea that feedback disrupts habitual behaviour, making
it available for conscious scrutiny. 59 of 72 studies show a beneficial effect of feedback on disrupting
habitual behaviour. Of those 13 studies that did not find this effect, 4 suffered from a lack of
statistical power for the type of analysis performed. Their null finding may very well be due to small
sample sizes, since descriptive results in all four studies did point towards a small positive effect of
the reported interventions. Where feedback did not lead to disruption of current behaviour, this was
sometimes due to misunderstanding of the design's purpose. Other studies showed contrary effects,
such as in a study on taking breaks at work, where participants used a design providing social activity
feedback not to take part in social activities, but to avoid colleagues, or to find empty rooms for
meetings (Kirkham et al., 2013).
However, current literature does not (yet) provide evidence for lasting effects of this disruption on
behaviour. Two causes underlie this: as yet, there has hardly been research into lasting effects of this
type of feedback on behaviour change; and research that has been performed so far, often suffers
from methodological shortcomings. Either the research designs lack statistical power for the type of
analysis performed in the study, which leads to a greater chance of false positives and inflated effect
sizes, or the research designs had no strategies to deal with demand characteristics ("I have this
beautiful design for you, and now I'm going to watch you use it. Does your behaviour change yet?").
2.2 Conclusions from our review
Our review enables us to at least partially answer our first question: yes, feedback from digital
technology is able to disrupt undesired habits; but whether this leads to lasting behavioural changes
remains unclear. To test this, we need research with higher quality research designs, data gathering,
and analysis than what is currently common; be it qualitative or quantitative, or action research
(such as the different flavours of research-through-design), which all have their relative merits to
add to our knowledge. Furthermore, our review showed that there is hardly any evidence about
what moderates sustained use of digital feedback technologies. The question who uses these
technologies in which circumstances to which effect still needs to be answered.
Conclusion I: feedback from digital technology can disrupt habitual behaviour, but
evidence for lasting behaviour change is lacking.
Challenge: we are in need of better evaluation methods of our designs.
Interestingly, our review shows that the disruptive effect of feedback on undesired habits occurs
independently from modality (e.g. visual, auditory, or tactile feedback), timing, frequency, and
medium (e.g. mobile phone apps, websites, or wearable devices). This is probably the result of
optimisation and iterative user testing in the design phase, which led to choices for feedback
modality, timing, frequency, and media which fit the target behaviour and user needs. For instance,
in the case of modality, the target behaviour often rules out specific feedback modalities. In driving a
car, the visual channel is more often than not occupied by keeping track of traffic. Visual feedback
on driving behaviour is more often than not dangerous instead of supportive, as anyone who has
ever attempted to text while driving will realise. At the dinner table, both the visual and the auditory
channel are occupied, and a designed artefact which relies on visual or auditory feedback on eating
behaviour needs to deal with the social practices of eating, which, for many people, has an
important social function as well. Disrupting this social aspect with feedback messages on eating
behaviours can be perceived as rude, and such designs are likely to be abandoned.
Conclusion II: there are no general guidelines for choosing optimal feedback properties.
These depend on the design's context of use and the target behaviour.
3 Design case I: Physical activity tracker
Evidence (Couper et al., 2010; Funk et al., 2010; Donkin et al., 2011; Perski et al., 2016) shows us that
lasting engagement with a design is essential for behaviour change. Unfortunately, our literature
review revealed that we hardly know anything about what factors (be it states, traits or context)
drive sustained use of our designs, and how this differs for individuals across different contexts. Even
the expected uptake of designs for behavioural change is as yet under-researched, let alone how
long we can expect people to keep on using a design. The only evidence available as yet comes from
industry whitepapers (Fox & Duggan, 2013; Chen, 2015), which claim abandonment rates of 3080%
in the first weeks, depending on technology.
3.1 711 Fitbit Zips moving about
To find out more about patterns in who will succeed in using designs that give feedback on
behaviour long enough for the behaviour change to occur, we performed an explorative study
among 711 participants from four urban areas in France. They received an activity tracker (Fitbit Zip)
and gave us permission to use their logged activity data for 320 days. They also filled out three Web-
based questionnaires: at start, after 98 days, and after 232 days to measure a range of potential
determinants of sustained use: demographic and socio-economical, psychological, health-related,
goal-related, technological, user experience-related and social predictors. We determined the
relative importance of all included determinants on the duration of tracker use by using machine
learning analysis techniques. Providing a detailed overview of the rationale, method, analysis, and
results of this study would go beyond the scope of the current paper, but can be found in Hermsen,
Moons, Kerkhof, Wiekens, & De Groot, 2017.
3.2 Slower attrition than expected
The data showed a slow exponential decay in physical activity tracking, with 73.9% (526/711) of
participants still tracking after 100 days and 16.0% (114/711) of participants tracking after 320 days.
On average, participants used the tracker for 129 days. This decay is exponential, but slower than
may be expected from what little literature exists on the topic. Most important reasons to quit
tracking were technical issues such as empty batteries and broken trackers or lost trackers (21.5% of
all respondents of our third questionnaire, 130/601). Major determinants of tracking duration were
age (the under 25 kept up tracking less long than older participants) and user experience-related
factors (those who liked the design and user interface of the Fitbit more and found it easier to use,
tracked longer than those who liked it less and found it more challenging). Other, smaller
determinants were mobile phone type (iPhone less than others), household type (single parents less
than others), perceived effect of the Fitbit tracker, and goal-related factors (having 'adjacent' goals
such as healthy eating and quitting smoking decreased Fitbit use, when compared to 'central' goals
such as increasing activity). Interestingly, many determinants had a smaller contribution to sustained
use than may be expected from literature, or no effect at all. Perhaps this means that in real life,
determinants such as education, character traits, income, and profession play a much smaller role
than in isolated lab conditions.
