Automated Personalized Goal-setting in an Activity Coaching

Application

Miriam Cabrita

1,2

, Harm op den Akker

1,2

, Reinoud Achterkamp

1,2

, Hermie Hermens

1,2

and Miriam Vollenbroek-Hutten

1,2

Roessingh Research and Development, Telemedicine Group,

Enschede, The Netherlands

University of Twente, Faculty of Electrical Engineering and Computer Science, Telemedicine Group,

Enschede, The Netherlands

Keywords:

Accelerometers, Physical Activity, Goal-setting, Personalization, Telemedicine, Body Area Networks.

Abstract:

The ageing population and the increase in sedentary lifestyles of knowledge workers has led to increasing

concerns about the physical activity habits of the European population. Pervasive technologies and theories of

behavioral change are being combined in an effort to promote physical activity. The Activity Coach is an ex-

ample of one such system. Whereas the previous version of the Activity Coach set a ﬁxed and permanent daily

goal, in this work we describe the addition of an automatically adaptive goal-setting feature to this existing

system. With the new feature, the daily goals for physical activity are set based on the user’s routine, contribut-

ing to the personalization of the system. A technical evaluation was performed to test the system’s adaptation

to the user’s routine. Additionally, a conversion factor between a unit of energy expenditure and number of

steps was determined. The evaluation indicates that our method of goal-setting provides more challenging

but still attainable goals when compared to the previous version. Additional evaluations are recommended to

evaluate the user’s perception and effects on physical activity behavior change of this new feature. The results

of this research are implemented in the existing Activity Coach and will be used in future patient evaluations.

1 INTRODUCTION

Due to the ageing population, the prevalence of

chronic disease is increasing worldwide. The growing

demand on healthcare services calls for cost-effective

treatments that reduce the demands on healthcare pro-

fessionals. From the socio-economical point of view,

the remaining labor force is responsible for cover-

ing the costs of a growing number of dependent el-

derly. This means that people have to work till a

later age and for longer periods of time, even when

not feeling in their healthiest condition. According

to the European Commission, nearly 25% of the Eu-

ropean working-age population suffers from a long-

standing problem which restricts their daily activi-

ties (Directorate General for Health and Consumers,

2011), chronic illnesses being the principal cause.

Provision of eHealth and Telemedicine services is

widely regarded as a promising paradigm to limit the

prevalence of chronic disease, reduce the burden on

the healthcare system and keep employees healthy

and at work. An important factor in reducing this bur-

den is the maintenance of a healthy lifestyle in terms

of regular physical activity. Regular physical activ-

ity is beneﬁcial for everyone and the American Col-

lege of Sports Medicine recommends that the major-

ity of adults perform moderate-intensity cardio res-

piratory exercise training for at least thirty minutes

a day (Garber et al., 2011). However, of all Dutch

employees, 50% exercises too little and 44% is over-

weight (Hooftman et al., 2011). This not only poses a

risk for the inactive subject, but can also result in in-

creased sick leave and in a smaller active labor force

to ﬁnance healthcare.

Over the past years, a telemedicine intervention

to promote sustainable behavior change in terms

of physical activity was designed, implemented and

evaluated in several different studies (Van Weering

et al., 2009; Evering et al., 2011). Subjects were given

a 3D-accelerometer based sensor to assess daily ac-

tivity patterns, combined with a smartphone for pro-

viding continuous visual feedback in the form of a

graph. By comparing the subjects’ daily activity to

389

Cabrita M., op den Akker H., Achterkamp R., Hermens H. and Vollenbroek-Hutten M..

Automated Personalized Goal-setting in an Activity Coaching Application.

DOI: 10.5220/0004878703890396

In Proceedings of the 3rd International Conference on Sensor Networks (SENSORNETS-2014), pages 389-396

ISBN: 978-989-758-001-7

 2014 SCITEPRESS (Science and Technology Publications, Lda.)

a pre-deﬁned reference activity pattern, subjects were

automatically given motivational cues based on their

performance at regular intervals throughout the day.

The smartphone system, called the Activity Coach,

and the telemedicine platform which it is part of is

described in more detail in Section 2.3. Earlier stud-

ies using the Activity Coach have already shown the

effectiveness of providing real time motivational cues

on the users level of physical activity. However, more

recent studies have shown that compliance to the in-

tervention tends to drop after several weeks of use

(Tabak et al., 2013; Dekker-van Weering et al., 2012).

