The Myth of 10,000 Steps: A New Approach to Smartphone-based

Health Apps for Supporting Physical Activity

Tom Ulmer, Edith Maier and Ulrich Reimer

Institute for Information & Process Management, University of Applied Sciences St. Gallen,

Rosenbergstrasse 59, St. Gallen, Switzerland

Keywords: Pedometer, Physical Activity, Physical Activity Goal, Mobile Health, Behaviour Change Support System.

Abstract: This paper introduces an alternative approach to conventional pedometer apps which measure the wide-spread

goal of 10,000 steps a day. Instead we focus on the intensity of physical activity, which is in line with recent

recommendations of renowned health institutions such as the WHO. These promote a minimum of moderate

to vigorous physically active time per week to achieve the desired health benefits. The paper discusses how

the guidelines have been implemented. It also outlines how we help maintain user motivation over time (e.g.

by integrating and personalising "nudges") and how we intend to solve the challenges posed by different

fitness levels and personal lifestyles.

1 INTRODUCTION

The importance and the positive effects of regular

physical activity (PA) are well known and have been

confirmed in many studies (e.g. Warburton et al.,

2006, Piercy et al., 2018). In developed countries,

around 1% to 3% of the total health care costs can be

attributed directly to physical inactivity. The indirect

costs are likely to be more than double of the direct

costs (Pratt et al., 2014). Encouraging people to

engage in more PA is a global priority to reduce the

burden of noncommunicable disease (World Health

Organization, 2015).

PA can take on many different forms, e.g.

walking, running, hiking, cycling, swimming, yoga,

resistance training etc. The more complex the

movement, the more difficult it is to track and

monitor. For walking and running, a smartphone with

an accelerometer is sufficient. For more complex

activities a combination of several sensors worn on

the body or placed in the environment are necessary

(Dernbach et al., 2012).

Pedometer apps (pure step counters) and running

apps are wide-spread. The Runtastic app, for

example, has over 300 million downloads (Adidas

Runtastic. 2019), the app “Pedometer – Step Counter”

has over 30 million downloads (Pedometer - Step

Counter - Apps on Google Play, n.d.). Moreover,

brisk walking, jogging and running are among the

most popular sports in the world and have a

significant impact on health and longevity (Lee et al.,

2017).

People who use such apps tend to be much more

active than non-users and have a lower body mass

index (BMI) (Litman et al, 2015). App-based

interventions aimed at encouraging PA have shown

significant health improvements for children and

adults (Schoeppe et al, 2016). The evidence to

support the health benefits of regular physical activity

has become increasingly compelling (see e.g.

Department of Health and Social Care, 2019).

However, most PA apps that are currently

available, have some severe deficits, especially those

which promote activities such as running or walking.

These are regarded as the most basic activities to

achieve an active lifestyle and are therefore also

recommended by international health organisations.

On the whole, current apps tend to rely on the wide-

spread goal of completing 10,000 steps per day. Such

a goal may be easy to implement in technical terms,

but rather hard to incorporate into an average user’s

everyday life. Moreover, this activity goal is quite

controversial among experts who doubt that counting

steps is the best approach towards an active lifestyle

(Wattanapisit & Thanamee, 2017).

This paper describes an alternative approach to

supporting an active lifestyle which is not based on

the numbers of steps per day. Our approach is based

on the most recent recommendations, for example the

Department of Health and Social Care (2019). These

Ulmer, T., Maier, E. and Reimer, U.

The Myth of 10,000 Steps: A New Approach to Smartphone-based Health Apps for Supporting Physical Activity.

DOI: 10.5220/0009142106410647

In Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2020) - Volume 5: HEALTHINF, pages 641-647

ISBN: 978-989-758-398-8; ISSN: 2184-4305

641

guidelines recommend moderate aerobic exercise for

150 minutes a week or vigorous aerobic exercise for

at least 75 minutes a week. Section 2 describes the

shortcomings of the currently available apps which

are mostly based on the 10,000 steps per day. In

Section 3 we present our alternative approach which

aims at overcoming these shortcomings and which

also tackles the problem related to incorporating

regular PA into a user’s everyday life. Section 4

discusses how we are evaluating the app and see how

its impact differs from apps with conventional

features. Section 5 outlines some challenges to target

in future work and possible solutions.