Conclusion III: User experience and the evaluation of the user interface are important
determinants for engagement with and sustained use of a design; technical failures are
the most important reason for abandonment
Figure 1: Usage decline of the Fitbits. The horizontal axis shows the number of days since the first day of use. The
percentage of participants who used the activity tracker for any number of days after a particular day is indicated with a
solid line. The other lines indicate habitual use: the percentage of participants who used the tracker for at least 3, 5, and 7
days in the preceding 7 days
It may not come as a surprise to designers and design researchers that user experience, aesthetic
preferences, and ease of use matter, but many other stakeholders such as commissioners and the
scientific community are relatively unaware of this importance. The latter tend to put more faith in
underlying general working mechanisms and neglect user experience design (Hermsen, Van der Lugt,
Mulder, & Renes, 2016) which may lead to clunky designs.
4 Design case II: An app that gives feedback on water drinking
To shed further light on potential moderators of sustained use and engagement of designs providing
feedback, we performed two smaller studies with mobile apps. In the first study, we adapted an
existing mobile app which gives users feedback on their water consumption and attempted to
influence sustained use by manipulating the kind of feedback participants received. In a second
study, we interviewed long-term and novice users of a mobile app which gives feedback on the
nutritional value of your meals.
In the first study, we looked at moderators of sustained use of an app in which participants could
register the amount of water they drank. The app then gives feedback on their water consumption.
We recruited 538 participants through the online iOS app store. After downloading and installing the
app, all participants completed a questionnaire about their motivation to record and change their
water drinking behaviour, their perceived self-efficacy in doing so, and the appropriateness of five
potential goals for their app use (Rooksby, Rost, Morrison, & Chalmers, 2014): documentary ('how
much water do I drink?'), diagnostic ('has my water drinking an effect on fatigue?'), behaviour
change oriented ('I want to drink more water'), reward-oriented (comparison to others, badges,
etcetera), and 'fetishized' (interest in gadgets for their novelty value).
We randomly assigned all participants to one of five conditions: app-as-is, negative feedback on
behaviour, positive feedback on behaviour, feedback aimed at competition against other app users,
feedback aimed at cooperation with other app users (common goals). Participants recorded their
water drinking behaviour using the smartphone app. as they saw fit, with no requirements on
duration and frequency of use.
The trial lasted for 68 days, but no participant made it that far; 23.8% (128 participants) downloaded
the app, but never used it. A further 23.6% (127 participants) only used the app a single time. Only
129 users (24%) made it past the first week. These findings are in line with what little evidence that
exists about the expected duration of the use of mobile apps for health. An 80% attrition rate in the
first week is quite normal (Chen, 2015), and people are likely to keep downloading different apps
until they find one that fits their needs.
All participants were highly motivated to use the app in the first place (µ = 5.38, SD = 1.36 on a
seven-point scale), but it took a very high motivation and the goal to change drinking behaviour to
actually start using the app. Once in use, age (older more than younger), motivation (extremely high
more than very high), and having the concurrent 'documentary' goal influenced sustained use.
Interestingly, our experimental manipulations did not affect sustained use whatsoever (a full
overview of experimental methods and results is provided in Hermsen & Frost, 2018).
Conclusion IV: A digital feedback design must have a close fit with user needs and goals.
Figure 2: Screens from the water drinking app, and a graphical display of the percentage of users that stopped logging on a
particular day
5 Design case III: An app that gives feedback on nutritional content of
In a second, qualitative study, we interviewed 20 long-term users and 8 novice users of Eetmeter, an
app that provides feedback on the nutritional value of your meals. This app has been developed by
the Netherlands Nutrition Centre and has a steady following of tens of thousands of active users.
Once again, initial motivation to use the app, documenting the nutritional value of participants
current diet, and motivation to change eating behaviour were the main drivers for app use. Social
influence and integration with other dietary interventions (diets, consults from dietary professionals,
etcetera) did not appear to have an effect on the use of the app at all (a full overview of the research
method and results will be provided in Hermsen & Van Eijl, 2018).
Figure 3: Screens from Eetmeter, an app to determine the nutritional value of your meals
5.1 Motivation is the key
Our studies indicate that when people are sufficiently motivated to change their behaviour, they
need some kind of 'scaffolding' to support and shape their behaviour change attempts. For some
people, this takes the form of a mobile health app, even if this involves some rather annoying self-
reporting of eating and drinking behaviours which would drive most people away from the app. For
others, digital technology that gives feedback on their behaviour is not the kind of motivational and
practical support they need to reach their goals; they are better off using other interventions or
designs that combine feedback with other behaviour change techniques.
This finding, that highly motivated people use the designed intervention as a sort of 'scaffolding' to
support and shape their behaviour change process, is in line with previous literature that sees
feedback technology as lived informatics, i.e. the idea that people will actively select those resources
that best support the behavioural change they seek, rather than with literature that follows the idea
of 'persuasive technology', i.e. the idea that technology is capable of driving behavioural change
itself (Rooksby et al., 2014). The notion of lived informatics also comprises the variety of uses and
motivations that people have for the design. Some people will see tracking their behaviour as a
social, collaborative process and will find more use for designs that encourage relatedness; others
track to achieve autonomy and self-determination and will find use for designs that encourage those
(Gouveia, Kapanaros, & Hassenzahl, 2015; Kapanaros, Gouveia, Hassenzahl, & Forlizzi, 2016).
Conclusion V: only those who display extreme motivation to persevere show lasting
engagement. People with less than extreme motivation are likely to abandon the
intervention before it has a change to affect behaviour.
Design Challenge: how do we engage people who are not already extremely motivated
to change their behaviour? This is a severe problem that concerns all designs for
behavioural change. When should we opt for 'broader' interventions, with different
components for people with the need for relatedness or autonomy, and when should we
restrict ourselves to designing for a target group who has goals and use styles that fit
our design?
6 Design case IV: A fork that vibrates when you eat too fast
To contribute to the existing knowledge on whether feedback through digital technology can change
undesired habits in the long run, we performed a range of studies that evaluate the acceptance and
efficacy of a design to slow down eating rate. This is a deeply engrained habitual behaviour, which is
strongly associated with stomach disorders and overweight (Robinson et al., 2014), which in itself
causes a range of debilitating health issues, such as diabetes type II and some forms of cancer
(Berenson, 2012). Because of its deeply automatic nature, eating rate is put-near impossible to
change by will alone. The solution may lie in using a 'smart' fork, equipped with sensors and
actuators to provide real-time feedback on eating rate; in other words, the fork vibrates when you
eat too fast.