In order to increase long term compliance, we in-

tend to tailor the system better to its individual users.

Ongoing research focuses on tailoring the motiva-

tional messages that are sent to the users, in particular

their timing (op den Akker et al., 2010) and content

(Wieringa et al., 2011; Achterkamp et al., 2013). In

the presented research, we describe the addition of a

complex new feature to the Activity Coach, which al-

lows it to automatically generate personalized daily

goals for its users. Considering that users might have

particular activity habits during the different days of

the week, the new feature of the Activity Coach auto-

matically sets daily goals based on previous measure-

ments. In this way adaptation to the user’s routine and

subsequently more realistic goals are guaranteed.

The rest of the paper is outlined as follows. Sec-

tion 2 describes the background on the use of mobile

technologies in the promotion of physical activity, the

Goal Setting Theory and the Activity Coach — the

speciﬁc system under consideration here. Section 3

describes the design and implementation of the new

smart goal setting module. Section 4 deals with the

evaluation of the system. Conclusions, discussion and

an overview of future work are given in Section 5.

2 BACKGROUND

In this section, relevant background information is

given regarding (1) mobile technology for the pro-

motion of physical activity (Section 2.1), (2) the goal

setting theory that forms the theoretical basic of the

generation of automated goal lines (Section 2.2), and

(3) the technology platform in which the system has

been implemented (Section 2.3).

2.1 Mobile Technology in the Promotion

of Physical Activity

It is estimated that the penetration rate of mobile

phones in 2013 is around 96% worldwide (Union,

2013). The development of new technologies and the

spread of mobile technology in the general population

opens a whole range of new possibilities for promo-

tion of physical activity, combining real-time moni-

toring and coaching features. Around the world, sev-

eral research groups evaluate the efﬁciency and efﬁ-

cacy of tailored interventions using pervasive technol-

ogy. Well-known examples of exercise tracking appli-

cations, e.g. Runkeeper

, Beeminder

, Endomondo

or Runtastic

, typically only use the smartphones

built-in global positioning system sensor for provid-

ing feedback in terms of e.g. speed and distance. Ap-

plications that encourage appropriate and sufﬁcient

physical activity throughout the day are less widely

available, and in most cases use external sensors. The

advantage of using external sensors over sensors in

the smartphone is that the external sensor is usu-

ally worn on the body continuously, whereas smart-

phones are typically not. Additionally, modern ex-

ternal accelerometers are usually more accurate than

the smartphones built-in accelerometers. An exam-

ple of a system using an external sensor is given in

(Mutsuddi and Connelly, 2012) that combines the use

of pedometers and a smartphone. The authors sent

text messages to the subjects during a period of three

months. The messages encouraged physical activity

and were based on personalized step goals. Results

showed that the subjects increased both their daily

physical activity and their motivation regarding phys-

ical activity during the intervention. Other examples

are UbiFit Garden (Consolvo et al., 2009) and Fish

‘n‘ Steps (Lin et al., 2006).

2.2 Goal-setting Theory

The Goal-Setting Theory is among the most used the-

ories of individual behavior change in interventions

aiming at the promotion of healthy lifestyles. Firstly

focused on the work setting, Locke and Latham’s the-

ory emerges as the result of nearly forty years of em-

pirical research on the relationship between conscious

performance goals and task performance level (Locke

and Latham, 2002).

Setting goals implies the choice of the goal time-

frame (when should the goal be achieved?), the goal

source (who sets the goal?) and the goal complex-

ity, or difﬁculty (how hard will it be to achieve the

goal?). Regarding complexity, the Goal-Setting The-

ory defends that individuals are more likely to change

http://www.runkeeper.com/

http://www.beeminder.com/

http://www.endomondo.com/

http://www.runtastic.com/

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390

a behavior the higher the speciﬁcity and (achievable)

difﬁculty of a goal. At the same time, when setting a

goal, one should bear in mind personal characteristics

of the subject, such as goal importance, self-efﬁcacy

and feedback.

The effectiveness of different goal-setting ap-

proaches has been researched extensively (Colineau

and Paris, 2010; Shilts et al., 2004). However, our

empirical experience with clinical trials suggests that,

within the therapeutic context, the currently available

systems tend to contain three speciﬁc ﬂaws. First, the

majority of the monitoring and feedback systems use

a common goal to all the users disregarding the in-

dividual health status and physical condition. As a

consequence, a goal that is easily achievable for a

certain user can be unattainable for others. Second,

the goal is regularly maintained throughout time, not

following a possible, and desired, behavior change.