2 SHORTCOMINGS OF

EXISTING APPS SUPPORTING

PHYSICAL ACTIVITY

Most of the currently available apps for tracking

activity including smartwatches and electronic

pedometers promote the widespread and well-known

goal of 10,000 steps per day. The origin of this marker

goes back to a Japanese pedometer nicknamed

“Manpo-kei” which can be translated as “10,000

steps meter” (Tudor-Locke & Bassett, 2004). There is

some scientific evidence that 10,000 steps/day may

have health benefits (Kang et al., 2009). However, to

integrate that step goal into one’s everday life is

challenging. Moreover, counting steps does not

represent an exact and scientific way to measure

energy expenditure. Neither is it in line with widely

approved international health recommendations

which focus on accumulated time of moderate to

vigorous physical activity (MVPA) in the course of a

week.

Walking 10,000 steps per day does not guarantee

that past nor current PA recommendations have been

met (Le-Masurier, 2003). Several approaches have

been tried in a variety of projects to achieve the daily

10,000 steps’ goal, largely without success. Most

people fail to reach that goal, missing on average

approximately 4000 steps (Choi et al., 2007). An

additional six hours and forty minutes of walking

would be necessary per week to reach the 10,000

steps’ goal (70 steps/min).

This corresponds to the factor of 2.5 of the

metabolic equivalent of task (MET), an objective

measure of energy expenditure. One MET is the

equivalent of the energy cost of sitting quietly. Based

on a cadence of 100 steps per minute to reach a

moderate intensity level, i.e. MET level 3.5, it would

be enough to do 3000 steps in 30 minutes on five days

a week instead (Tudor-Locke et al., 2018; Hendelman

et al., 2000). The threshold for vigorous PA is about

130 steps per minute, which is the equivalent of 6

MET (Tudor-Locke et al., 2018).

Instead of aiming for 10,000 steps a day with

questionable impact on one’s health it would be

possible to reach the recommended health goals based

on intensity with an equivalent of about 3000 steps

within 30 minutes on five days per week. Other

studies found that 6500 to 8500 steps per day would

suffice to achieve the recommended amount of PA

energy expenditure (Ayabe et al., 2008), which

however does not consider intensity.

All quantitative goals, be it just counting 10,000

steps per day or simply adapting the goal posts in line

with the number of steps already performed – a

common practice in many apps -, fall short of the

current health recommendations. This is due to the

fact that they fail to take into account the intensity and

thus the quality of the PA.

Although gamification elements and social

support, e.g. the interaction with peers, can contribute

to maintain people’s motivation to use a PA app, this

tends to decline considerably over time and adherence

is marked by high variability (Marin et al., 2019;

Ryan et al., 2017). The effects of step counting apps

such as those shown in Figure 1 are modest at best

(Bort-Roig et al, 2014, Coughlin et al., 2016).

Figure 1: Two typical pedometer apps with 10,000 steps

goal (Steps, Stepz).

3 ALTERNATIVE APPROACH TO

APPS SUPPORTING PHYSICAL

ACTIVITY

We intend to overcome the shortcomings of typical

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apps promoting physical activity by designing and

implementing a new approach.

3.1 Design Principles

As already said, pedometer apps which just count

steps are available in huge numbers. To our

knowledge, however, there are no apps which focus

on the intensities recommended for physical activity.

Whilst counting steps is rather trivial, incorporating

measurements and goals based on intensities is far

more challenging. This is especially true if no

physiological parameters like heart rate (HR) or heart

rate variability (HRV) are available.

These days, smartphone cameras are able to

measure HR and HRV based on changes in blood

volume in the fingers (photoplethysmography, PPG)

(Peng et al 2015). However, such measurements may

be difficult to carry out whilst running or jogging.

Other methods would require additional devices like

chest straps, smartwatches or other body sensors.

We propose to drop daily goals altogether and

integrate weekly goals as promoted in current health

recommendations from WHO and others. From a

health perspective, it makes more sense to adopt open

and continuous time windows instead of focusing on

single days or a 7-day calendar week pattern.

One advantage of longer timeframes is that the

user has more freedom to incorporate activities into

his or her routines without the need to perform an

activity every day. The downside could be that users

are tempted to postpone activities from one day to the

next. This could result in having to perform all the

activities required to achieve the weekly goal within

one or two days, for example on the weekend.

Although it would be better to engage in regular

physical activity, it is still better to be active only on

one or two days instead of not at all.

We have developed an approach that anticipates

the tendency to procrastinate and highlights the

consequences of postponing activity to later. Every

day without activity increases the amount of activity

needed to reach the goal.

3.2 Implementation

The above-mentioned design principles were

translated into a prototype by a team of researchers

with expertise in user experience and behavioural

economics.