6.1 User experience evaluation
To evaluate the usability and acceptance of this fork, which is available on the market under the
name 10sFork, we asked 11 participants to eat a single meal with the fork in our laboratory, and
then take the fork home for three days and use it as much as possible. After the laboratory meal and
upon returning the fork, we interviewed the participants. The fork proved an acceptable tool: users
reported enhanced awareness of their eating rate and felt comfortable using the fork in social
settings. However, none of the participants felt the fork was 'for them', even though they did
recognise the need to slow down their fast-eating. This self-perceived target group membership, and
the incapacity of the fork to take meal characteristics into account, may be issues affecting
acceptance of the fork as an intervention for healthy eating in real life (full report in Hermsen et al.,
Figure 4: The 10sFork, produced by SlowControl, Paris, France
6.2 Lab study on effect
To test the effect of the fork on eating rate, we invited 114 self-reported fast eaters to our lab. They
were randomly assigned to the feedback condition, in which they received vibrotactile feedback
from their fork when eating too fast (i.e., taking more than one bite per 10 seconds), or a non-
feedback condition, where they ate with the fork without feedback. To control for demand
characteristics, we told all participants about the importance of eating slowly, and that the fork
would record their eating speed. Participants in the feedback condition ate at a slower eating rate
and took fewer bites per minute than did those without feedback. A slower eating rate, however, did
not lead to a significant reduction in the amount of food consumed or level of satiation. This may
have to do with the artificial setting of the meal; uncertainty about norms in a social setting are
known to cause people to 'revert' to generally accepted ideas of portion size (Higgs, 2015).
Alternatively, a slower eating rate may take more meals to start having an effect on the amount of
food we eat (full report in Hermans et al., 2017).
6.3 Field study on real-life use and effect
Finally, we performed a field study, to learn more about the effect of using the fork in everyday life.
We enlisted 150 participants, all self-reported fast eaters. To make sure all participants were well
motivated to change their eating rate, we invited only participants currently under treatment of a
dietician for complaints related with their eating rate, such as overweight and stomach complaints.
All participants used the fork for one week without feedback to establish their baseline eating rate.
Then they were randomly assigned to one of three conditions: eating as many meals with the fork as
possible for one month, without feedback; same, but with vibrotactile feedback; and same, but with
vibrotactile feedback and access to an online dashboard that provides retrospective feedback on
eating rate. After this one-month training period, they once again ate with the fork without feedback
for a week, to establish the effect of the training. This one-week measurement was repeated two
months later.
The study revealed that people who received vibrotactile feedback managed to decelerate their
eating, with a lower eating rate and a higher success ratio (percentage of bites that have at least 10
seconds between them). This effect remained after two months. Even more surprisingly, people in
the experimental conditions managed to lose a bit of weight because of eating with the fork, where
people in the control condition remained at the same weight. After two months, this weight loss
persisted. This result shows that feedback from digital technology indeed has the potential to
change undesired behaviours in the long run. However, the impact on BMI was small. For people to
really lose weight, more 'holistic' approaches are needed, in which dietary interventions are
combined with physical activity plans and eating behaviour interventions (full report in Hermsen et
al., 2018).
Conclusion VI: our findings confirm the conclusions from our literary review, and also
show that in certain cases, feedback from digital technology can lead to lasting
behaviour change.
These effects, albeit small, give confidence in the potential effect of feedback from digital
technology on undesired habits that until recently proved put-near impossible to change. But new
challenges also emerged. Both in our user experience evaluation study and in our lab study,
participants did not particularly feel the need to change their eating rate. Even after receiving
information about the detrimental long-term effects of eating too fast on our health, they did not
feel motivated to slow down. In general, it is very hard to get people to accept the gravity of a
problem, and even harder to convince them to accept solution as being 'for them'. How do we get
people to start engaging with our feedback? We have seen previously that motivation to use a
design needs to be very high, but people also need to realise both the problematic behaviour and
the severity of its consequences.
Design challenge: How can we design a product or service in such a way that people
understand that they are the target group, and in such a way that motivates uptake,
without discouraging users by scaring them off or triggering cognitive dissonance
7 Further challenges: measurement and privacy
7.1 Where do the data for the feedback come from?
In the past years, we have seen a steep increase in designs that provide feedback on a range of
behaviours. Many of these rely on machine learning principles to signal events that warrant
feedback: there are designs that predict influenza (Barlacchi et al., 2017) and depression (Merothra,
Hendley, & Musolesi, 2016) from human activity patterns, and it is now possible to reliably predict
when people who just quit smoking are in danger to start again (Naughton et al., 2016). These
developments broaden the scope of potential designed solutions that provide us with feedback on
our behaviour. However, many behaviours and human practices are (and will be for quite some
time) too complicated to measure. For instance, the automatic analysis of nutrition is not yet
feasible, even though the first products that claim to do so have already appeared online (e.g. Fitly,
2017). To obtain feedback on eating behaviour, the user still has to painstakingly provide their own
This can be expected to have a detrimental effect on sustained use and may form part of the
explanation for why only a small segment of users of the water drinking app made it to the second
week. Similarly, it is as yet very hard, if not practically impossible, to reliably detect human
emotions. Yet, there are many products that claim to do just that (e.g. Sensoree, 2015; Bonte, 2017).
This practice of introducing designs to the marketplace before they are technologically feasible is
questionable, because it will kindle hope in people in need of such solutions, which will then
inevitably lead to disappointment.