Third, the majority of the systems available do not

concern the spread of physical activity throughout the

day, setting one single daily goal. Finally, to our best

knowledge, there is no study that either implements or

evaluates the effectiveness of automatic tailored goal-

setting, i.e. goals set to meet individuals needs. Along

these lines, we propose a new feature that sets the

daily goal as well as a set of successive goals spread

over the day based on the users routine, while not ne-

glecting the therapeutic objective.

2.3 The Activity Coach

The Activity Coach is a Body Area Network con-

sisting of an activity sensor to be worn on the hip

and a smartphone application and is part of the

Telemedicine platform described in more detail in

(op den Akker et al., 2012). The sensor device con-

tains (among others) a 3D accelerometer sensor that

can capture, process and communicate wireless full

3D motion and orientation information (Figure 1).

The processed data is then sent to the smartphone

over Bluetooth. The output used by the system to es-

timate physical activity is the Integral of the Modulus

of body Acceleration (IMA), a unit that correlates to

energy expenditure (Bouten et al., 1996).

In the present work, the daily goal is deﬁned as the

cumulative value of energy expenditure that the user

is recommended to achieve at the end of the day. In

the Activity Coach, this is seen as the ﬁnal point of

the goal line (displayed on the screen as a green line).

The daily end point is the energy expenditure level

that the user has actually reached (ﬁnal point of the

activity line, displayed on the screen as a blue line).

Figure 1: The Activity Coach, consisting of a smartphone

and accelerometer-based activity sensor.

Previous versions of the Activity Coach set the

daily goal either based on results from healthy control

subjects or to be 110% of the average of the daily end

points of the baseline period. The baseline period nor-

mally constitutes an initial seven days period during

which the user does not receive any kind of feedback.

In the older version the daily goal remained constant

throughout the whole intervention. Questions regard-

ing the efﬁciency of this way of goal-setting arose

during previous experiments. Our goal is to create

and evaluate a more efﬁcient and effective procedure

by automatically generating personalized daily goals

for each user.

3 IMPLEMENTATION

The new version of the Activity Coach includes auto-

matically self-adaptive goal-setting features. By au-

tomatically self-adaptive goal-setting we mean that

the system sets goals for the upcoming days based on

both the user’s weekly routine and a set of parameters

deﬁned by the healthcare professional via web-portal.

These parameters, explained in more detail in the fol-

lowing sections, are the ultimate goal, the deviation

allowance factor and the breakpoints. The high level

architecture of the system is explained in more detail

in (op den Akker et al., 2012). The self-adaptive pro-

cess is divided into two steps and is described in more

detail in the following sections.

3.1 Analysis of Physical Activity Daily

Routine

The daily data is analyzed in four parts: (1) average

of energy expenditure per minute during different day

AutomatedPersonalizedGoal-settinginanActivityCoachingApplication

391

parts (morning, afternoon, evening, and full day), (2)

deviation between the user’s physical activity and the

goal line for that day, (3) ratio between daily end point

and daily goal, and (4) a summary of the minute-by-

minute IMA values (smoothed over e.g. 15 minute

intervals) — referred to as saved IMA data. The val-

ues from the daily analysis are subsequently com-

bined with the equivalent values from previous ana-

lysis occurred on the same day of the week. There-

fore, the system keeps track of the parameters of the

four sets of data aforementioned in a speciﬁc ﬁle for

each weekday.

From the daily analysis the parameters used for set-

ting the goal line are the daily end point and the saved

IMA data. These values are combined with data pre-

viously analyzed using the Linear Moving Weighted

Average (LMWA) — Equation 1. For example, if the

system is analyzing the data obtained on a Monday,

the daily end point of this day is averaged with the

daily end points of all the previous Mondays using the

LMWA. Afterwards the resulting value will be used

for setting the goal line for the next Monday. This

method was chosen instead of an arithmetical average

to take into account the evolution of the user. In this

way the more recent a measurement, the bigger its

weight in the calculation of the average. From these

steps results the averaged end point and the averaged

saved IMA data.