For example, to counteract people’s tendency to

postpone activity till later, the consequences of such

behaviour are visualized by a specifically designed

bar chart which has been integrated into the graphical

user interface (GUI), see Figure 4.

The aim of the bar chart is to encourage people to

engage in regular physical activity instead of just

going for a run or brisk walk at the weekend. We do

not rely on the calendar week for visualising the PA

delta but have opted for a continuous floating 7-day

activity goal. Users can shift the visualized period

forth and back to see how the activities of different

intensities influence the PA necessary to achieve the

desired health benefits. The app calculates the effort

still required to reach the goal depending on the past

activities. The more days are taken into account from

the past, the fewer days are left for future activities to

reach the goal and vice versa. In line with the above-

mentioned health recommendations, the total period

to reach the activity goal is always 7 days (Figures 2

and 3). This helps the user to learn the benefits and

advantages related to frequent and regular rather than

extended isolated activities. This is illustrated in

Figure 3, which shows what happens if an extended

single activity drops out of the visualization period.

As a result, the effort to reach the 7-day goal goes up

to the maximum.

Figure 2: More frequent activities have a smaller effect on

the activity recommended to achieve the goal if the

observation window is moved by one day.

Figure 3: Single active days have a big effect on the effort

required to achieve the goal if they drop out of the

observation window.

As shown in Figure 2, the user has accumulated a

total of 90 minutes of physical activity at moderate

intensity over a period of three days, which means

that there are still 15 minutes to go on each of the

The Myth of 10,000 Steps: A New Approach to Smartphone-based Health Apps for Supporting Physical Activity

643

following four days. If the window is shifted forward

one day, the total activity amounts to 60 minutes and

18 minutes to go for each of the following five days.

The impact of moving the observation window

therefore is low.

In Figure 3, the user also has accumulated 90

minutes of physical activity at moderate intensity, but

in a single day with 15 minutes to go on each of the

following four days. If the observation window is

shifted forward by one day, the activity count drops

to zero, and 30 minutes of moderately intense PA is

recommended for each of the following five days. In

addition to intensity derived from walking cadence

our app considers the height profile of a track, which

is a critical factor for intensity as well. Our algorithm

is based on the formula of Naismith, which implies

that 1m of ascent is equivalent to 8m of horizontal

travel (Scarf, 2007).

The app brings together all the relevant

information in one single screen. There is no need to

go through a series of menus to collect relevant

information with goal achievement being the most

important. The default view shows the current day in

the centre of the 7-day window. It also shows the

activity over the last three days and the average

activity required to reach the goal within the next four

days including the current day. The area of

visualization can be shifted via touchscreen.

Moderate physical activity is visualized in light

green, vigorous activity in dark green and light

activity is grey. Only green activity is relevant for

achieving the goal and therefore stacked to fill the

accumulated bar on the right-hand side of the screen

(Figure 4, both left and right example).

Figure 4: App interface of the prototype.

To highlight the difference between quantifying

steps and MVPA, the total of steps per day is shown

as an orange circle for the current and past days. A

filled circle means the threshold of 10,000 steps has

been reached. However, it does not mean that any

MVPA has been achieved (see Figure 4, day Friday)

This is an important message conveyed by the app

and the interface.

The interface can be swiped up to get more insight

into the data. Active time in MVPA is shown for

every past day. It also shows the time with low

intensity (e.g. from slow walking) and the total steps

per day. The columns to the right of the current day

show how much MVPA is needed to reach the goal.

The column on the right shows the total active time in

each intensity zone (Figure 4).

3.3 Maintaining Motivation

As mentioned above, a major challenge is to maintain

user motivation over time (Nagler et al., 2013). Our

approach incorporates features based on insights from

behavioural economics which have been translated

into brief persuasive interventions, often described as

“nudges”. These may take on the form of alerts or

reminders as well as regular and possibly immediate

feedback on a user’s behaviour (Reimer et al., 2016).

Figure 5: Adaptive goal-achievement graph for triggering

situation-specific nudges.

Besides, we use a self-learning framework to

generate personalized and situation-specific

interventions because it has been shown that the “one-

size-fits-all” approach which disregards individual

preferences and contextual aspects fails to maintain

motivation (Reimer et al., 2016). The core element for

generating notifications is an adaptive goal-

achievement graph (see Figure 5). The graph triggers

different types of nudges depending on the time and

the progress towards the floating 7-day goal. A

learning algorithm adapts the segments to the

reactions of the user and thus continually improves

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the chances to trigger the right type of nudge at the

most appropriate time.