Design challenge: Automatic generation of data for feedback on behaviour can greatly
increase engagement with a design by taking away the frustrating task of self-
monitoring. However, this is at this moment only possible for a small range of
behaviours. How can we develop ways to measure more and more complicated
7.2 Where do the feedback data end up?
Machine learning techniques and other forms of automatic measurement of behavioural data have
their advantages, but they also come at a cost. Literature has described these forms of self-tracking
in Foucaultian terms, where subjects willingly regulate, govern, and optimise themselves (Whitson,
2014). There is indeed a fine line between beneficial self-regulation through feedback, and the use
of automatically generated behavioural data to subject people to standardisation and regulation. In
order to give feedback, most products rely on data analysis that takes place on the vendor's servers,
and visualisation of feedback through online and mobile applications. This process gives rise to
concerns about privacy. Who owns the data that is generated by measuring your behaviour? Who
guarantees that this data remains within the closed loop of measurement - analysis - feedback -
measurement, and does not get transferred on to third parties? Is your data accessible? The 10sFork
in the vibrating fork project registers each and every bite with a unique time stamp. This data is then
used for direct, vibrotactile feedback, and for retrospective visualisations of your eating rate
patterns. However, this data set was until recently unavailable for users of the fork. Similarly, many
activity trackers will tell you how many steps you have taken, will show you historic trends in your
activity and how you compare to others, but the entire data set with every registered step remains
unavailable, stored in the vendor's server park for who knows what use.
Design challenge: As yet, there are no best practices of designs that use feedback for
behavioural change that satisfactorily address privacy concerns. We need solutions that
provide open, usable data for their users which remain closed off to anybody else. In
other words, we need to start designing for privacy, or at least for privacy awareness.
8 Conclusion
This paper aimed to answer two questions: does feedback through digital technology have an effect
on undesired habitual behaviour, and what determinants and feedback properties enhance the
efficacy of the feedback? We have seen that feedback from digital technology can disrupt the
automatic cue-response-pair of habitual behaviour, which makes that behaviour available for
conscious scrutiny. All this does not necessarily mean that we can expect the technology and the
feedback itself to lead to behaviour change. Current evidence allows us to see feedback through
digital technology as a vehicle for behaviour change, but not (yet) as a driver (Patel, Asch, & Volpp,
2015). The vibrotactile fork project, however, shows that in specific cases, where people are
adequately motivated to choose the design that provides the feedback as their vehicle, feedback on
undesired habits can be an effective behaviour change technique.
Our second question: which determinants or feedback property enhance the efficacy of feedback
designs, proved harder to answer. Our research shows that user experience and engagement play an
important role. Challenges lie in keeping users engaged long enough for behaviour change to occur,
and in guaranteeing users' privacy.
Unfortunately, although research presented in this paper shows the potential efficacy of designs
that provide feedback, we are currently a long way from firmly establishing in what cases feedback
through digital technology can sustainably change behaviour, and from finding out what works for
whom in what contexts. This state of affairs is not helped by current methodological standards and
reporting traditions in design research and HCI. These are insufficient to generate generalisable
knowledge about the efficacy of our designs for behavioural change. To get there, we need to
overcome one final challenge: improve the way we as design researchers report our designs. We
need to put more effort in evaluating our designs, be it through qualitative or quantitative
measurements of its effects, or by more thorough reporting of the design process and its iterations.
Only then can we make more generalised conclusions about what works when and for whom.
Acknowledgements: I would like to thank all the people who worked with me on the
projects mentioned in this paper: (in alphabetical order) Tim van Eijl, Jeana Frost, Martijn
de Groot, Roel Hermans, Suzanne Higgs, Peter Kerkhof, Monica Mars, Jonas Moons, Reint
Jan Renes, Eric Robinson, and Carina Wiekens.
9 References
Barlacchi, G., Perentis, C., Mehrotra, A., Musolesi, M., & Lepri, B. (2017). Are you getting sick? Predicting
influenza-like symptoms using human mobility behaviors. EPJ Data Science, 6(1).
Berenson, G. S. (2011). Health Consequences of Obesity. Pediatric Blood & Cancer, 58(1), 117121.
Bonte, S. (2017). Numinous. URL: Accessed: 2017-11-08.
(Archived by WebCite at
Carver, C. S., & Scheier, M. F. (1985). A Control-Systems Approach to the Self-Regulation of Action. In: Kuhl, J.
(Ed.) Action Control: From Cognition to Behavior, 237265. doi:10.1007/978-3-642-69746-3_11.
Chen A. 2015. New data shows losing 80% of mobile users is normal, and why the best apps do better.
Retrieved May 17, 2016, from URL:
users-is-normal-and-that-the-best-apps-do-much-better/ (archived by Webcite at
Couper, M. P., Alexander, G. L., Zhang, N., Little, R. J., Maddy, N., Nowak, M. A., … Cole Johnson, C. (2010).
Engagement and Retention: Measuring Breadth and Depth of Participant Use of an Online Intervention.
Journal of Medical Internet Research, 12(4), e52. doi:10.2196/jmir.1430.
Darby, S. (2001). Making it Obvious: Designing Feedback into Energy Consumption. In: Energy Efficiency in
Household Appliances and Lighting, 685696. doi:10.1007/978-3-642-56531-1_73.
Donkin, L., Christensen, H., Naismith, S. L., Neal, B., Hickie, I. B., & Glozier, N. (2011). A Systematic Review of
the Impact of Adherence on the Effectiveness of e-Therapies. Journal of Medical Internet Research, 13(3),
e52. doi:10.2196/jmir.1772.
Fischer, C. (2008). Feedback on household electricity consumption: a tool for saving energy? Energy Efficiency,
1(1), 79104. doi:10.1007/s12053-008-9009-7.
Fitly. (2017). SmartPlate: Eating Just Got Smarter. URL: Accessed: 2017-11-08.
(Archived by WebCite at
Fox S, Duggan M. 2013. Tracking for Health. URL:
health/. Accessed: 2016-12-15. (Archived by WebCite at
Froehlich, J., Findlater, L., & Landay, J. (2010). The design of eco-feedback technology. Proceedings of the 28th
International Conference on Human Factors in Computing Systems - CHI ’10.
Funk, K. L., Stevens, V. J., Appel, L. J., Bauck, A., Brantley, P. J., Champagne, C. M., … Vollmer, W. M. (2010).
Associations of Internet Website Use with Weight Change in a Long-term Weight Loss Maintenance
Program. Journal of Medical Internet Research, 12(3), e29. doi:10.2196/jmir.1504.
Gardner, B. (2014). A review and analysis of the use of “habit” in understanding, predicting and influencing
health-related behaviour. Health Psychology Review, 9(3), 277295. doi:10.1080/17437199.2013.876238.