LW MA(point, N)

∑

j=1

point

i−N+ j

× (i −N + j)

∑

j=1

(1)

3.2 Determining the Goal Line for the

Upcoming Day

By goal line we mean both the quantity and distribu-

tion of physical activity that is recommended to the

user over the day. It has two main parameters: daily

end goal and daily pattern, i.e. distribution of phys-

ical activity over the day. In the new version of the

Activity Coach the healthcare professional sets an ul-

timate goal for the different days of the week. This

is seen as an upper limit for the daily end goal and

should be adjusted for each user. This ultimate goal

puts a maximum on the value of the daily goal set by

the system in order to avoid unattainable goals. An-

other value set by the healthcare professional is the

deviation allowance factor. This factor determines

the growth rate of the daily goal when compared to

the averaged end point. By default this value is set to

110%.

3.2.1 Determine End Goal

After the daily analysis, the averaged end point is

multiplied by the deviation allowance factor and the

result compared with the ultimate goal. If the result is

higher than the ultimate goal, the new daily goal has

the same value as the ultimate goal. If the result is

lower than the ultimate goal, the new daily goal is set

as the averaged end point multiplied by the deviation

allowance factor.

3.2.2 Determine Goal Line Pattern

The healthcare professional also sets the distribution

of physical activity that the user should follow over

the day. This happens by setting breakpoints —

<time,percentage>-pair points. As an example,

one can say that the user should achieve 40% of his

daily activity at 12 oclock. There is no limit to the

amount of breakpoints that can be set, allowing for

a ﬁne or coarse granularity of the goal line pattern.

When setting a new goal line, each one of these break-

points is compared to the percentage that the user ac-

complished at the same time of the day. This value is

determined by calculating the ratio between the cor-

respondent values of the averaged saved IMA data,

i.e. the one at the same (or closer) time of the day,

and the averaged end point. If this ratio is lower than

the percentage set on the breakpoint, the percentage

set in the goal line will be the average of the two val-

ues. Following the example given previously, if the

user should accomplish 40% of his daily activity at 12

o’clock and he accomplished only 20%, in the next

goal line for this day of the week, at 12 o’clock the

user is supposed to achieve 30% of his daily goal.

3.2.3 Runtime Procedure

When the application is launched the system checks

if there is data from previous days to analyze. If that

is the case, the system analyzes the data of each day

separately and veriﬁes if there are days with no valid

data (e.g. days when the user did not use the system,

or did not wear it for a long enough period of time).

In that case, the goal line is created based either on

stored data from that day of the week or in the param-

eters set by the healthcare professional. In this way it

is guaranteed that there is a goal line for every day. If

the data of a day is valid, i.e. if there is a signiﬁcant

amount of data points, the system analyzes the data

and checks if there is data stored about that day of the

week. If so, it combines the new and the old data and

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sets a new goal line after comparison with the param-

eters set by the healthcare professional. If not, it uses

only the data of the day to create a new goal line.

4 SIMULATIONS AND

EVALUATION

The presented paper reports exclusively the technical

evaluation of the new feature of the Activity Coach

and not the effectiveness of the application in terms

of behavior change. Ongoing research is being done

on the behavior change component and results are ex-

pected during the upcoming years. Additionally, our

only concern at this phase of the research regarded

the daily end goals set to the user and not the ones re-

ferred to in this paper as breakpoints. Although there

are clear guidelines on the amount of physical activ-

ity that should be performed per day/week, there is no

evidence on the way this activity should be distributed

over the day. Literature suggests that prolonged inac-

tivity is unhealthy independently of physical activity

level, meaning that single boosts of activity are not

the enough to meet the beneﬁts of an healthy lifestyle

(e.g. (van der Ploeg et al., 2012)). This supports the

general notion that physical activity should be equally

spread over the day. However, there is no clear evi-

dence on the exact health beneﬁts.

The Goal-Setting Theory emphasizes the impor-

tance of setting challenging but attainable goals. To

test the self-adaptive character of the system we an-

alyzed data from subjects with Chronic Obstructive

Pulmonary Disease acquired in a longitudinal study

that was executed between May and November of

2012, for the European Project IS-Active. From a

sample of 10 subjects, only the data of 7 patients was

used. Three subjects were excluded as a consequence

of the limited amount of viable data available. From

the 7 remaining subjects, two were female, four were

not working at the time of the study and all of the sub-

jects had low levels of physical activity as assessed

with the Baecke questionnaire (Baecke et al., 1982).