4 EVALUATION

The prototype has been pre-tested with a small group

of users (n=12) in an iterative process. Seven

participants were recruited from a school class (age =

14), five from the authors’ work environment (age 25

– 67). For the pre-test we used both interviews and

questionnaires (in the case of the pupils) which

addressed the following aspects: motivation for app

usage, goals related to physical activity, joy of use,

ease of use, other usability aspects such as error

tolerance and questions about the nudging approach.

The test users identified several shortcomings

related to the interface and interaction design, the

algorithm to trigger the appropriate nudges and some

technical issues (e.g. high energy consumption). As a

result of their feedback we have made minor changes

to the interaction design and the GUI, e.g. the colour

scheme. Currently, we are working on the onboarding

process which explains the main elements and the

benefits of the app after installation.

For the main evaluation we shall publish the app

via the Apple AppStore to reach larger numbers of

potential users. We expect to reach at least 150 to 200

users in the initial phase. To counteract the drop-off

effect, the evaluation process can be extended and

supported by online marketing activities if necessary.

Once it has been downloaded and installed, the app

will randomly activate one of two different

approaches for user motivation. The static version

uses the traditional approach known from most of the

common PA-promoting apps and makes use of a

reduced and hard-wired set of nudges. The dynamic

version includes the goal-achievement graph which

adapts over time in line with a user’s individual

performance. The graph should then trigger nudges at

promising times of the day and select nudge types that

are adapted to the individual user. The users will not

know which version they get to prevent bias.

The evaluation will examine the differences in

terms of PA between the two interventions groups.

PA can be measured by counting the number of steps

per single session of PA, steps per day, or per week

or to which extent the PA recommendations have

been reached. Additional parameters are number of

floors climbed and changes in walking cadence. We

also compare PA before the installation of the app

with the period from the installation and start of the

app usage. This can be done via access to the history

of PA data stored in the health applications of the

operating systems (e.g. HealthKit from iOS).

An important aspect besides the motivation to be

physical active is the motivation to use the app. Both

aspects shall be evaluated. Parameters for app usage

are number of times the app is opened, interactions

within the app and the time per session. We also differ

between nudge triggered and arbitrarily triggered user

interactions.

Additional outcome parameters are the users’

reactions to specific nudge types and their

engagement over time. The evaluation will

investigate both short-term (4 to 6 weeks) and long-

term effects (several months). Apart from the data

collected from the app we also plan to use a

questionnaire to obtain basic socio-demographic

information about the users (age, gender) and gain

further insights about usability, acceptance, and

motivation.

5 OUTLOOK & FUTURE WORK

One unsolved challenge is the problem of not

knowing if someone is within his or her individual

range of moderate or vigorous intensity. Currently we

rely on correlations between step frequency and

average intensity. The threshold for moderate

intensity is around 100 steps/min or 3.5 METs, for

vigorous intensity the threshold is around 130

steps/min or 6 METs (Tudor-Locke et al., 2018). We

are exploring different ways to optimize the

measurement of individual effort during exercise or

PA.

For example, we may calibrate the individual

intensity thresholds based on measuring the breathing

frequency whilst talking. The test can be used when

calibrating for the first time to define the individual

threshold and can be repeated to measure if the user

is in good or bad shape.

In the future, we also want to include optional

tracking of the heart rate to get a better feedback for

PA apart from walking and running. The idea is to let

the user select specific types of PA like cycling, yoga

or swimming and to derive the intensity based on

movement and heart rate. Using the METs as

suggested by Ainsworth et al. (2000), these activities

could then be added to the physically active time

shown by the app.

Another possibility to improve the app could

consist in combining the two approaches with

information from additional sensor data (GPS sensor,

accelerometer, barometer, gyro sensor, compass,

ambient sensor, ambient light sensor etc.). The data

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645

could be used to learn how to identify situations of

higher physical intensity. This method would

incorporate machine learning so as to be able to

recognize patterns in the sensor data gathered from

wearables, for instance, and as a result learn how to

identify high intensity activity based on data patterns.

Finally, we have to be aware of the fact that it

might be difficult for sedentary adults or older adults

to meet the recommended PA goals. We should

therefore consider adapting the MPVA thresholds

given in the guidelines to the individual fitness level

which could then be raised over time if a person’s

fitness improves. The positive effects of even low-

dose activity for older adults have already been

confirmed in various studies (e.g. Sparling et al. 2015,

Hupin et al. 2015). Besides, there is evidence for a

dose–response relationship between physical activity

and premature mortality (Warburton et al., 2017).

Inspired by these findings, we intend to further

develop our approach.

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