Gouveia, R., Karapanos, E., & Hassenzahl, M. (2015). How do we engage with activity trackers? Proceedings of
the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing - UbiComp ’15.
Gouveia, R., Pereira, F., Karapanos, E., Munson, S. A., & Hassenzahl, M. (2016). Exploring the design space of
glanceable feedback for physical activity trackers. Proceedings of the 2016 ACM International Joint
Conference on Pervasive and Ubiquitous Computing - UbiComp ’16. doi:10.1145/2971648.2971754.
Hänsel, K., Wilde, N., Haddadi, H., & Alomainy, A. (2015). Wearable Computing for Health and Fitness:
Exploring the Relationship between Data and Human Behaviour. arXiv preprint arXiv:1509.05238.
Hermans, R. C. J.*, Hermsen, S.*, Robinson, E., Higgs, S., Mars, M., & Frost, J. H. (2017). The effect of real-time
vibrotactile feedback delivered through an augmented fork on eating rate, satiation, and food intake.
Appetite, 113, 713. doi:10.1016/j.appet.2017.02.014. * shared first authorship
Hermsen, S., & Frost, J. (2018). Lessons from a failed attempt at increasing sustained use of a mobile app
providing digital feedback on water drinking. Extended abstract presented at the Etmaal voor de
Communicatiewetenschap 2018, Ghent, Belgium. Ghent, Belgium: NeFCA.
Hermsen, S., Frost, J., Renes, R. J., & Kerkhof, P. (2016). Using feedback through digital technology to disrupt
and change habitual behavior: A critical review of current literature. Computers in Human Behavior, 57, 61
74. Elsevier Ltd. doi:10.1016/j.chb.2015.12.023.
Hermsen, S., Frost, J. H., Robinson, E., Higgs, S., Mars, M., & Hermans, R. C. J. (2016). Evaluation of a Smart
Fork to Decelerate Eating Rate. Journal of the Academy of Nutrition and Dietetics, 116(7), 10661068.
Hermsen, S., Mars, M., Higgs, S., Robinson, E., Frost, J.H., & Hermans, R.C.J. (2018). Effects of a technology-
based intervention to decelerate eating rate on eating rate and BMI: a randomized controlled trial.
Manuscript under review.
Hermsen, S., Moons, J., Kerkhof, P., Wiekens, C., & De Groot, M. (2017). Determinants for Sustained Use of an
Activity Tracker: Observational Study. JMIR MHealth and UHealth, 5(10), e164. doi:10.2196/mhealth.7311.
Hermsen, S., Van der Lugt, R., Mulder, S., & Renes, R. J. (2016). How I learned to appreciate our tame social
scientist : experiences in integrating design research and the behavioural sciences. In: P. Lloyd & E.
Bohemia, eds. 2016 Design Research Society 50th Anniversary Conference, 4, 13751389.
Hermsen, S., & Van Eijl (2018). Determinants for Sustained Use of an App that provides feedback on nutritional
value of meals: Qualitative Study. Unpublished Manuscript.
Higgs, S. (2015). Social norms and their influence on eating behaviours. Appetite, 86, 3844.
Karapanos, E., Gouveia, R., Hassenzahl, M., & Forlizzi, J. (2016). Wellbeing in the Making: Peoples’ Experiences
with Wearable Activity Trackers. Psychology of Well-Being, 6(1). doi:10.1186/s13612-016-0042-6.
Kim, J. Y., Lee, K. H., Kim, S. H., Kim, K. H., Kim, J. H., Han, J. S., … Bae, W. K. (2013). Needs analysis and
development of a tailored mobile message program linked with electronic health records for weight
reduction. International Journal of Medical Informatics, 82(11), 11231132.
Kirkham, R., Ploetz, T., Mellor, S., Green, D., Lin, J.-S., Ladha, K., Wright, P. (2013). The break-time
barometer. Proceedings of the 2013 ACM International Joint Conference on Pervasive and Ubiquitous
Computing - UbiComp ’13. doi:10.1145/2493432.2493468.
Kluger, A. N., & DeNisi, A. (1996). The effects of feedback interventions on performance: A historical review, a
meta-analysis, and a preliminary feedback intervention theory. Psychological Bulletin, 119(2), 254284.
Kooiman, T. J. M., Dontje, M. L., Sprenger, S. R., Krijnen, W. P., van der Schans, C. P., & de Groot, M. (2015).
Reliability and validity of ten consumer activity trackers. BMC Sports Science, Medicine and Rehabilitation,
7(1). doi:10.1186/s13102-015-0018-5.
Li, I., Dey, A., & Forlizzi, J. (2010). A stage-based model of personal informatics systems. Proceedings of the
28th International Conference on Human Factors in Computing Systems - CHI ’10.
Lichtman, S. W., Pisarska, K., Berman, E. R., Pestone, M., Dowling, H., Offenbacher, E., … Heymsfield, S. B.
(1992). Discrepancy between Self-Reported and Actual Caloric Intake and Exercise in Obese Subjects. New
England Journal of Medicine, 327(27), 18931898. doi:10.1056/nejm199212313272701.
Ludden, G.D.S. (2013). Designing feedback. Multimodality and specificity. Paper presented at IASDR 2013,
Tokyo, August 25th 30th.
Mehrotra, A., Hendley, R., & Musolesi, M. (2016). Towards multi-modal anticipatory monitoring of depressive
states through the analysis of human-smartphone interaction. Proceedings of the 2016 ACM International
Joint Conference on Pervasive and Ubiquitous Computing Adjunct - UbiComp ’16.
Morozov, E. (2013). To save everything, click here: Technology, solutionism, and the urge to fix problems that
don’t exist. London, UK: Penguin. ISBN 9780241957707.
Nelson, R. O., & Hayes, S. C. (1981). Theoretical Explanations for Reactivity in Self-Monitoring. Behavior
Modification, 5(1), 314. doi:10.1177/014544558151001.
Naughton, F., Hopewell, S., Lathia, N., Schalbroeck, R., Brown, C., Mascolo, C., … Sutton, S. (2016). A Context-
Sensing Mobile Phone App (Q Sense) for Smoking Cessation: A Mixed-Methods Study. JMIR mHealth and
uHealth, 4(3), e106. doi:10.2196/mhealth.5787.