The experiment design followed the one described in

section 2.3. For each subject, the daily goal remained

the same throughout the intervention and was deﬁned

as 110% of the average daily end points of the base-

line period. From now the daily goals set based on

the previous version of the Activity Coach will be re-

ferred to as ﬁxed goals.

4.1 Fixed Versus Self-adaptive

Goal-setting

Our aim with this simulation was to compare the chal-

lenge and attainability of the provided goals set by

the previous (ﬁxed goals) and newer versions (adap-

tive goals) — of the Activity Coach. To clarify, the

ﬁxed goals correspond to what was, in reality, dis-

played on the screen of the smartphone during the

IS-Active experiment and the adaptive goals are hy-

pothetical goals that would have displayed in case of

using the automatically self-adaptive goal-setting fea-

ture. We intend to evaluate if the system would in

fact adapt to user’s routine as expected during the de-

sign phase. The goals were considered challenging

and attainable if the ratio between the averaged of the

goals and the averaged activity performed would be

between 0.75 and 0.95. The exact values are to be

taken as indicative. The procedure followed in this

study was as follows:

1. Calculate the average IMA count per minute for

each one of the days of the baseline period. All

the days with less than 300 data points were ex-

cluded (frequency of acquisition is 1 data-point

per minute);

2. Save the goal set by the system during the experi-

ment (ﬁxed goal);

3. Set an ultimate goal as 200% of the average of the

end points of the baseline period. This value was

chosen because it seems challenging but not im-

possible to double your level of physical activity;

4. Set automatically adaptive daily goals based on

the algorithm described in Section 3;

5. Compare the daily IMA averages during the inter-

vention period and the average of the ﬁxed (step

2) and adaptive goals (step 3 and 4).

Table 1 shows the results from both methods. It is

clear that the adaptive goals tend to be more challeng-

ing than the ﬁxed goals. Especially in the case of the

ﬁrst three subjects, the former method of goal-setting

provides daily goals that are, on average, lower than

the activity during the intervention period. Clearly,

this is not a desired system behavior considering that

the subject would not feel challenged to increase

physical activity levels.

To better evaluate the self-adaptive feature, both

old and new version were analyzed graphically. Fig-

ures 2 and 3 show the results of the simulations us-

ing data from subjects isa09 and isa10, respectively.

The black line shows the subjects daily activity (Daily

data), the dashed line represents the ﬁxed goal (Fixed)

and the light grey line the adaptive goal (Adaptive).

AutomatedPersonalizedGoal-settinginanActivityCoachingApplication

393

Table 1: Results of the evaluation performed with data from

subjects of the IS-Active project. Only days with more than

300 measured activity values were considered (Days). The

average of IMA counts per minute during the intervention

period was divided by the average per minute provided us-

ing the basic goal setting (Fixed) and the adaptive version

(Adaptive).

Goal Ratios

Subject Days Fixed Adaptive

isa07 40 1.05 0.83

isa09 30 1.24 0.98

isa10 38 1.26 1.01

isa11 61 0.94 0.90

isa12 53 0.99 0.93

isa13 59 0.90 0.90

isa14 36 0.92 0.74

600

700

800

900

1000

1100

1200

1300

1400

1500

1600

1700

Daily data

Adaptive

Fixed

Mon Mon Mon Mon Mon Mon Mon

Activity Average (IMA/min)

Time (day)

Figure 2: Simulation results comparing adaptive goals with

ﬁxed goals for subject isa09.

300

400

500

600

700

800

900

1000

1100

Daily data

Adaptive

Fixed

Mon Mon Mon Mon Mon Mon Mon Mon Mon Mon Mon Mon

Time (day)

Activity Average (IMA/min)

Figure 3: Simulation results comparing adaptive goals with

ﬁxed goals for subject isa10.

In both cases, conﬁrming the results shown in Table

1, the goal set by the previous version of the Activity

Coach is not challenging for the respective users. This

can lead to demotivation. For the adaptive goals, it is

clear that the system adapts to the users routine. This

is especially visible in the seventh week. (Figure 3).

The system not only sets speciﬁc goals to each day of

the week but also adapts these goals over time.

4.2 Other Simulations

As part of the research we performed a study to (1)

evaluate the reasonability of the default deviation al-

lowance factor, and (2) be able to provide more con-

crete, understandable and speciﬁc feedback to the

users. When providing feedback, it is important to

assure that the user fully understands the message re-

ceived. However, we are aware that, contrarily to

other commonly known measures of physical activ-

ity (e.g. calories expenditure, distance and number of

steps performed), IMA is not an understandable unit.