Northcraft, G. B., Schmidt, A. M., & Ashford, S. J. (2011). Feedback and the rationing of time and effort among
competing tasks. Journal of Applied Psychology, 96(5), 10761086. doi:10.1037/a0023221.
Robinson, E., Almiron-Roig, E., Rutters, F., de Graaf, C., Forde, C. G., Tudur Smith, C., … Jebb, S. A. (2014). A
systematic review and meta-analysis examining the effect of eating rate on energy intake and hunger.
American Journal of Clinical Nutrition, 100(1), 123151. doi:10.3945/ajcn.113.081745.
Ogihara, T., & Eshita, S. (2015). U.S. Patent No. D726,568. Washington, DC: U.S. Patent and Trademar k Office.
Quinn, J. M., Pascoe, A., Wood, W., & Neal, D. T. (2010). Can’t Control Yourself? Monitor Those Bad Habits.
Personality and Social Psychology Bulletin, 36(4), 499511. doi:10.1177/0146167209360665.
Patel, M. S., Asch, D. A., & Volpp, K. G. (2015). Wearable Devices as Facilitators, Not Drivers, of Health Behavior
Change. JAMA, 313(5), 459. doi:10.1001/jama.2014.14781.
Perski, O., Blandford, A., West, R., & Michie, S. (2016). Conceptualising engagement with digital behaviour
change interventions: a systematic review using principles from critical interpretive synthesis. Translational
Behavioral Medicine, 7(2), 254267. doi:10.1007/s13142-016-0453-1.
Rooksby, J., Rost, M., Morrison, A., & Chalmers, M. C. (2014). Personal tracking as lived informatics.
Proceedings of the 32nd Annual ACM Conference on Human Factors in Computing Systems - CHI ’14.
Schön, D. A. (1988). From technical rationality to reflection-in-action. In: Professional Judgment: A Reader in
Clinical Decision Making, 60-77. Cambridge, UK: Cambridge University Press. ISBN 9780521346962.
Sensoree (2015). GER Mood Sweater. URL: Accessed: 2017-
11-08. (Archived by WebCite at
Sheeran, P. (2002). Intentionbehavior relations: A conceptual and empirical review. European Review of
Social Psychology, 12(1), 1-36. doi:10.1080/14792772143000003.
Van Dijk, J. (2017). Dynamic Balance: a concept of an interactive wearable with the aim to empower people
with an autism spectrum disorder by supporting them in their process of emotion regulation.
URL: Accessed: 2017-10-31. (Archived by WebCite at
Verplanken, B., & Faes, S. (1999). Good intentions, bad habits, and effects of forming implementation
intentions on healthy eating. European Journal of Social Psychology, 29(5-6),
Verplanken, B., & Wood, W. (2006). Interventions to Break and Create Consumer Habits. Journal of Public
Policy & Marketing, 25(1), 90103. doi:10.1509/jppm.25.1.90.
Whitson, J. R. (2014). Foucault’s Fitbit: Governance and gamification. In: Walz, S.P., & Deterding, S. (Eds.) The
Gameful WorldApproaches, Issues, Applications. Cambridge, MA: MIT Press. ISBN: 9780262028004.
Wilson, T. D. (2002). Strangers to ourselves: discovering the adaptive unconscious. Cambridge, MA: Harvard
University Press. ISBN 9780674013827.
Wood, W., & Neal, D. T. (2009). The habitual consumer. Journal of Consumer Psychology, 19(4), 579592.
Zandian, M., Ioakimidis, I., Bergh, C., Brodi n, U., & Södersten, P. (2009). Decelerated and linear eaters: Effect of
eating rate on food intake and satiety. Physiology & Behavior, 96(2), 270275.
About the author:
Sander Hermsen researches design and behaviour change at the Utrecht University
of Applied Sciences. His work concentrates on enabling the creative industries to use
theory and evidence from the behavioural sciences to inform their work.
... -Ceux relatifs aux objectifs visés, notamment l'établissement des objectifs ( [17], [19], [25], [26], [28], [46], [52], [67]) et de la modification des habitudes indésirables (6 recherches : [6], [27], [32], [33], [52], [76]). ...
... 44 études quantitatives parmi les 49 intégrées à la revue systématique testent d'ailleurs l'effet du feedback délivré par des dispositifs digitaux, notamment via les applications mobiles. Pour autant, le rôle du feedback pour modifier durablement les habitudes n'a pas été établi de manière univoque (6 recherches : [2], [11], [21], [31], [32], [33], [45]). Par ailleurs, selon une étude du cabinet Gartner parue en 2016, les objets connectés connaissent un fort taux d'abandon chez les utilisateurs, notamment du fait d'une perte d'intérêt rapide. ...
Conference Paper
Les objets connectés offrent à leurs utilisateurs la possibilité d'enregistrer des traces de leurs activités, mais également d'intervenir sur leurs flux d'activités, facilitant ainsi l'auto-monitoring et la modification des habitudes. Pour autant, comprendre le lien entre les données du Quantified Self et les comportements nécessite d'effectuer un détour par le concept de feedback. Ce papier propose une revue systématique autour du concept de feedback afin de cerner les variables liées au feedback, au récepteur ou à la situation qui expliquent l'efficacité observée du feedback sur le changement comportemental. L'analyse des 76 articles et actes de conférences retenus témoigne de la fertilité de ce champ de recherche et permet de dresser un agenda de recherche autour des objets connectés et du changement comportemental.
... Enerzijds is het inderdaad zo dat de juiste kennis en houding alléén voor de meeste mensen niet voldoende is om zich gezonder te gaan gedragen; wanneer mensen een stappenteller gaan gebruiken, dan is het pure gebruik van de teller niet voldoende om meer te gaan bewegen (Hermsen 2018 ). De kennis en het zelfi nzicht die de feedback van de stappenteller oplevert, dient te worden gecombineerd met een ander werkzaam ingrediënt, zoals een hoge motivatie, praktische of sociale steun of gewoontevorming. ...
Dit hoofdstuk laat zien dat beklijvende gedragsverandering niet eenvoudig te bewerkstelligen is. Klassieke aanpakken die uitgaan van de premisse dat kennis en houding het gedrag bepalen, zullen weinig effect sorteren. Bij het ondersteunen van gezond gedrag bij patiënten dienen we niet alleen rekening te houden met kennis en houding, maar ook met weerstand, gewoonten, impulsgedrag, zelfinzicht en zelfregulatie, motivatie, vaardigheden en kansen om het gedrag uit te voeren. Om het gewenste gedrag vol te houden, kunnen we de volgende strategieën toepassen: verkennen en ondersteunen van intrinsieke motivatie en het voorkomen van frustratie; ondersteunen van zelfregulatie; aanboren van psychosociale hulpbronnen; hulp bij de vorming van gewoonten; en het ondersteunen van het aanpassen en inzetten van de fysieke en sociale omgeving.
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Background: A lack of physical activity is considered to cause 6% of deaths globally. Feedback from wearables such as activity trackers has the potential to encourage daily physical activity. To date, little research is available on the natural development of adherence to activity trackers or on potential factors that predict which users manage to keep using their activity tracker during the first year (and thereby increasing the chance of healthy behavior change) and which users discontinue using their trackers after a short time. Objective: The aim of this study was to identify the determinants for sustained use in the first year after purchase. Specifically, we look at the relative importance of demographic and socioeconomic, psychological, health-related, goal-related, technological, user experience-related, and social predictors of feedback device use. Furthermore, this study tests the effect of these predictors on physical activity. Methods: A total of 711 participants from four urban areas in France received an activity tracker (Fitbit Zip) and gave permission to use their logged data. Participants filled out three Web-based questionnaires: at start, after 98 days, and after 232 days to measure the aforementioned determinants. Furthermore, for each participant, we collected activity data tracked by their Fitbit tracker for 320 days. We determined the relative importance of all included predictors by using Random Forest, a machine learning analysis technique. Results: The data showed a slow exponential decay in Fitbit use, with 73.9% (526/711) of participants still tracking after 100 days and 16.0% (114/711) of participants tracking after 320 days. On average, participants used the tracker for 129 days. Most important reasons to quit tracking were technical issues such as empty batteries and broken trackers or lost trackers (21.5% of all Q3 respondents, 130/601). Random Forest analysis of predictors revealed that the most influential determinants were age, user experience-related factors, mobile phone type, household type, perceived effect of the Fitbit tracker, and goal-related factors. We explore the role of those predictors that show meaningful differences in the number of days the tracker was worn. Conclusions: This study offers an overview of the natural development of the use of an activity tracker, as well as the relative importance of a range of determinants from literature. Decay is exponential but slower than may be expected from existing literature. Many factors have a small contribution to sustained use. The most important determinants are technical condition, age, user experience, and goal-related factors. This finding suggests that activity tracking is potentially beneficial for a broad range of target groups, but more attention should be paid to technical and user experience-related aspects of activity trackers.
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Understanding and modeling the mobility of individuals is of paramount importance for public health. In particular, mobility characterization is key to predict the spatial and temporal diffusion of human-transmitted infections. However, the mobility behavior of a person can also reveal relevant information about her/his health conditions. In this paper, we study the impact of people mobility behaviors for predicting the future presence of flu-like and cold symptoms (i.e. fever, sore throat, cough, shortness of breath, headache, muscle pain, malaise, and cold). To this end, we use the mobility traces from mobile phones and the daily self-reported flu-like and cold symptoms of 29 individuals from February 20, 2013 to March 21, 2013. First of all, we demonstrate that daily symptoms of an individual can be predicted by using his/her mobility trace characteristics (e.g. total displacement, radius of gyration, number of unique visited places, etc.). Then, we present and validate models that are able to successfully predict the future presence of symptoms by analyzing the mobility patterns of our individuals. The proposed methodology could have a societal impact opening the way to customized mobile phone applications, which may detect and suggest to the user specific actions in order to prevent disease spreading and minimize the risk of contagion.
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“Engagement” with digital behaviour change interventions (DBCIs) is considered important for their effectiveness. Evaluating engagement is therefore a priority; however, a shared understanding of how to usefully conceptualise engagement is lacking. This review aimed to synthesise literature on engagement to identify key conceptualisations and to develop an integrative conceptual framework involving potential direct and indirect influences on engagement and relationships between engagement and intervention effectiveness. Four electronic databases (Ovid MEDLINE, PsycINFO, ISI Web of Knowledge, ScienceDirect) were searched in November 2015. We identified 117 articles that met the inclusion criteria: studies employing experimental or non-experimental designs with adult participants explicitly or implicitly referring to engagement with DBCIs, digital games or technology. Data were synthesised using principles from critical interpretive synthesis. Engagement with DBCIs is conceptualised in terms of both experiential and behavioural aspects. A conceptual framework is proposed in which engagement with a DBCI is influenced by the DBCI itself (content and delivery), the context (the setting in which the DBCI is used and the population using it) and the behaviour that the DBCI is targeting. The context and “mechanisms of action” may moderate the influence of the DBCI on engagement. Engagement, in turn, moderates the influence of the DBCI on those mechanisms of action. In the research literature, engagement with DBCIs has been conceptualised in terms of both experience and behaviour and sits within a complex system involving the DBCI, the context of use, the mechanisms of action of the DBCI and the target behaviour. Electronic supplementary material The online version of this article (doi:10.1007/s13142-016-0453-1) contains supplementary material, which is available to authorized users.