In our evaluation we decided to analyze the correla-

tion between IMA counts throughout the day and the

number of steps performed.

A single-study subject was performed in order to

determine a conversion factor between IMA counts

and number of steps. In this small study we used a

FitBit Zip

to measure steps taken during 11 days of

free living. Over the experiment period, the value of

IMA counts were compared to the number of steps

performed during each 5-minute interval. The rela-

tion between the two units found after data process-

ing is presented in Equation 2 (p<0.0001). The num-

ber of steps was then converted to average of minutes

walking according to recommendation from Ameri-

can Journal of Preventive Medicine — 100 steps cor-

respond to a minute walking. We considered that the

factor would be reasonable (i.e. challenging and at-

tainable) if it would add less than 20 minutes walking

to the user. For this evaluation, we used once again the

data acquired during the IS-Active project. For each

subject, we calculated the total IMA added in average

to the daily physical activity and converted to number

of steps and respective number of minutes walking.

Table 2 shows the results of this evaluation.

IMA

count

(steps

count

) = 30.24 × steps

count

+ 1680

(2)

When setting a new daily end goal, the average of

the daily end points of that weekday is multiplied by

the deviation allowance factor. Based on this ﬁrst ex-

plorative study we suggest that 110% is a reasonable

factor for increasing the daily physical activity of the

user. However, more studies should be performed in

http://www.ﬁtbit.com/uk/zip/specs

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394

Table 2: Total IMA (IMA), number of steps (Steps) and

minutes of walking (Minutes) added in average to the daily

physical activity using the self-adaptive goal-setting feature.

Only days with more than 300 measured activity values

were considered (Days).

10% Additional Effort

Subject Days IMA Steps Minutes

isa07 40 29,823 931 9

isa09 30 62,493 2,011 20

isa10 38 43,517 1,383 14

isa11 61 55,072 1,766 18

isa12 53 64,485 2,077 21

isa13 59 55,419 1,777 18

isa14 36 42,023 1,334 13

order to address the variance related to the method of

measuring acceleration during different types of ac-

tivities and expressing this as a number of steps per

minute.

5 CONCLUSIONS

In this work we implemented self-adaptive goals in

order to encourage daily physical activity, bearing in

mind the importance of both the ﬁnal goal of energy

expenditure and the distribution of activity over the

day. The level of challenge and the attainability of the

goals provided to the user was evaluated with (1) data

acquired during previous studies, and (2) newly gath-

ered data from a single-subject study. From simula-

tions using data of a 3-months-study we conclude that

self-adaptive goals tend to be more challenging than

ﬁxed goals (both methods provide attainable goals).

The main limitation concerns the conversion from

IMA counts to steps and consequent evaluation of the

additional effort required from the user when setting a

new goal. Along these lines we suggest two different

studies. First a study should be performed including a

larger sample of subjects in order to increase the accu-

racy of the conversion factor between IMA counts and

steps. Second, within the same subject, various mea-

suring contexts should be taken into account in order

to get a personalized conversion between number of

steps and minutes walking. Additionally, the simple

study showed in section 4.2 suggests that future im-

plementations should consider also a threshold to the

additional effort required from the user. As a sugges-

tion, the additional threshold can correspond to 10%

unless the case when this value adds more than 20

minutes walking to the daily activity.

Regarding the activity pattern, at the moment of

publication, there is no guideline that deﬁnes what

a proper daily pattern of physical activity is. If fu-

ture research gives insights into the most suitable dis-

tribution of physical activity throughout the day, the

breakpoints of the goal line can be adjusted through

a web-portal in order to be coherent with the new re-

sults.

To conclude, we believe that the incorporation of

self-adaptive goal-setting in the Activity Coach will

beneﬁt users in their way to become more active.

Also, healthcare professionals will beneﬁt by allow-

ing them to give more accurate recommendations to

their patients as they are more aware of their physi-

cal activity routines. The results from this research

will be used in future experiments using the Activity

Coach and can be adapted to other ambulatory feed-

back systems regarding promotion of physical activ-

ity.

ACKNOWLEDGEMENTS

This publication was supported by the Dutch national

program COMMIT (project P7 SWELL).

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