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Background: A major cause of lapse and relapse to smoking during a quit attempt is craving triggered by cues from a smoker's immediate environment. To help smokers address these cue-induced cravings when attempting to quit, we have developed a context-aware smoking cessation app, Q Sense, which uses a smoking episode-reporting system combined with location sensing and geofencing to tailor support content and trigger support delivery in real time. Objective: We sought to (1) assess smokers' compliance with reporting their smoking in real time and identify reasons for noncompliance, (2) assess the app's accuracy in identifying user-specific high-risk locations for smoking, (3) explore the feasibility and user perspective of geofence-triggered support, and (4) identify any technological issues or privacy concerns. Methods: An explanatory sequential mixed-methods design was used, where data collected by the app informed semistructured interviews. Participants were smokers who owned an Android mobile phone and were willing to set a quit date within one month (N=15). App data included smoking reports with context information and geolocation, end-of-day (EoD) surveys of smoking beliefs and behavior, support message ratings, and app interaction data. Interviews were undertaken and analyzed thematically (N=13). Quantitative and qualitative data were analyzed separately and findings presented sequentially. Results: Out of 15 participants, 3 (20%) discontinued use of the app prematurely. Pre-quit date, the mean number of smoking reports received was 37.8 (SD 21.2) per participant, or 2.0 (SD 2.2) per day per participant. EoD surveys indicated that participants underreported smoking on at least 56.2% of days. Geolocation was collected in 97.0% of smoking reports with a mean accuracy of 31.6 (SD 16.8) meters. A total of 5 out of 9 (56%) eligible participants received geofence-triggered support. Interaction data indicated that 50.0% (137/274) of geofence-triggered message notifications were tapped within 30 minutes of being generated, resulting in delivery of a support message, and 78.2% (158/202) of delivered messages were rated by participants. Qualitative findings identified multiple reasons for noncompliance in reporting smoking, most notably due to environmental constraints and forgetting. Participants verified the app's identification of their smoking locations, were largely positive about the value of geofence-triggered support, and had no privacy concerns about the data collected by the app. Conclusions: User-initiated self-report is feasible for training a cessation app about an individual's smoking behavior, although underreporting is likely. Geofencing was a reliable and accurate method of identifying smoking locations, and geofence-triggered support was regarded positively by participants.
Conference Paper
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Recent research reveals over 70% of the usage of physical activity trackers to be driven by glances – brief, 5-second sessions where individuals check ongoing activity levels with no further interaction. This raises a question as to how to best design glanceable behavioral feedback. We first set out to explore the design space of glanceable feedback in physical activity trackers, which resulted in 21 unique concepts and 6 design qualities: being abstract, integrating with existing activities, supporting comparisons to targets and norms, being actionable, having the capacity to lead to checking habits and to act as a proxy to further engagement. Second, we prototyped four of the concepts and deployed them in the wild to better understand how different types of glanceable behavioral feedback affect user engagement and physical activity. We found significant differences among the prototypes, all in all, highlighting the surprisingly strong effect glanceable feedback has on individuals' behaviors.
Conference Paper
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Designing solutions for complex behaviour change processes can be greatly aided by integrating insights from the behavioural sciences into design practice. However, this integration is hampered by the relative inaccessibility of behavioural scientific knowledge. Working in a multidisciplinary of design researchers and behavioural scientists may bridge the gap between the two fields. This paper shares our experiences in working as such a multidisciplinary group on a large project, amongst others consisting of the design of interventions for workplace safety. Our cooperation was fruitful, both for design researchers – being able to better structure the messiness of the design process –, behavioural scientists – gaining in ecological validity of their methods –, and commissioners – increased trust in potential outcomes of the design process. However, difficulties preventing synergy also transpired.
Full-text available
Background: Wearable activity trackers have become a viable business opportunity. Nevertheless, research has raised concerns over their potentially detrimental effects on wellbeing. For example, a recent study found that while counting steps with a pedometer increased steps taken throughout the day, at the same time it decreased the enjoyment people derived from walking. This poses a serious threat to the incorporation of healthy routines into everyday life. Most studies aim at proving the effectiveness of activity trackers. In contrast, a wellbeing-oriented perspective calls for a deeper understanding of how trackers create and mediate meaningful experiences in everyday life. Methods: We present a study of real life experiences with three wearable activity trackers: Fitbit, Jawbone Up and Nike + Fuelband. Using need fulfillment as a theoretical lens, we study recent, memorable experiences submitted by 133 users of activity trackers. Results: We reveal a two-dimensional structure of users' experience driven by the needs of physical thriving or relatedness. Our qualitative findings further show a nuanced picture of the adoption of activity trackers and their impact on wellbeing. For instance, while reflection about own exercising practices lost its relevance over time, users continued to wear the tracker to document and collect their runs. More than just supporting behavioral change, we find trackers to provide multiple psychological benefits. For instance, they enhance feelings of autonomy as people gain more control about their exercising regime. Others experience relatedness, when family members purchase a tracker for relatives and join them in their efforts towards a better, healthier self. Conclusions: The study highlights that activity trackers can be more than "tools" to change behavior. Through incorporation in daily life, they offer new social experiences, new ways of boosting our self-esteem and getting closer to our ideal selves.
Conference Paper
Remarkable advances in smartphone technology, especially in terms of passive sensing, have enabled researchers to passively monitor user behavior in real-time and at a granularity that was not possible just a few years ago. Recently , different approaches have been proposed to investigate the use of different sensing and phone interaction features , including location, call, SMS and overall application usage logs, to infer the depressive state of users. In this paper , we propose an approach for monitoring of depressive states using multi-modal sensing via smartphones. Through a brief literature review we show the sensing modalities that have been exploited in the past studies for monitoring depression. We then present the initial results of an ongoing study to demonstrate the association of depressive states with the smartphone interaction features. Finally, we discuss the challenges in predicting depression through multi-modal mobile sensing.
Over the last several years we have been working on the development of a theoretical account of the self-regulation of behavior. Our approach derives from many sources, including Duval and Wicklund’s (1972) self-awareness theory and the broader set of ideas known as control theory or cybernetics (e.g., MacKay, 1963, 1966; Powers, 1973a, 1973b; Wiener, 1948). Ours is a theory of the control of behavior, but not a theory of motor control per se. It is a theory of intentions and actions, but not a theory of cognition or comprehension. We believe, however, that the ideas that we have been using are eminently compatible with currently popular theories concerning motor control (see, e.g., Adams, 1976; Kelso, Holt, Rubin, & Kugler, 1981; Schmidt, 1976) and theories concerning cognition and comprehension (see, e.g., Anderson, 1980; Schank & Abelson, 1977). We also believe that the point of view we have adopted allows us to usefully address certain issues that traditionally have been approached from rather discrete and restricted theoretical perspectives. Thus, we suggest that the theory serves to pull together divergent ideas and research literatures in a way that is internally consistent, providing an integration that we view as highly desirable.